Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk (Endocrinology) 3031350634, 9783031350634

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Endocrinology Series Editor: Andrea Lenzi Series Co-Editor: Emmanuele A. Jannini

Massimo Federici · Rossella Menghini Editors

Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk

Endocrinology Series Editor Andrea Lenzi, Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, Rome, Italy Series Co-Editor Emmanuele A. Jannini, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Roma, Italy

Within the health sciences, Endocrinology has an unique and pivotal role. This old, but continuously new science is the study of the various hormones and their actions and disorders in the body. The matter of Endocrinology are the glands, i.e. the organs that produce hormones, active on the metabolism, reproduction, food absorption and utilization, growth and development, behavior control, and several other complex functions of the organisms. Since hormones interact, affect, regulate and control virtually all body functions, Endocrinology not only is a very complex science, multidisciplinary in nature, but is one with the highest scientific turnover. Knowledge in the Endocrinological sciences is continuously changing and growing. In fact, the field of Endocrinology and Metabolism is one where the highest number of scientific publications continuously flourishes. The number of scientific journals dealing with hormones and the regulation of body chemistry is dramatically high. Furthermore, Endocrinology is directly related to genetics, neurology, immunology, rheumatology, gastroenterology, nephrology, orthopedics, cardiology, oncology, gland surgery, psychology, psychiatry, internal medicine, and basic sciences. All these fields are interested in updates in Endocrinology. The aim of the MRW in Endocrinology is to update the Endocrinological matter using the knowledge of the best experts in each section of Endocrinology: basic endocrinology, neuroendocrinology, endocrinological oncology, pancreas with diabetes and other metabolic disorders, thyroid, parathyroid and bone metabolism, adrenals and endocrine hypertension, sexuality, reproduction, and behavior.

Massimo Federici • Rossella Menghini Editors

Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk With 45 Figures and 8 Tables

Editors Massimo Federici Department of Systems Medicine University of Tor Vergata Rome, Italy

Rossella Menghini Department of Systems Medicine University of Rome Tor Vergata Rome, Italy

ISSN 2510-1927 ISSN 2510-1935 (electronic) Endocrinology ISBN 978-3-031-35063-4 ISBN 978-3-031-35064-1 (eBook) https://doi.org/10.1007/978-3-031-35064-1 © Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Series Preface

Is there an unmet need for a new MRW series in Endocrinology and Metabolism? It might not seem so! The vast number of existing textbooks, monographs, and scientific journals suggest that the field of hormones (from genetic, molecular, biochemical, and translational to physiological, behavioral, and clinical aspects) is one of the largest in biomedicine, producing a simply huge scientific output. However, we are sure that this new series will be of interest to scientists, academics, students, physicians, and specialists alike. The knowledge in endocrinology and metabolism limited to the two main (from an epidemiological perspective) diseases, namely, hypo/hyperthyroidism and diabetes mellitus, now seems outdated and perhaps closer to the practical interests of the general practitioner than to those of the specialist. This has led to endocrinology and metabolism being increasingly considered as a subsection of internal medicine rather than an autonomous specialization. But endocrinology is much more than this. We are proposing this series as the manifest for Endocrinology 2.0, embracing the fields of medicine in which hormones play a major part but which, for various historical and cultural reasons, have thus far been “ignored” by endocrinologists. Hence, this MRW comprises “traditional” (but no less important or investigated) topics: from the molecular actions of hormones to the pathophysiology and management of pituitary, thyroid, adrenal, pancreatic, and gonadal diseases, as well as less usual and common arguments. Endocrinology 2.0 is, in fact, the science of hormones, but it is also the medicine of sexuality and reproduction, the medicine of gender differences, and the medicine of well-being. These aspects of endocrinology have to date been considered of little interest, as they are young and relatively unexplored sciences. But this is no longer the case. The large scientific production in these fields coupled with the impressive social interest of patients in these topics is stimulating a new and fascinating challenge for endocrinology. The aim of the MRW in Endocrinology is thus to update the subject with the knowledge of the best experts in each field: basic endocrinology; neuroendocrinology; endocrinological oncology; pancreatic disorders; diabetes and other metabolic

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disorders; thyroid, parathyroid, and bone metabolism; adrenal and endocrine hypertension; sexuality, reproduction, and behavior. We are sure that this ambitious aim, covering for the first time the whole spectrum of Endocrinology 2.0, will be fulfilled in this vast Springer MRW in Endocrinology Series. Andrea Lenzi Emmanuele A. Jannini

Volume Preface

Cardiometabolic diseases appear to be closely linked to the metabolic pathways triggered by the interaction of nutrients with the intestinal flora, and the emerging research in the last decade supports clear roles for microbiota in early development and progression of cardiometabolic risk. Identification of factors that influence gut microbiome composition and function and circulating biomarkers with prognostic value may help to identify relevant pathophysiological processes, to explore how changes in microbiota and microbial metabolism influence host metabolism and cardiometabolic risk and to improve preventive risk reduction. Recently, development of omics technologies has improved biomarker discovery, leading to the identification of a number of disease-associated microbial species and their metabolic products and several potential targets for diagnostic or therapeutic use. This reference work is strong informative about the role of the gut microbiome in organism metabolism and fully discusses the relationship between gut alterations and/or gut microbiome-derived metabolites and the pathogenesis of many diseases, as well as recent advances in clinical applications of microbiome and microbial effector molecules. It clearly shows how the microbiome research is a growing field in molecular and clinical sciences, due to technical advances based on highthroughput genetic sequencing technologies and omics analyses that empower systems biology-based methods for precision health monitoring and treatment. It will help in understanding that high diversity of the microbial communities in the gut is important to preserve health and microbiome alterations, not only in nutritionassociated diseases like obesity and diabetes, but also in many chronic inflammatory, cardiovascular, oncological, and neurological disorders. Written by renown experts in the field, this reference work is intended for clinicians, residents, specialists, and physicians involved in the diagnosis and treatment of affected patients. Rome, Italy January 2024

Massimo Federici Rossella Menghini

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Contents

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Methods to Study Metagenomics . . . . . . . . . . . . . . . . . . . . . . . . . . Antonia Piazzesi and Lorenza Putignani

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Methods to Study Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . Simona Fenizia, Egeria Scoditti, and Amalia Gastaldelli

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The Impact of Microbial Metabolites on Host Health and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sonia Fernández-Veledo, Anna Marsal-Beltran, Victòria Ceperuelo-Mallafré, Brenno Astiarraga, and Lídia Cedó

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From Leaky Gut to Tissue Microbiota in Metabolic Diseases . . . . Rémy Burcelin

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Gut Microbiota and Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giulia Angelini, Sara Russo, and Geltrude Mingrone

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Gut Microbiome and Brown Adipose Tissue . . . . . . . . . . . . . . . . . José María Moreno-Navarrete

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Gut Microbiome and Hepatic Steatosis (Steatotic Liver Disease) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lesley Hoyles

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Gut Microbiota and Type 2 Diabetes Mellitus . . . . . . . . . . . . . . . . Susanna Longo, Rossella Menghini, and Massimo Federici

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Gut Microbiome in Dyslipidemia and Atherosclerosis . . . . . . . . . . Andreas Puetz and Ben A. Kappel

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Gut Microbial Metabolism in Heart Failure . . . . . . . . . . . . . . . . . . Sahana Aiyer and W. H. Wilson Tang

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Gut Microbiome and Cognitive Functions in Metabolic Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna Motger-Albertí and José Manuel Fernández-Real

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Contents

The Other Microbiome: Oral Microbiota and Cardiometabolic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sylvie Lê, Chiara Cecchin-Albertoni, Charlotte Thomas, Philippe Kemoun, Christophe Heymes, Vincent Blasco-Baque, and Matthieu Minty Discovering the Nutrition-Microbiota Interplay in Inflammatory Bowel Disease: Are We There Yet? . . . . . . . . . . . . . . . . . . . . . . . . . Marilina Florio, Lucilla Crudele, Antonio Moschetta, and Raffaella M. Gadaleta

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Gut Microbiota and Diabetic Kidney Diseases . . . . . . . . . . . . . . . . Alessandra Stasi, Francesca Conserva, Maria Teresa Cimmarusti, Gianvito Caggiano, Paola Pontrelli, and Loreto Gesualdo

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Aging and Gut Dysbiosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Federica D’Amico, Marco Fabbrini, Monica Barone, Patrizia Brigidi, and Silvia Turroni

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Gut Microbiota and Specific Response to Diet . . . . . . . . . . . . . . . . Asma Amamou, Cian O’Mahony, Maria Antonia Lopis-Grimalt, Gaston Cruzel, Noel Caplice, Florence Herisson, and Subrata Ghosh

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The Role of Endothelial Dysfunction in the Connection Between Gut Microbiota, Vascular Injury, and Arterial Hypertension . . . . Rocco Mollace, Jessica Maiuolo, and Vincenzo Mollace

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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Editors

Massimo Federici is currently Professor of Internal Medicine at the University of Rome “Tor Vergata” Medical School and Director of the Center for Atherosclerosis at the Tor Vergata Medical School hospital. He trained in Endocrinology and Metabolism at the University of Rome (1999) and at the Joslin Diabetes Center (1997). Since 1999, he is actively working in both clinical and molecular research. His laboratory is focused on mechanisms causing diabetes and atherosclerosis, including the effect of gut microbiota on both diseases. He is a member of the EU-funded Florinash Consortium which is studying how the gut microbiome influences the onset and progression of metabolic dysfunction-associated steatotic liver disease (MASLD), Type 2 diabetes, and atherosclerosis in people living with obesity. Rossella Menghini is currently Associate Professor of Clinical Biochemistry at the Department of Systems Medicine, University of Rome Tor Vergata. After a Chemistry Bachelor Degree (1996), she has been Research Fellow at the Department of Cell and Developmental Biology of the “Sapienza” University of Rome, where she obtained a Board Certification in Chemical Science. She has been Visiting Scientist at the Department of Cellular Microbiology and Immunology of the Vienna Biocentrum, Austria. In 2003, she obtained the Ph.D. degree in Experimental Physiopathology at the University of Rome Tor Vergata, where from 2003 to 2007 she worked as Postdoctoral Researcher at the Molecular Medicine Laboratory Department of Internal Medicine. The research carried out in recent years has focused on the study of the xi

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mechanisms involved in metabolic and cardiovascular pathologies, with particular emphasis to the identification of molecular mediators and biomarkers of endothelial dysfunction, atherosclerosis, and obesity-induced adipose tissue inflammation.

Contributors

Sahana Aiyer Center for Microbiome and Human Health, Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA Asma Amamou APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland Giulia Angelini Università Cattolica del Sacro Cuore, Rome, Italy Brenno Astiarraga Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain Monica Barone Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy Vincent Blasco-Baque Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France Patrizia Brigidi Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy Rémy Burcelin Institut National de la Santé et de la Recherche Médicale (INSERM 1297) InCOMM team, Toulouse, France Unité Mixte de Recherche (UMR) 1297, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Team inCOMM, 1 rue Jean Poulhès, Université Paul Sabatier (UPS), Toulouse Cedex 4, France Gianvito Caggiano Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy xiii

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Contributors

Noel Caplice Centre for Research in Vascular Biology, Biosciences Institute, University College Cork, Cork, Ireland Chiara Cecchin-Albertoni Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, Toulouse, France Lídia Cedó Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain Victòria Ceperuelo-Mallafré Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain Universitat Rovira i Virgili (URV), Reus, Spain Maria Teresa Cimmarusti Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy Francesca Conserva Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy Lucilla Crudele Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, Bari, Italy Gaston Cruzel Centre for Research in Vascular Biology, Biosciences Institute, University College Cork, Cork, Ireland Federica D’Amico Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy Marco Fabbrini Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy Massimo Federici Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy Simona Fenizia Institute of Clinical Physiology, National Research Council, Pisa, Italy

Contributors

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José Manuel Fernández-Real Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute (IdibGi), Girona, Spain Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Girona, Spain Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Madrid, Spain Sonia Fernández-Veledo Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain Universitat Rovira i Virgili (URV), Reus, Spain Marilina Florio Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, Bari, Italy Raffaella M. Gadaleta Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, Bari, Italy Amalia Gastaldelli Institute of Clinical Physiology, National Research Council, Pisa, Italy Loreto Gesualdo Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy Subrata Ghosh APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland Florence Herisson Centre for Research in Vascular Biology, Biosciences Institute, University College Cork, Cork, Ireland Christophe Heymes UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/ Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France Lesley Hoyles Nottingham Trent University, Nottingham, UK Ben A. Kappel Department of Internal Medicine 1, University Hospital Aachen, RWTH Aachen University, Aachen, Germany Philippe Kemoun Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France

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Contributors

Sylvie Lê Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France Susanna Longo Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy Maria Antonia Lopis-Grimalt Centre for Research in Vascular Biology, Biosciences Institute, University College Cork, Cork, Ireland Jessica Maiuolo Department of Health Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy Anna Marsal-Beltran Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain Universitat Rovira i Virgili (URV), Reus, Spain Rossella Menghini Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy Geltrude Mingrone Università Cattolica del Sacro Cuore, Rome, Italy Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy Division of Diabetes & Nutritional Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK Matthieu Minty Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France Rocco Mollace Department of Experimental Medicine, University of Rome “Tor Vergata”, Rome, Italy Vincenzo Mollace Department of Health Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy Fondazione Dulbecco, Lamezia Terme, Italy

Contributors

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José María Moreno-Navarrete Department of Diabetes, Endocrinology and Nutrition, Institut d’Investigació Biomèdica de Girona, Girona, Spain CIBEROBN (CB06/03/010), Instituto de Salud Carlos III, Madrid, Spain Antonio Moschetta Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, Bari, Italy INBB National Institute for Biostructure and Biosystems, Rome, Italy Anna Motger-Albertí Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute (IdibGi), Girona, Spain Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Girona, Spain Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Madrid, Spain Cian O’Mahony APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland Antonia Piazzesi Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy Paola Pontrelli Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy Andreas Puetz Department of Internal Medicine 1, University Hospital Aachen, RWTH Aachen University, Aachen, Germany Lorenza Putignani Unit of Microbiology and Diagnostic Immunology, Unit of Microbiomics and Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy Sara Russo Università Cattolica del Sacro Cuore, Rome, Italy Egeria Scoditti Institute of Clinical Physiology, National Research Council, Lecce, Italy Alessandra Stasi Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy W. H. Wilson Tang Center for Microbiome and Human Health, Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA Kaufman Center for Heart Failure Treatment and Recovery, Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA

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Contributors

Charlotte Thomas Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France Silvia Turroni Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy

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Methods to Study Metagenomics Antonia Piazzesi and Lorenza Putignani

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods to Profile the Gut Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbial Microarray Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bait-and-Capture Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amplicon Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shotgun Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Third-Generation Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Metagenomic profiling of human fecal samples has long uncovered the importance of the microbiota in human health and disease. In recent years, metagenomic studies have demonstrated that the microbes that inhabit the human body have important commensal roles far beyond aiding digestion in the intestinal tract, leading the human microbiome to be defined as an endocrine organ in itself. In order to study these microbial communities, researchers have had to develop different methodologies to accurately characterize them, and to identify which microbes are commensal, and which can be considered pathogenic. In this chapter, we will discuss the next-generation sequencing methods of metagenomic A. Piazzesi Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy e-mail: [email protected] L. Putignani (*) Unit of Microbiology and Diagnostic Immunology, Unit of Microbiomics and Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_1

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profiling, which have made the expansion of our knowledge possible. Starting from more targeted approaches to untargeted ones, we will discuss the pros, cons, and potential applications of each sequencing strategy. Finally, we will introduce the recent advancements in third-generation sequencing technology, discussing the implications of the emergence of this new approach for the field of metagenomics. Keywords

Metagenomics · Microbiome · Shotgun sequencing · 16S sequencing · Bait-andcapture · Next-generation sequencing · Long-read sequencing · Third-generation sequencing

Introduction The human body is inhabited by thousands of species of microorganisms, collectively referred to as the human microbiota, and which are now known to play multiple, fundamental roles in human development and health. As appreciation for the importance of the human microbiota grew, the study of metagenomics has increased exponentially alongside it, defined as the theoretical collection of all microbial genomes found within a specific environment. While the gut microbiota may have once been thought to play no more than an auxiliary role in the final stages of digestion, insurmountable scientific evidence has demonstrated that gut microbes, as well as the metabolites that they produce, can either directly or indirectly impact virtually every organ in the human body, leading this community to be considered an endocrine organ in itself (Mutalub et al. 2022). Given the far-reaching effects of both intestinal pathogens and microbial commensals on human health, the gut microbiota has been proposed as a target for innovative therapeutic strategies for a number of human ailments, including metabolic and cardiovascular diseases (Mutalub et al. 2022). However, in order to develop these therapies, researchers must first characterize the human microbiome, which is associated with either good or poor health. In some cases, identifying the presence of one or a few microbial species is sufficient to diagnose the root cause of a disease. In others, more subtle disruptions in the relative abundances of certain microbial taxa may be responsible for precipitating specific disease symptoms. In others still, specific microbial species can act as markers for disease, giving early warning of the development or relapse of certain chronic disorders. Conversely, other microbial species could be positive prognostic indicators, or even, when supplemented, actively improve patient quality of life. To address all of these questions, multiple different metagenomic techniques have been developed to investigate the various roles that these microorganisms may have in human health. Historically, the study of metagenomics involved complicated and labor-intensive techniques, including isolating bacterial clones, growing them in culture, DNA cloning, and Sanger sequencing of these single DNA molecules (Escobar-Zepeda et al. 2015). For many years, Sanger sequencing by capillary electrophoresis was the gold standard

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for DNA sequencing, and was used to generate the first reference genomes, such as the one created during the Human Genome Project, that are now taken for granted. In Sanger sequencing, the principles of the polymerase chain reaction (PCR) are exploited to generate a complementary strand to the target DNA molecule that needs to be sequenced. Instead of incorporating typical nucleotides during synthesis, Sanger sequencing introduces chain-terminating, radioactively or (as is commonly used today) fluorescently labeled nucleotides, resulting in multiple DNA strands of various chain-lengths. These DNA molecules are then passed by an electrical current through a capillary tube filled with a gel polymer, which will separate the fragments based on nucleotide length. As the molecules move through the capillary, a laser excites each DNA fragment as it passes, a camera captures the fluorescent signal associated with the specific chain-terminating nucleotide, and a computer translates this signal into a base call. This information is then strung together to produce a single continuous nucleotide sequence (Escobar-Zepeda et al. 2015). While these studies were essential for laying the groundwork for modern metagenomic techniques, this study design suffered from far too many drawbacks to ever be sufficient to fully characterize the human microbiota. Even if the sheer number of microorganisms that inhabit the human body was not a limiting factor, many microbial species are exceedingly difficult, if not outright impossible to grow in a laboratory culture setting. Due to these limitations, metagenomic analysis has progressively moved away from laboratory culture, and toward “culture-free” methodologies (Escobar-Zepeda et al. 2015). The simplest of these culture-free methods is to perform a PCR for a specific, known microbe directly on human samples, a method that is still commonly used today to detect one or a few microbial species. While this method is undoubtedly useful in the screening of donor material and the diagnosis of pathogens in human samples, it is still a time- and cost-ineffective strategy for microbial community profiling. With humans being made up of more microbial cells than human ones, as well as being susceptible to infections from hundreds of pathogenic species, more comprehensive analyses of metagenomic profiling were needed to characterize these highly complex and dynamic communities. In addition to the ability to bypass the need to grow microbial species in culture, the biggest technological advancement that permitted community-based metagenomic profiling was the ability to sequence millions of DNA fragments at the same time, also known as next-generation sequencing (NGS), thus overcoming the time constraint imposed by the Sanger sequencing method (Hu et al. 2021). The first such technology to be released was 454 pyrosequencing, marketed by Roche. Like Sanger sequencing this platform also relied on sequencing by synthesis and, by the time it was discontinued, was capable of producing reads that were over 500 base pairs long. In pyrosequencing, each single-stranded DNA fragment was hybridized to a single bead, which would then be placed in one of the one million wells in a PicoTiterPlate. As DNA polymerase began synthesizing the complementary strand, luciferase in the well detected the phosphate group released during the addition of a base to the new DNA molecule, emitting a light signal. By exposing the plate to single nucleotide bases one at a time, and by capturing an image of the resulting light flashes from the wells, the sequence of each unique DNA strand could be inferred

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simultaneously (Balzer et al. 2010). Despite laying the groundwork for many metagenomic studies, especially those related to the study of fungi, other NGS technologies rapidly outperformed 454 pyrosequencing in terms of cost, time, and data quality, leading Roche to suspend production in 2016 (Nilsson et al. 2019). Currently, the two most commonly used NGS platforms are IonTorrent semiconductor sequencing and Illumina sequencing-by-synthesis. In IonTorrent semi-conductor sequencing developed by ThermoFisher Scientific, DNA fragments hybridize to millions of beads, which amplify the fragments so that each bead is covered in multiple copies originating from a single DNA fragment. Similar to 454 pyrosequencing, these DNA-coated beads are then placed on a chip, which is covered in microscopic wells each designed to house a single bead. These wells are then flooded with a single nucleotide which, if incorporated into the complementary DNA sequence, release a hydrogen ion, changing the pH of the solution and thus registering as a base call on the ion-sensitive layer beneath the wells. In Illumina sequencing by synthesis, DNA fragments tagged with specific sequences hybridize to a flow cell, a glass plate coated with oligos complementary to the DNA tag. These fragments are then amplified by PCR, producing multiple copies of each fragment tethered to the flow cell. Like Sanger sequencing, a DNA polymerase then uses fluorescently labeled nucleotides to generate a strand that is complementary to the target DNA fragment. Unlike Sanger sequencing, these nucleotides are reversibly chain-terminating, and thus the sequencer does not rely on the size separation of multiple DNA fragments to determine the DNA sequence. Instead, Illumina sequences are able to read the characteristic fluorescent signal emitted as the complementary strand is being generated. Also unlike Sanger sequencing, NGS platforms perform this reaction for millions of fragments simultaneously, allowing for metagenomic analyses that are orders of magnitude larger than those made possible by Sanger sequencing. Furthermore, NGS is capable not only of sequencing millions of DNA fragments within the same sample, but it can also sequence hundreds of different samples on the same flow cell (Hu et al. 2021). Thanks to the existence of special indexes, which are added to the DNA fragments before sequencing, DNA from up to 384 different samples can be pooled, or “multiplexed,” and sequenced within a single run, after which the reads produced during sequencing are “demultiplexed” based on their unique indexes and separated into their respective files. Once sequencing is completed and the reads are demultiplexed, quality control checks are performed to identify sequencing errors in the reads, and those that fall beneath a certain established parameter are discarded lest they lead to false positives later in the analysis. The adapters, i.e., the sequences that were added to the DNA fragments for the purposes of multiplexing and/or flow cell binding, are trimmed away, and similar reads are paired, aligned into contiguous sequences, or “contigs,” and mapped to reference genomes (Hu et al. 2021). NGS technology also improves upon Sanger sequencing in the potential downstream applications of these sequencing data. While the final aim of Sanger sequencing is to provide the actual nucleotide sequence of a DNA fragment of interest, the

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identified nucleotide sequences are only a means to an end in NGS workflows. Once the experiment is complete and the reads are demultiplexed, researchers can use the nucleotide sequences to identify the microorganisms present in their sample. Once identified, NGS data can also give quantitative information as to the relative abundances of the microbial species present in a given sample, which is crucial for the identification of shifts in microbial community composition, commonly referred to in the field as “dysbiosis,” and which itself has been associated with a wide range of human diseases. Statistical analyses can then be performed on these community-based datasets, with the ability to quantify the biodiversity of the sample, such as alpha- and beta-diversity measurements, or to identify factors such as age, disease state, or nutritional status, which most affect certain aspects of microbiome composition. While this technology far outshines the Sanger sequencing method for community-based metagenomic profiling, it is still important to remember that the number of DNA fragments that can be sequenced in a single experiment is still constrained by the surface area of the flow cell, or welled-chip, and the number of fragments that can hybridize it. Therefore, the more samples pooled together for a single sequencing experiment, the smaller proportion of DNA fragments from each sample will actually be sequenced, running the risk that less abundant DNA fragments, such as those from rare microbial species, will not be detected. On the other hand, despite the rapid decline in costs in recent years, NGS sequencing reagents still comport a significant expense, and it can be cost-ineffective to sequence too few samples at once. The decision, therefore, to perform a more “shallow” or “deep” sequencing experiment will depend on many factors, including the biological question, the importance of profiling rare microbial species or genes, and whether or not the researcher knows beforehand which microbes may be present in the sample. If the composition of the microbial community is completely unknown, the researcher may choose an unbiased, untargeted approach, whereby they sequence the entirety of the sample, knowing that they run the risk of either missing the rarer species or of designing a prohibitively expensive experiment. Alternatively, researchers might choose to target only specific sequences of interest and removing as much extraneous DNA as possible, knowing that this runs the risk of also removing novel or uncharacterized microbial species that may be biologically relevant. Although the terms “targeted” and “untargeted” may seem to be two distinct and mutually exclusive categories, they actually exist on more of a spectrum, ranging from the most targeted approaches that identify or characterize a single gene or species, such as laboratory culture, cloning or diagnostic PCR, to the most untargeted approach with the potential to identify every member of every kingdom of the human microbiota, known as shotgun sequencing (Fig. 1). In this chapter, we will describe the various methods commonly employed to study human metagenomics, beginning from the most targeted ones and ending finally with the most untargeted. With no one method being unequivocally superior to any other, we will discuss the pros and cons of each, illustrating how the biological question will inform which method is best suited to the researcher’s needs.

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Fig. 1 Overview of different metagenomics methods, from the most targeted (on the bottom) to the most untargeted (top). Figure generated with Biorender.com

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Methods to Profile the Gut Microbiome Microbial Microarray Analysis Even if one is only interested in a particular subset of microbial species, such as known pathogens, the sheer number of pathogenic microbes renders an individual screening of each one a time- and cost-ineffective strategy in the context of a research laboratory. To address this difficulty, microarray chips specific for human pathogens have been developed to allow for the screening of hundreds of different microbial species at once. In microarray analyses, chips are spotted with thousands to hundreds of thousands of oligonucleotides, which reverse complement specific genes of interest. Samples are then collected, DNA is extracted, digested, and labeled with fluorescent probes, which is then placed on the customized chip. Those labeled DNA fragments, which complement the oligonucleotides on the chip, will bind to them, providing a fluorescent signal at specific locations, which can then be mapped to the specific oligonucleotide and thus allow for the identification of the sequence of interest (Wang et al. 2002). Microarray technology has been used to streamline pathogen screening, such as for the presence of viruses in donated blood, or in the quality control of food and water (Ranjbar et al. 2017; De Giorgi et al. 2019). In the context of investigating the source of disease outbreaks, these microarray chips have been expanded to interrogate hundreds of viral species at once (Wang et al. 2002). Encompassing an even wider range of potentially infectious agents is the GreenChipPm, a microarray chip containing over 29,000 oligonucleotides, which reverse complement sequences from viral, bacterial, fungal, and parasitic pathogens, allowing for the differential diagnosis of outbreaks in situations where the kingdom of the responsible pathogen is unknown (Palacios et al. 2007). Beyond the identification of pathogens, microarrays have also been used to investigate the composition of commensal bacteria in the human intestine, for the purposes of providing an overview of the human gut microbiota. These microarray chips, such as the Human Gut Chip (HuGChip), the Human Intestinal Tract Chip (HITChip), the Human Microbiome Chip (HuMiChip), and the Genetic Analysis microbiota array platform (GA-map) are spotted with thousands of probes covering dozens of bacterial families, and have been successfully used to profile the microbial community response to colonic transit times, aging, and immune sensitization during infancy (Tu et al. 2014). Furthermore, some biotech companies offer custom-made microarray chips, which can be designed by the researcher to fit their experimental design, such as the detection of microbial species of particular importance to the production of clinically relevant metabolites. While microarrays have proven to be an elegant way to overcome the limitations of PCR-based methods, they suffer from significant shortfalls, which have seen them become somewhat obsolete in recent years. First of all, microarray data can suffer from cross-contamination of similar DNA sequences which, if not validated by sequencing, can lead to false positive results. Secondly, microarrays require a large amount of DNA input in order to successfully bind the probe, and thus can often

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miss those sequences, which are low in abundance in the sample. Having said this, microarrays laid down the founding principles for newer, more effective strategies in microbial DNA detection, which are still highly relevant today, especially in the context of differential diagnostics.

Bait-and-Capture Strategy Bait-and-capture, also known as hybridization capture or target enrichment, is largely based on the same principles as microarrays, though with significant improvements. As in the case of microarrays, bait-and-capture involves the generation of long, biotinylated oligonucleotides (probes, or “baits”) designed to reverse complement DNA sequences of interest. Unlike microarrays, DNA that is extracted and sheared from the samples is exposed to these probes in solution, which then bind (i.e., “capture”) the complementary DNA strands that are present in the sample. These are then amplified by PCR and purified with streptavidin-coated beads so as to enrich only the sequences of interest. This enriched, purified sample is then prepped for sequencing, creating a method with far higher sensitivity than classic microarray technology, as well as the ability to include far more probes than those that can fit on a single chip. Furthermore, bait-and-capture goes farther than providing a simple “yes or no” response to the presence of certain microbial species, as it involves the sequencing of the DNA fragments that have bound to the probes. This means that, if a novel variant of a known pathogen is similar enough to one of the probes present in the hybridization solution, it is also possible to identify novel species, strains, or variants of microbial species with this methodology. Most commonly, bait-andcapture is used to enrich either rare microbial species or rare classes of genes, in order to increase the sensitivity of NGS workflows without resorting to exceedingly deep sequencing. For example, bait-and-capture panels have been developed to enrich all pathogenderived RNA transcripts present in a sample, in order to overcome the swamping of pathogen mRNA by host transcripts. These methods have the aim of unbiasedly enriching pathogen-derived transcripts in patient samples, thus accurately maintaining pathogen gene expression level information in the host as well as bypassing the need for deep sequencing protocols (Bent et al. 2013). In other methods, bait-and-capture panels have been developed with a focus on identifying antimicrobial resistance genes, with the aim of reducing the use of unnecessary antibiotics, and thus combatting the rising threat of antibiotic resistance around the world. Given that the collection of antimicrobial resistance genes found in the human microbiota, commonly referred to as the “resistome,” often comprises less than 1% of the total human microbiome, bait-and-capture panels designed specifically to target the resistome provide an elegant, cost-effective solution for enriching and identifying the presence of these genes in human patients with infectious illnesses of unknown origin (Noyes et al. 2017). By relying on the Comprehensive Antibiotics Resistance Database (CARD), different platforms have been developed to profile the resistome in complex human samples, with some relying on more extensive profiling

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strategies to also include resistance to metals and biocides, and resistance genes gained by horizontal gene transfer, while others restrict their focus to antibiotic resistance genes only, to reduce the probability of false positives and sequencing costs (Guitor et al. 2019). Often, this strategy was also able to outperform deep sequencing techniques in the detection of particularly rare (yet still present and thus biologically relevant) antimicrobial resistance genes in the human gut microbiome, confirming its relevance as a method for the characterization of the human resistome (Guitor et al. 2019). In principle, given the downstream sequencing performed with bait-and-capture, some clues as to the identity of the bacterial species harboring antimicrobial resistance genes could be gleaned from these resistome platforms. However, other methods have been developed, which combine the identification of pathogenic bacteria and profiling of antimicrobial resistance, in order to quickly and effectively diagnose the cause of infection as well as selecting the most appropriate therapeutic strategy. For example, the bacterial capture sequencing (BacCapSeq) platform comprises a set of 4.2 million probes, which cover all known human bacterial pathogens, as well as all known antimicrobial resistance genes and virulence genes described. Samples from patients with an infection of unknown origin can then be sequenced with this method, in order to a) identify the pathogen responsible for infection and b) characterize any antimicrobial resistance genes possessed by that pathogen, giving clinicians all the information they need to choose the most effective antibiotic treatment (Allicock et al. 2018). This method is particularly effective in cases where the relative abundance of the pathogenic bacteria is likely to be very low, such as in blood samples, as the enrichment steps will amplify the species of interest and thus be detectable even with a more superficial depth of sequencing, reducing both the cost and the time necessary for the analysis. Similarly, the virome capture sequencing platform for vertebrate viruses (VirCapSeq-VERT) comprises almost two million probes covering the sequences of the 207 viral taxa known to infect vertebrates. This method also enriches viral reads up to 10,000 fold and thus can be used to diagnose both single and multiple viral infections in samples containing a high proportion of either host or other microbial DNA, with the additional step of retrotranscription of viral mRNAs, in order to effectively sequence cDNA from RNA viruses. This method has been used successfully to identify the causative agents of outbreaks of febrile illness in Tanzania, influenza-negative severe acute respiratory infection (SARI) in Uganda, pediatric gastroenteritis in Lebanon, and community-acquired meningitis in adults (Fahsbender et al. 2020). Another vertebrate viral bait-and-capture panel, named ViroCap, was similarly developed and used successfully to identify the causative infectious agent in a study of fever in children (Wylie et al. 2015). Beyond the diagnosing of human pathogens, a similar probe-based method has recently been developed for characterizing entire microbial community structures. Microbial abundances from genome tagged analysis (MA-GenTA) involves the design of multiple, specific probes matching each known genome of the microbial community in question (Benjamino et al. 2021). DNA from samples of interest is then extracted, fragmented, and combined with the barcoded hybridization probes,

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after which the samples are purified and sequenced. As the probes are designed to cover all bacterial species in the community, the aim of this method is not to enrich low abundance species, but rather to create a method that is capable of higher specificity than amplicon sequencing, while being more cost-effective than shotgun sequencing, both of which will be discussed later in this chapter. Since approximately 20 specific probes per microbial genome are generated in this workflow, this method not only allows for the identification of the microbial community composition down to the species and perhaps even the strain level, but it also allows for a more direct functional characterization, as these probes can also be designed to capture genes involved in specific biochemical pathways of interest, beyond those that are involved in antimicrobial resistance. For example, probes can be designed to cover genes involved in the production of clinically relevant metabolites, such as short-chain fatty acids (SCFAs), in order to directly estimate the relative enrichment of these pathways in the sample without the need for complementary metabolomics analysis. Similarly, a 16S hybridization capture method (16S-cap) has been developed to combine the benefits of 16S amplicon sequencing and shotgun sequencing. In this method, probes are designed to enrich multiple bacterial 16S rRNA hypervariable regions found within shotgun metagenomic libraries, increasing the specificity of the identified bacteria, identifying species that were missed with traditional 16S and shotgun sequencing, while simultaneously reducing the number of DNA fragments to be sequenced (Gasc and Peyret 2018; Beaudry et al. 2021). Alternatively, many methods have been developed to allow the researcher to design the baits that best suit their experimental model, which can then be customsynthesized and purchased from a company for use in a research or clinical setting. For example, the CATCH computational method was used to design a probe set designed to enrich pathogenic viral species, and was used to characterize the viral genomes responsible for the 2008 Lassa fever outbreak in Nigeria (Metsky et al. 2019). Other open source software packages, such as MrBait and BaitFisher, have also been used to design similar probe sets in the context of pathogen detection (Guitor et al. 2019). Shortly thereafter, the Syotti method was developed, optimizing both computational time and microbiome sequence coverage, allowing for the design of far larger probe sets in much shorter timeframes and thus expanding the potential for bait-and-capture in the context of large-scale metagenomic profiling (Alanko et al. 2022). In the context of differential diagnostics, bait-and-capture has proven to be an excellent tool for uncovering the cause of outbreaks of disease and represent a remarkable improvement on microarray technology, with the significant advantage of enriching targeted, low-abundance DNA species, and thus being able to detect sequences of interest with far more accuracy and specificity than is possible even with deep sequencing techniques. In the context of resistome profiling, bait-andcapture is arguably the most sensitive and comprehensive approach currently available to the scientific community. Furthermore, since bait-and-capture involves downstream NGS of the targeted DNA fragment, novel variants of known human pathogens can still be detected and characterized. Case in point, the VirCapSeqVERT platform, which was developed to include all known viral pathogens to infect

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vertebrates up until 2015, was still able to capture and enrich SARS-CoV 2 sequences, a virus which was not identified as a vertebrate-infecting pathogen until over 4 years later (Pogka et al. 2022). However, bait-and-capture does still suffer from some noteworthy drawbacks. In the context of diagnosing illness, infections caused by parasites or fungi will remain largely undetected, due to the lack of bait-and-capture platforms having been designed for more than a handful of parasitic or fungal species. Secondly, even if the infectious agent is bacterial or viral, but novel enough to not reliably bind any of the probes found in solution, the cause of the infection will likewise go undetected and uncharacterized. Finally, it is important to note that the study of human health and disease goes far beyond the characterization of pathogens and antimicrobial resistance. In recent years, it has become increasingly clear that the human body hosts a commensal microbial community in numerous different anatomical compartments, whose balance can be just as critical to human health as the absence of pathogens. While the MA-GenTA platform has been developed to address these questions, it still requires additional validation and adjustments to verify its adaptability to complex human microbial communities. Finally, microbes whose genomes have not been discovered, or whose genomes have not been completely annotated, will largely remain undetected in this method if they are different enough to escape the probes present in the hybridization solution. Methods that focus only on known species can only be complementary, therefore, to other metagenomic methods, which give a more comprehensive overview of microbial communities in the study of human health.

Amplicon Sequencing Far more comprehensive than bait-and-capture, amplicon sequencing relies on regions of DNA, which are conserved enough to be present in all known species within a kingdom, but containing variable enough regions that sequencing said region can lead to the identification of different members of that kingdom. Amplicon sequencing begins with DNA extraction from the sample of interest, followed by a PCR with universal primers, designed to bind to the highly conserved sequences found up- and down-stream of the hypervariable region of interest. These PCR primers are completed with adapters and barcodes, which already prepare the produced amplicons for later multiplexing and flow-cell binding. These amplicons are then purified, quantified, pooled, and prepped for NGS. Once the sequencing is completed, complex downstream bioinformatics analyses are required to interpret these data. First of all, sub-par sequencing reads must be removed and the data “cleaned” to remove sequencing errors. Secondly, the sequences must be assembled into contigs and mapped to those deposited in available databases, such as greengenes, SILVA, UNITE, or the Ribosomal Dabatase Project. Given the enormous amount of sequencing data produced by these more modern methods, bioinformatics pipelines have necessarily been developed to process these data, such as QIIME, Mothur, and VAMPS, which map sequences to these databases and assign

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them to an operational taxonomical unit (OTU). Finally, based on the number of reads that map to each OTU, relative abundances of microbial taxa are estimated, and the ecological diversity of the microbial community both within and between different individuals can be calculated by statistical methods (Kuczynski et al. 2011).

Bacterial 16S rRNA Sequencing By far the most widely used and cited example of amplicon sequencing is 16S sequencing for bacterial profiling. Long considered to be the gold standard for metagenomics, 16S sequencing has been widely used to characterize the bacterial communities that inhabit the human body, and provide the backbone of the data used to complete the Human Microbiome Project (Kuczynski et al. 2011). Traditionally, 16S sequencing targets one or more of the hypervariable regions (V1-V9) within the highly conserved gene encoding for the ribosomal small unit (16S rRNA) found across the prokaryotic kingdom. PCR primers are designed to bind to the highly conserved sequences found up- and down-stream of the selected hypervariable region, in order to bind all 16S genes in the sample, yet generating amplicons, which are different enough from each other to allow for the identification of the bacterial taxa. These amplicons are then sequenced and used to build global bacterial profiles of the ecological niche in question, be it a pond, a soil sample, or the large intestine of a human patient. Bacterial profiles from 16S sequencing have been used to demonstrate an association between gut dysbiosis and human diseases in many different contexts, including Inflammatory Bowel diseases, autism, and metabolic diseases (Piazzesi and Putignani 2022). While there is some debate as to how many of these changes are due to the behavior of the patients, such as the limited diet of an autistic child, and how many of them precipitate the patient’s symptoms, the fact that some bacterial species are drivers of disease is inarguable (Piazzesi and Putignani 2022). Conversely, ample evidence has uncovered a positive role for other bacterial species in human health, particularly those which are involved in key metabolic processes, such as the production of short-chain fatty acids. However, as the 16S gene used in amplicon sequencing does not, in itself, code for any of the key metabolic functions that have emerged as fundamental to human health, sequencing this gene cannot give us additional information as to the biological function of the bacterial community in question without additional computational investigations. Numerous computational approaches have been developed over the years to give predictive functional information of a bacterial community profiled by 16S sequencing. One of the most commonly used is an algorithm called Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), which interrogates well annotated 16S databases and predicts, based also on phylogenetic analysis and with quantifiable uncertainty, the metabolic processes that are likely to be enriched or depleted in a given bacterial community (Langille et al. 2013). Similarly, the Piphillin algorithm was developed as an improvement on PICRUSt, by bypassing the need for both data preprocessing and phylogenetic trees, but still giving functional profiles of bacterial communities similar to those uncovered by shotgun metagenomics (Iwai et al. 2016). While these computational approaches

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have optimized the amount of data that can be extracted from a 16S sequencing experiment, it is important to remember that they are still inferences, and need to be validated before a firm conclusion can be drawn. For example, if the habitat from which bacteria were sampled is a less-studied one, such as a specific marine niche, inferences drawn from data derived from habitats other than the one being studied can often lead to erroneous conclusions. In fact, when considering ecological niches other than the human microbiome, Tax4Fun2, an open-source R package updating its predecessor Tax4Fun, further improves functional profiling by incorporating habitat-specific information and functional genetic redundancy into its analysis, yielding more robust results when applied to bacterial communities drawn from habitats outside the human body (Wemheuer et al. 2020). However, none of these programs can ever fully account for horizontal gene transfer, a process by which bacteria can exchange genetic information, thus resulting in strains that have acquired or lost certain metabolic functions while keeping the same 16S identification sequence. Due to these limitations, researchers often prefer corroborating key functional findings with a metaproteomic or even a metabolomic analysis of the sample, in order to confirm the presence of the metabolites that these algorithms have predicted. Finally, the most commonly cited flaw of 16S sequencing is one of species resolution. Since 16S sequencing focuses on one or two hypervariable regions within a highly conserved gene, the nucleotide differences between closely related microbes often come down to a single nucleotide. Between strains of the same bacterial species, and sometimes even between species of the same genus, those hypervariable regions may even be identical. Therefore, while 16S sequencing may be sufficient for a general overview of the microbiota under investigation, and can be sufficient in the diagnosis of a strongly dysbiotic state, it lacks the finesse necessary to pinpoint the bacterial species that can be drivers of human disease. Given the fact that different bacterial species within genera, and even different strains within the same bacterial species, have been found to precipitate diametrically opposing effects on chronic inflammatory disorders (Piazzesi and Putignani 2022), the lack of resolution of classic amplicon sequencing is a notable setback in the study of the role of the human microbiota in health and disease. One amplicon sequencing method developed to overcome this problem is metagenomic multi-locus sequencing typing (MG-MLST), which combines NGS with the MLST method once used to characterize isolated bacterial strains in culture (Bangayan et al. 2020). Rather than sequencing a single amplicon, MG-MLST sequences four different bacterial housekeeping genes, and uses the combined allelic differences between these four genes to identify bacteria at the species and/or strain level, by analyzing the dataset produced with programs, which infer population structure based on curated MLST databases. While undoubtedly ingenious, this method is only applicable to the characterization of human microbial communities comprised of one or few bacterial species, such as the skin microbiome used as a proof-of-principle in this study. Given that this method cannot index the four housekeeping genes to each bacterium that expressed them, highly complex microbial communities, such as the gut microbiota, cannot be characterized by this

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method. Furthermore, since this method is based on known MLST profiles, any strains not present in the available databases or in the learning set will be erroneously assigned to one of the known populations, rather than informing the researcher that a novel, unknown profile has been detected. Having said that, MG-MLST can be a viable, cost-effective option for studying those anatomical compartments that are dominated by a single bacterial species, where strain-level profiling is paramount for disease-association studies.

ITS Sequencing Although the bacteriome dwarves the other microbial communities found in the human gut, whether in cell number, diversity, or in the amount of time researchers have spent studying it, other microorganisms have also been found to be of great importance to human health. The human gut mycobiome, i.e., the collection of genomes of the commensal fungi which inhabit the human body, has also been shown to mature and change with age (Zhang et al. 2021). Furthermore, mycobiome dysbiosis has also been independently linked to an array of human diseases, including inflammatory bowel disease, colorectal cancer, and metabolic syndromes (Zhang et al. 2021). The most commonly used fungal analog of bacterial 16S is the sequencing of the nuclear ribosomal internal transcribed spacer (ITS) region, which is approximately 650 bp long, found in large copy numbers within the cell, and contains two hypervariable regions, ITS1 and ITS2 (23). Given the higher complexity of eukaryotic genomes compared to their prokaryotic counterparts, these regions tend to be far more variable than bacterial V1-V9 16S regions, and thus ITS sequencing can often resolve fungal communities down to the species level (Zhang et al. 2021). Though the specifics of primer design and computational models vary, the basic principle is the same as with 16S bacterial profiling, beginning with the amplification of these highly variable regions by PCR, followed by NGS, identification of the fungal species present in the microbial community and estimating their relative abundances. Alternatively, the fungal 18S rRNA region can also be sequenced in a similar way, though this gene is used less often as a means to profile the human mycobiome (Zhang et al. 2021). In order to process raw sequencing ITS data and obtain results in a format which permits further downstream analysis, many of the same bioinformatics pipelines can be used, such as DADA2, QIIME2, and vegan, though these programs must interrogate different, fungal-specific databases in order to identify the composition of the mycobiome in the given sample. However, despite the potential for species-specific resolution of ITS sequencing, obtaining reliable results on the human mycobiome can be more computationally challenging than bacterial 16S profiling. Firstly, the hypervariability of the ITS region between fungal species leads to amplicons with a much wider size range than those produced during 16S sequencing. While this hypervariability is, incidentally, the very feature that allows for more species-specific information than does 16S profiling, it also leads to data that is less tolerant of cutoff restrictions, necessitating a researcher with significant bioinformatics experience to choose the appropriate parameters for their ITS dataset, lest they lose almost all of their

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biological information. Secondly, given the low diversity of the human mycobiome, the relative abundances between fungal species can be extreme, which can also lead to statistical artifacts if one is not experienced in biostatistics (Xie and Manichanh 2022). Therefore, given the complexity of pipelines such as DADA2 and QIIME2, other programs have been developed to give biologists with a limited bioinformatics background a chance to analyze their raw data without the need to outsource the computational part of their project. For example, the open-source web server Designing, Analyzing, and Integrating fungal Ecology to effectively study the systems of Life, or DAnIEL, can be used as a single program to perform sequencing quality control, trim adapter sequences, assemble contigs, align them to the appropriate fungal databases, and perform statistics to determine which fungal species are significantly more or less abundant between two groups (Loos et al. 2021). Other pipelines have also been developed to similarly analyze fungal community composition, as well as add some information as to the function of these fungi, such as FunFun, LIAS, DEEMY, FungalTraits, FunGuild, and FacesOfFungi, though these programs tend to be highly specific to certain habitats or ecological contexts (Tanunchai et al. 2022). Despite these advancements made in recent years, fungal databases still lag behind bacterial ones in both species and functional information, a drawback that will only be overcome in time with further mycobiome studies.

Amplicon Sequencing: The Archaeome and the Parasitome By far the least studied microorganisms, archaea and protists are, nonetheless, starting to become recognized in their own right as important players in the human intestinal ecosystem. By building on the principle behind bacterial 16S sequencing, researchers in Korea were able to annotate 685 archaeal ASVs in the Korean gut archaeome (Kim et al. 2020). Furthermore, unlike every other class of microorganism found in the human gut, no archaeal species has ever been identified as a human pathogen, leading to the hypothesis that these microorganisms inhabit the human gut for the purposes of performing commensal functions. While there is some evidence that human gut archaea may have a role in nutrient catabolism and immune system responsiveness, the human archaeome is still very poorly characterized (Mohammadzadeh et al. 2022). Similarly, amplicon sequencing has also been adapted to identify protists, by sequencing the V4 and V9 regions of the 18S rRNA gene and processing the data with the DADA2 pipeline (Salmaso et al. 2021). This method has wide applications, including the profiling of protists in water sources, marine environments, wild animals, livestock, and sewage, all of which may be reservoirs for parasitic outbreaks in humans (24). Furthermore, it is known that intestinal protists, as well as other parasites, have the ability to interact with other microorganisms with whom they share an ecological niche, exerting effects on both them and the host, not all of which are necessarily pathogenic (Parfrey et al. 2014). However, the role of protists in human health, especially in the context of a potential commensal relationship, remains to be both confirmed and characterized by researchers. With NGS technology becoming ever-more accessible to the scientific community, it is possible that the role of both archaea and protists in human health will be more thoroughly addressed in the near future.

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While this method can reasonably be described as far less targeted than bait-andcapture, it is still categorized as a “targeted” NGS approach due to the initial PCR step which, although designed to amplify all members of an entire kingdom, still results in the enrichment and purification of a single kingdom in the sequencing sample. By targeting one kingdom, amplicon sequencing effectively enriches that group of microorganisms, which can be particularly useful when studying a relatively low abundance kingdom, such as archaea in the gut, or when the sample may be strongly contaminated with host cells and DNA, such as tissue-associated microbiome profiling. However, unlike bait-and-capture, amplicon sequencing gives a far more comprehensive overview of the microbial communities that inhabit the human body, and has been routinely used to identify dysbiosis and altered biodiversity in the human gut, both of which are recognized as driving forces behind many different human pathologies, which are not necessarily infectious in origin. Given that it has been the standard operating procedure in the characterization of the human microbiota for decades, databases such as greengeens, SILVA, UNITE, and the Ribosomal Database Project are extensive and very well curated (Kuczynski et al. 2011). While it is always possible that there exists a bacterium, fungus, or protist, which diverges enough from other members of its kingdom to escape binding the “universal” primers designed for amplicon sequencing, the primer binding regions are conserved enough that amplicon sequencing is still far superior to bait-and-capture in its power to uncover new species. However, amplicon sequencing also suffers from some noteworthy flaws. One of the first drawbacks to be uncovered in this method was PCR bias, with the demonstration that some “universal” primers actually bind poorly to certain genera or phyla, leading to those taxa being erroneously classified as “rare” in samples in which they were actually far more abundant than previously estimated (Kuczynski et al. 2011). Another significant drawback to amplicon sequencing is the lack of any available analog for the profiling of the human virome. With the emergence of the role of several commensal viral species in the proper maintenance and functioning of the human microbiota, methods for virome profiling beyond the identification of known pathogens is increasingly in demand. In order to address these kinds of questions, the only method currently available is shotgun sequencing.

Shotgun Sequencing By far the most untargeted, and consequently the most expensive approach to metagenomic profiling is shotgun sequencing, which involves sequencing the entirety of the DNA found in a given sample. DNA is extracted and fragmented, either mechanically (e.g., sonication) or enzymatically (e.g., Illumina’s “tagmentation” protocol). Fragmented DNA is then “tagged” with universal adapters, purified, barcoded for multiplexing, pooled and prepped for sequencing. Unlike amplicon sequencing, shotgun metagenomics tags, barcodes, and sequences all DNA found in the sample, which means that subsequent reads can come from bacteria, fungi, viruses, archaea, parasites, the host, or even the food they

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ingested. By sequencing the entirety of the DNA present in the sample, shotgun metagenomics has, by far, the strongest potential for biological discovery among available NGS platforms. First of all, by sequencing the entirety of the metagenome rather than just one fragment of it, shotgun sequencing has the most capacity for subtle discrimination between different strains of the same species, allowing for the identification of specific microbial markers and drivers of disease. Secondly, shotgun sequencing does not need to rely on inferences to build functional profiles, as do pipelines such as PICRUSt, Tax4Fun2, and FunGUILD. Instead, contigs from a shotgun dataset can map to any part of the microbial genome in question, giving the researcher the ability to directly detect genes involved in antimicrobial resistance or metabolic functions. This way, even if horizontal gene transfer has occurred, genes involved in specific pathways of interest will still be detected in the dataset, resulting in a far more accurate functional profile. Furthermore, if the metagenome coverage is high enough, assembled contigs will reveal which bacterial strain, specifically, has acquired the gene of interest by horizontal gene transfer. Alternatively, researchers can apply Hi-C technology to link mobile genetic elements of interest to their bacterial host, in order to identify which microbial species, specifically, may act as antimicrobial gene reservoirs. Based on Chromosome Conformation Capture (3C) technology, Hi-C can be used to cross-link plasmids with their hosts, identifying which biologically relevant plasmids are most associated with which bacterial species (Stalder et al. 2019). This can be of particular importance, for example, to the medical field, whereby a bacterial strain which has acquired an antimicrobial resistance gene may be identified by shotgun sequencing and, with the information gathered from its assembled genome, a targeted diagnostic protocol for this particular pathogen can be readily implemented. Similarly, entirely novel microorganisms can be discovered this way, and if the sequencing coverage allows for their genomes to be assembled in silico, phylogenetic analysis can be performed to help identify novel microbial species. It was, in fact, a shotgun sequencing workflow that proved invaluable to the early identification and characterization of the SARS-CoV 2 virus in the early days of the COVID-19 pandemic. However, the applications of shotgun sequencing to viral detection go far beyond the identification of novel variants of human pathogens. It was only with a shotgun NGS approach that the role of the virome in human health and disease has a chance of one day being appropriately characterized. With this workflow, hundreds of commensal viral sequences have been identified in the human intestinal tract, although many of them still remain to be fully annotated and taxonomically classified (Fujimoto et al. 2022). Furthermore, virome dysbiosis, specifically, has been associated with a wide range of human diseases, including obesity, type 1 and type 2 diabetes mellitus, nonalcoholic fatty liver disease, and cardiovascular diseases. Intriguingly, the transplantation of the fecal virome from lean donor mice was sufficient to reduce weight gain in mice fed on a high fat diet, also strongly suggesting a causative role for the intestinal virome in the development of symptoms related to metabolic distress (Fujimoto et al. 2022). Furthermore, shotgun sequencing is capable of identifying trans-kingdom nodes of interaction. For example, with the most abundant viral species in the human

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intestinal tract being bacteriophages, and with the most abundant kingdom of microorganism in the human gut microbiome being bacteria, it is logical that the interaction between bacteriophages and their prokaryotic hosts would be of particular importance to human health (Fujimoto et al. 2022). Finally, by profiling the entirety of the microbiome in a single experiment, information can be more easily gathered on the under-characterized kingdoms of the human microbiome. For example, by compiling shotgun datasets from around the world, the catalogue of human gut archaeal genomes has been expanded to 1167, and changes in both bacterial and archaeal community compositions have been linked to diseases such as colorectal cancer (Coker et al. 2020; Chibani et al. 2022). While in principle this method could thus seem highly attractive for the universal profiling of an entire microbial community in a single sequencing step, obtaining a reliable trans-kingdom profile is far more complicated than it might initially appear. From the chemical perspective, there is the fact that DNA extraction protocols for one microbial kingdom often involve steps that are inadvisable for the DNA extraction of another. For example, extraction methods for bacteria from stool samples often involve different centrifugation steps to reduce the amount of dietary fiber and contaminants, with the recovery of the bacterial phase also losing the far lighter viruses, as well as the far heavier fungi. Even if centrifugation steps are not performed, the glass bead-bashing of the samples that is commonly used to break tough bacterial cell walls has also been found to lead to a significant reduction in viral particles (Conceição-Neto et al. 2015). Furthermore, there is the fact that, unlike prokaryotes and eukaryotes, viral genomes are often composed of nucleic acids other than dsDNA, including ssDNA and RNA, all of which need to be both recovered during the extraction process and transformed into dsDNA before they can be sequenced. Finding the perfect balance between the different kinds of genomes is still a significant challenge, and there is currently no universally applied nucleic acid extraction protocol for trans-kingdom shotgun profiling. From the sequencing perspective, one of the drawbacks to shotgun sequencing is inextricably linked to its greatest strength, which is the fact that it sequences all DNA present in a sample in a completely untargeted manner. While this is the feature that makes shotgun sequencing a truly untargeted approach it can also, depending on the biological question, lead to the production of a lot of confounding and unnecessary data. For example, if a researcher is interested in profiling the microbiota associated with tumerogenic tissue, shotgun sequencing of this sample will lead to the vast majority of the reads mapping to the human genome. Similarly, if one is interested in the gut virome, the enormous quantity of bacterial DNA in these samples will lead to a significant underrepresentation of viral reads in the final dataset, many of which may go completely undetected, unless the sample undergoes deep and costly sequencing. Even if such a deep sequencing is economically feasible, discarding over 90% of sequencing reads during analysis is not only wasteful, but it also inevitably leads to unreliable relative abundance measurements and statistical artifacts. Many experimental methods have been developed to attempt to overcome this problem and to keep the cost of shotgun sequencing down. To combat the issue of host DNA contamination, many kits are commercially available that can deplete host

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DNA during extraction, reducing the number of reads that map to the human genome to a minimum. If the researcher is particularly interested in the characterization of the virome, many methods of enriching all viral species in the sample, regardless of whether or not they have been previously characterized (as is necessary for bait-andcapture enrichment) have been proposed. Most protocols propose sequential filtering of the sample, in order to significantly reduce the presence of larger eukaryotic and prokaryotic cells. While this method does successfully enrich the virome in the sample, it can introduce a substantial bias, as there are many viral species (e.g., mimivirus) that are similar in size to bacterial cells, as well as underrepresenting viruses found within cell bodies, which will be eliminated along with the cells they are currently infecting (Conceição-Neto et al. 2015). Another approach is to use commercially available kits to deplete host rRNA and bacterial DNA, although this method can, in some contexts, lead to a final nucleic acid yield which is too low for reliable downstream applications. Conversely, another possibility is to amplify the entire viral genome, though these methods need to be tested and validated to minimize the introduction of amplification bias. For example, multiple displacement amplification, which has been used in many workflows to enrich viral reads in the human intestine, was demonstrated to be heavily biased toward circular singlestranded DNA genomes, leading researchers to question whether or not Microviridae ssDNA phages are truly as dominant in the human intestinal virome as initially reported (Fujimoto et al. 2022). Computationally, there are also significant challenges associated with the shotgun metagenomics approach. Firstly, given the genome-wide information that this method produces, mapping the obtained sequencing reads to shotgun metagenomic databases requires far more computational power than does mapping to those that only contain sequences from a small portion of the genome, such as the databases used for 16S rRNA sequencing. To overcome this issue, shotgun databases specific to certain highly studied habitats, such as the Unified Human Gastrointestinal Genome (UHGG) database, have been released, in order to reduce processing requirements, and thus data analysis costs (Almeida et al. 2021). Similarly, the FunOMIC pipeline was developed to combine the taxonomic identification and the functional characterization of the human mycobiome, with the potential of associating fungal species with specific diseases (Xie and Manichanh 2022). Nevertheless, while shotgun metagenomics has enormous potential in the functional characterization of microbial communities, this potential is still in part unrealized, as the databases available for shotgun sequencing are newer, and thus far less complete than those for 16S or ITS. Rather than being prone to sample-derived false positives, therefore, shotgun metagenomics is vulnerable to false positives produced when bioinformatics algorithms map sequences to the wrong organism, especially in the case of less studied microbial communities, such as those found in plants or in soil. While these limitations will be overcome in time with the expanding popularity of shotgun sequencing and the updating of reference databases, it is currently advisable to validate any unusual or remarkable finding (such as, for example, the identification of an unexpected tropical parasite in a human stool sample) with other, complementary methods (such as diagnostic qPCR for that parasite).

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Finally, one of the biggest drawbacks to shotgun sequencing is the cost, which is directly proportional to the amount of coverage needed in order to obtain the results that one is looking for. Despite the fact that the cost of sequencing has gone down significantly in recent years, shotgun sequencing can easily run in the thousands of dollars per sample if the researcher does not take measures to enrich their targets, and thus needs a very deep sequencing to detect them. To address this issue, one group performed shallow shotgun metagenomics by reducing the number of reads per sample to 0.5 million, making it similar in cost to 16S sequencing, and yet still managed to produce more accurate and specific OTU assignations than 16S sequencing (30). While shallow shotgun sequencing may, in the long run, beat 16S in a cost-benefit analysis in the context of the characterization of the gut microbiome, which incidentally is already highly enriched for bacterial DNA, it is clearly not a solution in the context of low abundance reads. By employing a shallow sequencing approach, the researcher is losing many of the desirable features of shotgun metagenomics, such as the characterization of novel species, functional analysis, and the identification of trans-kingdom nodes of interaction. However, given its relative novelty in the world of metagenomics, shotgun sequencing is still improving year by year, as researchers develop new strategies to overcome these limitations. One strategy that has been proposed to address the coverage versus sequencing depth trade-off is linked-read sequencing. In this approach, fragments from the same DNA strand are tagged with the same barcode. After shallow sequencing, algorithms such as Athena-meta recognize similarly barcoded fragments and assembles contigs accordingly, bridging what otherwise would have been a gap in the contiguous sequence (Zhang et al. 2020). Similarly, Element Biosciences developed a workflow, called LoopSeq, which applies synthetic long-read sequencing to metagenomic profiling. In this approach, DNA from a sample is exposed to a proprietary blend of millions of unique barcodes, one of which will bind to one DNA molecule. These barcoded DNA strands are then amplified by PCR, after which the unique barcode is randomly incorporated at various sites within the genetic material, resulting in short, barcoded DNA fragments. These short fragments are then sequenced by standard NGS platforms and reassembled into contiguous sequences based on the unique barcode identifiers, resulting in highly accurate long sequence reads of up to 6 kilobases (Callahan et al. 2021). By employing these systems, sequencing coverage has far less impact on microbial genome assembly, further optimizing the balance between sequencing costs and data quality. While intriguing, the researchers were clear that this method was still not able to compete with the capabilities of whole metagenome assembly made possible by newer, third-generation sequencing technologies.

Third-Generation Sequencing Next-generation sequencing has undoubtedly revolutionized the study of metagenomics, and with the continuous improvement and optimization of instruments

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and reagents, it is rapidly becoming more accessible and affordable to both medical and research laboratories across the world. However, it is likely that a plateau will soon be reached, in which the reagents and the instruments will be as compact, quick, and inexpensive as they can be, while still producing the same quality of data. Especially in the context of functional analysis and de novo genome assembly, which requires an adequate coverage of the genomes in the sample in order to yield reliable results, there is most likely an upper limit to how many samples can be sequenced together in a single experiment. However, new approaches are being developed, specifically to overcome these limitations of NGS, commonly referred to as long-read sequencing, or third-generation sequencing. As the name suggests, the principle behind long-read sequencing is very simple. The basics of NGS involves breaking up DNA into fragments of a few hundred base pairs, amplifying the fragments, sequencing these fragments, and then re-assembling them into the genomes they derived from. Long-read sequencing, on the other hand, does not rely on the fragmentation or amplification of DNA, but rather sequences DNA strands that range from thousands to millions of base pairs long, thus overcoming the need for deep coverage, and reducing how much repetitive elements can confound genomic assembly. With much larger strands of DNA being sequenced, much larger proportions of the genome are complemented by a single read, and thus far fewer reads are necessary for de novo genome assembly. This means that, not only would long-read sequencing require far less processing power for data analysis, but also far fewer DNA strands would need to be physically sequenced, completely obliterating the flow cell/sequencing chip size constraints discussed previously in this chapter. This technology, therefore, allows for exceedingly small, even portable sequencers, but which still have the capability of producing detailed and high-quality data. In order to make this concept a reality, new ways of sequencing DNA had to be developed in order to overcome the length constraints of classic, short-read sequencing by synthesis.

SMRT Sequencing In PacBio Single Molecule, Real-Time (SMRT®) Sequencing, nucleic acids are extracted from the sample, and hairpin adapter sequences are added to either side of the double-stranded DNA molecule, binding them together in a final, singlestranded, circular structure. DNA polymerase is bound to the strands, and the DNA is placed on a SMRT Cell, covered in millions of tiny wells, each of which will immobilize a single circular DNA molecule. Similar to NGS, sequencing occurs as the DNA polymerase incorporates labeled nucleotides into the complementary strand. Unlike NGS, the SMRT Sequencing system uses a characteristic light signal to identify the DNA sequence that is being generated, allowing for real-time monitoring of the sequencing read. This technology produces sequencing reads that can range from 20 to 100 kilobases in length. Though on average it also produces far fewer reads per sample than does NGS, far fewer long-reads are needed to adequately assemble a microbial genome. By eliminating the need for PCR amplification of DNA fragments, this method is far less prone to errors derived from amplification bias. Furthermore, by continuously running around the circular

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DNA template (i.e., circular consensus sequencing), the DNA polymerase essentially sequences the same DNA strand over and over again, producing very long reads known as HiFi reads, and thus addressing the issue of polymerase-introduced sequencing errors by an error-correction-by-consensus model (Nilsson et al. 2019). The low error rate, combined with the long sequencing reads produced by circular consensus sequencing are particularly relevant in the characterization of novel genetic variants, such as antimicrobial resistance genes, and/or other mobile genomic elements. In one study, researchers combined the principles of bait-and-capture and long-read SMRT sequencing in order to better annotate very low abundance antimicrobial resistance genes. In a process they called target-enriched long-read sequencing (TELSeq), biotinylated cRNA probes were used to target and capture long DNA strands containing the genes of interest. In this method, researchers were not only able to detect antimicrobial resistance genes that were commonly undetected with classic NGS techniques, but the long-read sequencing technology was also able to provide some biological context for these genes. Most biologically relevant was the demonstration that these genes often colocalize with mobile genetic elements, giving this technology wide applications in public health surveillance (Slizovskiy et al. 2022).

Nanopore DNA Sequencing In Oxford Nanopore DNA sequencing, the biochemical properties of protein nanopores were exploited to develop a completely different method of DNA sequencing, discarding the need for DNA polymerase and complementary strand synthesis altogether. Oxford Nanopore Technology platforms insert a protein nanopore into a synthetic, electrically resistant membrane, to which an electrical potential is then applied. DNA strands are tagged with an enzymatic complex, which recognizes and binds the nanopore embedded in the membrane and binds to it. The formation of this complex pulls one of the DNA strands through the nanopore which, while passing through, causes disturbances in the electrical current passing through the synthetic membrane. Since these disturbances are specific to the base that passes through the nanopore, the instrument can identify it from the electrical disturbance created, thus generating a sequencing read. Given that this technology does not require the synthesis of a new DNA strand in order to generate the sequence, it is capable of sequencing virtually any length of DNA molecule, without the introduction of amplification bias, or errors introduced by the DNA polymerase. Furthermore, Oxford Nanopore instruments are able to process the data while it is being produced, further streamlining the entire sequencing workflow. It concurrently uploads this data to a cloud server, which can be monitored by the researcher in real time, and who can thus decide when to interrupt the experiment based on whether the appropriate amount of information has been extracted from the sample. If the researcher is only interested in a specific target, such as a single microbial species, it is also possible to use software packages such as SPUMONI, minimap2, or readfish to direct the nanopore to expel the DNA molecule it is currently sequencing if the initial information indicates that it is sequencing a DNA strand that is not of interest (Payne et al. 2021).

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By eliminating many DNA processing steps, this technology is significantly faster and cheaper than both NGS and SMRT long-read sequencing. In metagenomic sequencing, this method has been applied to both mock microbial communities and human stool samples, resulting in the assembly of entire microbial genomes from a single contig (Moss et al. 2020). Furthermore, this technology has led to the release of portable sequencers, such as the MinION, which is the size of a large USB stick and which can therefore be used to sequence samples in real time in the field, with remarkable applications for ecological fieldwork and for tracking infectious disease outbreaks. However, Oxford Nanopore technology still suffers from significant sequencing error rates, primarily due to the speed by which the DNA strand passes through the nanopore. Nevertheless, the error rates are decreasing as the technology is continuously improved upon, though whether or not Nanopore sequencing will reach the accuracy of NGS or SMRT circular consensus sequencing remains to be seen. Oxford Nanopore technology has been applied to bacterial 16S sequencing, in order to increase its resolution down to the species level without the need to perform shotgun sequencing (Matsuo et al. 2021). In this workflow, the entire 1500 bp 16S gene is amplified by PCR, rather than only one or two hypervariable regions. By taking the entire 16S sequence into consideration, the probability of picking up differences between bacterial species is significantly increased, consequently also increasing the likelihood of discovering novel taxa. Combined with powerful biochemical and computational tools that can detect as few as single nucleotide differences between sequences, this method has been shown to be able to resolve down to the bacterial species, and even to the strain level within both simple and complex microbial communities (Matsuo et al. 2021). Similarly, the fungal intergenic spacer (IGS) sequence was used to identify potentially pathogenic fungal species with nanopore sequencing technology, with potential applications as a new, rapid, and cost-effective fungal infection diagnostic strategy (Morrison et al. 2020). While promising, relying on such miniscule sequencing differences to discriminate between strains also makes this method particularly prone to amplification and sequencing error, which needs to be taken into consideration when interpreting the data generated by this alternative amplicon-based method. Advancements in computational methods also had to be made in order to allow for this technology to fully realize its potential. Bioinformatics pipelines, which use short-read data to assemble genomes, are not applicable to long sequencing reads, which can range from the thousands to the millions of base pairs. Furthermore, in the context of microbiology, mapping long sequencing reads to the hundreds of thousands of annotated microbial genomes, in order to correctly identify the species in question, comports an additional challenge for classic long-read assemblers. Some metagenomic long-read pipelines, such as metaFlye, MAGPhase, and hifiasm-meta, have recently been released in order to address this need (Sereika et al. 2022). However, given the novelty of these approaches, it is still too early to tell whether long-read sequencing will end up complementing or supplanting NGS technology.

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Conclusion Since the development of high throughput sequencing techniques, publications on metagenomics have increased exponentially. Despite the expansion of this technology rapidly increasing the potential and applications of metagenomics profiling, no one technique has emerged to completely overshadow the rest, with each method coming with its own advantages and disadvantages. While, at face value, an untargeted, and thus unbiased, approach may seem like the new gold standard for metagenomics studies, the sheer vastness of the role of the human microbiota in various aspects of human health and disease has led to each method still retaining a relevant place in research today. In the context of infectious disease research and diagnostics, where only pathogenic species and the resistome are of relevance, untargeted methods such as deep sequencing shotgun metagenomics are costly, unnecessary, and often insufficiently sensitive. On the other hand, in the context of uncovering the role of commensal viral species in human health, shotgun sequencing remains the only viable method to date. Without the existence of any method that is completely free of weaknesses, the selection of an appropriate sequencing strategy will rely heavily on the biological question at hand. Furthermore, the development of combination metagenomic strategies, such as TELSeq, combining the benefits of bait-and-capture and long-read sequencing, or SLR sequencing, combining longread sequencing principles with existing NGS instruments, may provide the most innovative answers to the question; what is the best metagenomic method? Despite all that has been uncovered to date about the role of the human microbiome in the development of cardiometabolic diseases, there is still much, much more to be learned. Whether it be the identification of new pre/probiotic therapies that can alleviate symptoms, early microbial markers for disease, or the characterization of the metabolic functions of the underrepresented microbial kingdoms inhabiting the human intestinal tract, metagenomic methods will be continuously updated to keep up with the demand from the scientific community. With technological innovations being developed every year, it is possible that one method will emerge to render all others obsolete. Until then, a combination of metagenomic profiling strategies remain the most appropriate way to address the varied and complex microbiological questions of the day.

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Wylie TN, et al. Enhanced virome sequencing using targeted sequence capture. Genome Res. 2015;25(12):1910–20. https://doi.org/10.1101/gr.191049.115. Xie Z, Manichanh C. FunOMIC: pipeline with built-in fungal taxonomic and functional databases for human mycobiome profiling. Comput Struct Biotechnol J. 2022;20:3685–94. https://doi.org/ 10.1016/j.csbj.2022.07.010. Zhang L, et al. A comprehensive investigation of metagenome assembly by linked-read sequencing. Microbiome. 2020;8(1):156. https://doi.org/10.1186/s40168-020-00929-3. Zhang L, et al. The role of gut mycobiome in health and diseases. Ther Adv Gastroenterol. 2021;14: 17562848211047130. https://doi.org/10.1177/17562848211047130.

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Methods to Study Metabolomics Simona Fenizia, Egeria Scoditti, and Amalia Gastaldelli

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolomic Profile of Biological Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Essential Amino Acids and Related Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tryptophan-Kynurenine Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insulinotropic Amino Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amino Acids Involved in Brain Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amino Acids, Oxidative Stress and Lipotoxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Choline and Its Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glycolysis and Intermediates of TCA Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonesterified Fatty Acids (NEFA) and Short Chain Fatty Acids (SCFA) . . . . . . . . . . . . . . . . . . . Carnitine and Acylcarnitine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Primary and Secondary Bile Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vitamins and Phenol Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fluxomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of Glucose Fluxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of Aminoacid Turnover and Protein Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of Lipid Fluxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analytical Methodologies for Metabolomics and Fluxomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nuclear Magnetic Resonance (NMR) Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chromatography, Liquid Versus Gas, Coupled with Mass Spectrometry . . . . . . . . . . . . . . . . . . . Sample Acquisition and Purification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample Matrix, Sample Acquisition, and Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Derivatization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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S. Fenizia · A. Gastaldelli (*) Institute of Clinical Physiology, National Research Council, Pisa, Italy e-mail: [email protected]; [email protected] E. Scoditti Institute of Clinical Physiology, National Research Council, Lecce, Italy e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_2

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Metabolite Quantification and Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Targeted Versus Untargeted Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quantitative Versus Qualitative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Metabolic diseases, namely obesity, nonalcoholic fatty liver disease (NAFLD), and type 2 diabetes (T2D), are important risk factors for co-morbidities and mortality. However, not all subjects with metabolic diseases progress toward more severe forms. Thus, it is becoming urgent the discovery of biomarkers that can differentiate phenotypes with different associated risks as well as pathophysiological markers of disease that can be targeted. The use of new omics techniques (i.e., genomics, transcriptomics, metabolomics, lipidomics, and metagenomics) is now becoming a common way to discover biomarkers of metabolic alteration and progression toward more severe morbidity and mortality. Metabolomics has the advantage that it can be investigated directly in plasma samples taken during fasting or postprandial conditions, or in other biological samples, such as urine or feces. Metabolite concentrations are a mirror of alterations in metabolic fluxes. Moreover, also the gut microbiome is involved in the worsening of metabolic diseases since gut bacteria produce metabolites that can either stimulate hormone secretion or act as signaling molecules. Thus, the study of most relevant metabolites is necessary for the elucidation of pathophysiological pathways. This chapter focuses on the metabolites considered relevant for human metabolism and describes the approaches currently used to study the metabolomic profile in biological samples by discussing the targeted versus untargeted approaches and the analytical workflow required to study different classes of metabolites. Keywords

Metabolomics · Mass Spectrometry (MS) · Nuclear Magnetic Resonance Spectroscopy (NMR) · Biomarkers · Metabolic fluxes · Metabolic diseases · Microbial metabolites

Introduction Metabolomics, alone or in combination with other omics techniques, like fluxomics, lipidomics, metagenomics, transcriptomic, and/or proteomic, is used for the elucidation of pathophysiological pathways of metabolic diseases and in the discovery of biomarkers and possible pharmacological targets. Metabolic diseases, namely, obesity, nonalcoholic fatty liver disease (NAFLD), and type 2 diabetes (T2D) make the

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largest contribution to worldwide morbidity and mortality. The epidemic of metabolic diseases is now reaching dramatic numbers that are expected to continually increase. Worldwide obesity has nearly tripled since the 1970s with a growing trend in both adults and children. Over the next 25 years T2D is expected to increase by 51% (from 537 million to 783 million worldwide, IDF Diabetes atlas 10th edition 2021, https://www.diabetesatlas.org accessed on 3 January 2023). Fatty liver disease (for which both obesity and T2D are among the major risk factors) has a prevalence of 32% worldwide, of which ~10–30% progresses to nonalcoholic steatohepatitis (NASH) and severe liver disease (Riazi et al. 2022). However, it is recognized that not all subjects with metabolic diseases are progressing toward more severe forms. Thus, it is becoming urgent to discover biomarkers that can differentiate phenotypes with different associated risks as well as the pathophysiological markers of disease that can be targeted. Omics techniques (i.e., genomics, transcriptomics, metabolomics, lipidomics, and metagenomics) are now widely used in epidemiological studies to identify early biomarkers of alterations. In particular, metabolomics, lipidomics, and metagenomics are among the most promising techniques, since they are strictly related to alterations in metabolism and thus to the pathophysiology of metabolic diseases, providing an integrated biological profile downstream of genomic, transcriptomic, and proteomic variability. Moreover, the metabolomic profile can be easily investigated both in plasma samples, collected during fasting or postprandial conditions, and in other biological samples, such as urine or feces. Furthermore, integration of metabolomics and genetic variants might contribute to the understanding of disease mechanisms and provide novel markers of polygenic metabolic diseases. This chapter focuses on the metabolites found relevant for human metabolism and describes the methods currently used to study the metabolomic profile in biological samples. These methods include targeted versus untargeted analytical approaches and the analytical workflow to investigate various classes of metabolites.

Metabolomic Profile of Biological Samples Metabolomics focuses on small molecules (i.e., metabolites) with a mass range between 50 and 650 Dalton (Da), mainly polar metabolites (amino acids, bile acids, organic acids), sugars, fatty acids, and vitamins, while the analysis of the lipid profile is usually a matter of study of lipidomics, which will not be discussed in this chapter. Metabolites are produced during metabolic reactions, e.g., glycolysis, proteolysis, or lipolysis, or they derive from bacterial metabolism and in this case they are referred as microbial metabolites. Focus of metabolomics is the detection, identification, and quantification of metabolites in biological samples, mostly plasma, serum, or urine. Some of the major classes of biologically active metabolites are listed in Table 1. Metabolite concentration, either high or low, is considered a marker of dysfunctional metabolism, or a marker of microbial activity that can be either beneficial or

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Table 1 Major classes of biologically active metabolites, with molecular formula and mass Class/Pathway Aminoacids and derivatives

Compound Agmatine Alanine Arginine Asparagine Aspartate Cadaverine Citrulline Cysteine Dopamine Gamma-Aminobutyric acid Glutamate Glutamic acid Glutamine Glutathione Glycine Histidine Homocitrulline Homoserine Hydroxyproline Imidazole propionate Imidazolone-propionate Isoleucine Ketoglutaric acid Ketoisocaproic acid Ketoisovaleric acid Leucine Levodopa Lysine Methionine p-Cresol Phenylacetic acid Phenylacetylglutamine Phenylalanine Phosphoric acid Proline Putrescine Sarcosine (Nmethylglycine) Serine Spermidine Spermine Taurine

Formula C5 H14 N4 C3 H7 N O2 C6 H14 N4 O2 C4 H8 N2 O3 C4 H7 N O4 C5 H14 N2 C6 H13 N3 O3 C3 H7 N O2 S C8 H11 N O2 C4 H9 N O2

Mass 130.12 89.05 174.11 132.05 133.04 102.12 175.10 121.02 153.08 103.06

C5 H9 N O4 C5 H9 N O4 C5 H10 N2 O3 C10 H17 N3 O6 S C2 H5 N O2 C6 H9 N3 O2 C7 H15 N3 O3 C4 H9 N O3 C5 H9 N O3 C6 H8 N2 O2 C6 H8 N2 O3 C6 H13 N O2 C5 H6 O5 C6 H10 O3 C5 H8 O3 C6 H13 N O2 C9 H11 N O4 C6 H14 N2 O2 C5 H11 N O2 S C7 H8 O C8 H8 O2 C13 H16 N2 O4 C9 H11 N O2 H3 P O4 C5 H9 N O2 C4 H12 N2 C3 H7 N O2

147.05 147.05 146.07 307.08 75.03 155.07 189.11 119.06 131.06 140.06 156.05 131.09 146.02 130.06 116.05 131.09 197.07 146.11 149.05 108.06 136.05 264.11 165.08 97.98 115.06 88.10 89.05

C3 H7 N O3 C7 H19 N3 C10 H26 N4 C 2H7 NO3 S

105.04 145.16 202.22 125.01 (continued)

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Table 1 (continued) Class/Pathway

Tryptophan-kynurenine pathway

Carnitines

Choline metabolites

Compound Threonine Tryptophan Tyrosine Valine 2-Amino-3carboxymuconate semialdehyde Aminomuconate semialdehyde Hydroxyanthranilic acid Hydroxykynurenine Indole Indole sulfate Indoleacetic acid Indoleacrylic acid Indolecarboxaldehyde Indolelactic acid Indolepropionic acid Indolepyruvic acid Indoxyl sulfate Kynurenic acid Kynurenine N-formylkynurenine Nicotinamide-adeninedinucleotide(NAD) Picolinic acid Quinolinic acid Serotonine Tryptamine Xanthurenic acid Acetylcarnitine Butyrylcarnitine Carnitine Isovalerylcarnitine Myristoylcarnitine Octanoylcarnitine Palmitoylcarnitine Propionylcarnitine Choline Betaine Trimethylamine N-oxide TMAO Trimethylamine TMA

Formula C4 H9 N O3 C11 H12 N2 O2 C9 H11 N O3 C5 H11 N O2 C7 H7 N O5

Mass 119.06 204.09 181.07 117.08 185.03

C6 H7 N O3

141.04

C7 H7 N O3 C10 H12 N2 O4 C8 H7 N C8 H9 N O4 S C10 H9 N O2 C11 H9 N O2 C9 H7 N O C11 H11 N O3 C11 H11 N O2 C11 H9 N O3 C8 H7 N O4 S C10 H7 N O3 C10 H12 N2 O3 C11 H12 N2 O4 C21 H27 N7 O14 P2

153.04 224.08 117.06 215.03 175.06 187.06 145.05 205.07 189.08 203.06 213.00 189.04 208.08 236.08 663.11

C6 H5 N O2 C7 H5 N O4 C10 H12 N2 O C10 H12 N2 C10 H7 N O4 C9 H17 N O4 C11 H21 N O4 C7 H15 N O3 C12 H23 N O4 C21 H41 N O4 C15 H29 N O4 C23 H45 N O4 C10 H19 NO4 C5 H14 N O C5 H11 N O2 C3 H9 N O

123.03 167.02 176.09 160.10 205.04 203.12 231.15 161.11 245.16 371.30 287.21 399.33 217.13 104.11 117.15 75.07

C3 H9 N

59.07 (continued)

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Table 1 (continued) Class/Pathway Phenol metabolites

Vitamins

Glycolysis and TCA cycle intermediates

Organic acids

Compound Hydroxyphenyl-Acetic Acid Hydroxyphenyl-Propionic Acid Phenylacetic Acid Vitamin A (Retinol) Vitamin B1 (Thiamine) Vitamin B12 (Cobalamin) Vitamin B2 (Riboflavin) Vitamin B3 (Niacine) Vitamin B5 (Pantothenic Acid) Vitamin B6 (Pyridoxine) Vitamin B7 (Biotin) Vitamin B9 (Folate) Vitamin C (ascorbic acid) Vitamin D Vitamin E Vitamin K 1,3-Bisphosphoglyceric acid 2-phosphoglycerate/3phosphoglycerate Acetyl-CoA Aconitic acid Citric acid/iso-citric acid Dihydroxyacetone phosphate (DHAP) Fructose-1,6-bisphosphate Fumaric acid Glucose Glucose-6-phosphate/ Fructose-6-phosphate Glyceraldehyde-3phosphate Ketoglutaric acid Malic Acid (Malate) Oxalacetic acid Phosphoenolpyruvic acid Succinic acid Acetoacetic acid Aconitic acid Adipic acid Benzoic acid

Formula C8 H8 O3

Mass 152.05

C9 H10 O3

166.06

C8 H8 O2 C20 H30 O C12 H17 N4 O S C63 H88 Co N14 O14 P C17 H20 N4 O6 C6 N H5 O2 C9 H17 N O5

136.05 286.23 265.11 1354.57 376.14 123.03 219.11

C8 H11 N O3 C10 H16 N2 O3 S C19 H19 N7 O6 C6 H8 O6 C27 H44 O C29 H50 O2 C31 H46 O2 C3 H8 O10 P2

169.07 244.09 441.14 176.03 384.34 430.38 450.35 265.96

C3 H7 O7 P

185.99

C23 H38 N7 O17 P3 S C6 H6 O6 C6 H8 O7 C3 H7 O6 P

809.13 174.01 192.03 170.00

C6 H14 O12 P2 C4 H4 O4 C6 H12 O6 C6 H13 O9 P

340.00 116.01 180.07 260.02

C3 H7 O6 P

170.00

C5 H6 O5 C4 H6 O5 C4 H4 O5 C3 H5 O6 P C4 H6 O4 C4 H6 O3 C6 H6 O6 C6 H10 O4 C7 H6 O2

146.02 134.02 132.00 167.98 118.03 102.03 174.01 146.06 122.04 (continued)

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Table 1 (continued) Class/Pathway

Short chain fatty acids (SCFA)

Bile acids

Compound Creatine Creatinine Gluconic acid Glycolic acid Hippuric acid Homovanillic acid 2-Hydroxybutyric acid 3-Hydroxybutyric acid Hydroxyglutaric acid Maleic acid Malonic acid Methylmalonic acid Orotic acid Oxalic acid Oxooctanoic acid Phthalic acid Uric Acid Vanilmandelic acid Acetic acid Formic acid Propionic acid Butyric acid/iso-butyric acid Valeric acid/iso-valeric acid Methylbutyric acid Chenodeoxycholic acid Cholic acid Deoxycholic acid Lithocholic acid Tyrosocholic Acid Ursodeoxycholic Acid

Formula C4 H9 N3 O2 C4 H7 N3 O C6 H12 O7 C2 H4 O3 C9 H9 N O3 C9 H10 O4 C4 H8 O3 C4 H8 O3 C5 H8 O5 C4 H4 O4 C3 H4 O4 C4 H6 O4 C5 H4 N2 O4 C2 H2 O4 C8 H14 O3 C8 H6 O4 C5 H4 N4 O3 C9 H10 O5 C2 H4 O2 C H2 O2 C3 H6 O2 C4 H8 O2

Mass 131.07 113.06 196.06 76.02 179.06 182.06 104.05 104.05 148.04 116.01 104.01 118.03 156.02 90.00 158.09 166.02 168.03 198.05 60.02 46.03 74.04 88.05

C5 H10 O2

102.07

C5 H10 O2 C24 H40 O4 C24 H40 O5 C24 H40 O4 C24 H40 O3 C33 H49 N O7 C24 H40 O4

102.07 392.29 408.29 392.29 376.30 571.35 392.29

detrimental to the host and the microbial community itself. The effects linked to this activity will depend on the circulating levels of the metabolites and the type and metabolic status of the host tissues, including dietary (substrate availability) and other environmental factors (Krautkramer et al. 2021). Moreover, metabolomics can also reflect food and nutrient consumption, its interaction with the metabolic state of the individual, as well as the role of the gut microbiota phenotype in the modulation of physiological responses to diet, thus integrating metabolic phenotype into precision nutrition. During the lifetime, the structure and functioning of the gut microbial community and its interactions with the host are susceptible to changes in response to several

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host and microbial-derived selective pressures, thus resulting into a highly individualized gut microbiota composition, function, and metabolite production. In addition to its crucial role in normal development, gut microbiota exerts a number of benefits for human health, including nutrient metabolism and the consequent production of energy, vitamin synthesis, protection against pathogens through competitive exclusion, degradation of xenobiotic compounds, maintenance of a functional intestinal barrier, and maturation and function of the immune system (Fan and Pedersen 2021). In executing these important functions, with a collection of genes around 150 times larger than the human genome, the gut microbiome has a metabolic enzyme repertoire that complements the activity of human enzymes, both in the liver and the gut itself, and greatly influences the biochemistry of the host (Nicholson et al. 2012). Accordingly, the gut microbiota can be seen as a bioreactor capable of synthesizing and/or transforming in symbiotic association with the host, essential molecules for both microbes and host, and in constant crosstalk with different host tissues and organs. Bacterial metabolites are crucial for the host immune systems and metabolism, and can affect many aspects of the host physiology, behavior,and neuroendocrine functions (Fan and Pedersen 2021). The studies in germ-free mouse models underscore the important contribution of the gut microbiota to human metabolism by showing that most of circulating metabolites are dependent on the microbiome for their synthesis, although many are subsequently modified by the host. This finding is supported by other studies reporting the role of microbially derived metabolites in mediating the connection between gut microbiota and susceptibility to disease. Several studies have indeed demonstrated that alterations in the metabolomic profile are associated with functional changes in the microbiota and with the development of disease states. Notably, the high interindividual variability in gut microbiota-host interactions points toward the importance of developing strategies for a more personalized medicine. Considering this and because of the redundancy of many biochemical pathways among alternative members of the microbiota, metabolites are more robust and consistent redouts, compared with microbiota taxa, to comprehensively capture the metabolic function of the gut microbiota as well as the host-microbiota co-metabolism in both health and disease. The metabolites produced by bacteria in the gut provide a biochemical bridge for the gut microbiome to influence the metabolic status of the host. Microbially derived metabolites are directly produced not only from food intake, but also by the gut bacteria through a de novo synthetic pathway, or by the host and then biochemically modified by gut bacteria (Fig. 1). Once produced, all these metabolites provide a biochemical bridge for the gut microbiome to influence the metabolic status of the host. Gut microbiota converts undigested dietary or endogenous proteins (e.g., host enzymes, mucin) into shorter peptides, amino acids, organic derivatives (phenols, indoles, amines/polyamines, short chain fatty acids [SCFA]), and gases (e.g., ammonia, H2, CO2, and H2S), some of which are biologically active and even potentially toxic to the host. Although most of the gut bacterial metabolome is still far from being fully identified, different strategies have been used to determine the contribution of different bacterial species to metabolites synthesis, including metabolomic

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Fig. 1 Main gut microbiota-derived metabolites, their biosynthetic pathways and host co-metabolism. Serotonin is produced by enteroendocrine cells through the enzyme tryptophan hydroxylase 1, whose expression is induced by the gut microbiota. BCAA, branched-chain fatty acids; GABA, gamma-aminobutyric acid; SCFA, short-chain fatty acids; PAA, phenylacetic acid; PAGln, phenylacetylglutamine; PAGly, phenylacetylglycine; TMA, trimethylamine; TMAO, trimethylamine N-oxide

analysis applied to biofluids and statistically integrated with metagenomic data. Table 2 reports the contribution of gut bacteria to major classes of metabolites. In the following section, the major classes of metabolites are described.

Essential Amino Acids and Related Metabolites Amino acids are among the most studied metabolites, as they are involved in many metabolic pathways (Masoodi et al. 2021). They can derive either from the diet (i.e., the essential ones), be synthesized through metabolic processes from other metabolites (e.g., from glucose) or from protein catabolism. Changes in circulating amino acids during the fasting state reflect muscle proteolysis in response to increased energy demand, since they are involved in energy production (i.e., oxidized or used as gluconeogenic precursors) or in the synthesis of other important products, such as glutathione, a major intracellular antioxidant whose biosynthesis requires glycine, serine, glutamate, and cysteine. Essential amino acids are not produced by the body, but rather they derive from food sources, through protein digestion (that starts in the stomach and continues in the proximal small intestine, where the pancreas releases several proteases) or protein degradation. Essential amino acids include branched-chain amino acids (BCAA, valine, leucine, and isoleucine), some aromatic and heterocyclic amino acids, such as phenylalanine and tryptophan, or histidine, threonine, and lysine.

Acetate: Akkermansia muciniphila, Bacteroides spp., Bifidobacterium spp., Prevotella spp., Ruminococcus spp. Blautia hydrogenotrophica, Clostridium spp., Streptococcus spp. Propionate: Coprococcus catus, Eubacterium hallii, Megasphaera elsdenii, Veillonella spp., Bacteroides spp., Dialister spp., Phascolarctobacterium succinatutens, Veillonella spp., Roseburia inulinivorans, Ruminococcus obeum, Salmonella enterica. Butyrate: Coprococcus comes, Coprococcus eutactus, Anaerostipes spp., C. catus, E. hallii, E. rectale, Faecalibacterium prausnitzii, Roseburia spp.

Fermentation of carbohydrates and, to a lesser degree, other substrates (protein) depending on availability of fermentable carbohydrates

Genera: Clostridium, Fusobacterium, Bacteroides, Actinomyces, Propionibacterium, Peptostreptococci

Related bacteria

Pathway

Branched chain amino acids (BCAA) Valine, leucine, isoleucine From protein fermentation or de novo biosynthesis

Metabolites Short chain fatty acids (SCFA) Acetate, propionate, butyrate

Involvement in human obesity, insulin resistance, type 2 diabetes, and NAFLD

Decrease of colonic pH; inhibition pathogens growth; stimulation of water and sodium absorption; participation in cholesterol synthesis; energy supply to the colonic epithelial cells; involvement in human obesity, insulin resistance and type 2 diabetes, colorectal cancer

Potential biological effects

Table 2 Gut bacteria-derived metabolites, main microbial biosynthetic pathways, and potential pathophysiological roles (see text for further details)

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From tryptophan by promoting the induction of host synthesis

Decarboxylation of tryptophan

Hydrolytic β-elimination of tryptophan to indole (tryptophanase)

From tryptophan via multiple pathways

Decarboxylation of histidine (histidine decarboxylase)

Tryptamine

Indole

Indole derivatives: indoleacrylic acid, indoleacetic acid, indolelactic acid, indole propionic acid, indoxyl sulfate

Histamine

From tryptophan via the kynurenine pathway

Fermentation of BCAA

Serotonin

Amino acid-derived metabolites Kynurenines (kynurenine and its derivatives)

Branched chain fatty acids (BCFA) Isobutyrate, 2-methylbutyrate, and isovalerate

Clostridium sporogenes, Ruminococcus gnavus Achromobacter liquefaciens, Bacteroides ovatus, Bacteroides thetaiotaomicron, Escherichia coli, Paracolobactrum coliforme, Proteus vulgaris Bacteroides spp., Clostridium spp. (Clostridium sporogenes, C. cadaveris, C. bartlettii), E. coli, Lactobacillus spp., E. halli, Parabacteroides distasonis, Peptostreptococcus spp. E. coli, Morganella morganii, Lactobacillus vaginalis

Indigenous spore-forming bacteria, dominated by Clostridium spp. and Turicibacter spp.

Lactobacillus spp., Pseudomonas aeruginosa, P.fluorescens

Acidaminococcus spp., Acidaminobacter spp., Campylobacter spp., Clostridia spp., Eubacterium spp., Fusobacterium spp., Peptostreptococcus spp.

Methods to Study Metabolomics (continued)

Allergic inflammation; immune modulation; vasodilation; bronchoconstriction; gastric acid secretion; neurotransmission

Regulation of insulin secretion; modulation of mucosal homeostasis and immunity; neuroprotection

Association with vascular disease; kidney toxicity; modulation of mucosal homeostasis and immunity

Role in inflammation, cardiovascular disease; T2D; neurodegenerative disease Mood, sleep, behavior, appetite, learning, and memory; intestinal function; immune modulation, hemostasis, vascular tone, cardiac development and functions Intestinal motility; neurotransmitter

Anti-inflammatory effect; anticarcinogenic potential

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From tyrosine or phenylalanine via two pathways: direct tyrosine cleavage in p-cresol by tyrosine lyase; and reactions involving transamination, deamination, and decarboxylation of tyrosine or phenylalanine via formation of the cresol precursor phenylacetic acid From phenylacetic acid, an intermediate in microbial fermentation of phenylalanine, via conjugation to either glutamine or glycine by the liver

p-Cresol

Polyamine Putrescine, cadaverine, spermidine, spermine, agmatine

From arginine via decarboxylation and other enzymatic routes

Decarboxylation of levodopa (derived from phenylalanine and tyrosine) via tyrosine decarboxylase

Dopamine

Phenylacetylglutamine and phenylacetylglycine

Pathway Non-oxidative deamination of histidine to urocanate followed by reduction of urocanate to imidazole propionate by urocanate reductase

Metabolites Imidazole propionate

Table 2 (continued)

Campylobacter jejuni, Clostridium saccharolyticum

See above for microbial synthesis of p-cresol via phenylacetic acid

Related bacteria Aerococcus urinae, Adlercreutzia equolifaciens, Anaerococcus prevotii, Brevibacillus laterosporus, Eggerthella lenta, L. paraplantarum, Shewanella oneidensis, Streptococcus mutans Enterococcus spp. (Enterococcus faecalis, E. faecium, human isolates of Enterococcus spp.), L. brevis, Helicobacter pylori Blautia hydrogenotrophica, Clostridioides difficile, Olsenella uli, Romboutsia lituseburensis

Maintenance of gut barrier integrity; anti-inflammatory effects; cardioprotection, anti-obesity and antidiabetic properties

Association with renal damage, inflammation, fibrosis; cardiovascular disease; platelet activation, thrombosis

Association with renal damage, inflammation, fibrosis

Mood, motor function

Potential biological effects Association with type 2 diabetes; insulin resistance

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From dietary polyphenols and aromatic amino acids

Phenol compounds Hydroxyphenyl-acetic acids, hydroxyphenyl-propionic acids, etc.

Modified from (Krautkramer et al. 2021; Nicholson et al. 2012).

From decarboxylation of glutamate

GABA

Bacteroides genus, Gordonibacter spp., Bifidobacterium spp., Lactobacillus spp., Clostridium spp., Eubacterium spp., Enterococcus spp., Ruminococcus spp., Eggerthella spp., Slakia spp., Finegoldia magna, Veillonella spp.

Genus Bacteroides

Genera: Bacteroides, Eubacterium, and Clostridium

Antioxidant, anti-inflammatory, and microbial effects; maintenance of intestinal mucosal integrity; beneficial effects against metabolic syndrome; anti-cancer action; cardioprotection; neuroprotection

Absorption of dietary fats and lipidsoluble vitamins; maintenance of intestinal barrier function, regulation of lipid, cholesterol, glucose, and energy metabolism Inhibitory neurotransmitter; antidiabetic, hypotensive, anticancer, anti-inflammatory action; intestinal protection

Link with the risk of cardiovascular disease, obesity, NAFLD, T2D, chronic kidney disease

Genera: Firmicutes, Bacteroidetes, Protobacteria, Fusobacteria

From liver oxidation of trimethylamine (TMA), a gut bacteria metabolite derived from choline, L-carnitine, or betaine

From liver-derived primary bile acids via several biochemical pathways

Complementary endogenous sources of vitamins

Bifidobacterium

Various

Secondary bile acids Cholate, hyocholate, deoxycholate, chenodeoxycholate, uricholate, taurocholate, glycocholate, etc.

Vitamins Vitamin K, vitamin B12, biotin, folate, thiamine, riboflavin, pyridoxine Trimethylamine N-oxide (TMAO)

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Several studies demonstrated an association between high fasting concentrations of BCAA and metabolic diseases like obesity, T2D, and NAFLD (Newgard et al. 2009; Kalhan et al. 2011). Increased concentrations of serum BCAA were observed in the progression from simple steatosis to more severe NASH (Lake et al. 2015; Gaggini et al. 2018; Kalhan et al. 2008). Moreover, plasma BCAA concentrations may display sex-dimorphic changes with increasing severity of NAFLD, independently of BMI, insulin resistance, and age (Grzych et al. 2020; Della Torre et al. 2018). Some microbial species have high potential for BCAA biosynthesis (e.g., Prevotella copri and Bacteroides vulgatus) than for BCAA transport into bacterial cells (Pedersen et al. 2016). Accordingly, a BCAA-related metabolite signature, including changes in intermediary metabolites of the BCAA catabolic pathway (e.g., glutamate, alanine, short-chain acylcarnitines), in association with an increased microbial genomic potential for BCAA biosynthesis and a decreased genomic potential for BCAA uptake, were confirmed in obese and insulin-resistant humans (Newgard et al. 2009; Pedersen et al. 2016). This metabolite signature, together with the obese phenotype, is transmissible through gut microbiome transplantation from obese mice to germ-free lean mice (Ridaura et al. 2013). In mammalian and gut bacterial cells, histidine can be metabolized, by decarboxylation, into the immune modulator histamine, while it is transformed into imidazole propionate through a non-oxidative deamination performed by gut bacteria. Imidazole propionate has been shown to be present at higher concentrations in subjects with T2D versus healthy controls, and to impair insulin signaling at the level of insulin receptor substrate (Koh et al. 2018), thus contributing to the pathogenesis of T2D. Phenylalanine is an essential aromatic amino acid metabolized to tyrosine in the liver. Microbial fermentation of aromatic amino acids in the colon has been shown to produce phenylpropanoid-derived compounds, such as phenylacetic acid and 4-hydroxyphenyl-acetic acid: in preclinical models, the microbially produced phenylacetate has been recognized as one of the metabolites contributing to hepatic steatosis (Hoyles et al. 2018). Other amino acids derivatives are p-cresol and phenylacetylglutamine, which derive, respectively, from tyrosine and phenylalanine, and have been associated with inflammation, fibrosis, renal damage, and risk for chronic kidney disease and cardiovascular disease, independently of other cardiovascular risk factors (Ottosson et al. 2020). Moreover, 4-hydroxyphenyl lactate, a product of aromatic amino acid metabolism, has been associated with liver fibrosis and significantly correlated with the abundance of several gut-microbiome species belonging to Firmicutes, Bacteroidetes, and Proteobacteria phyla, previously reported as associated with advanced fibrosis.

Tryptophan-Kynurenine Pathway Tryptophan is an essential aromatic amino acid involved in the anti-inflammatory response and in the synthesis of serotonin, thus having an important role in brain metabolism. Tryptophan metabolism occurs through four pathways: the kynurenine

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pathway, the serotonin pathway, protein synthesis, or direct transformation to various compounds, including indoles, which also involve or are tightly regulated by gut bacteria. In the kynurenine pathway, tryptophan catabolism generates kynurenine and its derived metabolites, collectively named “kynurenines.” These metabolites display various physiological effects and are involved in immune tolerance and inflammation: anti-inflammatory and neuroprotective effects have, indeed, already been documented for kynurenic acid, while pro-inflammatory and neurotoxic activity have been rather linked to quinolinic acid. Activation of the kynurenine pathway is mediated by gut bacteria that contribute either to the induction of the required host enzymes (mostly indoleamine 2,3-dioxygenase 1 [IDO1], expressed in immune and gut epithelial cells) or to direct production of the required metabolites, such as kynurenic acid via activation of kynurenine aminotransferase (Cervenka et al. 2017). A role in metabolism has emerged depending on the balance between quinolinic acid and kynurenic acid production: while quinolinic acid interferes with insulin production and activity, kynurenic acid increases energy expenditure in adipose tissue in a mouse-model of obesity. Patients with T2D display a pattern of reduced kynurenic acid- versus quinolinic acid-pathway metabolism, and a longitudinal increase in quinolinic acid is detected in association with the risk of T2D (Yu et al. 2018). Dysregulation of the tryptophane/kynurenine pathway is a candidate pathogenic mechanism for neurodegenerative diseases (Cervenka et al. 2017). Moreover, gut bacteria can metabolize tryptophan into the monoamine tryptamine (through decarboxylation), which stimulates colonic peristalsis through activation of the serotonin type 4 receptor (5-HT4R), and also acts as neurotransmitter; into indole, via tryptophanase; and via oxidative and reductive pathways into indole derivatives, such as indoleacrylic acid, indoleacetic acid, indolelactic acid, indole propionic acid, and indoxyl sulfate (Krautkramer et al. 2021). Tryptophan and its metabolites are ligands for the aryl hydrocarbon receptor (AhR), a transcription factor playing a central role in the intestinal mucosa function, immune homeostasis, resistance to pathogens, and metabolism of xenobiotics (Heath-Pagliuso et al. 1998). Lastly, indole can be converted into indole sulfate in the liver and then excreted by the kidney, where it can exert toxic effects at high levels, thus being involved in the pathogenesis of chronic kidney disease (Wu et al. 2011).

Insulinotropic Amino Acids Among the nonessential amino acids, arginine is important since it has insulinotropic effects and its intravenous infusion is used to evaluate beta cell reserve and assess beta cell function. In the gut dietary, arginine is substantially processed by gut bacteria to produce polyamines including putrescine, cadaverine, spermidine, spermine, and agmatine. These metabolites play a role in the intestinal epithelium function and in the regulation of the immune system, thus providing antiinflammatory effects, cardioprotection, as well as in the beneficial regulation of the glucose, lipid, and energy homeostasis with anti-obesity and antidiabetic properties (Ramos-Molina et al. 2019; Sevilla-Gonzalez et al. 2022).

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Also, BCAA, primarily leucine, have insulinotropic effects, and considering that activation of muscle mTOR by leucine is a necessary step for muscle protein synthesis, the combination of increased insulin, which is an anabolic enzyme, and amino acids may help to counteract the “anabolic resistance” to feeding in older adults.

Amino Acids Involved in Brain Signaling Several amino acids are both involved in synthesis and act as a neurotransmitters in the central nervous system. Phenylalanine is the precursor of dopamine, an important neurotransmitter involved in the regulation of movement and mood and whose deficit, due to the loss of dopamine-producing neurons in the cerebral substantia nigra, causes Parkinson’s disease. In host cells, phenylalanine is transformed into tyrosine, and this latter into levodopa (L-dopa) by the rate-limiting enzyme tyrosine hydroxylase. L-dopa is then converted into dopamine by the host, through the aromatic amino acid decarboxylase (an enzyme present in the central nervous system and in the periphery), and by microbial species, through a conserved tyrosine decarboxylase. Tryptophan, another essential aromatic amino acid, serves as a precursor for the synthesis of serotonin (5-hydroxytryptamine [5-HT]) in the central nervous system. Serotonin exerts neuroactive properties involving the regulation of mood, sleep, behavior, appetite, learning, and memory, while in the periphery it affects intestinal motility and secretion, immune modulation, and cardiovascular functions. The tryptophan hydroxylase (TPH), a rate-limiting enzyme in the serotonin synthesis, exists in two different isoforms: the TPH1 isoform, which synthesizes the majority of the endogenous serotonin and is expressed in enterochromaffin cells of the intestine; and the TPH2 isoform, which is expressed in neurons of the central and enteric nervous systems. Glutamic acid acts as a neurotransmitter and is a precursor of GammaAminobutyric acid (GABA), a four carbon nonprotein amino acid whose synthesis is catalyzed by the glutamate decarboxylase that converts glutamic acid to GABA through an irreversible decarboxylation. GABA is the major inhibitory neurotransmitter in the central nervous system: its biosynthesis in the intestine is performed by the prominent human intestinal genus Bacteroides, which contributes to the regulation of the GABAergic system in humans. Besides systemic effects, the produced GABA may also affect the microbial community in the intestine by serving as a carbon source and signaling molecule between bacteria. Gut microbiota-derived GABA was also demonstrated to improve metabolic parameters (adiposity, insulin sensitivity and glucose tolerance) and depressive behavior in a mouse model of obesity (Patterson et al. 2019).

Amino Acids, Oxidative Stress and Lipotoxicity Glutamate is a nonessential amino acid, and its metabolism is strictly linked to glutamine and the TCA cycle. Moreover, glutamic acid, together with other amino

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acids like cysteine and glycine, is involved in the synthesis and transamination (this latter performed by gamma glutamyl transferase GGT) of glutathione, an important natural antioxidant that protects cells from oxidative stress and free radicals, considered among the causes of inflammation and cell damage. Glycine is also a nonessential amino acid whose endogenous synthesis may be insufficient to meet metabolic needs, thus making it a conditionally essential amino acid. Low plasma concentrations of glycine have been consistently reported in association with metabolic disorders such as obesity, insulin resistance, T2D, and NAFLD (Masoodi et al. 2021; Alves et al. 2019). Besides contributing to glutathione biosynthesis, glycine participates in protein synthesis and exerts several biological functions, including regulation of one-carbon metabolism, bile acid metabolism, and glucose homeostasis. Serine is a precursor of glycine; thus, it contributes to the synthesis of glutathione. Moreover, it is involved in the biosynthesis of ceramides, lipotoxic compounds whose accumulation within tissues has also been associated with metabolic diseases (Masoodi et al. 2021). Hence, serine supplementation has been shown to improve not only metabolism but also neuropathy (Handzlik et al. 2023). The GSG index (given by the ratio of glutamic acid to the sum of glycine and serine) measures alterations in glutamic acid metabolism and an increased GSG index was found to be associated with increased oxidative stress and more severe forms of NAFLD (Gaggini et al. 2018; Masoodi et al. 2021). Other metabolites involved in glutamate metabolism are alanine, aspartic acid, pyruvic acid, succinic acid, cysteine, and phosphoric acid: these metabolites are all found in plasma, urine, and stools (Jain et al. 2019).

Choline and Its Metabolites Choline is an essential nutrient involved in many metabolic pathways, such as the synthesis of phospholipids and acetylcholine synthesis, and, in liver mitochondria, it is irreversibly oxidized to betaine (trimethylglycine) by choline oxidases. Moreover, gut bacteria can metabolize choline to trimethylamine N-oxide (TMAO), although other precursors include carnitine and betaine. Anaerobic gut bacteria catalyze the conversion of choline, carnitine, or betaine first to trimethylamine (TMA), which is a toxic derivative further metabolized in the liver by flavine monooxigenases (FMO) 1 and 3 to produce TMAO. Foods rich in choline and carnitine are eggs, dairy products, seafood, and red meat, while betaine is present in wheat bran, wheat germ, and spinach. Interest toward TMAO has been increased in the last years due to reports of its involvement in the risk of cardiovascular and metabolic diseases, as well as colorectal cancer and neurodegenerative diseases, thus becoming a potential biomarker and therapeutic target (Naghipour et al. 2021). In a metabolomic study aimed at identifying plasma metabolites able to predict the risk for cardiovascular disease in humans, TMAO and its precursors choline and betaine were discovered as predictors of the risk of cardiovascular disease and capable of promoting atherosclerosis in

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mice in a microbiota-dependent manner, thus identifying a novel link between the microbiota and cardiometabolic health. These observations were further confirmed and extended by subsequent studies, which also associated TMAO with the risk of obesity, T2D, NAFLD, and NASH, chronic kidney disease, and their worse clinical course and mortality. These studies also provided some insights into the potential mechanism(s) underlying TMAO effects (Naghipour et al. 2021). For instance, it has been reported that TMAO inhibits cholesterol conversion into bile acids, the expression of the multiple bile acid transporters in the liver, and the farnesoid X receptor signaling, promoting steatosis and worsening the progression of NAFLD. Furthermore, choline conversion to TMA by gut microbiota decreases choline liver bioavailability, leading to inefficient export of VLDL particles out of the liver, lipid accumulation, and liver inflammation. Increased levels of TMAO in the liver increase insulin resistance and decrease glucose tolerance in mice fed a high-fat diet. Although TMAO levels can be modulated by lifestyle interventions (such as weight-loss programs), further investigation is warranted to determine if TMAOlowering interventions could improve the prognosis of patients with cardiometabolic diseases. Furthermore, TMAO has also been linked to colorectal cancer and neurodegenerative diseases. Determinants of circulating TMAO levels include diet, age, body weight, renal clearance, metabolic and endocrine status, diseases and polymorphisms in FMO3, but they only partially explain the variance in circulating TMAO in different cohorts. A critical role in determining TMAO concentrations is played by the gut microbiota composition, which is modulated by diet or disease states. Thus, diets with high intake of animal proteins favor carnitine/choline metabolizing bacteria, while a nonmeat diet (e.g., vegan/vegetarian) promotes non-TMA generating species. In line with this, a carnitine challenge increases plasma and urinary TMAO in omnivores but not vegans, a differential response eliminated by antibiotic treatment (Wang et al. 2014; Koeth et al. 2013). Since also healthy foods (such as seafood, wheat germ, and vegetables) are TMAO’s precursors, its universal validity as biomarker of cardiometabolic risk independent of the background diet has been questioned (Costabile et al. 2021).

Glycolysis and Intermediates of TCA Cycle Glycolysis is a cytoplasmic pathway that breaks down the six-carbon glucose into two three-carbon compounds and generates energy. In aerobic conditions, pyruvate is the final glycolytic product, while it is lactate in anaerobiosis. The tricarboxylic acid cycle (TCA, also known as Krebs cycle or citric acid cycle) is the central hub of energy production in mitochondria and coordinates glucose, fatty acid, and amino acid metabolism. These main catabolic pathways, glycolysis and TCA cycle, produce several metabolic intermediates that are substrates for further anabolic and catabolic processes, and hence their levels reflect physiological or pathological changes in metabolism. Furthermore, glycolysis and TCA-related metabolites can also act as intra- and extracellular signals able to influence several important

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biological metabolic and extra-metabolic processes, including redox homeostasis, regulation of immunity and inflammation, epigenetic control of gene transcription, signal transduction, cell proliferation, migration, and angiogenesis (Martinez-Reyes and Chandel 2020). Metabolomics studies showed that profiles of glycolysis- and TCA cycle-related metabolites were altered and associated with NAFLD, insulinresistance, and the risk of developing T2D and its complications (Morze et al. 2022; Masoodi et al. 2021). Moreover, a role for some of these intermediate metabolites, including fumarate and succinate, as onco-metabolites able to promote cancer development, has been reported.

Nonesterified Fatty Acids (NEFA) and Short Chain Fatty Acids (SCFA) Circulating fatty acids (i.e., nonesterified fatty acids, NEFA) reflect adipose tissue composition since they are released (mainly in the fasting state) during lipolysis. On the other hand, fatty acid produced by liver, e.g., during de novo lipogenesis, are immediately esterified to glycerolipids as dual- or tri-acylglycerols (DAG and TAG) and released in this form of very low-density lipoproteins (VLDL) (Masoodi et al. 2021; Morze et al. 2022). SCFA are saturated aliphatic organic acids and one of the most studied classes of bacterial metabolites that are synthetized by the anaerobic fermentation of dietary and host carbohydrates and, to a lesser extent, proteins. Acetate, propionate, and butyrate are the most abundant SCFA, present at molar ratio of around 60:20:20 in the colon and stool. Dietary fibers, including indigestible carbohydrates such as polysaccharides, oligosaccharides, and resistant starches are the predominant source of SCFA, being substrates for microbial carbohydrate-active enzymes (CAZymes). These pathways produce phosphoenolpyruvate and pyruvate, precursors for SCFA synthesis and for other intermediates, which are produced at lower abundance, including lactate, succinate, fumarate (Luo et al. 2022). SCFA can also be produced by microbial fermentation of dietary and host proteins (gut-secreted enzymes and mucus) that are first hydrolyzed into amino acids by the host and gut bacteria, and then converted to SCFA, branched-chain amino acids (BCAA, valine, leucine, and isoleucine), branched-chain fatty acids (BCFA, isobutyrate, 2-methylbutyrate, and isovalerate), and other minor compounds (amines, phenolic compounds, and volatile sulfur compounds). Different SCFA, mainly propionate and butyrate, can also be produced by specific bacteria: in particular, the Bacteroidetes phylum produces acetate and propionate, whereas the Firmicutes phylum mainly comprises butyrate-producing bacteria (Den Besten et al. 2013). However, depending on substrate availability and growth conditions, many bacterial species can alter their metabolism, thus producing different SCFA. The amount and type of fiber ingested influence the composition of the intestinal microbiota and thus the type and amount of SCFAs produced, which in turn affect the microbial community by reducing the luminal pH in the colon and exerting cross-feeding effects within microbes. A shift to utilization of protein versus carbohydrates for microbial fermentation, resulting in lower amount of SCFA and

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decreased gut microbial diversity, may be consequent of limited availability of fermentable carbohydrates. Accordingly, chronic consumption of diets low in fibers such as the Western diet compromises gut microbiota composition and induces a progressive loss of diversity with selective extinction of microbial taxa, mainly Bacteroidales, which is not recoverable upon the reintroduction of dietary microbiota-accessible carbohydrates. SCFA exert a growing number of biological functions important for host homeostasis acting as inhibitors for pathogenic bacteria and local energy suppliers for colonocytes and microbes. Moreover, SCFA are involved in the intestinal gluconeogenesis, gut barrier integrity, motility, hormone secretion (glucagon-like peptide-1 [GLP-1], Peptide YY [PYY], serotonin, cholecystokinin [CCK], gastric inhibitory peptide [GIP]), and immune function, as well as in the regulation of lipid, glucose, and cholesterol metabolism (Den Besten et al. 2013). The gut microbiota associated with obesity and T2D produces lower levels of SCFA with important implication for the pathogenesis of these diseases: SCFA supplementation in humans and animals suggest immunomodulatory role and beneficial metabolic effects of SCFA against obesity, insulin resistance, NAFLD, and NASH. On the other hand, some evidence points to unfavorable effects in NAFLD, including the induction by microbiota-derived acetate of hepatic lipogenesis and NAFLD development in an animal model of fructose-induced metabolic disease, and the association between increased fecal levels of SCFAs and NAFLD (butyrate and propionate) and liver fibrosis (formate and acetate) (Aron-Wisnewsky et al. 2020). Notably, at variance from fecal SCFA, circulating SCFA are more directly linked to metabolic health, therefore it is important to discriminate between the two biofluids.

Carnitine and Acylcarnitine L-carnitine

(L-3-hydroxy-4-N,N,N-trimethylaminobutyrate) is an essential amino acid that primarily derives from dietary source, mainly of animal origin, and secondarily from de novo biosynthesis from lysine and methionine in tissues such as the kidney, liver, and brain. Its homeostasis in the human body depends on the biosynthesis, dietary intake, and reabsorption by the kidney. Acylcarnitines are esters produced by the conjugation of L-carnitine with fatty acids (i.e., acyl groups) and represent a very large class of metabolites generally categorized according to the length of the acyl groups in short-chain (C2–C5), medium-chain (C6–C12), and long-chain (C13–C20) or very long-chain (>C21) acylcarnitines. Moreover, acylcarnitines are also grouped according to the saturation degree (i.e., unsaturated or saturated), the chemical structure (i.e., aliphatic, branched or cyclic), or the substitution (e.g., hydroxyl- or carboxyl groups) of the fatty acid moiety. Acylcarnitines play an important role in intermediary metabolism for energy production or synthesis of endogenous molecules (Dambrova et al. 2022). Acylcarnitine mediates the transport of activated long-chain fatty acids into the mitochondria for subsequent β-oxidation and energy production; it is involved in the transport of acetyl-coenzyme A from the mitochondria to the cytosol, and in the

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modulation of the mitochondrial acyl-coenzyme A/coenzyme A ratio. While shortchain acylcarnitines are mainly derived from glucose, amino acid, and fatty acid degradation, medium- and long-chain acylcarnitines are produced from fatty acid metabolism. Beyond fuel trafficking, acylcarnitines have shown to be actively involved in the regulation of carbohydrate and lipid metabolism, and to influence inflammation, insulin sensitivity, and cellular stress. Their levels are considered as indicators of energy metabolism pattern and historically used as key markers of inborn errors of fatty acid oxidation. An increase of plasma levels of acylcarnitines and/or distinct acylcarnitine profiles have been associated with metabolic dysfunctions and in particular, insulin resistance, T2D, and cardiovascular diseases, as well as liver cancer (Mccoin et al. 2015).

Primary and Secondary Bile Acids Primary bile acids synthesis occurs from cholesterol in the liver via either a classical (or neutral) or alternative (or acidic) pathway requiring cholesterol 7α-hydroxylase (CYP7A1) and sterol-27-hydroxylase (CYP27A1) as rate-limiting enzymes, respectively, and producing chenodeoxycholic acid (CDCA) or cholic acid (CA). These bile acids are then conjugated to taurine or glycine to form bile salts, secreted from the liver and accumulated as bile in the gallbladder until release following ingestion of food. Most of the bile acids (about 95%) in the ileum recirculate to the liver several times per day via the hepatic portal vein in the enterohepatic circulation (De Aguiar Vallim et al. 2013). In the colon, the liver-derived primary bile acids undergo deconjugation by the bacteria bile salts hydrolases, thus providing bacteria with a mechanism to reduce the toxicity of the bile acids and a source of nitrogen, sulfur, and carbon atoms. Free taurine generated by deconjugation is released in the intestinal lumen, where it promotes the mucosal integrity by increasing autocrine production of the gut-protective cytokine IL-18. Gut microbiota metabolism of primary bile acids produces secondary bile acids, including lithocholic acid, deoxycholic acid, ursodeoxycholic acid, through complex biochemical pathways such as dehydroxylation, epimerization, and oxidation of hydroxyl groups (De Aguiar Vallim et al. 2013). These secondary bile acids can be reabsorbed by the intestine, and passively reenter the enterohepatic circulation so that only a minor fraction is lost in the feces. The gut bacteria affect metabolism of primary bile acid also by regulating bile acid synthesis and uptake, the expression of both CYP7A1 and CYP27A1, and the synthesis of taurine. Microbiota can also drive the conjugation of primary bile acids with phenylalanine, tyrosine, or leucine generating phenylalanocholic acid, tyrosocholic acid, and leucholic acid. Interestingly, primary bile acids and mostly secondary bile acids possess antimicrobial properties. Beyond their role in fat digestion and absorption, bile acids are also signaling molecules in several host processes, including bile acid synthesis and transport, lipid and glucose metabolism, and energy homeostasis, as well as anti-inflammation, regulation of intestinal barrier and immune function.

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Several studies have reported that subjects who are overweight, obese, or have T2D had increased serum bile acid concentrations (Chavez-Talavera et al. 2017). Similarly, NAFLD and NASH, also in the transition from hepatic steatosis to NASH, are associated with disturbance in bile acid metabolism and altered circulating levels and composition of bile acids: higher plasma levels of total and conjugated bile acids have been consistently reported, with an increase in primary bile acids observed in some studies, and in primary and secondary bile acids in others (Chavez-Talavera et al. 2017). Bile acids not only play an important role in the digestion and absorption of dietary fats and fat-soluble vitamins, but they are also involved in both lipid and glucose metabolism through the activation of farnesoid X receptors (FXR) in the enterocyte and in the liver (Chavez-Talavera et al. 2017). FXR activation inhibits both carbohydrate responsive element-binding protein (ChREBP) and sterol responsive element-binding protein 1 (SREBP1c), thus decreasing glycolysis and lipogenesis; moreover, it has an effect on the synthesis of very-low-density lipoprotein (VLDL)/triglyceride (TG) (Chavez-Talavera et al. 2017). In this context, concomitant disorders including obesity, insulin resistance, and liver injury can contribute to bile acid dysregulation, also by modifying the gut microbiota. Being the gut bacteria involved in the biotransformation of primary into secondary bile acids and bile acid conjugation, gut microbiota dysbiosis might modify the balance between primary and secondary bile acids and the bile acid signaling, and hence have far-reaching effects on host metabolism. Interestingly, weight loss after bariatric surgery determined an increase in conjugated secondary bile acids that were significantly associated with the increase in insulin sensitivity (Ahlin et al. 2019).

Vitamins and Phenol Metabolites The human body can also synthesize several vitamins, i.e., vitamin B3 (niacin) and D, but not vitamins A, B1 (thiamine), B2 (riboflavin), B5 (pantothenic acid), B6 (pyridoxine), B7 (biotin), B9 (folate), B12 (cobalamin), E and K, which are either acquired through the diet or derive from gut bacterial metabolism. This implies that changes in gut microbial composition may affect dietary vitamin requirements. It is estimated that the 40–65% of the human gut microbiota genome is able to synthetize some or even all the B-vitamins (Magnusdottir et al. 2015), which are indeed important not only for the host, but also for bacterial metabolism and growth in cross-feeding interactions. Phenol metabolites are another example of the ability of the gut microbiota to metabolize dietary compounds into new metabolites that impact health and disease risk. Indeed, the gut microbiota has extensive capacity to metabolize dietary polyphenols (i.e., phenolic acids, flavonoids, stilbenes, lignans, and others), which are antioxidant compounds naturally present in plant foods and beverages and endowed with many biological activities important for the prevention of various chronic degenerative diseases (Jain et al. 2019). Most polyphenols (90%) are poorly absorbed and a great proportion of non-absorbed compounds reaches the colon, where they are metabolized by the resident microflora, generating an array of

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metabolites that are absorbed, further transformed, and released into the circulation (Di Lorenzo et al. 2021). Therefore, the bioavailability and possibly bioactivities of ingested polyphenols are thought to depend mostly on their gut bacteria-derived metabolites. Chemical reactions involved in the metabolic pathway of polyphenols include dehydroxylation, decarboxylation, and ring breakage, thus generating simpler phenolic compounds, such as hydroxyphenyl-acetic acid and hydroxyphenylpropionic acid, which can also derive from the fermentation of aromatic amino acids. A large interindividual variability exists in polyphenol metabolism and is a consequence of differences in gut microbiota composition, with important implications for polyphenol effect on health (Iglesias-Aguirre et al. 2021), For example, depending on the subjects gut microbiota the soy isoflavone daidzein, a known phytoestrogen, can be metabolized to O-desmethyl-angolensin and, in only 30% of subjects, (S)-equol, which is more bioactive compared with the parent isoflavone and is thought to be responsible for the soy isoflavone benefit on obesity, cardiovascular disease and estrogen-sensitive cancers. Another paradigmatic example is the gut bacteria-mediated metabolism of dietary ellagitannins and ellagic acid to urolithins, so that humans can be stratified in: metabotype-A that only produces urolithin-A; metabotype-B that produces urolithin-B and isourolithin-A in addition to urolithin-A; and metabotype 0 that does not produce detectable urolithins. Links exist between microbiota-dependent urolithin metabotypes and cardiometabolic outcomes. Indeed, overweight-obese individuals with metabotype-B exhibited a higher cardiovascular risk compared with metabotype-A, and have a greater cardiometabolic benefit than metabotype-A from consumption of ellagitannin-rich pomegranate. Moreover, a long-term and/or high intake of ellagitannins and ellagic acid can promote the shift of a subpopulation of metabotype-0 to metabotype-A or metabotype-B, underscoring the diet-mediated shaping of gut bacteria influencing subsequent metabolites production.

Fluxomics To investigate the impact of metabolic diseases on metabolite production or consumption it is necessary to study their metabolic fluxes. Fluxomics is the methodology that allows the measurement of metabolic fluxes, both organ and cellular metabolite synthesis and consumption. This technique is necessary to understand how different metabolic pathways are connected and regulated, and how they respond to changes in environmental or physiological conditions because metabolomics measure only plasma metabolite concentrations that are the results of the balance between secretion into the systemic circulation and clearance by the other organs. Thus, while metabolomics gives a static view of the metabolic profile, fluxomic analysis gives a dynamic view of metabolic alterations. Overall, fluxomics is an important tool for understanding the complex metabolic processes that occur in living organisms and for developing new technologies that can exploit these processes for various applications.

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Fluxes can be measured in vivo either by measuring arterial-venous organ differences in metabolite concentrations, or using tracers labeled with stable isotopes infused in vivo or used in cell culture and tracking their exchange with other molecules through various metabolic pathways (fluxomics). Stable isotope tracers are a powerful tool for investigating metabolic processes in living organisms since they are nonradioactive and thus safe to use in experiments (Wolfe and Chinkes 2004). Once stable isotope tracers are introduced in the organism (by infusion or oral intake) it is possible to track the exchange of these isotopes through various metabolic pathways. Indeed in vivo of stable isotopes tracers have been successfully used in humans for the identification of physiological mechanisms and alterations responsible for the development of metabolic diseases like obesity, diabetes, and nonalcoholic fatty liver disease (NAFLD) as well as mechanisms of action of drugs.

Measurement of Glucose Fluxes Glucose is produced by the hydrolysis of glycogen stored mainly in the liver or is de novo synthesized by metabolites like lactate and alanine (the most important glucogenic precursors) but also pyruvate, glycerol, and other aminoacids (the majority of amino acids are glucogenic, only leucine and lysine are not participating to glucose synthesis). The infusion of glucose labeled with either 2H or 13C is used to quantify the endogenous glucose production (EGP) that is mainly hepatic (Gastaldelli 2022). It has been shown that fasting hyperglycemia in subjects with type 2 diabetes is the result of increased endogenous glucose production. Moreover, it is possible to identify the separate contribution of gluconeogenesis and glycogenolysis to endogenous glucose production giving 2H2O orally and measuring the deuterium incorporated in the glucose molecule. It has been shown during de novo synthesis of glucose the deuterium bound to carbon-5 of glucose. These studies have shown that subjects with either obesity or diabetes have an increased production of glucose through gluconeogenesis that is the main contributor of increased endogenous glucose production. The studies based on 2H2O ingestion can estimate total gluconeogenesis without distinction of the separate contribution of the different organs. It has been shown that glucose is produced via gluconeogenesis mainly in the liver, but also the kidney and the gut can synthesize glucose via gluconeogenesis that is then released in the blood. Not only endogenous produced metabolite but also bacterial metabolites are used to produce glucose through gluconeogenesis since once absorbed they reach first the liver where they are taken up and metabolized. Free fatty acids do not participate to glucose synthesis, but the infusion of free fatty acids increases gluconeogenesis, probably because gluconeogenesis is a highly endergonic process and NEFA are used to produce the ATP necessary for the synthesis, e.g., the synthesis of glucose-6phosphate from pyruvate requires four molecules of ATP and two molecules of GTP to proceed spontaneously.

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Measurement of Aminoacid Turnover and Protein Synthesis Labeled amino acids (mainly with 13C and 15N) are used to investigate amino acid turnover, protein synthesis, and breakdown. The choice of amino acid tracer depends on the aim of the research. To study muscle protein breakdown and synthesis essential amino acids are infused (either phenylalanine or leucine), the same for apolipoprotein-B synthesis, while to study amino acid contribution to gluconeogenesis labeled alanine was used (Wolfe and Chinkes 2004). To study protein metabolism it is necessary to measure the incorporation into proteins. For muscle synthesis the abundance of labeled amino acids has to be measured in muscle biopsies making this technique highly invasive. However, surrogate methods based on mathematical models can be used, but they give an estimation of total body protein turnover (Wolfe and Chinkes 2004).

Measurement of Lipid Fluxes In the context of lipid metabolism, stable isotope tracers can be used to study the synthesis, breakdown, and transport of fatty acids, adipose tissue lipolysis, fatty acid release and clearance, but also de novo lipogenesis (DNL, which occurs mainly in the liver) and lipoprotein metabolism. Adipose tissue lipolysis is quantified by the infusion of 2H or 13C glycerol, because the glycerol produced during intracellular triglyceride hydrolysis is all released into the blood stream since it cannot be reused intracellularly, the NEFA can be re-esterified intracellularly (Wolfe and Chinkes 2004). Thus, the infusion of labeled fatty acids like palmitic or oleic acid can be used to measure the rate of secretion of these specific fatty acids by adipose tissue and clearance by peripheral organ, but underestimate lipolysis and intracellular triglyceride hydrolysis. De novo fatty acid synthesis is a complex pathway that occurs primarily in the liver and uses excess dietary carbohydrates to synthesize fatty acids (mainly palmitic acid). All complex carbohydrates, some sugars like fructose and sucrose, but not glucose, drive de novo lipogenesis (DNL). DNL can be measured in vivo using 2H2O as tracer, as for gluconeogenesis. Deregulations in the lipogenic pathway are present in the majority of metabolic diseases, but mainly in NAFLD, where it has been shown that of the newly synthesized liver triglycerides 26% arose from DNL, 59.0% from NEFA, and only 15% from the diet (Donnelly et al. 2005). Lipoproteins are complex molecules that transport lipids (fats) through the bloodstream. Their synthesis can also be investigated through fluxomics using stable isotope tracers measured by mass spectrometry. There are several types of lipoproteins including very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). The newly synthesized hepatic NEFA are esterified to triglycerides and transported from the liver to peripheral tissue by VLDL; their kinetics can be investigated using 2H2O and then measured by mass spectrometry. The newly synthesized 2H labeled fatty acids are incorporated also in

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other complex lipids like ceramides or phosphocholines that can be isolated and measured by Mass Spectrometry (MS) (lipidomics). Moreover, by analyzing the isotopic composition of the lipids, it is possible to determine the rate of lipoprotein synthesis and turnover, as well as the contribution of different tissues to lipid metabolism providing valuable insights into the mechanisms underlying metabolic disorders.

Analytical Methodologies for Metabolomics and Fluxomics The methodological pipeline for metabolomics studies is summarized below in Fig. 2 and depends on the class of metabolites and the type of biological sample. Metabolomic profile can be investigated in plasma samples, taken, for example, during fasting or postprandial conditions, as well as in other biological samples such as urine or feces. Circulating hormones or enzymes can rapidly degrade some metabolites, thus the study design and sample collection are an important part of a proper metabolomics workflow. Once the samples are collected, several techniques have been developed for their purification and metabolites extraction. Moreover, sample preparation is also an important step, since it depends on the type of analytical technique that will be applied, i.e., Mass Spectrometry (MS) or Nuclear Magnetic Resonance (NMR) Spectroscopy. Finally, quantification of metabolite concentrations using internal standards and statistical analysis, also with the use of machine learning techniques,

Fig. 2 Metabolomics analysis follows a precise workflow, which consists of five main procedures: (1) Sample collection; (2) Sample preparation; (3) Sample extraction and purification; (4) Chromatographic (LC or GC), Spectrometric (MS) and Spectroscopic (NMR) analysis; (5) Peak identification, integration and statistical analysis

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are important steps of metabolomic workflow (Fig. 2). The same workflow applies for fluxomics studies, with the difference that, in this case, also the labeled fragments will be monitored. Nuclear Magnetic Resonance (NMR) Spectroscopy and Mass Spectrometry (MS) are the most used analytical techniques in the field of metabolomics. The selection of the analytical platform depends on the available amount of sample to analyze, as well as on the classes of compounds of interest (Emwas et al. 2019), although the use of one technology does not exclude the other, as both mass spectrometry and NMR have specific advantages and disadvantages, summarized in Table 3 (from Emwas et al. 2019), and none of them is able to detect the entire sample metabolome.

Nuclear Magnetic Resonance (NMR) Spectroscopy NMR spectroscopy is one of the primary analytical technologies used in metabolomics. The versatility of NMR makes this spectroscopy a powerful tool to address metabolic questions in a variety of biological systems, aiding in the analysis of complex mixtures (as in metabolomics) to study fundamental aspects of biochemistry including metabolite identification, quantification, and metabolic activities. NMR spectroscopy is based on the magnetic properties of certain atomic nuclei. When a strong magnetic field is applied, a nucleus absorbs electromagnetic radiation at a characteristic frequency, giving an NMR signal, which is influenced, in a unique way, by the nature of neighboring atoms: the resulting signal, which is a shift in its resonance frequency, is called chemical shift. The measurements of all the frequencies produced in a sample can be used to identify metabolites in complex mixtures (Rhee and Gerszten 2012). Analyses of clinical tissues and biofluids such as plasma and urine are usually carried out by 1H-NMR, which generate catalogues of profiles of a large number of metabolites, while 2D NMR experiments (HMBC, HSQC, TOCSY and others) are less common in large metabolomic studies, as they take considerably more time, are energetically costly and computationally more intensive to process. Nevertheless, in addition to the widely used 1H-NMR, also 13C-, 15N-, and 31P-NMR analyses are used in the context of cellular metabolism to determine structure and abundance of metabolites as well as biological pathways (e.g., by fluxomics analyses using 13C tracers). Aside from the instrumental cost (Table 3), NMR is a powerful tool for structure elucidation and very advantageous considering that it is a nondestructive technology, which requires little sample preparation and no chromatographic separation or derivatization process, making the cost per sample very low. NMR can be applied to in vivo tissues or to biological fluids obtained from humans or cells in culture and can be used to perform metabolites quantification: steps for sample preparation are not discussed in this chapter, but for the details please refer to the article by Emwas (Emwas et al. 2019). Nevertheless, high amounts of biological samples are needed to obtain reliable NMR results (Fig. 2) and the identification, as well as the quantification, of extracted metabolites is limited to the most abundant, typically less than

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Table 3 Summary of the differences between Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) in metabolomics analysis. (Modified from Emwas et al. 2019) Sensitivity Selectivity

Targeted analysis Reproducibility Number of detectable metabolites Sample preparation

Sample sizesensitivity Tissue extraction Sample recovery

Sample analysis time

Instrument cost Sample cost

Nuclear magnetic resonance (NMR) Low Generally used for nonselective analysis. Peak overlaps from multiple detected metabolites pose major challenges Not optimal for targeted analysis Very high 30–100

Minimal sample preparation required, e.g., transferring the sample to an NMR tube and adding deuterated locking solvent. Can be automated Requires larger amount of sample than MS, usually 0.3–1.0 ml of plasma Not required – tissues can be analyzed directly NMR is nondestructive and, hence, several analyses can be carried out on the same sample. Additionally, the sample can be recovered and stored for a long time Fast – the entire sample can be analyzed in one measurement

More expensive and occupies more space than MS Low cost per sample

Mass spectrometry (MS) High Highly selective. In combination with chromatography (such as liquid and gas phase separation), is a superior tool for targeted analysis Better for targeted analysis than NMR Average-high 300–1000+ (depending on whether GC-MS or LC-MS is used) More complex sample preparation, requires purification and sample derivatization for gas chromatography (GC)-MS Requires smaller sample size, e.g. 0.01–0.30 ml of plasma Requires tissue extraction If a derivatization is needed MS, the sample cannot be recovered. However, it needs only a small amount of sample Longer than NMR – requires different chromatography techniques depending on the metabolites analyzed Cheaper and occupies less space than NMR High cost per sample

100 analytes in human biofluids (Rhee and Gerszten 2012). Given the fact that there are at least several thousand of metabolites in human matrixes, this is considered as the main drawback of the NMR approach to metabolomics.

Mass Spectrometry Mass spectrometry (MS) is currently the most used approach for metabolomic analysis. The mass spectrometer consists of three main components: the ion source, the mass analyzer, and the detector. For mass spectrometry analysis, the sample needs to be ionized and this occurs in the ion source where the sample is ionized

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usually to cations by loss of an electron (negative ions as M-H
), or it adds a proton becoming an anion (positive ions as M-H+). The precursor ion is mass-selected and fragmented (product ions) by single or multiple collisions induced by an inert gas (i.e., nitrogen or helium). Then, in the mass analyzer the ions are sorted and separated according to their mass and charge, and then separated in the detector where the abundance is recorded. A high number of metabolites, from 300 to more than 1000 (Table 3), can be detected during the run. This procedure is controlled by the software that not only acquires the data but also compares spectra according to the database to have the identification of the compounds (Want et al. 2013). The mass analyzer is the heart of the mass spectrometer, which takes ionized masses and separates them based on mass to charge ratios. Different types of mass analyzers (magnetic sector, time of flight, quadrupole, ion trap) are available to perform mass spectrometry metabolomic analysis, as each of them is more or less suitable for particular experimental conditions, including the mass of the analytes, the m/z range to consider, the limit of detection, the required resolving power of the analytes. The product ions are analyzed either using a targeted or an untargeted approach (i.e., known or unknown metabolites) unequivocally identifying a given mass spectrum as related to a specific metabolite (Johnson and Carlson 2015). The most common mass analyzers are quadrupoles, which can be single, or double and triple quadrupoles, to perform tandem mass spectrometric analysis (MS/MS) or highresolution mass spectrometers, such as time-of-flight (Q-TOF) or Orbitrap that are used for the identification of unknown metabolites and allow the determination of the elemental composition. For targeted analysis the MS most commonly used are double and triple quadrupoles (tandem MS/MS detector). Open-sources databases (Human Metabolome Database http://www.hmdb.ca; MoNA-MassBank of North America http://www.mona.fiehnlab.ucdavis.edu; MassBank http://www. massbank.jp; Metlin http://www.metlin.scripps.edu) are available to compare MS/MS experimental spectra with reference MS/MS spectra of known compounds, resulting in an easier identification. The fluxomics analyses of samples that contain tracers labeled with stable isotopes are performed by monitoring the peaks that contain the stable isotopes and for this reason have a higher mass (e.g., M þ 1 vs. M þ 0 mass for a fragment that contains one 13C or one 2H, M þ 2 if the fragment contains two 13C, etc.).

Chromatography, Liquid Versus Gas, Coupled with Mass Spectrometry To better identify the compounds in the matrix, different chromatographic systems are usually coupled to the mass spectrometer. In fact, since biological samples are mixtures of several compounds, the proper analytical separation reduces artifacts and background signals and increases MS detection limits and data quality (Dettmer et al. 2007). These chromatographic systems are based on columns that have either a liquid- (LC) or gas- (GC) phase and allow the separation of different compounds

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over time, prior to ionization and detection by the mass spectrometry system. Once separated through the chromatographic columns, the metabolites reach the ionization source of the mass spectrometer, where they are first ionized and then, once they reach the collector, quantified. Samples containing molecules enriched with stable isotopes have the same chromatographic characteristics as those without stable isotopes.

High-Performance Liquid Chromatography Mass Spectrometry (HPLC-MS) It is among the most powerful techniques in analytical chemistry, in particular in metabolomic analysis, as it does not require pre-analytical work before the analysis. Two main types of chromatographic methods have been developed to achieve the separation of different classes of metabolites: reversed phase liquid chromatography (RPLC), to detect nonpolar to moderately polar metabolites, and hydrophilic interaction liquid chromatography (HILIC), for ionic and polar compounds, which are not or poorly retained by RPLC. The combination of these two chromatographic techniques allows the direct analysis of the metabolites (hydrophilic, as well as hydrophobic molecules) present in biological fluids and tissues, without a prior derivatization step (Lv et al. 2020) (see paragraphs below). The most suitable stationary phases for RPLC analysis are nonpolar, like C8 and C18-bonded silica columns, where the most hydrophobic compounds elute later because the interactions established with the column are stronger than those established with the mobile phase, which in this case is polar, usually water or polar organic solvents, e.g., acetonitrile. To get a better metabolites separation, either an isocratic (i.e., the mobile phase has a constant concentration) or a gradient (i.e., the mobile phase has a varying concentration during the chromatographic run) elution can be used. HILIC columns, on the other hand, are selected for hydrophilic chromatography: in this case, the stationary phase is polar (e.g., amino, zwitterion, ciano), while the mobile phase is similar to the RPLC mobile phase, i.e., an organic solvent like acetonitrile. In both the polar and nonpolar analyses, samples may contain ionizable metabolites, which are affected by the pH of the mobile phase, which has to be under control. For this reason, mobile phases generally include a small amount of buffer to control the ionization of the analytes. The most common HPLC buffers are ammonium acetate, ammonium carbonate, and ammonium formate. Sometimes the addition to the mobile phase of an acid (e.g., formic acid, acetic acid) instead of a buffer is enough to keep the desired pH, aside from buffering capability (Greco and Letzel 2013; Buszewski and Noga 2012). Once the metabolites are separated through the right chromatographic column, electrospray ionization mass spectrometry (ESI-MS) is a versatile analytical technique to detect a broad array of metabolites. Gas Chromatography Mass Spectrometry (GC-MS) This is another widely used technique in metabolomic studies. This analytical technique is considered the most standardized method in metabolomic analysis, having established protocols to analyze different classes of metabolites, from

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amino acids to carbohydrates, fatty acids, and hormones (Fiehn 2016), where it provides reliable chromatographic separations, reproducible retention times, and accurate quantification. Nevertheless, the drawback of gas chromatography is that it requires volatile compounds, as the separation is mainly based on the characteristic boiling point and polarity of the analytes’ mixture; thus, a broad variety of compounds can be analyzed, as long as they are thermally stable and sufficiently volatile (Mcgarrah et al. 2018). Because the majority of biologically interesting metabolites are not intrinsically volatiles, a reaction of derivatization is required to improve the volatility and the stability of the analytes, as well as the detector response, to assure an appropriate analysis and accurate analytical interpretation of the results (Poojary and Passamonti 2016).

Sample Acquisition and Purification For metabolomic analysis, it is important to plan the acquisition and storage of the samples. Sample preparation for the analysis is important as well, since extraction of metabolites depends on the matrix (that could be tissue, serum or plasma, urine, or stools, Fig. 1) and type of analysis.

Sample Matrix, Sample Acquisition, and Storage Metabolomics profiling is carried out mainly in plasma or serum samples, but it can also be investigated in other biological samples as urine, stools, or tissue biopsies (although the last one is less feasible in human studies, Fig. 2). It is important to assure a rapid metabolic quenching of the sample. Plasma samples are preferred to serum since the last one is obtained by leaving whole blood to clot at room temperature for 15–30 min after collection; during the serum procedure, circulating enzymes are still active and this can potentially alter the metabolomic profile. For plasma samples, it is important to keep samples in ice until centrifugation, which should be done as soon as possible to avoid blood cells altering the plasma metabolomic profiles (e.g., glucose is metabolized to lactate by red blood cells, thus altering lactate concentration, or circulating lipoprotein lipase induces fatty acid spillover from triglycerides). It is preferable to use tubes with Ethylenediaminetetraacetic acid (EDTA) (antioxidant), since other anticoagulant agents can interfere with the measurement of some metabolites (e.g., tubes with sodium citrate should not be used if intermediates of TCA cycle like citrate are investigated, and Li-Heparin activates lipase and thus the hydrolysis of circulating triglycerides with spill-over of NEFA). For urine samples, there is no critical quenching problem but, since they are usually collected as spot samples, metabolites’ concentrations are highly dependent on urine volume, which is often not registered; for this reason, it is important to measure urine creatinine concentrations that is then used as a normalization factor. For cells and tissues, the quenching step assumes a central role to avoid sample

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degradation and to “capture” the metabolic picture at a specific time point, while rapid freezing using liquid nitrogen or dry-ice and the storage at
80  C prevent the sample degradation and ensure the accuracy and reproducibility of the analysis

Sample Preparation The great majority of metabolites are polar metabolites that are soluble in water, except fatty acids (either NEFA or SCFA). Sample preparation involves the separation of metabolites from proteins and lipids (Fig. 2) and this is usually done using organic solvents, i.e., employing chloroform/methanol/water (2/5/2, volumetric ratios) that allow the precipitation of proteins and peptides, and the separation from lipids, since the metabolites remain in the aqueous phase while the lipids are found in the organic phase (Folch’s method (Fiehn 2016)). For metabolomic analysis of tissues or biopsies, during the first step the tissue is homogenized at cold temperatures to prevent the hormone-sensitive metabolites from heat degradation during the homogenization process (Want et al. 2013). This can be done by using a tissue Lyser or by grounding the tissue in a mortar with liquid nitrogen until pulverized and freeze-dried in a vacuum freeze dryer to dry powder. Then, 0.1–0.5 mg of dried tissue is dissolved in 20 μL water, 200 μL methanol containing internal standards are added and sonicated at 60 Hz for 6 min to extract analytes. The obtained extracted mixtures are then centrifuged at 18,000 g, 4  C for 10 min and the supernatant is taken for analysis. Although cryogenic grinding with mortar and pestle is inexpensive, it has several limitations: the number of samples that can be processed is low; contamination issues may also be a concern as the grinding will generate dust; moreover, the amount of sample is relatively large. For this reason, new classes of homogenizers are equipped with a tool that allows the desired temperature between 0  C and 10  C before and during the tissue homogenization (e.g., the Precellys system by Bertin). Samples like plasma and tissues contain proteins and peptides that can interfere with the analysis. Thus, the first step is protein precipitation using an organic solvent at cold temperatures (e.g., methanol), and then the sample is centrifuged so proteins and peptides are precipitated and form a pellet (Fig. 2). After protein precipitation, filtrations and/or centrifugations steps might be necessary to remove further impurities (Want et al. 2013). The samples can be further purified using either liquidliquid extraction or Solid Phase Extraction (SPE). SPE technique is widely used for metabolomic analysis since the purification of the samples allows less interference in the analysis (Sitnikov et al. 2016). The sample is loaded into the SPE column (Fig. 2), passes through the stationary phase, and is either collected or discarded depending on whether it contains the desired analytes or undesired impurities. If the portion retained on the stationary phase includes the desired analytes, they can then be removed from the stationary phase for collection in an additional step, in which the stationary phase is rinsed with an appropriate eluent. The advantages of SPE versus liquid-liquid extraction include improved throughput, decreased organic solvent usage and waste generation, higher and more

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reproducible recoveries, cleaner extracts, no emulsions, tunable selectivity, and automation. The use of internal standard allows to estimate the recovery. Samples are then dried, usually under nitrogen flow to prevent oxidation, and either reconstituted with the appropriate solvent for HPLC-MS analysis or derivatized for GC-MS analysis.

Derivatization Methods As previously stated, the GC-MS analysis of multiple polar metabolites (e.g., amino acids, organic acids, sugars, amines, thiols) requires a derivatization step, which is achieved by using different reagents according to the functional groups that have to be modified (Dettmer et al. 2007; Fiehn 2016; Beale et al. 2018). While for GC-MS analysis the derivatization step is mandatory, for HPLC-MS analysis it is used only to detect metabolites with poor column affinity and improve sample analysis. Derivatization is performed on purified samples, dried under nitrogen flow to avoid the presence of water. The most used derivatization methods for GC-MS have been reviewed by Moldoveanu (C. Moldoveanu and David 2019), and here briefly summarized. Among the chemical derivatization reactions, a) alkylation/arylation, b) acylation, and c) silylation are commonly used to derivatize hydrogens from carboxylic (-COOH), alcoholic (-OH), amine (NH-), amidic (-CONH-), and thiolic (-SH) groups (Dettmer et al. 2007; Jones and Hugel 2013). Alkylation/arylation reactions are performed by using alkyl/aryl halides (mainly alkyl iodides and alkyl bromides) as reagents, together with a catalyst or a specific solvent that enhances the reaction (Moldoveanu and David 2019; Beale et al. 2018). Acylation reactions, instead, convert active hydrogens of amine, alcoholic, and thiolic groups into the corresponding amide, esters, and thioesters, thus improving the analyte thermal stability by reducing heat-decomposition and enhancing sample volatility. In silylation reactions, active hydrogens of -COOH, -NH, -OH, -SH groups are replaced by mainly trimethylsilyl groups (TMS, -SiMe3), to form TMS derivatives. All silylated derivatives are characterized by greater thermal stability and volatility, generating more reproducible chromatograms and MS spectra. Nevertheless, for primary amino groups, silylation might not be specific, producing more derivatives; moreover, this reaction is very sensible to small changes in experimental conditions. N,O-bis-trimethylsilyl-trifluoroacetamide (BSTFA) and N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) are common silylating reagents used for metabolomics applications (Dettmer et al. 2007; Jones and Hugel 2013; Moldoveanu and David 2019). MSTFA often replaces BSTFA, as it is the most volatile trimethylsilyl amide available, thus commonly used for several classes of metabolites (Jones and Hugel 2013); moreover, the use of anhydrous solvents (e.g., pyridine) accelerate the reaction, avoiding prolonged high temperatures (Beale et al. 2018). Besides TMS derivatives, N-methyl-N-(tert-butyldimethylsilyl)-trifluoroacetamide (MTBSTFA) is a common reagent containing the tert-butyldimethylsilyl group (TBDMS), whose derivatives are more stable to the hydrolysis than the TMS, thus preferred in

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silylation reactions of amino acids (Dettmer et al. 2007; Moldoveanu and David 2019; Fiehn 2016). Moreover, TBDMS derivatives are characterized by the loss of the tert-butyl moiety, which gives a fragment ion of [M-57]+ in the correspondent EI-MS spectra, thus making the metabolite identification easier (Dettmer et al. 2007; Moldoveanu and David 2019; Fiehn 2016). The silylation reactions mainly derivatize exchangeable protons, but carbonyl groups may also be transformed and derivatized through a methoxylation reaction, with methoxy- (MOX) or ethoxy-amine hydrochloride (EtOX), see Fiehn et al. (Fiehn 2016), yielding the correspondent oxime-derivative. This method stabilizes α-ketoacids and locks sugars in open-ring conformation, prior to silylation, eliminating side products and improving the quality of the analysis. In general, whatever the applied derivatization method will be, to achieve successful outcomes it is important to optimize the sample extraction and preparation protocols, in particular for complex matrixes, such as plasma, urine, or tissues, and spend some time investigating the best derivatization method that ensures the detection of the highest percentage of metabolites. The success of this step can be further increased by the simultaneous use of both commercially available synthetic standards and spiked sample matrixes (Beale et al. 2018), also allowing good and correct identification and quantification of the metabolite of interest within the biological matrix.

Metabolite Quantification and Data Analysis Targeted Versus Untargeted Protocols The human metabolome has not been fully identified yet, and several metabolites involved in biochemical processes are still unknown and the variety of low-molecular weight molecules identified up to now actually reflects only a small part of the entire metabolome (Fiehn 2016; Mcgarrah et al. 2018). Prior to the analysis, the type of metabolomic investigation has to be defined: whether it will be an untargeted approach or a targeted one, it will in any case determine the experimental design, the sample preparation, and the analytical analysis. (a) Untargeted metabolomics is an unbiased, comprehensive analysis of all the measurable metabolites from each sample, without previous knowledge of the constituents his a “discovery” process based on the statistical evaluation of peak abundance, through which variations among the groups of analyzed samples can be detected. The major advantage of this approach is the discovery of novel metabolites relevant for the study context: nevertheless, it is not possible to structurally identify all of them, and sample preparation, as well as analytical instrumentation and parameters will influence the set of identified metabolites. It does not require the use of internal standards since it is merely qualitative. However, it is a challenging approach in terms of data analysis and peak identification through matching with known spectra available in published databases (Schrimpe-Rutledge et al. 2016; Wishart et al. 2022; Mcgarrah et al. 2018).

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(b) Targeted metabolomic analysis focuses its measurements on a specific and defined number of chemically characterized and biochemically annotated metabolites and, through the use of the corresponding chemical standard, it is possible to confirm the identity of the metabolite and quantify it in different matrixes. This approach allows the optimization of the sample preparation to focus mainly on a class of metabolites, reducing the dominance of other abundant molecules through a specific sample preparation workflow. The use of labeled internal standards allows the full quantification of metabolites in the matrix (Fiehn 2016; Kalhan et al. 2011; Mcgarrah et al. 2018). Therefore, as both the approaches have their advantages and disadvantages, they are often used in combination, for an accurate identification and determination of differential metabolites.

Quantitative Versus Qualitative Methods The outcomes of a LC/GC-MS or NMR analysis are peak areas whose integration is used to determine the amount of the correspondent compound and to highlight its different abundances among different biological samples. The use of internal standards, or calibration using external standards, is necessary and are discussed below. External Standardization: When quantification is performed by external standard calibration, the detector response of known concentrations of the analytical standard are compared to the response of biological samples containing unknown concentrations. A calibration curve is generated by injecting the analytical standard at different concentrations (calibrators) and plotting the absolute analyte response against the analyte concentration. The slope of the curve is then used to generate a calibration factor, used to calculate the concentration of the metabolite. Internal Standardization: Because external standardizations may not provide acceptable results when considerable sample preparation or instrumental errors are expected, a proper internal standard (IS) can improve the precision and accuracy of the analysis. Internal standards are either structural analogues or stable isotopically labeled (SIL) analogues of the analyte, where several atoms are replaced by their stable isotopes (e.g.,2H,13C, 15N, 17O) (Stokvis et al. 2005; Fiehn 2016). The IS peak will coelute with the correspondent unlabeled metabolite, but the shift in the m/z value allows a proper peak integration and a precise quantification.

Data Analysis Bioinformatics and advanced tools are needed for qualitative and quantitative analysis of the large datasets generated by metabolomics, including data processing, peak picking, and statistical analysis of the dataset originated from the resulting chromatograms to address the vast amount and variety of data (Fig. 2).

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Overall, bioinformatics and advanced statistical tools are crucial for the analysis of metabolomic data, allowing researchers to extract meaningful information from complex datasets and gain insights into the underlying biology. To investigate and identify patterns or relationships between different variables, data are often analyzed using multivariate analysis tools such as principal component analysis (PCA), partial least squares (PLS), and hierarchical clustering. Moreover, machine learning techniques, such as random forest, neural networks, and support vector machines, are often used to classify samples, predict protein expression levels, and identify biomarkers. Among the available open-source software, MetFlow (http://metflow.zhulab.cn/, accessed on 3 January 2023) and MetaboAnalyst (https://www.metaboanalyst.ca accessed on 3 January 2023) can be used not only to discover metabolites of interest, but also for pathway enrichment analysis. To deepen this aspect, the works by Chen (Chen et al. 2022), and Gardinassi (Gardinassi et al. 2017) are suggested. Moreover, pathway analysis tools are used to identify the biological pathways and networks that are affected by changes in metabolites or protein expression levels. Several websites are available to investigate metabolic pathways like the KEGG (Kyoto Encyclopedia of Genes and Genomes), Reactome, or BioCyc, containing information on biological pathways and reactions involved in biological processes, genomic information, and chemical compounds, including metabolites, providing a comprehensive and reliable source of information on biological pathways and reactions. KEGG provides information on metabolites such as their chemical structure, molecular weight, and biological function. Unknown metabolites can be searched by their mass to find information on chemical structure, biological function, and related pathways. Reactome provides a comprehensive knowledge base of molecular events in human biology and other organisms, including information on cellular metabolism, signaling pathways, and cell cycle regulation. Moreover, the program provides interactive pathway diagrams that allow users to explore the details of a pathway or reaction. BioCyc is a collection of Pathway/Genome Databases (PGDBs) that provide computational access to genome and metabolic pathway information for thousands of organisms and can be accessed through the BioCyc website or through a variety of software tools and APIs.

Conclusion Over the last few years, metabolomics has made a substantial contribution to providing systematic insights into biochemical processes and potential biomarkers of pathophysiological states and diseases. Metabolomics complements genomics, transcriptomics, and proteomics, and contributes to bridge the gap between genotype and phenotype while also capturing the complex and functional interactions with

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environmental factors. In this chapter, we reported a summary of the main metabolites that are measurable in biological samples and involved in metabolic diseases, as well as the methods currently used to measure the metabolomic profile. Moreover, we discussed the fluxomics methods that, together with metabolomics, help to describe different phenotypes and relate them to metabolic alterations, thus providing a biological and physiological interpretation of the results. An important system biology strategy and research challenge is the integration of metabolomics-based signatures with other multilevel -omics data (genomics, transcriptomics, metagenomics, [phospho]proteomics) from the host and in response to the environment, in order to define novel metabolic disease mechanisms and targets. Nevertheless, analytical development and standardization are the current methodological challenges to achieve more reliable analyses and a cross-laboratory comparability.

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The Impact of Microbial Metabolites on Host Health and Disease Sonia Ferna´ndez-Veledo, Anna Marsal-Beltran, Victo`ria Ceperuelo-Mallafre´, Brenno Astiarraga, and Lídia Cedo´

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Short-Chain Fatty Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Production of SCFAs from Dietary Fiber and Bacterial Carbohydrate Fermentation . . . . . 74 Physiological Roles of SCFAs in Host Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Impact of SCFAs on Metabolic Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 SCFAs Precursors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Lactate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Succinate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Bile Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Microbial Interaction with BAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Impact of BAs on Host Metabolism: Physiology and Mechanisms of Action . . . . . . . . . . . . . 86 Impact of BAs on Metabolic Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Amino Acid-Derived Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Hydrogen Sulfide (H2S) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Phenolic and Indolic Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Polyamines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Branched-Chain Fatty Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 S. Fernández-Veledo (*) · A. Marsal-Beltran · V. Ceperuelo-Mallafré Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain Universitat Rovira i Virgili (URV), Reus, Spain e-mail: [email protected] B. Astiarraga · L. Cedó Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III (ISCIII), Madrid, Spain © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_3

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Trimethylamine-N-Oxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biosynthesis of TMAO and Determinants of Its Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Gut Microbiota in TMA/TMAO Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of TMAO on Metabolic Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beneficial Effects of TMAO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

There is compelling evidence that the gut microbiota functions as an endocrine organ through the production of various metabolites with hormone-like properties that play multifaceted roles in host metabolism. It is increasingly appreciated that gut dysbiosis, which disturbs the production and utilization of microbial metabolites, is a major determinant of several metabolic diseases. In this chapter, we discuss the influence of some of these microbial-related metabolites on host (patho)physiological processes known to be involved in the development of metabolic disorders, such as obesity and its related comorbidities. The pace of progress in this field is rapid, and a deeper understanding of the mechanistic roles that microbial metabolites play in the interaction between the host and gut microbiota will undoubtedly reveal new insights into the etiology of metabolic disorders. Microbial metabolites are also emerging as useful biomarkers and clinical tools to evaluate disease predisposition and progression, and further research will hopefully define new uses of microbial metabolites for not only the treatment of metabolic disease but also its prevention. Keywords

Short-chain fatty acids · Succinate · Lactate · Bile acids · Amino acid-derived metabolites · TMAO · Obesity · Diabetes · CVD · NAFLD

Introduction The gut microbiota and the host have coevolved in a mutualistic association. The host provides nutrients and a stable environment for microbes to survive and, in return, gut microbes fulfill many essential functions in the host. Indeed, it is now well established that the pleiotropic roles of gut microbiota in physiology go beyond the digestion of complex dietary macronutrients and providing essential nutrients and vitamins to contributing in the defense against pathogens, including shaping and sustaining the immune system. In addition to these “housekeeping” functions, it has become evident during the last decade that the gut microbiota acts as an endocrine organ, converting nutritional cues into hormone-like signals that enter the circulation and travel to distal sites with a direct impact on host physiology. In particular, several microbial-derived metabolites work as critical signaling molecules with pivotal roles in the maintenance of energy balance, both centrally and peripherally. For example, some microbial metabolites can target the hypothalamus to modulate appetite, or participate as substrates in metabolic tissues such as adipose tissue, liver, and

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pancreas, to regulate energy storage and expenditure. This can occur either directly, sensed by specific host receptor systems, or indirectly, by modulating the production of other hormones. Maintaining a healthy gut microbiota is important for achieving a good nutritional and physiological state and for reducing the risk of developing some diseases. The dysregulation of gut microbiota composition – termed dysbiosis – can trigger multiple adverse effects to host physiology and enhance the susceptibility to many chronic illnesses and conditions, including metabolic disorders such as obesity, type 2 diabetes (T2D), cardiovascular disease (CVD), and nonalcoholic fatty liver disease (NAFLD), all of them closely associated with a chronic inflammatory state. It is becoming clearer that the role of gut dysbiosis in the development of metabolic disorders is more likely due to changes in the metabolic activity of the microbiota, rather than to changes in its composition. However, despite the convergence of basic research and epidemiological and clinical studies indicating that intestinal dysbiosis contributes to chronic inflammation, it cannot be confirmed categorically that gut microbiota imbalance is the causal factor of these metabolic diseases or just a consequence. Instead, and considering the complex symbiotic bidirectional relationship that exists, dysbiosis should be thought of as a major contributing factor to the etiopathogenesis of metabolic disorders, rather than a decisive and exclusive cause. Although a large number of microbial metabolites have been identified and partially characterized, this chapter will focus on the most representative signaling metabolites with broad impact on host physiology. Emphasis will also be placed on how disturbances in the production of these metabolites and/or the increase in detrimental microbiota-derived metabolites contribute to the initiation and progression of metabolic diseases.

Short-Chain Fatty Acids Perhaps the best-studied microbial-derived metabolites are the short-chain fatty acids (SCFAs), which are the major metabolites derived from the anaerobic fermentation of complex resistant carbohydrates and indigestible polysaccharides (e.g., fructo-oligosaccharides, inulin, sugar alcohols, resistant starch, and polysaccharides from plant cell walls) in the large intestine by the gut microbiota. Small quantities of SCFAs can be directly obtained from plant oils and animal fats. SCFAs are small organic monocarboxylic acids with a chain length of 70%) cannot be cultured (Raymond et al. 2019). Within the gut, which represents the largest epithelial diversity of the body, the bacterial gene counts are ranging from 400,000 to 800,000 (Le Chatelier et al. 2013). When compared to the ~25,000–30,000 human genes the gut microbiota diversity is about 20–30 times higher. Therefore, such large functional diversity must contain numerous mechanisms through which the gut microbiota is involved for the control of health. It is noteworthy that the microbiome encompasses not only genes from the bacteria but from the viruses (Townsend et al. 2021; Bai et al. 2022), the fungi, the yeast, and some parasites. The gut microbiota is composed in most mammals of two major phyla the Firmicutes and the Bacteroidetes and represent around 80–90% of the bacteria. Some Actinobacteria and Proteobacteria phyla are also present but at lower proportion. However, it is noteworthy that although the latest phyla are present at a lower frequency their importance is crucial. Another rare phylum the Verrucomicrobia that includes the Akkermansia genera and muciniphila specie is also of major importance. It resides in the mucosal layer and is considered as a major regulator and degrader of mucins. Recent evidences show its importance in the control of energy metabolism in mice (Depommier et al. 2019). Altogether, thanks to

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its diversity at the individual level the identification of the microbiome opens an immense opportunity to identify novel personalized food, therapeutic approaches, and biomarker solutions to help maintain health in humans, as well as animals. The tight interaction between the microbiota, the host, and its environment represents a new scientific opportunity to better understand the mechanisms through which the host has evolved within its environment. The gut microbiota is inherited at birth from the mother’s intestine, vagina, and the closed environment when the babies are born naturally. Over the following days, weeks, and months the microbiota progressively diversifies and shapes the host functions. The main physiological functions that include the innate and adaptive immune systems, the intestinal absorption, the neuronal developments, the vascular network, the development of the organs involved in metabolism, i.e., the adipose depots, the liver, the muscles, and the endocrine pancreas are under the control of gut microbiota diversity. However, this is a two-way crosstalk since changes in food intake, hormone secretion, behavior, stress, physical activity, neural system, and the immune system are imprinting dramatically the ecology of the gut microbiota. Furthermore, numerous environmental factors including drugs, diet, and pollution modify the gut microbiota ecology. Altogether, the host to microbiota interaction shapes both parties. A tremendous number of original manuscripts and reviews have reported the cross talk between the environment and the gut microbiota. All conclude that such relationship is bidirectional. However, a temporal dimension clearly shows that the host is first under the control of the gut microbiota colonization at birth which secondarily shapes the gut microbiota to reach a steady balance that evolves rapidly over the first days, weeks, months, and years of life to stabilize at adulthood. Recent advances further show that aging could be a risk factor responsible for gut microbiota dysbiosis. However, it is clear that the latter contributes to accelerated aging. Hence, this tight relationship between the host and the microbiome is a dynamic mechanism continuously interacting to maintain an apparent balance. Exogenous factors, such as drugs, diets, and stress to cite a few, interact with both the host and the microbiome thereby impairing the balance. The host and the microbiome have developed throughout the evolution resilient mechanisms that allows the equilibrium between both parties to afford acute impacts from the environment. Certainly, long term changes of the environment are deleterious to both the host and the microbiome leading to a shortening of the lifespan and overall health. The molecular mechanisms of this tight interaction between the host and the microbiome are daily deciphered by the scientific community. Over the last 2 decades numerous potential regulatory molecules have been uncovered among them the LPS, peptidoglycans, flagellin, short chain fatty acids, indole derivates, aromatic aminoacids to cite a few. In addition, this new understanding of the potential physiological role of the gut microbiome on the host health suggest that novel physiological functions of the host could be uncovered. We here detail that the intestine, in addition to be a barrier to the microbiota protecting thereby from infections, allows to some extent the passage of gut microbiome, notably bacteria, to the host tissues interacting, hence with the physiological functions of the targeted organs.

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The Intestinal Defense Systems: The Leaky Gut In the blood almost two thirds of the circulating molecules are either produced by the gut microbiome or their concentration is influenced by the gut microbiome as shown in metabolomic studies on germ free mouse serum samples (Li et al. 2008). Hence, circulating molecules from the microbes, or under the control of the microbes, could be considered as potential regulatory factors of the host physiology. Such molecules circulate into the blood and would directly target organs to regulate their function (Fig. 1). In addition to this mode of action of the host microbiota another mechanism involves the education or the programming, notably at birth, of numerous physiological functions, including the intestinal immune system and thereby the efficacy of the defense against pathogens but commensals as well (Al Nabhani and Eberl 2020). First, the innate immune cells identify bacteria which are in closed contact with the epithelial intestinal cells (Fig. 1). Dendritic cells and macrophages as well as intra epithelial cells identify bacteria which are phagocytosed. The innate immune cells can capture bacteria from the luminal side when the bacteria are reaching the

Interactions between the gut microbes and the host tissues Lamina propria filter system

Luminal Bacteria and molecules

LPS, PG, indols, SCFA… Mucosal Bacteria nd molecules

M Immune cells: LB-LT, APC , ILC, IL17 producing cells, antibodies, defensins, Reg M III, Mucus (Muc2)

Translocation of bacteria to tissues

Luminal and mucosal bacteria LPS, PG, indols, SCFA…

Intestinal lamina propria Vascular, muscular, neuronal… cells

Fig. 1 Molecular interactions between the gut microbes and the host tissues. Importance of the intestinal barrier. Luminal and mucosal bacteria and related molecules, interact with the intestinal lamina propria to control bacterial translocation to tissues. The lamina propria could be considered as a filter where the intestinal immune system and notably some specific immune cells producing IL17 are of importance. Innate Lymphoid cells (ILCs), T and B lymphocytes (LT, LB), some antibodies secreted into the mucosal and luminal layers, defensins, notably RegIII and the release of mucosal protein Muc2 are some of the principal regulators of the interactions between the gut microbiota and the host. Different mechanisms are at play at different location of the long digestive track including the mouth. In addition to the immune and epithelial cells, vascular and neural cells are contributing to the barrier although through indirect ways

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epithelial layer and attach to it. Dendritic cells can cross the epithelial barrier through diapedesis and reach the lumen. Others reach the luminal side through the M cells. These epithelial cells do not have villi and can easily get in contact with bacteria. The surrounding Payer patches will behave as ganglions to educate the adaptive immune cells. Eventually, innate immune cells translocate to the luminal side through Goblet cells which are specialized for the secretion of mucus. In all instances, bacteria from the closest mucosal layer are either destroyed in the lamina propria of the intestine through mechanisms involving oxidative stress and lysozyme degradation or transported to ganglions to be presented to the naïve T lymphocytes. The latter then through their T cell receptor will generate a specific immune response, educate B lymphocyte to secrete specific antibodies. Among them a specific isoform of the immunoglobulins the IgA are secreted into the luminal side of the intestine and opsonize the corresponding bacteria. Hence, milligrams of IgA are secreted every day which help to protect the host against bacterial invasions. Beside the defense mediated by the innate and adaptive immune system, the epithelial cells do secrete anti-microbial peptides such as defensins (Fig. 1). A large set of antibacterial peptides have been described such as RegIII. The corresponding mode of action varies according to the peptide and has not been very clearly identified. The mucosal layer on itself can be considered as a protection since according to its thickness it prevents numerous bacteria to attached to the epithelial cells and thereby release toxic xenobiotics aggressing the epithelial and immune cells (Fig. 1). The mucus is composed mainly by a glycoprotein, the muc2 which harbors and extremely large number of O-glycation that the length of the sugars varies according to the intestinal location and the individuals. The mucus is continuously degraded by the bacteria and resynthesized by the host. It is a source of energy for numerous bacteria notably including Akkermansia muciniphila. The bacteria extract sugars from the mucin as an energy source. Notably, during fasting the mucosal layer is the only source of energy for the bacteria. An altered mucosal layer could be detrimental as it exposes epithelial cells to the adherence of bacteria which can alter the cells and generate inflammation. However, the fermentation of sugars from the mucosal layer generates short chain fatty acids and notably butyrate and some propionate which are known to be beneficial for the host. The butyrate activates colonocyte proliferation and hence improve the thickness of the epithelial layer. The molecule modulates the intestinal immune system and prevents excessive inflammation. Eventually, the butyrate activates the gut brain axis and informs the host of its fasting state. The mucosal layer varies according to the intestinal segment. The mucus is extremely poor in the duodenum and jejunum. It is fluffy in the ileum and thicker in the colon. Interestingly, less bacteria are present in the duodenum (104/ml) than in the colon (1012). The bile acids secreted in the duodenum, the high tension in oxygen, and very low pH are responsible for such low bacterial count in the duodenum; thereby, less mucus is secreted suggesting that the mucosal layer is released in response to the bacteria. During bariatric surgery such as the Y en Roux where the last segment of the ileum is connected to the stomach a dramatic change of the gut microbiota has been observed. The increased nutrient concentration, the pH, and oxygen tension are

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most likely responsible for such changes. Consequently, bariatric surgery has an important impact on body weight and glycemia that occurs within weeks and months following the surgery. The causal role of the change in gut microbiota has been proposed but no clear mechanisms have been proposed for such metabolic impacts. One could suggest that the novel bacterial ecology and notably its high complexity could be encompassing numerous mechanisms at play in the metabolic control. The intestinal immune system could as well be dramatically impacted by the rewiring of the intestine during the surgery. Hence, a modified intestinal defense could be a mechanism altering the metabolism. In line with its role in the defense against potential commensal bacteria and impaired immune system could lead to the translocation of bacteria from the intestine towards the host tissues.

Metabolic Disease and Gut Microbiota Metabolic diseases have been characterized more than three decades ago by a metabolic inflammation thereby altering insulin action and secretion, as observed during diabetes, and altering adipose precursor cell proliferation, including vascular cells during obesity, to cite a few mechanisms (Holmes et al. 2012). The origin of metabolic inflammation has been attributed first to the impact of fatty acids on the host tissues notably the muscles, liver, and insulin secreting beta cells. A chronic dyslipidemia could alter these tissues. The insulin receptor transmembrane signaling pathway, the glucose sensing mechanisms, and the overall glucose metabolic pathways could be impaired by ectopic fat accumulation notably ceramides. However, dyslipidemia occurs mostly after the onset of insulin resistance and beta cells glucose unresponsiveness. Hence, other mechanisms, early in the time course of the disease, should be hypothesized. The last two decades demonstrated that such diseases are linked to a gut microbiota dysbiosis mostly described in feces (Ley et al. 2006; Wu et al. 2017). The mechanisms causal to metabolic inflammation and hence metabolic diseases have been originally linked to an increased endotoxemia (Cani et al. 2007). The circulating LPS, while bound to circulating lipoproteins (Verges et al. 2014), eventually reach metabolic tissues such as the adipose depots, the liver, and the muscles to activate tissue macrophages and adipose precursor cells (Luche et al. 2013). Consequently, inflammatory cytokines are released and further trigger the local tissue inflammation leading to insulin resistance. As mentioned above bariatric surgery dramatically changes the gut microbiota ecology as well as the body weight loss and the control of glycemia. So far, no clear demonstration of the mechanisms through which the change of gut microbiota during bariatric surgery could impact the host metabolic functions, have been proposed. Importantly and along the same line of evidence, it has been shown that the treatment of the early onset of type 2 diabetes by metformin leads to a change in gut microbiota rapidly after the initiation of the treatment. The transfer of the gut microbiota from patients treated with metformin to germ free mice lead to an improve glucose control thereby suggesting that microbial mechanisms could be at play for the control of glycemia.

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Another mechanism is directly linked to the presence of bacteria DNA, and potently bacteria in tissues, issued from the translocation through the gut of bacteria (Amar et al. 2011a; Burcelin et al. 2012; Burcelin et al. 2013; Massier et al. 2021; Massier et al. 2020; Anhe et al. 2020) that could activate inflammation notably through TLR9 DNA receptors (Burgueno et al. 2016; Henao-Mejia et al. 2012). Metabolic endotoxemia and tissue microbiota have been associated and potentially linked to a leaky gut (Riedel et al. 2021; Zhao et al. 2020). We previously described in the mouse that a short term high-fat diet induces ileum mucosa microbiota dysbiosis leading to a leaky gut due to a reduced ileum mucosal IL17 producing cells (Garidou et al. 2015) (Figs. 2 and 3). The transfer of the dysbiotic microbiota to germ free mice demonstrated the causal role of the microbiota by reducing the ileum content in IL17 producing cells of the recipient mice while the glycemic control was simultaneously impaired. Notably, the dramatic frequency reduction of ileum Th17 lymphocyte was due to an impaired antigen presenting cell efficacy to activate Th lymphocyte into IL17 producing cells. It is noteworthy that the later cytokine is involved in the defense against commensal microbiota (Hanson et al. 2013) as well as against fungi (Leonardi et al. 2022). Similarly, intestinal Il22 producing cells can be activated through the metabolism of tryptophan by bacteria within the intestine (Zelante et al. 2013) and contribute to the daily defense against commensal bacteria that could impair metabolism (Mao et al. 2018).

Non leaky gut: Eubiosis Gut microbiota Eubiosis

Intestinal defense

Intestinal epithelium

ICAM1 CD86 Th17

IL-6, IL-23 TLR

Healthy Nutrition

LPS/PG Inflammation

IL17, RegIII, Def; Muc2 …

Non inflammatory tissue microbiota

Optimal metabolic control

Fig. 2 Non leaky gut: Eubiosis. A non leaky gut is characterized by a non dysbiotic gut microbiota ecology, i.e., defined as Eubiosis. The corresponding commensal bacteria and their molecules regulate immune, epithelial, vascular cells that produce their related molecules, which are triggering a specific non pathologic metabolic inflammation characterized notably by the release of IL17, which favors RegIII and other defensin production, as well as muc2 to protect the epithelial layer against excessive or inappropriate bacterial translocation. Hence, an optimal non inflammatory tissue microbiota is set leading to an optimal metabolic control of tissue functions

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Leaky gut: Dysbiosis Gut microbiota Dysbiosis

Lack of defense

Intestinal epithelium

ICAM1

Fat-enriched Nutrition

CD86 Th17 TLR

IL-6, IL-23

LPS/PG Inflammation

…

Inflammatory and dysbiotic tissue microbiota

Obesity, insulin resistance, diabetes, hepac steatosis

Fig. 3 Leaky gut: Dysbiosis. A leaky gut is characterized by a dysbiotic gut microbiota ecology, i.e., defined by the change in the abundance of commensal bacteria so that the microbial profile is different from that of non dysbiotic individuals. The corresponding commensal bacteria and their molecules regulate immune, epithelial, vascular cells that produce their related molecules, which are triggering a specific non pathologic metabolic inflammation characterized notably by the release of IL17, which favors RegIII and other defensin production, as well as muc2 to protect the epithelial layer against excessive or inappropriate bacterial translocation. Hence, an optimal non inflammatory tissue microbiota is set leading to an optimal metabolic control of tissue functions. A non-healthy diet, such as a fat enriched and dietary fiber lowered diet will favor the growth of commensal bacteria in a different abundance when compared to a healthy individual. In such conditions, the intestinal immune cells are not properly triggered for their IL17 production, thereby lowering some defensins and muc2 production. Hence, the mucosal and luminal bacteria are no longer efficiently selected by the intestinal barrier and they translocate to tissues establishing a proinflammatory and dysbiotic tissue microbiota. Consequently, the chronic long term dysbiotic tissue microbiota generate excessive metabolic inflammation leading to insulin resistance, diabetes, hepatic steatosis, and obesity to cite a few.

The Tissue Microbiota Evidences suggest the existence of live bacteria in tissues. This has been notably described many times in solid cancers such as breast tumors (Costantini et al. 2018). Recent studies have reported dysbiosis of the microbiome in breast tissue collected from patients with breast cancer and the association between the microbiota and disease progression. They observed that breast microbiota differs between women who have breast cancer and those who are disease-free. Lipidomics show tight associations between some bacterial families such as the Gammaproteobacteriaceae and ceramides to cite a few suggesting some causal relationship interactions. Some tissue bacteria were present in breast prior to the development of breast cancer (Hoskinson et al. 2022) suggesting some causality as well. These bacteria would originate from the gut, the skin, the lungs and basically any kind of leaky epithelium (Fig. 1). The translocation of bacteria from these leaky epithelia towards tissues has

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been described in many instances. However, most of the time such bacteria translocation was linked to an acute and important inflammatory process such as cancer or Crohn’s disease or other intestinal inflammatory diseases, or intestinal infections with dysenteric bacteria. In addition to gut, skin, and lung injuries could lead to a porous epithelium and hence to the translocation of bacteria from these epithelia toward tissues as well (Figs. 2 and 3). The main issue is that commensal bacteria could translocate as well and chronically stay within tissues. Hence, some tissues would host live bacteria establishing a “tissue microbiota” (Fig. 1). Although, it is expected that in most of the tissues the large majority of the translocated bacteria are dead. We here define tissue microbiota as live bacteria or simply bacteria DNA notably when characterizing the 16SrRNA gene. It is counter intuitive to think that bacteria would be chronically hosted without generating infections. However, in absence of major virulence factors commensal bacteria remain mostly silent and under the control of the immune system but in a tolerant manner. Furthermore, it is now considered that the intestinal commensal bacteria would be defined as self, and hence not fully degraded by the innate immune system (Bresciani et al. 2016). The identification of the tissue microbiota in chronic disease is at its infancy. The understanding of its potential role remains unclear but could be related to the establishment of a chronic inflammatory process. A low-grade inflammation is an important process involved in the control of cell proliferation and hence tissue growth or reparation such as the adipose tissue development and heart or liver fibrosis following an injury like an infarction or a cell death. Therefore, tissue microbiota could be a player in the control of the tissue inflammatory process. A major rate limiting step in the identification of a tissue microbiota ecology is the impact of environmental contaminants. Bacteria and bacteria DNA are everywhere in the environment. Chemicals currently used to isolate bacterial DNA, amplify, and sequence it, do contain lots of bacterial DNA contaminants such as the bacterial vector used to produce and extract the polymerase, for example. Therefore, extreme care should be taken when analyzing tissue microbiota by PCR and sequencing. The other important paradigm that needs to be taken into account is that all coelomates live within and in close contact with the environment, thereby drinking, eating, and breathing environmental bacteria which hence could contribute to establish ecologies with the host and thereby considered as host residents. Although this does not preclude upon their hosting in tissues. The mechanisms linked to the chronic hosting in tissues could be related to the immune to microbiota crosstalk, where bacteria, considered as self (Bresciani et al. 2016) and without virulence factors will remain intracellular and more or less silence, since deprived from virulence factors. Their outbreak, although moderate for a commensal bacterium, could come from a reduction of the immune system surveillance. In case of moderate immunodepression, the tissue bacteria could take over the innate immune system and release factors able to interact with the host cell. Local infinite amounts of Microbial Associated Molecular Molecules (MAMM) could be required to activate some of the host corresponding receptors such as TLRs and NLRs. The activation of such receptors would engage numerous different functions such as the promotion of a local inflammation, stem cell and precursor cellular proliferation, increased energy oxidation, thermogenesis to cite a few.

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In metabolic diseases characterized by a low-grade inflammation, a tissue microbiota has been observed notably in adipose depots and more precisely in the stroma vascular fraction of the fat pad (Burcelin et al. 2013). Recent reports have independently characterized adipose tissue microbiota by sequencing the 16SrRNA gene (Massier et al. 2020) and showed relationship with the BMI and the inflammation. Others showed microbial profile found in plasma, liver, and in three distinct adipose tissues of individuals with morbid obesity. They, as well, identified specific tissue bacteria signatures associated with type 2 diabetes. Live bacteria were characterized using the CARD-FISH technique and similarly used to characterize blood bacteria (Massier et al. 2021). An increased frequency of bacteria DNA characteristics from gram-negative bacteria was associated with an increased BMI, while fewer grampositive bacteria were associated with leanness and/or the lack of type 2 diabetes. The Proteobacteriaceae are characteristics of the adipose depots and represent approximately 70% of the ecology. The leftover been shared among Firmicutes and Actinobacteriaceae. Conversely to the gut microbiota ecology almost no Bacteroidetes were detected demonstrated that the tissue microbiota is tissue specific. Eventually, all tissues where the 16SrRNA bacteria gene has been characterized show a different tissue microbiota ecology. In the blood more than 85% of the bacteria DNA is from Proteobacteriaceae. Different ratios of Proteobacteriaceae to Firmicutes, and/or Actinobacteria characterize each tissue specifically suggesting a precise selection process of the bacteria residing within a specific tissue. In metabolic diseases most of the tissue microbiota ecology seems to originate from the gut ecology. Although, we cannot rule out that the lung, the oral cavity, or the skin could be at the origin of some of the tissue microbiota. This feature is specific to metabolic disease. In cancer, or other inflammatory disease, the tissue microbiota could be certainly different and in large amount. The mode of selection from the source to the tissue hosting the microbiota is most likely different between major inflammatory diseases, cancer, Crohn’s and low-grade inflammatory diseases such as diabetes and obesity. In humans with Crohn’s disease appears a surprising observation which is development of extra-intestinal fat depots, i.e., the creeping fat. It is defined as expansion of mesenteric adipose tissue around the inflamed and fibrotic intestine (Ha et al. 2020). The authors discovered a subset of mucosal-associated gut bacteria that consistently translocated and remained viable in the creeping fat depots in Crohn’s disease and identified Clostridium innocuum as a specific signature. The mechanisms through which tissue microbiota could be responsible for the control of the tissue function remains to be identified in humans. However, in mouse models we initially described that the bacterial LPS are strong triggers of precursor cell proliferation and inflammatory processes (Luche et al. 2013). The large spectrum of gram negative Proteobacteriaceae translocated to the adipose depots could be responsible for its plasticity during obesity favoring hence, the enlargement of the depot, thereby accumulating more fat. The proliferation of precursor cells would be enhanced by the bacteria determinant, notably LPS, triggering CD14 and TLR4 receptor, leading to their proliferation. Some level of inflammation would further favor such proliferation, while blocking their differentiation momently. Upon and increased energy intake the newly produced preadipocytes would accumulate

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exogenous fat or newly synthesize fat to be stored in adipose cells (Luche et al. 2013; Luche et al. 2017; Cavallari et al. 2016; Denou et al. 2015; Moreno-Navarrete et al. 2013; Ortega et al. 2013). Certainly, the molecular mechanisms identified in the gut microbiome such as the production of short chain fatty acids, the metabolism of aromatic amino-acids, and of bile acids to cite a few could be players as well as the mechanisms within tissues responsible for the control of the tissue functions. Although, the adipose tissue seems to be an important site for tissue microbiota it is still a matter of debate since it remains difficult to grow bacteria from tissues. In previous trials the cultures remained negatives suggesting that beside bacterial DNA or eventually dead bacteria there are no living bacteria within tissues. The blood microbiota could be considered as a good biomarker for metabolic diseases since, conversely to the adipose or liver microbiota, its access is easy. Hence, we previously reported in a human cohort of apparently healthy individuals that the amount of DNA into the blood could predict the onset of type 2 diabetes (Amar et al. 2011b). Another signature was observed in patients with different cardiovascular outcomes (Amar et al. 2013). In these studies, the cases are mostly men who had died from cardiovascular diseases or hospitalized for heart failure. Eventually, a specific blood bacteria DNA signature was observed in patients with liver fibrosis (Lelouvier et al. 2016). This biomarker is of great interest since it could help avoid liver biopsies, notably in patients with a low risk of liver disease. An early prediction of liver fibrosis could be of great interest to initiate treatments at the early onset of the disease. From a therapeutic point of view, it could be important to control bacterial translocation, preventing hence proinflammatory and proliferative syndromes. It was proposed that probiotics could control intestinal bacterial translocation, affecting hence the tissue microbiota. In rodent’s models tissue microbiota has been extensively characterized since it can be standardized from a technical angle, which is essential to demonstrate the lack of potential contaminants. Blood has been fragmented to identify that the major part of the bacterial DNA was concentrated within the buffer layer, i.e., the while blood cells suggesting that the blood bacteria DNA would be intracellular (Lluch et al. 2015; Paisse et al. 2016). The origin of the tissue microbiota is gut bacteria translocation, i.e., the leaky gut. Although, it is a well described process in cancer disease (Faber et al. 2011), in Crohn’s disease (Gabele et al. 2011), in HIV-infected patients (Merlini et al. 2011), in cirrhosis (Steed et al. 2011), and other liver diseases (Fouts et al. 2012) we initially demonstrated the existence of a leaky gut in metabolic disease (Amar et al. 2011a) leading hence to a tissue microbiota.

Hypotheses Regarding Bacterial Translocations A growing body of evidences demonstrate the importance of a leaky gut in numerous diseases, thereby allowing gut microbes to translocate to tissues. Therefore, the intestine is the first barrier to the development of numerous disease that has a leaky gut as an origin (Fig. 1). The mechanisms through which the gut protects the

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host from the excessive and non-specific translocation of commensal bacteria are numerous. They involve notably the production of a thick mucosal layer. In the colon this layer of mucus is thick and sealed. The Muc2 protein is the main component of this layer. This protein is highly glycosylated and probably the substrate of numerous bacteria notably in the fasting states. When nutrients are rare, the mucosal layer, and notably the sugars of the muc2 proteins, is partially digested by the bacteria thereby producing short chain fatty acid which regulates the immune system and the proliferation of enterocytes to favor the thickness of the intestinal epithelium and hence prevent from leakiness. The innate and the adaptive immune systems are major regulators of the intestinal protection against gut microbiota. The immune cells continuously sample microbes on the luminal side of the intestine to identify potential pathogens. The Th17, innate lymphoid cells are responsible for the regulation of commensals, while Th1 react against viruses and Th2 against parasites to cite the most common defense systems involved. In animal models of type 2 diabetes, such as the high fat diet fed mice, we showed that a reduced frequency of intestinal IL17 producing cells was associated with the bacterial translocation towards adipose depots and liver (Garidou et al. 2015). The immune cells are in tight control with the enterocytes that sense directly molecules secreted by the microbes. The fractalkine is a signal molecule that binds CX3CR1 receptor on the immune cells, thereby, engaging the intestinal immune system defense. An impaired bacterial recognition by enterocytes and signal transmission of the immune cells could lead to a leaky gut and bacterial translocation (Pomie et al. 2021). The education of the immune cells with bacterial antigens used as a vaccine help the host to block the bacterial translocation (Pomie et al. 2016) and the development of diabetes and obesity in response to a fat-enriched diet. Another mechanism important for the control of the leaky gut are defensins. Numerous defensins are produced by epithelia to control the gut or oral microbiota dysbiosis (Yilmaz et al. 2015). Although most of the studies have been performed in Crohn’s disease models, in metabolic diseases observation suggest an impaired RegIII production (Garidou et al. 2015). The leaky gut has been observed in many diseases notably liver diseases. It is largely intuitive that an inflammatory liver disease could have bacteria extracts as triggering factors. The portal vein is directly issued from the intestine and hence provides a major route for bacterial determinants, including bacteria, to trigger inflammatory processes in the liver. The gut brain axis is as well under the influence of the leaky gut and bacterial translocation impacting hence the development of Alzheimer disease. The diabetic kidney disease is as well connected to the leaky gut hypothesis. Altogether, distant organs are connected to the gut. A leaky gut would initiate the translocation of bacteria or bacterial fragments leading to inflammation of tissues (Nagpal and Yadav 2017). The mechanisms through which the gut microbes and the corresponding fragments are transported specifically to tissues are unknown. It remains surprising that a specific bacterial ecology is set per tissues, establishing a signature, and that the gut leakiness would be specific as well allowing the translocation of some specific bacteria that will be addressed to an unknown mechanism to a specific tissue.

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Potential Therapeutic Strategies The control of a healthy bacterial translocation and the translocation of bacterial molecules leading to the tight regulation of a low-grade inflammatory process is of importance since is associated with health and disease. In healthy situation, a low-grade inflammation induced by bacteria and their components is essential in the tonus of the immune system, allowing the latter to promptly react in front of an inflection. This process is also of importance to allow the organism to adapt to a new metabolic situation. As an example, in excess of nutrients, sugars, and fat, the blood excess of these nutrients needs to be stored in adipose cells rather than in the liver, the muscles, the pancreas etc. . . . where they could be damaging the functions of the cells altering viability and promoting senescence and death. LPS and triggering adipose precursor cells and immune cells allowing the storage of fat and glucose into new adipocytes (Luche et al. 2013; Luche et al. 2017; Denou et al. 2015). On the other hand, triggering chronically, low-grade inflammation through a continuous flux of LPS and other bacterial molecules from the gut to the tissues would lead to excessive inflammation and adipose cell production, thereby developing insulin resistance, diabetes, obesity, and hepatic steatosis to cite a few diseases. To properly target metabolic inflammation in a way that it does not promote diseases there is a need to fine tune bacterial translocation. However, since this process is still nowadays poorly molecularly defined it still represent a challenge for pharmaceutical and agro food companies. Only a few molecular mechanisms at the level of the intestinal immune system and the epithelial layer have been determined. IL17 producing cells seem to be an interesting target as a causal demonstration has been shown in rodents (Garidou et al. 2015). Similarly RegIII a defensin like molecule produced by epithelial cells in response to mucosal bacteria could an important target to regulate the impact of excess mucosal bacteria. Hence, a first control of the translocation would be using regulators (R), such as small chemical molecules thereby targeting the bacteria pattern recognition molecules involved in translocation such as, NOD1.2, other NLRs and TLRs receptors (Fig. 4). A second strategy would be by using Regulators that are able to block the recognition of secreted bacterial molecules. The latter are triggering low-grade inflammation in tissues. Hence, blocking or down regulating low-grade inflammation by modulating the bacterial molecules using regulators would reduce their impact on insulin resistance, adipose precursor proliferation etc. . . . eventually, but not finally, a third strategy would be to control the potential role of host intermediate targets of gut microbiota, such as incretin secretion, at the level of the gut and which are interacting with other target tissues such as the muscle, the pancreas, the liver, brain, and adipose depots. In addition, there is an important need to precisely decipher the tissue microbiota that interacts with the host tissues. The bacterial molecules liberated within the tissues, could help design Regulators, as well. Eventually the tissue host genes sensitive to these tissue bacteria would be of interest for a potential target. Altogether, the control of bacterial translocation should be under the control of different potential therapeutic strategies to fine tune such process and avoid

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Therapeutic targeting of the gut to tissues microbial crosstalk R

Regulators of intestinal gene functions

Intestinal regulation

Immune cells, LB-LT, APC , ILCs, IL17 producing cells, antibodies, defensins, REGIII, Mucus (Muc2),…

3

Remote control

Controlling intermediate targets

R Translocation

Intestinal translocation regulators

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R

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control Regulator of Post biotic control

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R Regulators of post biotic target

Circulation S

Potential Regulator

Fig. 4 Therapeutic targeting of the gut to tissue microbial crosstalk. In 1, regulators (R), could be generated that control the bacterial recognition molecules at the level of the immune and epithelial cells. The production of IL17, defensins, muc2, and others could be quantified as biomarkers. In 2, the microbial post biotics, such as LPS, peptidoglycan, flagellin, could be targeted by other Regulators to reduce their impact on inflammation. Such Regulators could block the blood and tissues post biotics impacts. Eventually in 3, Regulators could block the impact of gut bacteria and molecules on mechanisms considered remotely controlling host functions such as incretins. The remote control mechanism could be as well considered as a target. The gene functions of the host once engaged by the bacteria and the corresponding post biotics could be targeted as well

excessive or insufficient regulation of metabolic inflammation. Such fine tuning will be necessary at the individual level since the gut microbiota ecology is different between individuals and hence different mechanisms could be at play for the regulation of bacterial translocation. Biomarkers classifying patients with different leaky gut phenotypes would be necessary. They could be issued from the genotyping of the gut microbiota diversity and complexity, including the genome. Shotgun sequencing could complement 13SrRNA targeted sequencing. Metabolomics, targeting bacterial molecules could be a useful approach to identify such biomarkers and even some potential regulatory molecules. Eventually, one could think of a targeted biotics strategy where cocktails of probiotics, prebiotics, and cobiotics could be thought.

Conclusions This novel paradigm of tissue microbiota is at infancy regarding its description, role, and mechanism but it opens a wide route to discover novel mechanisms explaining the development of the host within its environment. The fact that at birth the intestine

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is immature but, upon colonization by commensal bacteria, does launch several biological processes, the immune, vascular, and neuronal maturation, strongly suggest that in tissues similar processes could occur. Hence, the development of a tissue could be requiring a tissue microbiota. Germ free mice are lean and the adipose tissue develops as soon as they are conventionalized by commensal bacteria. A gut microbiota dysbiosis could lead to a leaky gut thereby modifying the tissue microbiota ecology. A tissue microbiota dysbiosis would consequently alter the tissue development and its function. From a therapeutic point of view, it could be recommended to regulate bacterial translocation by triggering specifically the gut. However, since the mechanisms of the translocation and how the tissue microbiota ecology is set are yet unknown it would require more studies. Although, once identified the prevention and treatment of chronic diseases, such as metabolic disease, could benefit from a topic treatment where only the gut epithelium would be targeted, thereby dramatically limiting the potential risks linked to off target effects of small molecules.

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Gut Microbiota and Obesity Giulia Angelini, Sara Russo, and Geltrude Mingrone

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Gut Microbiota in Host Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota Alteration in Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evidences from Animal Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evidences from Human Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of Gut Microbial Metabolites in the Development of Obesity . . . . . . . . . . . . . . . . . . . . . . Short-Chain Fatty Acids (SCFAs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lipopolysaccharide (LPS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interaction Between Diet Composition and Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timing of Food Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Western Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ketogenic Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mediterranean Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bariatric Surgery and Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modulation of Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Probiotic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prebiotic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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G. Angelini · S. Russo Università Cattolica del Sacro Cuore, Rome, Italy G. Mingrone (*) Università Cattolica del Sacro Cuore, Rome, Italy Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy Division of Diabetes & Nutritional Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_5

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Abstract

The worldwide prevalence of obesity has nearly tripled in the past ~50 years. The health risks associated with obesity have reached an alarming stage and have become a significant problem worldwide. The pathophysiology of obesity has been proven to be more complex and involves a combination of both genetic and environmental factors, which regulate food intake and energy expenditure. Recent evidences suggest that that certain types of gut bacteria may be more efficient at extracting energy from food than others, which could potentially lead to increased calorie absorption and weight gain. Additionally, it has been suggested that alterations in the gut microbiota may lead to increased inflammation and development of insulin resistance, both of which are closely linked to obesity and related metabolic disorders. Several studies have suggested that the composition of the microbiota may be influenced by factors such as diet, and that interventions aimed at modifying the microbiota (such as lifestyle modification, metabolic surgery, probiotics) could be useful in the prevention or treatment of obesity. Several studies in animal models and humans are showing how gut microbiota is associated with the onset and progression of metabolic disorders. Moreover, these studies are elucidating how the type of diet and surgical approaches, aimed at weight loss, modulate the abundance and the composition of gut microbiota. In this chapter, we will outline which alterations of gut microbiota are associated with obesity, its role in the development of obesity, and how diet and bariatric surgery can modulate the gut microbiota. Keywords

Obesity · Gut microbiota · Bariatric surgery

Introduction The worldwide prevalence of obesity has nearly tripled in the past ~50 years. A comprehensive analysis showed that almost one-third of the world’s population is overweight, and approximately 10% is affected by obesity. Moreover, it has been predicted that by 2030, the number of people affected by obesity will reach 1.12 billion worldwide (Jaacks et al. 2019). The health risks associated with obesity have reached an alarming stage and have become a significant problem worldwide. Indeed, obesity not only manifests as an excessive accumulation of body fat but is also associated with an imbalanced metabolism of lipid and glucose, chronic inflammation, oxidative stress, and an increased risk of a variety of diseases associated with an increased mortality, including type 2 diabetes mellitus, hyperlipidemia, nonalcoholic fatty liver disease (NAFLD), metabolic syndrome, cardiovascular diseases (CVD), hypertension, obstructive sleep apnea, and osteoarthritis (Kivimäki et al. 2022).

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According to current health recommendations, obesity is the result of a long-term energy imbalance between calories consumed and calories expended (Piaggi 2019). However, weight-loss interventions aimed at reducing calorie intake and increasing energy expenditure often fail to achieve long-lasting results at the individual level. Indeed, the pathophysiology of obesity has been proven to be more complex and involves a combination of both genetic and environmental factors, which regulate food intake and energy expenditure. Recent evidences suggest that certain types of gut bacteria may be more efficient at extracting energy from food than others, which could potentially lead to increased calorie absorption and weight gain. Additionally, it has been suggested that alterations in the gut microbiota may lead to increased inflammation and development of insulin resistance, both of which are closely linked to obesity and related metabolic disorders. Finally, some studies have suggested that the composition of the microbiota may be influenced by factors such as diet and lifestyle, and that interventions aimed at modifying the microbiota (such as lifestyle modification, metabolic surgery, probiotics, and fecal microbiota transplants) could be useful in the prevention or treatment of obesity. Several studies in animal models and humans are showing how gut microbiota is associated with the onset and progression of metabolic disorders, such as obesity and type 2 diabetes. Moreover, these studies are elucidating how the type of diet and surgical approaches, aimed at weight loss, modulate the abundance and the composition of gut microbiota. In this chapter, we will outline which alterations of gut microbiota are associated with obesity, its role in the development of obesity, and how diet and bariatric surgery can modulate the gut microbiota.

The Gut Microbiota The human body contains trillions of microorganisms that inhabit our bodies during and after birth. The gut microbiota is composed mainly of bacteria followed by smaller proportions of fungi, viruses, and archaea. The intestinal microbiota comprises more than 1500 species and more than 50 different phyla. The most dominant phyla of bacteria in the gut microbiota are Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Verrucomicrobia. Firmicutes and Bacteroidetes are the most frequent in the microbiota accounting for at least 90% of the total microbial population in human’s gut. The microbial composition of the gut differs across the gastro-intestinal tract. Indeed, the stomach and small digestive tract, are populated by relatively few species of bacteria while, the colon contains a heavily populated microbial community with over 1012 cells for every gram of intestinal tissue (Fig. 1). The human body relies on the gut microbiota to perform numerous important functions, such as colonizing mucosal surfaces and generating various antimicrobial substances. Additionally, the gut microbiota enhances the immune system, plays a critical role in digestion and metabolism, regulates epithelial cell proliferation and

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Stomach

Duodenum

Jejunum Ileum

101-103 CFU/ml Lactobacillus, Enterobacteriacee

101-103 CFU/ml Lactobacillus, Streptococcus Enterobacteriacee 104-107 CFU/ml Bifidumbacterium, Bacteroides, Lactobacillus, Streptococcus, Enterobacteriacee

Colon 1010-1012 CFU/ml Bacteroides, Eubacterium, Clostridium, Lactobacillus, Streptococcus, Enterobacteriacee, Bifidobacterium

Fig. 1 Differences in the microbial composition across the gastrointestinal tract

differentiation, alters insulin resistance, and affects its secretion. Moreover, the gut microbiota influences brain-gut communication, ultimately affecting the mental and neurological functions of the host. Therefore, maintaining a healthy gut microbiota is essential for maintaining normal gut physiology and overall health. In this section we will discuss how the gut microbiota metabolized dietary substrates.

Role of Gut Microbiota in Host Metabolism The gut microbiota utilizes a variety of substrates, including nutrients from the diet (such as carbohydrates, proteins, and lipids) and components derived from the host (such as shed epithelial cells and mucus), to produce energy for cellular processes and growth. As the microbiota metabolizes these substrates, the microbiota generates several metabolites that can have a significant impact on human health and metabolism. Nondigestible dietary carbohydrates, such as cellulose, hemicelluloses, resistant starch, pectin, oligosaccharides, and lignin, are metabolized into short-chain fatty acids (SCFAs). Carbohydrate fermentation by the gut microbiota leads to the production of three major SCFAs, acetate, propionate, and butyrate. While various bacteria are capable of producing acetate, propionate and butyrate are typically synthesized by specific bacterial species. In the gastrointestinal tract, Firmicutes,

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including certain Lachnospiraceae and Faecalibacterium prausnitzii, are the primary producers of butyrate. Meanwhile, Bacteroidetes such as Negativicutes and certain Clostridium species are known for producing propionate (Singh et al. 2023). Indeed, genetic analysis of gut microbiota has revealed that Bacteroidetes have the capacity to produce a wide range of enzymes involved in carbohydrate metabolism. These enzymes include glycoside hydrolases, glycosyl transferases, polysaccharide lyases, and carbohydrate esterases, among others. This genetic potential enables Bacteroidetes to efficiently break down complex carbohydrates found in the human diet, such as plant polysaccharides and dietary fibers, into simpler sugars that can be utilized for energy production and growth (Singh et al. 2023). SCFAs are absorbed from the colon, with butyrate being a primary energy source for colonic epithelial cells. SCFAs can promote colon health by enhancing tight junction integrity, increasing epithelial cell proliferation rate, aiding in epithelial repair following injury, and promoting epithelial cell differentiation. Although acetate and propionate are absorbed into the portal circulation and metabolized in the liver, a portion of the acetate produced in the colon can also reach other tissues, including adipose tissue. Propionate, on the other hand, is primarily metabolized in the liver, where it can help reduce serum cholesterol and blood glucose levels. Given the key role of SCAs in host metabolism, the disruption in the biosynthesis of SCFAs can result in numerous pathological consequences for the host. In addition to SCFAs, gut microbiota plays a major role in the synthesis s of bile acids. The microbial biotransformation of bile acids involves modifications of the steroid nucleus and both side chain. Several bacteria found in the gut possess bile salt hydrolase (BSH) enzymes, which can hydrolyze the amide bond between the bile acid and its conjugated amino acid. BSH genes have been identified in several genera, including Bacteroidetes, Bifidobacterium, Clostridium, Lactobacillus, and Listeria (Bourgin et al. 2021). The deconjugation step reduces bile acids toxicity and provides the bacteria with nitrogen, sulfur, and carbon atoms. After this step, bile acids can return to the liver for re-conjugation or can undergo further bacterial processing before reentering the enterohepatic circulation. Microbial modifications can also occur at the steroid nucleus leading to the production of secondary bile acids. Once the bile acid is deconjugated, the C7 hydroxyl group becomes available for microbial dehydroxylation. Some genera, such as Clostridium and Eubacterium, can transform chenodeoxycholic acid (CDCA) and cholic acid (CA) into the secondary bile acids lithocholic acid (LCA) and deoxycholic acid DCA, respectively (Guzior and Quinn 2021). This modification reduces the bile acid’s toxicity, creating a more favorable microenvironment for the bacteria. Changes in the composition of bile acids can affect the physical and chemical properties of the overall bile acid pool. Indeed, when bacteria remove the conjugated portion of bile acids, it reduces their efficiency in emulsifying dietary lipids and forming micelles, which can alter the host’s digestive function. Additionally, bile acids act as signaling molecules by binding to the farnesoid X receptor (FXR) and G protein-coupled receptor (TGR5), which regulate genes involved in bile acid synthesis, conjugation, transport, detoxification, lipid and glucose metabolism, and

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energy homeostasis. Therefore, changes in the bile acid composition caused by the gut microbiota can have downstream effects on various metabolic processes in the host. The human gut microbiota plays also an active role in the metabolism of dietary proteins, including the catabolism of dietary proteins into peptides and amino acids and their subsequent utilization as a source of energy. Indeed, studies conducted on human gut contents have revealed that the colonic microbiota has significant proteolytic capabilities, breaking down ingested dietary protein and endogenous protein into shorter peptides, amino acids and derivatives, short and branched-chain fatty acids, and gases, including ammonia, H2, CO2, and H2S. Up to hundreds of different identified proteases are found in several gut microbiota species, such as Clostridium, Bacteroides, and Lactobacillus. Indeed, some bacteria, like lactic acid bacteria, have developed complex proteolytic systems to compensate for their reduced amino acid biosynthesis capabilities. Bacterial species that ferment peptides and amino acids include Bacteroides, Prevotella, Clostridium, Veillonella, Megasphaera, Acidaminococcus, and Selenomonas. Some of these species have highly active dipeptidyl peptidase and dipeptidase activities, suggesting their potential importance for protein digestion and amino acid absorption in the mammalian digestive tract (Thomas et al. 2022).

Gut Microbiota Alteration in Obesity Obesity is a significant public health concern that is associated with a number of comorbidities, such as type 2 diabetes, cardiovascular disease, and certain forms of cancer. While obesity has a multifactorial origin, recent research has identified a potential link between the gut microbiota and obesity development. Subjects affected by obesity have been shown to have a less diverse gut microbiota, with an overrepresentation of certain bacterial species associated with inflammation and metabolic dysfunction. The purpose of this chapter is to examine the changes in the gut microbiota that have been linked to obesity based on evidence from both animal and human studies.

Evidences from Animal Studies The relationship between body weight and the gut microbiota has been the focus of several studies that showed how the alteration of gut microbiota in rodents causes a reduction in fat mass and body weight and the reversion of insulin resistance (Rastelli et al. 2018). The first clues suggesting a possible role for gut microbiota contribution to the development of obesity originated from the observation that germ-free mice are protected from diet-induced obesity.

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In 2004, Bäckhed et al. reported that germ-free mice were leaner and accumulated less body fat than conventional mice. Colonization of germ-free mice using gut microbiota obtained from obese mice induced weight gain and doubled the deposition of body fat (Bäckhed et al. 2004). This study establishes the gut microbiota as a key player in weight gain and fat deposition and that the gut microbiota of obese mice could extract energy from the host diet more efficiently than the microbiota of lean mice. In 2005, Ley et al. explored the alterations in the gut microbiota of genetically obese mice (ob/ob mice, a mouse strain that carries a mutation in the leptin gene) and lean mice. They identified a shift in the relative abundance of gut microbiota in obese mice compared to lean ones. Indeed, obese mice displayed a lower abundance of Bacteriodetes and an increased proportion of Firmicutes than lean mice (Ley et al. 2005). In 2009, Turnbaugh et al. assessed the effects of gut microbiota transplantation from both obese and lean mice into germ-free mice. Fourteen days after the colonization, the germ-free mice that underwent microbiota transplantation from obese mice, which had a higher Firmicutes/Bacteroidetes ratio than lean donors, displayed an increased total body weight compared to those who had received microbiota from the lean mice (Turnbaugh et al. 2009). Remarkably, they observed that the composition of gut microbiota in the receiver was comparable to that of the donor, suggesting that the composition of gut microbiota is critical for the development of the obese phenotype. In 2013, Ridaura et al. performed transplantation of human fecal microbiota in germ-free mice. The gut microbiota was obtained from each member of a discordant twin pair (one of the subjects was affected by obesity while the other was lean). Mice colonized with the “obese” microbiota showed an increase in their total body and fat mass compared with those colonized with the “lean” microbiota. Fecal sequencing confirmed the successful integration of the human donor microbiota, including the transmission of several functions related with the respective microbial communities (Ridaura et al. 2013). These results represent the first evidence of a causative role of the microbiota in the development of obesity.

Evidences from Human Studies In the last decade, the role of human gut microbiota in the development and progression of obesity has attracted considerable interest. Changes in the diversity and structure of the microbiota community can affect host metabolism and lead to obesity (Peters et al. 2018). In subjects affected by obesity, low fecal bacterial diversity is associated with more pronounced overall adiposity and dyslipidemia, impaired glucose homeostasis, and greater low-grade inflammation (Le Chatelier et al. 2013). Ley et al. using 16S rRNA sequencing showed that the gut microbiota of subjects affected by obesity had an increased proportion of Firmicutes and a decreased proportion of Bacteroidetes compared to lean subjects. When subjects affected by

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obesity followed a low-fat or low-carbohydrate diet and lost more than 25% of their body weight, the proportion of Firmicutes decreased while the proportion of Bacteroidetes increased (Ley et al. 2006). Several studies have reported a decrease in the abundance of the families Rikenellaceae and Christensenellaceae and a decrease in the abundance of the genera Bifidobacterium and Akkermansia (Gao et al. 2018; Vallianou et al. 2019; Singh et al. 2017). Bifidobacterium is linked with increased production of short-chain fatty acids (SCFAs), with decreased levels of lipopolysaccharide (LPS) and improvement in intestinal barrier function (Cani et al. 2009). Christensenellaceae and Akkermansia are associated with a decrease in visceral fat mass that, when in excess, is considered to be a risk factor for cardio-metabolic disease (Beaumont et al. 2016), while Alistipes finegoldii and Alistipes senegalensis, two species belonging to the Rikenellaceae family, have been shown to correlate negatively with BMI (Zhernakova et al. 2016). The abundance of the species belonging to the families of Prevotellaceae, Coriobacteriaceae, Erysipelotrichaceae and Alcaligenaceae has been reported to increase in subjects affected by obesity (Zhang et al. 2009). The increase in the abundance of the genus Roseburia is associated with elevated BMI. Indeed, Roseburia has the ability to increase energy harvest from the diet through the hydrolysis of polysaccharides into SCFA (Moran-Ramos et al. 2017). Finally, Eubacterium dolichum is positively correlated with visceral fat mass and used as a surrogate marker for obesity (Pallister et al. 2017). All these evidences suggest that a reduced diversity of gut microbiome is associated with obesity, but there are still many open questions on the specific bacterial signature correlated with the onset and the progression of obesity. Whether it is more accurate to explore the composition of the microbiota at phyla or genus or species levels and whether the presence or absence of a particular bacteria contributes to the development of obesity remains to be discussed.

The Role of Gut Microbial Metabolites in the Development of Obesity Considering that obesity is associated with a decrease diversity of gut microbiota, several mechanisms have been explored to describe how gut microbiota could promote obesity. The gut microbiota appears to play a crucial role in the metabolism and degradation of several dietary components. Indeed, nearly 10% of all circulating metabolites originate from gut microbiota and are involved in several human metabolic pathways. In this section, we summarize the main pathophysiological mechanisms by which gut microbiota could influence the development of obesity, although the exact mechanism is not yet understood.

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Short-Chain Fatty Acids (SCFAs) In humans, short-chain fatty acids (SCFAs) are primarily produced by gut microbes from dietary sources, with some SCFAs coming directly from fermented foods or synthesized by the liver. Specific types of carbohydrates, such as those found in plants, are selectively fermented by SCFA-producing bacteria in the gut. These bacteria include those in the Clostridium cluster IV and XIVa, such as Clostridium leptum, Coprococcus, Faecalibacterium prausnitzii, Eubacterium, Anaerostipes, and Roseburia. Other bacteria, such as Lactobacillus and Bifidobacteria, indirectly contribute to the SCFA pool through metabolic cross-feeding (Portincasa et al. 2022). The functionality of SCFAs as secondary messengers depends on their production, absorption, and distribution into the body. SCFAs alter gut pH, regulate gastrointestinal motility and blood flow, influence nutrient bioavailability, promote immune function, and maintain gastrointestinal health. SCFAs do not require micellarization to be absorbed nor re-esterification once inside cells due to their short-chain length. After absorption, SCFAs enter the portal vein and can be used as an energy source, or can be incorporated into endogenous molecules, or function as signaling molecules; while remaining SCFAs are excreted in the stool, urine, and breath. The gut microbiota produces several SCFAs, including acetic, propionic, butyric, valeric, and caproic acids, with acetate, propionate, and butyrate being the most abundant, comprising up to 95% of the SCFAs present in the colon. Acetate is the most prevalent SCFA, accounting for 60–75% of the total SCFAs, and is produced by various enteric bacteria, including Akkermansia muciniphila, Bacteroides, Bifidobacterium, Prevotella, Ruminococcus, Blautia hydrogenotrophica, Clostridium, and Streptococcus. Propionate is produced by Megasphaera elsdenii, Coprococcus catus, Bacteroides, Phascolarctobacterium succinatutens, Dialister, and Veillonella through the acrylate and succinate pathways. Butyrate is mainly produced by Coprococcus comes and Coprococcus eutactus from butyryl-phosphate through the phosphotransbutyrylase/butyrate kinase routes (Portincasa et al. 2022). The molar ratio of acetate, propionate, and butyrate production in the colon is typically 60:25:15 but varies depending on factors such as diet, microbiota composition, and expression of transporters. Despite the long-recognized metabolic benefits of dietary fiber, such as reducing the risk of obesity and diabetes, the underlying molecular mechanisms behind these benefits have remained unclear until recent times. Recently, SCFAs have emerged as key mediators linking the gut microbiota and diet to host physiology. Indeed, they interact with receptors to modulate endocrine responses, leukocyte development and function, and enzyme and transcription factor activity. Other SCFAs, especially butyrate, are essential for maintaining the intestinal epithelial barrier, which protects against harmful luminal contents. Indeed, butyrate regulates the expression of tight junction proteins, including claudin-1, zonula occludens-1, and occludin, to modulate intestinal permeability and solute transport between intestinal cells. In addition, butyrate can upregulate the expression of mucin

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2 (MUC2), a major mucin on the intestinal surface, to enhance the mucus layer’s protective function against pathogens. In addition to their role in modulating the function, integrity, and motility of the colon, microbial SCFAs, produced by gut microbiome, may influence the metabolism of liver, muscle, and adipose tissue (Bäckhed et al. 2005). Indeed, subjects affected by obesity and insulin resistance display a decreased production of SCFA. Increased levels of SCFAs might also affect body weight by influencing energy expenditure. Indeed, in obese mice, the administration of butyrate resulted in a reduction of body weight, caused by an increase in energy expenditure and lipid oxidation (Gao et al. 2009). Interestingly, the acute infusions of acetate in the distal colon improved lipid oxidation and resting energy expenditure in subjects who were overweight or affected by obesity (Canfora et al. 2017). In line with these data, a study performed on healthy subjects showed that the oral administration of propionate increased resting energy expenditure and fasting lipid oxidation, despite normal levels of glucose or insulin (Chambers et al. 2018). However, more studies are needed to confirm that the improvement in oxidative metabolism, induced by SCFAs, also confers long-term benefits in weight control. Another beneficial role of SCFAs is the modulation of central appetite and energy intake through several mechanisms. One of the most studied mechanisms is their ability to stimulate the production of satiety hormones. In vitro studies performed in human and rodent cell lines have shown that SCFAs actively stimulate the secretion from enteroendocrine cells of peptide YY (PYY) and glucagon-like peptide 1 (GLP1) and the secretion from adipocytes of the adipose tissue-derived satiety hormone, leptin (Al-Lahham et al. 2010). Moreover, acute rectal infusion of sodium acetate and SCFA mixtures increase circulating concentrations of PYY in subjects affected by obesity (Freeland and Wolever 2010). In line with these findings, the oral ingestion of inulin propionate ester, in subjects with obesity, increase postprandial plasma concentrations of PYY and GLP1 resulting in a reduction of food intake (Chambers et al. 2015). Although the metabolic effects of SCFA appear promising, the long-term metabolic consequences, especially the effects on satiety hormones, need further investigations.

Lipopolysaccharide (LPS) The ability to break down food into nutrients is dependent on both digestive secretions, microbial composition, and metabolic activity. The diversity of the gut microbiota is crucial as it increases the host’s ability to degrade food. However, the gut microbiota could also produce potentially harmful metabolites that can directly damage the gastrointestinal tract and promote the development of obesity by entering the bloodstream. Lipopolysaccharide (LPS) is one of the most powerful inducers of inflammation and is implicated in the onset and progression of metabolic diseases such as obesity

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(Lassenius et al. 2011). LPS is a key component of the outer membrane of most Gramnegative bacteria, consisting of three structural elements: O-antigen, core oligosaccharide, and lipid A. Lipid A is recognized through cluster of differentiation 14 (CD14) and toll-like receptor 4 (TLR4). LPS triggers the immune response by acting as an agonist on TLR4, which leads to the assembly of a complex formed by LPS-binding protein (LBP), CD14, and Myeloid Differentiation factor 2 (MD-2). This complex recognizes a common pattern in different LPS molecules and activates the TLR4-MD-2-LPS complex, initiating the Myeloid Differentiation Primary Response Protein 88 (MyD88)-dependent signaling pathway leading to activation of Nuclear factor kappaB (NF-kB). Studies have shown that reducing MyD88 expression in intestinal cells does not alter serum levels of LPS and interleukin 6 (IL-6), suggesting its involvement in high fat diet-induced endotoxemia. Additionally, CD36 knockout mice fed a high fat diet exhibit reduced adipose tissue inflammation and lower pro-inflammatory cytokine response to LPS (Lassenius et al. 2011). Under physiological conditions, it has been demonstrated that about 90% of LPS in the circulation is bound to lipoproteins. All plasma lipoprotein subclasses have been found to sequester LPS, with high-density lipoproteins (HDL) having the highest binding capacity for LPS. Indeed, LPS that is not bound to lipoproteins in the circulation is rapidly captured by soluble CD14 (sCD14) or LPS-binding protein, both of which are exclusively found on HDL. LBP facilitates the transfer of LPS to lipoproteins, as well as the transfer of LPS from LPS micelles to sCD14 and from LPS-sCD14 complexes to HDL, and potentially to other lipoproteins as well. While the intestinal epithelium of healthy individuals is considered to be a reliable barrier for LPS, its function can be compromised in disease states, such as obesity, resulting in LPS translocation. Indeed, several studies conducted in subjects with obesity have shown a correlation between unhealthy dietary habits and reduced levels of sCD14 in the bloodstream (Laugerette et al. 2020). Metabolic endotoxemia is the term used to describe elevated levels of endotoxins, such as LPS, in individuals with obesity. This term is used to emphasize that the effects of these endotoxins are related to metabolism rather than being a measure of infectious diseases. One possible explanation for the development of metabolic endotoxemia in obesity is the connection between gut permeability, low-grade inflammation, and insulin resistance via the gut microbiota. It has been shown that a high-fat diet can disrupt tight-junction proteins involved in gut barrier function, namely, zonula occludens-1 and occludin and that this effect is dependent on the gut microbiota, as antibiotic treatment can reverse diet-induced gut permeability. Supporting this hypothesis, recent studies have found that the modulation of gut microbiota composition with nondigestible carbohydrates can improve gut barrier integrity, reduce metabolic endotoxemia, and alleviate inflammation and glucose intolerance (Bäckhed et al. 2005; Chambers et al. 2018). Another potential mechanism linking the gut microbiota to obesity and related disorders is the endocannabinoid system (eCB). This system consists of endogenous lipids that activate cannabinoid receptors 1 and 2 (CB1R and CB2R), which are G protein-coupled receptors. The most widely studied lipids in this system are N-arachidonoylethanolamine (anandamide, AEA)

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and 2-arachidonoylglycerol (2-AG). Both AEA and 2-AG are present throughout the body, and their levels are regulated by their synthesis and inactivation. Importantly, recent evidences suggest a connection between the gut microbiota, eCB system tone, and metabolic comorbidities associated with obesity (Muccioli et al. 2010). Indeed, the gut microbiota appears to regulate CB1R expression, AEA content, and its degrading enzyme fatty acid amide hydrolase (FAAH) in the intestine and adipose tissue. However, the precise mechanisms behind this interplay in subjects affected by obesity and/or type 2 diabetes remain largely unknown. Interestingly, it has been demonstrated a potential association between the eCB system and LPS. The infiltration of macrophages in adipose tissue and liver during obesity plays a vital role in metabolic disorder development, and it has been shown that LPS regulates eCB synthesis in macrophages (Muccioli et al. 2010). Moreover, macrophage infiltration is driven by LPS activation and its interaction with its co-receptor CD14, which is in turn dependent on gut microbiota composition. Indeed, pharmacological interventions indicate that the eCB system contributes to gut barrier function and metabolic endotoxemia via putative CB1 receptor-dependent mechanisms, independent of food intake behavior. Specifically, the eCB system governs gut barrier function by modulating the distribution and localization of tight junction proteins (zonulin-1 and occludin), and plays a crucial role in adipogenesis regulation in the gut and adipose tissue (Muccioli et al. 2010). Taken together, these findings highlight the interplay between the gut microbiota, the innate immune system, and the endocannabinoid system in the development of obesity and associated disorders. Obesity is associated with high levels of Gram-positive (Firmicutes) and low levels of both Gram-negative (Bacterioidetes) and Gram-positive (Bifidobacterium) and the decrease in Bifidobacterium results in lower expression of gut peptides, such as glucagon-like peptide-2 (GLP-2). GLP-2 is produced in the intestine by L cells and regulates tight junction integrity and gut permeability (Cani et al. 2009). Moreover, studies performed in both humans and animal models revealed that a high-fat diet is responsible for a shift in LPS producing bacteria as well as an increase in LPS plasma levels (Amar et al. 2011). Studies performed in humans revealed a positive correlation between circulating LPS and obesity (Moreno-Navarrete and Fernández-Real 2014). Moreover, the consumption of a high-fat diet increases plasma LPS while diet-induced weight loss or bariatric surgery exert the opposite effect, decreasing plasma levels of LPS and improving obesity-associated metabolic comorbidities (Moreno-Navarrete and Fernández-Real 2014). All these studies show the importance of circulating LPS in the progression of obesity, pointing at LPS as key metabolites in gut microbiota-induced endotoxemia.

Interaction Between Diet Composition and Gut Microbiota Increasing evidences suggest that the composition of diet is a key factor in influencing gut microbiota diversity. Indeed, a large number of evidences suggest that different dietary patterns are associated with specific bacteria in the intestine (Fig. 2).

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Increase: Firmicutes Proteobacteria Actinobacteria Decrease: Bacteroidetes Akkermansia muciniphila

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Increase: Bacteroidetes Streptococcus Lactococcus Eggerthella

Increase: Bacteroidetes Bifidobacteria Bacteroides uniformis Prevotella stercorea

Decrease: Firmicutes Ruminoccocus Eubacterium Clostridium Bifidobacterium

Decrease: Firmicutes Ruminococcus Escherichia coli

Fig. 2 Graphical representation of different dietary pattern and their influence on the composition and the function of gut microbiota

Numerous studies have shown that the host diet modulates the composition and function of the gut microbiota in humans (David et al. 2014; De Filippo et al. 2010; Wu et al. 2011; Cotillard et al. 2013; Kovatcheva-Datchary et al. 2015; Walker et al. 2011; Ley et al. 2008). The human gut microbiota responds rapidly to significant changes in diet composition and this notion is supported by several studies focused on people who switched between plant-based and meat-based diets. In these studies, the composition and function of gut microbiota changed within 1–2 days (David et al. 2014; Wu et al. 2011; Walker et al. 2011). Despite these rapid changes, long-term dietary patterns are the dominant factor shaping the composition of the gut microbiota. Here, we discuss how different dietary pattern can influence the composition and the function of gut microbiota.

Timing of Food Consumption Although the core bacterial taxa in the gut are typically resistant to short-term external influences, the overall gut microbial community tends to display significant interindividual variability. The gut microbiota is highly dynamic and has the capacity to double in number within an hour. Dietary interventions are known to induce rapid changes in the microbial composition at species and family level within

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24–48 h. Similarly, in mouse models, manipulating macronutrient intake can consistently alter the gut microbiota composition within a day. However, this variability is only partially explained by diet composition, as other factors such as circadian rhythm and feeding behaviors are also thought to contribute. Despite the fact that the gut microbiota is not directly exposed to the light-dark cycle associated with the circadian rhythm, its composition and functionality are still affected. In humans, approximately 10% of Operational Taxonomic Units (OTUs) oscillate in response to the circadian rhythm. Nutrient availability and the level of host-derived auto-antibodies and peptides, both of which are associated with circadian rhythm oscillations, contribute to fluctuations observed in the microbiota. It is believed that the microbiota regulates these synchronized diurnal oscillations through epithelial histone deacetylase 3 (HDAC3), which regulates intestinal lipid uptake and that the disruption of this interaction could promote dietinduced obesity. Studies have shown that jet-lag and disrupted sleep patterns can alter the gut microbiota, increase dietary intake, and promote an inflammatory response that can lead to metabolic stress and obesity. Manipulating feeding time, including time and duration of consumption and frequency, may also influence the gut microbiota composition, function, and host health. Observational and experimental studies (Kaczmarek et al. 2017; Thaiss et al. 2014; Collado et al. 2018) have shown that meal timing can affect the diurnal rhythms of the microbial profile and increase microbial abundance. These studies reported that the timing of food intake is associated with specific bacteria in humans, and that the timing of food intake leads to a 15% fluctuation of commensal bacterial taxonomic units throughout the day and increased microbial abundance. Moreover, in a randomized crossover study the timing of a meal was found to impact the diurnal rhythms of the microbial profile, with late main meal consumption associated with increased taxa that are typically considered pro-inflammatory, affecting various aspects of host health such as body weight, cortisol rhythm, basal metabolic rate, glucose tolerance, and body temperature (Kaczmarek et al. 2017; Thaiss et al. 2014; Collado et al. 2018). Although the gut microbiota can fluctuate within an hour, it remains unclear whether delayed feeding related to hunger can influence the composition of the gut microbiota. Furthermore, while certain bacteria and bacterial metabolites have been implicated in the regulation of hunger and satiety, their production is dependent on bacterial growth cycles. The fundamental characteristics of how fasting or time-restricted feeding affects the gut microbiota are not yet well-understood, and current research is limited to a small number of observational and experimental studies, many of which have a sample size of fewer than 50 participants.

Western Diet Western diet is characterized by the consumption of foods with a high energy density and a high proportion of fats, sugars, and animal proteins, while at the same time consuming a very low intake of fruit and vegetables.

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Numerous studies are now focusing on the impact of Western diet on the composition of gut microbiota. For instance, Western diet consumption has been associated with a decrease in Bacteroidetes and an increase in Firmicutes and Protebacteria in mice (Hildebrandt et al. 2009). Similar effects were reported using a high-fat and high-sucrose diet (Parks et al. 2013). In a recent study, Carmody et al. used more than 200 strains of mice to investigate whether variations in gut microbiota were primarily due to the host genetics or to nutritional factors. Their results suggest that a high-fat, high-sugar diet alters the gut microbiota despite the differences in host genotype (Carmody et al. 2015). Similarly, Hildebrandt et al. confirm that a Western diet promotes changes in the gut microbiota of wild-type mice compared with genetically obese mice. These changes were characterized by an increase in the abundance of Firmicutes, Proteobacteria, and Actinobacteria, and by a decrease in the abundance of Bacteroidetes. Because wild-type mice became obese and genetically obese mice did not gain weight, the authors concluded that the effect of diet rather than the obese state, was responsible for the changes in gut microbiota composition (Hildebrandt et al. 2009). Western diets, through the increase of Gram-negative bacteria proportion, promote the translocation of LPS and reduce intestinal mucosa integrity increasing the blood concentrations of LPS (Cani et al. 2007). Indeed, a reduced expression of tight junction proteins was showed in intestinal mucosa of animals under Western diet (Cani et al. 2007). Western diet can also reduce the number of mucin-degrading bacteria (Akkermansia muciniphila) that colonizes mucus layer (Belzer and de Vos 2012). Akkermansia muciniphila represent approximately 3–5% of the microbial community of healthy humans (Belzer and de Vos 2012) and is inversely correlated with body weight in animals (Everard et al. 2013) and humans (Collado et al. 2008; Karlsson et al. 2012). Moreover, Akkermansia muciniphila has a key role in the stimulation of immune system and the production of anti-inflammatory cytokines (Zhang et al. 2009). Recent studies have shown that dietary fat composition and not just the fat content of diet can alter the composition of gut microbiota (Wu et al. 2011; Mujico et al. 2013; Patterson et al. 2014). In a metagenomic study involving healthy volunteers, Bacteroidetes were found to be significantly correlated with the consumption of monounsaturated fatty acids (MUFAs) and saturated fatty acid (SFAs) (Wu et al. 2011). In another study, Patterson et al. have shown a reduction in Bacteroidetes in animals fed with highfat dietary palm oil diet compared to high-fat olive oil diet (Patterson et al. 2014). These data suggest that consumption of SFA (palm oil) could lead to adverse changes in the gut microbiota, whereas consumption of MUFA (olive oil) could have a beneficial effect on the host microbial ecosystem. Modulation of gut microbiota by different types of dietary fat could alter body weight (Mujico et al. 2013) or visceral fat mass even at very low doses. Supplementation of omega-3 polyunsaturated fatty acids (PUFAs) significantly increased the abundance of Firmicutes (especially Lactobacillus) without reducing body weight. This study suggests that fatty acids of the omega-3 series have the

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potential to favorably influence the gut microbiota and regulate weight gain (Mujico et al. 2013). However, further studies are needed to confirm the relationships between the modulation of gut microbiota by different types of dietary fat and obesity. Despite the changes in the composition of the gut microbiome promoted by HFD consumption, dietary manipulations can reverse high-fat-induced dysbiosis and obesity.

Ketogenic Diet The ketogenic diet is a type of diet that limits calories and carbohydrates intake to 5–10% of total daily caloric intake while consuming reasonable amounts of protein. This results in increased lipid metabolism and reduced glucose levels, promoting the production of ketone bodies such as 3-hydroxybutyrate, acetate, and acetoacetate through hepatic ketogenesis. The rise in ketones can provide energy to colon cells, while also promoting anti-inflammatory and antioxidant activity, immune regulation, and improved intestinal mobility and barrier function. This diet can promote rapid weight and fat loss, particularly visceral fat, while also preserving lean mass and reducing hunger and cravings. The primary mechanism responsible for the anorexigenic effect of the ketogenic diet is believed to be ketogenesis, which decreases circulating ghrelin and maintains cholecystokinin meal response. For these reasons, the ketogenic diet can be a viable therapeutic option for individuals with obesity, especially when obesity related is associated with comorbidities, such as type 2 diabetes and insulin resistance. Recent evidences suggest that the ketogenic diet may positively impact the gut microbiome by increasing Bacteroidetes and decreasing Firmicutes (Attaye et al. 2021). This is in contrast to what is typically observed in individuals affected by obesity who consume a Western diet. Indeed, the Bacteroidetes/Firmicutes ratio is an indicator of obesity and inflammation, and its modulation has been linked to improved metabolic health. It has been reported that the ketogenic diet can decrease intestinal Bifidobacteria, thereby reducing Th-17 cells that contribute to inflammation. Additionally, the ketogenic diet has been shown to cause a shift in the gut microbiota composition, with increased levels of Streptococcus, Lactococcus, and Eggerthella, and decreased levels of Ruminoccocus, Eubacterium, Clostridium, and Bifidobacterium (Attaye et al. 2021). This shift is beneficial, as Streptococcus and Lactococcus produce folate, which can improve lipid metabolism and reduce oxidative stress and inflammation. Overall, the evidence suggests that the ketogenic diet may increase gut microbiome diversity and composition potentially leading to weight loss.

Mediterranean Diet The Mediterranean diet is a dietary approach that has been associated with various health benefits, such as reducing the risk of cardiovascular disease, obesity, type

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2 diabetes, degenerative diseases, and cancer. In addition, the Mediterranean diet has been linked to beneficial effects on gut microbiota. One of the defining characteristics of the Mediterranean diet is the high intake of fiber, particularly insoluble fiber. Consumption of fruits, vegetables, and legumes has been shown to increase levels of SCFAs in feces, as well as the proportion of fiber-degrading microorganisms in the gut. The PREDIMED trial has shown that a plant-based Mediterranean-style diet was associated with a significant reduction in all-cause mortality. Indeed, a higher proportion of the Bacteroidetes phylum is found in plant-based diets compared to omnivorous diets, likely due to increased fiber intake. Following a Mediterraneanstyle diet has been linked to lower levels of Escherichia coli and higher levels of Bifidobacteria (Estruch et al. 2018). Moreover, the adherence to a Mediterraneanstyle diet led to an increase in Bacteroides uniformis and Prevotella stercorea, and a decrease in families belonging to the Firmicutes phylum, such as Ruminococcus. High consumption of animal protein, saturated fats, and sugars negatively impacts gut microbiota diversity and is linked to a high Firmicutes/Bacteroidetes ratio, which is associated with type 2 diabetes and obesity. Adherence to a Mediterranean-style diet has been shown to reshape the gut microbiota of obese individuals, increasing populations of fibrolytic bacteria like Bacteroides, Prevotella, Roseburia, Ruminococcus, and Faecalibacterium prausnitzii that produce SCFAs by metabolizing carbohydrates (Merra et al. 2020). These bacteria are known to confer anti-inflammatory effects via inducing CD4+ T cells, which secrete anti-inflammatory interleukin-10. Moreover, the high content of vegetables, legumes, and fruit in a Mediterranean-style diet is also associated with increased levels of SCFAs in the intestine. Overall, following a Mediterraneanstyle diet promotes the growth of beneficial bacteria in the gut, which subsequently produces beneficial metabolites.

Bariatric Surgery and Gut Microbiota Bariatric surgery has become a key model to understand the pathophysiological mechanisms underlying both obesity and its associated complications. Not only bariatric surgery results in significant and rapid weight loss, but it also allows researchers to evaluate shifts in gut microbiota composition and to address whether these changes contribute to the improvement of obesity-related diseases. Bariatric surgery causes alterations in both environmental and systemic factors in addition to changes in the anatomy of the digestive tract, all of which might affect the composition of the gut microbiota. Currently, Sleeve gastrectomy and Roux-en-Y gastric bypass (RYGB) are the most common surgical procedures. Sleeve gastrectomy consists of a surgical resection of a large portion of the stomach along the greater curvature, causing a reduction of gastric volume and a significant decrease in the production of gastric acids. Moreover, sleeve gastrectomy is associated with an increase in incretin levels such as GLP1 and PYY and higher levels of the orexigenic hormone, ghrelin.

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RYGB is a restrictive and malabsorptive procedure that creates a gastric pouch, which is connected by the Roux limb to the distal jejunum. RYGB induces an alteration in bile acid production and accelerates gastric emptying. Despite the physiological changes linked to the anatomic rearrangements, caloric restriction, and malabsorption due to the surgery itself, there are additional mechanisms that induce weight loss and the resolution of obesity-associated comorbidities, which have not been fully understood. In this section, we discuss recent studies explaining the link between bariatric surgery and gut microbiota alterations. A number of human studies, using various sequencing techniques, have shown that bariatric surgery increased gut bacterial richness and diversity, Table 1 summarizes which microorganisms are affected by bariatric surgery. Table 1 List of bacteria affected by bariatric surgery

Decreased Firmicutes Lactobacillus reuteri Clostridium difficile Clostridium hiranonis Blautia spp. Dorea spp. Bacteroidetes Staphylococcus epidermis Roseburia intestinalis Eubacterium rectale Dialister invisus Coprococcus comes Anaerostipes caccae Gemella sanguinis Faecalibacterium prausnitzii Increased Streptococcus spp. Veillonella spp. Veillonella parvula Veillonella dispar Bacteroides/Prevotella spp. Akkermansia muciniphila Proteobacteria Escherichia coli Klebsiella pneumoniae Shigella boydii Salmonella enterica Enterobacter cancerogenus Citrobacter spp. Pseudomonas spp. Enterococcus faecalis

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Most of the studies report an increase in the abundance of Bacteroidetedes and Proteobacteria and a decrease in Firmicutes after bariatric surgery (Palmisano et al. 2020; Davies et al. 2020; Farin et al. 2020; Ikeda et al. 2020). The changes in the anatomical and physiological structure of the gastrointestinal tract, following bariatric surgery, induce a lower gastric acid secretion that leads to a modification in pH. These changes increase the availability of oxygen, which facilities the expansion of anaerobic bacteria belonging to Proteobacteria phylum. In particular, the Haemophilus genus and some members of Firmicutes phylum, namely, Veillonella, Gemella, and Streptococcus, has been described to increase their abundance following RYGB and SG (Schenck et al. 2016). Several studies report the increased abundance of Akkermansia and its species Akkermansia muciniphila after bariatric surgery (Palmisano et al. 2020; Farin et al. 2020; Ilhan et al. 2020). Akkermansia muciniphila use mucus as a carbon and nitrogen source to produce SCFAs such as propionate, acetate, and succinate and has been proposed as a potential therapeutic target for the treatment of obesity. Nevertheless, the assumption that the abundance of Akkermansia muciniphila could drive the metabolic improvements observed after bariatric surgery is far from been confirmed. Indeed, a recent study investigating the abundance of Akkermansia muciniphila in patients following SG or RYGB showed a high abundance of this species after bariatric surgery but its increase was not correlated with the metabolic outcomes of the patients (Dao et al. 2019). A long-term study evaluating the alterations of gut microbiota induced by RYGB and vertical banded gastroplasty (VBG) 9.4 years after surgery did not find differences in microbiota composition induced by the two bariatric surgery procedures (Tremaroli et al. 2015). However, the composition of the gut microbiota was significantly different between patients who underwent RYGB compared with subjects affected by obesity. These observations were linked to an increase in the abundance of Proteobacteria (Escherichia, Klebsiella, and Pseudomonas) and a decrease in a few species from the Firmicutes phylum (Clostridium difficile, Clostridium hiaronis, and Gemella sanguinis) (Tremaroli et al. 2015). Nowadays, it is still a matter of debate if the changes in microbiota composition after bariatric surgery are long-lasting or reversible. The studies evaluating this issue are limited and these observations need to be confirmed in large cohorts and by long term-study.

Modulation of Gut Microbiota Various methods have been employed to manipulate the gut microbiome as a way to explore its functionality and to develop new therapeutic modalities. Prebiotics (which induce bacterial growth) and probiotics (nonpathogenic beneficial strains of various microorganisms) are among the most popular and widely used modulators of gut microbiome composition. Although prebiotic and probiotic supplements have

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been shown to provide some health benefits in humans, there is still a great deal of speculation and inconsistency between findings.

Probiotic The use of probiotics to increase beneficial microorganisms in the gut microbiome of individuals with obesity has been shown to be an effective method for combating obesity and related health issues. Indeed, this approach is supported by numerous scientific evidences from animals and humans that prove how different probiotics can exert their beneficial effects through species and strain-specific mechanisms (Everard et al. 2013; Núñez et al. 2014; Karimi et al. 2015; Hulston et al. 2015; Osterberg et al. 2015). Prebiotics, which come from a variety of sources such as vegetables and fruits, are nondigestible oligosaccharides with different molecular weight, monosaccharide type, and branching degree. Probiotics help regulate gut microflora, decrease insulin resistance, and increase feelings of fullness, resulting in anti-obesity effects. Table 2 lists several probiotic species used in humans based on their mechanisms of action and location. Lactobacillus and Bifidobacterium species have been tested on animal models of obesity due to their low pathogenicity and resistance to antibiotics, resulting in varying degrees of weight and fat reduction. Bifidobacterium, in particular, was effective in reducing inflammation, insulin sensitivity, fat accumulation, Table 2 List of probiotic species used in humans based on their location and mechanisms of action Microorganism Lactobacillus plantarum Lactobacillus rhamnosus Lactobacillus salivarius Lactobacillus reuteri Lactobacillus acidophilus Lactobacillus casei Lactobacillus bulgaricus Lactobacillus plantarum Lactobacillus paracasei Bifidobacterium animalis

Location Gastrointestinal tract Gastrointestinal tract and brain Gastrointestinal tract Gastrointestinal tract Intestine

Mechanism of action Improves IL-10 production in the colon

Intestine Intestine

Inhibits bacterial translocation, reduces cholesterol mycelia formation, enhances NK cell activity Decreases cholesterol

Intestine

Decreases translocation of pathogens

Intestine

Increases cell apoptosis

Intestine

Increases intestinal motility and hydrolysis bile acid salts

Produces lactic acid, regenerate epithelial cells. Secretion of bacteriocins Produces 3-hydroxypropionaldehyde Reduces cholesterol

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cholesterol, and triglyceride levels, mainly by reducing intestinal permeability. Furthermore, administering probiotics containing Lactobacillus strains to obese animals led to significant reductions in body fat mass and improved lipid distribution and blood glucose homeostasis by promoting fatty acid oxidation or inhibiting lipoprotein lipase activity. Several strains of Lactobacillus have been studied in humans. For example, the probiotic Lactobacillus rhamnosus was found to influence weight gain during the early years of life and initial stages of excessive weight gain, but not in later years, as compared to a placebo group. In another study, obese individuals were given Lactobacillus gasseri SBT2055 and BNR17 for a period of 12 weeks. Lactobacillus gasseri SBT2055 resulted in reduced abdominal fat and body weight, while Lactobacillus gasseri BNR17 did not show any significant effect. Administration of Lactobacillus curvatus HY7601 and Lactobacillus plantarum KY1032 showed a similar outcome. Additionally, in a separate study, overweight adults who were given Bifidobacterium animalis ssp. lactis 420 (B420) for 6 months, with or without Litesse® Ultra polydextrose fiber, experienced a significant reduction in body fat mass. Another study showed that a symbiotic formula containing Lactobacillus rhamnosus, inulin, and oligofructose resulted in weight loss and a reduction in body fat mass over a period of 12 weeks. Furthermore, Aspergillus flavus CECT7765 was found to induce significant weight loss in obese children with insulin resistance. Finally, the consumption of Lactobacillus and Bifidobacterium showed substantial reductions in body weight, BMI, and body fat (León Aguilera et al. 2022). Overall, these studies suggest that probiotic supplementation enhance atherogenic indices by lowering the levels of LDL and total cholesterol, as well as improving body composition, weight, and visceral adipose tissue in the abdominal region. Moreover, probiotics have also been acknowledged for their antibacterial properties and their ability to promote barrier and immunomodulatory functions. While definitive conclusions cannot be drawn at this time, these studies indicate that probiotics may play a valuable role in preventing and reversing weight gain, dysbiosis, and inflammation.

Prebiotic Prebiotics have been defined by FAO/WHO as nondigestible food ingredients that beneficially affect the host by selectively stimulating the growth and/or activity of intestinal bacterial species that can contribute to host health. According to this concept, prebiotics usually include nondigestible, non-hydrolyzable carbohydrate forms, such as galacto-oligosaccharides (GOSs), fructo-oligosaccharides (FOSs), soybean oligosaccharides, inulin, cyclodextrins, gluco-oligosaccharides, xylo-oligosaccharides, lactulose, lactosucrose, and isomaltooligosaccharides. These carbohydrates have the ability to reach the distal sections of the human gastrointestinal tract where they are used as nutrients by host gut microbiota. A number of experimental studies have demonstrated that consuming foods rich in prebiotics can significantly reduce obesity through different mechanisms of action.

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Among them, there is growing evidence that prebiotic-based therapy changes gut microbiota composition, stimulating the growth of Lactobacillus and Bifidobacterium in the gastrointestinal tract of obese animals and, at the same time, reducing the population of pathogenic microorganisms including Firmicutes and Bacteroidetes. Some studies have shown that these changes were related to improved enteroendocrine cell activity, glucose homeostasis, and leptin sensitivity (Parnell and Reimer 2012). Interestingly, these changes were also associated with increased endogenous GLP-2 production, thus reducing both obesity-related systemic and hepatic inflammatory disorders. In addition to modulate gut microbiota composition, anti-obesogenic effects of prebiotics also involve improvement of lipid and glucose metabolism. In this regard, it has been showed that oligofructose-treated animals showed non-obese metabolic phenotypes characterized by lower triglycerides levels, adipose tissue mass, and muscle lipid infiltration (Everard et al. 2013). Short-chain fructo-oligosaccharides treatment also had beneficial effects on plasma lipid metabolome and insulin, which were associated with changes in composition and activity of the intestinal microbiota of diet-induced obese mice. Moreover, supplementation with α-cyclodextrins not only modulated intestinal gut microbiota, but also increased lactic acid and SCFAs levels in obese mice (Nihei et al. 2018). These effects were associated with changes in expression of those genes involved in lipid metabolism, including the upregulation of peroxisome proliferatoractivated receptor (PPAR) γ and PPARα, and the downregulation of sterol regulatory element-binding protein-1c (SREBP-1c) and fatty acid synthase, which could partly explain the anti-obesogenic effect of α-cyclodextrins (Nihei et al. 2018). As noted above, prebiotic-induced changes in gut microbiota composition lead to improvement in the activity of enteroendocrine cells, which release hormones involved in the modulation of food intake, energy homeostasis, and body weight. As a consequence, anti-obesity properties of prebiotics seem also to be strongly related to the control of satiety hormones. In this regard, it has been found that obese rats fed with a diet rich in prebiotic fiber, including inulin and oligofructose, showed higher circulating glucagon-like peptide-1 (GLP-1) levels as well as enhanced expression of pro-glucagon and peptide YY (PYY) genes. However, prebiotic treatment failed to reduce body weight and fat mass, although energy intake was reduced. Another study suggests that prebiotic effects on the control of satiety and food intake are directly attributed to higher SCFA levels, which improve GLP-1, PYY, and ghrelin production and consequently trigger hypothalamic reward mechanisms (Parnell and Reimer 2012). Although the beneficial role of prebiotics on obesity has been supported by experimental studies, results obtained from clinical trials are contradictory. Long-term treatment for 6 months based on daily intake of dietary fiber (Litesse ® Ultra polydextrose) failed to modulate body composition in adult subjects with obesity, although fat mass, waist circumference, and food intake were markedly reduced using dietary fiber in combination with Bifidobacterium animalis subsp. lactis 420. Regarding their role on energy intake, some trials did not support any effect of either long-term prebiotic supplementation or short-term fructo-

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oligosaccharides treatment, but others reported that dietary intake of oligofructose or inulin for at least 2 weeks reduced total energy intake in adult subjects affected by obesity. These results seem to suggest that prebiotic supplementation over long periods is needed to obtain beneficial effects on energy intake and consequently on body weight loss. Interestingly, it has been reported that daily consumption of diet enriched with oligofructose, chicory-derived fructan, or FOS for 2 weeks improved satiety cues in healthy normal weight subjects. Nevertheless, prebiotic effects on satiety were not associated with subsequent weight loss, which may be related to a short duration of treatment. Conversely, research efforts have focused on the role of prebiotics on hormones involved in the body’s energy homeostasis. Clinical evidence showed that circulating levels of peptide YY, GLP-1, and GLP-2 increased after dietary prebiotic supplementation for 2 weeks in overweight individuals, but these effects may be partly explained by a high content of non-prebiotic dietary fibers used in dietary interventions. In light of these findings, there is no conclusive evidence supporting dietary prebiotics for obesity management, although their beneficial effects on the regulation of appetite and obesity-related metabolic parameters have been suggested. Thus, further investigations based on randomized placebo-controlled trials are still needed to implement both prebiotic treatment as an efficient tool for the prevention and control of obesity and related diseases.

Lean Subjects

Subjects with Obesity

Gut Microbiota Bacteroidetes

Firmicutes

Diversity

Diversity

Gut Epithelium Intestinal Permeability

Intestinal Permeability

SCFAs

SCFAs LPS Metabolic Outcomes

Insulin sensitivity

Insulin Resistance

Inflammation

Inflammation

CVD Risk Adiposity

CVD Risk Adiposity

Fig. 3 An overview of the dysbiosis occurring in gut microbiota correlated with the onset of obesity and other related diseases. Short-Chain Fatty Acid (SCFAs); Lipopolysaccharide (LPS); Cardiovascular Disease (CVD)

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Conclusion Obesity and its related diseases have increased worldwide and are considered the main risk factor for developing other diseases. Studies in animal models and human subjects have demonstrated that dysbiosis occurring in gut microbiota are correlated with the onset of obesity and other related diseases (Fig. 3). To date, since data are conflicting, it is unclear which microbial signature contributes the most to the development of obesity. This effect could be due to the intricate nature of the gut microbiota. Nowadays, the challenge is to determine whether the changes observed in gut microbiota are the cause or the consequence of weight loss and improvement in obesity comorbidities.

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Gut Microbiome and Brown Adipose Tissue Jose´ María Moreno-Navarrete

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Studies in Rodents Linking Gut Microbiota to BAT Activity or WAT Browning . . . . . . . . . . . . . Effects of Cold Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Plant Extract-Derived Bioactive Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Bariatric Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Intermittent Fasting and Caloric Restriction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Probiotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Depletion of Gut Microbiota: Beneficial or Detrimental? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intestinal AMPK, a New Link Between Gut Microbiota and Adipose Tissue Thermogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Studies in Humans Did Not Support the Relationship Between Gut Microbiota and Adipose Tissue Thermogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The high prevalence of worldwide obesity and its associated metabolic disorders has a very negative impact on health, resulting in increased prevalence of cardiovascular disease, type 2 diabetes, liver steatosis, arteriosclerosis, and some types of cancer. For this reason, the search for new therapeutic solutions to reduce obesity is strongly required. A possible therapeutic approach might be to increase energy expenditure through the enhancement of thermogenic pathways in white (WAT) and brown adipose tissue (BAT). Studies based on mice J. M. Moreno-Navarrete (*) Department of Diabetes, Endocrinology and Nutrition, Institut d’Investigació Biomèdica de Girona, Girona, Spain CIBEROBN (CB06/03/010), Instituto de Salud Carlos III, Madrid, Spain e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_6

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experiments performed in the first decade of the twenty-first century indicated that gut microbiota can play a relevant role in the modulation of host metabolic homeostasis and energy balance. However, the impact of gut microbiota on energy expenditure through the induction of BAT activity or browning of WAT is still unclear. While some studies point to a possible role of gut microbiota as endogenous modulator of BAT activity or WAT browning, other studies dissociate the impact of the microbiota from this process. Here, we will further review most of these studies in an attempt to understand the reasons for these discrepancies and also go over the few studies in humans exploring the relationship between gut microbiota and WAT browning or BAT activity. Keywords

Gut microbiota · Adipose tissue · Obesity · Thermogenesis · Lipopolysaccharide · Bile acids · Short chain fatty acids

Introduction In recent decades, the worldwide prevalence of obesity has exponentially raised over the last 30 years, largely due to the lifestyle of industrialized countries, where poor eating habits and sedentary lifestyles predominate. Obesity is a serious health problem for the world’s population, and it is considered one of the most prominent epidemics of the twenty-first century (Czech 2017). Obesity is a consequence of an imbalance between energy input and energy expenditure, in which excess of fat mass is produced when energy intake exceeds energy expenditure. In obesity progression, the chronic positive energy balance promotes adipocyte hypertrophy, a dysfunctional condition characterized by decreased adipogenic capacity, and increased pro-inflammatory activity, in which adipose tissue exceeds its capacity to store excess energy. Consequently, increased amounts of lipids enter the circulation, leading to an ectopic accumulation of fat in nonfat but metabolically relevant insulin-dependent tissues such as the liver and skeletal muscle. This process increased tissue lipotoxicity and is associated with increased production of pro-inflammatory cytokines, leading to a chronic low-level inflammatory state. Both low-level inflammation and ectopic fat accumulation contribute significantly to the development of insulin resistance, the most important cause of type 2 diabetes (Czech 2017). Obesity-associated metabolic disturbances (including dyslipidemia, insulin resistance, and hypertension) increase the risk of serious diseases such as type 2 diabetes, metabolic-associated fatty liver disease, atherosclerosis, and cardiovascular events and some type of cancers (Czech 2017). In recent years, it has also been shown that obesity may be an important risk factor for susceptibility to severe complications arising from viral infections, possibly due to the negative impact of obesity (especially abdominal and visceral obesity) on the immune system. Treatments to reduce obesity currently used, such as diet programs and bariatric surgery to restrict energy capture, physical exercise to increase energy expenditure or

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pharmacological therapy to modulate appetite, often have undesired side effects (LeBlanc et al. 2018). In the search of new therapeutic alternatives against obesity more effective and with fewer side effects, the increase of energy expenditure through an enhancement of thermogenic pathway in white adipose tissue (WAT) and brown adipose tissue (BAT) has been postulated (Rothwell and Stock 1979). Studies in mice demonstrated that the inhibition of thermogenic activity in WAT and BAT through the depletion of key proteins for this pathway, uncoupling protein 1 (Ucp1) and PR domain containing 16 (Prdm16), attenuated energy expenditure, and led to an obesogenic phenotype (Cohen et al. 2014; Feldmann et al. 2009). In mice, BAT is located in specific regions, such as the interscapular, perirenal, and axillary fat depots, and is specialized for the efficient production and distribution of heat. BAT is densely innervated by the sympathetic nervous system, which in response to cold stimulates sympathetic outflow to BAT, resulting in noradrenaline release by nerve fibers that interacts with adrenergic receptors on brown adipocytes to activate thermogenesis. In addition, the high vascularization of BAT permits substrates and oxygen to be delivered to brown adipocytes for thermogenesis, increasing the whole body energy expenditure. UCP1 is only present in the inner mitochondrial membrane of brown and beige adipocytes. This protein function translocating protons (H+) from the intermembrane space into the mitochondrial matrix, dissipating in consequence the proton motive force used by ATP synthase and increasing the respiratory activity. A fine-tuned regulation of UCP1 activity is required to avoid disturbing the body’s thermal and energy balance. Otherwise, WAT, which is larger, is located at subcutaneous (inguinal) and visceral (perigonadal and mesenteric) fat depots, and acts by storing and releasing energy in the form of fatty acids in response to systemic demands, and has a low mitochondrial density and oxidative capacity. Even though, WAT depots also contain dispersed UCP1expressing and mitochondrial-rich adipocytes, named “beige” or “brite” (brownin-white) adipocytes, which can appear as a result of the browning of WAT in response to cold exposure and other stimuli. Similar to brown adipocytes, beige adipocytes also have the capacity to expend energy. In humans, while browning of white adipose tissue seem to have a minor impact on energy expenditure and weight reduction (Loh et al. 2017), due to the maximal rate of oxygen consumption per unit of tissue volume in humans is unlikely to approach that of mice, several studies demonstrated that the activation of BAT has beneficial metabolic effects (Blondin et al. 2017; Orava et al. 2011). Of interest, human studies using positron emission tomography imaging with radiotracers showed a significant increase in BAT activity upon cold exposure in parallel to enhanced activity of sympathetic nervous system (Ouellet et al. 2012; Virtanen et al. 2009). A major challenge in current obesity research is to find new endogenous mechanisms to promote BAT activity or browning of WAT in physiological conditions at room temperature. In the search for these endogenous mechanisms, some researchers have focused on the gut microbiota. In the last 15 years, the gut microbiome has been catalogued as an important regulator of energy metabolism in the host. Alterations in the composition of the gut microbiota or in its functionality could affect the

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pathophysiology of obesity and diabetes. The human intestinal microbiome contains between 500 and 1000 bacterial species that express approximately 2,000,000 genes and outnumber human genes by 100 times. The primary intestine is colonized by a low abundance of facultative anaerobic microorganisms, with a microbial density of approximately 100 microorganisms per gram of tissue, while the cecum and colon reach a density of more than 1000 microorganisms per gram of tissue. Microbial composition and abundance depend on endogenous factors such as pH, motility, intestinal mucosa, and antimicrobial peptides, and external factors such as the consumption of medicines and diet. Nondigestible but fermentable foods play an important role in shaping the composition of the intestinal microbiota. The first studies demonstrating the role of the intestinal microbiota in the modulation of host metabolic homeostasis and energy balance appeared at the beginning of the twentyfirst century (Bäckhed et al. 2004; Tremaroli and Bäckhed 2012). The microbiome comprises the genetic makeup of the gut microbiota and is much more influenced by environmental factors such as diet, drug consumption, and lifestyle, and by anthropometric measures, than by host genetic factors. Microbiome information can accurately predict many human traits, such as metabolic parameters, including body weight and basal glucose. The human microbiota consists of approximately 250 bacterial species of which 57 are shared by all individuals, constituting the common bacterial core of the human species. In fact, human gut microbiome, which reflects the microbiota genetic load, significantly improve the precision of prediction models for glucose tolerance and obesity, compared to those prediction models that only use information from host genetics and the environment (Rothschild et al. 2018). The gut microbiota facilitates, directly or indirectly, several vital functions for the host, including digestion, obtaining energy from nondigestible but fermentable carbohydrates, and the synthesis of certain vitamins. It is also involved in the formation of immune cells. Many of the metabolites produced by bacteria through the fermentation of complex carbohydrates such as short-chain fatty acids modulate signaling pathways involved in maintaining health. The fermentation of nondigestible carbohydrates, named saccharolytic fermentation, produces short-chain fatty acids such as acetate, butyrate, propionate, and other metabolites such as ethanol, lactate, and succinate, as well as gases (H2, methane, and CO2). This fermentation occurs in the proximal part of the colon, and animal studies suggest that these short-chain fatty acids play an important role in modulating energy metabolism. On the other hand, the distal colon microbiota uses mainly residual peptides and proteins to obtain energy, through protein fermentation. As a consequence of proteolytic fermentation, a wide variety of metabolites are produced, such as phenolic compounds, indole, hydrogen sulfide, and amino acids, and branchedchain fatty acids, many of which can be harmful to intestinal integrity and metabolic health. Other microbial metabolites, such as dimethylamine, trimethylamine, and lipopolysaccharide may also contribute to the onset of metabolic disorders, but it seems that they are not directly dependent on these fermentative processes. The gut microbiota also contribute to bile acids metabolism, converting primary into secondary bile acids enhancing deconjugation, dehydrogenation, and dehydroxylation

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via bacterial enzymes (Rastelli et al. 2019). Interestingly, these specific microbialderived metabolites and structural components arising from gut microbial activity, which are called postbiotic components, exert a relevant contribution in the development of obesity and its associated metabolic diseases (Canfora et al. 2019). The impact of gut microbiota on energy expenditure is still unclear. While some studies point to a possible role of gut microbiota as endogenous modulator of BAT activity or WAT browning, other studies dissociate the impact of the microbiota from this process. Here, we will further review most of these studies in an attempt to understand the reasons for these discrepancies.

Studies in Rodents Linking Gut Microbiota to BAT Activity or WAT Browning Mice experiments reveal a large number of factors, such as cold, exogenous compounds, bariatric surgery, caloric restriction, probiotics, and antibiotics that can contribute to the relationship between gut microbiota and energy expenditure by different mechanisms (Fig. 1). These studies are described below. Cold exposure Cold exposure Caloric restriction Probiotic

Caloric restriction Plant extract-derived bioactive compounds (Allicin, pectins, nobitelin)

(Lactobacillus acidophilus)

Roux-en-Y gastric bypass Probiotic (Lactobacillus reuteri J1)

Plant extract-derived bioactive compounds

Intestinal AMPK activity

(Camu camu, resveratrol)

Improved healthy microbiota composition and activity

Proinflammatory microbial products

Short-chain fatty acids

Secondary bile acids

Circulating methylglyoxal GPR43 signaling

LPS signaling

TGR5 signaling

BAT activity and WAT browning Fig. 1 This figure illustrates the different factors and mechanisms that can modulate the relationship between gut microbiota and energy expenditure. The related factors and mechanisms have frames of the same color. The mechanisms described by the violet and green colors consist in an increase in short-chain fatty acids and secondary bile acids that stimulate catabolic activity in adipose tissue, increasing GPR43 and TGR5 signaling, respectively. The mechanisms described by the blue and yellow colors consist in a reduction of proinflammatory microbial products (such as LPS) and methylglyoxal, which exert anti-adipose tissue browning effects. The reduction of these compounds improves adipose tissue function, attenuating tissue immune activation and enhancing thermogenic activity

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Effects of Cold Exposure First evidences suggesting a possible contribution of gut microbiota to WAT browning or BAT activity arise from studies in which the effects of cold exposure were assessed (Chevalier et al. 2015; Worthmann et al. 2017; Ziȩtak et al. 2016). Cold exposure led to a relevant shift in the composition of gut microbiota, increasing Firmicutes abundance over Bacteroidetes and inhibiting the presence of Verrucomicrobia phylum in both feces and cecum (Chevalier et al. 2015). The transplantation of cold exposure-associated microbiota improved insulin sensitivity and adipocyte function in parallel to increased browning in both subcutaneous and visceral WAT (Chevalier et al. 2015). The changes in gut microbiota after cold exposure ran in parallel to increased levels of SCFAs (specifically propionate, butyrate, lactate, and succinate) and a larger small intestine, indicating increased nutrient absorption capacity. In line with this, cold exposure also induced a significant decrease in Akkermansia muciniphila, a microbe with anti-obesity actions that exerts beneficial metabolic effects partly due to the reduction of intestinal absorptive capacity and promoting a situation of caloric restriction in mammalian host (Plovier et al. 2017). Of interest, cold exposure increased bile acid synthesis, enhancing the hepatic conversion of cholesterol to bile acids via the alternative synthesis pathway, in association to relevant changes in gut microbiota and enhanced BAT activity (Worthmann et al. 2017). The inhibition of bile acid synthesis attenuated coldinduced bacterial composition shift and reduced thermogenic actions (Worthmann et al. 2017). Supporting this study, Zietak et al. also demonstrated that cold environments induced hepatic primary bile acids synthesis and conjugation enzymes and promoted the cholesterol conversion into primary bile acids in association with changes in gut microbiota composition (Ziȩtak et al. 2016). Of note, these modifications were observed only 1 day after cold exposure, suggesting that they did not depend of weight loss or adiposity reduction (Ziȩtak et al. 2016). Bile acids impact on gut microbiota to modulate energy homeostasis and lipid and glucose metabolism through activation of farnesoid X receptor and G proteincoupled bile acid receptor 1 (GPBAR1 or also named TGR5) (Wahlström et al. 2016). The capacity of gut microbiota to regulate host thermogenesis via conversion of primary into secondary bile acids also have been demonstrated in other recent studies (Li et al. 2022; Somm et al. 2017). Strikingly, Somm et al. found that the absence of B-Klotho (Klb), the required mediator of FGF21 and FGF15/19 signaling, resulted in a mice phenotype of diet-induced obesity resistance with increased energy expenditure and BAT activity, which was not explained by the beneficial action of FGF21. Interestingly, these anti-obesity phenotype of Klb KO mice was produced by the enhanced hepatic cholic acid production that results in increased microbiota-derived deoxycholic acid. Mechanistically, deoxycholic acid activates TGR5 receptor, stimulating in consequence BAT thermogenic activity, and showing how the microbiota can modulate host energy expenditure and thermogenesis via conversion of primary into secondary bile acids (Somm et al. 2017).

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In another recent study that was focused on the mechanism of weight regain after caloric restriction, a similar microbiota composition shift was found after caloric restriction compared to high-fat diet feeding, leading both conditions to decreased abundance of Parabacteroides distasonis in parallel to a significant reduction of non-12OH secondary bile acids, such as ursodeoxycholic acid and lithocholic acid. Importantly, the recovery of P. distasonis abundance and these non-12OH secondary bile acids prevents weight regain after caloric restriction through increasing energy expenditure and BAT UCP1 levels (Li et al. 2022). This study demonstrated a positive impact of this bacteria and its activity on BAT thermogenic activity and confirmed the relevance of secondary bile acids in this process (Li et al. 2022). It is important to consider some discrepancies in relation to the fat depotdependent effect reported (Chevalier et al. 2015; Ziȩtak et al. 2016), which could be due to some differences in cold exposure intervention. Whereas Chevalier study reported that cold-associated microbiota promoted WAT browning, performing cold exposures at 6  C (Chevalier et al. 2015), Ziętak study highlighted induced BAT activity at 12  C (Ziȩtak et al. 2016).

Effect of Plant Extract-Derived Bioactive Compounds Recent studies reported positive metabolic effects of natural food compounds, reducing body weight and fat mass, and improving obesity-associated metabolic disturbances in parallel to enhanced energy expenditure, WAT browning or BAT activity, and changes in gut microbiota composition (Anhê et al. 2019; Chen et al. 2022; Houron et al. 2021; Hui et al. 2020; Kou et al. 2021; Quan et al. 2020; Xu et al. 2020; Zhang et al. 2020). These compounds include nobiletin that is only present the citrus fruit rinds (Kou et al. 2021), allicin that is the main bioactive component of garlic (Zhang et al. 2020), camu camu (Myrciaria dubia) that is an Amazonian fruit with potent antioxidant and anti-inflammatory activities (Anhê et al. 2019), ginseng extracts (Quan et al. 2020), tangeretin that is a key citrus polymethoxylated flavone with potential beneficial effects on health (Chen et al. 2022), pectins (Houron et al. 2021), and resveratrol (Hui et al. 2020). While these studies reported a possible link between gut microbiota and the compound-induced potential beneficial metabolic effect, they pointed to different mechanisms. For instance, allicin administration improved high fat diet-induced gut dysbiosis, increasing the abundance of Bifidobacterium, Lactobacillus, and Blautia, and in consequence, raising SCFAs levels in the cecum. These improvements in gut microbiota composition also result in healthier intestine, increasing intestinal absorptive surface and reducing inflammation (Zhang et al. 2020). Nobitelin treatment increased abundance of Bacteroidetes and Bacteroidetes/Firmicutes ration compared to gut microbiota in control mice fed with HFD, highlighting at the bacterial genus level, Akkermansia and Bacteroides. These changes were specifically associated with increased acetate levels in cecum and serum (Kou et al. 2021). Acetate can enhance energy expenditure, inducing BAT activity and WAT browning though GPR43 (Hu et al. 2016; Kimura et al. 2013). Pectins administration displayed similar

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effects improving obesogenic diet-associated microbiota dysbiosis to those observed with allicin or nobitelin, also increasing SCFAs levels, specially acetate (GarcíaCarrizo et al. 2020; Houron et al. 2021). Camu camu administration prevented the high-fat and high-sucrose (HFHS)-associated reduction in Bifidbacterium and Barnesiella, reduced the relative abundance of Lactobacillus, explaining in part most of the reduction observed in Firmicutes, and led to bloom of A. muciniphila in parallel to a relevant alteration in plasma bile acids pool size and composition (Anhê et al. 2019). These microbiota-associated changes in plasma bile acids observed after camu camu administration ran in parallel with BAT activation and the promotion of WAT browning (Anhê et al. 2019). Similar to this study, the administration of resveratrol, a natural polyphenol found in grapes and berries with positive effects attenuating obesity-associated metabolic disturbances, prevents obesity-associated gut dysbiosis resulting in a significant improvement in glucose tolerance by increasing both BAT activity and subcutaneous WAT browning through the modulation of bile acid metabolism (Hui et al. 2020). Specifically, resveratrol administration increased intestinal bacteria-produced secondary bile acids, such as lithocholic acid (LCA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), and hyodeoxycholic acid (HDCA), which can induce BAT activity or WAT browning through the activation of TGR5 (Hui et al. 2020). In addition to SCFAs and secondary bile acids, other mechanisms might explain the relationship between gut microbiota and adipose tissue thermogenesis (Quan et al. 2020; Xu et al. 2020). The administration of ginseng extracts increased the abundance of Enterococcus faecalis, a bacterium that produces high levels of myristoleic acid, a metabolite with anti-obesity effects by the induction of BAT activity and WAT browning through the enhancement of UCP1 and mitochondrial activity (Quan et al. 2020). The administration of Panax notoginseng saponins, a commercial herb product that contains ginsenosides Rb1, Rd., Re, Rf and Rg1 and notoginsenosides R1 with putative beneficial effects on weight management, might promotes WAT browning-induced weight loss possibly through gut microbiotainduced leptin activation (Xu et al. 2020). P. notoginseng saponins administration resulted in increased abundance of A. muciniphila and P. distasonis and antibioticinduced microbiota depletion attenuated WAT browning via the leptin signaling pathway (Xu et al. 2020). Specifically, leptin promotes adipose tissue browning directly impacting on white adipocytes, through the induction of AMP-activated protein kinase-α (AMPKα) and signal transducer and activator of transcription 3 (STAT3) signaling pathway, and in consequence, increasing the expression of UCP1 and PRDM16 and the amount of mitochondria in these cells (Xu et al. 2020).

Effect of Bariatric Surgery Bariatric surgery, the most effective treatment for obesity and its associated metabolic disturbances, resulted in important gut microbiota shifts in humans with obesity (Kong et al. 2013; Tremaroli et al. 2015) and rodents (Liou et al. 2013) in correlation with metabolic improvements.

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Liou et al., in a mouse model of Roux-en-Y gastric bypass (RYGB) that recapitulates many of the metabolic outcomes in humans, demonstrated that gut microbiota perturbations after RYGB were conserved among humans, rats, and mice, leading to a fast increase in Gammaproteobacteria (Escherichia), and Verrucomicrobia (Akkermansia) in relative abundance. These changes in microbiota composition did not depend on body weight change or caloric restriction and were most evident in the distal gut, just after the surgical manipulation site (Liou et al. 2013). Similar to this study, RYGB intervention has long-term effects on gut microbiota composition in humans, resulting in increased abundance of several species in the Gammaproteobacteria class and some facultative anaerobes in the Proteobacteria (such as Escherichia, Klebsiella, and Pseudomonas), but decreased species in the Firmicutes phylum (Clostridium difficile, Clostridium hiranonis, and Gemella sanguinis), and these effects did not depend on weight loss (Kong et al. 2013; Tremaroli et al. 2015). RYGB intervention also impact on gut microbiota functional activity, conditioning microbial genetic content, slightly decreasing the production of SCFAs (acetate, butyrate, and propionate) and enhancing the biosynthesis of secondary bile acids (Tremaroli et al. 2015). Of note, fecal transplantation from RYGB operated humans (Tremaroli et al. 2015) and mice (Liou et al. 2013) into germ-free mice suggest a possible role of gut microbiota in post-RYGB-induced weight and fat loss. Supporting this suggestion, microbiota depletion after RYGB surgery impairs the expected weight loss and metabolic improvements in diet-induced obese rats after this intervention (Münzker et al. 2022). This study also confirmed that the transfer of RYGB-associated gut microbiota into HFD-induced obese rats replicates the beneficial effects of RYGB through the modulation of taurine and bile acid metabolism. Specifically, this transfer led to increased abundance of cecum and plasma taurineconjugated bile acids that stimulated intestinal FXR and systemic TGR5 signaling to enhance thermogenesis-associated energy expenditure (Münzker et al. 2022).

Effects of Intermittent Fasting and Caloric Restriction Another important nonpharmacological intervention for reducing obesity and type 2 diabetes is calorie restriction. In mice, caloric restriction has been shown to have a linear effect on life span. Some studies indicate that the positive effects of intermittent fasting on weight loss, weight control, and other metabolic benefits, might be mediated in part by gut microbiota-induced WAT browning (Li et al. 2017). Intermittent fasting increased small intestinal length and produced substantial changes in the content and composition of the intestinal microbiota, resulting in a microbial shift similar to that previously observed in cold-exposed mice (Chevalier et al. 2015). Importantly, microbiota transplantation from mice subjected to intermittent fasting led to enhanced browning of WAT in parallel to improved glucose tolerance in recipient mice, and these beneficial effects disappeared when gut microbiota were depleted (Li et al. 2017). The high production of acetate and lactate by intermittent fasting-derived microbiota was postulated as the possible mechanism to explain enhanced WAT browning (Li et al. 2017). Otherwise, other study that confirmed

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the effect of caloric restriction-derived gut microbiota enhancing WAT browning and its associated metabolic improvements (Fabbiano et al. 2018), pointed to another mechanism. This study demonstrated that caloric restriction caused changes in gut microbiota that reduced LPS biosynthesis, resulting in attenuated WAT inflammation, and in consequence, increasing WAT browning (Fabbiano et al. 2018). Supporting this idea, several studies demonstrated that the activation of LPS-TLR4 axis and relevant mediators of LPS response pathway can inhibit WAT browning probably through the activation of inflammatory pathway in this fat depot (Gavaldà-Navarro et al. 2016; Latorre et al. 2022; Nagata et al. 2017; Okla et al. 2015). For instance, glucoraphanin, a stable glucosinolate precursor of sulforaphane that is abundant in broccoli sprouts, impacts on obesity and associated metabolic disturbances, attenuating weight gain, decreasing hepatic steatosis, and improving insulin sensitivity in high-fat diet-fed mice. These positive effects were associated with reduced plasma LPS levels in parallel to increased energy expenditure and UCP1 levels in inguinal and epididymal fat depots, suggesting a possible link between the attenuation of metabolic endotoxemia (LPS levels) and the promotion of white adipose tissue browning (Nagata et al. 2017). Consistently, TLR4 stimulation in mice by high-fat feeding or LPS administration led to display relevant signs of impaired thermogenic activation, such as reduced core body temperature and heat release, or decreased expression of browning-related genes and mitochondrial function in subcutaneous adipose tissue (Okla et al. 2015). In addition, lipopolysaccharide-binding protein (LBP), a crucial mediator in LPS signaling pathway that is highly expressed in adipose tissue in association to obesity and insulin resistance (Moreno-Navarrete et al. 2013), also impacts negatively in white adipose tissue browning (Gavaldà-Navarro et al. 2016; Latorre et al. 2022) and seems to impair mitochondrial respiratory function in differentiated 3T3-L1 adipocytes (MorenoNavarrete et al. 2017). Specifically, Gavaldà-Navarro et al. demonstrated that in vivo and in vitro stimulation of white adipose tissue browning resulted in decreased Lbp expression, and that in vitro downregulation of Lbp expression increased expression of thermogenesis-related genes in 3T3-L1 adipocytes, which was attenuated after administration of recombinant LBP (Gavaldà-Navarro et al. 2016). Of interest, in vivo experiments also supported the negative role of LBP on adipose tissue browning, showing increased expression of Ucp1 and Pgc1α in white fat depots when LBP was depleted (Gavaldà-Navarro et al. 2016) or downregulated (Latorre et al. 2022).

Effect of Probiotics Some recent studies suggest that the beneficial effect of specific probiotic administration might be mediated by boosting BAT activity or WAT browning. For instance, the administration of Lactobacillus reuteri J1 on obese mice resulted in reduced weight and fat mass and improved glucose tolerance and insulin sensitivity. The administration of this bacteria also produced changes in gut microbiota and bile acid composition that consisted in increased relative abundances of Lactobacillus,

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Akkermansia, and Clostridium in correlation to the secondary bile acids, ursodeoxycholic acid (UDCA) and lithocholic acid (LCA). Similar to other studies, increased levels of UDCA and LCA led to activated TGR5 signaling in WAT and promotes adipose browning (Zhang et al. 2022a). In other study, Yoshida et al. reported that whereas catabolic defects of branchedchain amino acids in BAT were associated with reduced BAT activity and obesity, the administration of Bacteroides dorei and B. vulgatus improved branched-chain amino acids catabolism, enhancing BAT activity and preventing weight and fat gain in mice fed with obesogenic diet (Yoshida et al. 2021). Strikingly, the administration of Bifidobacterium adolescentis strains isolated from the feces of newborn and elderly humans in high-fat diet-fed male mice displayed different effects on energy metabolism and immunity (Wang et al. 2021). B. adolescentis isolated from elderly humans attenuated body weight gain in parallel to increased expression of thermogenic- and lipid metabolism-related genes in BAT in parallel to the increase in relative abundance of beneficial bacteria in gut microbiota (including Bacteroides, Parabacteroides, and Faecalibaculum). In contrast, B. adolescentis strains isolated from the feces of newborn increased food intake and body weight (Wang et al. 2021). The probiotic L. acidophilus isolated from the porcine gut also has anti-obesity effects increasing energy expenditure through the enhancement of BAT activity in association with improved obesity-induced gut dysbiosis (characterized by decreased Firmicutes-to-Bacteroidetes ratio and levels of endotoxin bearing Gramnegative bacteria), and in consequence, preserving intestinal barrier integrity, reducing metabolic endotoxemia, and inhibiting the TLR4/NF-Κb signaling pathway (Kang et al. 2022). One of the most relevant probiotic with anti-obesity effect is A. muciniphila (Everard et al. 2013). Several mechanisms to explain these effects have been reported (Depommier et al. 2020; Yoon et al. 2021). One study suggests that administration of A. muciniphila increased thermogenesis by induction of BAT activity and improved glucose metabolism by increasing systemic glucagon-like peptide-1 secretion (Yoon et al. 2021). Contrary to this study, Depommier et al using metabolic chambers, found that daily oral administration of pasteurized A. muciniphila increased energy expenditure caused by energy excretion in the feces (reducing carbohydrates, but not lipid, absorption), but not by adipose tissue thermogenesis (neither WAT browning nor BAT activity) (Depommier et al. 2020). These discrepancies related to the mechanism of action of A. muciniphila indicate that further studies should be required to confirm if the impact of L. reuteri J1, Bacteroides spp, B. adolescentis, and L. acidophilus on metabolism is due to the enhancement of adipose tissue thermogenesis.

Depletion of Gut Microbiota: Beneficial or Detrimental? The bidirectional relationship between host and gut microbiota is required to modulate energy harvest and storage in host and the composition and diversity of gut

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microbiota. Germ-free mice are characterized by larger cecum, thinner intestinal villi, and decreased inflammatory responses that resulted in reduced host energy harvest (Bäckhed et al. 2004). First studies using germ-free mice or microbiota depletion using antibiotic treatment reported increased WAT browning in subcutaneous and visceral WAT, without any effects on BAT activity (Suárez-Zamorano et al. 2015). The impact of microbiota depletion on WAT is associated with reductions in fat mass, adipocyte hypertrophy, and adipose tissue inflammation in parallel to increased levels of type 2 cytokines (IL-4, IL-13, and IL-15), eosinophils, and alternative activated M2 macrophages in WAT and improvements in systemic glucose tolerance and insulin sensitivity (Suárez-Zamorano et al. 2015). However, in 2019, another very well-conducted study displayed opposite findings (Li et al. 2019). This study showed that similar to germ-free mice (Bäckhed et al. 2004), antibiotic-induced gut microbiota depletion also increased intestinal and cecal size, but impaired thermoregulation and decreased UCP1 protein levels in BAT and WAT when mice were exposed to cold stress. No UCP1 induction was also observed at room temperature (22  C) or thermoneutrality (30  C) (Li et al. 2019). In a more recent study, the contribution of the gut microbiome to the regulation of energy homeostasis was evaluated, quantifying whole-body energy expenditure in several models of gut microbial depletion or cold- and diet-induced perturbation (Krisko et al. 2020). Compared with control mice, the intestinal contents of both antibioticinduced depletion and germ-free mice were significantly increased, being the most pronounced effects observed in cold-housed mice and accounting for a considerable proportion of total body weight, indicating reductions in both lean and fat mass, possibly caused by reduced efficiency of nutrient absorption (Krisko et al. 2020). Using indirect calorimetry, neither gut microbiome depletion nor the relevant microbial perturbations induced by cold ambient temperatures influenced energy expenditure during cold exposure or high-fat feeding (Krisko et al. 2020). In addition, no significant changes in thermogenic-related gene and protein expression or other signs of browning in WAT in the different gut microbiota depleted and perturbed mice models, indicating that in contrast to previous studies (Chevalier et al. 2015; Li et al. 2019; Suárez-Zamorano et al. 2015; Ziȩtak et al. 2016), microbiota did not impact on energy expenditure, BAT activity, or WAT browning (Krisko et al. 2020). However, the low glucose concentrations observed in mice models with gut microbiota depletion, which in previous studies were attributed to browning-associated improved insulin sensitivity (Chevalier et al. 2015; Suárez-Zamorano et al. 2015; Ziȩtak et al. 2016), seems to be explained by other mechanisms, such as the induction of glucagon like peptide-1 secretion (Hwang et al. 2015), increased glucose utilization in enterocytes (Zarrinpar et al. 2018), enhanced glucose uptake in BAT in dissociation with adaptative thermogenesis (Li et al. 2021), or the inhibition of hepatic gluconeogenesis, without impacting on glucose tolerance or insulin sensitivity (Krisko et al. 2020). Discrepancies from mice experiments could be explained in part by the loss of microbial complexity and function due to a restrictive laboratory environment (Rosshart et al. 2017). The reconstitution of laboratory mice with the microbiome of wild mice (wildings) prevented weight gain in obesogenic conditions and protects

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from obesity-associated metabolic disturbances in association to increased microbial alpha diversity (Hild et al. 2021). The anti-obesity effect of mice gut microbiota wildlings was produced by increased BAT activity-linked energy expenditure. This phenotype is transferable in neonatal mice during the first 2 weeks of life, but not in adult mice, and wildlings maintained their protective metabolic phenotype after antibiotic treatment-induced microbiome alteration. Interestingly, the narrow window in early life (2 weeks) of wild-type microbiome exposure required to promote the obesity-resistant phenotype coincides with the period of the greatest proliferation of brown adipocytes after birth (Hild et al. 2021).

Intestinal AMPK, a New Link Between Gut Microbiota and Adipose Tissue Thermogenesis Mice with intestinal AMPKα1 deletion displayed a dysfunctional BAT, with marked adipocyte hypertrophy, impaired mitochondrial function, and decreased expression of lipolysis- and thermogenesis-related genes associated with a significant reduction of energy expenditure (measured by indirect calorimetry), which resulted in increased weight gain and hepatic gluconeogenesis and impaired glucose and insulin tolerance (Zhang et al. 2022b). Important changes in gut microbiota composition, which led to increased circulating methylglyoxal levels, were found in mice with intestinal AMPKα1 deletion, suggesting that gut microbiota through the synthesis of this metabolite might participate in the effects of intestinal AMPK in metabolism. Interestingly, additional in vitro and in vivo experiments supported the negative effects of methylglyoxal on UCP1 levels and BAT thermogenesis. Supporting this idea, fecal microbiota transplantation from intestinal AMPKα1 KO mice led to attenuated BAT function and increased circulating methylglyoxal levels compared to that of the wild-type mice, and this impaired thermogenesis was rescued by using microbiota from wild-type mice in parallel to methylglyoxal reduction. Furthermore, this study also demonstrated that intestinal AMPK can impact on the production of intestinal antimicrobial proteins, which may modulate gut microbiota composition, and that the metabolic actions of metformin are dependent on intestinal AMPK, which would be more relevant, than on hepatic AMPK (Zhang et al. 2022b).

Studies in Humans Did Not Support the Relationship Between Gut Microbiota and Adipose Tissue Thermogenesis The functional effect of gut microbiota on browning of human adipose tissue has been far less explored compared to that of mice. In fact, few studies investigated the relationship between gut microbiota and adipose tissue thermogenesis or BAT activity in humans. These studies reported associations among gut microbiota, levels of the postbiotic acetate, insulin sensitivity, and gene expression markers of adipose tissue browning (Moreno-Navarrete et al. 2018) or between gut microbiota and brown adipose tissue activity (Ortiz-Alvarez et al. 2023). One of these studies found

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decreased relative abundance of Firmicutes and increased relative abundance of Bacteroidetes and Proteobacteria in fecal samples of obese subjects in association with insulin resistance. Interestingly, the relative abundance of Firmicutes (mainly those bacteria from Ruminococcaceae family) was positively correlated with expression of thermogenesis-related genes in subcutaneous adipose tissue, systemic insulin sensitivity, and plasma acetate levels, whereas Bacteroidetes’s relative abundance was negatively correlated with gene expression markers of adipose tissue browning (Moreno-Navarrete et al. 2018). This other study (Ortiz-Alvarez et al. 2023) investigates the association of fecal microbiota composition of young non-obese adults with BAT volume and activity, which was measured using static 18F-fluorodeoxyglucose positron emission tomography-computed tomography scan after 2 h of personalized cooling protocol. Of note, this study demonstrated a negative association between the relative abundance of Akkermansia, Lachnospiraceae sp., and Ruminococcus genera and BAT volume and activity and a positive correlation between the relative abundance of Bifidobacterium genus and BAT activity. However, it is important to note that these observational studies (Moreno-Navarrete et al. 2018; Ortiz-Alvarez et al. 2023) do not establish a cause-effect relationship. In humans, BAT is located mainly in the neck and supraclavicular region (Virtanen et al. 2009). Even though BAT thermogenesis is a minor contributor to whole-body energy expenditure (Loh et al. 2017), cold exposure-induced human BAT activity is associated with the clearance of circulating glucose, nonesterified fatty acids and triglycerides (Blondin et al. 2017; Orava et al. 2011), indicating that the induction of BAT activity might impact positively on obesity-associated metabolic disturbances, improving glucose tolerance and liver steatosis. In a very recent study, the association between BAT activity, gut microbiota, and hepatic fat content in adult humans was investigated. This study reported an inverse correlation between BAT activity and liver fat, without any association with fecal microbiota diversity and composition. This study also confirmed that higher cold-stimulated BAT activity is associated with improved glucose tolerance. In addition, fecal microbial transplantation from human donors with high BAT or low BAT activity into germ-free mice did not result in alterations in body and fat mass, BAT activity, whole-body energy expenditure, or UCP1 levels in the recipient mice, indicating that in humans BAT activity was not influenced by gut microbiota (Ahmed et al. 2021). This study also examined the percentage decline in abdominal subcutaneous adipose tissue proton density fat fraction after cold exposure, and this remained unchanged, in agreement with other studies, showing that proton density fat fraction of BAT and WAT had different responses to cold. However, this study did not evaluate relationship between expression of thermogenic genes (UCP1 and PRDM16) and fecal microbiota. In addition, as stated by the authors in discussion, limitations as the relative small sample size, the absence of subjects with severe obesity and type 2 diabetes, and non-standardization of dietary intake, suggest that further studies in humans are needed to generalize these findings. In line with this, another recent study that investigated the relationship between high-glucose and -fructose diets and BAT dysfunction in humans, concluded that high-fructose but not high-glucose diet impaired BAT glucose uptake without impacting on thermogenesis and on gut microbiome (Richard et al. 2022). According

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to this study, high-carbohydrate diets did not alter taxonomic composition, including α- and β-diversity, nor metabolites produced by its fermentative activity (such as shortchain fatty acids). This study highlighted a potential confounding factor for studies based on 18F-fluorodeoxyglucose to assess BAT thermogenesis. Supporting these studies, previous studies in humans also reported very small or no metabolic effects of the oral fecal microbial transplantation from healthy donors into obese subjects (Allegretti et al. 2020; Yu et al. 2020), indicating that previous observations from mice experiments about the connection between gut microbiota and BAT activity might have little translational capacity to humans.

Conclusion Experiments in mice demonstrated that exogenous or endogenous factors that promote a healthy shift in gut microbiota composition and abundance might mediate their beneficial effects on metabolism (reducing body weight and fat mass or improving insulin sensitivity) by enhancing BAT activity or browning of WAT. Mechanistically, these effects are produced as a result of gut microbial activity, which include the biosynthesis of secondary bile acids and SCFAs as putative inductors or proinflammatory microbial products (LPS) as putative inhibitors of adipose tissue thermogenesis and catabolic processes. The main factors that promote this thermogenic microbiota shift are cold exposure, plant extract-derived exogenous bioactive compounds, bariatric surgery, caloric restriction, or administration of specific probiotics (Fig. 1). However, these studies should be considered cautiously, because recent studies in mice suggest that the reductions in fat mass and other metabolic benefits observed in some of these experimental conditions that perturbed intestinal microbiota might be caused by reduced efficiency of nutrient absorption rather than increased BAT activity or WAT browning. These discrepancies could be explained in part by the loss of microbial complexity and function due to a restrictive laboratory environment. In humans, even though some observational studies suggest a possible contribution of gut microbiota in BAT activity or WAT browning, other studies dissociate gut microbiota from these processes. A major limitation in human studies is the difficulty of performing microbial transfer experiments and having this transferred microbiota successfully established in the gut over a long period of time, e.g., 6 months that is the required time to perform an adequate metabolic experiment. To overcome this constraint, perhaps it would be more interesting and feasible to design clinical trials in humans to evaluate those postbiotics or specific microbialderived metabolites with a putative impact on energy expenditure in mice experiments by enhancing adipose tissue browning. Future studies in humans should be more focused on investigating the impact of circulating specific microbial-derived metabolites on BAT activity or WAT browning, rather than fecal microbial composition (Fig. 2). First, taking advantage of omics analysis, the relationship between circulating microbial metabolites and adipose tissue should be further examined. Next, the functional effects of this metabolites should be tested in in vitro

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Fecal microbiota composition

BAT activity WAT browning Circulating specific microbial-derived metabolites

In vitro experiments

In vivo experiments

Clinical trials / Preclinical studies

Fig. 2 This figure illustrates the methodological approach of studies focused on investigating how gut microbiota activity might impact on energy expenditure and adipose tissue physiology in humans. First, the associations among circulating specific microbial-derived metabolites, adipose tissue, and systemic metabolic parameters should be analyzed, giving less weight to fecal microbial composition. Next, functional in vitro and in vivo experiments in murine experimental models should be tested to confirm the relevance of target microbial metabolites on adipose tissue physiology. Finally, clinical trials or preclinical studies in animal models closer to humans (such as nonhuman primates) should be designed to validate their therapeutic use

experiments and in murine experimental models, and finally clinical trials or preclinical studies in animal models closer to humans (such as nonhuman primates) should be designed.

Cross-References ▶ Gut Microbiome and Hepatic Steatosis (Steatotic Liver Disease) ▶ Gut Microbiome in Dyslipidemia and Atherosclerosis ▶ Gut Microbiota and Obesity ▶ Gut Microbiota and Specific Response to Diet ▶ Gut Microbiota and Type 2 Diabetes Mellitus ▶ The Impact of Microbial Metabolites on Host Health and Disease

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Gut Microbiome and Hepatic Steatosis (Steatotic Liver Disease) Lesley Hoyles

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Defining and Diagnosing Hepatic Steatosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Contribution of the Gut Microbiome to Steatotic Liver Disease . . . . . . . . . . . . . . . . . . . . . . . . . . The Gut Microbiota and Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Host–Microbiota Co-metabolism, and the Gut–Liver Axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SLD and the Gut Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbiome-Targeted Interventions to Ameliorate SLD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The gut–liver axis represents complex pathways of communication between the gut, the gut microbiota, and the liver that influence hepatic and systemic health. Hepatic steatosis, characterized by an accumulation of lipids in liver tissue, is one of the leading causes of chronic liver disease worldwide, with the potential to progress to more serious and irreversible forms of hepatic disease. It is becoming apparent that interactions between the host and its gut microbiota contribute to hepatic steatosis, but determining whether these interactions initiate hepatic steatosis and/or drive disease progression is difficult. What is clear is that metabolites produced by the gut microbiota influence aspects of hepatic steatosis, and they may prove invaluable in non-invasive diagnostic tools to allow earlier detection of fatty liver disease, reducing the global burden of disease and improving patient outcomes. This chapter summarizes our current knowledge on the role of the gut–liver axis in hepatic steatosis and provides information on microbiotaL. Hoyles (*) Nottingham Trent University, Nottingham, UK e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_7

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targeted interventions that may be able to ameliorate or treat hepatic steatosis in the future. Keywords

Microbiota · Metagenome · Microbial metabolites · NAFLD · Non-alcoholic steatohepatitis · NASH · Gut–liver axis

Introduction Defining and Diagnosing Hepatic Steatosis Hepatic steatosis (HS), characterized as an excess of fat (triglycerides) in liver tissue, contributes to a spectrum of liver diseases affecting approximately 25% of the global population (Del Barrio et al. 2023). Because of the complexity of HS-associated conditions, recent advances in research, a need to remove exclusionary confounder terms from clinical use, and the desire to remove potentially stigmatizing language from the medical lexicon, the way in which different disorders linked with HS are defined has changed (Rinella et al. 2023). Steatotic liver disease (SLD) is now used as an overarching term to encompass the various aetiologies of HS (Rinella et al. 2023). There has also been recognition that similar, or even the same, biological processes contribute to both non-alcoholic fatty liver disease (NAFLD) and alcoholrelated liver disease (ALD) (Rinella et al. 2023). As such, along with the introduction of the term SLD to describe HS, changes have been made to the ways in which variants of NAFLD and non-alcoholic steatohepatitis (NASH) are described (Rinella et al. 2023). NAFLD, also known as non-alcoholic HS, represents the hepatic component of metabolic syndrome. This condition is now known as Metabolic dysfunctionAssociated Steatotic Liver Disease (MASLD). It is the most prevalent chronic liver disease globally and increases the risk of cardiovascular disease, stroke, hepatocellular carcinoma (40% of patients present without cirrhosis associated with ALD), cholangiocarcinoma, gallstone diseases, diabetes, and chronic kidney disease in those affected (Xiao et al. 2023). MASLD encompasses patients who have HS, as determined by biopsy or imaging, and at least one of five cardiometabolic risk factors known to be associated with insulin resistance (Table 1) (Rinella et al. 2023). However, it is important to note that making a diagnosis of MASLD does not mean that other causes of SLD (e.g. parenteral nutrition, Wilson disease, α-1 antitrypsin deficiency, steatogenic medication) should not be considered, especially in children (Kaufmann et al. 2023; Rinella et al. 2023). Those with no known cause of SLD and with no cardiometabolic risk factors are considered to have cryptogenic SLD. MetALD describes those with MASLD who consume greater amounts of alcohol per week (Fig. 1) than MASLD patients, and within this group “exists a continuum across which the contribution of MASLD and ALD will vary” (Rinella et al. 2023). NASH is now called Metabolic dysfunction-Associated SteatoHepatitis (MASH). It is predicted that by 2030 a large proportion of populations in industrialized

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Table 1 Cardiometabolic risk factors that, together with the presence of SLD, can be used to give a pediatric or adult diagnosis of MASLD. (Adapted from Rinella et al. (2023)) Cardiometabolic risk factor Obesity/ metabolic syndrome

Insulin resistance

Hypertension

Dyslipidaemia

Dyslipidaemia

Pediatric criteria Body mass index (BMI)
85th percentile for age/sex OR Waist circumference > 95th percentile OR Ethnicity adjusted Fasting serum glucose
5.6 mmol/L (100 mg/dL) OR Serum glucose
11.1 mmol/L (
200 mg/dL) OR 2-h post-load glucose levels
7.8 mmol/L (140 mg/dL) OR HbA1c
5.7% (39 mmol/L) OR Type II diabetes mellitus OR Treatment for type II diabetes mellitus Blood pressure
95th percentile for 99% of the genes in the gastrointestinal tract are of microbial not human origin (>3,000,000 vs ~22,000) (Qin et al. 2010). This increased genetic diversity within the gastrointestinal tract allows the microbiota to break down ingested foodstuffs and medications that cannot be acted upon by human digestive functions and enzymes (Clarke et al. 2019). The by-products of microbial metabolism in the gastrointestinal tract are associated with a range of biological functions in mammals through a “network” of microbial–host co-metabolism such that these by-products can influence host intestinal and systemic health (Hoyles and Swann 2019; Zheng et al. 2011) (Fig. 4). Microbial metabolism of dietary substrates and medications in the upper gut is poorly studied because samples can only be obtained from this environment in an invasive manner. However, the microbiota of the small intestine contributes to synthesis of essential amino acids, with up to one-fifth of circulating plasma lysine

Fig. 4 Host–microbiota co-metabolism. Dietary substrates and/or medications are ingested. The gut microbiota breaks down components of these substrates. The contribution of intestinal phase I metabolism to modification of microbial metabolites is unknown, but intestinal phase II metabolism does contribute to modification of some metabolites (e.g. p-cresol ! p-cresol sulfate). Over 75% of the blood supply to the liver is delivered by the hepatic portal vein (draining venous blood from the small and large intestines), transporting potentially hundreds (Han et al. 2021) of microbial metabolites to the liver. These metabolites can pass through the liver and enter systemic circulation unchanged (e.g. short-chain fatty acids). Other metabolites are subject to phase I (e.g. trimethylamine ! trimethylamine N-oxide) or phase II (e.g. benzoate + glycine ! hippurate; p-cresol + glucuronide ! p-cresol glucuronide) metabolism in the liver before entering the circulation. Whatever biotransformation(s) they undergo in the intestine and/or liver and their route to the systemic circulation, microbiota-associated metabolites can interact with organs (including the brain) and cells (including cancer cells) throughout the body, influencing human biological processes. In healthy individuals, microbiota-associated metabolites are efficiently cleared from the body in urine and/or feces. Plasma-bound microbiota-associated metabolites such as trimethylamine N-oxide, indoxyl sulfate, and p-cresol sulfate accumulate in the blood of those with chronic kidney disease, due to the characteristic impaired renal function of these patients

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and threonine produced by the ileal microbiota (Hoyles and Swann 2019). Phase I and II metabolism of xenobiotics (including products of microbial metabolism of dietary substrates and medications) occurs in many organs in the body, but predominates in the liver (Clarke et al. 2019; Pant et al. 2023). Phase I metabolism involves modification of xenobiotics (via oxidation, reduction, hydrolysis, deamination, demethylation, dehalogenation, epoxidation, and/or peroxigenation) to make them more water soluble, facilitating their excretion from the body. Phase II metabolism involves conjugation (via glutathionylation, glucuronidation, sulfation, acylation, and/or glycation) of xenobiotics or their phase I derivatives to (in general) increase their metabolic weight, reduce their biological activity, and make them more hydrophilic for excretion. Dietary substrates such as choline, trimethylamine N-oxide (TMAO), and carnitine are broken down to trimethylamine (TMA) by bacteria in the small intestine, with TMA subject to phase I metabolism in the liver (Hoyles et al. 2018b). Benzoate, an end-point product of microbial polyphenol metabolism and likely produced in the small intestine, is subject to phase II conjugation in the liver, producing hippurate, known to be a marker of and contributor to improved metabolic health (Brial et al. 2021). The microbiota in the large intestine gains most of its nutrients from undigested dietary substrates and host-derived products such as mucins, desquamated intestinal epithelial cells, and gastric and pancreatic secretory products. By-products of microbial fermentation of dietary fiber include short-chain fatty acids (acetate, propionate, butyrate, succinate), CO2, CH4, H2S, H2, lactate, pyruvate, and ethanol (Hoyles and Swann 2019). Butyrate is the main energy source for epithelial cells lining the large intestine, protecting against colorectal cancer, and stimulating the antimicrobial activity of macrophages and regulatory T cells (Hsu and Schnabl 2023). Microbiota-driven proteolysis in the large intestine leads to the formation of potentially toxic metabolites (Fig. 5), including ammonia, amines, sulfides, phenols, and indoles, though the impact of many of these on host health is still poorly defined (Clarke et al. 2019).

Metabolic Retroconversion The term “metabolic retroconversion” was originally used to describe the way in which TMAO, derived predominantly from fish-based foodstuffs, was subject to host–microbiota co-metabolism. The process was suggested to involve “a cycle of reductive followed by oxidative reactions to regenerate TMAO” (Al-Waiz et al. 1987). Studies using in vitro fermentation systems inoculated with mixed microbiotas derived from human feces demonstrated that TMAO is broken down by Enterobacteriaceae to TMA (Hoyles et al. 2018b). Metabolic retroconversion of TMAO was confirmed in a mouse model in the same study. As described above, microbially produced TMA is subject to phase I metabolism in the liver, with hepatic flavin mono-oxygenases converting it to TMAO. If one considers metabolic retroconversion as the breakdown of compound A to compound B by the gut microbiota with conversion back to compound A by the host, urea is also subject to this process (Fig. 6). There is increasing interest in the role of nitrogen metabolism in MASLD, as increased microbial production of NH3 and decreased hepatic removal of this metabolite are characteristics of the disease (Delgado et al. 2022).

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Fig. 5 Microbial use of proteins, peptides, and amino acids in the large intestine. Amino-acidutilizing bacteria are more dominant than peptide-utilizers in the human fecal microbiota. Compared with carbohydrate fermentation, protein and amino acid use by the gut microbiota results in a wider range of metabolites. Non-protein sources of ammonia are shown in red. Phenolic compounds produced by the gut microbiota include the methylphenol p-cresol, a by-product of microbial metabolism of tyrosine and phenylalanine. (Adapted from Hughes et al. (2000); Mafra et al. (2013))

The exact contribution of the microbiota to host NH3 levels (i.e. within the gut, accumulation within the liver and/or in hyperammonia) is unknown. Re-establishment of the nitrogenous balance in MASLD may, however, be one approach to tackling the disease (Delgado et al. 2022).

The Gut–Liver Axis The interactions of microbial metabolites with the liver constitute one aspect of the gut–liver axis, involving the complex interplay between the gut, gut microbiota, and the liver (Fig. 7). Some microbiota-associated metabolites contribute directly to liver function: e.g. D-lactic acid produced by microbes in the small intestine promotes phagocytosis of circulating/translocating pathogens by Kupffer cells (Hsu and Schnabl 2023). There is extensive communication between the liver and gastrointestinal tract via the biliary tract, portal vein, and other mediators (Tripathi et al. 2018). It was proposed that SLD results from “multiple hits” from the gut and/or adipose tissue that together promote liver inflammation (Tilg and Moschen 2010). If one considers the complexity of the gut–liver axis (Fig. 7) it is not difficult to visualize the “multiple hit” concept, and the difficulty of distinguishing cause from

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Fig. 6 Production of ammonia by the gut microbiota, and its metabolic fates. Breakdown of urea to ammonia (NH3) by the gut microbiota and its conversion back to urea by the liver is an example of metabolic retroconversion. Numerous members of the gut microbiota (e.g. bacteroides, bifidobacteria, clostridia, enterobacteria) have urease activity, enabling them to convert urea to NH3 and CO2. Microbial-mediated NH3 production occurs in the stomach, small intestine, and large intestine (Delgado et al. 2022). Urea is also subject to enterohepatic recirculation, a process in which it and a range of different chemicals (e.g. bile acids, bilirubin, drugs) circulate from the liver to the bile and enter the small intestine, from where they are absorbed by enterocytes and transported back to the liver. HCO3, bicarbonate. Bicarbonate produced during renal NH3 generation is used in the hepatic metabolism of NH3 to urea (Weiner 2017)

effect with respect to the onset and progression of SLD. As such, gut- and adiposederived modulators are unlikely to be the only contributors to fatty liver disease. Bile is produced in the liver and comprises cholesterol, secretory IgA, antimicrobial molecules, phospholipids, bicarbonate (Fig. 6), and the primary lipid-soluble bile acids cholic acid and chenodeoxycholic acid, and their water-soluble taurine- and glycine-conjugated bile salts taurocholic acid, glycocholic acid, taurochenodeoxycholic acid, and glycochenodeoxycholic acid (Hsu and Schnabl 2023). Primary bile acids, synthesized in the liver from cholesterol, and their conjugates are stored in the gall bladder prior to their excretion in bile. Bile ducts channel bile into the duodenum, where bile acids play a role in the absorption of lipids and aid in the innate immune system via their direct bacteriostatic effect, where by acting as detergents they disrupt microbial cell membranes and contribute to modulation of the microbiota (Joyce and Gahan 2016). Secretory IgA and antimicrobial molecules present in bile also contribute to modulation of the microbiota. Bile acids influence this modulatory process indirectly via activation of farnesoid X receptor (FXR), a bile acid receptor, stimulating production of antimicrobial molecules by intestinal epithelial cells and maintaining the gut barrier (Hsu and Schnabl 2023). Bacteria in the large intestine dehydroxylate or deconjugate primary bile

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Fig. 7 The gut–liver axis and its complexity. Foods and medications we ingest are broken down by gut bacteria, releasing a range of microbiota-associated metabolites into the gastrointestinal tract. Lipopolysaccharides from the cell walls of Gram-negative bacteria (e.g. Escherichia, Klebsiella, Citrobacter, Bacteroides) are also present in the gastrointestinal tract. By-products of microbial metabolism and cell-wall components, along with nutrients (carbohydrates, proteins, lipids) from foods, can be transported to the liver via the portal circulation, where they remain unchanged or are subject to hepatic phase I/II metabolism before entering systemic circulation. Enterohepatic recirculation refers to the passage of substances (e.g. bile acids and other steroids, urea, bilirubin, drugs) from the liver to bile, and their transport to the duodenum. These substances are actively absorbed from the small intestine by ileal enterocytes into the hepatic portal circulation, and then transported back to the liver. (Incorporates information from Hsu and Schnabl (2023))

acids to a range of secondary bile acids, with up to 66 and 55 bile acids detected in blood and urine, respectively (Sarafian et al. 2015). These bile acids are reabsorbed in the ileum, before being transported back to the liver and converted to primary bile acids before being excreted again (Joyce and Gahan 2016). Bile acids can selfregulate their synthesis: activation of the FXR target gene fibroblast growth factor 19 (FGF19) in the ileum suppresses hepatic bile acid synthesis. Activation of FGF19 also contributes to regulation of insulin sensitivity, and stimulation of hepatic protein

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and glycogen synthesis (Hsu and Schnabl 2023). The movement of bile acids from the liver to the gut and back to the liver is referred to as enterohepatic recirculation (Fig. 7). Up to 95% of bile acids and salts delivered to the duodenum are subject to enterohepatic recirculation. The role of the metabolic endotoxin lipopolysaccharide (LPS) in the gut–liver axis is complex. It is often assumed that the presence of LPS, derived from the cell walls of Gram-negative bacteria, is never a good thing, contributing to increased permeability of the gut barrier and hepatic inflammation. In homeostasis, the gut barrier prevents translocation of living bacteria across the lamina propria lining the intestinal tract into the portal circulation. LPS can activate toll-like receptor 4 (TLR4) on Kuppfer and stellate cells in the liver, activating NF-κB and NLRP3 (inflammasome) signaling pathways that produce pro-inflammatory and pro-fibrotic mediators that aggravate SLD. LPS also contributes to innate immune suppression in the liver and control of local inflammation, through so-called “LPS tolerance.” That is, continuous exposure of Kuppfer cells to LPS in the portal venous blood leaves these cells immunosuppressive, resulting in downregulation of the TLR4 signaling pathway. LPS tolerance also affects sinusoidal endothelial cells of the liver (Horst et al. 2016). Increased intestinal permeability is observed in less than half of patients with early liver disease (Hsu and Schnabl 2023). This suggests that increased hepatic LPS, such as that seen in individuals affected by SLD, may not be as important in early-stage liver disease as often cited.

SLD and the Gut Microbiome Perturbation of the fecal microbiota, with respect to its taxonomic diversity and functional capacity, has long been associated with metabolic diseases such as SLD (Sharpton et al. 2019; Walker and Hoyles 2023). This perturbation, characterized by an increase in the presence of facultative microbes (i.e. those that can grow in the presence or absence of oxygen) at the expense of more-specialist anaerobic bacteria (such as those responsible for the production of short-chain fatty acids, e.g. Faecalibacterium and Roseburia spp.), is referred to as dysbiosis. Few early studies into NAFLD characterized both the microbiota and its metabolites (Sharpton et al. 2019). In NAFLD, there are inconsistencies in the species or groups of bacteria associated with dysbiosis and HS: the facultative bacteria Escherichia spp. tend to increase in abundance, while obligately anaerobic bacteria (i.e. Coprococcus spp.) tend to decrease in abundance as disease severity increases. As SLD progresses to NASH, NAFLD-related advanced fibrosis or NAFLD-related hepatocellular carcinoma – all associated with concomitant increases in gut permeability, circulating LPS and systemic inflammation – dysbiosis becomes more pronounced with significant increases seen in Escherichia coli, Enterococcus and Bacteroides spp., and significant decreases seen in Faecalibacterium spp. (Sharpton et al. 2019). These trends are seen across a range of diseases (e.g. obesity, type II diabetes, Alzheimer’s disease, coeliac disease, gastrointestinal cancers, inflammatory bowel diseases) associated with inflammation (Walker and Hoyles 2023). Dysbiosis is considered a

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risk factor for progression of fatty liver disease, but whether it is the cause of disease progression or merely an indicator of the increased systemic inflammation and metabolic dysregulation associated with SLD remains to be determined. In the limited number of early studies that examined fecal or serum microbiotaassociated metabolites and their association with hepatic disease, no single metabolite was consistently associated with SLD (Sharpton et al. 2019). It is only in recent years and with the advent of multi-omics studies incorporating microbiomic, metabolomics, and/or transcriptomic approaches that the roles of microbial metabolites in SLD have started to be appreciated (Koh et al. 2018; Hoyles et al. 2018a; Caussy et al. 2018; Yuan et al. 2019; Zhao et al. 2020). Histidine can be metabolized directly to imidazole propionate by Adlercreutzia equolifaciens, Shewanella oneidensis, and Brevibacillus laterosporus, or via the intermediate urocanate by bacteria encoding urocanate reductase (Anaerococcus, Aerococcus, Streptococcus, Adlercreutzia, Eggerthella, Lactobacillus, Shewanella, Brevibacillus spp.). Imidazole propionate impairs insulin signaling and glucose tolerance, contributing to the development of type 2 diabetes (Koh et al. 2018). In European patients with type 2 diabetes levels of imidazole propionate were found to be higher when compared to their non-diabetic counterparts, with imidazole propionate associated with inflammatory bowel disease and reduced diversity in the gut microbiota (Molinaro et al. 2020). Although imidazole propionate has not been directly implicated in SLD, it may play a role in impairing insulin signaling in humans, as the metabolite was associated with increased activation of p62 and mTORC1 in liver from subjects with type 2 diabetes (Koh et al. 2018). Dysbiosis and significantly increased levels of imidazole propionate have been observed in the serum of male Göttingen Minipigs fed a choline-deficient amino-acid-defined highfat diet (Lützhøft et al. 2022). Expression of mTOR and binding of mTORC1 were increased, and expression of insulin substrate receptors 1 and 2 (INSR1, INSR2) and the glucagon receptor were decreased. At the time of writing, the influence of imidazole propionate on human SLD has not been investigated, but the observations of Koh et al. (2018) and Lützhøft et al. (2022) suggest increased circulating levels of this metabolite could influence not only metabolic processes associated with type II diabetes (Koh et al. 2018) but also those involved in the onset/progression of fatty liver disease. In a study of Spanish and Italian female non-diabetic patients with HS, increased circulating levels of the branched-chain amino acids valine, leucine, and isoleucine were seen as hepatic lipid load increased (Hoyles et al. 2018a). Valine and leucine can be produced by the gut microbiota and the host (Shoaie et al. 2015). Therefore, it was not possible for Hoyles et al. (2018a) to determine whether the increased levels of these branched-chain amino acids seen in patient sera were from the microbiota or host. It was only through an additional combined analysis of liver transcriptomic and serum metabolomic data that these authors were able to demonstrate the entire host hepatic pathway involved in degradation of branched-chain amino acids was downregulated in HS (Menghini et al. 2022). This exemplifies the complexity of host– microbiota co-metabolism and its contribution to disease, and the need to consider host- and microbiota-associated processes together in microbiome studies.

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Hoyles et al. (2018a) noted that it was possible to detect significant changes in the functional potential of the fecal microbiota as hepatic lipid load increased in HS patients when no obvious changes in species representation were apparent. That is, functional changes in the fecal microbiota preceded obvious taxonomic changes in HS, and thus may be a more-suitable focus for future microbiome studies in early liver disease. This observation may also go some way to explain the inconsistent results seen in studies focused solely on taxonomic changes in the NAFLDassociated fecal microbiota (Sharpton et al. 2019). Microbial gene diversity and hepatic INSR expression were inversely associated with progression of HS. Along with decreased hepatic INSR expression in HS, reduced p-Akt phosphorylation was seen (reducing the response to insulin), concomitant to increased expression of hepatic lipoprotein lipase and increased levels of liver triglycerides. An integrated analysis of microbiomic and metabolomic data suggested a direct role for phenylacetate, a product of aromatic amino acid metabolism, in the development of HS (Hoyles et al. 2018a). Phenylacetate is the main phenolic compound found in feces and is a product of microbial metabolism of phenylalanine. As hepatic transamination of phenylalanine to phenylacetate is low, except in the case of phenylketonuria, the increased levels of phenylacetate seen in HS are of microbial origin (Delzenne and Bindels 2018). Treatment of primary hepatocytes with phenylacetate triggered accumulation of lipids in hepatic cells. When phenylacetate was applied to hepatocytes together with palmitic acid, a synergistic effect on lipid deposition was observed, suggesting phenylacetate is not the sole metabolite contributing to HS. Feeding phenylacetate to mice contributed to HS in the mice, confirming phenylacetate does have a role in mammalian HS. 3-(4-Hydroxyphenyl)lactate, a microbiota-associated metabolite sharing structural similarity with phenylacetate, has been proposed to influence HS and fibrosis based on predicted shared gene effects, but this metabolite’s role in SLD has yet to be demonstrated (Caussy et al. 2018). Similar to phenylacetate, the microbiota-associated metabolite N,N,N-trimethyl5-aminovaleric acid (TMAVA) has been implicated in HS (Zhao et al. 2020). TMAVA is produced by Enterococcus faecalis and Pseudomonas aeruginosa from trimethyllysine, and initially was found in higher levels in the plasma of 15 patients with HS compared with controls. Analyses in replication cohorts (1157 subjects with or without diabetes, 767 subjects with or without HS) confirmed the association of TMAVA with HS. Inclusion of TMAVA in water in combination with high-fat feeding caused mice to develop HS, confirming the role of TMAVA in HS. In murine models, TMAVA inhibits γ-butyrobetaine hydroxylase, reducing carnitine synthesis and fatty acid oxidation to promote steatosis (Zhao et al. 2020). Hepatocytes convert ethanol into acetate and triglycerides, contributing to the development of HS. High-alcohol-producing strains of Klebsiella pneumoniae were found in moderate to high abundance in over 60% of NAFLD/NASH patients in a Chinese cohort (Yuan et al. 2019). Transfer of these K. pneumoniae strains to mice caused the mice to develop HS. This is an unusual finding and may represent a cause of SLD in only a small subset of patients, though further studies are required to determine the exact contribution of high-alcohol-producing bacteria to the development of HS (Yuan et al. 2019).

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Findings from the above-mentioned studies have led to the suggestion that diagnostic tests based on measuring serum metabolites could be used for non-invasive screening of SLD. A proof-of-concept study, including derivation and validation cohorts, identified ten serum metabolites (5α-androstan-3β monosulfate, pregnanediol-3-glucuronide, androsterone sulfate, epiandrosterone sulfate, palmitoleate, dehydroisoandrosterone sulfate, 5α-androstan-3β disulfate, glycocholate, taurine, fucose) that had greater diagnostic accuracy than the FIB-4 index or the NAFLD fibrosis score for detecting advanced fibrosis (Caussy et al. 2019). A combination of 31 serum metabolites linked to amino acid, lipid, nucleotide, and peptide pathways had excellent accuracy for the detection of NASH in patients with biopsy-proven NAFLD. However, the diagnostic performance of this panel of metabolites for the detection of NASH requires follow-up in independent cohorts (Caussy et al. 2019).

Microbiome-Targeted Interventions to Ameliorate SLD Current recommendations for the treatment of SLD revolve around dietary and lifestyle interventions, as there are no licensed drug-based therapies for the disease (Del Barrio et al. 2023; Kaufmann et al. 2023). A Mediterranean diet is recommended, along with regular moderate- or vigorous-intensity physical activity, to encourage 10% weight loss with the intention of reducing levels of SLD biomarkers (e.g. intestinal permeability, circulating aspartate transferase and alanine transferase levels, altered metabolome, inflammation) (Del Barrio et al. 2023; Kaufmann et al. 2023). However, many individuals affected by SLD are unable to reach weight-loss goals and/or fail to make the necessary long-term changes to dietary and exercise habits required to reverse the disease. As such, tractable alternatives that modulate the microbiota/microbiome are being investigated to improve patient outcomes in SLD (Del Barrio et al. 2023).

Probiotics, Prebiotics, Synbiotics, and Postbiotics Probiotics are “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” (Hill et al. 2014). Probiotic products (usually dietary supplements or foods) have been formulated using one or more species of bacteria representing lactobacilli, bifidobacteria, streptococci, or bacilli. The probiotic market is largely unregulated, and these functional foods are not considered a medicine. As such, there are no clear guidelines on their prescription or administration for treatment or amelioration of SLD (Kaufmann et al. 2023). Another issue around their clinical use is that not one probiotic has been identified that elicits health benefits in all individuals who ingest it. A small number of studies have shown the potential of probiotics in improving markers of health in rodent models of SLD, but many of these interventions remain untested in patient cohorts (Kaufmann et al. 2023). Although their long-term effectiveness is unknown, short-term (8 week to 12 month, depending on duration of study) benefits of probiotics in NAFLD/NASH patients include decreases in alanine and aspartate transaminase, high-sensitivity

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C-reaction protein, inflammation, total body fat, intrahepatic fat, and/or γ-glutamyl transferase levels (Kaufmann et al. 2023). However, the lack of standardized tests and reporting and different probiotic strain(s) and doses used across studies make it impossible to compare outcomes. Reports in the literature also focus on small (n ¼ 28–89) cohorts in which disease status is often heterogeneous. Many larger and unified clinical studies are required to determine the true value of probiotics in SLD (Kaufmann et al. 2023). Recent improvements in our understanding of microbial genomics and the biological functions of gut bacteria have led to the development of next-generation probiotics (Khan et al. 2023) that have yet to be tested in SLD patient cohorts, but which offer greater potential for targeted improvements to metabolic health. A prebiotic is “a substrate that is selectively utilized by host microorganisms conferring a health benefit” (Gibson et al. 2017). Traditionally considered carbohydrate-based products (e.g. inulin) that could be selectively broken down by bifidobacteria or lactobacilli present in the large intestine, the subtle change made to the definition in 2017 suggests that plant-derived polyphenols or polyunsaturated fatty acids converted to respective conjugated fatty acids could be considered as prebiotics in future if sufficient evidence of their efficacy in humans could be demonstrated. Gastrointestinal effects of prebiotics (i.e. inulin, the oligosaccharides FOS, GOS, XOS, MOS) include inhibition of pathogens and immune stimulation, while cardiometabolic benefits include, among other things, increased insulin sensitivity and a reduction in blood lipid levels (Gibson et al. 2017). Fewer prebiotic than probiotic studies have been conducted in humans with SLD (i.e. NAFLD/ NASH). These studies suffer the same issues as described for probiotics with respect to low numbers of participants (n ¼ 14–89), short duration (12 weeks to 9 months), lack of uniformity of intervention/design and reporting, though similar changes in biomarkers of disease have been seen as for probiotic interventions (Kaufmann et al. 2023). In theory, because of their ability to selectively modulate the microbiota to generate by-products of microbial metabolism known to be beneficial to metabolic health (e.g. short-chain fatty acids) (Canfora et al. 2019; Canfora et al. 2015), prebiotics should have a more profound effect on SLD than probiotics, but this has not been demonstrated clinically. A synbiotic is “a mixture comprising live microorganisms and substrate(s) selectively utilized by host microorganisms that confers a health benefit on the host” (Swanson et al. 2020). Host microorganisms here include microbes usually found in the gastrointestinal tract or introduced microbes (e.g. probiotics), as they can both be targets for the substrate component of the synbiotic. Synergistic synbiotics comprise co-administered substrates designed to be selectively used by the co-administered microbe(s), and the selective use of the substrate included in these synbiotics must be demonstrated in the same study demonstrating a health benefit. Complementary synbiotics include a probiotic combined with a prebiotic. As with probiotics and prebiotics, the few studies to date incorporating synbiotics have shown variable effects on SLD, include low numbers of patients, and non-unified approaches in study design (Kaufmann et al. 2023). It should also be noted that inclusion of a Bifidobacterium or Lactobacillus sp. in a synbiotic intervention does

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not mean the bacterium in question has been demonstrated to be a probiotic (i.e. not all bifidobacteria or lactobacilli have probiotic traits), so it is often not possible to determine whether a synergistic or complementary synbiotic has been used in a specific trial. Postbiotics are preparations “of inanimate microorganisms and/or their components that confers a health benefit on the host” (Salminen et al. 2021). These constitute “deliberately inactivated microbial cells with or without metabolites or cell components that contribute to demonstrated health benefits,” and the microbial cells do not have to come from recognized probiotics (Salminen et al. 2021). Pure microbial metabolites are not considered postbiotics, even though several studies have demonstrated the benefits of microbiota-associated metabolites to host health in rodent and human studies (Canfora et al. 2019; Canfora et al. 2015; Clarke et al. 2019; Hoyles and Swann 2019). To date, no postbiotic studies have been conducted in SLD cohorts.

Fecal Microbiota Transplant Fecal microbiota transplantation (FMT) involves taking a fecal sample from a healthy donor and using processed fecal material to transfer the donor’s microbiota to the gastrointestinal tract of a patient affected with a disease, the implication being that restoration to a “healthy” microbiota could treat or ameliorate the patient’s condition (Forlano et al. 2022). To date, FMT has only been used successfully in a clinical setting for the treatment of recurrent Clostridioides difficile infections. FMTs from humans to mice have been used to demonstrate the role of the gut microbiota in HS (Hoyles et al. 2018a). Similarly, FMT of a microbiota from a NASH patient harboring high-ethanol-producing Klebsiella pneumoniae induced NAFLD in mice (Yuan et al. 2019). Mice with high-fat-diet-induced HS had reductions in liver lipids and pro-inflammatory cytokines, an increase in intestinal butyrate, and improved intestinal-barrier function after an 8-week FMT intervention (Forlano et al. 2022). On the back of such studies, it has been proposed that FMT could be used to modulate the whole microbiota of SLD patients to reduce their intestinal permeability and hepatic liver load (Del Barrio et al. 2023; Forlano et al. 2022). As of September 2023 there were 11 clinical trials registered with ClinicalTrials.gov (https://clinicaltrials.gov/search?cond¼NAFLD&intr¼FMT) involving FMT and NAFLD/NASH/steatosis. None of these trials has returned their findings to date. A pilot study in NAFLD patients delivered FMT by endoscope to the distal duodenum of 15 patients who received fecal material from one of three healthy, lean fecal donors (allogenic FMT) and six patients who received their own fecal material (autologous FMT) (Craven et al. 2020). By chance, patients randomized to the autologous group were healthier (i.e. less severe NAFLD) than those in the allogenic group. FMT recipients were monitored up to 6 months post-FMT. It was concluded that duodenoscopy administered FMT did not improve insulin sensitivity or the hepatic proton density fat fraction in NAFLD patients but did contribute to repair of intestinal permeability, but this may have been an artefact of the higher baseline permeability observed in the allogenic group. A more recent study aimed to determine the clinical efficacy and safety of colonoscopy-delivered (daily for 3 days, from

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healthy donors) FMT for NAFLD patients (Xue et al. 2022). The study was non-randomized, as patients were allowed to select whether they joined the FMT or non-FMT group. No significant differences were seen in baseline information between the groups of patients, nor were any significant differences seen in blood lipid and liver function tests before and after treatment in the FMT group. There was, however, a significant (p < 0.05) reduction in hepatic fat (measured by attenuation) after FMT. As our knowledge develops around the components of FMTs that elicit effects in patients, it might become possible to design defined synthetic consortia of microbes (and/or metabolites) that could be used to treat patients with SLD rather than use FMT (Del Barrio et al. 2023). This would remove the need to conduct expensive screening exercises to identify “healthy” fecal donors, in which between only 3 and 25% of all individuals make it through screening (Del Barrio et al. 2023) and would reduce the risk of side-effects (e.g. diarrhea, abdominal cramps, abdominal distension, bloating, abdominal pain, fever, flatulence or constipation; (Del Barrio et al. 2023)) to patients.

Phage Therapy The association of high-ethanol-producing strains of K. pneumoniae with NAFLD (Yuan et al. 2019) and cytotoxin-excreting Enterococcus faecalis with hepatocyte death and injury (Duan et al. 2019) has led to attempts to use phage therapy as a means to treat liver disease (Duan et al. 2019; Gan et al. 2023). While lytic phages can be isolated against a range of pathogenic and commensal bacteria with relative ease, these entities often have narrow host ranges, sometimes infecting only one to a few different strains of a single species of bacteria. Each individual’s gut microbiota is unique, so no two individuals will harbor exactly the same strains of, for example, K. pneumoniae or E. faecalis, limiting the clinical efficacy of phage therapies designed based on studies involving only one to a few different strains of bacteria. With rapid advances being made in the field of synthetic biology, it is likely that broad-host-range phages will be engineered in future to target a range of bacteria implicated in SLD.

Conclusion Microbiota characterization based on taxonomic profiling is of limited use in studies concerning early-stage liver disease, and more focus should be put on functional characterization of the gut microbiota in SLD. The contribution of individual members of the microbiota to SLD is difficult to assess, with only high-alcoholproducing strains of K. pneumoniae having been linked to development of HS in some individuals. It is clear a range of microbiota-associated metabolites contribute to the phenotype of HS/SLD, but the roles of these metabolites in the initiation and/or progression of hepatic disease remain largely unproven. More mechanistic studies are required to determine the numerous ways in which microbiota-associated metabolites influence hepatic biological processes and metabolic pathways, so that

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targeted interventions can be designed to reduce the global burden of SLD. Interventions intended to modulate the microbiota for improved metabolic and hepatic health have potential, but have yet to be tested in large-scale, well-designed clinical studies involving well-characterized patient cohorts.

Cross-References ▶ Gut Microbiota and Obesity ▶ The Impact of Microbial Metabolites on Host Health and Disease

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Gut Microbiota and Type 2 Diabetes Mellitus Susanna Longo, Rossella Menghini, and Massimo Federici

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Type 2 Diabetes Mellitus Etiology, Pathogenesis, and the Role of Host Hormonal Dysfunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Microbiota in the Onset of Metabolic Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of the Intestinal Barrier and the Immune System in the Control of the Gut Microbiota-Host Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Potential Mechanisms of Microbiota Effects on Metabolism in the T2DM Patient . . . . . . . Effects of Microbiota on Glucose Metabolism, Fatty Acid Metabolism, and Energy Expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiometabolic Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contribution of Microbiota to the Success of Drug Therapy for T2D . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Type 2 diabetes mellitus (T2DM) is characterized by poor insulin secretion from pancreatic beta cells together with reduced insulin sensitivity of adipose tissue, liver, and muscle. This results in an improper response to fasting and refeeding, and hyperglycemia causing acute and chronic complications associated with T2DM. The International Diabetes Federation estimates that there are 537 million adults aged 20–79 with diabetes in 2021 and this number is projected to rise to 643 million by 2030. The incidence of T2DM is largely driven by obesity pandemic and is increasing at alarming rates. In the development and progression of T2DM, the primary role of environmental factors such as smoking, poor diet, and sedentary lifestyle is well known. Genetic susceptibility has also been studied, and many genes that determine the hormonal regulation of glucose and S. Longo · R. Menghini · M. Federici (*) Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_8

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lipid metabolism have been found to be associated with T2DM. The human gut microbiota can be considered an organ with important functions for human metabolism, digestion, maintenance of the intestinal barrier function, and immunomodulation. It helps to modulate host metabolism and its change appears to be related to the development of T2DM and cardiometabolic disorders associated with overweight and obesity. Keywords

Diabetes mellitus · Gut microbiota · Insulin resistance · Metabolic diseases · Meta-inflammation · Atherosclerosis · Obesity · NAFLD · Antidiabetic therapy · Bariatric surgery

Introduction Type 2 diabetes mellitus (T2DM) is characterized by poor insulin secretion from pancreatic beta cells along with reduced insulin sensitivity of tissues and organs with an important role in glucose clearance, such as adipose tissue, liver, and muscle (Herrema and Niess 2020). This results in inappropriate control of metabolism, resulting in an improper response to fasting and refeeding, and hyperglycemia causing acute and chronic complications associated with T2DM (Massey and Brown 2021). Hyperglycemia has been extensively linked both to the detrimental micro- and macrovascular complications typically observed in humans with T2DM and other effects (Scheithauer et al. 2020). Indeed, damage from hyperglycemia contributes to the development of diseases associated with T2DM such as nonalcoholic fatty liver disease (NAFLD), cardiovascular disease, and chronic kidney disease (CKD) (Massey and Brown 2021). The International Diabetes Federation estimates that there are 537 million adults aged 20–79 with diabetes in 2021 and this number is predicted to rise to 643 million by 2030 (International Diabetes Federation 2021). T2DM accounts for approximately 90% of diabetic patients. T2DM incidence is in large driven by the obesity pandemic and is increasing with alarming rates (Scheithauer et al. 2020). Clinically, T2DM can be defined by the level of hemoglobin A1c (5.7–6.4% considered prediabetic and > 6.5% are diabetic) and fasting blood glucose (100–125 mg/dL considered prediabetic and > 125 mg/dL are diabetic) (Massey and Brown 2021). In most cases, T2DM is guided and preceded by metabolic syndrome (MS), a group of interconnected lifestyle-related clinical features consisting of elevated fasting blood glucose, increased blood pressure, decreased HDL cholesterol, increased circulating triacylglycerols, and obesity. Although obesity is a critical and obvious sign of metabolic syndrome, other clinical features often go unnoticed or are absent (Herrema and Niess 2020).

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Type 2 Diabetes Mellitus Etiology, Pathogenesis, and the Role of Host Hormonal Dysfunction In the development and progression of T2DM, the primary role of environmental factors such as smoking, poor diet, and sedentary lifestyle is well known (Massey and Brown 2021). The genetic susceptibility has also been investigated and many genes setting hormonal regulation of glucose and lipid metabolism have been found associated with T2DM (Massey and Brown 2021). Many gene variants have been identified that increase the risk of T2DM by 10–30% (Herrema and Niess 2020); however, environmental factors play a predominant role in driving the progression of T2DM, being the most important the chronic nutrient excess (Massey and Brown 2021). As previously mentioned, both insulin secretory deficit and reduced tissue sensitivity to secreted insulin coexist in the development of T2DM. The concept of insulin resistance implies an insensitivity of the tissue to the action of the hormone itself, so the action of insulin on peripheral tissues including skeletal muscle, liver, and adipose is impaired (Scheithauer et al. 2020). This results in reduced insulinstimulated glucose disposal, insulin-induced suppression of hepatic glucose production, and lipolysis (Scheithauer et al. 2020). During the early phase of T2DM progression, pancreatic β cells can increase the production of insulin to counteract tissue insulin resistance. However, overtime pancreatic β cells exhibit reduced capacity for glucose-stimulated insulin secretion and, in some cases, die, leading to loss of insulin production and hyperglycemia (Massey and Brown 2021). When the body is unable to utilize all the nutrients it is receiving, the excess of nutrients is stored into adipose tissue leading to its expansion (adiposity). Therefore, the hyperglycemia caused by insulin resistance is associated with an increase in weight up to the development of obesity and dyslipidemia. Furthermore, when the lipid storage capacity of adipose tissue is exhausted, as in obese individuals, other nonadipose tissues can begin to store ectopic lipids: the liver and skeletal muscle. They can promote lipotoxicity and associated insulin resistance (Massey and Brown 2021) Fig. 1. Chronic low-grade local and systemic inflammation contributes to the development of insulin resistance and progression to T2DM. It is also called “meta-inflammation” and it has been linked to both impaired insulin secretion and action (Scheithauer et al. 2020). Inflammation is a biological response of the immune system triggered by exposure to pathogens, damaged cells, and toxic compounds. It could be acute or chronic and potentially lead to damage in several tissues. Adipose tissue contains a plethora of immune cells including resident monocytes-macrophage, T cells, eosinophils, and mast cells. All these cells are involved in the production of several inflammatory mediators, such as cytokines, of which secretion could be impaired in people with MS and could have negative effects on peripheral tissue metabolism (Scheithauer et al. 2020).

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Fig. 1 Pathophysiology of T2DM: Many factors can contribute to the development and progression of T2DM. Thus, β-cell dysfunction and insulin resistance, linked to abdominal obesity, coexist and lead to decreased insulin secretion, increased free fatty acids (FFA) and triglycerides (TG). There is impaired glucose tolerance with glucose and lipid toxicity leading to type 2 diabetes

M1 macrophages generally secrete pro-inflammatory cytokines and are associated with the development of T2DM. Some of these cytokines, called chemokines, attract immune cells to active metabolic tissues. For instance, the monocyte chemoattractant protein-1 (MCP1) has a strong chemoattractant action on monocyte-macrophages and its expression is increased in obese human adipose tissue and in rodents with increased infiltration of macrophages and induction of insulin resistance. Human pancreatic islets also secrete MCP1, suggesting a possible role of MCP1 in the pathogenesis of insulin resistance and beta cell dysfunction (Scheithauer et al. 2020). Another important cytokine is IL-1 β. Although it plays a physiological role in glucose metabolism, in T2DM, pancreatic islets are infiltrated by pro-inflammatory macrophages that induce the production of IL-1β through the NOD-, LRR-, and pyrin domain–containing protein 3 (NLRP3) inflammasome. Initially, IL-1β at low concentrations may be useful by promoting the proliferation of beta cells; however, chronically high concentrations could lead to beta cell failure. The mechanisms by which IL-1β mediates insulin resistance have been attributed, at least in part, to the downregulation of insulin receptor substrate 1 (IRS-1) and aberrant activity of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription factors and forkhead box O1 (FOXO1) (Scheithauer et al. 2020). Obesity activates resident liver macrophages, promoting the activation of altered inflammatory pathways. It also induces a pronounced increase in inflammatory infiltration in the liver. This leads to the production of inflammatory cytokines which generate insulin resistance (Scheithauer et al. 2020).

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Tumor necrosis factor alpha (TNFa) is mainly expressed and secreted by macrophages of adipose tissue and is increased in the circulation of people with T2DM and obesity, and its expression in adipose tissue correlates inversely with insulin sensitivity. Furthermore, within the pancreatic islets, the production of TNFa by macrophages determines the dysfunction of beta cells and can directly mediate the insulin resistance (Scheithauer et al. 2020). Regarding the mechanism by which TNFa impairs insulin sensitivity, it has been hypothesized that TNFa activates the intracellular kinase IκB (IKK) alpha and beta, leading to the activation of nuclear factor NF-κB and the transcription of inflammatory genes. Furthermore, TNFa can activate c-jun N-terminal kinase (JNK), which is a direct inhibitor of the insulin signaling pathway. Both IKK and JNK interfere with IRS1 and thereby disrupt insulin signaling (Scheithauer et al. 2020). IL-6 also appears to have physiological effects on pancreatic islets, but the underlying mechanism has yet to be revealed. IL-6 increases cytokine 3 signaling suppressor (SOCS3) activity, which inhibits several mediators downstream of insulin receptor signaling. This is the reason why chronically elevated levels of IL-6 decrease hepatic insulin sensitivity, induce hyperinsulinemia, and mediate insulin resistance of muscle tissue, in vitro and in murine models. In pancreas, the IL-6 receptor is mainly expressed in the endocrine portion, with higher levels in alpha cells. Increased IL-6 expression has been observed in the pancreatic islets of obese mice associated with alpha cell expansion, a typical histological feature of people with T2D (Scheithauer et al. 2020). M2 macrophages produce the anti-inflammatory cytokine IL10 which protects lean mice from insulin resistance. Its expression is reduced in obese mice and in people with T2DM and MS, suggesting a critical role of IL-10 in the prevention of inflammation in people with metabolic abnormalities. Also alterations in IL-4 levels, which promotes glucose tolerance, inhibits adipogenesis, and promotes alternative activation of macrophages, have been associated with T2DM susceptibility, as well as with IL-13 that promotes an alternative activation of macrophages (Scheithauer et al. 2020). Innate lymphoid cells (ILC) correspond to the innate counterpart of T cells but without T lymphocytes adaptive antigenic receptors. These cells protect barrier tissues from pathogens and maintain immune homeostasis in different tissue types. Additionally, some of the ILCs have cytotoxic features that are important for removing transformed cells and keeping macrophages in homeostasis. Hence, in conditions of high-fat diet (HFD), they kill the processed macrophages of the adipose tissue to maintain homeostasis. They are divided into five subsets: natural killer (NK) cells, ILC1, ILC2, ILC3, and lymphocyte tissue inducer (LTi) cells. HFD is associated with an increased number of NK and ILC1 cells that contribute to the obesity phenotype by promoting a pro-inflammatory environment. In fact, the proliferation of NK cells stimulates the production of interferon-γ (IFN-γ) which in turn stimulates the differentiation of macrophages and promotes insulin resistance. Conversely, the HFD reduces the number of ILC2 in adipose tissue, which are important for supporting metabolic homeostasis and for maintaining macrophages in an M2 phenotype (Scheithauer et al. 2020).

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About T helper lymphocytes (Th), the number of Th1-producing IFN γ in human visceral adipose tissue appears to support systemic inflammatory tone but is not associated with insulin resistance; on the other hand, Th2, with their antiinflammatory effect, have a negative correlation with insulin resistance (Scheithauer et al. 2020).

Role of Microbiota in the Onset of Metabolic Diseases The human gut microbiota can be considered an organ with important functions for human metabolism, digestion, maintenance of the intestinal barrier function, and immunomodulation. Additionally, it has been linked to many diseases not classically associated with microbes, such as metabolic diseases, rheumatoid arthritis, and psychiatric disorders (Herrema and Niess 2020). In fact, it is known that the gut microbiota contributes to modulating the host metabolism. Among the various factors contributing to the regulation of energy balance, the gut microbiota has received increasing attention due to its link with cardiometabolic disorders associated with overweight and obesity (Régnier et al. 2021). Studies conducted on germ-free (GF) mice have shown that GF mice have a lower percentage of body fat and a reduced accumulation of macrophages in white adipose tissue compared to healthy mice, characteristics that are reflected in a better regulation of glucose and insulin levels. Moreover, introduction of Escherichia coli into GF mice resulted in reduced glycemic regulation and M1 macrophages accumulation (Caesar et al. 2012). It has also been shown that a Western diet increased the intestinal abundance of Escherichia coli in mice. It has been also proven that microbiota-free mice were characterized by specific energy metabolism and resistance to diet-induced obesity. Furthermore, a causal association has been identified between the gut microbiota and the development of low-grade inflammation and insulin resistance associated with obesity and HFD (Cani et al. 2007b).

Effects of the Intestinal Barrier and the Immune System in the Control of the Gut Microbiota-Host Relationship It is known that there exists a symbiotic relationship between the gut microbiota and the human body, so microbes can survive in a favorable environment, and can participate in metabolic processes that humans alone are unable to carry out. In the regulation of coexistence and collaboration, gut barrier and immune system play a fundamental role (Régnier et al. 2021). Since gut microbes are found near gut epithelial cells, gut barrier prevents gut microbiota and its potent immunostimulant molecules from entering the circulation. However, it must also allow the absorption of essential nutrients and fluids (Régnier et al. 2021). The intestinal barrier is composed of several physical and chemical components. A single layer of epithelial cells, in which cells show densely packed

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microvilli (brush border) and are joined on the apical side by tight junction proteins (TJP), acts as a transcellular barrier. This monolayer is constantly renewed every 4–5 days and is covered with a protective layer of mucus impregnated with immune factors produced by the host. Both, the mucus layer and specific immune factors, help to keep gut microbes at some distance from the intestinal epithelial cells (Régnier et al. 2021; Herrema and Niess 2020). Innate immune system and the adaptive immune system are other important contributors in regulating the symbiotic relationship between gut microbiota and its host. Factors such as macrophages located under the epithelium that ingest and destroy pathogenic bacteria, or production of immunoglobulin A (IgA), which can prevent the interaction of antigens with epithelial cells, attenuate bacterial motility, growth, and adhesion to epithelial cells, inhibit bacterial penetration into mucus and mucous tissue, and promote the growth of beneficial bacteria (Régnier et al. 2021, Herrema and Niess 2020). In the rare event that all these barriers are overcome, and symbiotic microorganisms spread through blood vessels or portal veins, they are killed by macrophages in the spleen and Kupffer cells in the liver (Herrema and Niess 2020).

Potential Mechanisms of Microbiota Effects on Metabolism in the T2DM Patient Alteration of intestinal homeostasis, in terms of abnormal gut microbiota composition, metabolites produced, and altered gut barrier function, disturbs production and secretion of intestinal endocrine hormones, thus triggering metabolic diseases (Régnier et al. 2021). There are many mechanisms through and by which the gut microbiota can contribute to the onset and development of T2DM (Table 1).

Gut Barrier Alteration The gut barrier function is a very complex and multifaceted model and the alterations of this line of defense are the first signal that allows the penetration of bacteria contributing to a local inflammatory response (e.g., inflammatory bowel disease or IBD) and metabolic disorders (e.g., T2DM) (Régnier et al. 2021). Maintaining the integrity of gut barrier is critical for preventing various ailments and requires finely tuned mechanisms that also depend on microbial composition (Régnier et al. 2021; Herrema and Niess 2020). It was found that Bacteroides vulgatus and Bacteroides dorei, two species potentially beneficial for T2DM, upregulate the expression of tight junction genes in the colon leading to the reduction of intestinal permeability and lipopolysaccharide (LPS) production and improvement of endotoxemia in a mouse model. Akkermansia muciniphila reduces intestinal permeability by using extracellular vesicles that improve intestinal tight junctions by activating AMP-activated protein kinase (AMPK) in the epithelium, improves the expression of occludin and tight junction protein-1 (Tjp-1), and reduces systemic

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Table 1 Contribution of gut microbiota to the onset and the development of T2DM:MI Gut microbiota feature Gut barrier

Impairment Impaired integrity

Composition

Impaired

PAMPs

LPS increasing in plasma

Metabolites

Amino acid–related metabolites

Branched-chain amino acids Short-chain fatty acids

Bile acids

Neurotransmitters

Effect Increased intestinal permeability Endotaxemia Microbial translocation Low-grade inflammation Low-grade inflammation Reduced production of SCFA Dysregulation of the immune system Low-grade inflammation Modulation of GLP-1 secretion Decrease inflammation Impaired glucose tolerance and insulin signaling Insulin resistance Hypersecretion of insulin GLP-1 PYY secretion Reduced food intake Serotonin secretion Epigenetic effects GLP-1 secretion Immunosuppressive effects Regulators of lipid and cholesterol metabolism Improve beta cell function

levels of LPS (Chelakkot et al. 2018). Finally, Faecalibacterium and Roseburia have the potential to reduce intestinal permeability through the production of butyrate which interacts with serotonin transporters and peroxisome proliferator-activated receptor γ (PPAR-γ) pathways (Kinoshita et al. 2002). In humans with metabolic disorders, the altered composition of the microbiota along with a defective gut barrier has been suggested to facilitate the translocation of microbes, thus contributing to low-grade inflammation (Herrema and Niess 2020) (Fig. 2). Diet, particularly a Western diet, has been implicated in altering the gut barrier function in mice, resulting in the translocation of LPS into the circulation causing adipose and systemic inflammation in several animal models (Warmbrunn et al. 2020).

Gut Microbiota Composition It is difficult to define the characteristics associated with a healthy or diseased microbiota, due to the interindividual differences in both healthy and diseased populations. Although there is no clear definition of a healthy microbiota, that of a diseased population it is generally referred to as a “dysbiotic.” In the case of obesity and T2DM, microbiota composition can be altered and include both a reduced alpha diversity and an overrepresentation of microorganisms that are lacking in the healthy population (Herrema and Niess 2020).

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Normal gut barrier

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T2DM Impaired gut barrier

LPS and other PAMPS GLP-1 PYY SCFAs

Liver

Adipose tissue

Inflammation Oxidative stress Steatosis

Inflammation Oxidative stress Fat mass

Insulin sensitivity

Insulin sensitivity

Muscle tissue Inflammation

Brain Inflammation Food intake

Insulin sensitivity Insulin sensitivity

Fig. 2 T2DM impaired gut barrier: An impairment in gut barrier due to T2DM has important consequences such as the translocation of microbes and the production of microbial metabolites in plasma. It leads to a decrease in insulin sensitivity and an increase in inflammation, oxidative stress, fatty liver disease, fat mass and food intake by acting on the liver, adipose tissue, muscle tissue, and the brain

However, because microbiota configuration is regulated by many factors, notably diet, drug, environment, and genetic makeup of the host, it can be difficult to determine whether the observed altered microbiota composition is a cause or a consequence of a disease (Herrema and Niess 2020). Lifestyle and diet can induce dysbiosis, thus contributing to the development of obesity and T2DM, as shown by studies in mice fed a HFD, excess fat causes reduced insulin sensitivity, increased activation of the Toll-like receptor (TLR), and inflammation of the white adipose tissue, compared to mice that eat a diet rich in fish oil. The inflammatory phenotype that characterizes fat-fed mice has been linked to differences in the composition of their gut microbiota (Caesar et al. 2015). Surely, the composition of the microbiota in T2DM and obesity patients has some important differences compared to healthy ones. Conflicting evidence is reported regarding the subscription of specific genes. In some studies, bacteria considered proinflammatory because of carrying LPS on their cell wall, such as Escherichia coli, are more abundant in T2DM. On the other hand, bacteria such as Fecalibacterium prausnitzii, with anti-inflammatory properties attributable to the ability to produce short chain fatty acids (SCFA), are less abundant in obese and T2DM patients (Qin et al. 2012). An association study of the entire gut microbiota metagenome of 345 Chinese individuals showed that T2DM patients were characterized by a decrease in Eubacterium rectale, Faecalibacterium prausnitzii, and Roseburia intestinalis, which produce butyrate, and an increase in Bacteroides caccae, Clostridium hathewayi, Clostridium ramosum, Clostridium symbiosum,

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Eggerthella lenta, and Escherichia coli, which are opportunistic pathogens (Qin et al. 2012). Another study in a Nigerian population showed that patients with T2DM had a decrease in the butyrate-producing bacteria Clostridium butyricum, Cellulosilyticum ruminicola, and Clostridium paraputrificum and an increase in Desulfovibrio piger, Prevotella, Peptostreptococcus, and Eubacterium (Doumatey et al. 2020). The Lactobacillus genus shows the most discrepant results. This genus is very diverse and contains the largest number of operational taxonomic units (OTUs) in the human gut of all potentially probiotic bacteria. Its effects on T2DM appear to be species specific or even strain specific, which could explain the discrepancy of the results in the analyses. This phenomenon could be associated with the diversity of a given genus. The greater the number of strains that are found in the human gut for the single genus, the more strain-specific effects are observed (Gurung et al. 2020). Contrasting results also exist for the genera Ruminococcus, Fusobacterium, and Blautia (Gurung et al. 2020). Interestingly, several diversity indices and the Bacteroidetes/Firmicutes ratio, previously suggested as markers of metabolic diseases, did not show consistent associations with T2DM (Gurung et al. 2020). On the contrary, a possible pathogenetic and biomarker role of five phylogenetically distant genera has been suggested, decreasing in association with the development of T2DM: Bacteroides, Bifidobacterium, Roseburia, Faecalibacterium, and Akkermansia (Gurung et al. 2020). Bacteroides and Bifidobacterium represent the most frequently reported potentially protective genera in T2DM studies. In fact, almost all studies report a negative association between Bifidobacterium and T2DM (Gao et al. 2018), while only one article reported opposite results (Sasaki et al. 2013). In particular, some studies have found a negative association between specific species such as Bacteroides adolescentis, Bacteroides bifidum, Bacteroides pseudocatenulatum, Bacteroides longum, and Bacteroides denteum and diseases in patients treated with metformin or after undergoing gastric bypass surgery (Murphy et al. 2017). Furthermore, administration of Bacteroides acidifaciens and Bacteroides uniformis improved glucose intolerance and insulin resistance in diabetic mice (Cano et al. 2012). Roseburia, Faecalibacterium, and Akkermansia are constantly negatively associated with T2DM, although with conflicting evidence. In five case control studies, Roseburia was found in lower frequencies in the T2DM group than in healthy controls (Cano et al. 2012; Zhang et al. 2013; Salamon et al. 2018). Two case control studies reported lower frequencies in the T2DM group for Faecalibacterium (Salamon et al. 2018) and Faecalibacterium prausnitzii species was negatively associated with T2D in four out of five human case control studies (Zhang et al. 2013). However, administration of Faecalibacterium prausnitzii resulted in improved liver function and decreased inflammation of liver fat in mice with dietinduced metabolic disease without affecting blood glucose (Munukka et al. 2017). Finally, it has also been shown that Faecalibacterium was associated with diabetes remission after bariatric surgery (Murphy et al. 2017).

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Also Akkermansia muciniphila shows diverging data. Its beneficial effect on host glucose metabolism has been reported in some animal models, and the negative association between the abundance of this bacterium and T2DM has been reported in human studies (Zhang et al. 2013). One of the features consistently observed in multiple cohorts across different geographic locations is the reduction of butyrate-producing bacteria in individuals with T2DM such as Roseburia and Faecalibacterium prausnitzii (Arora et al. 2021). The positive association between butyrate microbial production and normoglycemia was supported by the results of a Dutch study that combined fecal microbiota metagenomics and human genome sequencing (Sanna et al. 2019). A possible protective role of butyrate-producing bacteria also in prediabetes is explored by a study conducted on a Danish cohort of subjects diagnosed with prediabetes. Compared to age- and sex-matched individuals with normal glucose regulation, prediabetes subjects exhibited a lower abundance of butyrate-producing bacteria (Allin et al. 2018).

Pathogen-Associated Molecular Models and Low-Grade Inflammation A dysregulated immune system is strongly associated with obesity and T2DM, and several inflammatory components directly alter glucose tolerance and insulin sensitivity. The microbiota itself, or through its components and metabolites, contributes to altering the immune system (Scheithauer et al. 2020). Low-grade inflammation is chronic inflammation that has no definite cause but, like mild acute inflammation, has elevated inflammatory markers that positively correlate with visceral adiposity and cardiovascular disease (CVD) risk both in healthy and T2DM subjects (Warmbrunn et al. 2020). Several studies, conducted in both animals and humans, have associated the low diversity and relative abundance of certain species of the gut microbiota with markers of systemic inflammation such as C-reactive protein (CRP) and cytokines, as well as the degree of inflammation within the white adipose tissue (Caesar et al. 2012; Cotillard et al. 2013). These data suggest that the intestinal microbiota can influence the host metabolism by modulating its inflammatory state according to its composition (Warmbrunn et al. 2020). Some constituents of the bacterial structure, called pathogen-associated molecular models (PAMPs), could be the key factors that trigger the onset of low-grade inflammation and insulin resistance, for instance flagellin and peptidoglycans in gram-positive or LPS of gram-negative bacteria (Régnier et al. 2021). Flagellin, a structural component of the flagellum of the bacterial locomotor appendage, also possesses immunomodulatory properties (Herrema and Niess 2020). LPS is a component of the cell wall of gram-negative bacteria. It has been proposed as a source for metabolic inflammation, since LPS was found to be increased in the circulation of people with diabetes and metabolic syndrome (Scheithauer et al. 2020). HFD regimen has been shown to increase circulating LPS in mice, thereby triggering inflammation of the adipose tissue and insulin resistance of the liver (Arora et al. 2021). Humans with T2DM have a similar metabolic phenotype with increased intestinal permeability and elevated levels of CRP, IL-6, and TNF-α (Warmbrunn et al. 2020).

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Two phenomena have been noted: “metabolic-endotoxaemia” that defines the increase in circulating endotoxins and other inflammatory markers after a meal, particularly meals rich in fat, and “postprandial inflammation” that describes an inflammatory response to increased systemic levels of endotoxins due to a leaky gut barrier (Scheithauer et al. 2020). LPS may trigger inflammatory reactions of the host, crossing the intestinal barrier with two mechanisms: bacterial translocation from the intestinal lumen to the bloodstream by loss of tight intestinal junctions, or direct transport of the bacteria into chylomicrons and lipoproteins after dietary lipid ingestion. Once circulating, LPS causes inflammation through activation of the innate immune system by recognizing pattern recognition receptors (PRRs) such as TLR 4, while flagellin is recognized by TLR 5. These pattern recognition receptors are mainly expressed on epithelial cells and innate immune cells and play critical roles in the activation of the immune response (Herrema and Niess 2020). It is interesting to note that the lipid domain A of the LPS is the most immunogenic part of the LPS and its composition varies between the different species of bacteria which can be immunogenic (Warmbrunn et al. 2020). The number of acyl chains in the lipid A domain, as well as the phosphate groups linked to the LPS, are related to the host inflammatory response, characterized by an increase in TNF-α levels and in the ratio of macrophages M1/M2 in white adipose tissue (Caesar et al. 2012). Interestingly, LPS and long-chain saturated fatty acids, such as palmitate, act synergistically in activating inflammatory signaling in macrophages, highlighting a link between a westernized diet rich in saturated fatty acids and postprandial inflammation (Scheithauer et al. 2020). Furthermore, administration of LPS was found to reduce glucose tolerance and glucose-stimulated insulin secretion in mice, in which activation of inflammatory pathways is considered a central mechanism in the development of the disease phenotype (Herrema and Niess 2020). Lots of inflammatory pathways are involved into regulation of glucose homeostasis and the gut microbiota could act as an immunomodulator, playing a critical role in human metabolism. Although it is essential for human health, its alteration can also have harmful consequences. Consequently, the gut microbiota has been named as a driver of the meta-inflammation observed in obesity and T2DM which are characterized by an altered composition of the gut microbiota (Scheithauer et al. 2020). Some species associated with a protective effect against T2DM like Roseburia, Bacteroides, Akkermansi, and Lactobacillus stimulate the production of antiinflammatory cytokines and chemokines, such as IL-10 and IL-22, improving insulin sensitivity and thus contributing to the improvement of glucose metabolism. Others, such as Bacteroides thetaiotaomicron, promote the differentiation of regulatory T cells by inducing TGF-b exposure and suppress intestinal inflammation (Hoffmann et al. 2016), Other Lactobacillus species, as well as Bacteroides and Akkermansia, inhibit the production of pro-inflammatory cytokines and chemokines such as IL-1b, IL-8, IL-6, and TNF-α preventing inflammation. Similarly, Roseburia and Faecalibacterium are butyrate producing bacteria and butyrate is also known to inhibit NF-kB activity (Kinoshita et al. 2002).

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Gut Microbiota Metabolites The gut microbiota produces a multitude of metabolites. Under normal conditions, they can enter the bloodstream, processed by endogenous enzymes and recognized by endogenous G protein–associated receptors (GPR) which mediate a wide range of effects on host metabolism (Régnier et al. 2021). In turn, the microbiome can modify endogenously generated metabolites in the gut (Herrema and Niess 2020). The increased exposure of host organs to microbial metabolites also has the effect of increasing the activation of the immune system, an event strongly correlated with the development of T2DM (Herrema and Niess 2020). Some of the gut microbiota metabolites act through GPR which activates downstream intracellular signal transducers; others bind nuclear receptors (NRs) that play a role in various physiological and pathological processes by acting as intermediaries between the gut microbiota and the host (Wang et al. 2021). NRs are a superfamily of ligand-binding transcription factors and mediators of various metabolic and signaling pathways. They include various members such as the farnesoid X receptor (FXR), the liver X receptor (LXR), the retinoid X receptor (RXR), the pregnane and xenobiotic receptor (PXR), the receptors activated by the proliferator of the peroxisome (PPAR), the constitutive androstane receptor (CAR), vitamin D receptor (VDR), and aryl hydrocarbon receptor (AHR) (Wang et al. 2021).

Amino Acid–Related Metabolites Some amino acids can be transformed by specific gut microbes into a variety of bioactive metabolites. Tryptophan (Trp), an essential aromatic amino acid, is the biosynthetic precursor of many microbial metabolites (Du et al. 2022). The metabolism of food-borne tryptophan occurs through two endogenous pathways (the kynurenine and serotonin pathways) and a microbiota-dependent pathway (the indole pathway). In the kynurenine pathway, tryptophan is metabolized into kynurenine and kynurenic acid, while in the serotonin pathway, tryptophan is metabolized into the neurotransmitter serotonin. In the indole pathway, gut-resident microbes use tryptophan as a nitrogen source and thus also produce indole (Herrema and Niess 2020). Trp can be metabolized into indole by various bacterial species, such as Bacteroides thetaiotaomicron, Bacteroides ovatus, Clostridium limosum, and Clostridium bifermentans (Du et al. 2022). Indole binds the transcription factor AHR that detects xenobiotic chemicals, such as aromatic aryl hydrocarbons, and indole by facilitating various functions in the gastrointestinal tract, such as intestinal motility, pathogen overgrowth, and lipid absorption by inhibition of IL-22 production through vasoactive enteric neurons expressing vasoactive intestinal peptide (VIP) during nutrient uptake (Herrema and Niess 2020). AHR is a regulator of inflammation and immune metabolism especially in the central nervous system, intestinal barrier, lymphatic system, and alcoholic hepatitis, through mechanisms involving the protein 9 containing the caspase recruitment domain (CARD9), IL-22, or the lymphocyte activation gene 4 – gene 3 differentiation cluster (CD4 + -LAG-3) (Wang et al. 2021).

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Furthermore, it has been reported that indole could modulate glucagon-like peptide 1 (GLP-1) secretion from intestinal enteroendocrine L cells, which in turn stimulates insulin secretion and inhibits glucagon secretion, thus contributing to glucose homeostasis in the postprandial phase (Du et al. 2022). Recently, it has been found that the reverse occurs with prolonged exposure to indole. This disparity in acute versus chronic effects of indole on GLP-1 secretion observed ex vivo may be suitable for regulating GLP-1 levels in vivo in response to fluctuations in the proteinor carbohydrate-enriched diet (Arora et al. 2021). Trp can also be converted into indolpropionic acid (IPA) using a deamination reaction by some gut microbes, such as Clostridium botulinum, Clostridium caloritolerans, and Clostridium paraputrificum. IPA is a bioactive compound that has effects on intestinal barrier integrity and glucose homeostasis and has been described as inversely related to type 2 diabetes in humans (Régnier et al. 2021). In the Finnish Diabetes Prevention Study, higher serum IPA was associated with a reduced likelihood of developing T2DM. The beneficial effect of IPA on T2DM could be mediated by reduced inflammation, or by the preservation of β-cell function (de Mello et al. 2017). Trimethylamine (TMA) is another microbial metabolite derived from amino acids and it is relevant in the development of T2DM. TMA is produced by the gut microbiota from dietary choline, carnitine, or betaine and from phosphatidylcholine. Phosphatidylcholine is found mainly in meat, fish, and eggs, but it can also be produced by the liver. However, its dietary intake is required to produce high levels of trimethylamine-N-oxide (TMAO). These amino acids are hydrolyzed by the intestinal microbiota TMA lyase into TMA. This metabolite then enters the portal circulation and flavin monooxygenases (FMO), mainly FMO3, oxidize the TMA in the liver to TMAO. TMAO can then enter the circulation and will eventually be excreted by the kidneys (Régnier et al. 2021; Warmbrunn et al. 2020). Although TMAO has been implicated in the regulation of inflammatory pathways and endoplasmic reticulum stress (both of which are relevant to the development of T2DM), evidence of a role for TMAO in T2DM is scarce. However, because most people with T2DM will develop cardiovascular disease, we assume that TMAO may also play a role in T2DM (Herrema and Niess 2020). In fact, TMAO has been linked to the development of cardiovascular disease in humans and mice through mechanisms including hyperreactivity of blood platelets, decreased reverse cholesterol transport, and accumulation of cholesterol in macrophages (Herrema and Niess 2020). Indeed, many clinical findings support a close link between elevated TMAO levels and increased risk of T2DM. A study based on 2694 participants demonstrated a positive association between plasma concentrations of TMAO and T2DM in the Chinese population (Shan et al. 2017). Similarly, an observational study involving 475 subjects showed that an elevated plasma TMAO is a strong marker of risk in diabetes (Lever et al. 2014). A clinical study reported that plasma TMAO concentration was significantly increased in subjects with T2DM and higher TMAO levels were associated with an increased risk of major adverse cardiac events and mortality in T2DM patients (Tang et al. 2017). At the molecular level, TMAO appears to be associated with increased mRNA expression of the pro-inflammatory cytokine MCP1 and reduced mRNA expression

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of the anti-inflammatory cytokine IL-10 (Gao et al. 2014). Recently, a study reported that TMAO could promote metabolic dysfunction and hyperglycemia by selectively activating endoplasmic reticulum protein kinase R-like (PERK) (Chen et al. 2019). TMAO has also been correlated with renal inflammation and fibrosis in diabetic rats through increased levels of transforming growth factor-β1 (TGF-β1) and its downstream molecule smooth muscle α-actin (α-SMA) promoting renal fibrosis; it also activates NLRP3 to release IL-1β and IL-18 and promoting renal inflammation. These results indicate that TMAO appears to be harmful to T2DM and its complications (Du et al. 2022). Another microbially produced amino acid–derived metabolite involved in the development of insulin resistance is imidazole propionate (ImP), which results from the degradation pathway of histidine. It is increased in T2DM subjects and impairs glucose tolerance and insulin signaling in HFD-fed mice by a mechanism involving inhibition of IRS through activation of the p38y/p62/mTORC1 pathway (Régnier et al. 2021). These results indicate that imidazole propionate may contribute to the development of insulin resistance impairing hepatic signaling of insulin and ultimately lead to the onset of T2DM (Du et al. 2022). Tyrosine can be metabolized into tyramine by tyrosine decarboxylase of gut microbes such as Ruminococcus gnavus, Enterococcus faecalis, and Clostridium sporogenes. Some results suggest that tyramine may be a beneficial microbial metabolite for T2DM. A recent study found that tyramine concentrations in patients with MS were significantly lower than those in the control group (Patel et al. 2019). It was also negatively correlated with multiple biomarkers of inflammation and cardiometabolic risk factors (Du et al. 2022). In a recent human study of terminal cardiometabolic disease in obese and hyperglycemic subjects, the microbial metabolite 4-cresol is negatively correlated with T2DM (Herrema and Niess 2020). Although 4-cresol can be derived directly from food, it is also a product of the fermentation of tyrosine and phenylalanine in colon. Subcutaneous administration of 4-cresol prevented the development of hyperglycemia and fatty liver in HFD-fed mice (Brial et al. 2020), and in diabetes models reduced obesity, glucose intolerance, and liver fat, improved glycemic control, increased insulin secretion, and stimulated islet density and pancreatic β-cell proliferation (Brial et al. 2020). Branched-Chain Amino Acids (BCAAs) and Aromatic Amino Acids Insulin resistance and the risk of developing T2DM have been strongly associated with BCAAs and aromatic amino acids, respectively. BCAAs have been implicated in the short- and long-term regulation of insulin secretion by pancreatic β-cells and inducing hypersecretion, an important hallmark of T2DM. Aromatic amino acids mechanically alter insulin signaling in muscle skeletal and therefore reduce the absorption of glucose (Herrema and Niess 2020). Individuals with insulin resistance exhibit altered microbiota composition and altered microbiota-derived metabolite profile with higher levels of circulating BCAAs (Warmbrunn et al. 2020). Interestingly, interventions on a healthy diet (i.e., a moderately restrictive diet enriched in fiber) improve gut microbiota diversity and metabolic alterations. Furthermore, an effective intervention in pre-diabetic patients can modify the microbiota and

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the profile of its metabolites, increasing the synthesis capacity of SCFA and catabolism of BCAAs (Liu et al. 2020). An increase in BCAA catabolism could therefore be beneficial for host metabolism, as the high levels of BCAAs found in insulin resistance conditions have been associated with a reduced catabolic capacity of white adipose tissue, liver tissue, and skeletal muscle. The identification of microbiota-derived metabolites through metabolomics comparing subjects with T2DM and without diabetes showed increased plasma BCAA levels in individuals with insulin resistance, which in turn correlates with specific bacterial species (Provatella copri and Bacteroides vulgatus) in their gut microbiota (Pedersen et al. 2016). Short-Chain Fatty Acids (SCFA) Nondigestible carbohydrates, such as dietary fiber and resistant starches, are anaerobic fermented by bacteria in cecum and colon to produce SCFA, which are organic fatty acids composed of 1–6 carbon atoms. The main SCFA derived from gut microbiota are acetate, propionate, and butyrate, which represent about 95% of the total amount (Du et al. 2022) and are present in the lumen of colon in a molar ratio of 3: 1: 1 (Herrema and Niess 2020). These three SCFA have different roles: butyrate is a preferred energy source for colonic epithelial cells and increases mucus production and tight junction protein expression; acetate is used as a precursor to fatty acids or for cholesterol synthesis; and propionate is the primary substrate for gluconeogenesis (Warmbrunn et al. 2020). SCFA act as mediators in several pathways, including local, immune, endocrine functions, and microbiota-gut-brain communication (Portincasa et al. 2022) They are also implicated in the regulation of glucose homeostasis, modulation of satiety, the browning of white adipose tissue, and the accumulation of fat (Du et al. 2022). Some SCFA-sensitive receptors have been identified, such as the GPR41 receptor (free fatty acid receptor (FFAR 3), GPR43 (FFAR2), and GPR109A (niacin receptor 1 [NACR1]) (Herrema and Niess 2020). These receptors are expressed in colonocytes, enterocytes, and many other cells in the body, including neural cells (Arora et al. 2021). SCFA also interact with PPAR pathway. They are a series of nuclear receptor subfamilies including PPARα, β, γ, and δ activated by peroxisome proliferators and fatty acids. They are essential regulators that play key roles in various physiological and pathological processes related not only to lipid and fatty acid metabolism and tumor generation, but also to glucose metabolism, inflammation, and immunology. Mediating host-microbiome gut interaction, they also contribute to enteric epithelial homeostasis (Wang et al. 2021). PPAR are also involved in lipid and glucose metabolism and, therefore, associated with obesity and diabetes. PPARγ and PPARα could modulate the cross talk between some microbial and host species, as in the case of Bacteroides involved in the regulation of diseases related to glucose and lipid metabolism (Wang et al. 2021). After recognition with a specific receptor, butyrate and propionate can activate the signaling pathway of extracellular signalregulated kinase (ERK) which has the ultimate effect of PPARγ phosphorylation. Activated PPARγ targets angiopoietin-like 4 (ANGTPL4) and adipose

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differentiation–related protein (ADRP), through which it could inhibit the expression of nitric oxide synthase 2, reducing the synthesis of luminal nitric oxide and thus inhibiting the dysbiotic expansion of bacteria such as Enterobacteriaceae (Wang et al. 2021). Acetate has been reported to inhibit the accumulation of body fat and liver lipids by upregulating PPARα genes and fatty acid oxidation–related proteins in the liver (Du et al. 2022). The main action of SCFA on metabolism is to modulate glucose homeostasis by promoting the production of satiety hormones and modifying inflammatory responses (Herrema and Niess 2020; Régnier et al. 2021). In fact, SCFA stimulate the release of peptide YY (PYY) and GLP-1 from enteroendocrine L cells and the release of the satiety hormone leptin from adipose tissue by activating GPR41 or GPR43 (Du et al. 2022). At the level of enteroendocrine L cells, SCFA-mediated receptor stimulation is independent of the bile acid pathway involving the ileal membrane receptor G bile acid receptor 1 (GPBAR-1) (Arora et al. 2021). Consequentially, they promote insulin secretion, improvement of insulin sensitivity, increase in energy expenditure, and reduction of glycolysis and gluconeogenesis pathways, fat accumulation through lipid oxidation, and systemic and adipose tissue inflammation (Du et al. 2022). The reduction in glycolysis, also evoked in skeletal muscle, causes an accumulation of glucose-6-phosphate with an increase in glycogen synthesis (Portincasa et al. 2022). Through these hormones, SCFA also affect appetite and food intake by acting on the microbiota-gut-brain axis via systemic circulation or vagal afferents. In fact, GLP-1 slows down gastric emptying and intestinal transit, which, together with GLP-1, acts directly on the appetite centers and evokes satiety. Furthermore, SCFA stimulate the production of serotonin by intestinal enterochromaffin cells, evoking further effects on intestinal motility that contribute to the sense of satiety (Arora et al. 2021). SCFA also have epigenetic effects, through the inhibition of histone deacetylases (HDACs) with consequent hyperacetylation of histones in the lysine residues present in the nucleosome, whereby the expression of approximately 2% of all mammalian genes is repressed (Herrema and Niess 2020; Arora et al. 2021). This pathway is expressed in the gut, associated immune tissue, peripheral nervous system, and central nervous system and allows SCFA to influence the function of intestinal immune T cells 3 (Arora et al. 2021). Their anti-inflammatory properties are also due to inhibition of NF-κB pathway (Régnier et al. 2021). Bile Acids (BA) BA have long been implicated in human metabolism regulation and in the development of metabolic diseases including T2DM. Bile acids are produced from cholesterol in the liver and secreted as primary bile acids in the small intestine, where they aid in the absorption of dietary lipids and vitamins. The most abundant primary bile acids in humans are chenodeoxycholic acid (CDCA) and cholic acid and are derived from conjugation with glycine, to reduce toxicity and increase solubility in bile. In mammals, conjugation preferably occurs with taurine in mammals, but since humans usually ingest small amounts of taurine, most bile acids are conjugated to glycine in

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humans (Herrema and Niess 2020, Régnier et al. 2021). Bile is temporarily stored in the gallbladder until food intake triggers its release into the duodenum. Once secreted in the intestine, 95% of primary bile acids are reabsorbed by enterocytes, mainly in the ileum. The conversion of the remaining primary bile acids into secondary bile acids takes place in colon thanks to the microorganisms present. This pathway, called enterohepatic circulation, is controlled by a negative feedback circuit in which the BA themselves regulate their own production (Régnier et al. 2021). The conversion of primary bile acids to secondary bile acids includes a multitude of biotransformations in which different intestinal bacteria are required. The type of composition of the intestinal microbiota therefore has an important influence on the production of secondary bile acids (Régnier et al. 2021). These hydrophobic molecules readily diffuse into the circulation to bind to a wide range of receptors, such as the G protein–bound receptor TGR5 and the farnesoid X receptor (FXR), both of which have a high affinity for hydrophobic BAs [1]. BAs allow the absorption of lipids and fat-soluble vitamins, and act as key regulatory molecules that influence other metabolic pathways such as lipid, energy, and glucose metabolism (Régnier et al. 2021). Activation of Takeda G protein coupled receptor (TGR5) could be helpful in improving T2DM. The binding of BAs to TGR5, expressed on the basolateral side of EEC, increases their secretion of GLP-1 in a dose-dependent manner (Arora et al. 2021). In mice, TGR5 has been shown to induce GLP-1 secretion from intestinal L cells, to be immunosuppressive and to increase energy expenditure (Herrema and Niess 2020). Furthermore, TGR5 is also expressed in pancreatic α cells and can cause the conversion of proglucagon to GLP-1 with the release of GLP-1 (Du et al. 2022). TGR5 can also promote mitochondrial uncoupling in brown adipose tissue in vitro and increase its activity and whole-body energy expenditure in vivo. Furthermore, activation of TGR5 also stimulates the conversion of inactive into active thyroxine, which affects the metabolism of the whole body (Callender et al. 2022). FXR is a subset of receptors that includes metabolic regulators such as vitamin D receptor (VDR), chimeric antigen receptors (CAR), pregnane X receptor (PXR), and liver X receptor α (LXRα). The physiological ligands of FXR are BAs, and being a transcription factor, FXR can bind to DNA as a monomer or heterodimer with RXR and regulate the expression of the target gene. In this way it modulates various physiological activities such as cholesterol catabolism, liver regeneration, inflammation, and glucose metabolism. FXR is expressed in the liver and intestine and regulates the synthesis of BAs through a negative feedback mechanism by acting on the cholesterol-limiting enzyme 7 – a hydroxylase (Arora et al. 2021). FXR and the gut microbiome have a reciprocal influence and regulation relationship in the gut. The interaction between microbiota and FXR appears to be involved in the development of T2DM. Zhang et al. demonstrated that FXR activation increased hepatic glycogen synthesis and significantly improved hyperglycemia and hyperlipidemia in diabetic mice (Zhang et al. 2006). In terms of mechanisms, the activation of intestinal FXR can induce the release of fibroblast growth factor 15 (FGF-15) in mice and FGF-19 in humans, which can stimulate hepatic glycogen

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synthesis and suppress hepatic gluconeogenesis by inhibiting the pathway cAMP response element-binding protein (CREB) – peroxisome-proliferator-activated receptor gamma coactivator (PGC-1α). One of the key mechanisms is the FXR-GLP-1 pathway (Wang et al. 2021). In addition to interacting with transcription factors, FXR also dialogues with TGR5 and SCFA receptors to regulate GLP-1 levels. Administration of the double activating agonist of both TGR5 and FXR increases GLP-1 levels and improves hepatic metabolism in mice, suggesting a potential positive interaction of FXR5 with TGR5 in the regulation of GLP-1 (Arora et al. 2021). Furthermore, BAs are also key regulators of lipid and cholesterol metabolism (Aydin et al. 2018). A gut restricted FXR agonist (fexaramine) has been found to promote browning of adipose tissue and reduce obesity, inflammation, and insulin resistance in mice (Fang et al. 2015). Activation of the nuclear FXR receptor has also been associated with reduction of body weight and decrease of inflammatory markers in mice (Fang et al. 2015). Therefore, FXR is an important factor that, through regulation of the expression or activation of other receptors and through cross talk with the gut microbiota, serves to regulate GLP-1 levels. Thus, its effects are complex, as it can increase or decrease the expression of hormones, depending on the interaction with other receptors (Arora et al. 2021). However, there are conflicting results regarding the role of this receptor. In fact, several studies have reported that inhibition of FXR also has a beneficial effect on glucose metabolism, obesity, and diabetes (Trabelsi et al. 2015). Therefore, both activation and inhibition of FXR have been shown to have beneficial effects on obesity and insulin resistance. A possible explanation for this could lie in the fact that FXR can be activated as well as inhibited by BA (Herrema and Niess 2020; Callender et al. 2022). In addition to their role in glucose and lipid homeostasis, BAs possess digestive properties and bactericidal action. They modify the relative abundance of different bacterial phyla and promote microbial diversity, favoring BA-resistant bacteria (Callender et al. 2022). Dysbiosis in cardiometabolic patients could therefore also lead to a reduction in the anti-inflammatory effects of bile acids (Warmbrunn et al. 2020). Neurotransmitters Many bacteria are also capable of producing mammalian neurotransmitters, such as dopamine, noradrenaline, serotonin, or γ-aminobutyric acid (GABA). GABA has been implicated in glucose homeostasis and has been shown to improve beta cell function (Herrema and Niess 2020). 5-hydroxytryptamine (5-HT), also called serotonin, is an important Trp catabolite. Although 5-HT can be produced in the brain, 95% is synthesized in the gut so the gut microbiota plays a key role in gut production of 5-HT. As a multifunctional signaling molecule, 5-HT plays various roles in human health and disease (Du et al. 2022). In recent years, many studies have shown that 5-HT is closely related to the development of T2DM, but the specific role of 5-HT in T2DM is still controversial (Watanabe et al. 2016).

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Effects of Microbiota on Glucose Metabolism, Fatty Acid Metabolism, and Energy Expenditure The gut microbiota can influence the development of T2DM by affecting glucose homeostasis, insulin sensitivity in major metabolic organs (liver, muscle, and fat), digestion of sugars, and the production of intestinal hormones (Gurung et al. 2020). Some taxa are associated with an antidiabetic effect. For example, Bifidobacterium lactis increases the synthesis of glycogen, decreases the expression of genes related to hepatic gluconeogenesis, and promotes the translocation of the glucose transporter-4 (GLUT4) and therefore the uptake of glucose stimulated by insulin. Lactobacillus gasseri BNR17 also increases the expression of GLUT-4 in muscle (Gurung et al. 2020). Lactobacillus casei improves insulin resistance by increasing the mRNA level of phosphatidylinositol-3-kinase (PI3K), the substrate of the insulin receptor substrate 2 (IRS2), AMPK, serine/threonine kinase Akt2, and the synthesis of glycogen in the liver; it also has a hypoglycemic effect through a bile acid-chloride exchange mechanism that involves the upregulation of multiple genes, and reduces the insulin degrading enzyme (IDE) in Caco-2 cells and insulin-like growth factor-3 (IGFBP-3) in white adipose tissue (Gurung et al. 2020). Lactobacillus rhamnosus increases the level of adiponectin in epididymal fat, thereby improving insulin sensitization (Gurung et al. 2020). Some species of Lactobacillii and Akkermansia muciniphila have potent α-glucosidase inhibitory activity that prevents the breakdown of complex carbohydrates and reduces postprandial hyperglycemia (Gurung et al. 2020). Bifidobacterium and Lactobacillus express a bile salts hydrolase, producing secondary BAs capable of stimulating TGR5 and inducing the production of GLP-1 (Gurung et al. 2020). Some bacteria modulate the metabolism of fatty acids, increasing their oxidation and reducing their synthesis. This results in an increase in energy expenditure which improves obesity and consequently T2DM (Gurung et al. 2020). For example, Akkermansia muciniphila increases the levels of 2-oleoylglycerol (2-OG), 2-palmitoylglycerol (2-PG), and 2-acylglycerol (2-AG) in the adipose tissue which increases the oxidation of fatty acids and the differentiation of adipocytes (Gurung et al. 2020). Bacteroides acidifaciens also improves the oxidation of fatty acids in adipose tissue via the TGR5-PPAR-a pathway (Gurung et al. 2020). Lactobacillus gasseri reduces obesity by increasing the expression of fatty acid oxidation genes and reducing that of genes related to their synthesis (Gurung et al. 2020).

Cardiometabolic Diseases The definition of cardiometabolic diseases (CMD) encompasses several diseases, affecting different organs, which share similar risk factors and phenotypes that are in turn influenced by a combination of environmental and genetic factors (Warmbrunn et al. 2020).

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These conditions include T2DM, MS, obesity, NAFLD, and pathologies related to atherosclerosis like CVD. They represent a serious global health problem characterized by increasing prevalence and high morbidity (Warmbrunn et al. 2020). In 2012, 2.2 million deaths occurred globally from complications of T2DM, primarily due to CVD. CVD is still the leading cause of death worldwide, with 17.6 million deaths in 2016 representing 48% of overall global mortality, mainly due to atherosclerosis (Warmbrunn et al. 2020). CMD are characterized by intestinal microbial dysbiosis, and the gut microbiota could therefore have predictive value for assessing the susceptibility of individuals to develop CMD (Warmbrunn et al. 2020; Callender et al. 2022). CMD are accumulated by a chronic low-grade inflammation, probably induced by dysbiosis and bacterial translocation. Although the interaction between the local immune system and bacteria can help support the inflammatory tone in CMD, it is mainly caused by the passage of bacterial components such as LPS and flagellin in the blood and the consequent activation of the immune system in different tissues (Callender et al. 2022). Low gut microbiome diversity has been shown to be associated with obesity and a higher prevalence of insulin resistance, NAFLD, and low-grade inflammation (Aydin et al. 2018). Furthermore, low bacterial diversity was characterized by pro-inflammatory properties, related to the reduction of butyrate-producing bacteria and an increase in mucid grading bacteria (Cotillard et al. 2013. These features potentially compromise intestinal integrity by causing low-grade inflammation through endotoxemia. Low-grade inflammation of visceral adipose tissue may provide a link between obesity and insulin resistance (Aydin et al. 2018). Atherosclerosis is a chronic inflammatory disease caused by damage to endothelial cells with subsequent formation of atherosclerotic plaque (Callender et al. 2022). Diabetic dysregulation of circulating lipids (i.e., dyslipidemia) may lead to lipid accumulation in artery walls and dysregulated blood pressure increasing the risk for major adverse cardiovascular events (Massey and Brown 2021). A link between the gut microbiota, its metabolites, and atherosclerosis has been demonstrated. Indeed, some studies show that increased plasma endotoxin levels are linked to the development of CVD and consecutive injections of endotoxin appear to accelerate the accumulation of cholesterol and the consequent plaque in some animal models (Caesar et al. 2010). To explain how the intestinal microbiota can influence the mechanisms underlying atherosclerosis, a link has been hypothesized with the alteration of its composition. For example, some evidence supports Porphyromonas gingivalis’ role in activating macrophage in carotid plaques, although specific antibiotic treatment did not reduce the incidence of CVD events in a high-risk cohort (Figuero et al. 2011). In some studies, coronary heart disease patients showed a different microbiotic profile than healthy controls, more enriched in Lactobacillales and less in phylum Bacteroidetes such as Bacteroides and Prevotella (Emoto et al. 2016). Furthermore, individuals with atherosclerosis appear to show a reduction in butyrate-producing bacteria such as Roseburia intestinalis and Faecalibacterium prausnitzii and an increase in Enterobacteriaceae such as Escherichia coli, Klebsiella spp., and Enterobacter aerogenes compared to healthy controls (Jie et al. 2017).

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The intestinal microbiota and its metabolites seem to influence the risk of CVD through various pathways involving bile acids, SCFA, TMAO, and the metabolism of lipids and glucose. A negative effect of BAs on atherosclerosis probably mediated by GLP-1 has been established. Activation of their specific TGR5 receptor can promote GLP-1 secretion, which reduces monocyte adhesion and atherosclerotic lesions (Callender et al. 2022). TMAO was found to be associated with atherosclerosis and major adverse cardiovascular events. In two large cohort studies performed in patients with coronary angiography-proven plaques, elevated TMAO levels were associated with major adverse cardiovascular events during follow-up (Wang et al. 2014). There is evidence that a TMAO threshold of approximately 6 μM can be a good predictor of adverse cardiac events. Furthermore, increased levels of FMO3, the most important enzyme in TMAO production, are associated with increased plasma TMAO levels in mice. Consequently, FMO3 silencing reduced plasma TMAO levels, revealing FMO3 as a potential target to regulate TMAO levels (Bennett et al. 2013). The increase in TMAO levels appears to affect various aspects of lipid metabolism and lead to an increase in platelet hyperreactivity, which can occur as a complication of atherosclerosis. Increased TMAO levels have been shown to reduce reverse cholesterol transport, increase its accumulation in macrophages, vascular dysfunction, and endogenous inflammation in mice (Seldin et al. 2016). However, the role of TMAO is not yet fully understood. In fact, it may be a surrogate marker of atherosclerosis rather than being a driver of it. Further studies are needed to elucidate the full mechanistic effect of TMAO on atherosclerosis. Obesity derives from a positive energy balance, causes insulin resistance, an essential aspect of the multifactorial pathology of T2DM, and leads to the infiltration of immune cells into adipose tissue, thus contributing to low-grade inflammatory tone (Callender et al. 2022). T2DM-related obesity and inflammation are mediated by LPS, a known activator of the innate immune system through binding to TLR4 and subsequent activation of the intracellular NF-κB signaling pathway. The final effect of this pathway is to activate JNK which inhibits the phosphorylation of IRS by inducing insulin resistance (Callender et al. 2022). In obese individuals, this pro-inflammatory stimulation is added to the action of adipocytes that secrete the MCP-1 attracting macrophages. Activated adipocytes and macrophages secrete cytokines such as IL-1β, IL-6, and TNF-α which also drive insulin resistance through several mechanisms including activation of serine/threonine kinases with subsequent activation of JNK and protein kinase C (PKC) (Callender et al. 2022). The role of gut microbiota composition remains central in the development of CMD. Low bacterial diversity is generally associated with insulin resistance, dyslipidemia, and adiposity (Callender et al. 2022). Additionally, obese individuals with low bacterial diversity have a higher risk of gaining weight and lose less weight on a moderately restrictive diet than obese individuals with high bacterial diversity (Cotillard et al. 2013). It has been suggested that the gut microbiota of obese subjects

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extracts more energy from the diet due to the alterations in the relative abundance of Firmicutes and Bacteroidetes compared to individuals with high bacterial richness (Callender et al. 2022). Studies have evaluated the role of the gut microbiota by comparing phenotype variations in mice undergoing fecal microbiota transplant (FMT). The transfer of the microbiota from obese mice to lean mice induced an obese phenotype in the latter (Callender et al. 2022). In a study on genetically obese ob/ob mice, they induced a 50% reduction in Bacteroidetes and an increase in Firmicutes compared to their lean brethren (Ley et al. 2005). These ob/ob mice showed an increased ability to harvest energy from ingested food and this trait appeared to be transmissible to germ-free mice with FMT resulting in increased obesity (Turnbaugh et al. 2006). These results have also been confirmed in humans (Aydin et al. 2018). The composition of the gut microbiota and the proportions of SCFA are correlated with obesity. Some obese mouse models show increased propionate levels compared to lean mice with a higher proportion of Bacteroidetes (Schwiertz et al. 2010). In other models, GPR43-deficient mice become obese when fed a normal diet, while GPR43-overexpressed mice remain lean even when consuming an HFD (Kimura et al. 2013). Nonalcoholic fatty liver disease (NAFLD) is another disease classified on the CMDs spectrum. NAFLD is a highly prevalent condition characterized by accumulation of hepatic fat and inflammation. It can progress to nonalcoholic steatotic hepatitis (NASH), cirrhosis, and hepatocellular carcinoma. The incidence of this spectrum of diseases is increasing and is expected to continue to do in the coming years. Furthermore, it is one of the main indications for liver transplants (Callender et al. 2022). It is characterized by an ectopic fat accumulation in the liver because of adipose tissue insulin resistance and then immune activation from extracellular nutrients contributes to its development and progression (Massey and Brown 2021). Diet and the gut microbiota play an essential role in the progression of NAFLD, and a key role is played by the interaction of the immune system with the gut microbiota (Callender et al. 2022). The development of fatty liver is associated with the composition of the intestinal microbiota. For example, a choline-deficient diet in humans modulates intestinal bacteria with altered levels of Gammaproteobacteria and Erysipelotrichi (Spencer et al. 2011). An observational study in humans found that primary BAs were associated with fibrosis in subjects with NASH due to an increase in Bacteroides and Lactobacilli strains in the microbiome that can deconjugate BA (Kwan et al. 2020). Another study in mice showed that lower secondary BA concentrations lead to less activation of FXR, and ultimately to higher serum levels of triglycerides and glucose (Chávez Talavera et al. 2017). Downregulation of FXR appears to be involved in the development of NAFLD, leading to downregulation of butyrate-generating microbes and thereby decreasing levels of butyrate, a regulator of liver inflammation (Wang et al. 2021). An important role seems to be also played by PPAR which regulates the expression of IL-22 and is involved in the differentiation process of anti-inflammatory M2 macrophages (Wang et al. 2021).

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Contribution of Microbiota to the Success of Drug Therapy for T2D Therapeutic options for T2DM vary and imply a nonpharmacological and pharmacological approach. Specifically, lifestyle modification includes improving diet to restrict excess nutrient intake and increasing physical activity. The best strategy to maintain intestinal barrier integrity is to adopt a healthy nutritional status, as some dietary patterns have been shown to be associated with improved health (e.g., a Mediterranean or Western diet), while diets high in fat and sugars depleted of some nutrients, such as zinc, glutamine, and tryptophan, could compromise the integrity of the intestinal barrier (Régnier et al. 2021). The long-term effects of a diet rich in carbohydrates or animal proteins and fats shape the composition of the gut microbiota. Indeed, obesity has been associated with reduced gut microbiota diversity, altered relative abundance of the main phyla Firmicutes and Bacteroidetes, and the presence of low-grade inflammation in both mice and humans (Cotillard et al. 2013; Ley et al. 2005). Even if gut microbiota composition can change rapidly when exposed to large and rapid changes in diet, short-term dietary interventions have failed to change the main characteristics and classification of the microbiota. Only long-term eating habits can significantly shape the composition of the intestinal microbiota (Aydin et al. 2018). In evaluating the effect of diets on intestinal flora, the high interpersonal variance must be considered given the individualized nature of intestinal microbiota (Aydin et al. 2018). Dietary fructose, if consumed in significant quantities, can reach the intestinal microbiota of the colon which metabolizes it into toxic metabolites such as glycerate. Therefore, the reduction of fructose consumption or the improvement of the colonic catabolism of fructose in the diet by altering the composition of the intestinal microbiota in an ameliorative sense takes place, reducing NAFLD (Callender et al. 2022). Fiber in the diet can affect glucose metabolism. Furthermore, dietary fiber modulates the microbial composition and bacterial production of some metabolites, such as SCFA, with final effects on glucose metabolism. Fiber viscosity, water solubility, and fermentation rate are important properties in this regard (Portincasa et al. 2022). In line with this evidence, a diet enriched in fiber, thanks to a prebiotic effect, promote an enrichment in SCFA-producing microbial groups, playing a role in the risk of developing T2DM. Therefore, the consumption of whole grains, legumes, fruits, and vegetables should be increased in patients with prediabetes or diabetes (Portincasa et al. 2022). It remains to be seen whether soluble and insoluble fiber can affect the microbiota differently on glucose homeostasis (Portincasa et al. 2022). Over the years, interventions with fermentable fibers, such as inulin, fructo-oligosaccharides, flaxseed, or resistant starch in animal models, have shown to improve glucose tolerance through an increase in GLP-1 or PYY, thanks to the modulation of the intestinal microbiota and in particular the production of SCFA (Arora et al. 2021). The integration of

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nonfermentable fibers, such as cellulose, does not seem to affect the level of GLP-1 (Arora et al. 2021). This shows that not all interventions on dietary fibers report an increase in intestinal hormones, due to the multifactorial nature of the diet-microbiota interactions which depends on the characteristics of the cohort, the fermentability of dietary fibers, and the duration of the interventions. Furthermore, the extent of fermentation and the consequent improvement in glycemic control depend on the basic interpersonal differences in the gut microbiota they determine (Arora et al. 2021). Short-term low-carbohydrate diets are associated with a reduction in SCFA and SCFA-producing microbial species. Furthermore, ketogenic diets could also dramatically affect the composition of the gut microbiota (Rondanelli et al. 2021). The importance of iron as a substrate was demonstrated in rats: iron deficiency correlated with lower levels of cecal SCFAs, such as propionate and butyrate, compared to rats with sufficient iron. A greater abundance of Lactobacilli and Enterobacteriaceae was observed as well as a significant decrease in the abundance of Roseburia spp./Eubacterium rectale group, and the iron replenishment significantly increased cecal butyrate concentrations (Dostal et al. 2012). In recent decades, it has become clear that many innate metabolic, inflammatory, and immune mechanisms are also coordinated by diet-derived lipids. However, they also act as proinflammatory ligands, binding to nuclear receptors such as PPARs and LXR. The binding with the three members of the PPAR family also improves the action of insulin and suppresses the production of pro-inflammatory cytokines such as TNF-α (Aydin et al. 2018). Diet-derived lipids can also bind G-coupled protein receptors such as GPR43 which, when activated by food-derived acetate, directly reduces lipolysis in adipocytes leading to a decrease in plasma levels of free fatty acids. This suggests a potential therapeutic role for GPR43 in the regulation of lipid metabolism (Fang et al. 2015). A study in mice showed that dietary resveratrol could alter the risk and progression of atherosclerosis by modifying the composition of the gut microbiota. In fact, it increases the levels of Bifidobacterium and Lactobacillus which convert conjugated BA into unconjugated. Additionally, resveratrol lowered TMAO levels by reducing trimethylamine production (Attaye et al. 2020). Pharmacological treatment is represented by a set of options that have been considerably updated over the years, making some previously widely used drugs obsolete (Massey and Brown 2021). In general, drugs used for the therapy of T2DM are recombinant insulin peroxisome proliferator-activated receptor gamma agonists (“glitazones”), sulfonylureas, metformin, sodium-glucose cotransporter-2 inhibitors (SGLT2i), GLP-1 receptor agonists, and slow or fast acting human insulin analogs (Massey and Brown 2021). Certain antidiabetic agents are known to modulate the microbiota and improve diabetes. Similarly, the underlying microbiota can positively and negatively affect the pharmacokinetics and pharmacodynamics of drugs and numerous chemicals through a variety of mechanisms. Several studies have been reported on the ability of the intestinal microbiota to modulate the effect of antidiabetic therapy (Gurung et al. 2020).

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Metformin, used in the first line of treatment in newly discovered T2DM patients, affects the composition of the gut microbiota in diet-induced obese mouse models, where it led to the enrichment of Akkermansia muciniphila, and in large human cohorts in which the intestinal microbiota underwent an enrichment of the butyrate producers and in some cases also of Akkermansia muciniphila. It has been assumed that the change in gut microbiota could be linked to some of the antidiabetic effects of metformin (Arora et al. 2021). A recent study looked at the effects of a probiotic Bifidobacterium animalis ssp. lactis 420, prebiotic polydextrose, and their combination with sitagliptin in diabetic mice. The combination of sitagliptin with pre- and probiotics was effective in reducing several T2DM parameters (Stenman et al. 2015). A similar study in diabetic rats has showed that the combination of prebiotic polysaccharides with the antidiabetic drugs metformin and sitagliptin reduced hyperglycemia and adiposity compared to using the drugs alone (Reimer et al. 2014). In another study, streptozotocin-induced diabetic mice were treated with a combination of a prebiotic and metformin. Improvements in fasting glucose, glucose tolerance, and insulin resistance were observed with combination therapy, compared with metformin alone (Zheng et al. 2018). In the common case where T2DM develops in obese subjects, surgical options such as bariatric surgery can show dramatic improvements in diabetic complications (Massey and Brown 2021). Interestingly, the attenuation of T2DM following bariatric surgery is accompanied by a sharp increase in GLP-1 levels, which may be due to the rapid release of nutrients to the distal intestine, where there is a high density of L cells, or changes in the composition of the microbiota (Arora et al. 2021). Indeed, it is known that Roux-en-y gastric bypass (RYGB) and vertical sleeve gastrectomy (VSG) increase the total level of BAs and vary their composition, probably affecting both TGR5 and FXR signaling in L cells. GLP-1 appears to be an important effector of bariatric surgery (Arora et al. 2021). The gastric sleeve procedure increases the levels of endogenous cholic acid-7-sulfate in the intestine, that is a BA agonist of TGR5, and induces the production of GLP-1 (Massey and Brown 2021). Bariatric surgeries, such as RYGB and VSG, show superior weight reduction and better regulation of glucose in obese individuals with T2DM. Both are associated with changes in gut microbiota composition in humans, and GF mice transplanting microbiota using feces or intestinal contents obtained from individuals or rodents operated on by RYGB or VSG partially transfer post-bariatric surgery phenotypes as a reduction of adiposity and improvement of glucose tolerance (Arora et al. 2021).

Conclusion The gut microbiome has emerged as a targeted organ with the potential to alter disease development. This is important considering the ever-increasing global prevalence of T2DM, which still needs clinical prevention and treatment (Herrema and Niess 2020).

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Considering the associations between differences in gut microbiota composition and CMD, modifying gut microbial composition could reduce the risk of CMD morbidity and mortality. Greater diversity of gut microbial alpha is known to be associated with improved health outcomes (CMD). Therefore, influencing the gut microbial composition could be an effective and novel method to reduce morbidity and mortality due to CMD (Callender et al. 2022). The butyrate production potential of the gut microbiome seems to be exhausted already in the prediabetes state, which may suggest that replenishment of butyrate producers or butyrate levels could be important to delay or prevent progression to T2DM (Arora et al. 2021). In addition to the rules for a correct lifestyle and therapy with hypoglycemic drugs, the restoration of intestinal levels of butyrate could represent a new therapeutic route for T2DM (Arora et al. 2021). Probiotics, nextgeneration probiotics, and fiber supplements could be effective strategies for increasing butyrate-producing bacteria and improving both hyperglycemia and insulin resistance, but their effects could depend on the preexisting gut microbiota. There may therefore be responders and nonresponders (Arora et al. 2021). Changing the gut microbiome composition with prebiotics has also been shown to affect portal-dosed GLP-1 levels, resulting in modulation of food intake, followed by a decrease in body weight and fat mass (Cani et al. 2007a). The technique of human-to-human FMT has recently been used. When insulinresistant individuals receive duodenal infusion of their own fecal microbiota (autologous transplant) or fecal microbiota from a lean healthy donor (allogeneic transplant), insulin sensitivity was improved in the allogeneic group (Kootte et al. 2017; Vrieze et al. 2012). In both studies, improvement in phenotype was observed 6 weeks after transplant, but not 18 weeks after FMT (Kootte et al. 2017). In addition, a change in butyrate producers (such as Roseburia, Eubacterium, and Butyrivibiro) in the feces and small intestine was found in both, but only in the allogeneic group increased butyrate values were found (Vrieze et al. 2012). Overall, FMT studies indicate that while this procedure may improve insulin sensitivity in the short term, the host’s gut microbiota is resilient enough to outweigh the foreign microbial community in the long term. Furthermore, although FMT is generally associated with mild side effects, major adverse events have also been reported (Baxter and Colville 2016). These observations question the feasibility and applicability of FMT as a treatment method for T2DM. FMT appears promising for restoring the gut microbiota and improving insulin sensitivity, but it is impractical to perform such a highly invasive procedure in humans for short-term benefits (Arora et al. 2021). Performing FMT from lean human donors to obese human recipients with metabolic syndrome has been shown to improve insulin sensitivity (Aydin et al. 2018).

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Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C, et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring). 2010;18:190–5. https://doi.org/10. 1038/oby.2009.167. Seldin MM, Meng Y, Qi H, Zhu WF, Wang Z, Hazen SL, et al. Trimethylamine N-oxide promotes vascular inflammation through signaling of mitogen-activated protein kinase and nuclear factorkappaB. J Am Heart Assoc. 2016;5:e002767. https://doi.org/10.1161/JAHA.115.002767. Shan ZL, Sun TP, Huang H, Chen SJ, Chen LK, Luo C, et al. Association between microbiotadependent metabolite trimethylamine-N-oxide and type 2 diabetes. Am J Clin Nutr. 2017;106: 888–94. https://doi.org/10.3945/ajcn.117.157107. Spencer MD, Hamp TJ, Reid RW, Fischer LM, Zeisel SH, Fodor AA. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology. 2011;140:976–86. https://doi.org/10.1053/j.gastro.2010. 11.049. Stenman LK, Waget A, Garret C, Briand F, Burcelin R, Sulpice T, et al. Probiotic B420 and prebiotic polydextrose improve efficacy of antidiabetic drugs in mice. Diabetol Metab Syndr. 2015;7:75. https://doi.org/10.1186/s13098-015-0075-7. Tang WH, Wang ZN, Li XS, Fan YY, Li DS, Wu YP, et al. Increased trimethylamine N-oxide portends high mortality risk independent of glycemic control in patients with type 2 diabetes mellitus. Clin Chem. 2017;63:297–306. https://doi.org/10.1373/clinchem.2016.263640. Trabelsi MS, Daoudi M, Prawitt J, Ducastel S, Touche V, Sayin SI, et al. Farnesoid X receptor inhibits glucagon-like peptide-1 production by enteroendocrine L cells. Nat Commun. 2015;6: 7629. https://doi.org/10.1038/ncomms8629. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31. https:// doi.org/10.1038/nature05414. Vrieze A, Van Nood E, Holleman F, Salojarvi J, Kootte RS, Bartelsman JF, et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology. 2012;143:913–6.e7. https://doi.org/10.1053/j.gastro.2012.06.031. Wang Z, Tang WH, Buffa JA, Fu X, Britt EB, Koeth RA, et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur Heart J. 2014;35:904–10. https://doi.org/10.1093/eurheartj/ehu002. Wang Z, Chen WD, Wang YD. Nuclear receptors: a bridge linking the gut microbiome and the host. Wang et al. Mol Med. 2021;27:144. https://doi.org/10.1186/s10020-021-00407-y. Warmbrunn MV, Herrema H, Aron-Wisnewsky J, Soeters MR, Van Raalte DH, Nieuwdorp M. Gut microbiota: a promising target against cardiometabolic diseases. Expert Rev Endocrinol Metab. 2020;15:13–27. https://doi.org/10.1080/17446651.2020.1720511. Watanabe H, Nakano T, Saito R, Akasaka D, Saito K, Ogasawara H, et al. Serotonin improves high fat diet induced obesity in mice. PLoS One. 2016;11:e0147143. https://doi.org/10.1371/journal. pone.0147143. Zhang YQ, Lee FY, Barrera G, Lee H, Vales C, Gonzalez FJ, et al. Activation of the nuclear receptor FXR improves hyperglycemia and hyperlipidemia in diabetic mice. Proc Natl Acad Sci U S A. 2006;103:1006–11. https://doi.org/10.1073/pnas.0506982103. Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One. 2013;8:e71108. https://doi.org/10.1371/journal. pone.0071108. Zheng J, Li H, Zhang X, Jiang M, Luo C, Lu Z, et al. Prebiotic mannan-oligosaccharides augment the hypoglycemic effects of metformin in correlation with modulating gut microbiota. J Agric Food Chem. 2018;66:5821–31. https://doi.org/10.1021/acs.jafc.8b00829.

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Gut Microbiome in Dyslipidemia and Atherosclerosis Andreas Puetz and Ben A. Kappel

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota Composition in Dyslipidemia and Arthrosclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota Composition in Dyslipidemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota Composition in Atherosclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reduced Bacterial Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbiota-Related Mechanisms Influencing Dyslipidemia and Atherosclerosis . . . . . . . . . . . . . Microbiome-Related Influence on Lipid Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Influence of Dietary Lipids on the Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbiota-Dependent Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chronic Inflammation and Immunomodulatory Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atherothrombotic Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endocannabinoid System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-HETE, C18-3OH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of Drugs on the Intestinal Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Classical risk factors for cardiovascular disease, such as dyslipidemia, arterial hypertension, obesity, diabetes mellitus, smoking, and genetic predisposition, are well-studied, and their impacts on the pathogenesis of atherosclerotic disease remain beyond question. Recently, intestinal microbiota became of more interest in cardiovascular research. Their crucial role in the pathogenesis of obesity has been elucidated previously. Over the last two decades new scientific findings indicate a further influence of the gut microbiome on lipid metabolism and A. Puetz · B. A. Kappel (*) Department of Internal Medicine 1, University Hospital Aachen, RWTH Aachen University, Aachen, Germany e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_10

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atherosclerosis. Alterations in the microbiota composition can be observed in patients with dyslipidemia as well as with atherosclerosis. The intestinal microbiota influence lipid metabolism and also directly contribute to atherosclerosis. Here, microbiota-derived metabolites, such as TMAO or short chain fatty acids (SCFA), are of particular interest as mediators in the crosstalk between microbiome and host. An impact on adaptive and innate immune response and inflammatory pathways linked to atherosclerosis has also been described. This chapter will enlighten interactions of the intestinal microbiome with lipid metabolism, atherosclerosis, and subsequently cardiovascular disease. Keywords

Atherosclerosis · Cardiovascular disease · Dyslipidemia · Lipids · Cholesterol · Microbiome · Microbiota · Bacteria · Gut · Intestine

Introduction Atherosclerosis is a disease of the arterial wall It ultimately results in pathological accumulation of lipids and subsequent inflammatory processes leading to chronic ischemia by arterial obstruction. Due to endothelia dysfunction leukocytes migrate into the intima of the artery. By the uptake of lipids these leukocytes transform into so-called foam cells. Local inflammation caused by cytokine release promotes accumulation of extracellular matrix, which eventually leads to the formation of atherosclerotic plaques (Libby 2021). Atherosclerotic plaques and subsequent stenosis of blood vessels cause chronic ischemia, for example in peripheral arterial disease. Rupture of plaques may result in partial or complete occlusion of the arterial vessel due to arteriothrombosis. This may eventually result in acute life-threatening diseases such as myocardial infarction, peripheral arterial embolism, and ischemic stroke. Therefore, atherosclerosis is a major cause of morbidity and mortality all over the world and especially in western countries. However, also in developing countries the prevalence is rising in the last years (Libby 2021). Established risk factors for atherosclerosis are age, arterial hypertension, smoking, obesity, diabetes mellitus, chronic kidney disease, and dyslipidemia. Dyslipidemia is defined as a deviation of measurable blood lipids compared to individuals with normal lipids. Since hyperlipidemia occurs more often than hypolipidemia, hyperlipidemia and dyslipidemia are generally used as equivalents. Dyslipidemia is known to be one of the mayor risk factors for atherosclerosis. Here, especially cholesterol is crucial for the pathogenesis of cardiovascular disease. It is either synthesized in the liver or resorbed in the intestine. Because of their lipophile structure transportation of cholesterol and other lipids in the blood stream is mainly facilitated through lipoproteins. Lipoproteins in the human body are represented by chylomicrons, very low-density lipoprotein (VLDL), low-density lipoproteins (LDL), high-density lipoprotein (HDL), and LP (a). Very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) are the lipoproteins transporting cholesterol to their target organs. LDL is a major driver of

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atherosclerosis and known to predominantly contribute to the formation of foam cells and atherosclerotic plaques. Non-HDL cholesterol, which is total cholesterol minus HDL cholesterol, represents the actual risk factor and causal contributor to cardiovascular disease (Libby 2021). In contrast, high-density lipoprotein (HDL) is able to transport cholesterol back to the liver. High HDL levels are considered to be athero- and cardioprotective (Libby 2021). However, cholesterol is not the only lipid facilitating to atherosclerosis. Also other lipids may play a role. For example, hypertriglyceridemia is an independent risk factor for cardiovascular disease. However, pharmacological reduction of triglycerides by fibrates did not result in a reduction of major adverse cardiovascular events (MACE). In contrast, cholesterol lowering drugs, represented by statins, ezetimibe PCSK-9 inhibitors and bempedoic acid, are effective to reduce MACE in cardiovascular high-risk patients (Libby 2021). Beyond the above-mentioned classical risk factors for atherosclerosis and dyslipidemia, recently other “new” risk factors such as work-related factors and psychological influences have been discussed (Libby 2021). As for many other diseases, the intestinal microbiome with its enormous metabolic impact became of interest. The human body hosts a large number of bacteria in different surfaces. Within the intestine more than 36,000 different bacterial species, which harbor more than 10 million different microbial genes, have been described. This largely exceeds the number of genes of the human genome. The amount of genes indicate that bacteria have metabolic functions vaster than the host and may contribute to the metabolism of the human organism, i.e., by generation of metabolites, synthesis of essential amino acids, as well as vitamins. The intestinal microbiome is considered to have an enormous metabolic potential with the ability to influence the host metabolism. Therefore, the intestinal microbiome has emerged as important contributor to the pathogenesis of cardiometabolic diseases. Studies from Gordon’s group showed the connection between intestinal bacteria and metabolic diseases and underlined the metabolic potential of the intestinal microbiome. The intestinal bacterial flora from overweight mice was transplanted into germ-free recipient mice. Independently of the diet, the recipient mice developed obesity and insulin resistance. These results suggest that the obese phenotype is at least partly transferred by the intestinal microbiome. A similar relationship could be shown in studies of the microbiome structure in human monozygotic twins (Turnbaugh et al. 2006). The composition of the intestinal microbiome is variable and altered by factors such as age, diet, genetic background, antibiotics, and coexistent disorders. Dysbiosis is referred to a disproportion of the bacterial composition within the microbiome and is linked to a pathological phenotype. Dysbiosis has been associated with metabolic diseases such as obesity, type 2 diabetes mellitus, and cardiovascular disease. In this chapter, the impact of the microbiome on lipid metabolism and atherosclerosis will be reviewed. In the first part, gut microbiota composition in dyslipidemia and atherosclerosis will be described. Microbiota-related mechanisms influencing the pathogenesis of both conditions will then be discussed.

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Fig. 1 Alterations of the intestinal microbiome in dyslipidemia and atherosclerosis

Gut Microbiota Composition in Dyslipidemia and Arthrosclerosis Several studies have been performed to unravel alterations in the gut microbiota composition in atherosclerosis and dyslipidemia. Notably, individuals with cardiovascular disease also frequently exhibit dyslipidemia. Therefore, alterations in the intestinal microbiome in these conditions are at least partwise overlapping. In this chapter we show the “shared” as well as “unique” alterations of the microbiome in both diseases (Fig. 1).

Gut Microbiota Composition in Dyslipidemia Alterations in the intestinal microbiome composition can be observed in dyslipidemia patients as well as in animal models. Dyslipidemia is strongly connected to obesity and high fat diet (HFD) which is known to induce changes in the intestinal microbiome composition (Libby 2021; Turnbaugh et al. 2006). In obese patients, the composition of the phyla Bacteroidetes and Firmicutes is altered with an increased Firmicutes/Bacteroidetes ratio compared to healthy individuals (Turnbaugh et al. 2006). Recent findings indicate similar findings in individuals with hypercholesterinemia (Kappel et al. 2023). In diabetic mice with dyslipidemia a depletion of bacteria that are known to produce short chain fatty acids (SCFA, see section “Short Chain Fatty Acids (SCFA)”), such as Eubacterium rectale, Clostridium ramosum, and Faecalibacterium prausnitzii, has been described. These animals showed an upregulation of possible opportunistic pathogens such as Clostridia and Eggerthella lenta instead eventually leading to an enhanced gut-derived inflammation (Wang et al. 2020). Indeed, in hyperlipidemic rats higher abundance of lipopolysaccharide (LPS)-producing bacteria and bacteria known to be able to damage the intestinal mucosa (e.g., Bilophila, Akkermansia muciniphila, and Sutterella) could be detected. This might lead to enhanced systemic inflammation (see section “Chronic Inflammation and Immunomodulatory

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Mechanisms”). Interestingly, these effects were attenuated by administration of the probiotic bacterium Lactobacillus plantarum (Song et al. 2017). These results were at least partly translatable into humans. Higher abundance of LPS-producing bacteria (Escherichia coli and Enterobacter cloacae) was present in fecal samples of patients with hyperlipidemia. In contrast, eubacteria (e.g., Bifidobacterium, Lactobacillus, and Faecalibacterium prausnitzii) were diminished (Moreno-Indias et al. 2016). High serum-lipid levels are linked to depletion of commensal bacteria in the gut (e.g., Bacteroidetes and Clostridia) as shown by a human cohort including 893 subjects. In the same study, a significant correlation between serum levels of high-density lipoprotein (HDL) as well as trigylcerides and microbiota composition was discovered. Here, Eggerthella positively correlated with triglycerides but negatively with HDL. The intestinal microbiome composition explained differences between individuals regarding LDL, HDL, and BMI independently of age and sex (Fu et al. 2015).

Gut Microbiota Composition in Atherosclerosis While in the past gut microbiome research mainly focused on microbiome composition of metabolic disorders, such as obesity and insulin resistance, less investigations focused on atherosclerosis. Jie et al. compared the gut microbiome composition of 218 patients with cardiovascular disease to 187 healthy control subjects. Similar to findings in dyslipidemia, patients with atherosclerosis revealed higher abundance of LPS-producing enterobacteria (e.g., Escherichia coli, Klebsiella spp., and Enterobacter aerogenes) and opportunistic pathogens such as Eggerthella lenta. In addition, SCFAproducing bacteria, such as Roseburia intestinalis and Faecalibacterium prausnitzii, were reduced (Jie et al. 2017). Ruminococcus gnavus was also upregulated in patients with atherosclerosis. Previously, this bacterium revealed a strong association to inflammatory bowel disease. Therefore, it potentially plays a role in atherosclerosis by facilitating inflammation (Jie et al. 2017). Comparing symptomatic to asymptomatic patients with atherosclerosis, Actinobacteria were elevated in symptomatic disease, whereas defined Clostridia species, Lachnospiraceae, and Eubacteriaceae, were more prominent in asymptomatic patients (Lindskog Jonsson et al. 2017). In a longitudinal cohort study including more than 5,000 participants, measurements of bacterial DNA in the blood stream via 16SrRNA PCR was used as a surrogate marker for alterations in the intestinal microbiome. Here, circulating bacterial DNA in the blood stream of the phylum Proteobacteria correlated positively with cardiovascular events (myocardial infarction, ischemic stroke) within the follow-up period of 9 years. In contrast, DNA of bacteria contained in the physiological gut flora (e.g., Bacteroidetes) showed a negative correlation. These findings suggest a potential influence of a reduced bacterial diversity resulting in depletion of commensal bacteria such as Bacteroidetes and Firmicutes and higher abundance of species of different phyla such as Proteobacteria (Amar et al. 2013). Interestingly, the abundance of members of the oral microbial flora (e.g., Streptococcus spp., Lactobacillus salivarius) within the intestinal microbiome is higher in

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patients with atherosclerosis. DNA of streptococci can also be found in atherosclerotic plaques itself. Patients with periodontal disease are known to exhibit more cardiovascular events than healthy individuals. Therefore, a potential crosstalk of the oral and intestinal flora might be present in atherosclerosis patients. Enhanced systemic inflammation and direct effects of bacterial DNA might both contribute to atherosclerotic heart disease (Jie et al. 2017). Taken together, these findings underline the strong association of microbiome alterations and atherosclerosis potential displaying a proatherogenic gut flora.

Reduced Bacterial Diversity Reduced bacterial diversity is present in dyslipidemia as well as atherosclerosis. Menni et al. showed on more than 600 female patients an association between reduced bacterial diversity and arterial stiffness measured by pulse-wave velocity, as marker for endothelial dysfunction and atherosclerosis. Moreover, the influence of reduced bacterial diversity on arterial stiffness in this study was bigger than the influence of classical atherosclerosis risk factors such as visceral obesity and insulin resistance (Menni et al. 2018). Microbial diversity/richness also showed significant negative correlation with body mass index (BMI) and triglycerides levels and a positive correlation to HDL in a human cohort of 893 human subjects. Interestingly, no significant correlation to LDL was present in this cohort (Fu et al. 2015). Microbial diversity in the intestine is reduced by antibiotic treatment. These medication-dependent alterations of the microbiome have been shown to be longlasting and partly persistent after the termination of drug administration. Reduction of bacterial richness can still be observed long after last application of the antibiotic drug. Interestingly, long-term use of antibiotics has been shown to be an independent predictor for cardiovascular events among women indicating a possible influence of the intestinal microbiome (Heianza et al. 2019). In an Apolipoprotein E knockout (ApoE / ) mouse model antibiotic treatment largely reduced bacterial diversity in the gut and augmented atherosclerotic lesion burden. In the same study, patients with atherosclerosis patients exhibited diminished levels of commensal bacteria, such as Lachnospiraceae, Ruminococcaceae, Porphyromonadaceae, and Prevotellaceae. In this study a negative correlation between atherosclerotic lesion size and bacterial richness/diversity could be observed (Kappel et al. 2020). These discoveries are in accordance with investigations of the gut flora in other (partly related) metabolic diseases. Reduced microbial diversity has also been described for patients in type 2 diabetes mellitus (Libby 2021). Even though, the previous discussed findings indicate a reduction of microbial diversity in atherosclerosis, there are also studies showing contrary results. No difference in gene richness or microbial diversity could be detected for example in a study comparing stool samples from healthy controls and patients with atherosclerotic cardiovascular disease (Jie et al. 2017).

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Microbiota-Related Mechanisms Influencing Dyslipidemia and Atherosclerosis Different pathomechanisms linking the intestinal microbiome to cardiovascular disease have been described in the past decades. In this chapter we will first describe mechanisms of microbiota interaction with lipid metabolism in general. In addition, unique or shared microbiota-associated mechanism on dyslipidemia and/or atherosclerosis, i.e., gut-derived metabolites and immunomodulation, will be discussed in depth (Fig. 2).

Microbiome-Related Influence on Lipid Metabolism The intestinal microbiome has been shown to have an enormous impact on human lipid metabolism. Dyslipidemia, especially hypercholesterolemia, remains a major risk factor for atherosclerosis. Alterations in dyslipidemia of the intestinal

Fig. 2 Microbiota related mechanisms influencing dyslipidemia, atherosclerosis and cardiovascular disease

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microbiome composition have been described in patients and mice (see section “Microbiome-Related Influence on Lipid Metabolism”). Therefore, a functional influence of the gut flora on lipid metabolism can be assumed. Serum lipid levels are to some extend dietary dependent. However, the gut microbiome has been shown to affect lipid resorption in the intestine (Fu et al. 2015; Jie et al. 2017; Kappel et al. 2020). Several mechanisms on bacterial cholesterol metabolism have been described. Deconjugation of bile acids by bile-salt hydrolase is dependent on specific bacteria of the gut flora, e.g., Lactobacilli. Distinct Lactobacillus strains are also capable of incorporation and conversion of cholesterol to coprostanol, leading to lower cholesterol serum levels. These strains may therefore be promising candidates for probiotic treatment in hypercholesterinemia (Lye et al. 2010). Several members of the microbiome are known to tolerate higher levels of bile acids which have in gerenal toxic effects on microorganisms. This might lead to microbiome-dependent differences in serum cholesterol levels due to varying bile acid deconjugation. Cholesterol assimilation was discovered ex vivo for Lactobacillus fermentum, Bifidobacterium infantis, Streptococcus bovis, Enterococcus durans, Enterococcus gallinarum, and Enterococcus faecalis (Pereira and Gibson 2002). Modified bile acids (secondary bile acids) are resorbed in the intestine and have impact on the cardiovascular system (see section “Bile Acids”). Different animal studies were performed to investigate the influence of the intestinal microbiome on lipid metabolism. In vivo data from mouse models revealed a microbiome-dependent elevation of lipid and especially cholesterol levels in the serum (Kappel et al. 2020). Le Roy et al. also showed serum cholesterol elevation due to transplantation of human microbiome from a high-cholesterol individual into mice (Le Roy et al. 2019). While some groups reported elevations in serum cholesterol in germ-free mice compared to conventionally raised ApoE / mice fed a chow diet, this result was not present under a Western-type diet (Jonsson et al. 2018). Similar results were revealed in a low-density lipoprotein receptor knockout (LDL-R / ) mouse model (Kiouptsi et al. 2019). However, some study groups found no alterations in serum cholesterol in germ-free conditions neither on chow diet nor Western diet (Kasahara et al. 2017). Oral, largely non-absorbable antibiotics have been shown to largely diminish bacterial quantity as well as diversity in the gut. Also, an increase of serum cholesterol induced by broad-spectrum antibiotics in ApoE / mice and in LDL-R / mice could be observed (Kappel et al. 2023; Le Roy et al. 2019). A reduction of intestinal cholesterol transporter Npc1l1 mediated by diminished plant sterols by antibiotic treatment was a suggested mechanism (Haghikia et al. 2022; Kappel et al. 2023). Haghikia et al. identified a propionate-dependent IL-10 upregulation. This subsequently resulted in suppression of Npc1l1, which is an important intestinal cholesterol transporter (Haghikia et al. 2022). The microbiome in obese patients is known to display the capacity for an increased energy harvest leading to reinforcement of the obese phenotype (Turnbaugh et al. 2006). Increased energy harvest in obesity might lead to enhanced absorption of lipids, subsequently causing dyslipidemia. Alterations of the

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microbiome by antibiotics in mice have been shown to shift the composition to an atherogenic phenotype. Here, lipid and bile acid metabolism were among the most affected metabolic pathways. In particular, saturated long-chain fatty acids and hydroxy fatty acids were enhanced due to antibiotics administration (Kappel et al. 2020). Functional analyses of the microbiome of atherosclerosis patients revealed a higher capacity in metabolism of glycolipids and the degradation of fatty acids whereas the synthesis of short-chain fatty acids (SCFA), which are known to have anti-inflammatory function, was impaired (Haghikia et al. 2022; Jie et al. 2017).

Influence of Dietary Lipids on the Gut Microbiota Interestingly, not only gut microbiota have an influence on lipid metabolism. Also, lipids contained in the diet may shape the composition of the intestinal microbiome vice versa. Fatty acids for example have antibacterial properties, such as lysing bacterial cell membranes and inhibiting ATP production. These antibacterial effects are influenced by different characters of the lipids, such as carbon chain length and saturation. Not only antibiotic properties of fatty acids but also metabolic pathways impact microbiota composition. Anaerobic bacteria are by definition unable to generate energy via beta-oxidation but can still metabolize fatty acids through anaerobic pathways. In animal models, dietary supplementation of saturated longchain fatty acids was able to counteract dysbiosis induced by alcohol and subsequent stabilization of the intestinal gut barrier. Mechanistically, this was due to an increase of Lactobacillus bacteria, which are able to metabolize saturated long-chain fatty acids (Schoeler and Caesar 2019). Furthermore, the interaction of bacteria with fatty acid double bonds leads to microbiome-dependent metabolites that can either be beneficial or maleficent for the host lipid metabolism and other atherosclerosislinked pathways. An important example for this mechanism is represented by conjugated linoleic acid. Linoleic acid is an essential fatty acid and therefore completely diet-dependent. Conjugated linoleic acid is either able to improve or to worsen insulin sensitivity and atherosclerosis dependent on the synthesized isomer. Thus, bacteria producing these isomers may also impact the aforementioned diseases (Schoeler and Caesar 2019).

Microbiota-Dependent Metabolites The number of bacteria and subsequent bacterial genes outcompetes the amount of human genes by far. Therefore, effects of gut bacteria on the host can be expected and potentially impact several metabolic diseases. Particularly, a vast majority of metabolites are produced by gut bacteria, which exhibit local effects as well as enter the circulation. Not only the uptake of several essential amino acids and vitamins but also different other metabolites have been described to be microbiota-dependent. Some of them are additionally modulated in the liver resulting in microbiota-host-

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co-metabolites. Microbiota-dependent metabolites are known to play a role in dyslipidemia as well as in atherosclerotic/cardiovascular disease. In this section the most important microbiota-derived metabolites will be described.

Bile Acids Primary bile acids are synthesized in the liver. Their formation is a complex process which can be carried out via two pathways: the classical and the alternative (acidic) pathway. The enzyme of the classical pathway is cholesterol 7a-hydroxylase (CYP7A1). In the alternative pathway, sterol-27-hydroxylase (CYP27A1) is the predominant enzyme. The intestinal microbiome regulates the expression of several enzymes involved in bile acid synthesis. Bile acids are conjugated with glycine or taurine at position C-24 in the liver and transported actively into the bile. The bile is then secreted in the intestinal lumen. Here, bacterial hydrolases (expressed for example in Bifidobacteria, Clostridia, and Bacteroidetes) deconjugate and dehydroxylate primary bile acids, resulting in so-called secondary bile acids. Bacterial enzymes therefore have direct influence on blood cholesterol. The majority of biliary secreted bile acids are reabsorbed, predominantly as conjugated/“secondary” bile acids in the distal ileum. Then, they re-circulate via the portal vein to the liver. Here, they are secreted again. This enterohepatic circulation is essentially for the uptake of dietary lipids and lipophile vitamins. Secondary bile acids partly cause their metabolic effects via binding to the Farnesoid-X receptor (FXR) and the Takeda-G-protein-receptor-5 (TGR5). Activation of these receptors increases glycogen synthesis and insulin sensitivity in the liver, insulin secretion by the pancreas, and ultimately resulting in a decrease in body weight. Artificial agonist on these receptors might therefore be able to reduce the atherosclerotic severity. The above-mentioned mechanisms might play a synergetic pathogenetic role in atherosclerosis. The activation of TGR5 by secondary bile acids leads to energy consumption in brown adipose and muscle tissue in mice. This finding was also observed in a small human cohort orally supplemented with a secondary bile acid (Broeders et al. 2015). Whereas FXR activation in the liver causes inhibition of adipogenesis, intestinal FXR activation leads to bile acids synthesis and reduced cholesterol levels. TGR5 improves glucose tolerance with increased secretion of glucagon-like peptide 1 (GLP-1) and also insulin (Agus et al. 2021). In metabolic disorders, alterations of the intestinal microbiome strongly impact bile acid metabolism. Dysbiosis can cause a diminished ability to metabolize primary bile acids which subsequently leads to their accumulation. This alteration of the primary to secondary bile acid ratio might contribute to low-grade intestinal inflammation. This is because primary BAs show pro-inflammatory effects whereas secondary bile acids have anti-inflammatory properties. Therefore, alterations of the primary/secondary bile acids ratio do not only impact lipid metabolism but might have a crucial impact on low-level inflammation influencing also atherosclerotic diseases (Agus et al. 2021). Not only has the intestinal microbiome impacted bile acids. Also, an influence of bile acids on the microbiome composition itself was reported. Activation of FXR

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was shown to exhibit antimicrobial functions and prevent from bacterial overgrowth (Agus et al. 2021). Bacterial overgrowth, dysbiosis and furthermore bacterial translocation might therefore be the result of a disbalance or absence of bile acids with impact on cardiovascular and metabolic diseases. Bacterial translocation might cause local and systemic inflammation, which again might trigger atherosclerosis. Moreover, bile acids show antimicrobial effects themselves. By these mechanisms, the intestinal microbiome and bile acids have impact on each other in both directions. Disturbance of this balance might promote the pathogenesis of dyslipidemia and/or atherosclerosis (Agus et al. 2021). Individuals with obesity show a higher amount of circulating bile acid levels compared to healthy individuals. Here, the bile acid level positively correlates with body mass index and serum level of triglycerides. The intestinal microbiome’s underlying effect on obesity might also depend on the FXR pathway (Agus et al. 2021). Bile acid synthesis is regulated by a feedback loop via FXR and the activation of a transcript factor named FGF19. Activation of this transcript factor inhibits bile acid synthesis in the liver. Gut microbiota alterations impact ileal absorption of bile acids which can result in decreased expression of FXR and FGF19. This also leads to an altered resorption of dietary lipids and subsequently to alterations of the host’s lipid metabolism. The effect of the interaction between bile acids and the intestinal microbiome is massively impacted by diet. Especially high consumption of animal fat causes a shift in microbiota composition and therefore has an enormous impact on lipid metabolism and cardiovascular disease (Agus et al. 2021). Even though the evidence so far suggests an important role for bile acids in the pathogenesis of dyslipidemia and atherosclerosis further research is needed. To understand the pathomechanism in depth, molecular targets of bile acids in human must be identified and further characterized. Most research so far is done in animal (mostly mouse) models with a bile acid composition different than in the human body. Therefore, evidence from human cohorts is needed to unravel the underlying bile acid pathways in detail and provide insights in the crosstalk with cardiovascular disease.

Short Chain Fatty Acids (SCFA) The short chain fatty acids (SCFA) acetate, butyrate and propionate are gut-derived metabolites that have been shown to impact human lipid metabolism and cardiovascular disease. They are synthesized via bacterial fermentation of indigestible plantderived carbohydrates. Here, especially Roseburia and Faecalibacterium prausnitzii have major impact on the SCFA serum level (Jie et al. 2017). SCFA are known to serve as local energy suppliers for enterocytes and moreover cause systemic effects after resorption. Their binding to G-protein-coupled receptors GPR41 and GPR43 mediates positive effects on atherogenesis by reducing cytokine release and expression of vascular adhesion molecules (Ge et al. 2008). SCFA are therefore widely understood as positive modulators of cardiovascular disease. SCFA have also been reported to impact the regulation of lipid metabolism. Reduced SCFA levels in feces of hyperlipidemia pediatric patients indicate a

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reduction of SCFA-producing bacteria (e.g., Akkermansia, Bacteriodes, Roseburia, and Faecalibacterium) as observed in hyperlipidemic mice. Interestingly, these alterations were partly attenuated by dietary interventions (Wang et al. 2020). Recently, Haghikia et al. showed that propionate regulates cholesterol metabolism via immunomodulatory mechanisms. In ApoE / mice, supplementation of propionate was able to reduce cholesterol serum levels. Moreover, this finding was likewise observed in a placebo-controlled human study, in which propionate supplementation reduced total cholesterol, non-HDL cholesterol, and LDL cholesterol. Haghikia et al. identified a propionate-dependent IL-10 upregulation. This subsequently resulted in suppression of Npc1l1, which is an important intestinal cholesterol transporter (Haghikia et al. 2022). Therefore, antibiotic-dependent cholesterol exacerbation via SCFA-reduction could also be a possible side effect in humans. This mechanism may contribute to the increased cardiovascular risk by long-term antibiotic treatment, which was observed by Heianza et al. (2020). SCFAs such as butyrate are also known to activate peroxisome proliferatoractivated receptor gamma (PPAR-gamma). This receptor is responsible for microbial homeostasis in the intestinum. Activation of PPAR-gamma additionally results in enhanced insulin sensitivity and beta-oxidation. All these mechanisms together result in the crucial role of PPAR-gamma in maintaining homeostasis. In accordance with these findings, PPAR-gamma signaling is subsequently disrupted in metabolic and inflammatory diseases (Hills et al. 2019). In accordance with the previous studies, SCFA were significantly reduced in feces of patients with hyperlipidemia (Jia et al. 2021). SCFA acetate has been shown to reduce serum levels of fatty acids by GPR43 activation in adipocytes. This effect was abrogated in GPR43 / mice (Ge et al. 2008). Mice fed a high fat diet (HFD) show upregulation in acetate serum levels resulting in ghrelin secretion, glucose stimulated insulin secretion, and activation of the parasympathetic nervous system. These effects underline the interaction of microbiota-derived metabolites on metabolic (insulin secretion) and central nervous (ghrelin, parasympathetic nervous system) levels (Perry et al. 2016). Propionate is not only able to reduce serum cholesterol levels, but also to reduce atherosclerotic aortic lesion size in ApoE / mice (Bartolomaeus et al. 2019; Haghikia et al. 2022). Moreover, it reduced hypertrophy, fibrosis, and arrhythmic events in a mouse model of hypertensive heart disease (Bartolomaeus et al. 2019). Butyrate was able to lower atherosclerosis in a mouse model by a reduction of pro-inflammatory cytokines and subsequent impairment of macrophage migration and adhesion (Aguilar et al. 2014). Intestinal colonization of germ-free ApoE / mice with butyrate producing species (Roseburia intestinalis) resulted in attenuation of atherosclerotic lesions. Interestingly, serum lipid levels were not affected by this intervention (in contrast to studies with propionate) (Kasahara et al. 2018). SCFA do not only mediate direct mechanisms linked to atherosclerosis or dyslipidemia. It has been shown that SCFA also impact other cardiovascular risk factors. Diabetes mellitus is a major risk factor for atherosclerosis. Metformin is a commonly used antiglycemic agent in patients with type 2 diabetes. Wu et al.

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revealed a direct influence of metformin on the gut microbiome, which partly mediated the beneficial effects of the drug by enhancing SCFA levels (Wu et al. 2017).

Trimethylamine-N-Oxide (TMAO) Trimethylamine-N-Oxide (TMAO) is a microbiota-host-co-metabolite, which has been linked to cardiovascular disease. Its precursor trimethylamine (TMA) is generated by bacteria from phosphatidylcholine and L-carnitine, both contained in animal-derived food, e.g., milk, eggs, and red meat. Interestingly, carnitine is also contained in several energy drinks and supplements for athletes. TMA is then absorbed by the intestine and oxidized by flavin containing dimethylaniline monooxygenase 3 in the liver producing TMAO (Koeth et al. 2013; Wang et al. 2011). TMAO serum levels have been shown to be predictive for cardiovascular events and mortality (Wang et al. 2011). The gut microbiome composition of atherosclerosis patients shows enhanced enzymes involved in TMA production compared to healthy controls (Jie et al. 2017). To date, the influence of TMAO on cardiovascular disease is not completely understood. Direct effects of TMAO on atherogenesis have been suggested since supplementation of L-carnitine with subsequent elevated TMAO levels exaggerated atherosclerosis in a mouse model (Koeth et al. 2013). Furthermore, TMAO serums levels positively correlate with the severity of coronary heart disease and have been shown to be an independent predictor for cardiovascular events (myocardial infarction, stroke) as well as mortality (Koeth et al. 2013). Koeth et al. also suggested a microbiota-dependent influence of carnivorism on cardiovascular disease by increased TMAO synthesis. Individuals with a vegetarian diet displayed lower TMAO levels and less cardiovascular events, whereas meat eaters showed the opposite phenotype. This effect might be mediated by the high content of L-carnitine in red meat, which serves as precursor for TMAO (Koeth et al. 2013). Whereas carnitine shows a strong association to the diet, choline is only partly dependent on food intake since its concentration in the bile is high as it serves as a nutrient precursor in patients independent of the diet (Witkowski et al. 2020). Additional studies support that TMAO associated heart failure is primarily due to atherosclerotic events and subsequently myocardial ischemia. Trøseid et al. showed a strong association of TMAO levels to heart failure of ischemic origin, but not to dilatative cardiomyopathy. In heart failure of ischemic origin, TMAO levels correlated with disease severity measured by the New York Heart Association scale (NYHA) and cardiovascular mortality (Trøseid et al. 2015). In line with this study, Schuett et al. revealed in a cohort of 2490 patients referred to coronary angiography that TMAO predicts cardiovascular mortality in patients with reduced ejection fraction (HFrEF) (mainly from ischemic heart failure), but not in individuals with preserved ejection fraction (HFpEF) (Schuett et al. 2017). However, the underlying pathomechanism how TMAO modulates atherosclerosis or cardiovascular events have not fully been elucidated. In animal models, TMAO modulated the reverse cholesterol transport in macrophages with an

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elevation of cholesterol serum levels (Koeth et al. 2013). Other studies indicate an influence on platelet reactivity and thus atherothrombotic events. However, in the LURIC cohort including more than 1,600 patients with high cardiovascular risk and a median follow-up of 9.7 years, TMAO and platelet reactivity were found to be independent predictors for cardiovascular mortality (Berger et al. 2020). One proposed pathomechanism of TMAO’s impact on atherosclerosis is vascular inflammation. In mice infused with TMAO vascular inflammation was enhanced compared to control animals. This effect was also observed in human endothelial cells in vitro. TMAO was also found to increase oxidative stress and vascular senescence. Moreover, TMAO has been shown to activate the NLRP3 inflammasome in endothelial cells causing enhanced local inflammation. However, the exact pathways by which TMAO induces inflammasome activity remain to be studied (Witkowski et al. 2020). TMAO elimination is strongly dependent on the kidney function, respectively glomerular filtration rate (GFR), leading to accumulation in renal failure. Interestingly, after correcting TMAO serum levels for GFR, no predictive value for cardiovascular events could be seen by Müller et al. (2015). In another large cohort, after correcting TMAO levels for albumin excretion, no connection between mortality and TMAO levels could be seen. An association of TMAO and cardiovascular events was here only present in patients with mildly impaired renal function (GFR < 90 ml/min) (Gruppen et al. 2017). Interestingly, TMAO theoretically might also have beneficial functions in the human body. This idea is in accordance with a potential confounder of enhanced TMAO levels and chronic kidney failure regarding the cardiovascular effects (as mentioned above). In some fish TMAO protects against pressure-induced protein destabilization via osmotic mechanisms. In vertebrate TMAO may also be involved in tissue osmolality in the kidney where high levels of TMAO might impact osmoregulation and renal function. As mentioned before, TMAO levels are associated with elevated risk for thrombotic complications. These same “complications” however may theoretically benefit the host during situations with high bleeding risk, such as childbirth or traumatic bleeding. Sex-specific differences in TMAO levels have been observed in animal models. Here, females show higher TMAO levels maybe indicating a beneficial function during pregnancy and delivery. In human, the prognostic value of TMAO seems to be similar in both, men and women (Witkowski et al. 2020). TMAO is one of the most interesting and well-studied microbiome-dependent metabolites. However, its exact role in the pathogenesis of cardiovascular disease is still not discovered. Further studies need to be performed to finally unravel its exact role in atherosclerosis and cardiovascular disease.

Aromatic Amino Acids The intestinal microbiome has been identified as an important regulator of amino acid metabolism. Reduced tryptophan serum levels for example have been shown to serve as predictors for cardiovascular mortality. Therefore, effects of the intestinal microbiome on amino acid metabolism might also impact dyslipidemia and atherosclerosis. Tryptophan can be synthesized by certain different bacteria in the gut. In contrast, some

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bacteria are able to metabolize it via bacterial tryptophanase-producing metabolites containing an indole ring, which cannot be produced via human enzymes. Thus, metabolites containing indole rings are either gut microbiota-derived or supplied via diet (Agus et al. 2021). Tryptophan is contained in dietary products such as oats, fish, milk, and milkproducts (e.g., cheese). It plays a pivotal role as a precursor for several metabolites and in the synthesis of proteins. Tryptophan can follow three main pathways in host cells: the kynurenine pathway, the serotonin pathway, and the so-called indole pathway. The kynurenine pathway is upregulated in metabolic diseases. In accordance to this finding, inhibition of indoleamine-2,3-dioxygenase, the rate-limiting enzyme of the kynurenine pathway, protects against dietary-induced obesity and metabolic alterations (Agus et al. 2021). Indoleamine-2,3-dioxygenase (IDO) could play a pivotal role in cardiovascular disease. IDO is a human enzyme catalyzing tryptophan degradation via the kynurenine-pathway. This pathway is also known to modulate T-cell immunity. High IDO activity is associated with enhanced cardiovascular risk potentially by causing vascular inflammation. High IDO activity leads to lower tryptophan availability in the gut lumen and thereby to reduce production of gut-derived tryptophan metabolites (“indoles”). Some indoles are able to function as aryl hydrocarbon receptor (AhR) ligands (Agus et al. 2021). Via this pathway indoles have been shown to mediate IL-22 release, which in turn impacts metabolic diseases such as metabolic syndrome. On the other hand, an IL-22 knockout in mice led to an expansion of pathogenic bacteria and subsequent upregulation of proatherogenic metabolites, such as TMAO (Fatkhullina et al. 2018). Furthermore, disruption of the AhR-pathway leads to enhanced LPS translocation, subsequently causing systemic inflammation. In a human cohort of 119 patients with peripheral artery disease, gut-derived indole-3-proprionic acid (IPA) negatively correlated with disease burden (Ho et al. 2022). Recently, IPA supplementation was shown to reduce atherosclerotic plaques in ApoE / mice. Mechanistically, besides impact on the AhR pathway also an IPA-dependent efflux of cholesterol from macrophages has been proposed (Xue et al. 2022). In a mouse model of atherosclerosis, diminished microbial tryptophan metabolism was suggested as mediator of antibiotics-induced atherosclerosis. Tryptophan supplementation was subsequently able to attenuate atherosclerosis due to antibiotic treatment (Kappel et al. 2020). Besides its positive effects on the vasculature, IPA also revealed direct effects on the heart. IPA modulated mitochondrial respiration in cardiomyocytes and enhanced cardiac contractility in an ex vivo mouse model (Gesper et al. 2021). These findings show a connection between the kynurenine pathway and indole producing pathways by the intestinal microbiome. IDO1-activity is essential for the amount of tryptophan in the intestinal lumen. Therefore, activation of the kynurenine pathway, while having atherogenic properties itself, also leads to diminished indole levels and a disruption of the AhR-pathway worsening systemic and local inflammation. The complex interaction of the kynurenine pathway and the indole pathway is shown in Fig. 3. While previous data suggest a beneficial role of indoles on the cardiovascular system at physiological concentrations, certain indole metabolites, i.e., indoxyl sulfate and indoleacetate, accumulate in patients with chronic kidney disease

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Fig. 3 Impact of Tryptophan metabolism on inflammation and metabolic disorders

(CKD). Here, high serum levels of these metabolites are positively correlated to cardiovascular mortality. Indole-3-acetic acid levels displayed an independent predictor for cardiovascular events and mortality also after correcting for CKD stage (Dou et al. 2015). In a rat model of CKD, indoxyl sulfate supplementation stimulated vascular inflammation (Adelibieke et al. 2014). Another group found that indoxyl supplementation in a rat model caused senescence of endothelial cells resulting in aortic wall thickening thus promoting cardiovascular disease (Niwa and Shimizu 2012). Moreover, indoxyl sulfate also targets the myocardium. By induction of reactive oxygen species (ROS), it has been shown to induce hypertrophy in cardiomyocytes (Yang et al. 2015). The gut microbiota also modulates the intestinal production of serotonin (5-HT), which affects feeding behavior and satiety and is crucial in development of obesity and the metabolic syndrome. Deficiency in the production of peripheral serotonin protects from dietary derived obesity by inhibition of fat tissue degradation. In accordance with that, elevated plasma levels of the serotonin end 5-hydroxyindole-3-acetic acid are increased in these patients (Agus et al. 2021). Interestingly, gut microbiota have also been linked to hepatic steatosis via aromatic and branched-chain amino acids. Hepatic steatosis is associated with systemic dyslipidemia. Thus, microbial modulation of these amino acids may also play a role in dyslipidemia (Mavilio et al. 2016). Analogous to tryptophan metabolism, an association between cardiovascular mortality and different other gut-derived aromatic amino acid metabolites has been observed. Here, phenylacetylglutamine (Poesen et al. 2016) and p-cresyl (Meijers et al. 2008) are metabolites of interest. Phenylacetylglutamine is synthesized in the liver by conjugation of microbial phenylalanine metabolite phenylacetate with glutamine. Negative effects on the cardiovascular system of these metabolites have

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been described. Phenylacetylglutamine was found to be a predictor for cardiovascular events and mortality (Nemet et al. 2020; Poesen et al. 2016). Even though upregulated in CKD, it remains a predictor independent of eGFR (Poesen et al. 2016). In female identical twins phenylacetylglutamine predicts arterial stiffness and cardiovascular risk, thus suggesting a role of the gut flora (Menni et al. 2015). Nemet et al. proposed an adrenergic-dependent platelet activation leading to thrombocyte reactivity (Nemet et al. 2020). P-cresyl has also been discovered to predict cardiovascular events (myocardial infarction, myocardial ischemia, onset of peripheral artery disease, and ischemic stroke) and mortality in hemodialysis patients. Interestingly, this effect was absent in individuals with diabetes mellitus despite high p-cresyl levels in this population (Meijers et al. 2008).

Chronic Inflammation and Immunomodulatory Mechanisms The intestinal microbiome has an influence on adaptive and innate immune mechanisms with an impact on inflammatory processes in different diseases (Jie et al. 2017). Chronic inflammation is also present in cardiovascular disease and causative for its pathogenesis (Libby 2021). The potential impact of the intestinal microbiome on inflammatory processes in atherosclerosis is therefore of interest and will be discussed in this chapter (Figs. 2 and 3).

Endotoxemia Chronic low-grade inflammation is linked to metabolic diseases, e.g., obesity, type 2 diabetes mellitus, and atherosclerosis. Chronic low-grade inflammation can be defined by elevated levels of inflammatory parameters, for example c-reactive protein (CRP) in the absence of infectious diseases. The importance of chronic inflammation for cardiovascular diseases is underlined by the fact that pharmacological reduction of inflammation is able to reduce MACE. Anti-inflammatory drugs like colchicine antagonists were able to reduce cardiovascular events in human cohorts (Tardif et al. 2019). Inflammation can be triggered by bacterial components such as lipopolysaccharide (LPS) even in the absence of bacterial infection. LPS is an endotoxin and a conservated part of bacterial cell membranes of gram-negative bacteria. In contrast to gram-negative infections where LPS and CRP levels are drastically elevated, these parameters are only slightly upregulated in chronic low-grade inflammation. LPS as a bacterial structure can activate innate immune responses by interaction with pattern recognition receptors (PRRs). The four major subfamilies of PRRs are toll-like receptors (TLRs), nucleotide-binding oligomerization domain-leucine-rich repeats (LRR)-containing receptors, the retinoic acid-inducible gene 1 (RIG-1)-like receptors, and the C-type lectin receptors. Toll-like receptors are however the most wellcharacterized of these. Each TLR mediates different responses to distinct microbial components. For example, TLR-4 recognizes bacterial LPS. TLRs are present in intestinal epithelial cells as well as in different immune cells. The activation of downstream signaling pathways of TLRs is dependent on the myeloid differentiation

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factor 88 protein (MyD88), an essential adaptor protein for all TLRs except TLR-3. Deletion of MyD88 in the intestines partially protects against diet-induced obesity, diabetes, and inflammation showing the importance of inflammatory pathways in metabolic disorders (De Vos et al. 2022). As part of the innate immune response Tolllike receptor 4 (TLR-4) plays a crucial role in LPS detection. Activation of the NFkB-pathway downstream of TLR-4 leads to cytokine release and initiation of innate immune response. Dysbiosis of the intestinal microbiome could play an important role in LPS translocation causing low-grade inflammation and subsequent pathogenesis of the so-called metabolic endotoxemia (Jie et al. 2017; Libby 2021). In metabolic endotoxemia, high-fat diet and weight gain have been linked to a higher gut permeability, leading to a subsequent mild elevation in circulating plasma LPS that causes low-grade inflammation, a pathological feature of chronic conditions such as atherosclerosis. Interestingly, LPS from different types of bacteria have varying effects indicating that the effect of metabolic endotoxemia levels on host metabolism can vary depending on the composition of the intestinal microbiome (De Vos et al. 2022). In accordance with this hypothesis, high fat diet (HFD) was able to induce metabolic endotoxemia displayed by chronic inflammation, insulin resistance, and lipid metabolism disorder (Cani et al. 2007). Indeed, HFD has been shown to upregulate serum LPS levels by induction of microbiome dysbiosis in mice. Also in humans, obesity is linked to higher LPS serum levels indicating a similar link between HFD, intestinal dysbiosis, and chronic low-grade inflammation. Interestingly, mice lacking LPS-receptors (CD14, TLR-4) do not show HFD- or LPS-induced metabolic endotoxemia. This indicates an LPS-dependent pathway in chronic low-grade inflammation (Cani et al. 2007; Libby 2021). As expected, LPS serum levels in human correlate with the prevalence of atherosclerotic disease. LPS application was also able to induce atherosclerosis in mouse models (Malik et al. 2010). In accordance, Michelsen et al. showed that knockout of TLR-4 results in decreased severity of atherosclerosis in mice. Interestingly, a TLR-4 knockout also impaired atherosclerosis in mice with hypercholesterinemia. This finding underlines the enormous effect of inflammatory signaling on the pathogenesis of cardiovascular disease (Michelsen et al. 2004).

Leaky Gut Syndrome The intact mucosal barrier normally protects bacterial translocation as well as undirected translocation of endotoxins and bacterial metabolites. The concept of the “leaky gut” serves as an explanation for the translocation of metabolites and endotoxins (LPS), whose levels are higher in the gut lumen than in the blood. Therefore, the permeability of the gut mucosa might be a crucial regulator for chronic low-level inflammation. Interestingly, the intestinal microbiome is able to regulate mucosal permeability itself by interaction with a glucagon-like-peptide-2 mechanism targeting tight-junctions as important cell adhesion molecules. In a highfat-diet mouse model, Bifidobacterium supplementation significantly diminished serum LPS-levels and hence obesity (Cani et al. 2009). Here, probiotic treatment with Bifidobacterium increased the host proglucagon-derived peptide-2 (GLP-2)

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synthesis leading to decreased gut leakage and subsequently diminished systemic inflammation (Cani et al. 2009). A leaky gut, however, does not only enable higher LPS serum levels. The translocation of other bacterial derived molecules, e.g., metabolites is also facilitated.

Bacterial Translocation Moreover, the concept of a leaky gut is not limited to smaller molecules. Bacteria itself might translocate into the host blood stream causing inflammation in human tissue and/or blood vessels. This mechanism may take part in maintaining chronic low-grade inflammation. Large numbers of gram-negative bacterial DNA have been detected in blood and fatty tissue of mice fed with a HFD. Potentially, translocation of bacteria from the gut microbiome into the blood stream was mediated by HFD intestinal leakage. Probiotic treatment with a Bifidobacterium strain was able to improve bacterial translocation and both, systemic inflammation, and insulin resistance in this mouse model (Amar et al. 2011a). Also, in human similar findings could be observed. In a patient cohort consisting of 3936 participants with high cardiovascular risk, bacterial DNA in the blood stream was investigated. Here, the presence of Proteobacteria DNA could be identified as predictor for cardiovascular events (Amar et al. 2011b). Immune System Modulation and Influence on T-Cell Response The gut microbiome has been shown to have a close interaction with the immune system. Therefore, interactions with atherosclerosis pathogenesis via immunomodulatory pathways are implicated. Impact on innate immune response via TLR has in part been discussed earlier. TLR activation leads to recruitment of adapter proteins, for example, MYD88 resulting in proinflammatory signaling cascades. Fatkhullina et al. showed a dependency of macrophage activation and subsequent atherosclerotic lesions on protective cytokines like IL-22 and IL-23. In their experiments, IL-22/IL23 knockout mice had upregulated serum LPS levels as well as larger atherosclerotic lesions (Fatkhullina et al. 2018). Also, the adaptive immune responses play a pivotal role in cardiovascular disease. T-lymphocytes normally promote atherogenesis. However, regulatory T cells (Tregs) are able to mitigate this process. Tregs are in balance with proinflammatory Th17 cells and known to have anti-inflammatory effects. An imbalance in the Treg/Th17-ratio has been suspected to play a role in the pathogenesis of autoimmune diseases, but may also affect atherogenic processes. Within the intestinal flora, segmented filamentous bacteria have been shown to regulate Th17 cells resulting in increased IL-17/22 release and therefore promoting inflammation (Ivanov et al. 2009). T-cell response is mediated by gut-derived metabolites. SCFA have been shown to stimulate Treg. In a mouse model, supplementation of the SCFA propionate was able to reduce atherosclerosis via activation of Treg (Bartolomaeus et al. 2019). Besides direct effects on atherosclerosis, Treg/Th17-cell axis may also regulate cardiovascular risk factors. High salt intake has been linked to arterial hypertension. Wilck et al. revealed that salt supplementation diminishes Lactobacilli in the gut.

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This in turn was linked to the depletion of Th17 cells, which mediated arterial hypertension (Wilck et al. 2017). Interestingly, it was suggested that gut-derived tryptophan metabolites were responsible for the regulation of Th17 cells. Moreover, IDO-dependent tryptophan metabolism plays a crucial role in the T-cell response. IDO-activity leads to tryptophan reduction in the gut lumen and thereby to reduced production of indole metabolites via the microbiome causing decrease in IL-22. Th17 cells are regulated via this IL-22 axis. An IL-22 knockout therefore leads to expansion of pathogenic bacteria and upregulation of proatherogenic metabolites (Fatkhullina et al. 2018). These findings show an influence of the intestinal microbiota on both pro- and anti-inflammatory T-cell responses that in turn may result in exaggerated inflammation in situations where this balance is altered, e.g., dysbiosis.

Clonal Hematopoiesis Clonal hematopoiesis of indeterminate potential (CHIP) is a condition where clones of leukocytes with mutations in several genes associated with leukemia are present in patients without hematological diseases. Individuals with CHIP have a higher chance to develop leukemia and/or other hematological diseases compared to individuals without these mutations. In patients with CHIP, a higher mortality compared to healthy individuals can be observed. However, this is not due to the enhanced risk of transformation to hematological diseases, but rather explained by cardiovascular causes. Thus, presence of CHIP was identified as an independent risk factor for cardiovascular disease with doubling the cardiovascular risk (Jaiswal et al. 2017; Libby 2021). Among others, Tet-methylcytosindioxygenase 2 (TET2) mutations have been pinpointed as risk factor for CHIP. In a mouse model, TET2-knockout animals exposed more CHIP and subsequently more atherosclerosis (Jaiswal et al. 2017). Here, inflammatory pathways like the NLRP inflammasome were activated resulting in elevated IL-1beta and Il-6 indicating an influence of CHIP on chronic inflammation (Fuster et al. 2017). Interestingly, microbial signaling in TET2knockout mice was shown to be crucial for CHIP-associated pre-leukemic myeloproliferation (Libby 2021). Further studies must reveal whether intestinal microbiota might also have an impact on cardiovascular disease via modulation of CHIP.

Atherothrombotic Potential Atherosclerosis can lead to significant morbidity and mortality by acute and chronic cardiovascular diseases. In acute conditions, such as ischemic stroke and myocardial infarction, the concepts of localized thrombosis and plaque rupture are noteworthy. Despite its influence on atherosclerosis, intestinal microbiota may also directly modulate atherothrombotic processes. Gut-derived metabolites TMAO and phenylacetylglutamine have been suspected to have a direct impact on platelet reactivity and sequentially mediate cardiovascular events (Nemet et al. 2020; Zhu et al. 2016). In mice, an alteration of hepatic von Willebrand-Factor synthesis by gut

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microbiota has been described, which can lead to arterial thrombosis (Jäckel et al. 2017). These results are in agreement with investigations of Kiouptsi et al. who suggested a protection against the atherothrombotic phenotype by germ-free conditions (Kiouptsi et al. 2019). These studies indicate that the gut microbiome not only impacts atherogenesis but also influences acute cardiovascular events via its atherothrombotic potential.

Endocannabinoid System The endocannabinoid (eCB) system has various physiological effects including impact on lipid metabolism, inflammation, and microbiota-host interaction. Endocannabinoids act via two receptors, CB1 and CB2. Both receptors belong to the group of G protein-coupled receptors (GPCRs) and share downstream signaling mechanisms. Endocannabinoid receptors are activated by several different agonists. The most prominent and first reported agonist is the lipid andanamide (N-arachidonoylethanolamine; AEA) as part of the N-acylethanolamines (NAE). However, the family of cannabinoid receptor agonists has been expanded to other compounds that also interact with other receptors causing pleiotropic effects. Besides the “true” eCBs, other eCB-like compounds have been discovered, which can interfere with the eCB system without directly activating the cannabinoid receptors. These molecules are for example bioactive lipids. The eCB system was recently discovered to play a major role in the regulation of the gut barrier function. In accordance with this investigation, the intestinal eCB system is altered during metabolic disorders, for example, obesity and diabetes. An increased abundance of AEA in these diseases causes increased gut permeability via a CB1-dependent mechanism. This is subsequently associated with changes in the gut microbiota. In animal models, obese and diabetic mice present a shift in their gut microbiota composition associated with alterations in the eCB-system. These findings underline the importance of the endocannabinoid system on metabolic disorders and subsequently on atherogenesis and lipid metabolism (De Vos et al. 2022).

12-HETE, C18-3OH Enterosynes are a newly described class of molecules targeting the enteric nervous system (ENS) originated in the gut. Patients with type 2 diabetes mellitus display duodenal hypermotility leading to increased glucose absorption and hyperglycemia. Enterosynes can restore the normal duodenal contraction by acting on ENS. For example, oligofructose is able to decrease duodenal contraction subsequently leading to reduced hyperglycemia and decreased inflammatory markers in the adipose tissue of diabetic mice. The administration of oligofructose selectively increased the abundance of an intestinal bioactive lipid: 12-hydroxyeicosatetraenoic acid (12-HETE). 12-HETE improves glucose metabolism in diabetic mice. Human data supported these findings, showing a reduction in the levels of 12-HETE in the

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duodenum of patients with diabetes as compared with healthy subjects. In another example, specific bacteria in the gut microbiota produced 3-hydroxyoctadecaenoic acid (C18-3OH), which revealed anti-inflammatory properties decreasing gut inflammation. The gut mircrobiome is the source of numerous bioactive compounds acting on host receptors involved in the regulation of metabolism and inflammation (De Vos et al. 2022). Molecules impacting host metabolism and inflammatory processes, such as 12-HETE and C18-OH, might also influence lipid metabolism and therefore atherosclerotic potential in the host. Here, mechanistic studies to enlighten the impact of these newly discovered molecules on these diseases remain to be performed.

Impact of Drugs on the Intestinal Microbiome As mentioned before, Metformin, a commonly used antiglycemic agent in patients with type 2 diabetes, has direct influence on the gut microbiome composition (Wu et al. 2017). Studies of healthy volunteers show that metformin treatment results in significant changes in gut microbiome, including an increase in Escherichia coli. This effect might be due to metformin’s effect on butyrate-producing bacteria and the abundance of Akkermansia muciniphila. Interestingly, gastrointestinal side effects are often reported by patients receiving metformin which may be related to changes in the gut microbiome. Also, other commonly used drugs such as proton pump inhibitors have been shown to affect the microbiome composition. Proton pump inhibitors (PPIs) are widely used to treat gastroesophageal reflux, gastritis, as well as to prevent gastroduodenopathy as well as gastric and duodenal bleeding. Interestingly, PPI use leads to decreased diversity of the gut microbiome, alterations in the abundance of commensal bacteria and an increase in oral cavity bacteria. The shift toward typical oral bacteria is reflected in increases in specific bacterial species, such as Rothia dentocariosa and Rothia mucilaginosa, Actinomyces, and Micrococcaceae. A potential mechanism might be the reduction of gastric pH induced by PPIs which could enable oral bacteria to colonize the gut microbiome, leading to the above-mentioned changes (Weersma et al. 2020). Moreover, PPIs are associated with increased mortality due to cardiovascular disease, gastrointestinal tumors, and chronic kidney disease. Even though the underlying pathomechanism are not known so far, these diseases are all influenced by the intestinal microbiome. Therefore, the microbiome might play a potential role in the PPI-dependent mortality. Both examples, Metformin and PPIs, show how commonly used drugs alter the intestinal microbiome with potential impact on metabolic and subsequently cardiovascular diseases. A potential effect of the drug on host metabolism dependent on the microbiome is also possible and would explain differences in drug effects between different individuals. Further research unraveling the complex interactions between different medications, the intestinal microbiome and cardiovascular disease and lipid metabolism needs to be performed. In the future, phenotyping of the gut microbiome may forecast medication (side-) effects.

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Antibiotics of course also have an impact on the intestinal microbiome composition and have been used to demonstrate the involvement of gut microbiota. However, they are not a good long-term therapeutic option due to potential developments of antibiotic resistances. Commonly prescribed non-antibiotic drugs may also impact human gut microbial communities, thereby indirectly impacting host phenotypes. Defined microbial compositions (so-called probiotics) and non-microbial substances that may alter microbial community structure (so-called prebiotics) have been proposed to improve cardiovascular disease via the gut-heart axis. A study using a distinct Lactobacillus rhamnosus strain improved systolic and diastolic left ventricular function in an animal model of coronary artery disease. Akkermansia muciniphila was associated with reduced aortic atherosclerosis in a hypercholesterolemic Apoe / mouse model. This probiotic is also used in human cohorts and was already associated with LPS-reduction in obese patients (Witkowski et al. 2020). Although there are promising results from preclinical and clinical intervention studies using prebiotics and probiotics, clinical evidence is lacking until now. Further research with large cohorts needs to be performed in order to prove beneficial effects in cardiovascular disease and dyslipidemia. However, some preclinical studies show a direct impact of alterations in gut microbiome-related processes and the host phenotype.

Conclusion This chapter has summarized the influence of the intestinal microbiome on dyslipidemia and atherosclerosis. These conditions are at least partly overlapping. Individuals with cardiovascular diseases frequently exhibit dyslipidemia, which is a causal risk factor for atherosclerosis. Thus, microbial mechanism linked to either dyslipidemia or atherosclerosis is challenging to distinguish, particularly when studying human cohorts. While gut dysbiosis has clearly been linked to cardiovascular disease by increasing circulating lipids (i.e., cholesterol), there are also other gut microbiota-dependent mechanisms that independently mediate atherosclerosis. Noteworthy is the influence of gut-derived metabolites. While some, e.g., SCFA, have an impact on lipid metabolism and atherosclerosis, others, such as TMAO, have been linked to cardiovascular disease independently of dyslipidemia. Many studies have highlighted to role of microbial aromatic amino acid metabolism, in particular tryptophan. Placebo-controlled randomized trial, as performed by Haghikia et al. for the SCFA propionate, must now proof whether gut-derived tryptophan metabolites may be potential targets for pharmacological interventions in the future (Haghikia et al. 2022). Besides gut-derived metabolites, it has been reported that the intestinal microbiome impacts cardiovascular disease also via inflammatory and immunomodulatory processes. In addition, a crosstalk between microbiota-derived metabolites (e.g., indoles, SCFA) and immunomodulatory pathways is evident. This highlights the complex interaction between the gut microbiota and the host. Human studies with

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microbiota intervention might therefore impact more than a single targeted pathway and cause pleiotropic effects. In conclusion, the gut flora has been identified as modifiable risk factor for dyslipidemia and atherosclerosis. Further studies, including pharmacological and probiotic intervention, need to be conducted in order to reach clinical application. The influence of the intestinal microbiome on lipid metabolism and atherosclerosis is therefore a promising and interesting field that needs, however, further attention and research.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introducing the Gut Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Healthy Gut Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Gut Hypothesis of Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Dysbiosis Patterns Associated with Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbial Metabolites – Physiological Mediators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Short Chain Fatty Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bile Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amino Acid Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trimethylamine N-oxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phenylacetylglutamine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lipopolysaccharide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Gut Microbiome in Heart Failure Comorbidities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chronic Kidney Disease and Cardiorenal Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insulin Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiac Cachexia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strategies to Target the Gut Microbiome to Treat Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dietary and Lifestyle Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prebiotics and Probiotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbial Enzyme Inhibition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fecal Microbiota Transplant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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S. Aiyer Center for Microbiome and Human Health, Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA W. H. W. Tang (*) Center for Microbiome and Human Health, Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA Kaufman Center for Heart Failure Treatment and Recovery, Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_11

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Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Abstract

Despite significant advances in drug development and therapeutic strategies, heart failure (HF) continues to have a high burden of morbidity and mortality. Current treatment options for advanced HF are limited to cardiac replacement therapies such as heart transplantation and left ventricular assist devices, but even these therapies pose restrictions of related complications and high rehospitalization rates. There remains a strong need for novel insight into the pathophysiology of HF to develop more precise, personalized, and complementary therapeutics. Recent evidence has demonstrated that alterations in the gut microbiome could play a role in HF progression and development. Changes in gut microbiota composition and metabolism, or gut dysbiosis, has been linked to HF’s pathogenic disease outcomes. While understanding of the specific mechanisms behind HF-related gut dysbiosis is limited, research has illustrated the effects of several gut microbial metabolites such as short-chain fatty acids, bile acids, trimethylamine N-oxide, amino acid metabolites, and phenylacetylglutamine and their implications in HF pathogenesis and overall cardiac health. The use of gut microbial metabolites as diagnostic biomarkers and therapeutic targets for HF demonstrates great clinical potential. Further insight into the complex gut microbiome–host interactions opens the door to improved treatment options and more comprehensive and personalized HF care. Keywords

Gut microbiome · Gut dysbiosis · Heart failure · TMAO · Cardiovascular

Introduction Cardiovascular disease (CVD) is the leading cause of death globally, responsible for approximately 17.9 million lives each year (World Health Organization 2021). Specifically, heart failure (HF) has been identified as a global pandemic, affecting at least 26 million patients across the world (Savarese and Lund 2017). Heart failure is a chronic, progressive condition in which the heart has an impaired ability to fill or eject blood (Savarese and Lund 2017). The heart cannot effectively pump enough blood to maintain the body’s metabolic and physiological needs (Savarese and Lund 2017). Despite major advances in treatment that significantly reduce HF hospitalization rates and risk of death, patients’ prognosis after a first hospitalization remains poor with a mortality rate of over 50% during the next 5 years (Branchereau et al. 2019). Thus, novel therapeutics and preventative strategies for HF are required to treat and manage the chronic illness. Accumulating evidence suggests that the gut microbiome plays a significant role in the development and progression of HF and its comorbidities. Growing data

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shows a correlation between imbalances in gut microbiome composition and function and the development of HF. Alterations of the gut microbiome composition may affect host metabolism. The gut microbiome also interacts with the immune system, modulating the host’s response to infection, inflammation, and other stimuli. In patients with HF, the gut microbiome affects the production and activation of cytokines. In this chapter, we will discuss the interplay between the gut microbiota and HF and evaluate evidence linking the gut microbiome’s role in HF pathogenesis. We will discuss several gut microbiota-mediated metabolic pathways and their products that have been implicated in HF. We will close with potential therapeutic strategies that target gut microbiota to treat and prevent HF.

Introducing the Gut Microbiome The gut microbiome is a complex and dynamic system that plays a crucial role in maintaining human health. It is a diverse community of microorganisms that inhabit the digestive tract and interact with each other, as well as with the host, in a symbiotic relationship. The gut microbiome consists of communities of bacteria, fungi, archaea, and viruses in the human intestine (Tang et al. 2019a). There are over 2000 species of organisms co-existing with the human body and a healthy human adult contains approximately 100 trillion bacteria in the gut (Harikrishnan 2019). Crucial for the body’s metabolic system and physiological processes, the gut microbiota aid in food digestion, regulate the mucosal barriers, filter and metabolize nutrients, generate vitamins and hormones, and produce signaling molecules for the organ systems (Neish 2009). The gut microbiota is largely responsible for immune system stimulation, helping to defend against pathogens and forming a symbiotic relationship with their human host to maintain overall health (Neish 2009).

Healthy Gut Microbiome The human gastrointestinal tract hosts a dynamic and diverse community of microbes. The small intestine is home to trillions of microorganisms such as bacteria, fungi, archaea, and viruses, the majority of which are bacteria (Tang et al. 2019a). The known gut microbial community is dominated by bacteria in the phyla: Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, and Verrucomicrobia (Tang et al. 2019a). In healthy guts, the anaerobic phyla Bacteroidetes and Firmicutes account for more than 90% of the total bacterial population (Qin et al. 2010). Originally, traditional culture-based approaches were utilized to evaluate microbial richness and diversity in individuals (Tang et al. 2019a). Culture-based methods allow information about bacterial growth and metabolism to be directly obtained (Tang et al. 2019a). However, the process is tedious and time consuming (Tang et al. 2019a). Recent efforts have focused on culture-independent and next-generation sequencing to determine microbial organism identity and abundance utilizing nucleic sequences (Lagier et al. 2016). Two common strategies are the targeted

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amplicon sequencing and the whole genomic shotgun sequencing. Targeted amplicon sequencing uses the hypervariable region of the bacterial 16S ribosomal RNA to identify sequence differences of the present bacteria (Lefterova et al. 2015). Whole genomic shotgun sequencing allows for a more specific taxonomic and functional understanding of the present bacteria by using high-throughput genomic sequencing along with advanced computational bioinformatics (Lefterova et al. 2015). Although these methods mark advancements in gut microbial sequencing, they pose limitations in their clinical applications (Cao et al. 2017). The gut microbiota is essential for the human body and health by synthesizing important vitamins and minerals, aiding in nutrient absorption, promoting mucosal immune system development, establishing a barrier to harmful compounds, and producing diverse chemical mediators (Rowland et al. 2018). These crucial functions help maintain homeostasis in the gut microbiome (Rowland et al. 2018). The gut microbiome is an ecosystem comparable to the body’s endocrine system due to its ability to produce substances that absorb into the bloodstream and signal organ systems (Schiattarella et al. 2019). Due to genetic and environmental factors, there is variation in microbial composition, richness, abundance, and diversity between individuals. The microbial community develops during childhood, stabilizing during adulthood (Ahmad et al. 2019). However, changes in environmental factors such as diet, hygiene, and antibiotic use may alter one’s microbial diversity and composition (Tang et al. 2017). Imbalance in gut microbiota richness and composition is known as dysbiosis (Branchereau et al. 2019). These alterations in gut microbial communities have been associated with numerous diseases such as HF, atherosclerosis, and hypertension (Ahmad et al. 2019). Indeed, patients with various comorbidities (e.g., renal insufficiency, HF) have consistent microbial composition patterns that are characterized by a reduction in bacterial diversity, a decrease in abundance of beneficial bacteria, such as Bifidobacterium and Lactobacillus, and an increase in harmful bacteria, such as Proteobacteria and Firmicutes (Tang et al. 2019a). These changes are often associated with systemic inflammation and oxidative stress, which are known to contribute to disease progression (Tang et al. 2019b) (Fig. 1).

The Gut Hypothesis of Heart Failure The gut microbiota can be directly linked to HF pathogenesis through the “gut hypothesis of HF,” which postulates that HF can promote changes in microbial composition of the gut (Tang et al. 2017). Such host–microbial interactions can modulate various pathogenic processes in HF including inflammatory pathways and pathologic immune responses (Tang et al. 2017). Indeed, studies have demonstrated that the presence of cardiac dysfunction can directly lead to hemodynamic imbalance that affects intestinal mucosa structure and function (Tang et al. 2017). Specifically, heightened gut venous pressures, decreased blood flow in the splanchnic arteries, and decreased cardiac output can result in gut ischemia and intestinal hypoperfusion (Sandek et al. 2014). Systemic venous congestion along with ischemia disrupts the intestinal mucosa and creates changes in the gut barrier function (Sandek et al.

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Fig. 1 Contributions of gut microbiome in the pathophysiology of worsening heart failure

2007). Such changes increase gut permeability, gut dysbiosis, bacterial infection, and the presence of circulating toxins (Mamic et al. 2021). With increased intestinal permeability, it is easier for bacterial endotoxin and metabolites to enter the systemic circulation, promoting inflammation and immune responses (Mamic et al. 2021). These endotoxins can contribute to HF’s underlying inflammation by producing pro-inflammatory cytokines (Mamic et al. 2021). Inflammatory cytokines and endotoxins also increase intestinal permeability, generating a vicious cycle of systemic inflammation and HF progression (Mamic et al. 2021). This increased gut mucosal permeability, referred to as a “leaky gut,” is the primary source of systemic inflammation in HF (Mamic et al. 2021), although this phenomenon may be more prominent in those with more advanced diseases especially with elevated filling pressures (Kitai et al. 2021).

Gut Dysbiosis Patterns Associated with Heart Failure A variety of changes in human gut microbiota have been correlated with HF. Patients with HF have increased gut microbial density and disruption of the epithelial mucus layer, the barrier that prevents interactions between the gut microbiome and human immune system (Sandek et al. 2014, 2007). Patients with HF were more susceptible to infections by certain bacteria such as Clostridium difficile (Mamic et al. 2016). This finding supports that HF may be correlated with gut microbiome depletion and the abundance of specific Gram-negative bacteria (Mamic et al. 2016). Other bacteria observed to be rich in patients with HF include Ruminococcus gnavus, Bacteroidetes, Prevotella, Hungatella, and Succiclasticum (Cui et al. 2018; Mayerhofer et al. 2020). These microbes colonize patients with chronic HF more frequently and at an increased

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abundance than healthy individuals (Cui et al. 2018; Mayerhofer et al. 2020). Analysis of intestinal surfaces have shown heightened bacterial overgrowth with mucosal biofilm and bacterial adhesions in patients with HF compared to healthy individuals (Sandek et al. 2007). Overall, the gut microbiome in patients with chronic HF has lessened diversity and diminished abundance of microbes with anti-inflammatory properties. These findings are the first steps toward characterizing gut microbiota in HF patients. However, reproducibility still must be seen when identifying and comparing bacterial communities. Increased technologies to develop more comprehensive and standardized gut microbiota identification methods will help characterize gut microbiota more effectively in HF patients. One should caution, however, that analyses of gut microbial compositions have primarily relied on fecal sampling, which has well-recognized limitations and may not reflect the microbial population in various colonic segments (Tang et al. 2019b).

Gut Microbial Metabolites – Physiological Mediators The gut microbiome also influences the metabolic processes in the gut and elsewhere in the body. The gut microbiota influences the host body through circulating small molecules known as metabolites (Mamic et al. 2021). The gut microbiome filters dietary nutrients and produces these bioactive metabolites, which are then reabsorbed into the bloodstream to function as signaling molecules (Chaikijurajai and Tang 2021). These microbial metabolites are the avenue through which the gut microbiome interacts with the human body and organ systems in an endocrine manner (Tang et al. 2019a). Studies have demonstrated that these metabolites may bind directly to target host receptors or undergo biochemical modifications by host enzymes (Tang et al. 2019a). Indeed, various microbial metabolites support physiological functions in digestion and exert hormonal effects to promote host health through the immune system, digestive system, and circulatory system (Branchereau et al. 2019). In patients with HF, the gut microbiome contributes to the production of short-chain fatty acids (SCFA) and various amino acids that may influence oxidative stress and influences the synthesis and metabolism of bile acids that may play a key role in the regulation of cholesterol levels, energy metabolism, and glucose homeostasis (Fig. 2).

Short Chain Fatty Acids Some gut microbes produce short-chain fatty acids (SCFA) from the fermentation of indigestible nutrients, such as complex carbohydrates, dietary fiber, resistant starch, and prebiotics (Topping and Clifton 2001). SCFAs, defined by containing one to six carbons in length, include acetate, propionate, and butyrate, and are instrumental in providing energy, protecting intestinal mucosa, and functioning as signaling molecules (Ohira et al. 2017). SCFAs bind to specific G-protein-coupled receptors to regulate blood pressure and autonomic functions (Chaikijurajai and Tang 2021). By exerting

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Fig. 2 Physiologic consequences of gut microbial metabolites

physiological effects on the host’s organ systems, SCFAs help control autonomic systems, blood pressure, and inflammatory responses (Tang et al. 2019a). Specifically, SCFAs regulate mucus production, tight junction protein expression, and ileal motility to maintain intestinal barrier function (Branchereau et al. 2019). SCFAs exhibit biochemical functions such as histone deacetylase inhibition, chemotaxis and phagocytosis regulation, induction of reactive oxygen species, and cell proliferation (Ohira et al. 2017). Butyrate and propionate play a key role in regulating blood pressure and vascular tone (Pluznick 2014; Pluznick et al. 2013). Through regulatory T-cell proliferation and colonocyte PPAR-γ activation, butyrate prevents pathogen growth and promotes anti-inflammatory effects (Pluznick 2014; Pluznick et al. 2013). Propionate maintains immune homeostasis and reduces cardiac hypertrophy through regulatory T-cell activation (Pluznick 2014; Pluznick et al. 2013). Acetate is protective against cardiac hypertrophy and fibrosis by downregulating a transcription factor (Egr1) that regulates inflammation, cardiorenal fibrosis, and ventricular hypertrophy (Pluznick 2014; Pluznick et al. 2013). Reduction of SCFA microbial producers and disruption in SCFA biochemical pathways has been linked to HF (Mamic et al. 2021). Depletion of SCFA producers is correlated with excess pathogen growth, HF-associated gastrointestinal symptoms, and insulin resistance (Mamic et al. 2021). Due to SCFA’s function in regulating vascular smooth muscle tole, decreased SCFA production may result in heightened cardiac afterload (Mamic et al. 2021). HF patients have been shown to have a significantly lower amount of SCFA-producing gut microbes (Chaikijurajai and Tang 2021). SCFAs have a protective role in cardiac repair and immune responses (Chaikijurajai and Tang 2021). Mice fed a diet without fiber developed cardiac hypertrophy, fibrosis, and hypertension (Kaye et al. 2020). However, after

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dietary supplementation with SCFAs, these conditions were alleviated (Kaye et al. 2020). Another study supported these findings as mice fed a high-fiber diet with SCFA acetate supplementation had reduced hypertension, cardiac hypertrophy, and fibrosis (Marques et al. 2017). Similarly, a human study with 84 patients with HF showed that a low fiber diet was correlated with gut microbiome dysbiosis and an increased risk of mortality and heart transplantation (Mayerhofer et al. 2020). Although SCFAs appear to exhibit a cardioprotective role in HF, more research is needed to determine the benefits of SCFAs in patients with HF.

Bile Acids The liver produces primary bile acids, emulsifiers, from cholesterol (Chaikijurajai and Tang 2021). Primary bile acids are then metabolized into secondary bile acids by gut bacteria and recycled through the enterohepatic cycle to function as signaling molecules for the host’s organ systems (Branchereau et al. 2019). Bile acids help with digestion and absorption of dietary fats and fat-soluble vitamins as well as lipid and glucose metabolism (Branchereau et al. 2019). Bile acids can regulate energy metabolism by activating nuclear receptors such as G-protein-coupled receptor 1 (TGR5) and the farnesoid X receptor (FXR) (Branchereau et al. 2019). These receptors are expressed in cardiomyocytes and help bile acids exert a variety of pleiotropic effects such as inflammation and fibrosis control and intestinal barrier restoration (Mamic et al. 2021). Bile acids’ function as signaling molecules has been shown to play a major role in cardiovascular physiology and HF-associated gut dysbiosis. Evidence suggests that bile acids can exert both favorable and unfavorable effects on cardiac health and function, with the full role of bile acids in HF development still poorly understood (Chaikijurajai and Tang 2021). A recent study found that HF patients exhibited decreased serum levels of primary bile acids and an increased ratio of secondary bile acids to primary bile acids compared to control patients (Mayerhofer et al. 2017). Another study found that HF patients treated with a specific secondary bile acid, ursodeoxycholate, improved peripheral blood flow (Tousoulis et al. 2012). In vitro, FXR agonists heightened expression of cardiomyocyte FXR expression, triggered myocyte apoptosis, and decreased myocyte viability (Branchereau et al. 2019). After myocardial ischemia/reperfusion, FXR levels increased along with reduction in myocardial apoptosis and improvement of cardiac health and function (Branchereau et al. 2019). Interestingly, contrary results were seen when evaluating the TGR5 receptor. Mice induced with HF given dietary supplements of a specific TGR5 agonist experienced improved ventricular remodeling and contractile dysfunction (Eblimit et al. 2018). Similarly, after induced aortic constriction, mice with deletion of the TGR5 receptor experienced increased mortality and contractile dysfunction compared to control mice (Eblimit et al. 2018). These conflicting results on the role of bile acids in HF suggest that bile acid imbalance may be associated with HF development and progression. It is important for the optimal values of primary and secondary bile acids to be known to target bile acids as a potential biomarker and therapeutic target for HF.

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Amino Acid Metabolites Gut microbes also ferment and metabolize dietary amino acids such as tryptophan, tyrosine, and phenylalanine (Chaikijurajai and Tang 2021). These amino acids are metabolized by the intestinal microbiome into “uremic toxins” (Chaikijurajai and Tang 2021). Tryptophan is metabolized into indole, which is then converted into indole-3-propionate (IPA) or indoxyl sulfate (IS), important mediators of cardiotoxicity and vascular inflammation (Chaikijurajai and Tang 2021). Specific gut microbes such as Clostridium sporogenes help produce IPA, which has been shown to protect the intestinal mucosal barrier by decreasing permeability as well as reduce inflammation and gut dysbiosis (Chaikijurajai and Tang 2021). IPA regulates secretion of GLP-1, improving insulin sensitivity (Mamic et al. 2021). IPA seems to exert cardioprotective effects in HF patients as patients with chronic HF display significantly lower amounts of circulating IPA compared to control patients (Mamic et al. 2021). In contrast, gut microbial metabolism of dietary amino acids can also result in harmful metabolite production. IS appears to stimulate maladaptive renal and cardiac remodeling effects through upregulation of cardiac hypertrophy, cardiorenal fibrosis, and inflammation (Mamic et al. 2021). Similarly, another study showed that heightened levels of IS are correlated with diastolic dysfunction and negative cardiac outcomes (Shimazu et al. 2013). Tyrosine and phenylalanine are metabolized into p-cresyl sulfate (PCS), a metabolite that can activate RAAS and trigger cardiomyocyte apoptosis (Mamic et al. 2021). PCS also negatively impacts cardiomyocytes’ calcium regulation and disrupts gap junctions (Peng et al. 2012). PCS was observed to better indicate mortality and hospitalization in HF patients compared to IS (Wang et al. 2016). The function of these uremic toxins plays an important role in HF development and progression and should especially be considered in patients with cardiorenal syndrome (Chaikijurajai and Tang 2021). These amino acid metabolites have the potential to serve as biomarkers for HF with further investigation. In addition to these “uremic toxins,” recent evidence has pointed toward the significance of the valine-leucine-isoleucine degradation family in HF progression (Tang 2022). This degradation pathway, the branched chain amino acid (BCAA) catabolism pathway, plays a role in epithelial cell metabolism, transportation, growth, and differentiation (McGarrah and White 2023). HF involves an imbalance between the availability of BCAA and its actual usage (McGarrah and White 2023). As a result, BCAA oxidation is hindered, increasing the circulating levels of BCAAs, which then detriments cardiac health and function (McGarrah and White 2023). This mechanism is seen in HF patients as patients with HF having reduced ejection fraction have higher levels of BCAA than patients with preserved ejection fraction or without HF at all (Hunter et al. 2016). Increased levels of BCAA have been shown to hinder mitochondrial function, autophagy function, oxidative stress mechanisms, and calcium homeostasis (Tang 2022). The BCAA catabolism pathway produces myocardial branched chain alpha-keto acids (BCKA) first; this metabolite shows heightened levels because of the downregulation of the branched chain alpha-

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keto acid dehydrogenase (BCKDH) complex (Tang 2022). As a result, BCKA levels remain high due to decreased catabolism. Interestingly, animal studies have shown that restoring BCAA catabolism avoids the detrimental BCKA buildup and prevents cardiac impairment (Tang 2022). These findings suggest that preserving BCAA oxidation and reducing BCAA levels plays an important role in maintaining cardiac health and function. It is important to explore strategies that target BCAA pathways and maintain BCAA catabolism. A potential strategy is the stimulation of the mitochondrial branched-chain aminotransferases (BCATm), which decrease BCAA levels; however, this may also result in elevated BCKA levels (Karwi and Lopaschuk 2022). Other strategies include the direct inhibition of BCKA dehydrogenase kinase, which decreases BCAA and BCKA levels and the upregulation of mitochondrial protein phosphatase 2C (PP2Cm), which also decreases both BCAA and BCKA levels (Karwi and Lopaschuk 2022). These approaches have only been investigated in preclinical models so further study is required.

Trimethylamine N-oxide Trimethylamine N-oxide (TMAO), the most widely studied gut microbiome-related metabolite, is another biomarker, produced by gut microbes from dietary nutrients such as choline, betaine, phosphatidylcholine, and L-carnitine (Branchereau et al. 2019). These dietary compounds, mainly found in animal products such as eggs, red meat, dairy, and fish, are metabolized into trimethylamine (TMA) and converted into TMAO in the liver using flavin monooxygenases (Mamic et al. 2021). The enterohepatic cycle aids in choline recycling, generating a consistent supply for gut microbes (Chaikijurajai and Tang 2021). TMAO plays a role in lipid and glucose homeostasis and is linked to diseases such as chronic kidney disease, diabetes, and atherosclerosis (Ahmad et al. 2019). Plasma TMAO levels are also positively correlated with coronary artery disease, peripheral artery disease, and myocardial infarction (Mamic et al. 2021). Although the specific TMAO host receptor is still unknown, studies have shown that TMAO mediates the metabolism of cholesterol, sterol, and bile acids, triggers platelet sensitivity, promotes vascular damage, and disrupts ventricular remodeling (Mamic et al. 2021). These negative effects result in a proinflammatory and proatherogenic outcome. Several large clinical studies have demonstrated that circulating TMAO levels are associated with increased risk of major adverse cardiac events. Hence, TMAO may serve as a direct connection between gut dysbiosis and HF progression and development. Overall, patients with HF had higher TMAO levels compared to controls, and there is a correlation between higher circulating TMAO levels with greater severity of diastolic dysfunction and functional class (Tang et al. 2015b). This finding suggests that elevated venous congestion may impact the gut microbiome even though that was not observed in the decompensated state with direct hemodynamic correlations (Tang et al. 2015b). Nevertheless, there was a positive correlation between TMAO and inflammation markers and endothelial dysfunction in patients

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with HF, reinforcing how TMAO’s effect on the gut microbiome disrupts cardiac function (Mamic et al. 2021). Other studies have shown that TMAO levels are directly associated with HF severity, unfavorable adverse outcomes in acute HF patients, and an increased mortality risk (Tang et al. 2015b). TMAO’s specific mechanism of action remains unknown. Animal model studies suggest that manipulation of the gut microbial TMAO pathway may contribute to HF development (Branchereau et al. 2019). When inducing HF in mice, ventricular remodeling, fibrosis, and cardiac function were disrupted in mice fed a TMAO diet when compared with the control diet (Branchereau et al. 2019). Another study supported these findings, finding that TMAO treatment triggered fibrosis and cardiac hypertrophy in rats (Branchereau et al. 2019).

Phenylacetylglutamine Phenylacetylglutamine (PAGln) is another gut microbiota-derived metabolite that may induce cardiovascular events by activating platelets and increasing the risk of thrombosis (Romano et al. 2022). Original observations from patients with chronic kidney disease linked elevated PAGln levels with increased cardiovascular mortality risk (Poesen et al. 2016). Elevated PAGln with more advanced HF, and Romboutsia and Blautia were negatively correlated with PAGln while Esherichia-Shigella was positively correlated with PAGln (Zhang et al. 2022). Recently, several cohort studies revealed that elevated PAGln levels were observed in patients with HF, and mechanistic studies reveal both PAGln (and its murine counterpart, phenylacetylglycine) attenuate cardiomyocyte sarcomere contraction, and induce B-type natriuretic peptide gene expression in both cultured cardiomyoblasts and murine atrial tissue (Romano et al. 2022). PAGln also increased the susceptibility of ventricular tachyarrhythmia in thoracic aortic coarctation-induced HF mouse model by activating the TLR4/AKT/mTOR signaling pathway (Fu et al. 2023).

Lipopolysaccharide Lipopolysaccharide (LPS) is an endotoxin that is located on the outer membrane of Gram-negative bacteria and stimulates proinflammatory cytokine production and release (Niebauer et al. 1999). LPS is a microbial product and easily enters blood circulation when gut barrier permeability increases (Niebauer et al. 1999). LPS plays a role in the “leaky gut” seen in HF by downregulating intestinal tight junctions (Niebauer et al. 1999). To promote inflammatory cytokine production and release, induce insulin resistance, and exert pro-atherothrombotic effects, LPS attaches to the host TLR 4 (Niebauer et al. 1999). This binding to the host TLR 4 results in cardiac and vascular dysfunction (Niebauer et al. 1999). Patients with decompensated HF have increased LPS levels due to its role in the “leaky gut” (Branchereau et al. 2019).

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Role of Gut Microbiome in Heart Failure Comorbidities The gut microbiome not only plays a role in HF progression, but it also actively contributes to the development of other conditions that can arise as complications of HF. These conditions and diseases then exacerbate the high morbidity and mortality of HF patients, worsening their outcome (Mamic et al. 2021). It is important to understand how the gut microbiome is connected to such HF-related comorbidities to prevent pathogenesis of disease and identify potential therapeutic targets (Fig. 3).

Chronic Kidney Disease and Cardiorenal Syndrome HF and chronic kidney disease (CKD) are closely connected, with the cardiorenal syndrome worsening the clinical outcome of HF (Löfman et al. 2016). More than 50% of patients with chronic HF have comorbid CKD (Löfman et al. 2016). Both conditions involve the disruption of the sympathetic and renin-angiotensin-aldosterone systems, oxidative stress, endothelial dysfunction, increased inflammation, and improper fluid handling (Löfman et al. 2016). As comorbidities, each disease worsens the pathogenesis of the other disease, establishing a dangerous cycle. CKD patients have an altered gut microbiota, with an increase in circulating urea and uremic toxins in the gut (Vaziri et al. 2013). The products of the influx of urea, ammonia, and ammonium hydroxide are toxic to epithelial cells and harm the intestinal mucosal barrier (Vaziri et al. 2013). This hydrolysis of urea into ammonia and ammonium hydroxide appears to be the source of intestinal barrier dysfunction

Fig. 3 Therapeutic targets to modulate gut microbial metabolism and dysbiosis

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in CKD, allowing for movement of uremic toxins and gut bacterial DNA, promoting inflammation (Tang et al. 2017). TMAO has been connected to the progression of both HF and CKD (Tang et al. 2017). Patients with CKD have been shown to have elevated levels of TMAO in their plasma, with such high TMAO levels correlated to their increased mortality and loss of kidney function (Tang et al. 2015b). Animal models have shown that TMAO has a direct link to the progression and dysfunction of renal fibrosis and is correlated with negative long-term outcome and prognosis in patients with CKD (Mamic et al. 2021). When supplementing a diet with choline, researchers found exacerbated renal fibrosis and worsened renal function with high levels of tubular injury markers, suggesting the role of TMAO in CKD progression (Tang et al. 2015a). Further study is needed to explore TMAO metabolism in CKD and to target its prothrombotic effects and endothelial disruption to develop therapeutic strategies for treatment.

Insulin Resistance Insulin resistance (IR) is a common comorbidity and complication of chronic HF, impacting more than 50% of HF patients (Doehner et al. 2005). It is an independent risk factor of HF and contributes to a negative overall and long-term outcome in HF patients (Doehner et al. 2005). IR plays a role in HF progression by directly disrupting cardiac muscle and indirectly producing harmful systemic effects (Doehner et al. 2005). The exact mechanism in which IR develops independently after HF onset is still unknown. It is likely that gut microbiome imbalance and dysregulation play a role in HF-related IR development (Mamic et al. 2021). There are many similarities between gut microbiome dysregulation in patients with HF and in patients with IR (Mamic et al. 2021). Both patients with chronic HF and IR lack SCFA-producers such as Eubacterium rectale and Faecalibacterium prausnitzii and have elevated levels of pathogenic bacteria such as Escherichia coli (Pedersen et al. 2016). Patients with HF and IR have been shown to display heightened levels of branched chain amino acids and their metabolites (leucine, isoleucine, and valine) (Pedersen et al. 2016). Animal students have indicated that the ability to synthesize and metabolize these catabolites is directly related to IR development (Pedersen et al. 2016). Further research is needed to continue building on the relationship between HF and IR, pinpointing the gut microbiome’s function.

Cardiac Cachexia Cardiac cachexia or “body wasting” is a condition that HF patients often face where they lose an extreme amount of muscle, body fat, and bone (Genton et al. 2019). Cardiac cachexia represents a metabolic shift toward a more catabolic state, which has been shown to involve the gut microbiota (Mamic et al. 2021). Cardiac cachexia arises from disrupted intestinal permeability caused by altered splanchnic blood flow (Mamic et al. 2021). With elevated intestinal epithelial permeability, there is movement of gut bacteria and their products, promoting inflammation (Mamic et al. 2021).

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There is an increase in inflammatory cytokines such as IL-6 and TNF-α in these HF patients with comorbid cardiac cachexia (Genton et al. 2019). The inflammatory cytokines mediate skeletal myocyte apoptosis and endothelial disruption correlated with lowered blood supply to the muscles (Genton et al. 2019). The dysregulation in these processes leads to sarcopenia and skeletal muscle wasting (Genton et al. 2019). The gut microbiome plays a role in HF-associated cardiac cachexia through bowel wall permeability regulation (Mamic et al. 2021). One study found that in rats, colonization with Escherichia coli, Klebsiella pneumoniae, and Streptococcus viridans significantly increased bowel permeability (Mamic et al. 2021). Research has shown that bacteria’s attachment to the gut epithelium disturbs epithelial barrier function and promotes cytokine generation and thus inflammation (Mamic et al. 2021). Along with elevated epithelial barrier permeability, gut microbiome imbalance in patients with HF as well as cardiac cachexia results in significant fat and protein malabsorption (Mamic et al. 2021). This malabsorption is caused by the excess growth of pathogenic gut bacteria associated with chronic HF, which disrupts metabolic functioning by lowering absorptions of key vitamins such as B12, folate, and K (Mamic et al. 2021). These key vitamins are necessary for protein metabolism (Mamic et al. 2021). Evidence suggests that HF-associated gut microbiome dysbiosis drives cardiac cachexia, but more studies are required to fully understand the host–microbiota interactions to properly prevent and treat cardiac cachexia.

Strategies to Target the Gut Microbiome to Treat Heart Failure Due to the gut microbiota’s clear role in HF and other metabolic diseases, it serves as a promising target for therapeutics to prevent and treat HF. The gut microbiome affects susceptibility to HF by metabolizing a variety of diverse dietary components that exert beneficial and adverse cardiovascular effects. Modulating the gut microbiome will provide more insight on how gut dysbiosis damages cardiovascular health. Scientific evidence and clinical observations demonstrate the connection between the altered gut microbial community, its metabolites, and the risk for HF, making gut microbiome manipulation an attractive approach for preventing and treating HF. Here, we will discuss microbiota-targeted strategies to treat HF: dietary and lifestyle interventions, prebiotic and probiotic therapy, gut microbial enzyme inhibition, and fecal microbiota transplant.

Dietary and Lifestyle Interventions Diet plays a tremendous role in the gut microbiome as a balanced diet helps maintain a healthy and diverse flora which is important for digestion and nutrient absorption. The gut microbiota metabolizes food into different dietary components that then impact the host’s metabolism, organ systems, and immune system. For example, dietary components such as fiber, macronutrients, vitamins, cholesterol, and polyphenols generate metabolites such as SCFAs and bile acids that then influence the

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host’s physiological systems. A nutritional approach to HF, focusing on dietary intake, has been shown to exert cardioprotective effects (Mamic et al. 2021). The Mediterranean diet, a diet rich in unprocessed foods such as fruits, vegetables, legumes, fish, and whole grains and low in meat products, refined sugar, and processed foods has been shown to promote healthy microbial composition (Mamic et al. 2021). A multicenter and randomized study found that the Mediterranean diet was correlated with a 30% relative reduction in heart attack, stroke, and cardiovascular-related death (Estruch et al. 2018). The Mediterranean diet has also been shown to delay HF and reduce incidence of the disease as a study involving 10,950 individuals found that the Mediterranean diet reduced HF incidence by 70% (Liyanage et al. 2016). One hypothesis suggested that these cardioprotective benefits may arise from the TMA lyase inhibitor, 3,3-dimethyl-1-butanol (DMB), which is found in extra virgin olive oil, commonly used in the Mediterranean diet (Mamic et al. 2021). Similarly, the Dietary Approaches to Stop Hypertension (DASH) diet and additional plant-based diets are correlated with a reduced risk of HF (Levitan et al. 2009). Plant-based diets are beneficial to the cardiovascular system by bringing in antioxidants, inhibiting LDL cholesterol, elevating HDL cholesterol, and lowering overall cholesterol concentrations (Mamic et al. 2021). The plant-based diets’ richness in fiber and complex carbohydrates help reduce inflammation and maintain insulin sensitivity (Mamic et al. 2021). Fermentation of fiber produces SCFAs, which benefit physiological functions by maintaining energy homeostasis, insulin sensitivity, blood pressure, and immune function (Mamic et al. 2021). The benefits of plant-based diets and the Mediterranean diet on HF outcome and heart health has been investigated in various populations. Studies have found these diets to increase gut microbial diversity and the amount of microbes associated with fibrolytic activity, SCFA generation, and anti-inflammatory potential. A study found that in HF patients, increased fiber intake was correlated with elevated bacterial diversity and abundance, suggesting that HF-related gut dysbiosis is associated with low fiber intake (Mayerhofer et al. 2020). Increasing fiber intake and modifying HF patients’ diet is a potential therapeutic strategy (Mayerhofer et al. 2020). The cardioprotective impact of fiber has been pinpointed in animal studies as well. Mice assigned to a fiber-depleted diet developed cardiac hypertrophy, hypertension, and fibrosis (Marques et al. 2017). When these mice were supplemented with fiber and SCFA, their conditions were reversed (Marques et al. 2017). This discovery suggests that fiber and SCFA supplementations are cardioprotective interventions that alter gut microbial composition and decrease gut dysbiosis. Salt intake is another important dietary factor that influences gut microbial composition and function (Mamic et al. 2021). Past research in mouse models has shown that a high salt diet decreases levels of the microbe Lactobacillus murinus (Wilck et al. 2017). This microbe is important for anti-inflammatory and cardioprotective effects (Wilck et al. 2017). Other studies show similar results with a high salt diet depleting important gut microbes, expanding pro-inflammatory cytokines, and increasing blood pressure (Wilck et al. 2017). Restricting and managing salt intake is a crucial part of HF treatment and is recommended by multiple guidelines (Mamic et al. 2021). Further research and data are required to provide HF

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patients with more personalized nutritional guidelines and recommendations based on their unique gut microbiome. In addition to diet, exercise training could be another promising intervention to promote health in the gut microbiome (Mailing et al. 2019). Exercise training is an established prevention strategy but it could also be complementary as a therapy for HF (Mailing et al. 2019). While there is insufficient research to link exercise training to HF-related microbiota modifications, mouse studies have shown that diabetic mice assigned to exercise exhibited microbiota higher in Firmicutes species and lower in Bacteroides and Prevotella species (Lambert et al. 2015). Similarly, a study investigating amateur half-marathon runners found that certain microbiota species were significantly higher before and after running (Zhao et al. 2018). A healthy and balanced lifestyle based on diet and exercise training has the potential to treat HF and various diseases, due to its positive impact on the gut microbiome.

Prebiotics and Probiotics Prebiotics are food substances and compounds that are indigestible by the host and exhibit a beneficial effect on gut microbial composition and function (Tang et al. 2019b). Prebiotics are metabolized mainly by the gut microbiome and promote growth of favorable microbial organisms (Tang et al. 2017). Prebiotics include indigestible dietary fibers, oligosaccharides, and other complex saccharides (Tang et al. 2017). In the presence of prebiotics and fiber-rich substances, gut microbes will prioritize metabolizing dietary fiber instead of the gut mucus lining (Desai et al. 2016). This preferential fermentation of dietary fiber exerts anti-inflammatory effects for the host, lowers gut dysbiosis, maintains insulin sensitivity, and promotes overall metabolic regulation (Desai et al. 2016). As mentioned above, dietary fiber fermentation also produces SCFAs, which protects physiological function and exerts cardioprotective effects (Mamic et al. 2021). Prebiotics such as insulin are able to improve gut microbiome function and diversity (Trøseid et al. 2020). A past study found that dietary supplementation with insulin boosted insulin sensitivity and decreased systemic inflammation markers by increasing SCFA propionate production (Trøseid et al. 2020). Another study showed that a prebiotic, β-glucan, significantly decreased low-density lipoprotein and total cholesterol in healthy individuals and improved overall endothelial function by producing SCFA (Ahmad et al. 2019). Although current research on utilizing prebiotics as therapy for HF is limited, targeting microbial SCFAs production through prebiotic and insulin supplementation offers potential future strategies to treat HF and cardiovascular disease. Probiotics are live microorganisms consumed by the host that interact with the gut microbial community to produce a healthy microbial balance and alter metabolic output (Mamic et al. 2021). Probiotic food products usually consist of Bifidobacteria, Lactobacilli, Lactococci, and Streptococci (Azad et al. 2018). Probiotics exert benefits such as pathogen inhibition, immune function support, and protection against inflammation (Ahmad et al. 2019). Evidence suggests that probiotics can have cardioprotective effects by modulating cardiac remodeling and

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function (Azad et al. 2018). Probiotics’ potential benefits in HF alleviation has been shown mainly in animal studies. One study found that the probiotic, Lactobacillus plantarum 299v, exerted cardioprotective effects in rats, by improving cardiac function and reducing smaller left ventricular infarct size after myocardial ischemia/reperfusion (Lam et al. 2012). Similar cardioprotective results were seen in a rat model after supplementation of the probiotic Lactobacillus rhamnosus GR-1 (Gan et al. 2014). After induced ischemic HF, probiotic supplementation resulted in improved systolic and diastolic left ventricular function as well as in left ventricular hypertrophy (Gan et al. 2014). Research on the effects of probiotic therapy on HF in humans has not been studied thoroughly yet, with only one published study (Mamic et al. 2021). This study found that HF patients treated with the probiotic Saccharomyces boulardii (S. boulardii) exhibited improved left ventricular systolic function and left atrial size (Costanza et al. 2015). These findings are very preliminary but signified an overall decrease in inflammatory and biochemical biomarkers, suggesting potential cardioprotective effects (Costanza et al. 2015). An ongoing human controlled clinical trial by Gut-Heart is randomly assigning patients with stable HF to receive either S. boulardii, antibiotic rifaximin, or no treatment (Mayerhofer et al. 2018). The study will evaluate changes in left ventricular systolic function, quality of life, functional capacity, and different biomarkers of inflammation and gut permeability (Mayerhofer et al. 2018).

Microbial Enzyme Inhibition Early HF studies have looked at decontaminating the gut with antibiotics to decrease inflammation and bacterial translocation (Trøseid et al. 2020). No clear clinical impact has been shown although this technique has effectively decreased inflammatory biomarkers (Trøseid et al. 2020). While antibiotic treatment can reduce TMAO levels and inflammation, long-term use of antibiotics may promote dysbiosis and lead not only to multidrug resistance but other adverse consequences such as increased risk of aortic ruptures, aortic dissection, and other cardiovascular events (Trøseid et al. 2020). Targeting gut microbial enzymes that are not found in the host in a safe manner may avoid these adverse cardiovascular effects. This strategy is used to target the TMAO pathway and inhibit the conversion of dietary choline to TMA, decreasing TMAO levels (Tang et al. 2017). In an experimental mouse model, DMB (a trimethylamine formation inhibitor as mentioned above) was shown to decrease TMAO plasma levels as well as cardiac hypertrophy, lung congestion, and reverse impaired cardiac function (Wang et al. 2015). DMB also decreased foam cell formation and the amount of atherosclerotic plaques (Wang et al. 2015). DMB is naturally found in red wine, extra virgin olive oil, and balsamic vinegar, core ingredients in the Mediterranean diet (Wang et al. 2015). Other enzymes can also be inhibited in the TMA/TMAO pathway, which results in suppression of TMAO generation and decreased platelet aggregation and formation of artery clots in mice (Mamic et al. 2021). Although this strategy still needs to be implemented in human studies, manipulation and targeting of the TMAO pathway offers potential strategies to manage the progression of HF.

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Fecal Microbiota Transplant Fecal microbiota transplant (FMT) is a current radical intervention that entails the transplantation of stool and flora from a healthy individual into a diseased recipient’s GI tract (Mamic et al. 2021). This strategy has been effective by introducing a community of beneficial microorganisms and flora into a diseased intestinal system to restore function and dysbiosis (Tang et al. 2017). FMT is the established and most effective treatment of Clostridium dificile infection (Branchereau et al. 2019). No research has linked FMT to HF treatment but it is possible that the strategy could have protective effects since studies have shown its positive impact on gastrointestinal and systemic diseases (Mamic et al. 2021).

Conclusion Current research overall supports an important link between HF and gut microbiome dysbiosis. Gut microbiome dysregulation is key to understanding the pathophysiology of HF, and a crucial factor to be targeted in medicine. The complex host–microbe relationship involves metabolites such as SCFAs, bile acids, TMAO, amino acid metabolites, and PAGln, which serve as promising therapeutic targets. Metabolism of certain dietary components release SCFAs, which exhibit cardioprotective effects while the production of TMAO exerts pro-inflammatory and pro-atherogenic effects. Understanding the role these microbial metabolites play suggest promising therapeutic developments and strategies to treat and prevent HF. Although translating the potential of gut microbiomics into high-risk clinical settings to treat HF is complex, with a multidisciplinary and collaborative approach, the development and progression of HF can be managed. Further research and investigation into large population-based longitudinal studies is needed to gain a more thorough mechanistic understanding of the gut microbiome and the role its metabolites play in HF pathogenesis in order to generate therapeutic strategies. Disclosure Ms. Aiyer has no relationships to disclose. Dr. Tang served as consultant for Sequana Medical, Cardiol Therapeutics, Genomics plc, Zehna Therapeutics, Renovacor, WhiteSwell, Kiniksa, Boston Scientific, and CardiaTec Biosciences and has received honorarium from Springer Nature and American Board of Internal Medicine.

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Gut Microbiome and Cognitive Functions in Metabolic Diseases

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Anna Motger-Albertí and Jose´ Manuel Ferna´ndez-Real

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cognitive Function and Brain Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolic Diseases and Cognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolic Diseases and Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Gut–Brain Axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota and Its Relationship to Neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota and Cognitive Functions in Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiome and Cognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiome Is Associated with Brain Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiota and Mental Health in Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genes and Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

In recent years, there has been an increase in the study of metabolic illnesses and the gut microbiota. According to certain research, the pathogenesis and development of various metabolic illnesses, including type 1 and type 2 diabetes and obesity, may be significantly influenced by bacterial microbiome dysbiosis, and A. Motger-Albertí · J. M. Fernández-Real (*) Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute (IdibGi), Girona, Spain Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Girona, Spain Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Madrid, Spain e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_12

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additionally, it has affected brain functions through metabolism. Recently, human research has discovered specific microbiota that are favorable and unfavorable to brain function. For instance, the Firmicutes phylum, which includes Clostridium, Ruminococcus, and Eubacterium, was beneficial to cognition, whereas the Bacteroidetes and Proteobacteria were detrimental. These results might encourage more investigation into the connections between the gut and cognition and mental health as well as to study the mechanisms of these relationships. With a focus on the gut microbiota and its relationships with physiological systems, the novel results that have so far been made show promise for identifying new therapeutic targets through food and nutrition. Keywords

Gut microbiome · Metabolism · Cognition · Brain · Gut–brain axis

Introduction Cognitive decline is becoming increasingly common as people live longer, and is one of the world’s leading public health problems. The prevalence of dementia has risen sharply and is expected to increase by more than 78 million and 139 million by 2050 (Prince 2015). Obesity, on the other hand, is a metabolic disease of concern due to its exponential upward trend. Obesity has become a serious public health issue in recent decades, with hundreds of millions of people classified as overweight (Blüher, 2019; WHO 2019). Obesity and type 2 diabetes (T2D) are both complex metabolic disorders and in developed and developing countries, the prevalence of metabolic illnesses has risen dramatically in recent decades, with obesity leading the way. Environmental variables such as calorie intake and decreased physical activity have been attributed to the rapid rise in obesity and metabolic disease prevalence. Obesity and T2D are both complex metabolic disorders, and the microbiome has been shown to modulate several pathways involved in their progression. Because it promotes the release of gut hormones, the gut microbiota is vital for satiety signaling, thermogenesis, and energy balance. Firmicutes and Bacteroidetes are the most prevalent phylum, followed by Proteobacteria, Actinobacteria, Verrucomicrobia, and Fusobacteria (Blaut 2013). It has been demonstrated that the proximal gut intestinal tract (GIT) contains an enriched population of bacteria from the phyla Firmicutes and Proteobacteria, particularly from the genus Lactobacilli, as opposed to the distal GIT, which is primarily made up of bacteria from the phylum Bacteroidetes and Firmicutes, with special attention to the species Akkermansia muciniphila (Scheithauer et al. 2016). The microbial composition of the small intestine varies greatly from person to person and is constantly altered by both endogenous and exogenous factors. Although the host genome plays a key role in shaping the composition of the gut microbiota, other regional and environmental factors, such as nutrition, disease, lifestyle, hygiene, and medication, can affect population changes. There is evidence that individuals with

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metabolic disorders like obesity or T2D have gut dysbiosis, which can lead to inflammatory processes that affect both the peripheral neurological system (PNS) and the central nervous system (CNS). Fifty seven percent of gastrointestinal variability may be accounted for by dietary changes. In addition, because the microbiota may play a role in the etiology of obesity, there may be significant differences between people with and without obesity, thus impacting cognition. Despite the fact that preclinical research in murine models unequivocally ties the gut microbiota to changing brain development, morphology, function, and behavior, proving causal connections in humans is still challenging. The study of the microbiome and its impact on cognition began with significant discoveries in an animal model. This chapter will concentrate on the findings from the recent human research that has just commenced. According to certain research, excessive adipose tissue is linked to decreased cognitive function, specifically the visceral adipose tissue. In particular, obesity has been related to impulsivity, self-control, and motivation as well as memory impairment. These cognitive functions are implicated in eating control behavior. Moreover, it has been associated with an increased risk of mild cognitive impairment (MCI) and dementia, and anatomical alterations to the brain, including excessive age-related atrophy and white matter pathology. The ineffectiveness of existing approaches and therapies in dealing with these pathologies is a compelling reason to discover new ways to understand and treat them. One of the mechanisms that has recently gained strength for addressing and comprehending these disorders is the gut–brain axis (GBA) approach, a bidirectional communication system that connects the brain (emotional, behavioral, and cognitive processes) with peripheral intestinal functions. In this way, people with metabolic disorders have cognitive dysfunction and emotional alterations such as depression or anxiety, or even eating disorders, which taken together, could be a cause and consequence of the maintenance of the disease. For these reasons, it is important to approach metabolic disorders not only by focusing on the peripheral nervous system (PNS) but also on the central nervous system (CNS). Recent research breakthroughs have discovered the microbiome that influences these relationships. The microbiota influences gut–brain communication via endocrine, immunological, and neuroactive pathways. Microbial neurotransmitters (e.g., gammaaminobutyric acid GABA, catecholamines), and metabolites such as short-chain fatty acids (SCFAs), bile acids (BAs), and tryptophan are the most well-known examples of microbial-derived intermediates that communicate from the gut microbiome to the CNS. Although some of these intermediates directly interact with enteroendocrine cells, enterochromaffin cells, and the mucosal immune system to spread bottom-up signaling, others can pass the intestinal barrier and enter systemic circulation, and may even breach the blood–brain barrier (BBB). Microbial signals may also be sent through neurological pathways involving vagal and/or spinal afferents. In order to better understand the different cognitive functions, this chapter will provide a brief introduction and description of the cognitive domains according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). Then, the link between the gut microbiome and cognition through the GBA will be explored.

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Cognitive Function and Brain Structure Cognitive functions are processes in the CNS that take place in different areas of the brain, which in turn, are interconnected with each other. CNS integrates data from the entire body and coordinates activities throughout the whole organism. Despite the fact that this region of the organism serves a variety of functions, this chapter focuses solely on cognitive processes and their relationship with gut microbiome. The Diagnostic and Statistical Manual of Mental Disorders divides cognitive function into six cognitive domains, each of which has subdomains (DSM-V-R. 2022), which have been acknowledged by the neuropsychological and psychiatric societies. The brain is divided into four cortical lobes: the frontal lobe; temporal lobe; parietal lobe; and the occipital lobe. These are not only connected to each other but also to subcortical structures (caudate nucleus, globus pallidus, amygdala, etc.). Despite this interconnectivity between areas, studies of brain damage have revealed which region of the brain is primarily responsible for each cognitive function, as well as emotion and behavior. The frontal lobe is involved in attention and executive function (working memory, inhibition, self-regulation, and planning, and phonemic verbal fluency). Executive function is a set of cognitive abilities that enable us to manage our behavior, set goals, and analyze information in order to be as adaptable as possible in our environment. This specific area is involved in psychiatric and movement disorders. The temporal lobe is implicated in language (processing and understanding verbal information and speaking, among others) and memory function (storing and retrieving information). The parietal lobe is involved in processing and integrating sensory information, orientation, and visuospatial skills. Finally, the occipital lobe is involved in visual perception, memory formation, and face recognition (Fig. 1). A neuropsychologist evaluates cognitive function using particular neurocognitive tests that produce a raw score that is then translated into normative-standard scores according to the test manual.

Metabolic Diseases and Cognition In recent years, there has been a lot of research on the connection between metabolic disorders and cognition. There is a very substantial correlation between type 2 diabetes or obesity and cognition. The main cognitive processes that have been affected and studied in metabolic diseases are attention, executive function (ArnoriagaRodríguez et al. 2021), and memory processes (Arnoriaga-Rodríguez et al. 2020; Loprinzi and Frith 2018). Obesity has been associated with impaired attention, executive function, and memory, and these specific functions have been regarded as one of the causes of the pathology’s maintenance and the challenge of weight loss. Subjects with obesity often struggle to maintain a healthy weight, creating a stigma or prejudice about the idea that they are obese because they want to be, when, in fact, they have serious difficulties exercising or dieting, due in part to cognitive dysfunction and emotional

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Visuoconstructional reasoning and Perceptual-motor coordination

Executive function Planning, decision making, working memory, responding to feedback, inhibition, and flexibility

Parietal lobe Complex attention

Frontal lobe Perceptual-motor function

Sustained, divided, selective attention and processing speed Social recognition

Occipital lobe

Recognition of emotions, theory of mind, and Insight

Visual perception

Temporal lobe Learning and memory Free and cued recall, recognition memory, semantic and autobiographic, long-term memory, and implicit memory

Language Objects naming, word-finding, fluency, grammar and syntax and receptive language

Fig. 1 Cognitive function in the specific domains according to DSM-V: Cognitive function is divided into six cognitive domains by the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), each of which has subdomains

issues. On the other hand, T2D is linked to dementia and cognitive deterioration. A systematic review (Herrmann et al. 2019) found that people with obesity have structural anomalies in their gray matter volume compared to lean people and also, there is compelling evidence of white matter loss correlated with increased BMI (Papageorgiou et al. 2017). Numerous studies have shown a negative relationship between obesity and cognitive function. This relationship is biologically plausible for a number of reasons, including pro-inflammatory states (neuroinflammation), the potential role of triglycerides in blocking NMDA receptor activation, and glutamate release. Leptin’s capacity to traverse the BBB is also compromised by hypertriglyceridemia (Banks et al. 2004), which may have an impact on memory since leptin is crucial for episodic memory (Farr et al. 2006). In addition, it has been demonstrated that high levels of glucocorticoids are linked to decreased hippocampal brain activity (Oei et al. 2007). Adipose tissue, especially visceral adipose tissue, is also harmful to memory performance. Since metabolites are the primary means through which the brain and peripheral systems communicate with one another, it is essential to understand how metabolic illnesses affect cognition. In people with obesity, there was a positive relationship between impulsive behavior and changes in tryptophan metabolism (Arnoriaga-Rodríguez et al. 2021). Concretely, the plasma levels of tryptophan and some microbial-derived catabolites were positive in relation to this cognitive function. It is known that tryptophan metabolism is altered in obesity and it is related to systemic inflammation. Dopamine (DA) is known to significantly affect eating patterns and is thought

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to have a role in the rewarding effects of both natural and artificial incentives. Its purpose is to cause loss of control and addiction-related compulsive behaviors. In fact, to examine these links, particularly in the context of obesity, numerous researchers have recently considered the concept of “food addiction.” Brain imaging research supports the notion that obesity is associated with structural and functional abnormalities in brain areas involved in executive function (Syan et al. 2019). Neuroimaging studies such as positron emission tomography (PET) have been critical in describing the involvement of the brain’s DA systems in addiction (along with their role in drug reward) and obesity (Val-Laillet et al. 2015). There is now evidence that similar dopaminergic responses are linked to food reward and that these pathways are likely to play a role in overeating and obesity. It is commonly recognized that certain foods, especially those high in sugar and fat, are highly rewarding and contribute to obesity and are detrimental to brain structure and function (Freeman et al. 2018). According to findings from both longitudinal and cross-sectional investigations, type 1 diabetes (T1DM) is linked to cognitive impairment (Moheet et al. 2015). Most frequently, areas of psychomotor speed, mental flexibility, attention, and general intelligence are impacted (Brands et al. 2005). T1DM is frequently discovered in children and teenagers. There have been concerns that the developing brain may be particularly vulnerable to glycemic extremes during this time of fast CNS development. Magnetic resonance imaging (MRI) structural investigations have demonstrated that subjects with T1DM had reduced gray and white volume compared to subjects without T1DM and poor glycemic regulation was linked to lower gray matter density. Poor glycemic control, a greater frequency of severe hypoglycemia episodes, and diabetes’ age of onset and persistence were all linked to lower gray matter density (Musen et al. 2006). Hughes et al. (2013) found decreased volumes in patients with T1DM compared to controls seen in the frontal lobe. At the same time, the Framingham Study found that subjects with T1DM have increased white matter lesions compared to controls (Jeerakathil et al. 2004). A known risk factor for cognitive impairment and neurodegenerative disorders such Alzheimer’s disease or mild cognitive impairment (MCI) is T2DM. The connections between the two disorders are substantial, and the root of this association still remains unclear T2DM has serious negative effects on the heart and blood arteries, which increases the risk of stroke and small cerebral artery disease (Abner et al. 2016). Alterations in insulin signaling, hyperglycemia, advanced glycation, and chronic low-grade inflammation, which are relevant to the pathogenesis of dementia regardless of the presence of diabetes, could be common underlying mechanisms for pathways leading to both vascular and neurological degradation (Arnold et al. 2018). Executive function, information processing speed, memory, and attention are some of the cognitive domains that have been found to be affected (Van Den Berg et al. 2010). When compared to controls, people with T2DM have lower total gray matter, specifically in the medial temporal, anterior cingulate, and medial frontal lobes, as well as white matter loss in the frontal and temporal regions (Moran et al. 2013).

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Metabolic Diseases and Gut Microbiota Many studies have shown a link between gut dysbiosis and the development of metabolic diseases via the production of pro-inflammatory molecules as well as decreased intestinal integrity and permeability (Fig. 2). Obesity: People with obesity show dysbiosis in the gut microbiota, with an imbalance between Firmicutes and Bacteroidetes even in metabolically healthy obesity (MHO). The Firmicutes/Bacteroidetes ratio increased significantly in the MHO, with Bacteroides vulgatus being the dominant species. This bacterium could protect subjects with MHO from developing metabolic syndrome. In contrast, Faecalibacterium prausnitzii and Butyrivibrio crossotus, species that protect against obesity, were two species with low MHO scores (Kunnackal and Mullin 2016). People with obesity have shown increased abundant Actinobacteria, and Firmicutes (Faecalibacterium prausnitzii, Staphylococcus aureus) phyla. Parabacteroides genus and Archea also have been found to be abundant in subjects with obesity. Conversely, people with obesity have been shown to have a decreased abundance of genera belonging to the Bacteroidetes phylum (Bacteroides, Moryella, Turicibacteraceae, Prevotella, Clostridium histolytica, Coccoides, Roseburia genus, Eubacterium, Ruminococcaceae, and Lachnospiracea genera). Also, genera belonging to Verrucomicrobia (Akkermansia), Firmicutes (Christensenellaceae, Clostridia), and Eubacterium rectale have found a decreased abundance (John and Mullin 2016). In T2DM, there are increased counts of Proteobacteria (Escherichia coli, Shigella, and Desulfovibrio), Bacteriodetes (Prevotella copri and Bacteroides vulgatus), Bacillota (Ruminococcus gnavus), Firmicutes (Streptococcus dorea and Streptococcus mutans), and Actinobacteria (Eggerthella lenta) phyla. One the other

OBESITY

Firmicutes Staphylococcus Faecalibacterium prausnitzii Actibobacteria Proteobacteria Betaproteobacteria Bacteroidota Parabacteroides Archea

T2DM

T1DM

Firmicutes Eubacterium Turibacteraceae Ruminococcaceae Clostridia Christensenellaceae Verrucomicrobia Akkermansia Euryarchaeota Dehalobacteriaceae Bacillota Clostridium histolytica Coccoides Roseburia Moryella Lachnospiraceae Bacteroidetes Bacteoroides Prevotella

Firmicutes Clostridium perfringens Dialester invisus Bacillus cereus Bacillota Gemella sanguinis Actibobacteria Bifidobacterium longum Fusobacteriota Leptotrichia goodfellowii Bacteroidota Bacteroides dorei Bacteroides vulgatus

Firmicutes Faecalibacterium prausnitzii

Verrucomicrobia Akkermansia Bacillota Roseburia faecis Actinobacteria Bifidobacterium adolescentis Bifidobacteria

Bacteroidetes Bacteoroides adolescentis Prevotella

Bacteroidetes Bacteroides

More abundant

Fig. 2 Gut microbiota in metabolic diseases

Firmicutes Streptococcus Dorea Streptococcus mutans Actibobacteria Eggerthella lenta Bacterioidetes Prevotella corpi Bacteroides vulgatus Bacillota Ruminococcus gnavus Proteobacteria Escherichia Shigella Desulfovibrio

Firmicutes Subdoligranulum Faecalibacterium Eubacterium Clostridium Eubacterium listeri Lactobacillus Streptococcus Eubacterium siraeu

Verrucomicrobia Akkermansia A. Muniphila Bacillota Roseburia Anareotruncus colihominis Buyrivibrio crossotus Ruminococcus Actinobacteria Bifidobacterium

Bacteroidota Bacteroides Parabacteroides

Less abundant

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hand, people with T2DM have decreased Bacillota (Roseburia, Ruminococcus, Anareotruncus colihominis, Butyrivibrio crossotus), Firmicutes (Subdoligranulum, Faecalibacterium, Eubacterium, Eubacterium siraeum, Clostridium, Listeria, Lactobacillus, Streptococcus), Verrucomicrobia (Akkermansia, A. muciniphila), Actinobacteria (Bifidobacterium), Bacteroidetes (Bacteroides) and Bacteroidota (Parabacteroides) phyla (Liu et al. 2022). T1DM is also linked to changes in the composition of the gut microbiota, with a large amount of Bacteriodes, resulting in an increase in the Bacteriodes to Firmicutes ratio. The studies have demonstrated that people with T1DM showed increased Firmicutes (Clostridium perfringens, Dialister invisus, and Bacillus cereus), Bacillota (Gemella sanguinis), Actinomycetota (Bifidobacterium longum), Fusobacteriota (Leptotrichia goodfellowii), and Bacteroidota (Bacteroides dorei, Bacteroides vulgatus, and Bacteroides genus) phyla. In contrast, patients with T1DM showed decreased Firmicutes (Faecalibacterium prausnitzii), Bacteriodetes (Prevotella), Verrucomicrobia (Akkermansia), Actinomycetota (Bifidobacterium adolescentis and Bifidobacteria), Bacillota (Roseburia faecis), and Bacteroidota (Bacteroides adolescentis) phyla (Mokhtari et al. 2021).

The Gut–Brain Axis The gut–brain axis approach is rethinking the study of various diseases affecting the central nervous system, from neurodegenerative diseases such as Alzheimer’s or Parkinson’s, neurodevelopmental disorders such as attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder, or even psychiatric diseases such as depression(Mayer et al. 2022). Previously, these diseases were approached from the perspective of brain mechanisms. However, in recent years, the focus of these diseases has shifted toward the gut and its microbiota, as well as its impact on the brain. Furthermore, not only can the aforementioned disorders influence cognition, but metabolic diseases like obesity also affect cognitive function, especially executive skills and memory. Moreover, cognitive dysfunction has been also described as both a cause and a consequence of obesity (Hartanto et al. 2019). The connection between the brain and the intestinal tract is known as the GBA. The two systems have a strong and reciprocal interaction. The gut and the brain are connected by an information exchange network that encompasses the central nervous, endocrine, metabolic, and immune systems. The GBA has the ability to transfer information in both directions: “top-down” from the brain to the gut and “bottom-up” from the gut to the brain (Fig. 3). In addition to the hypothalamicpituitary-adrenal (HPA) axis and endocrine pathways (i.e., intestinal peptides and hormones), there is mounting evidence that bacteria metabolites (e.g., SCFAs, neurotransmitters, and their precursors) affect the levels of related metabolites in the brain via blood circulation, regulating brain functions and cognition. Gut microbiota can also communicate with the brain via the local neurological system (e.g., enteric nerves, vagus nerve), sending messages very rapidly to the brain (Durgan et al. 2019).

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Emotional Behaviour Cognition

Vagus nerve

BBB permeability

GBB permeability

Bottom up

Fig. 3 The Gut–Brain Axis (GBA): The gut microbiome communicates with the Central Nervous System (CNS) predominantly through microbial-derived intermediates, with ShortChain Fatty Acids (SCFAs), secondary Bile Acids (2Bas), and tryptophan metabolites. The GBA can send and receive data messages in both directions: from the brain to the gut (top-down) and from the gut to the brain (bottomup). BBB blood–brain barrier, GBB gut–blood barrier

Top-down

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SCFAs 2Bas Tryptophan metabolites

Microbiota

Bidirectional biochemical pathways Endocrine, Immune, Metabolic and Neural

Both neuronal and nonneuronal methods enable the brain to engage in gut communication. The gut wall receives direct communication via parasympathetic and sympathetic nerve fibers for top-down signaling (brain–gut), or indirectly following stimulation of the enteric nervous system, a highly developed system of neuronal connections found in the gut wall’s submucosal and myenteric plexus (Naveed et al. 2021). Gut motility, permeability, microbiota composition, and

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resident immune cell activation are all influenced by neural inputs. The HPA axis is particularly significant as a communication mechanism in response to stress (Carabotti et al. 2015). Bottom-up signaling (gut–brain) is theorized to occur in a variety of ways. First, the vagus nerve has a dual function in transmitting information from the gut to the brain. Microbial chemicals and metabolites, as well as hormones (e.g., serotonin, cholecystokinin, glucagon-like peptide-1 (GLP-), and peptide YY released from enteroendocrine cells of the gut epithelial layer can stimulate these afferent fibers, causing bottom-up signaling. The stimulation of these afferent projections sends messages throughout the brain, including the hypothalamic neurons that control pituitary secretions and the nucleus tractus solitarius’ projections. Second, immunogenic endotoxins produced by the microbiota, such as lipopolysaccharides (LPS), can cause neuroinflammation directly or indirectly by activating peripheral immune cells that then transfer to the brain. Third, the microbiota produces or stimulates the release of a variety of metabolites, including neurotransmitters, SCFAs, indoles, and bile acids, which are thought to enter the systemic bloodstream and travel to the brain, modulating the function of neurons, microglia, astrocytes, and the BBB. Not only can microbiota produce neurotransmitters that can influence the host, but multiple studies have discovered neurotransmitter binding sites on bacteria, which can affect bacterial metabolism and proliferation. Several cardiovascular and metabolic illnesses, including stroke, hypertension, obesity, and diabetes mellitus, have been found to have fewer SCFA-producing bacteria.

Gut Microbiota and Its Relationship to Neurotransmitters The signal transmission between neurons and glial cells, which is mostly dependent on neurotransmitters, is essential for the brain’s functioning processes. Glutamate is mainly an excitatory neurotransmitter, and GABA and glycine, on the other hand, are the most important inhibitory neurotransmitters. Another important neurotransmitter, dopamine, is involved in a variety of brain functions, including learning, motor control, reward, emotion, and executive functions. Dopamine has also been linked to mental and neurological illnesses. The CNS and sympathetic nerves produce norepinephrine, which is a monoamine that influences the autonomic nervous system’s responses. Histamine is a neurotransmitter that regulates motivational behavior and mediates homeostatic activities in the body. It increases alertness, affects feeding behavior, and motivation. All of these neurochemicals exert an important role in a variety of brain activities such as movement, emotion, learning, and memory. Neurotransmitter imbalances can lead to neurological and psychological illnesses like Alzheimer’s disease, Parkinson’s disease, autism spectrum disorder, anxiety disorders, and depression, as well as metabolic disorders like obesity. As a result, studying the dysregulation of neurotransmitter synthesis in the CNS and peripheral organs could reveal new information about the underlying origins and mechanisms of these diseases and disorders.

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In addition to SCFAs and BA, recent research has found that gut bacteria produce neurotransmitters such as glutamate, GABA, serotonin, and dopamine (Strandwitz et al. 2019). Furthermore, certain bacteria contain genes that code for particular enzymes that catalyze the conversion of substrates into neurotransmitters or precursors. Meanwhile, some bacterial metabolites can act as signaling molecules, causing enteroendocrine cells to produce and release neurotransmitters. Because neurotransmitters like glutamate, GABA, dopamine, and serotonin cannot cross the BBB, they must be generated in the brain from local pools of precursors. The majority of these precursors are amino acids (such as tyrosine and tryptophan) that enter the bloodstream, penetrate the BBB or send rapid signals to the brain via the vagus nerve (Fig. 4), and are taken up by related neurotransmitter-producing cells. The precursors are subsequently transformed into functioning neurotransmitters like dopamine, norepinephrine, and serotonin via a series of intermediate stages involving a variety of host enzymes. As a result of the dietary origins of these precursors, the gut microbiota can impact host behavior via controlling neurotransmitter precursor metabolism. However, the relationship between abnormal neurotransmitter levels in the brain and deficiencies in the manufacture of neurotransmitters or precursors in the gut is still unknown. The mechanisms by which the specific microbiota modulates the main neurotransmitters (Chen et al. 2021) of the CNS are now discussed:

GABAergic Glutamatergic neurons neurons

SEROTONINE ACETILCOLINE DOPAMINE

GABA

-

Glutamic acid decarboxylase (GAD) ENZYMES

GLUTAMATE +

GABA metabolic cycle

Tricarboxylic acid cycle

Staphylococcal aromatic amino acid decarboxylase (SadA) ENZYMES

Choline Acetyltransferase (ChAT) ENZYMES

Tryptophan decarboxylase (TDC) and tryptamine 5-hydroxylase (T5H) ENZYMES

Choline

Tryptophan

L-DOPA

Acetate Staphylococcus

Lactobacillus plantarum, Bacteroides vulgatus Campylobacter jejuni

Bifidobacterium, Bacteroides fragilis, Parabacteroides, and Eubacterium

Bacillus acetylcholini, Lactobacillus plantarum, Bacillus subtilis, Escherichia coli, and Staphylococcus aureus

Clostridia and Staphylococcus

Fig. 4 Microbiota, precursors, and the neurotransmitters: Bacteria are able to manufacture the precursors, and metabolites created by the microbiota’s intestinal fermentation of carbohydrates, and they can also cross the BBB via the associated metabolic cycles and enzymes to make the corresponding neurotransmitters

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Glutamate is the most abundant excitatory neurotransmitter in the brain and is responsible for signal transmission between nerve cells. Because glutamate cannot cross the BBB, its synthesis in the brain is dependent on the collaboration of neurons and astrocytes, which use tricarboxylic acid cycle intermediary metabolites as precursors. However, cells other than neurons can create glutamate in other parts of the body, such as the intestinal system. A subset of enteroendocrine cells in the colon synthesizes glutamate and uses it to send quick signals to the brain via the vagus nerve. The vagus nerve transmits sensory information from the intestine to the brain. Its precursor is acetate, which can be produced by some bacteria such as Lactobacillus plantarum, Bacteroides vulgatus, and Campylobacter jejuni. GABA is a neurotransmitter that plays a role in a variety of metabolic and physiological processes. GABA is produced in the brain by GABAergic neurons, which convert glutamate to GABA via the glutamic acid decarboxylase enzyme, which is found only in these neurons. The fact that certain bacteria such as Bifidobacterium, Bacteroides fragilis, Parabacteroides, and Eubacterium can manufacture GABA in the gut suggests that GABA produced by gut microbes may act locally on the enteric nervous system. However, metabolites from the microbiota’s colonic fermentation of carbohydrates, such as acetate (precursor), can cross the BBB and enter the GABA metabolic cycle, predominantly in the hypothalamus, similar to the glutamate production pathway in the CNS. It also has the ability to modulate inflammation and intestinal motility. Acetylcholine: By transducing excitatory impulses across neurons, it acts as a local mediator in the central and peripheral nervous systems. Its dysfunction is linked to neurodegenerative illnesses like Alzheimer’s disease. Bacillus acetylcholini, Lactobacillus plantarum, Bacillus subtilis, Escherichia coli, and Staphylococcus aureus have all been discovered to produce acetylcholine. B. subtilis, in particular, manufactures more acetylcholine than E. coli or S. aureus. Because acetylcholine cannot cross the BBB, CNS neurons generate it from choline and acetyl coenzyme A, which is catalyzed by choline acetyltransferase enzyme. Choline from the periphery can be transferred to the brain via carriers on capillary endothelial cells. Dopamine: it is produced in the substantia nigra and ventral tegmental regions of the brain in the CNS. The dopamine system is dysregulated in a number of neurological illnesses, including schizophrenia and Parkinson’s disease. Dopamine is the most prevalent catecholamine neurotransmitter in the brain, and it is made in dopaminergic neurons from tyrosine, which is plentiful in foods, and may cross the BBB to reach the brain. Dopamine production has been discovered in the human intestine by Staphylococcus, which can take up the precursor L-3,4dihydroxyphenylalanine (L-DOPA) and convert it to dopamine using the staphylococcal aromatic amino acid decarboxylase (SadA) enzyme produced by these bacteria. The gut synthesizes more than half of the dopamine in the human body. Gastric secretion, motility, and mucosal blood flow are all affected by dopamine and its receptors, which are broadly dispersed in the intestinal system. The dopamine system is mainly involved in attention, executive functions, and in emotional processes.

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Serotonin: It is primarily produced in the CNS by serotonergic neurons in the raphe nuclei region of the brain. The pathophysiology of mental health diseases, such as depression and anxiety disorders, is linked to abnormal serotonin expression and activity in the brain. It is worth noting that 90% of serotonin is produced in the peripheral areas of the human body. Serotonin, on the other hand, is unable to pass the BBB, but its precursor, tryptophan, may. Enterochromaffin cells in the gut use tryptophan from dietary protein as a substrate to produce serotonin, and the bacterial kynurenine production pathway regulates this process. Bacteria in the gut, mostly Clostridia and Staphylococcus, can increase the gene expression of tryptophan hydroxylase 1 (TPH1). Numerous staphylococcal strains have recently been discovered in the human intestine that produce the trace amines tryptamine, tyramine, and phenylethylamine through the decarboxylation of their respective aromatic amino acid substrates: tryptophan, tyrosine, and phenylalanine. Clostridium sporogenes and Ruminococcus gnavus, both members of the Firmicutes phylum, have newly been discovered to manufacture tryptamine by decarboxylating tryptophan with their own tryptophan decarboxylase enzymes.

Gut Microbiota and Cognitive Functions in Humans A limited number of researchers have explored the human microbiome and cognitive areas. In fact, only a few cognitive domains have been studied, mainly executive functions and memory processes (Arnoriaga-Rodríguez et al. 2020, 2021). The gut microbiota is a diverse population of bacteria, archaea, eukaryotes, and viruses that inhabit the digestive system, but only gut bacteria and viruses have been studied in relation to cognitive function.

Gut Microbiome and Cognition Gut Microbiota and Attention and Executive Function There is evidence that executive function, specifically the ability to inhibit behavior, (the opposite of impulsivity), is related to the Firmicutes phylum (Eubacterium sp CAG 603 and Firmicutes bacterium CAP: 238). Thus, the presence of these bacteria could reduce impulsivity. In contrast, Bacteroides was associated negatively with this cognitive process, specifically, with Plebeieus, Gallinarum, Mediterrantis, Desulfovibrio fairfieldenris, Lachnospirace bacterium 5_1_57FAA and Lachnospirace bacterium 6_1_37FAA (Arnoriaga-Rodríguez et al. 2021). Higher numbers of Verrucomicrobia and Lentisphaerae were linked to higher inhibition control in healthy older individuals, according to an exploratory investigation by Anderson et al. (2017). Moreover, three bacterial functions are also implicated in executive cognitive function: dUTP pyrophosphatase, dut; thymidylate synthase, thyX, and exodeoxyribonuclease V have a negative relationship with the inhibitory process. dut and thyX are involved in one-carbon metabolism mediated by folate as they relate to the plasma folic and acid concentrations, and it could seem that plasma folic acid was

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found to be favorably connected with a function linked to poor cognitive function. Other enzymes involved in folate-mediated one-carbon metabolism or folate biosynthesis may decrease inhibitory control. At the same time, impulsivity was negatively associated with several functions related to vitamins involved in folate and one-carbon metabolism, specifically B6, B12, and B2 (Arnoriaga-Rodríguez et al. 2021). Low tryptophan levels as well as the presence of microbial-derived methionine catabolites were associated with increased impulsivity. Tryptophan and tyrosine amino acids are precursors of serotonin and dopamine synthesis. Both are especially implicated in attention and executive functions. In the same line, there is a relationship between one-carbon metabolism and metagenomics functions (thyX and dut) and inhibition. Betaine and its precursor, choline, can be metabolized by intestinal microbiota to trimethylamine and methylamine, two amines that have also been associated with inhibitory capacity. The significant associations between inhibition function and bacterial betaine transport functions (proW, betaine/proline transport system permease protein; proV, betaine/ proline transport system ATP-binding protein; proX, betaine/proline transport system substrate-binding protein) are also consistent with these changes in betaine levels. Moreover, plasma histidine, a precursor of histamine, is a neurotransmitter implicated in anxiety, stress response, learning, and memory, which are associated with being more impulsive (Arnoriaga-Rodríguez et al. 2021). In people with obesity, there was a positive relationship between inhibition function and changes in tryptophan metabolism. In particular, the plasma levels of tryptophan and some microbial-derived catabolites were positive in relation to this cognitive function. It is known that precisely, tryptophan metabolism is altered in obesity and it is related to systemic inflammation (Cussotto et al. 2020). Obesity is frequently associated with poor working memory when compared to healthy-weight people. Executive function such as working memory is assumed to aid adherence to weight-loss regimens. Histidine may be converted to urocanic acid in obese subjects, which is also involved in the inhibition. In fact, urocanate has recently been proven to pass the BBB, promoting glutamate production and release in numerous brain regions, therefore improving learning and memory. Histidine may thus be metabolized differently in obesity (Zhu et al. 2018). As previously discussed, working memory is one of the cognitive executive functions. It is the ability to mentally hold and manipulate information (i.e., to remember goal-relevant knowledge). Its dysfunction or impairment has serious clinical implications, such as psychiatric disorders (depression, ADHD, or neurodegenerative diseases). Working memory is commonly processed in the prefrontal regions. The presence of Acetitomaculum ruminis bacteria in human fecal samples has been associated with an increased volume of the right prefrontal cortex (PFC) area and working memory, while the presence of various Bacteroides sp. decreases the volume of the right PFC and, consequently, working memory (ArnoriagaRodríguez et al. 2020). Some bacteria from Firmicutes phylum were associated with better working memory, namely, Clostridium and Ruminococcus. There is also a positive

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relationship between working memory and Selenomonadaceae, Lactococcus sp., and Colinsella sp. In contrast, bacteria corresponding to Bacteroides phylum such as Bacteroides fragilis, Bacteroides, Bacteroides caccae, together with phylum Proteobacteria, specifically, Citrobacter freundii, Enterobacter cloacae, Salmonella enterica, and Klebsiella aerogenes are detrimental for this cognitive function (Arnoriaga-Rodríguez et al. 2020). Moreover, bacterial metagenomics functions were linked to this cognitive trait. The presence of vitamin B metabolism-related functions, such as riboflavin (ribBA, aphA, fre, ubiB, folX), vitamin B6 (pdxA), folic acid (pabB, queE, pabC, folM, and folX), and vitamin B12 (btuB) could impair working memory, and it is known that these vitamins are crucial for one-carbon metabolism (Arnoriaga-Rodríguez et al. 2020). The presence of Eubacterium sp., Ruminococcus sp., Clostridium sp., and Faecalibacterium sp. could improve working memory in subjects with obesity. The link between bacterial activity and thiamine was stronger in people with obesity, who have been shown to be more vulnerable to thiamine deficiency (ArnoriagaRodríguez et al. 2020). Recent research has shown that viruses can have a significant impact on the physiology of their bacterial hosts. Bacteriophages are well known to constitute the most common members of the human virome. Temperate (lysogenic) bacteriophages can transfer genes to their bacterial hosts, changing their phenotype and modifying gene expression. Despite this fact, prophages are found in more than 80% of bacterial genomes. As a result, bacteriophages may have a significant impact on bacterial diversity and function, as well as human health (Keen and Dantas 2018). A recent study (Mayneris-Perxachs et al. 2022a) has explored the relationship between gut-resident bacteriophages and the microbiome’s structure and metabolism, as well as their effects on cognition. The authors found that the presence of Caudovirales bacteriophages in the gut microbiome was associated with improved executive function, specifically, cognitive flexibility and working memory. Specific Caudovirales (the former Siphoviridae family with the old taxonomy comprising the new Demerecviridae, Drexlerviridae, and Siphoviridae families) levels were positively associated with cognitive flexibility, while Microviridae counts were negatively associated with this trait. According to gene and genome analysis of unassembled and assembled data, most of the Caudovirales were uncultured and uncharacterized, while others putatively infected predominantly Lactococcus spp. and other gut bacteria belonging to Enterobacteriacaea, Firmicutes (e.g., Eubacterium rectale), or Bacteroidetes. The gene content and annotation of these Caudovirales revealed common gene traits, as those coding for structural proteins (capsid, portal, neck, and tail) and other Caudovirales functional proteins (e.g., terminases). For several of these Caudovirales, metagenomics assembly resulted in a fragmented genome assembly, particularly for Lactococcus viruses, which have been linked to higher performance in central executive processes. Within the Caudovirales, a strong positive relationship between Siphoviridae levels (as per new genome-based taxonomy) and cognitive flexibility was also disclosed. Unlike other Caudovirales levels, Siphoviridae levels were likewise associated with improved inhibitory control (meaning being

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less impulsive) and short- and long-term memory, underlining the potential importance of the Siphoviridae family in cognitive function. On the other hand, some counts of single-stranded DNI (ssDNA) Microviridae were linked to a worsening in executive function. Microviridae levels correlated positively with fat mass, confirming recent findings that showed their rise after a high-fat diet (Schulfer et al. 2020). In both unassembled and assembled data, Microviridae signature genes and proteins were clearly recognized. Some of them resembled Escherichia phage alpha3 and uncultured Microviridae seen in the stomach before. Surprisingly, identification of putative hosts revealed that some Microviridae infect Bacteroidetes (most likely Alistipes onderdonkii), and one Microviridae virus showed a broad host range because CRISPR spacers from Ruminococcus spp., Oscilobacteriales, and Lachnospiracea matched viral protospacers of this virus. Bacteriophages may play a crucial role in host health and disease by altering bacterial communities through transposition, induction, and horizontal gene transfer (Keen and Dantas 2018). When Mayneris-Perxachs and co-authors (2022a, b) looked at the relationships between these bacteriophages and bacterial composition and functionality, Lactic acid bacteria (Lactobacillales order), particularly Streptococcus, Lactobacillus, Lactococcus, and Enterococcus species, were positively associated with specific Caudovirales levels, while Bacteroides species were inversely associated. In fact, all known lactic acid bacteria phages are classified as Caudovirales, with the majority of them belonging to the Siphoviridae family (Murphy et al. 2017). In this study, 40% of the species most associated with certain Caudovirales were also associated with cognitive flexibility. Microviridae levels, on the other hand, were negatively linked to various Lactobacillus, Streptococcus, and Enterococcus species, while they were positively linked to Bacteroides and Prevotella species. In addition, subjects with increased specific Caudovirales had better phonemic verbal fluency (specific executive function related to language) and information processing speed (Mayneris-Perxachs et al. 2022a, b). There was a consistent positive correlation in three of the four cohorts between certain Caudovirales levels and Lactococcus lactis, as well as numerous Lactobacillus and Streptococcus species (S. mitis, S. salivarius, S. vestibularis, and Lactobacillus). S. salivarius and S. mitis are the most common streptococcal species in human milk microbiota, whereas L. lactis and Lactobacillus sp. are commonly employed in dairy product fermentation. The Microviridae family, on the other hand, showed a negative relationship with medium-chain fatty acids. In this context, it is interesting to mention that the supplementation of medium-chain fatty acids in mice and humans has been demonstrated to promote synaptic plasticity and cognitive performance (Mayneris-Perxachs et al. 2022a, b). Significant correlations between bacterial pathways, bacteriophages, and human host executive functions were also discovered using functional analyses based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Caudovirales levels were found to be substantially linked to folate-mediated one-carbon metabolism (Mayneris-Perxachs et al. 2022a, b). Folate metabolism is important for a variety of physiological activities because it provides 1C units needed for cellular operations. It

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is also a part of the methionine cycle, which is required for the production of S-adenosylmethionine (SAM), the universal methyl donor in a variety of methylation processes, including DNA methylation. It modulates redox defense by generating antioxidants like taurine and glutathione from cysteine via the trans-sulfuration mechanism. All of these bacterial pathways were positively linked to specific Caudovirales levels and negatively with Microviridae levels. A link between the metabolism of vitamins B2 and B6 (important cofactors in the folate cycle) and the presence of some Caudovirales was also uncovered. The bacterial genes thyX and dut, both involved in folate-mediated pyrimidine biosynthesis, had the strongest relationships with Caudovirales levels, according to functional analyses at the enzyme level. Thymidylate synthase is encoded by the thyX gene (TYMS in humans). Reduced TYMS expression is known to cause a misalignment of DNA synthesis and methylation, which is crucial for neurodevelopment, synaptic plasticity, and memory. In addition, a lack of folate-mediated one-carbon metabolism has been linked to neurodegenerative illnesses, which could be caused by a lack of deoxythymidine monophosphate (dTMP) synthesis and subsequent uracil misincorporation into DNA. Other critical pathways in the central nervous system, such as glutamatergic, GABAergic, dopaminergic, serotonergic synapse, and retrograde endocannabinoid transmission, were similarly negatively related with certain Caudovirales levels. Other closely connected bacterial genes were found to be involved in folate-mediated histidine catabolism (FTCD, ftcd) and purine biosynthesis (purH, purU) (Mayneris-Perxachs et al. 2022a, b). Finally, many circulating and fecal metabolites were also associated with Microviridae and Caudovirales levels. Most of these metabolites were directly implicated in one-carbon metabolism: Choline, glycine, formate, histidine, and glucose are among the metabolites that feed 1 Carbon (1C) units to the folate pool, as are related catabolites (urocanate, glutamate, inosine, β-aminoisobutyric acid, and methionine sulfoxide). The most important sources of folate 1C units, choline and glycine (Mayneris-Perxachs et al. 2022a, b), exhibited the closest relationships with Microviridae and specific Caudovirales levels. Closing the circle, bacterial metabolic pathways for glycine and histidine were also linked to particular Caudovirales and Microviridae levels. Glycine is produced by the breakdown of dietary choline and serine, which provide carbon units to the 1C-metabolism. Serine can also be made from 3-phosphorylglycerate, which is a glycolysis intermediate. The glycine cleavage system (GCS), which produces a carbon unit for the methylation of tetrahydrofolate (THF), is also a 1C source. Bacteriophage levels were consistently linked to genes involved in the GCS (gcvH, gcvP, and gcvR), serine synthesis (serB and serA), and choline transport and catabolism (sox and opuD). The GCS transcriptional repressor (gcvR) was associated with the Microviridae family, whereas GCS genes exhibited the largest negative correlation with particular Caudovirales levels. In both mice and humans, mutations in genes encoding the GCS have been demonstrated to lead to neural tube abnormalities and neurological dysfunction (Mayneris-Perxachs et al. 2022a, b). In order to validate the findings, the authors performed a microbiota transplantation from humans to mice. A dose-response effect based on the specific

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Caudovirales levels in the donor’s microbiome was found 4 weeks later: the higher the Caudovirales levels, the higher the scores in the novel object recognition test, which is used to assess cognition, particularly immediate memory. Increased Microviridae levels in the donor’s microbiome, on the other hand, were linked to recipient mice’s cognitive impairment. They also investigated whether fecal microbiota transplantation had an effect on the transcriptome of the recipient’s prefrontal cortex of mice, involved in executive processes and memory. Of note, 23 and 18 genes were up- and downregulated, respectively, in response to the donor’s specific Caudovirales levels, according to RNA sequencing. Microviridae levels in donors were linked to up- and downregulation of 18 and 10 genes, respectively. Several of the most upregulated gene transcripts with increased donor’s specific Caudovirales levels were well known memory-promoting genes (e.g., Arc, Fos, Egr2, and Btg2), whereas those downregulated (Ide and Ppp1r42) were memory suppressors. Based on gene ontology analysis, cognition was identified as the most overrepresented biological function linked to the donor’s unique Caudovirales levels. In the hippocampus and retrosplenial cortex of adult mice, learning and memory acquisition is known to lead to increased expression of the immediate early genes (IEGs) Arc, Fos, Btg2, Sik1, Dusp1, Ier2, and Egr2 (Peixoto et al. 2015). Finally, in this study, the exposure of Drosophila melanogaster to lactococcal 936-type bacteriophages led to improved memory retention, changing the expression of memoryrelated genes in the brain. These findings revealed that thermolabile components in whey powder, including the presence of bacteriophages in this product, could improve memory (Mayneris-Perxachs et al. 2022a, b). Recent findings seem to go far beyond initial expectations. Not only Siphoviridae and Microviridae counts were reciprocally associated with executive function in two independent cohorts, but also with a specific microbiome profile in four independent cohorts. It was also noted that gut bacterial functions and plasma and fecal metabolites run in parallel to bacteriophage counts, integrated in a network impacting cognition. A kind of dose–response effects of bacteriophages was observed in the human gut microbiome transplanted to mice: the genes that most changed in recipient mice were precisely those involved in memory in a concordant manner with mice cognition (Mayneris-Perxachs et al. 2022a, b).

Gut Microbiota and Memory Processes Learning and memory processes have been linked to specific microorganisms and metabolites. Several common Firmicutes phylum species, such as Clostridium, have been found to be positively associated with learning and verbal memory. The phyla Bacteroides (Bacteroides fragilis, Bacteroides, Bacteroides caccae) and Proteobacteria, on the other hand, showed unfavorable relationships between the gut microbiota and memory scores: Citrobacter freundii, Enterobacter cloacae, Salmonella enterica, and Klebsiella aerogenes. Some species, such as Ruminococcus, Roseburia, Veillonella magna, and Pararhodospirillum photometricum, were also found to be particularly and favorably related to learning and memory (ArnoriagaRodríguez et al. 2020).

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At the same time, bacterial activities associated to vitamin B metabolism, such as riboflavin (ribBA, aphA, fre, and ubiB), vitamin B6 (pdxA), folic acid (pabB, queE, pabC, folM, and folX), and vitamin B12 (btuB), were negatively correlated with all memory domains. Because all of these vitamins are required for one-carbon metabolism, there is evidence of a relationship between B vitamins and cognition. Particularly, memory has been linked to thiamine and folate. Low memory scores were also connected to bacterial activities involved in thiamine (vitamin B1) metabolism (thiB, thiK, and ABC.VB1X. P), and participants with lower plasma thiamine levels had significantly lower memory (Arnoriaga-Rodríguez et al. 2020). Other metagenomics processes linked to Aromatic Amino-Acid (AAA) metabolism, one-carbon metabolism, and endocannabinoid signaling were shown to be correlated with various memory domains (Arnoriaga-Rodríguez et al. 2020). Memory is processed mainly in the frontal and temporal areas, specifically in the hippocampus. Obesity and a certain gut microbiota profile are both linked to memory impairment. Several Bacteroidetes and Enterobacter species have been found to be more common in insulin-resistant and adult people with obesity and have been linked to a worse cognitive profile. Taxa of the phylum Firmicutes, such as Clostridiales and Roseburia, which were associated with higher memory scores, showed reduced relative abundance in patients with T2D (Tilg et al. 2020). The presence of Eubacterium and Clostridium sp. led to improved cognition in people with obesity. Proteobacteria, however, appear to have the opposite impact (Arnoriaga-Rodríguez et al. 2020), (Fig. 5).

Gut Microbiome Is Associated with Brain Structure A limited number of researchers have studied the relationship between brain structure and microbiota. The prefrontal cortex (frontal lobe) plays a crucial role in attention and executive function. The orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsomedial prefrontal cortex (DLPFC), respectively, are involved in salience attribution, inhibitory control, emotion regulation, and decision-making. Consistent with the implication of the OFC, ACC, and impulsivity, there is a negative relationship between Bacteroides sp., Anaerovibrio, Selenomonas, and the ACC volume. This could indicate that their presence is associated with being more impulsive. The bacterial genes kinB, dut, and thyX, on the other hand, are linked to ACC volume and are thought to have less inhibitory control. Clostridium sp. CAG: 226, Roseburia sp. CAG: 182, and Ruminococcus sp. CAG: 417 are associated with strong inhibitory control and ACC volume (Arnoriaga-Rodríguez et al. 2021). Metagenomics functions were associated with brain volume in several brain areas and memory domains. The relative abundance of Firmicutes and Bacteroidetes was linked to the opercula gray matter volume (orbital and triangularis areas), as well as the temporal cortex, which has a detrimental effect on verbal memory and the left hippocampal volume. Roseburia sp., on the other hand, leads to increased hippocampus volume and memory (Arnoriaga-Rodríguez et al. 2020).

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Fig. 5 Gut bacteria are associated with worse executive function and memory in obesity

Finally, Actinobacteria phylum was linked with diffusion tensor imaging (DTI) in the thalamus, hypothalamus, and amygdala on magnetic resonance imaging, and also with better speed of information processing, attention, and cognitive flexibility (Fernandez-Real et al. 2015).

Gut Microbiota and Mental Health in Obesity Major depressive disorder (MDD) has become the leading cause of disability worldwide, and it is more frequently associated with death and suicide than any other mental or physical disorder. MDD symptoms include persistent low mood, feelings of worthlessness or guilt, anhedonia, sleep and appetite disturbances, fatigue, slowed movements and speech, and suicidal thoughts. Quite apart from CNS alterations, specially through the dysregulation of neurotransmitters, patients with depression have changes in their metabolic, immune, and endocrine systems (Caspani et al. 2019). Obesity has been strongly linked to depression and anxiety. Depression levels are linked to a specific microbial ecosystem. Patients with depressive symptoms have increased Parabacteroides spp. and Acidaminococcus

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spp., but decreased Bifidobacterium pseudolongum and species from the butyrateproducing Lachnospiraceae family. Lower counts of Actinobacteria and Lachnospiraceae species, as well as increased levels of Prevotella and Enterobacter species, were associated with higher depression scores 1 year later. Microbial functions and metabolites converging onto glutamate/GABA metabolism, particularly proline, were linked to depression. High proline consumption was the dietary factor with the strongest impact on depression (Mayneris-Perxachs et al. 2022a, b). Anxiety is the second most common mental health disorder. Some research found that patients with anxiety had increased levels of Bacteroidaceae from the Bacteroidetes phylum; E. coli, Shigella, and Enterobacterales from the Proteobacteria phylum; Bacteroidetes (Bacteroides), and Tyzerella from the Firmicutes phylum. At the same time, people with anxiety present decreased counts of the species Mollicutes belonging to the Firmicutes, Prevotellaceae belonging to the phylum Bacteroidota, and species belonging to the phylum Bacillota: Ruminococcaceae, Subdoligranulum, Coprococcus, and Dialister (Y.-H. Chen et al. 2019).

Conclusion Recently, there has been a great deal of interest in understanding the relationship between microbiota and psychiatric diseases (depression, anxiety, autism spectrum disorder, or epilepsy) since much of it has psychological and cognitive consequences. This interaction could also help to understand pathologies of the CNS, such as neurodegenerative diseases (Alzheimer’s disease and Parkinson’s disease) opening the door to new treatments that would have an impact on the gut and not only on the brain. Therefore, a novel strategy might include not only recognizing these illnesses but also understanding how to treat them. These bacterial strains could be produced to induce favorable changes in the human metabolic and neurological profiles once the causal relationships between the strains and the human metabolic profile have been identified. With this method, the gut microbiota composition of patients with metabolic or neurological illnesses could be largely established as they grew. This invention could help in the creation of personalized therapy for patients so they can optimize the benefits they can receive as new technologies are developed. As has been noted in the previous decade, this association extends beyond the pathophysiological consequences of the aforementioned diseases. However, it is certain that these disorders, whether metabolic, neurological, or mental, have often been the focus of microbiome research. In order to gather information that can help with the treatment of many diseases, microbiome research has recently focused on the healthy microbiota and its relationship with the CNS. There is little doubt that the innovative findings that have been so far presented are promising to identify new therapeutic targets through diet and nutrition, focusing on the gut microbiota and its connections with physiological systems. The impact on the CNS through treatments for cognitive and memory impairment could include the use of known bacteriophages.

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Glossary AAA AAC ADHD BBB BA BMI CNS CRISPR DA DLPFC DSM-V dTMP FTCD GABA GBA GCS GIT GLP-1 IEGs KEGG LPS MHO MRI NMDA receptor OFC PNS PET PFC T1DM T2DM THF TYMS SAM SCFAs ssDNA 1C

Aromatic Amino Acids Anterior Cingulate Cortex Attention-Deficit Hyperactivity Disorder Blood–Brain Barrier Bile Acids Body Mass Index Central Nervous System Clustered Regularly Interspaced Short Palindromic Repeats Dopamine Dorsomedial Prefrontal Cortex Diagnostic and Statistical Manual of Mental Disorders deoxythymidine monophosphate formimidoyltransferase cyclodeaminase Gamma-aminobutyric acid Gut–Brain Axis Glycine Cleavage System Gut intestinal tract Glucagon-like peptide-1 Immediate early genes Kyoto Encyclopedia of Genes and Genomes pathway Lipopolysaccharides Metabolic health obesity Magnetic Resonance Image N-metil-D-aspartate receptor Orbitofrontal Cortex Peripheral Neurological System Positron Emission Tomography Prefrontal cortex Type 1 Diabetes Mellitus Type 2 Diabetes Mellitus Tetrahydrofolate Thymidylate synthase S-adenosylmethionine Short-Chain Fatty Acids Single-stranded DNA 1 Carbon

Genes and Proteins Microbial ABC.VB1X. P aphA

putative thiamine transport system permease protein gene acid phosphatase/phosphotransferase

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btuB folM folX fre gcvH gcvP gcvR nudB opuD pabB pabC pdxA proV proW proX purH purT purU queE ribBA SadA serA serB sox ThiB thyX ubiB

cobalamin outer membrane transporter dihydromonapterin reductase dihydroneopterin triphosphate 20 -epimerase NAD (P) H-flavin reductase glycine cleavage system H glycine decarboxylase GCS transcriptional repressor dihydroneopterin triphosphate diphosphatase glycine betaine transporter OpuD aminodeoxychorismate synthase subunit 1 aminodeoxychorismate lyase 4-hydroxythreonine-4-phosphate dehydrogenase glycine betaine/L-proline ABC transporter ATP betaine/proline transport system permease protein betaine/proline transport system bifunctional AICAR transformylase/IMP cyclohydrolase phosphoribosylglycinamide formyl transferase 2 formyltetrahydrofolate deformylase putative 7-carboxy-7-deazaguanine synthase bifunctional 3,4-dihydroxy-2-butanone-4-phosphate synthase/GTP cyclohydrolase II staphylococcal aromatic amino acid decarboxylase enzyme phosphoglycerate dehydrogenase phosphoserine phosphatase Sarcosine oxidase Thiamine-binding periplasmic protein thymidylate synthase ubiquinone biosynthesis protein UbiB

Human TPH1 tryptophan hydroxylase 1 gene TYMS Thymidylate synthase gene

Mice Arc Fos Egr2 Btg2 Ppp1r42 Sik1 Dusp1 Ier2

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Activity-regulated cytoskeleton-associated protein gene c-Fos protein gene E3 SUMO-protein ligase EGR2 protein gene Protein BTG2 protein gene protein phosphatase 1 regulatory subunit 42 gene Serine/threonine-protein kinase SIK1 Dual specificity protein phosphatase 1 gene Immediate early response gene 2 protein gene

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The Other Microbiome: Oral Microbiota and Cardiometabolic Risk

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Sylvie Leˆ, Chiara Cecchin-Albertoni, Charlotte Thomas, Philippe Kemoun, Christophe Heymes, Vincent Blasco-Baque, and Matthieu Minty

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oral Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Salivary Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Periodontal Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dysbiosis of Oral Microbiota and Cardiometabolic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Sylvie Lê and Chiara Cecchin-Albertoni had contributed equality in this chapter. S. Lê · C. Thomas · P. Kemoun · V. Blasco-Baque (*) · M. Minty Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected] C. Cecchin-Albertoni Département Dentaire, Université Paul Sabatier III (UPS), Toulouse, France Service d’Odontologie Toulouse, CHU Toulouse, Toulouse, France RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, Toulouse, France e-mail: [email protected] C. Heymes UMR1297 Inserm/Université Paul Sabatier|Team InCOMM/Intestine ClinicOmics Metabolism & Microbiota – Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_20

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Pathophysiological Mechanisms Between Periodontitis and CMDS . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physiopathology of Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathophysiology Linking Periodontitis and CMDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular Mechanisms of Bacterial Translocation Inducing Cardiometabolic Phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment Strategies and Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oral Hygiene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pre-/probiotics Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phytotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vitamin D Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Periodontal Treatment and Attenuation of Systemic Inflammatory Markers . . . . . . . . . . . . . . . . . . Modification of the Oral-Intestinal Axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Over the last decade and following technological advances, particularly in the high-throughput analysis of “omics,” the relationship between microbiota and the host has not yet been fully recognized and despite progress in the pathophysiological understanding of cardiometabolic diseases. These pathologies are still the first cause of mortality worldwide with a disease morbidity rate remaining unacceptably high. The link between intestinal microbiota and cardiometabolic risks is well extended and developed, unlike the role of the oral microbiota (2nd most important microbiota in the human body) and more precisely the dysbiosis of this microbiota causing these complications still remains, not fully defined. In this chapter, we will talk about the link between oral microbiota and cardiometabolic diseases, with a first part on the dysbiosis of the oral microbiota and in particular periodontal disease; a second mechanistic part of this dysbiosis contributing to these complications and finally a last part on potential prevention and treatment strategies (prebiotic, probiotics, etc.). Keywords

Microbiota · Cardiometabolic diseases · Microbiome · Prebiotics/Probiotics · Translocation · Periodontitis

Introduction Cardiometabolic disease (CMD) is a spectrum of conditions beginning with insulin resistance, progressing to the metabolic syndrome, pre-diabetes, and finally to more severe conditions including cardiovascular disease (CVD) and type 2 diabetes (T2DM). Recognized cardiometabolic risk factors are increased waist circumference, inflammation measured by high-sensitivity C-reactive protein (hsCRP), hypertension, dysglycemia, dyslipidemia, decreased HDL levels, tobacco use, unhealthy

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diet, with lack of consumption of fruits and vegetables, sedentary lifestyle, and psychosocial stress (Lopez-Neyman et al. 2022). A rising body of evidence suggests that inflammation is a key contributor to CMDs, with a recognized role of cytokines (such as TNF-α, IL-1, and IL-6) in coordinating inflammatory response. CMDs represent a significant global healthcare problem and have become an epidemic worldwide, with a substantial social and economic burden. The number of people with CVD worldwide is reported to be 523 million and CVD is the leading cause of death globally, taking an estimated 17.9 million lives each year (Ruan et al. 2018). In Europe, CVD is responsible for 3.9 million deaths (45% of deaths). Type 2 diabetes affects almost 426 million people worldwide (Khan et al. 2020) (with an estimate of 600 million by 2035) and is directly associated with 1.5 million deaths each year. Both incidence and prevalence of diabetes have been steadily increasing over the past few decades. CVDs and T2DM are therefore a serious public health concern, with a considerable impact on human life and health expenditures. As described, the main cardiovascular risk factors alone do not explain the interindividual variability in the risk of cardiovascular mortality and morbidity, hence the importance of identifying new explanatory risk factors. The immunoinflammatory process increasingly appears to be a common factor for these cardiovascular risk factors and a determining element in the development of the pathology. Indeed, despite the support in recent years of societal awareness aimed at reducing cardiometabolic risk factors and in particular improving lifestyle and diet, thus improving the intestinal microbiota, we see that the global prevalence of cardiovascular pathology continues to increase, suggesting that there are other risk factors such as the oral microbiota, which are increasingly studied. In fact, the oral cavity is one of the human body’s microbiota with one of the largest reservoirs of known bacteria. The presence of numerous ecological niches on soft and hard tissues gives a very important role of this oral microbiota in its relationship with the general health. Manifestations of underlying systemic disease, including CMDs, are often reflected in the oral cavity and oral health can serve as an indicator of overall health. Many articles have been published on PubMed in the last 5 years evaluating the relations between periodontitis and CMDs; many of them suggest a strong epidemiologic association between periodontitis and CMDs (Sanz et al. 2020a). In fact, the existence of an epidemiological correlation between CMDs and periodontitis is nowadays well documented. As a consequence of that, a great deal of research is focusing on the study of putative biological mechanisms involved in this association, aiming not only to clarify the pathophysiological bases shared by these two conditions, but also to develop effective and optimal interventions for their management. We analyze the evidences on the association between oral microbiota and cardiometabolic risk (obesity, diabetes, hypertension) leading to cardiovascular diseases (atherosclerotic cardiovascular diseases, heart failure, infective endocarditis). We describe the molecular mechanism by which oral dysbiosis and its oral pathologies could be a risk factor for the CVD. In the last part, we discuss the treatment strategies for future research and possible prevention for clinical and therapeutic application.

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Oral Microbiota Introduction Human life is accompanied by microbial communities specific to its ecological niche called the “microbiota,” and by all of its genes, called the “microbiome.” Many human organs such as the mouth, gut, or lungs have a complex microbiota, which varies with age and many life events, and which is different from those of other organs. These various microbiotas have a role, either local or remote, in the health and balance of an individual as well as in the development of pathologies. The key is to succeed in maintaining a balance between these populations so that they can live together. We call this state “eubiosis.” The definition of eubiosis is a fragile and particular equilibria in the quantitate and quality of microbes inside the microbiota. The dysbiosis (the break in the equilibria) in the event of an imbalance in the biodiversity of the microbial flora can result in a decrease in the bacteria present, or an increase in pathogenic bacteria with an increase in particular of gram-negative bacteria, but more generally a decrease in diversity. If the bacteria of the oral eubiotic microbiota play a protective role, the dysbiotic microbiota will induce cavities or local inflammatory reactions: gingivitis and distant reactions during microbiota dysbiosis. Each individual plays a role in maintaining the quality of their own microbiota. We can now consider human microbiota to be “super organisms,” a concept developed by W. Morton Wheeler in 1917, which shows that different organisms that live together maintain a permanent and beneficial exchange. The oral cavity is the most important “gateway” for germs at the kick-off of the digestive tract; it harbors the second microbiota of our organism (Verma et al. 2018) and has been a subject of intense study for several years. The oral microbiota is the second most important microbial reservoir in the body after the intestinal microbiota. It is mainly composed of bacteria, but also of viruses, protozoa, fungi, archaea, phages, and very small bacteria belonging to the candidate phyla radiation group (Hug et al. 2016). In their physiological state, these micro-organisms cohabit with the human body in symbiosis. This balance within the microbiota is called eubiosis and its disruption is called dysbiosis. The accessibility of the oral cavity makes this microbiota one of the best-known bacterial communities in the human body with over 500 different species identified in adults. The oral cavity is a complex ecosystem with several niches. The main niches are the oral epithelium, the tongue, the supra-gingival dental surfaces, and the sub-gingival space (Mougeot et al. 2017). The composition of the oral microbiota can vary according to several factors such as age, diet, lifestyle (e.g., smoking) without necessarily leading to a pathological state. Dysbiosis is an imbalance that can occur between the bacteria of a community or between the microbiota and the host. The persistence of oral dysbiosis can lead to the development of oral pathologies and also have repercussions at the systemic level. With more than 400–500 different species identified in adults the acquisition of the microbiota begins in the first minutes of life in contact with the maternal microbiota: the skin, the vagina, and the mouth. A recent study (Nanci and Bosshardt

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2006) underlines that the child’s environment is essential to the construction of his microbiota and is not due solely to his heredity. In addition, the maternal microbiota has already played an important role in the neurodevelopment of the fetus before birth (Arzate et al. 2015). This oral microbiota is subjected to a great reorganization during the eruption of the permanent teeth. Indeed, the appearance of teeth will allow bacteria to access a new habitat and create the periodontium with its microbiota. After birth, the bacteria established for the first time are anaerobic bacteria of the genus Streptococcus from contact with maternal microflora. Later, strict anaerobic bacteria, such as the genus Veillonella (Firmicutes) and the Phylum Fusobacteria, colonize the mouth. The composition of this microflora varies depending on life events: the diversification of foods, hormonal changes (puberty, menstrual period, pregnancy), medications, including antibiotics, and, of course, aging. The oral microbiota is acquired from birth through vertical transmission from mother to child. The acquisition of this microbiota, and its diversity are closely linked to the mode of delivery, and breastfeeding: Lactobacilli are dominant in breast-fed children. The particularity of the oral microbiota is linked to the different types of mucous membranes that compose it and especially to the presence of the teeth, which constitute the only non-desquamating surface of the human body. This latter surface creates biofilms, a kind of unique refuge for microorganisms by accumulating on the surface of the teeth, thus creating dental plaque (ten Cate 2006). It should therefore be noted that the bacteria in the mouth can adopt totally different lifestyles: either in the planktonic state, when microorganisms are present in the liquid of this oral environment, or as we have just seen, attached to the hard-dental surface by constituting a biofilm. Confocal laser scanning microscopy imaging allowed 3D visualization of the bacterial microbiota in the biofilm. This visualization is possible thanks to the dyes that bind to proteins specific to the bacteria of the biofilm (ten Cate 2006). Socransky grouped the bacterial species forming the biofilm into five major complexes (Socransky et al. 1998). In a healthy person this biofilm contains mainly the species Actinomyces (blue complex) and in smaller quantities, the species Streptococcus (yellow complex) and Veillonella (purple complex). The orange (Fusobacterium and Prevotella) and red (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola) complexes associated with periodontal disease do not even represent 4% of bacteria in a healthy subject. An increase in these bacteria, mainly gram-negative, is the cause of dysbiosis in this oral microbiota (Socransky et al. 1998). The first complex was strikingly related to clinical measures of periodontal disease, particularly pocket depth and bleeding: the parameters indicating the presence of periodontal disease. Subsequently Lovegrove J. studied bacteria associated with periodontal disease, which are mainly gram-negative anaerobic bacteria and may include Aggregatibacter actinomycetemcomitans, P. gingivalis, P. intermedia, Bacteroides forsythus, Campylobacter rectus, Eubacterium nodatum, P. micros, Staphylococcus Intermedius, and Treponema sp. Bacteria associated with disease are up to ten times more numerous than those associated with health (Li et al. 2004).

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The Salivary Microbiota The various ecological niches that make up the oral microbiota consist mainly of hard and soft tissues, but Marsh et al. shows us the major influence of saliva on this microbiota (Marsh et al. 2016). Indeed, the composition of saliva plays a determining role on the microbiota through a cascade of mechanisms linked to the molecules in suspension, which condition the composition of the biofilm. This will allow bacterial attachment to the oral surfaces. Salivary components such as glycoproteins are a source of nutrition to ensure the growth of bacteria present in the mouth. All of these bacteria act in synergy, at very low concentrations, allowing a complex balance between the surrounding microbiota and the host oral cavity. An imbalance in the emission of salivary flow can easily lead to dysbiosis. The microbiota present in saliva is made up of bacteria “released” by the environmental surfaces of the mouth (teeth, gingival sulcus, cheeks, hard and soft palates, gums, tongue, tonsils, etc.). This microbiota is unique and specific to each individual, relatively stable over time, variable according to the state of nutrition, for example, and therefore influenced by lifestyle (Singh et al. 2019). The number of bacterial species of this microbiota is estimated between 500 and 700. In Takeshida’s study (Takeshita et al. 2016), the authors identified 550 different species, of which a set of 72 species was common to the majority (75%) of people. This “hard core” of bacteria constitutes 90% of the salivary microbiota of each individual. This oral microbiota contains archaebacteria, protozoa such as Entamoeba gingivalis and Trichomonas tenax, but we can also find up to 85 species of fungi, mainly Candida, Cladosporium, Aureobasidium, Saccharomycetales, Aspergillus, Fusarium, and Cryptococcus, which together with Streptococcus form a pathogenic biofilm (Wang et al. 2012). Viruses, generally associated with pathologies, can also be present and are mainly bacteriophages. Among these viruses, we find the “mumps” virus, the human immunodeficiency virus or HIV-1, and more recently the strain of coronavirus SARS-CoV-2, which causes an infectious disease of the viral zoonosis type, the COVID-19 (Bao et al. 2020).The analysis of the salivary microbiota allows the prognosis of the presence of cavities. The presence of Streptococcus mutans, Rothia, Candida albicans, and species of Fusobacterium, Prevotella, Leptotrichia, and Capnocytophaga allows us to diagnose a carious state. Perhaps more surprisingly, the composition of the salivary microbiota is also characteristic of several other oral or systemic physiological and pathological states.

The Periodontal Microbiota Periodontium The periodontium represents the different supporting tissues of the tooth. It consists by a superficial periodontium, the gum, and a deep periodontium composed of cementum, periodontal ligament, and alveolar bone. The superficial periodontium or the gingiva is an oral mucosa of masticatory type covered by a keratinized

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epithelium penetrated by deep invaginations of a fibrous connective tissue rich in collagen fibers and inserted to the bone tissue. It is a very resistant covering tissue that protects the deep periodontium. The gum is attached to the tooth by a permeable epithelial-conjunctive attachment forming the attachment system or biological space that allows for fluid exchange. The groove between the tooth and the gum line, called the sulcus, contains a fluid that contains a mixture of microorganisms, cell debris, and electrolytes. The evacuation of this gingival fluid, composed mainly of waste, is sufficient for the protection of the deep periodontium. The gum has several defense mechanisms: the first is the constitution of a mechanical barrier toward bacteria and other oral fluids, the second is the presence of numerous defense cells playing a role in the immune reaction in case of chemical or mechanical attack (Nanci and Bosshardt 2006). The deep periodontium is made up of the periodontal ligament or desmodontium, the cementum and the alveolar bone. They constitute the alveolar attachment system and allow the stability and the damping of the dental organ. The cementum that builds up during tooth formation is made up of a primary cementum present before the eruption of the tooth and a secondary cementum formed after the eruption. The latter is applied throughout life thanks in particular to cementoblasts. It is composed of an extracellular matrix of mineralized collagen, proteins, and cement-derived growth factor (CGF) (Arzate et al. 2015). The periodontal ligament, unlike the cementum, is vascularized and innervated. It is made up of an extracellular matrix made up of fibroblasts, immune system cells, and also blood vessels and nerves. The fibers that make up this periodontal ligament, connecting the cementum of the tooth and the alveolar bone are called Sharpey’s fibers. By its innervation, the periodontal ligament is responsible for the proprioception of the tooth and contributes, through its vascularization, to the formation of cementoblasts. The last part of the deep periodontium is the alveolar bone, which makes up our jaws supporting our dental organs. The alveolar bone “originates, lives and dies” with the tooth. It is composed of an external cortical zone and an internal trabecular part. It is renewed throughout the life of the tooth by a succession of appositions and resorptions of bone tissue mediated by the presence of osteoblasts, osteocytes, and osteoclasts.

The Periodontal Microbiota It appears during the eruption of the first teeth. It is special compared to other microbiota because it combines planktonic bacteria and biofilm bacteria included in an exo-polysaccharide matrix. The microorganisms that develop in the sulcus and adhere to the root surface develop in an environment that is less rich in oxygen and more protected from shear forces than those found at the supra-gingival level. The subgingival environment has a higher amount of gram-negative anaerobic bacteria. Periodontal health is associated with a predominance of gram-positive cocci and rods such as Actinomyces naeslundii which, by aggregating with other bacterial species of the streptococcus family, are part of the primary colonizers of the root surface and will serve as a support for the development of the dental plaque biofilm. At the level of the periodontal lesion, profound changes are observed in the periodontal microbiota with the emergence of other gram-negative bacterial species. Although the bacterial species associated with periodontal health are still present, the

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bacterial triad traditionally described in 1979 as belonging to Socranski’s “red complex” (Treponema denticola, Porphyromonas gingivalis, and Tannerella forsythia) is found in abundance and dominates (Socransky et al. 1998). However, metagenomic sequencing has revealed new concepts concerning the periodontal dysbiosis associated with periodontitis. At the periodontal level, dysbiosis results more from a change in the dominant species than from de novo bacterial colonization (Scannapieco and Dongari-Bagtzoglou 2021). Periodontitis is associated with an increase in the diversity of subgingival microbiota. In periodontal disease, this dysbiotic periodontal microbiota is essentially composed of strict anaerobic gramnegative bacteria such as rods or bacilli of the genus Fusobacterium, Porphyromonas, Prevotella, or Tannerella (Minty et al. 2019). Concerning the clinical interest, periodontal bacterial sampling allows the qualitative and quantitative analysis of the bacterial species present in this gingival fluid. It is carried out using small cones of blotting paper introduced into the sulcus for a few seconds to impregnate the bacteria on these paper tips. This type of fingerprint allows the analysis of the periodontal microbiota and the diagnosis of the dysbiotic state at the origin of an immune disorder leading to the onset of periodontitis and other systemic pathologies.

Periodontitis Periodontitis is an oral age-related dysbiotic chronic inflammatory disease including a number of alterations affecting the tooth-supporting tissues or periodontium (that includes gingiva and deep periodontium). Gingiva comprises a connective tissue sustaining different types of epithelia, and especially the junctional epithelium, located at the bottom of the periodontal sulcus, a space between the inner aspect of the gingiva and the tooth. In healthy periodontium, this crevice is colonized by microorganisms and their products, that co-aggregate as the “ecological plaque or periodontal microbiome.” Deep periodontium is constituted of two mineralized tissues, the cementum – an avascular connective tissue that coats the root dentin of teeth – and the alveolar bone proper, and of a soft tissue, the periodontal ligament (PDL). Through the PDL anchorage, deep periodontium tissue maintains the tooth in the jaws, as a specific synarthrotic joint: the gomphosis. Because the junctional epithelium is highly permeable, periodontal steady state has to be carried out in a “septic” environment. Indeed, the oral microbiome perpetually communicates with the periodontal connective tissues. Therefore, periodontal health is maintained by homeostatic interactions between host, environment, and symbiotic periodontal microbiota, composed mostly of facultative bacterial genera such as Actinomyces and Streptococci. Thus, a supra-physiologic immune vigilance ensures a periodontal host-microbiome homeostasis, that prevents periodontal dysbiosis by sustaining the ecological plaque stability (Berezow and Darveau 2011). The nonpathogenic, eubiotic gingival microbial community sustains a permanent stimulation of oral defenses (e.g., structural and functional adaptations of the epithelium, antimicrobial peptides synthesis, and salivary IgA secretion) to control the microbiological challenge and to maintain healthy tissue. Moreover, leukocytes from innate immunity are recruited to the periodontal sulcus to form a

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“defense wall” against the tooth-associated biofilm (Schenkein 2006). Despite dense bacterial colonization, acute infections are rare in the oral mucosa, suggesting that this site is predominantly tolerogenic. Mucosal dendritic cells (DCs) are the arbitrators of periodontal tolerance by employing complex regulatory mechanisms, involving the induction of the regulatory T cells (Tregs) activity via interleukin (IL)-10 and TGF-ß secretion, both factors secreted during connective tissue wound healing by M2 pro-healing macrophage and MSC. The host may also use secretory immunoglobulin A, anti-microbial peptides including cathelicidin LL-37, and αand β-defensins to limit and control microbial adhesion and colonization (Schenkein 2006). Periodontal health depends on a constant interrelation of both microbial and defense systems. As described in the next section, it is now admitted that a dysregulation of host-microbiome homeostasis drives a chronic inflammatory disease of the whole tooth-supporting tissue, called periodontitis. Periodontitis clinical features includes redness or bleeding of gingiva, gum swelling, and gingival recession (Fig. 2) (Cekici et al. 2014) as results of local inflammation and dysbiosis, and deep periodontium destruction, which may in fine lead to tooth loss, altered quality of life, and aesthetics. Periodontitis defects result from a breakdown of the junctional epithelium between bacterial plaque and periodontal connective tissue, which might be associated with a significant change in the immune response allowing direct access to plaque antigens and metabolites. Disease extends as vicious circle, with development of the pathogenic microflora and the extension of the inflammatory infiltrate. Some circumstances (genetics, epigenetics, lifestyle, and environmental factors) may lead to periodontal microbiome destabilization through the emergence of virulent pathogens (including Prevotella intermedia, Fusobacterium nucleatum, and Porphyromonas gingivalis (Pg)). A shift to pathogenic status from non-pathogens like Streptococcus or Corynebacterium contributes to the emergence of the dysbiosis. There is increasing evidence that periodontitis only develops in “susceptible hosts.” Indeed, in some permissive subjects, genetic immunoregulatory defects, systemic diseases, environmental factors (e.g., smoking, stressors, epigenetic factors, drugs, diet), epigenetic modifications in response to environmental changes, and age (Wu et al. 2016), can accelerate dysbiosis and contribute to hostmicrobiome homeostasis unbalance. Then, in these vulnerable hosts, the dysbiotic community promotes an overall inflammatory response. Inflammation and dysbiosis reinforce each other, and the escalating environmental changes further select a pathobiotic community (anaerobic genera from the phyla Firmicutes, Proteobacteria, Spirochaetes, Bacteroidetes, and Synergistetes) that promotes an overall destructive immuno-inflammatory response, which, in turn, generates a nutritionally favorable environment for selective expansion of periodontitis-associated organisms: inflammation and dysbiosis reinforce each other, in a vicious cycle that determines the onset and progression of periodontitis. As a result of dysbiosis, toll-like-receptor (TLR) recognition pathways are activated, resident macrophages produce pro-inflammatory cytokines (i.e., Il-1β, Il-6, TNF-α) and chemokines that initiate macrophage/PMN crosstalk. This innate immune response can be confined to gingiva as stable chronic inflammation, called gingivitis.

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However, with persistence of the pathogenic biofilm in association with the inability of the host immune system to control it, the immune response may extent to the deep periodontium, resulting in a significant influx of monocytes, dendritic and T-helper cells to the site of inflammation. The newly recruited macrophages will polarize to an M1 phenotype in the context of increasing levels of pro-inflammatory mediators: Il-1β, Il-6, TNF-α, matrix metalloproteinases, and the pro-osteoclastic mediator RANKL generate an inflammation amplification loop, leading to irreversible deep periodontium tissue destruction. As the disease progresses, the destruction of collagenous PDL along with bone resorption leads to periodontal pocket formation. Hence, periodontitis is characterized by progressive destruction of the soft and hard tissues of the periodontal complex, manifesting with gingival inflammation, clinical attachment loss, periodontal pocketing, and radiographically assessed alveolar bone loss. Left untreated, such a disease may lead to tooth loss. Oral biofilm-associated diseases have broad implications on systemic health and quality of life, and pose a significant cost burden on societies. Almost 50% of the world’s adult population have periodontal disease and, as reported by the 2022 WHO Global Oral Health Status Report, severe periodontitis affects around 19% of the global adult population, representing more than 1 billion cases worldwide (Global oral health status report: towards universal health coverage for oral health by 2030 n.d.). More than one third of teeth extracted each year are concerned by periodontal diseases (Beikler and Flemmig 2011). A study performed in the UK demonstrated that for each tooth preserved an additional year and each millimeter of attachment loss avoided, the incremental cost-effectiveness ratios were of €217 and €1130 respectively (Beikler and Flemmig 2011). Interestingly, prevalence, extent and severity of periodontitis increase with age. Epidemiologic studies reported periodontitis is the leading cause of tooth loss after the age of 65, with major negative consequences on masticatory function, aesthetics, social interactions, and overall quality of life. By prevalence, periodontal disease is among the most infectious disease affecting mankind. Due to its high prevalence and impact in the quality of life, periodontitis represents a major public health concern. Furthermore, on a global scale, it is estimated to cost $54 billion per year in direct treatment costs and further $25 billion per year in indirect costs. Periodontitis is influenced by numerous independent factors. Some of them are modifiable, including lifestyle factors, such as smoking, psychological stress, or alcohol consumption. More care should be given to the prevention and treatment of periodontitis in order to maintain functional teeth in dental arch and improve patient’s oral health-related quality of life and selfesteem, including in patients with chronic diseases. Indeed, periodontitis has been linked to multiple systemic diseases and in some cases a causal relationship has been demonstrated (e.g., glycemic control, endothelial function). Periodontitis has thus been correlated to at least 57 comorbid conditions, including diabetes, adverse pregnancy outcomes, and cardiovascular diseases (Monsarrat et al. 2016). Among extraoral pathologies suspected to be related to periodontitis, cardiometabolic diseases have largely been considered in the literature. Here, we report the latest evidence on the relations between periodontal diseases and cardiovascular diseases (CVDs) and periodontal diseases and metabolic diseases.

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Dysbiosis of Oral Microbiota and Cardiometabolic Risk Introduction Cardiovascular diseases are complex conditions of major public health importance, as they are one of the leading causes of morbidity and mortality in industrialized countries. Knowledge of cardiovascular physiopathology is therefore a major issue, both fundamental and clinical. It is well established that diseases of the cardiovascular system are often linked to atherosclerosis and develop gradually under the influence of environmental factors and genetic predispositions. These are therefore multifactorial diseases. Age, smoking, dyslipidemia, arterial hypertension, insulin resistance, and overweight have been identified as the main cardiovascular risk factors. However, these factors alone do not explain the interindividual variability in the risk of cardiovascular mortality and morbidity; hence the importance of identifying new explanatory risk factors. The immuno-inflammatory process increasingly appears to be a common factor for these cardiovascular risk factors and a determining element in the development of the pathology. Indeed, despite the support in recent years of societal awareness aimed at reducing cardiometabolic risk factors and in particular improving lifestyle and diet, thus improving the intestinal microbiota, we see that the global prevalence of cardiovascular pathology continues to increase, suggesting that there are other risk factors such as the oral microbiota, which are increasingly studied. In fact, the oral cavity is one of the human body’s microbiota with one of the largest reservoirs of known bacteria. The presence of numerous ecological niches on soft and hard tissues gives a very important role of this oral microbiota in its relationship with the general health (Thomas et al. 2021). Many studies are investigating the role of the oral microbiota in the occurrence and/or aggravation of metabolic diseases. Indeed, studies have shown that some bacterial genus (like Streptococcus and Veillonella) found in the oral microbiota were also found in atherosclerotic plaques which is one of the main causes of cardiovascular disease (Koren et al. 2011) (Fig. 1).

Epidemiologic Evidence of the Association Between Periodontitis and Cardiovascular Diseases Mattila et al. reported for the first time periodontitis and myocardial infarction correlation (Mattila et al. 1989). Since then, many subsequent epidemiological studies showed a significant association between periodontitis and CVD, although a causal pathophysiological relationship is yet to be elucidated. It has been shown that periodontitis can contribute to endothelial dysfunction and elevated arterial calcification scores (Sanz et al. 2020a). There is also a documented positive association between periodontitis and cerebrovascular disease and risk of stroke: Campanella et al. (Campanella et al. 2019), in a recent review, highlighted that patients with stroke exhibit a higher prevalence of periodontitis than controls. A recent meta-analysis concluded that individuals with periodontitis are twice as likely to suffer stroke and have a higher risk of

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Oral microbiota dysbiosis

Actinomyces Lactobacillus Bifidobacterium spp. Scardovia spp. Dialister spp. Selenomonas spp. Solobacterieum Capnocytophaga

Atheroscloric plaque dysbiosis

Veillonella Rothia Granulicatella Streptococcus Porphyromonas Propionibacterium Prevotella Fusobacterium Treponema Tannerella

Burkholderia Corynebacterium Staphylococcus Bacteroides Chryseomonas Chlamydia Lachnospiraceae Bryantella Enterobacter Enterobacteriaceae Ruminococcus

Fig. 1 Bacterial genus crossover between dysbiotic oral microbiota and atherosclerotic plaque microbiota found in cardiovascular diseases

experiencing stroke compared to individual without periodontal disease (Baniulyte et al. 2021). Periodontitis has also been positively associated to increased hearth failure risk and coronary heart disease (CHD). Tiensripojamarn et al. (Tiensripojamarn et al. 2021) prospectively evaluated the association between periodontitis and the incidence of coronary health diseases in 1850 Thai adults during a 13-year follow-up and demonstrated the association between severe periodontitis and an increased incidence of CHD, independently of cardiovascular risk factors. Ngamdu et al. (2022) recently examined the relationship between periodontitis and the composite of coronary artery disease and stroke using the National Health and Nutrition Examination Survey (which involved 2830 adult participants): patients with stage III and IV (severe) periodontal disease were more likely to have CVD than those with stage I (mild/subclinical). There is, finally, limited but consistent evidence that individuals with periodontitis have a higher prevalence and incidence of peripheral artery diseases (PAD) compared to individuals without periodontitis and that the association is independent of other risk factors such as diabetes, smoking, and socioeconomic status (Yang et al. 2018). Yang and colleagues demonstrated that PAD patients presented a higher risk of developing periodontitis compared to non-PAD subjects (Yang et al. 2018). According to Sanz et al. (2020a), there is a lack of consistent data on the correlation between periodontitis and secondary cardiovascular events and there are no studies evaluating the association between periodontitis and the incidence of Major Adverse Limb Events. Overall, there are nowadays no scientific evidence highlighting the role of CVD as a risk factor for the onset or progression of periodontitis (Sanz et al. 2020a).

Epidemiologic Evidence on the Association Between Periodontitis and Metabolic Diseases Epidemiological associations are also well documented between periodontitis and metabolic diseases. The association between T2DM and periodontitis has been

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shown to be bidirectional (King et al. 2022). The results of a 2021 systematic review and meta-analysis of 15 cohort studies (Stöhr et al. 2021) showed that patients with diabetes had a 24% (95% CI 13%, 37%) increased incidence of periodontal disease and that, on the other hand, for patients with periodontitis, the relative risk of developing diabetes mellitus was elevated by 26% (95% CI 12%, 41%). In addition, patients affected by type 1 and type 2 diabetes appear to have a higher prevalence and severity of periodontitis than the general population (Adda et al. 2021). As reported in a recent review (Graziani et al. 2018), among T2DM people, periodontitis is significantly associated with poorer glycemic control as measured by HbA1C. Patients with periodontitis have also a higher level of HbA1C, when compared to individuals with better periodontal health. The majority of studies in literature report a higher association between periodontitis and diabetes complications (retinopathy, nephropathy, neuropathic foot ulcerations). Moreover, T2DM may worsen periodontitis, in part, by increasing the inflammatory burden on the periodontal tissues and by adversely impacting the composition of the periodontal microbiome (Hajishengallis 2022). Scientific literature consistently supports a relationship between periodontitis and insulin resistance, a chronic condition involved in metabolic disease and T2DM pathogenesis (Nazir 2017). More specifically, it has been argued that periodontal disease exacerbates insulin resistance (Nazir 2017). In a 2022 meta-analysis, Kim et al. (Kim et al. 2022) provided strong evidence supporting the positive association between obesity and periodontitis: 17 among 29 selected studies showed a significant increased odds ratio of periodontitis in the obesity group compared to control group in both elderly and young people.

Pathophysiological Mechanisms Between Periodontitis and CMDS Introduction Dysbiosis of the oral microbiota, one of the causes of periodontal disease, leads to an immuno-inflammatory reaction as previously described. Atherosclerosis, being a reaction and a direct link between cholesterol and inflammation, is increased in case of periodontal disease, causing a 24% increase in periodontal disease in patients with cardiovascular disease. There is evidence that CVD risk factors can be reduced by appropriate periodontal treatment: according to a meta-analysis, periodontal treatment improved plasma concentrations of inflammatory [C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-a (TNF-a)], thrombotic (fibrinogen), and metabolic [triglycerides, total cholesterol, high density lipoprotein (HDL) cholesterol, glycated hemoglobin (HbA1c) markers]. Conversely, some authors have called for more studies on the long-term effects of periodontal treatment on CVD. The scientific community agrees that the common etiopathogenicity between periodontal disease and cardiovascular disease is chronic low-grade inflammation. This inflammation leads to the massive release of pro-inflammatory cytokines, which will in particular lead to an alteration of the lipoproteins and their connection with their

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receptors associated with a reduction in the receptors expressed due to the inflammation, which leads to a modification toward a profile of pro-artherogenic lipoproteins due to a decrease in the clearance of these lipoproteins. This promotes the presence of pro-artherogenic lipids, the dysbiosis of the oral microbiota also leads to a decrease in anti-artherogenic processes. An association between periodontitis and unbalanced lipoprotein metabolism seems to relate in particular to the apo(lipoprotein) classes of lipoproteins containing B-100, very low density lipoproteins (VLDL), intermediate density lipoproteins (IDL), and low density lipoproteins (LDL).

Physiopathology of Cardiovascular Disease The first step in the atheromatous process is the accumulation and oxidation of LDL particles in the subendothelial space (Jiang et al. 2022) following a dysfunction of the endothelial cell wall. Accumulation and oxidation of LDL in the intima is followed by activation of adhesion and penetration of peripheral blood leukocytes through the endothelial wall. Once in the subendothelial space, the monocytes differentiate into macrophages, which will capture and internalize large quantities of oxidized LDL to give rise to foam cells, precursors of lipid streaks. In addition, inflammatory cells, in particular macrophages in contact with oxidized LDL in the subendothelial space, are in an active state and secrete a wide variety of inflammatory mediators such as pro-inflammatory cytokines, the main ones being IL-1, TNF-α, and IL-6 (Jiang et al. 2022). These pro-inflammatory cytokines promote the recruitment of new leukocytes by stimulating the production of chemokines, such as MCP-1, by the cells of the plaque in formation, as well as their adhesion by inducing the production of cell adhesion molecules (ICAM-1, VCAM-1, and selectins). All of these phenomena thus ensure the maintenance of a chronic inflammatory reaction at the vascular level. Many biological pathways are involved in the development and progression of cardiovascular diseases, notably in the atherosclerotic process. The local release of cytokines and other inflammatory mediators leads to changes in the structure of the vascular walls, which are now recognized as factors in cause of atherosclerosis.

Pathophysiology Linking Periodontitis and CMDs As recently reported by Hajishengallis et al.(Hajishengallis and Chavakis 2021), the pathophysiology linking periodontitis and CMDs is mainly consistent with bacteremia, endotoxemia, and low-grade systemic inflammation. Furthermore, there is now a significant body of evidence to support independent associations between periodontitis and CMDs (Monsarrat et al. 2016).

Bacteremia Periodontal bacterial species can invade the circulation and induce bacteremia through periodontal tissue following daily life activities or professional interventions

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(Sanz et al. 2020a). Episodes of bacteremia in periodontitis patients are demonstrated to be more frequent, longer, and to involve more virulent bacterial species than in non-periodontitis patients (Polak and Shapira 2018). DNA, RNA, or antigens derived from oral bacteria, mainly periodontal pathogens (such as Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, and Prevotella intermedia) have been detected in atherothrombotic tissues (Sanz et al. 2020a). In animal models, periodontal pathogens have been shown to be involved in increased incidence of CVDs risk factors. P. gingivalis, together with A. actinomycetemcomitans, has been shown to accelerate atherosclerosis in murine models, to induce fatty streaks in rabbits aorta and to provoke aortic and coronary lesions after bacteremia in normocholesterolemic pigs (Sanz et al. 2020a). Bacteremia may thus be responsible for bacterial graft and growth over atherosclerotic coronary artery plaques and worsening of coronary artery diseases. Recently, El-Awady et al. (El-Awady et al. 2022) investigated the relation between P gingivalis and myeloid dendritic cells (DCs), mobilized in both lymphoid and non-lymphoid tissues as well as in the bloodstream, in response to oral microbial challenge. According to the authors, P gingivalis has the ability to invade and survive within DCs and this could explain the systemic dissemination of this pathogen including to atherosclerotic plaques. P gingivalis is one of the major periopathogens and could induce periodontitis with dysbiotic microbiota associated with the disruption of adaptative immune reaction. Hence, the family Porphyromonadaceae is detected in atherosclerotic plaques and upgrades the risk of CVD. The systemic effects of P gingivalis could be linked to an altered periodontal barrier, leading to increased local and systemic inflammation and spread of live bacteria into the systemic circulation to target CVD. Other periodontal pathogens can also directly invade several organs and tissues, including the cardiovascular system: they have been identified in pericardial fluids of patients with pericarditis (Louhelainen et al. 2014) as well as in cardiac valve tissue of patients with valve disease or in both atrial and ventricular tissues in patients that underwent aortic valve surgery. Bacteremia is therefore an important factor in the initiation of endothelial lesion as well as in the potentiation of the vascular wall inflammatory process (Cardoso et al. 2018).

Endotoxemia Although the presence of viable periodontal bacteria in extraoral tissues is transient, their release of virulence factors may have deleterious impact, given the chronicity of periodontitis and the frequency of bacteremia. Among virulence factors, endotoxins, especially lipopolysaccharides (LPS), play a prominent role. LPS are major components of gram-negative bacteria outer membrane, released when bacteria die; they have the capacity to activate both innate and adaptive immunity, in both local and systemic inflammation (Pussinen et al. 2022). LPS stimulates the production of inflammatory mediators and cytokines that, in turn, promotes the release of matrix metalloproteinases, the activation of osteoclasts via toll-like receptor signaling, the induction of cytokines, chemokines, and prostaglandins synthesis, leading to the destruction of soft and hard periodontal tissues (Pussinen et al. 2022). The translocation of LPS into the bloodstream leads to endotoxemia and insulin resistance,

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which significantly increases the risk of cardiovascular disease (CVD). In our previous results, cardiac functions also underwent metabolic adaptation following metabolic glucose impairment to high fat diet. The occurrence of periodontal pathogens in diabetic patients could be targeted for the management of cardiac parameters and prevent the development of CVD. Interestingly, chronic endotoxemia is involved in the pathogenesis of many inflammation-driven conditions, including cardiometabolic disorders (Hajishengallis 2022). Though the major source of endotoxemia is the gut microbiota, the dysbiotic periodontal microbiota (typically enriched with gram-negative bacteria) may also contribute to endotoxemia in patients with periodontitis (Pussinen et al. 2022). As suggested by Hajishengallis (Hajishengallis 2022), endotoxemia not only support systemic inflammation but may have direct effects on the vessel walls (e.g., endothelial dysfunction) and atherosclerotic lesions (e.g., contribution to the formation of fatty streaks). Moreover, endotoxemia have a direct effect on lipid metabolism since it is positively correlated with the concentration of triglycerides, cholesterol, and apolipoprotein B. Indeed, a highfat diet can lead to increased intestinal permeability and augmentation of LPS levels in the circulation (metabolic endotoxemia), thus raising the risk not only of cardiometabolic disorders but also of other inflammatory diseases, including periodontitis (Pussinen et al. 2022). Endotoxemia having been recognized as a risk factor of cardiometabolic disorders, LPS could be considered as a molecular link between periodontitis and cardiometabolic diseases.

Low-Grade Inflammation A possible mechanism contributing to the association between periodontitis and extraoral inflammatory comorbidities involve periodontitis-associated low-grade systemic inflammation, common denominator of many chronic diseases (Hajishengallis 2022). Periodontitis is characterized by an immune-inflammatory response, resulting in production of pro-inflammatory cytokines, such as TNFα, IL-1β, IL-6, which mediate the tissue damages. Cytokines also enter the bloodstream inducing the release of C-reactive protein, the activation of cytokine networks, and the release of oxygen radical by neutrophils, thereby contributing to the development of low-grade systemic inflammation (King et al. 2022). Patients with severe periodontitis have elevated blood levels of pro-inflammatory mediators (such as IL-1, IL-6, C-reactive protein (CRP), and fibrinogen) and increased neutrophil numbers, which contribute to maintaining a state of inflammation throughout the organism. The chronic systemic inflammation caused by periodontitis increases the risk for CVD (e.g., atherosclerosis) and metabolic disease. Low-grade systemic inflammation supports the generation of advanced glycation end-products, contributes to poor beta cell function and insulin resistance, thereby representing a possible linchpin between T2DM and periodontitis (King et al. 2022). Moreover, the pro-inflammatory cytokines inhibit insulin receptor signaling by activating phosphatases and serine threonine kinases, thus altering insulin action (Minty et al. 2019). Vice versa, the increase in adiposity and the state of general inflammation that may result from metabolic diseases contributes to maintaining and exacerbate periodontal inflammation (Minty et al. 2019) under NF-kB pathway activation. These mechanisms may be involved in the formation of atherosclerotic

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lesions. Finally, oxidative stress driving pro-inflammatory pathways common to CMDs and periodontitis, appears to be another major link between these pathologies. Toll-like receptor 4 (TLR4) signaling is know to be the key pro inflammatory signaling in the induction of hypertension and diabetes, thus leading to cardiometabolic diseases. In fact, activation of the link between LPS and TLR4 initiates an intracellular signaling pathway involving NF-κB, resulting in the production of inflammatory cytokines. These cytokines are responsible for activating the innate immune system. Knowing that TLR4 is the major receptor for lipopolysaccharide present in gram-negative bacteria, we can predict the influence of oral microbiota and its dysbiosis on high blood pressure. Chronic infections, including periodontal infections, may predispose to cardiovascular disease (Minty et al. 2023).

Molecular Mechanisms of Bacterial Translocation Inducing Cardiometabolic Phenotypes The mechanism that links the dysbiosis of the oral microbiota and the cardiometabolic phenotypes such as insulin resistance, hepatic steatosis, heart failure, requires identifying a molecular discourse between the microbiota and the host. The first mechanisms involve bacterial molecules produced in the periodontal space and which interact with the epithelium and the local immune system of the host. As a reminder, LPS can act locally for the triggering of metabolic inflammation but also remotely because they are transported by lipoproteins and plasma-binding proteins, in particular released by adipose tissue such as LPS-binding proteins (MorenoNavarrete et al. 2013). Many transport systems and therefore buffers prevent the accumulation of free forms of LPS. However, transport proteins such as lipoproteins, LPB-BP, sCD14 are all mechanisms capable of targeting tissues, in particular via their membrane receptors (Moreno-Navarrete et al. 2013). TLR4-MD2 bind the sCD14-LPS complex, the LDL receptors bind these lipoproteins loaded with LPS, etc. The origin of these circulating LPS is therefore linked to a certain oral mucosa permeability. During metabolic syndrome, the oral cavity, like the gut, becomes permeable in multiple ways (Miele et al. 2009). The contraction of tight junctions promotes their opening and thus the passage of LPS multimers or other bacterial macromolecules. A transepithelial passage via the M cells of the Peyers patches or even the mucus-secreting goblet cells has been observed (Hirose et al. 2016). In the latter case, the translocated bacteria are generally destroyed very effectively by the local intestinal immune system. However, during type 2 diabetes induced by a fatty diet and following the consequent dysbiosis, a certain immune hypovigilance sets in. This has been observed in particularly morbidly obese subjects of bariatric surgery. Dysbiosis itself is a vector of this reduction in immune competence. Analysis of the transcriptome of antigen-presenting cells and helper lymphocytes show a decrease in the transmission of information resulting from bacterial recognition between the two cell types (Garidou et al. 2015). From then on, the bacteria are no longer degraded in situ by the local immune system. On the other hand, these translocated bacteria are well phagocytosed in the intestine and then transported by

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the phagocytes to the metabolic tissues, liver, adipose deposits, heart, or even brain. Indeed, the latter, following a metabolic attack of the excess fat type, generate chemokines, which attract phagocytes, in particular those activated following bacterial phagocytosis. Phagocytes are intestinal and periodontal, i.e., the two main sites of bacterial translocation. Transepithelial translocation from the skin or the lungs is physiologically possible but has never been demonstrated to date. Concerning the oral microbiota, we have been witnessing for several years a new paradigm of insulin resistance, involving inflammation, immunity, and the microbiota. In this paradigm, changes in the microbiota would be at the origin of the phenomena of obesity and insulin resistance (Cani et al. 2007). This new concept for type 2 diabetes proposes that the “driver” of this pathology could be chronic low-grade inflammation (Cani et al. 2007), which would itself be the result of a dysbiosis of this oral microbiota. Indeed, diabetics patients present in their blood and in their adipose tissue an increase in the level of differents pro-inflammatory cytokines, such as TNF-α and IL-1 (Shoelson et al. 2006). But correlation does not necessarily indicate cause! However, some studies have dissected the molecular mechanisms linking inflammation to diabetes. For example, treatment with anti-TNF-α significantly improved adipose tissue glucose uptake and blood glucose levels in mice in response to insulin (Shoelson et al. 2006). How can these pro-inflammatory cytokines alter insulin sensitivity? Like fatty acids, these cytokines will activate JNK kinases (c-Jun N-terminal Kinases). These kinases will phosphorylate the effectors, IRS-1 and 2, involved in the transmission of the insulin signal. Once IRS-1 and 2 are phosphorylated on serines, they can no longer bind to the insulin receptor, preventing signal transduction from this receptor. Other transduction pathways are also affected: we can cite JNK, which regulates the transcription factor AP-1 responsible for inhibiting the signal to insulin and IKK (IkB kinase), inhibiting the action of insulin by activating the transcription factor NF-κB (Hotamisligil 2017) (Fig. 2). Numerous studies have investigated the role of the oral microbiota in these cardiometabolic diseases (Lamster and Pagan 2017). From an epidemiological point of view, a positive association has been demonstrated between periodontal disease and metabolic pathologies: indeed patients with type 2 diabetes have a higher risk of developing periodontitis; conversely, patients with periodontitis have a higher risk of suffering from type 2 diabetes (Musskopf et al. 2017). This has been demonstrated even if the molecular mechanisms responsible are still poorly understood. Dysbiosis of this oral microbiota leads to local inflammation, which contributes to maintaining/ aggravating a systemic metabolic inflammatory state throughout the body that can notably cause insulin resistance and vascular and cardiometabolic disorders. Insulin resistance characterizes a particular metabolic pathology, diabetes, whose relationship with the oral microbiota has been the most studied. You should know that 80% of T2D could be avoided with a healthier diet and regular physical activity (Kikui et al. 2017). However, despite the management of these risk factors, the number of diabetics in the world is constantly changing suggesting that there are other risk factors for diabetes such as intestinal dysbiosis and more recently described oral dysbiosis (Sharma and Tripathi 2019).

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Fig. 2 Link between oral microbiota and atherosclerotic cardiovascular disease. Local inflammation increases permeability of the local periodontal tissues, allowing oral bacteria to reach the systemic circulation, where they inoculate atherosclerotic plaques and aggravate inflammatory processes. GS gingival sulcus, JE Junctional epithelium, G gingiva, CT connective tissue, C cementum; PDL periodontal ligament; AB alveolar bone, D dentine (tooth root)

The bidirectional link between diabetes and periodontitis has been demonstrated with inflammation as a common mediator (Matsha et al. 2020) making periodontitis the sixth complication of T2DM also suggesting that the “driver” of this pathology could be chronic low-grade inflammation (Hirose et al. 2016), which is itself the result of dysbiosis of the oral microbiota. Differences in oral and periodontal microbiota have been found in diabetic subjects compared to healthy subjects. A significant increase in the genera Aggregatibacter, Neisseria, Gemella, Eikenella, Selenomonas, Actinomyces, Capnocytophaga, Fusobacterium, Veillonella, and Streptococcus has been observed in diabetics (Miele et al. 2009). Additionally, the involvement of oral pathogens such as Pg has been demonstrated in insulin resistance (Blasco-Baque et al. 2017). Indeed, high levels of tumor necrosis factor α (TNF-α) and Interleukin-6 (IL-6) (produced by periodontal macrophages in the presence of Pg) increase the permeability of the epithelial barriers of the oral cavity (Amano 2007) and thus promote the passage of gram-negative bacteria and their virulence factors such as LPS into the bloodstream. In addition to the inflammatory state it causes in the organs, LPS is also responsible for inhibiting the transduction of insulin receptor-initiated signaling pathways and thus the development of insulin resistance (Cani et al. 2007). In addition, periodontal treatment could reduce glycated hemoglobin (HbA1c) levels by up to 0.4% in T2DM patients (Mealey and Oates 2006). In addition, the oral microbiota can influence the progression of diabetes and certain oral hygiene measures, such as the overuse of mouthwashes, can even have a detrimental effect on the progression of diabetes. Nevertheless, many mechanisms on this two-dimensional interaction still remain to be elucidated.

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Treatment Strategies and Prevention Oral Hygiene Oral bacterial species and their metabolites play an important role in the initiation and progression of periodontal disease and dental caries such as Mutans Streptococci, Lactobacilli. By managing these acid-producing microorganisms through tooth brushing, we can prevent the development of caries and/or periodontal disease. Toothbrushing is one of the easiest individual practices used to provide a good oral hygiene. Studies have shown that the use of fluoride toothpastes significantly reduces the prevalence of caries. Indeed, fluoride contributes to increasing the enamel resistance to acidic pH, and combined with arginine, can maintain oral microbial equilibrium. Oral hygiene is essential to maintain a balanced oral microbiota but very limited research has been done on the influence of good oral hygiene on general health. A study showed that tooth brushing and flossing are associated with reduced cardiovascular risk. More research is needed to determine the exact role of oral hygiene on the control of cardiometabolic disease.

Diet It has been shown that dietary and lifestyle habits may play a role in oral diseases. Daily consumption of dairy products is inversely associated with the prevalence of periodontal disease. Indeed, the nutrients, proteins, and probiotic bacteria present in dairy products are thought to have a beneficial effect on periodontitis (Lee and Kim 2019). It is also well known that in order to decrease the cardiovascular risk, certain diets can have a beneficial influence on the risk of heart disease. For example, the Mediterranean diet, characterized by intake of fruits, vegetables, nuts, legumes, and olive oil, fewer red meats and refined grains, and low-to-moderate wine consumption, has been recommended for the prevention of cardiovascular disease and type 2 diabetes.

Pre-/probiotics Treatment As mentioned previously, S. mutans is known to be the main pathogen involved in the development of caries. It has been shown that the addition of probiotics such as Lactobacillus acidophilus is capable of modifying the pathogenicity of S. mutans and could therefore change this balance toward inhibition of pathogenic microorganisms and stimulation of host defense mechanisms (Nunpan et al. 2019). At the dawn of a new, and less invasive medicine, we are asking ourselves the question of the benefit of eradicating broad-spectrum oral bacteria. Moreover, the will to fight against microbial resistance due to antibiotic treatments and to neutralize the causal bacteria is a growing reality. It seems obvious that the use of pre-/probiotics that can

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lead to new prevention strategies becomes essential. This has already been done for the gut microbiota with the use of specific probiotics, Bifidobacterium pseudocatenulatum and Bifidobacterium catenulatum, to treat liver damage by attenuating D-galactosamine. In mice, treatment with the probiotic Bifidobacterium pseudocatenulatum reduced obesity and inflammation by improving the epithelial barrier of the oral cavity (Moya-Pérez et al. 2015).

Phytotherapy Other new therapeutic strategies exist, and we can cite here herbal medicine, with for example puerarine, an active ingredient in the root of pueraria lobate, suggested to have a powerful anti-obesity effect. Puerarin treatment increases the abundance of Akkermansia muciniphila thus protecting the intestinal barrier function by increasing the expression of ZO-1 and occludin.

Vitamin D Treatment There is another link between periodontitis and metabolic disorders: Vitamin D levels. The relationship between vitamin D defficiency and insulin resistance could be explained by inflammation, because the deficiency in vitamin D is associated with increased inflammatory markers, initial insulin resistance, and later onset of diabetes caused by β-cell death. In fact, we know that chronic periodontitis is associated with low vitamin D levels and more specifically with low serum 1.25 (OH) 2 D levels. This type of link corresponds to previously reported associations between serum 1,25(OH)2D and other inflammatory diseases. Epidemiological studies have shown an association between low serum 25-hydroxyvitamin D3 (25(OH)D3) concentration and an increased risk of metabolic syndrome and type 2 diabetes. This may be partly explained by an increased fat mass. In addition, genetic polymorphisms in vitamin D-related genes may predispose to impaired glycemic control and type 2 diabetes.

Periodontal Treatment and Attenuation of Systemic Inflammatory Markers Several studies demonstrated a decrease in systemic inflammatory biomarkers following periodontal treatment and consequent possible benefit in the reduction of cardiometabolic risk (Cardoso et al. 2018). Periodontitis treatment results in a reduction of systemic inflammatory markers, including C-reactive protein, IL-6, TNF-α, and circulating lipids and of systolic blood pressure in patients with hypertension (Muñoz Aguilera et al. 2020). However, while there is evidence suggesting that periodontal therapy reduces systemic inflammation, there are no large

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randomized controlled trials demonstrating the impact of periodontitis treatment on cardiometabolic outcomes (Cardoso et al. 2018). The effectiveness of etiological periodontal therapy in improving glycemic control has been demonstrated, with reported HbA1c reduction ranging from 0.27% to 0.48% at 3–4 months following periodontal therapy (D’Aiuto et al. 2018). There is mechanistic evidence that improving the control of diabetes reduces oxidative stress, improves lipid profiles, and reduces circulating cytokine levels. However, no study investigated the biological mechanisms involved in these clinical observations (Sanz et al. 2020b). Interestingly, administration of statins to decrease LDL cholesterol and prevent CVD appear to have a positive effect in the prevention of alveolar bone loss in experimental periodontitis rodent models and to be associated with reduced tooth loss and lower gingival bleeding and probing depth (Sangwan et al. 2016).

Modification of the Oral-Intestinal Axis Before considering a treatment of the oral microbiota, it is important to remember that when faced with dysbiosis, the body adapts. Failure to adapt, particularly immune, increases the impact of dysbiosis on the body. Thus, biomarkers classifying the type of dysbiosis, its intensity, and adaptation defects, particularly immune, will be essential to obtain therapeutic efficacy. A large number of studies have collected dysbiosis compared to nondiabetic controls. With regard to oral diseases, they are now considered to be the consequence of a deleterious change in the balance of the oral microbiota. As shown above, there is a link between periodontitis and diabetes. Indeed, treatment of periodontitis reduces HbA1c in T2D patients and many studies show the effect of antimicrobial periodontal treatment on TNFalpha. Thus, antibiotics and mechanical treatment of periodontitis have a beneficial effect on Type 2 diabetes. Once a dysbiosis of the oral microbiota has been identified and classified, it is possible to directly treat the dysbiosis and/or the organism in response. to dysbiosis. Dysbiosis are very often characterized by an imbalance in the production of short-chain volatile fatty acids, which makes this treatment suitable for several reasons. Recent data show that butyrate protects insulin-secreting beta cells against the deleterious effects of cytokines. Butyrate is also involved in strengthening the immune response to better fight against inflammation of the metabolic type. The effects of butyrate are numerous but essentially intestinal because its circulating concentration is very low. GPR43 receptors in particular are located in the intestine, and the cells of the intestinal epithelium, in particular colonic.

Conclusion In conclusion, we propose that oral diseases and its dysbiotic oral microbiota could be a new risk factor of cardiovascular diseases. We have many evidences to focus on the role of this super organ on the CVD. In order to develop new therapeutic

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strategies for the management of CVD, we have to set up clinical study protocols on the use of pre/probiotics targeting specifically pathogene bacteria. The plethoric studies that cardiac diseases are associated with a change in oral microbiota opens a new era of putative therapeutic and preventive strategies to reduce the development and the incidence of CVD. Hence, the importance of the future personalized medicine is primordial to manage the oral microbiota associated to CVD.

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Discovering the Nutrition-Microbiota Interplay in Inflammatory Bowel Disease: Are We There Yet?

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Marilina Florio, Lucilla Crudele, Antonio Moschetta, and Raffaella M. Gadaleta

Contents Introduction: Etiopathogenesis and Clinical Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetic Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathophysiological Aspects Involving the Intestinal Mucosa and the Immune System . . . The GM in IBD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Macronutrients in IBD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of Lipids in IBD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of Proteins in IBD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of Carbohydrates in IBD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deficiency of Micronutrients in IBD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Western-Style Diet or High-Fat Diet (HFD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Low FODMAP Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anti-inflammatory Dietary Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Anti-inflammatory Diet (AID) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Semi-vegetarian Diet or Plant-Based Diet (PBD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Mediterranean Diet (Med Diet) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Protein-Based Dietary Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The High-Protein Diet (HPD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Paleolithic Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Antonio Moschetta and Raffaella M. Gadaleta contributed equally with all other contributors. M. Florio · L. Crudele · R. M. Gadaleta (*) Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, Bari, Italy e-mail: [email protected] A. Moschetta (*) Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, Bari, Italy INBB National Institute for Biostructure and Biosystems, Rome, Italy e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_14

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Other Restrictive Dietary Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Specific Carbohydrate Diet (SCD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Gluten-Free Diet (GFD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Lactose-Free Diet (LFD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current Therapeutic Approaches in IBD and Their Impact on the GM . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Inflammatory bowel disease (IBD) is a set of chronic inflammatory disorders affecting the gastrointestinal tract, occurring in a relapsing-remitting fashion, and requiring lifelong treatments. Although great advancements in the IBD research field have been made, its exact etiology remains uncertain. It has been widely acknowledged that IBD represents a multifactorial disorder resulting from a complex interplay between genetic, host immune system, and environmental factors. Emerging evidence points at diet as a crucial factor in the pathogenesis and progression of IBD. A common feature of people suffering from IBD is intestinal dysbiosis, characterized by a reduction of Firmicutes, Bacteroidetes, Actinobacteria, and other beneficial commensal species and a concomitant increase of Proteobacteria and pathobionts, all together leading to loss of intestinal homeostasis. The consumption of industrialized and processed food, rich in saturated fatty acids, typical of a Western-like type of diet, has been shown to contribute to the onset of an aberrant hyperactivated mucosal immune response to commensal bacteria and impairment of the intestinal barrier integrity, thereby triggering intestinal inflammation. Conversely, the Mediterranean diet, rich in anti-inflammatory bioactive compounds, appears to be one of the most efficient dietetic regimen able to restore the host intestinal physiology. Given IBD molecular and clinical heterogeneity, we are far from establishing a universal nutritional protocol for IBD patients; however, data demonstrate that specific dietetic protocols and GM manipulation are useful as adjuvant therapies to prevent nutritional deficiencies, promote eubiosis, alleviate inflammatory symptoms, and ameliorate the clinical progression of IBD. Keywords

Intestinal inflammation · Dietary fibers · Dietary patterns · Gut microbiota · Metagenomics

Introduction: Etiopathogenesis and Clinical Presentation Inflammatory bowel disease (IBD) is a set of chronic inflammatory disorders affecting the gastrointestinal (GI) tract, occurring in a relapsing-remitting fashion, and requiring lifelong treatments. Ulcerative colitis (UC) and Crohn’s disease (CD) are the two main clinical phenotypes of IBD, each presenting with specific pathological and clinical features (Podolsky 2002).

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UC is a non-transmural inflammation partially or entirely involving the colon. UC lesion pattern typically begins proximally in the rectum and extends along the colon. The small intestine might occasionally exhibit lesions, leading UC patients to experience distal ileal inflammation, which is probably due to the “backwash” of cecal content; therefore, this manifestation is called “backwash ileitis.” Patients can be classified as having proctitis, left-sided colitis, distal colitis, or pancolitis, depending on the involved anatomical area: proctitis affects the lining of the rectum; left-sided colitis affects the descending colon; distal colitis affects the descending colon and reaches the sigmoid colon; and pancolitis affects the entire large intestine. The clinical diagnosis is supported by objective findings from endoscopic and histological analysis. The major clinical symptoms of UC are bloody diarrhea (often nocturnal and postprandial), bleeding from the rectum, passage of pus, mucus, or both, during bowel activities, and abdominal pain in the lower left part. Moreover, UC patients experience weight loss. UC is categorized as mild with up to four bloody stools per day without systemic toxicity, moderate with four to six bloody stools per day with minimal toxicity, or severe with more than six stools per day with signs of toxicity, such as fever, tachycardia, anemia, and high serum level of erythrocyte sedimentation rate (ESR). More than 10 bloody stools per day, anemia requiring blood transfusions as a consequence of persistent bleeding, and colonic dilatation are common in patients with fulminant UC. Such patients also experience high fever, high level of inflammatory markers, as well as weight loss. CD is a transmural inflammatory disorder, potentially involving the entire GI tract, from the oral cavity to the rectum. As previously mentioned, a small number of patients experience backwash ileitis, difficult to distinguish from Crohn’s ileocolitis. Clinical diagnosis of CD is based on physical examination, supported by objective findings from endoscopic, radiographic, laboratory, and histological analyses. Differently from the UC, CD lesion pattern is discontinuous, with inflamed areas alternated to uninflamed ones. Strictures, abscesses, and fistulas also occur as complications in CD patients. The major clinical symptoms of CD are abdominal pain in the lower right part, weight loss, diarrhea, fever, bowel obstruction due to swelling, which results in thickening of the bowel wall, and passage of mucus or blood or both during bowel activities. Because of poor absorption, individuals with CD frequently experience malnutrition or nutritional deficiencies. CD is categorized as mild to moderate (tolerance to oral nutrition without signs of toxicity, dehydration and abdominal tenderness), moderate to severe disease (failure to respond to treatment for mild disease, symptoms of fever, abdominal tenderness, weight loss, intermittent nausea, and vomiting), and severe to fulminant disease (persisting symptoms despite corticosteroids therapy). Musculoskeletal, dermatological, ocular, and hepatobiliary comorbidities occur as extraintestinal manifestations in both UC and CD patients. IBD patients may also present with comorbidities such as cardiovascular and respiratory diseases and have higher risk of developing colorectal cancer (CRC). Although great advancements in the IBD research field have been made, its exact etiology remains uncertain. It has been widely acknowledged that IBD represents a multifactorial disorder resulting from a complex interplay between genetic, host

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immune system, and environmental factors. Several studies support the hypothesis that IBD mainly occurs in genetically susceptible individuals, also presenting with a disbalanced intestinal microbiota composition. Studies have reported that IBD susceptibility is strongly influenced by hereditary factors, since up to 12% of IBD patients have shown a family history of such condition (Turpin et al. 2018). In these patients, an aberrant hyperactivated mucosal immune acquired response to commensal bacteria is the leading cause of chronic inflammation. Environmental factors, such as drugs like nonsteroidal anti-inflammatory drugs and oral contraceptives, and Western-style diet, including alcohol, can trigger the onset and progression of disease since they have the potential to alter the intestinal epithelial barrier integrity and influence the microbiota composition. Moreover, the “hygiene hypothesis” has also been proposed as risk factor for IBD. It postulates that improvements in sanitation and the consequent reduction of exposure to bacteria in childhood could compromise the immune system training, resulting in the onset of immunological-mediated disorders, including IBD. Last, but not least, stress and anxiety are correlated to IBD recurrence. However, they are currently not included as risk factors for the pathogenesis of IBD.

Epidemiology In the past, IBD had the highest incidence in Western countries such as North America and Europe, whereas Asia, South America, and southern and eastern Europe recorded a much lower IBD prevalence and incidence. The high impact of environmental factors has been indicated by a study conducted in a Swedish twin cohort where a low concordance rate in identical twins was found (~50% for CD and ~10% for UC), and the genetic influence on IBD diagnoses was mostly evident in CD than UC. Worldwide, the number of individuals suffering from IBD has increased from 3.7 million in 1990 to more than 6.8 million in 2017 (Alatab et al. 2020). The onset of IBD usually occurs during adulthood but its diagnosis is constantly growing in children. Both in the USA and Europe, a geographical gradient has been observed, showing that northern and southern regions have the highest and the lowest incidence of IBD incidence, respectively. Recently, a West-East gradient in IBD incidence in Europe has been observed by the Epidemiological Committee (EpiCom) study, confirming the gradual lifestyle change in these countries in which the progressively transitioning diet, currently “westernized,” has a pivotal influence in IBD pathogenesis. As regarding to mortality rate, IBD is known to induce serious short- and long-term consequences in the affected individuals, some of which can be deadly. Nonmalignant gastrointestinal causes, gastrointestinal malignancies, and chronic obstructive pulmonary disease are the main mortality causes in CD. A low percentage of patients who suffer IBD, especially UC patients, have a higher risk of developing CRC as a longterm complication, while extraintestinal cancers, including lymphoproliferative disorders (LD) and intra- and extrahepatic cholangiocarcinoma, are significantly higher

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among IBD patients as a whole. However, the overall survival of IBD results to be similar to the general population.

Genetic Aspects Genome-wide association studies (GWAS) have resulted in the identification of about 230 single nucleotide polymorphisms (SNPs) involved in IBD pathogenesis. It is reported that most of them are associated with the hosts’ intestinal barrier integrity, microbial clearance and/or homeostasis (Moustafa et al. 2018), innate and adaptive immune regulation (Turpin et al. 2018), reactive oxygen species (ROS) generation, autophagy, endoplasmic reticulum (ER) stress, and metabolic pathways related to cellular homeostasis. Since the barrier is involved in the hostmicrobiota interaction, it is not only conceivable but also shown in numerous studies (Birchenough et al. 2019) that alteration of the gut microbiota (GM) can trigger barrier dysfunction, ultimately leading to chronic inflammation. A peculiar dysbiosis has been observed in both UC and CD patients. There are 110 chromosomal locations that have been directly linked to the development of IBD. It has been reported that nucleotide-binding oligomerization domain containing protein 2 (NOD2) is the genetic risk locus on chromosome 16 with the strongest association with IBD, particularly in CD patients. It encodes for a pattern recognition receptor, a member of the NOD-like receptors (NLRs), which is a class of intracellular innate immune proteins. NOD2 is expressed in monocytes, macrophages, gut epithelial cells, Paneth cells, and lamina propria lymphocytes, including T cells. It is essential in the host-microbe immune response, since it binds the muramyl dipeptide (MDP), part of the ubiquitous bacterial cell wall peptidoglycan. Transcription of pro-inflammatory cytokines is induced by the activation of nuclear factor (NF)-κB and mitogen-activated protein kinase (MAPK), after dimerization of NOD2. NOD2 mutations contribute to dysbiosis and inflammation. Furthermore, it has been also reported that clearance of pathogen bacteria is dependent on NF-κB activation through the cell-death regulatory protein named gene associated with retinoid-IFNinduced mortality 19 (GRIM-19), which also regulates pathogen invasion of intestinal epithelial cells. Inflamed mucosa of patients with IBD has been reported to have lower GRIM-19 expression. Moreover, in CD patients presenting with NOD2 mutations, Paneth cells, bodyguards of the ileal mucosa, display altered morphology and secrete fewer α-defensins, antimicrobial peptides maintaining the balance between the host immune system and the intestinal microbiota. Paneth cells are selectively expressed in the ileum, potentially contributing to the distal ileal involvement of CD in patients with NOD2 mutations. Mutations in the transcribed region of solute carrier family 22 member 4 (SLC22A4) and promoter region of solute carrier family 22 member 5 (SLC22A5), encoding for carnitine and organic cation transporters 1 (OCTN1) and 2 (OCTN2), respectively, have been associated to NOD2 mutations in CD, suggesting their contribution in multiple pathways. Such mutations, actively expressed in the intestinal epithelium, macrophages, and T cells, result in reduced carnitine transport expression and altered OCTN1 and OCTN2 transport

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functions. These mutations make the transporters less tolerant to carnitine and other endogenous substrates, and more tolerant to xenobiotics and potential toxins, increasing the risk of IBD development. Moreover, the specific polymorphism OCTN1/1672 T has been associated to increased risk of developing CRC in UC patients, thus serving as a predictor marker of malignant IBD progression. Mutations on the discs large MAGUK scaffold protein 5 (DLG5) gene have also been identified. DLG5 encodes a scaffolding protein involved in the maintenance of epithelial integrity. The non-synonymous SNP G113A, causing the amino-acid substitution R30Q, represents one of the risk-associated DLG5 haplotypes for IBD. The mutation, which probably prevents DLG5 scaffolding, has also been associated to NOD2 mutations. Variants in the multidrug resistance 1 (MDR1) gene have been also associated with IBD. The gene encodes the P-glycoprotein 170, an efflux transporter mainly expressed in intestinal epithelial cells, regulating the transport of drugs and xenobiotic compounds out of cells. It has been observed the strong association of the MDR1C3435T SNP with UC. Moreover, a significant association of MDR1Ala893 polymorphism with IBD was also observed. The SAMP1/YitFc (SAMP1/Fc) mouse strain has been widely used as a genetic IBD model as it spontaneously develops chronic ileal inflammation and has several features resembling human CD. Taking advantage of this model, the peroxisome proliferative-activated receptor gamma (Pparγ) gene has been identified as a susceptibility gene of spontaneous chronic ileitis in bothSAMP1/YitFc mice and human CD. PPARγ is highly expressed in intestinal epithelial cells, adipose tissue, liver, colon, heart, and skeletal muscle, and – to a lesser extent – in immune cells. It regulates lipid metabolism, insulin sensitivity, cell proliferation, and differentiation. Moreover, it plays a central role in the transduction pathways, involved in controlling inflammatory and reparative responses. Interestingly, in IBD, PPARγ exerts its anti-inflammatory properties by stimulating the production of NF-κB inhibitor IkBα. Moreover, it has been observed that the expression of PPARγ in the intestinal is decreased in patients with UC, as compared to CD patients, although it does not seem to be associated with disease severity. This suggested an important role of PPARγ in both pathogenesis and therapeutic management of UC. Other genetic variations contributing to IBD pathogenesis have been found in additional genes, including autophagy-related 16-like 1 (ATG16L1), leucine-rich repeat kinase 2 (LRRK2), and immunity-related GTPase family M (IRGM). The ATG16L1 gene encodes a protein that processes intracellular bacteria through the autophagosome pathway. TheLRRK2 gene encodes a large cytosolic protein kinase, having a functional role in mucosal immunity. High expression of LRRK2 in lysozyme-positive Paneth cells as well as in myeloid cells in the lamina propria has been reported. It has also been reported a strong association between CD and a 20-kb deletion polymorphism (SNP rs13361189), immediately upstream of the IRGM gene. Altered expression of IRGM affects the efficacy of autophagy, a process involved in CD pathogenesis. Recently, it has been demonstrated that epigenetic modifications, including DNA methylation and noncoding RNAs, also have an important role in the pathogenesis

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and progression of IBD. In fact, environmental factors could influence the risk of IBD development through epigenetic modifications. As reported in a pediatric IBD cohort, specific epigenetic variation in intestinal epithelium could have an impact on the progression of the disease. In the study, differentially methylated regulatory regions were identified. Among them in the colon, BACH2 showed a decrease in DNA methylation, and this matched with the increase in gene expression levels in both CD and UC patients. Moreover, several site-specific changes in DNA methylation of genes associated with pathways involved in IBD onset and progression have been identified. For example, the promoter region of tripartite motif-containing 39 (TRIM39)-ribonuclease P protein subunit P21 (RPP21) gene was found hypomethylated in the colonic mucosa from pediatric UC patients, while tumor necrosis factor receptor-associated factor 6 (TRAF6) was hypermethylated in the peripheral blood mononuclear cells (PBMCs) of IBD patients. In addition, TRAF6 mRNA expression level was lower in IBD patients compared to controls, indicating that reduced gene expression and hypermethylation are correlated. Emerging evidence has also elegantly shown how the gut microbiota (GM) influences the intestinal epithelial cell methylome, thus contributing to intestinal homeostasis. Intestinal bacteria may directly or indirectly regulate the expression of epithelial genes and the intestinal inflammatory immune response through epigenetic modifications such as DNA methylation and/or histone acetylation. For instance, during dietary fibers fermentation, bacteria produce butyrate, a potent inhibitor of histone deacetylase (HDAC) activity. Butyrate-dependent HDAC inhibition upregulates the expression of NOD2, essential to activate the immune response against microbial components. Moreover, through epigenetic modifications, bacteria modulate interleukin (IL)-23/helper (Th) 17 and IL-12/Th1 axes, thus ensuring the correct expression of Th1-type and Th17-type cytokines, based on the specific activated immune response. Although the role of epigenetics in IBD is still to be fully understood, genes regulating several pathways associated with IBD might be differentially methylated or acetylated in the epigenome, suggesting their role in IBD pathogenesis. Last but not least, microRNAs (miRNAs) could also contribute to IBD pathogenesis since they have been found differentially expressed in the intestinal mucosa and peripheral blood of IBD patients compared to control individuals. miRNAs are small noncoding RNA molecules of approximately 22 nucleotides regulating several signaling pathways and targeting specific genes. They act posttranscriptionally, regulating gene expression without any variation on the DNA sequence. Recently, they emerged as potential biomarkers for diseases. In UC, miRNAs play a role in the immune response involved in bacteria colonization, modulation of cytokine expression, and regulation of the intestinal barrier integrity. Studies have analyzed the miRNome in tissue obtained from IBD patients and reported that miR-7, miR-26a, miR-29a, miR-29b, miR-31, miR-126, miR-127-3p, miR-135b, and miR-324-3p were increased in the inflamed mucosa of UC patients, whereas miR-9, miR-126, miR-130a, miR-181c, and miR-375 were increased in the mucosa of patients with active CD compared to control subjects.

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Pathophysiological Aspects Involving the Intestinal Mucosa and the Immune System The intestinal wall consists of subsequent protrusions called villi and invaginations known as Lieberkühn crypts. The intestinal mucosa is the inner layer of the gastrointestinal (GI) tract, coated by a monolayer of epithelial cells held together by tight junctions (TJ). The intestinal epithelium consists of enterocytes and enteroendocrine cells, as well as goblet cells (GC) and Paneth cells, producing mucus and antimicrobial peptides, respectively. The intestinal mucosa is inhabited by a large number of macrophages, lymphocytes, and antigen-presenting cells (APC). Epithelial cells contain intraepithelial lymphocytes (IELs) and CX3CR1+ phagocytes penetrating the epithelium and extending dendrites to sample luminal antigens. The intestinal epithelium acts as a physical barrier to the intestinal lumen’s content while also taking a major role in nutrient absorption. Moreover, the epithelium communicates with the immune system and the GM. The lamina propria is situated under the epithelium, and then there is the connective tissue containing stromal cells and lymphoid tissue, which aggregate in the ileum to form Peyer’s patches (PPs). PPs contain a significant number of dendritic cells (DCs), macrophages, and lymphocytes. At their apex, PPs contain microfold cells (MCs) that internalize antigens. Then, M cells take them to the underlying APCs, such as DCs and macrophages, coming into close contact with lymphocytes. Due to their ability to produce chemokines and the immune-system regulator IL-33, lamina propria cells also play roles in fibrosis and wound healing and may contribute to the worsening of pathological conditions such as IBD. For instance, intestinal fibrosis leads to stricture formation and bowel obstruction which are frequent complications in CD. IL-33 has been reported to be a protective immune modulator that leads to the attenuation of a dextran sulfate sodium (DSS)-induced chronic colitis in mice and modulates mucosal healing. Conversely, excessive IL-33 production has been reported in UC, and IL-33 mRNA levels have been positively correlated with UC disease activity. The intestinal mucosa is separated from the intestinal submucosa by a layer of muscularis mucosae. PPs also extend into the submucosa of the ileum and as a result of their immune function can be considered the immune sensors of the gut, representing the interface between innate or adaptive immunity. In IBD patients, the innate and the acquired immune response are abnormally activated since their cells have a hyperactivated phenotype characterized by a dysregulated expression of cytokines, chemokines, adhesion, and co-stimulatory molecules (Saez et al. 2023). The majority of IBD patients present with an impaired intestinal barrier function, deregulated expression of TJs, altered apoptosis, and the presence of mucosal lesions. Moreover, IBD patients present with a lower number of GC (Singh et al. 2022) and therefore a dramatically reduced levels of mucus, resulting in an increased ability of microbial products and toxins to directly affect intestinal cells. In CD patients, a reduction of the mucus layer thickness has been associated with a significant reduction in the GC population, and also aberrant mucus composition has been documented in UC (Van Der Post et al. 2019). A discontinuous pattern of TJs has been observed in CD patients. Moreover, the upregulation of epithelial apoptosis leads to loss of the

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epithelial integrity. These alterations allow molecules such as microbial and food antigens to get through the epithelial barrier, triggering the immune response. Beside deregulation of structure and function of TJs and apoptosis, epithelial barrier dysfunction in UC is also caused by the presence of inflammatory lesions, which in turn increase ion permeability of the tissue. In both IBD phenotypes, altered intestinal barrier results in increased permeability, deregulation of the osmotic system thus contributing to the onset of diarrhea. Macrophages, neutrophils, DCs, and natural killer T cells (NKT) mediate the innate immune response. In both forms of IBD, macrophages and DCs are increased in the lamina propria, present an activate phenotype, and produce pro-inflammatory cytokines, such as IL-1β, tumor necrosis factor (TNF-α), IL-6, IL-8, IL-18, and chemokines, which are small peptides useful in cell-to-cell communication, growth of antigen-specific effector cells, and mediation in local and systemic inflammation through autocrine, paracrine, and endocrine mechanisms. Cells of the innate immune response in the lamina propria are susceptible to activation by bacterial products, such as lipopolysaccharide (LPS), peptidoglycan, flagellin, and CpG sequences, all known as pathogen-associated molecular patterns (PAMPs). They selectively bind to various pattern-recognition receptors (PRRs). The family of Toll-like receptors (TLRs) is the most extensively characterized class of PRRs in mammalian species. Most mammalian species contain between 10 and 15 types of TLRs, although the exact gene numbers vary between species. TLRs are expressed in innate immune cells, intestinal epithelial cells, and mesenchymal cells. Since the distal ileum and colon contain high bacterial concentration, intestinal epithelial cells express low levels of TLRs. TLRs are differentially expressed on the effector cells of the innate immune response which selectively bind to specific microbial adjuvants. The triggering mechanism of an infection is the detection of PAMPs by TLRs. When TLRs are ligated, NF-κB and MAPKs are activated; this in turn triggers the transcription of several inflammatory mediators. Moreover, NF-κB stimulates the expression of protective molecules such as its own inhibitor IκBα, β-defensins, and PPAR-γ. It has been reported that NF-κB is hyperactivated in intestinal macrophages and epithelial cells of IBD patients. In inflammatory conditions, such as IBD, the innate immune cells from susceptible individuals release cytokines, primarily TNF-α and the chemokines monocyte chemoattractant protein 1 (MCP-1), which create a chemotactic gradient to attract circulating effectors, neutrophils, and monocytes to the inflammatory site. In colonic tissue, serum, and macrophages of both CD and UC patients, TNF-α is highly expressed, increasing the production of IL-1β. The secreted inflammatory molecules upregulate the local endothelial expression of adhesion (e.g., ICAM1), co-stimulatory molecules CD40, CD80, CD86, and the inducible T-cell co-stimulator (ICOS) (Amersfoort et al. 2022). Thus, leukocytes increase their capacity to adhere to the inflamed endothelium. It has been reported that they are recruited and retained in a large number in the inflamed intestine of IBD patients, where they become activated and release pro-inflammatory cytokines. In IBD, cytokines are also crucial in determining the differentiation of T cells into their subgroups, including T regulatory Treg), Th1, Th2, and Th17 cells. These T cell subgroups together with B lymphocytes mediate the acquired immune response. Th

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cells are crucial in mediating the immune response playing a pivotal role in defining the specificity of antibodies generated by B lymphocytes as well as in activating other immune cells. Th cells differentiate into Th1 cells, Th2 cells, or additional Th lineages. Th1 cells promote cell-mediated immune response by producing pro-inflammatory cytokines like interferon gamma (IFN-γ), TNF-α, and IL-12. Conversely, Th2 cells promote the humoral immune response by producing antiinflammatory cytokines such as IL-4, IL-5, IL-6, and IL-10. In healthy individual, the ratio of Th2 to Th1 cells is balanced. Differently from the innate immune response which is equally overstimulated in both forms of IBD, T-cell profiles differ in UC and CD. UC is associated with an atypical Th2 response mediated by nonclassical NKT producing IL-13 and having cytotoxic potential for epithelial cells. Conversely, CD is associated with a high Th1/Th17 response producing cytokines, which results in persistent gut inflammation. Th1 response is mediated by IFN-γ, whose production is stimulated by the secretion of IL-12 and IL-23 by APCs, following bacterial colonization. In conclusion, when the mucosal immune system is exposed to external luminal contents due to increased permeability, the production of pro-inflammatory cytokines increases, promoting the differentiation of T cells in Th1 or Th2 effector cells. On the other hand, the increased permeability of epithelial cells causes a continuous stimulation of the mucosal immune system which sustains the chronic intestinal inflammation that characterizes IBD. Then, inflammatory cytokines recruit additional inflammatory cells into the intestinal wall to sustain inflammation.

The GM in IBD The human GI tract is colonized by more than 1000 different bacterial species, as well as fungi, viruses, and even parasites which are fundamental for intestinal homeostasis, and all together constitute the GM. It has been estimated that the total amount of genes expressed by the GM, collectively called microbiome, is more than 100 times larger than its host. The GM symbiotically acts with its host contributing to normal human physiology: it has trophic functions, produces vitamins (e.g., vitamin K and constituents of vitamin B), performs metabolic functions, facilitates the digestion of dietary substrates, and ferments nondigestible dietary fiber. Moreover, the GM has protective functions, produces bacteriocin and hydrogen peroxide, and modulates the host immune system. Fiber fermentation results in the production of short-chain fatty acids (SCFAs), such as butyrate, acetate, and propionate, all involved in a wide range of metabolic functions. The amount and diversity of the GM increase from the small to the large intestine, with fewer species living in the stomach due to its acidic environment. The GM is unique for each individual, and even identical twins present significant differences. The number of microorganisms inhabiting the gastrointestinal tract and its diversity is not only genetically determined but is also strongly influenced by multiple factors, including epigenetics, gestational age at birth, birth route (natural and cesarean delivery), breast- or formula-feeding, diet, drug administration, hygiene, physical exercise, and general lifestyle. Emerging data show that the acquisition of the

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microbiota already occurs during pregnancy from the mother, and bacterial colonization becomes pivotal at birth. Firmicutes and Bacteroidetes phyla begin to colonize the individual within the first week of the birth and they dominate the gut. They represent the 90% of the total bacterial composition. The remaining percentage of bacterial composition is represented by Proteobacteria, Fusobacteria, Actinobacteria, and Verrucomicrobiota phyla. Newborns after natural birth route present higher level of Bifidobacterium and Bacteroides, while neonates born with cesarean section are mostly colonized by Clostridium. The colonizing microbiota is initially unstable, with poor diversity and complexity; its composition changes when newborns switch to a solid and varied diet. Then, the GM increases until it reaches a diverse, complex, and stable population at about the age of 3 years. Then, within the 3rd–4th year of age, the GM composition evolves towards an adult-like shape. Once formed, GM is generally stable throughout adulthood; however, it can vary as a result of bacterial infections, antibiotic use, lifestyle, surgery, and long-term dietary changes. Another broad shift in the composition of GM occurs later in life, from the adult stage to the elderly stage, in which low levels of Firmicutes (mainly Clostridium cluster XIVa and Faecalibacterium prausnitzii), Actinobacteria (mainly Bifidobacteria), and high levels of Proteobacteria have been found. Moreover, in elderly, also Bacteroidetes, specifically Lactobacilli, are reduced. As previously mentioned, the GM produces SCFAs as a result fermentation of dietary fibers. SCFAs act as important regulators of metabolic and endocrine pathways, as well as immunological homeostasis. They represent a crucial energy source for colonic mucosa cells (Lee and Chang 2021) and act as mediators of the communication between the gut and distant organs, such as the brain, liver, lung, and heart, thus regulating the so-called gut-brain, gut-liver, gut-lung, and gut-heart axes. Also, the GM and its metabolic byproducts influence the immune system (Cohen et al. 2019). Intestinal dysbiosis deeply affects the host intestinal physiology since it changes the balance between commensal and potentially pathogenic microorganisms (Teofani et al. 2022). Although a wide variability in the GM exists, there are common features in IBD patients’ GM, including a reduced total number of species and a loss of microbial diversity. In particular, Firmicutes, Bacteroidetes, and Actinobacteria are reduced, while high level of Proteobacteria has been observed. Biopsies and fecal samples of IBD patients and control subjects have been subjected to metagenomic analysis to study mucosa-associated microbiota and fecal-associated microbiota. Patients with IBD present with different fecal and mucosal microbial composition and a lower diversity (He et al. 2019). Fusobacterium, Enterococcus faecalis (Zhou et al. 2016), and members of Enterobacteriaceae family seem to be more abundant in fecal samples from UC patients than controls. CD patients present with a significantly decreased diversity of Firmicutes, and lower levels of the Clostridium leptum, compared to control subjects. Patients affected by either CD or UC also present with lower level of Bifidobacteria and butyrate-producing bacteria, including Faecalibacterium, Eubacterium, Roseburia, Lachnospiraceae, and Ruminococcaceae. A high mucosa-associated bacterial density has been observed in IBD patients, and the ratio between Firmicutes and Bacteroidetes, which represents a measure of dysbiosis, was significantly reduced (Lee et al. 2022). Clostridium leptum (cluster IV),

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Clostridium coccoides, and Eubacterium rectale (cluster XIV) constitute the Firmicutes phylum in the colonic microbiota, while Bacteroides-Prevotella-Porphyromonas group constitutes the Bacteroidetes phylum in the colonic microbiota. Bacteroides-PrevotellaPorphyromonas group was increased in the mucosa of UC patients compared with CD and controls, as well as Lactobacillus and Escherichia coli (Enterobacteriaceae) levels, while Clostridium coccoides, Eubacterium rectal, and Clostridium leptum group were significantly less abundant in the mucosa of CD patients compared to controls and UC mucosa patients. A lower abundance of Faecalibacterium (Firmicutes) was found in patients with active CD. Particularly, Faecalibacterium prausnitzii and other SCFAproducers are reduced in CD patients (Barberio et al. 2022a). Bacterial populations within the Clostridium (Firmicutes) clusters IVand XIVa were reduced in fecal samples from CD and UC patients, respectively (Zhang et al. 2017). In vitro experiments and murine models have demonstrated that Faecalibacterium prausnitzii (Firmicutes) and Lactobacillus rhamnosus (Firmicutes) improve the intestinal barrier integrity by upregulating the expression of occludin and cadherin proteins. Roseburia hominis (Firmicutes), another major species producing butyrate, also seems to be greatly reduced in UC patients and inversely correlates with UC disease activity and duration. Eubacterium rectale was also shown to be significantly reduced, especially in pediatric UC. Reduced levels of Bifidobacterium (Actinobacteria) were also found in patients with active CD (Qiu et al. 2020) and it has been tested as a probiotic strain in UC patients (Bozkurt and Kara 2020). In a research study, an intracolonic single-dose injection of Bifidobacterium animalis subsp. Lactis and xyloglucan combination was administrated to 10 UC patients with severe disease. After 6 weeks, it was observed that combination of the single-strain and its strain-specific prebiotic was effective in healing the intestinal mucosa and ameliorating colonic symptoms in UC patient (Bozkurt and Kara 2020). An enrichment of the enterotoxigenic strain Bacteroides fragilis was reported in UC (Zamani et al. 2017). Bacteroides fragilis is crucial for preserving the host physiological health because it induces an anti-inflammatory immune response in intestinal tissue. However, it has been reported that the secretion of the pro-inflammatory toxin by the enterotoxigenic Bacteroides fragilis contributes to the development of UC and diarrhea symptoms in susceptible patients (Zamani et al. 2017). A decreased relative abundance of Bacteroides uniformis and greater relative abundances of Bacteroides ovatus and Bacteroides vulgatus were also seen in CD patients likely indicating their involvement in IBD progression (Zhang et al. 2017). Several Fusobacterium species, including adherent, invasive, and pro-inflammatory species, have been identified as UC triggers. In murine experimental colitis models or IBD patients, there is an increase in the relative abundance of Fusobacterium, with Fusobacterium nucleatum accounting for 69% of all IBD patient-derived Fusobacterium (Strauss et al. 2011). Moreover, the commensal bacteria Fusobacterium varium was identified as a potential pathogen in UC, since it can actively invade the host epithelial cells through extracellular adhesion and invasion molecules, thus contributing to a severe mucosal inflammation, which might ultimately lead to UC. In both types of IBD, it has been reported that the enteric virome undergoes disease-specific alterations. It has been observed an increased number of Caudovirales bacteriophages in CD patients, while in IBD patients, an increased number of bacteriophages infecting

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Clostridiales (Firmicutes), Alteromonadales, and Clostridium acetobutylicum, and viruses from the Retroviridae family was observed. The GM is sensitive to bacteriophages since they are primarily responsible to horizontal transfer of genetic material among bacteria. Therefore, changes in bacteriophages composition might also lead to changes in bacterial microbiome. In addition, fungal components also regulate the immune responses and are related to the pathogenesis and progression of UC, including Candida albicans. In 91.7% of UC patients, it is the most frequently isolated strain, and it can delay UC recovery. Pathobionts are also a relevant cause of dysbiosis. Pathobionts are distinguishable from pathogens since they are generally innocuous. Nonetheless, they are potential pathogenic symbionts of intestinal bacteria, causing immune-mediated diseases under specific conditions that also involve environmental and genetical alterations. Gastrointestinal pathobionts are Clostridioides (formerly Clostridium) difficile, Helicobacter pylori, invasive Escherichia coli, Proteus mirabilis, Klebsiella pneumoniae, and vancomycin-resistant Enterococcus. Recent studies have reported increased levels of Escherichia coli in UC patients, particularly the strains of adherent-invasive Escherichia coli (AIEC). Because of their capacity to invade, AIEC strains can pass the human intestinal barrier and reach deep tissues. Emerging evidence points to the relationship between the bile acid (BA) pool and the GM, since species-specific bacteria strains are able to metabolize BAs, amphipathic compounds synthesized in the liver from that facilitate the solubilization, digestion, and absorption of dietary lipids and lipid-soluble vitamins in the small intestine. BAs perform their functions travelling along the whole small intestine and are reabsorbed in the terminal ileum and then recirculated to the liver via the portal vein. This so-called enterohepatic circulation occurs in human 4–12 times every day. Altered BA metabolism can cause dysbiosis and, in turn, trigger intestinal mucosal inflammation. The GM is important to regulate the BA pool size and composition. Moreover, BAs flow along the intestine prevents bacterial overgrowth and preserves from intestinal mucosal damage. Only 5% of BAs escape ileal reuptake and reach the colon where they are mainly biotransformed by GM and excreted with feces. BAs biotransformation processes include deconjugation, oxidation, epimerization, esterification, and desulfatation (Gadaleta et al. 2022). In terminal ileum and proximal colon, which is the primary colonization site for bacterial strains with bile salt hydrolase (BSH) activity, GM-dependent BA metabolization occurs (Gadaleta et al. 2022). Lactobacilli, Bifidobacteria, Clostridium, and Enterococcus strains are among the gram-positive gut bacteria with a BSH activity. In gram-negative bacterial strains, BSH activity has only been identified in bacteria of the phylum Bacteroidetes. BSH-competent bacteria convert primary into secondary BAs, mainly deoxycholic acid (DCA) and lithocholic acid (LCA). Unconjugated BAs and secondary BAs are more hydrophobic than the corresponding conjugated and primary forms, and therefore, they are more cytotoxic. Abnormal BA homeostasis in the gut-liver axis contributes to the pathogenesis of several diseases, including colon cancer, cholesterol gallstone disease, and GI disease. Moreover, it has been reported that IBD patients have low level of BSH activity due to the lack of BSH-competent bacteria, indicating a dysbiosis-dependent alteration of BA metabolization in these patients (Sinha et al. 2020). BAs also act as hormone-like signaling molecules.

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Primary BAs bind to the farnesoid X receptor (FXR), a nuclear receptor highly expressed in the small intestine and liver, which acts as a transcription factor of tissue-specific gene networks, primarily involved in the regulation of BA homeostasis. It has been reported that changes in BA metabolism are associated with several pathological conditions, including IBD. BA-activated FXR has important anti-inflammatory (Gadaleta et al. 2011) and antitumorigenic functions (Cariello et al. 2022), and it contributes to the maintenance of intestinal barrier integrity and modulation of intestinal immunity (Gadaleta et al. 2011). Among other effects, inhibited FXR activation causes a decreased production of its main intestinal targets: the enterokinase fibroblast growth factor 15/19 (FGF15/19 in mouse and human, respectively). Physiologically, once produced, FGF15/19 is secreted into the portal circulation and reaches the liver, where it binds to its cognate co-receptor heterodimer fibroblast growth factor receptor (FGFR) 4-Klotho-beta (KLB). A phosphorylation signaling cascade is thus activated resulting in the inhibition of cytochrome P450 family 7 subfamily A member 1 (CYP7A1), ultimately suppressing the BA de novo hepatic synthesis. As a result, de novo BA synthesis is increased in IBD patients together with their susceptibility to cancer development and BA-related IBD comorbidities, such as primary sclerosing cholangitis. Pharmacological FXR activation and/or FGF19 protect the intestinal barrier integrity and have an antiinflammatory effect in the intestine while positively modulating the GM (Gadaleta et al. 2020), promoting the enrichment of gram-positive strains, including Streptococcus thermophilus, Lactobacillus casei and paracasei, Bifidobacterium breve, and Lactococcus lactis (Gadaleta et al. 2022). A summary of GM changes in IBD, and specifically in CD and UC, is depicted in Fig. 1.

INFLAMMATORY BOWEL DISEASE REDUCED NUMBER OF SPECIES AND DIVERSITY AND REDUCED FIRMICUTES/BACTEROIDETES FIRMICUTES BACTEROIDETES ACTINOBACTERIA

PROTEOBACTERIA

FUSOBACTERIUM NUCLEATUM

BIFIDOBACTERIA FAECALIBACTERIUM EUBACTERIUM ROSEBURIA LACHNOSPIRACEAE RUMINOCOCCACEAE

CROHN'S DISEASE BACTEROIDES OVATUS BACTEROIDES VULGATUS

BACTEROIDES UNIFORMIS BIFIDOBACTERIUM FECALIBACTERIUM PRAUSNITZII EUBACTERIUM RECTALE CLOSTRIDIUM LEPTUM CLOSTRIDIUM COCCOIDES

ULCERATIVE COLITIS ROSEBURIA HOMINIS BACTEROIDES PREVOTELLA PORPHYROMONAS FUSOBACTERIUM ENTEROCOCCUS FAECALIS ENTEROBACTERIACAE FAMILY LACTOBACILLUS ESCHERICHIA COLI (AIEC) BACTEROIDES FRAGILIS

Fig. 1 Summary of changes in gut microbiota composition in patients affected by IBD (left side panel), and specifically in CD (right upper panel) and UC (lower right panel) patients

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Macronutrients in IBD Emerging evidence strongly points at nutrition as one of the contributors in IBD pathogenesis. However, clear mechanistic insights are still lacking. It is commonly accepted that specific dietary elements with a pro-inflammatory potential could alter the immune system and GM, resulting in damage of the intestinal barrier and the hyperactivation of the immune response typical of IBD condition. The intestinal barrier is the first physical and chemical interface protecting the intestine from pathogens and food antigens and its components can be altered by diet. As discussed earlier, structural changes result in increased intestinal permeability followed by the activation of the immune response (Birchenough et al. 2019). All nutrients, either in excess or defect, may putatively have a pivotal impact on the IBD. For example, patients with IBD may show deficiencies of macronutrients (lipids, proteins, and carbohydrates) and micronutrients (vitamins and fibers), in either the active or quiescent phase of the disease (MacMaster et al. 2021). Malnutrition is very common in IBD, with a prevalence ranging between 20% and 85%, mostly represented by protein-energy malnutrition and micronutrient depletion. Malnutrition is more frequently observed in patients with CD than in patients with UC, especially occurring in CD patients with ileal localization affecting nutrient digestion and absorption (Bischoff et al. 2020). Anyhow, malnutrition results from several factors including decreased food intake, nutrients malabsorption, enteric nutrients loss, increased energy expenditure, and iatrogenic factors. Reduced food intake is mainly caused by loss of appetite due to nausea, vomiting, abdominal pain, and diarrhea, which could be also induced or worsen by medications. Reduced nutrients digestion and absorption could also be caused by bacterial overgrowth which increases intestinal motility and produces osmotically active metabolites contributing to discomfort and diarrhea, together ultimately leading to severe nutrient loss.

The Role of Lipids in IBD In the contest of lipids, fatty acids (FAs) represent the major lipid building blocks and have primarily been studied for their immune-regulatory properties potentially influencing IBD pathogenesis. Based on the length of carbon chains, FAs are classified into long chain (LCFAs) and short chain (SCFAs). LCFAs consist of 13–21 carbons long chains. Based on the number of double bonds, LCFAs can be further categorized into saturated fatty acids (SFAs) and unsaturated fatty acid (UFAs), according to the precence of absence of double bonds. UFAs can be further divided into two subgroups: monounsaturated (MUFAs), which have a single double bond, and polyunsaturated (PUFAs), which present with more than one double bond. UFAs can be also classified into ω-3, ω-6, and ω-9 subgroups, according to the position of the final double bond in FA chemical structure. α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) are the well-known members of the ω-3 family.

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SFAs are present in animal fat-containing products such as meat, butter, whole milk, and dairy products. Myristic, palmitic, and stearic acids are the most common ones. It has been reported that diets rich in SFAs, known as high-fat diet (HFD) or Western diet, have a negative impact on human health and increase the risk of developing inflammatory diseases, including IBD (Barnes et al. 2017). However, their contribution to IBD pathogenesis is still not completely clear. In a study, human colonic LS174T goblet cells were treated with palmitic acid or ω-9, ω-6, or ω-3 UFAs alone or in co-treatment with palmitic acid. It has been observed that palmitic acid downregulated Muc2 mRNA expression and significantly decreased GC differentiation after 3, 6, and 24 h of treatment. In addition, palmitic acid induced ER stress. In this study, only EPA and DHA protected GC against altered Muc2 secretion induced by palmitic acid. Among MUFAs, oleic acid, the most prevalent ω-9 MUFA, is present in a high number of vegetable oils, including olive, avocado, soybean, and canola oils, as well as in plants like safflower, sunflower, macadamia nuts, hazelnuts, and almonds. The Mediterranean diet, which is rich in MUFAs, particularly oleic acid, has been shown to have beneficial effects in several pathological conditions, including metabolic syndrome and cardiovascular diseases. MUFAs could also have a protective effect in IBD, but clear mechanisms of action remain uncovered. The anti-inflammatory effects of oleic acid, leading to the attenuation of gut inflammation, have been observed in UC patients. Indeed, it has been reported that dietary oleic acid intake is inversely associated with UC development. Moreover, in an experimental UC rat model, it has been shown that dietary intake of oleic acid together with a low intake of ω-6 FAs caused changes in the GM composition, with a significant increase in the bacterial species characterized by anti-inflammatory properties, namely Alistipes, Blautia, Dorea, and Parabacteroides. In addition, a diet with high amount of oleic acid reduced UC-related parameters, including epithelium alteration in colonic mucosa, inflammatory cell density in the colon, and secretion of pro-inflammatory cytokines IL-17 and INF-γ. Among ω-3 PUFAs, ALA cannot be synthetized by the human body, and therefore, it must be introduced from food sources. It is present in products such as vegetable oils, dairy products, and eggs. EPA and DHA can be derived on a certain extent from ALA conversion and found in fish and seafood. A research study has been conducted in an experimental colitis rat models. Colitis was induced by intrarectal injection of 2-4-6-trinitrobenzen sulfonic acid (TNBS). It has been observed that TNBS-induced colitis increased inflammatory markers such as the inducible nitric oxide synthase (iNOS). Dietary ω-3 PUFAs decreased iNOS, cyclooxygenase-2 (COX-2), and leukotriene B4 (LTB4) expression (Charpentier et al. 2018). In addition, in IL-10 / mice model suffering chronic colitis, it has been demonstrated that DHA treatment attenuates colonic inflammation, reduces inflammatory cells infiltration in the colonic mucosa, and decreases the levels of pro-inflammatory cytokines such as IL-17, TNF-α, and IFN-γ. Moreover, DHA was able to restore the expression of the tight junctions occludin and zona occludent-1 (ZO-1) and decrease immune cell infiltration in the colonic mucosa of IL-10 / mice. Linoleic acid (LA) and its derivatives γ-linolenic acid (GLA), di-homo-γ-linolenic acid (DGLA), and arachidonic acid (AA) belong to the ω-6 PUFAs. Similar to ALA,

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LA cannot be synthesized by the human body and must be introduced with diet. ω-6 PUFAs are present in vegetable oils such as corn, safflower, sunflower, soybean, hydrogenated oils such as margarine, and foods derived from livestock animals and poultry. Studies have demonstrated that ω-3 PUFAs have anti-inflammatory properties and display a protective role in UC, whereas ω-6 PUFAs display pro-inflammatory properties and long-term consumption of trans FAs is mainly associated with an increased risk of UC. Thus, ω-3 PUFAs have been studied as potential supplemental treatments for IBD; however, prevention and maintenance of remission could not be achieved. It has been reported that balancing ω-3 and ω-6 PUFAs might be more promising to reduce recurrence rates. It has been observed that ω-6 PUFA derivatives are the main cause of several inflammatory disorders. Indeed, human health may be negatively impacted by dietary imbalances in the ω-6/ω-3 PUFAs ratios. Western diet lacks in ω-3 FAs and is excessively rich in ω-6 FAs. Increased levels of ω-3 PUFA (a low ω-6/ω-3 ratio) exert suppressive effects on the pathogenesis of many diseases, such as cardiovascular disease, cancer, and inflammatory and autoimmune diseases, differently from excessive amounts of ω-6 PUFA and a high ω-6/ω-3 ratio. Moreover, a high ω-6 to ω-3 PUFA ratio is correlated with a significant risk of developing IBD. Indeed, a higher concentration of AA and a lower concentration of EPA are features of inflamed colonic mucosa in UC patients, also leading to the increased expression of pro-inflammatory eicosanoid metabolites of AA including the potent neutrophil chemoattractant leukotriene (LT) B4 (Ma et al. 2019). It is generally accepted that ω-6 PUFAs contribute to the pro-inflammatory effect since LA can be converted into AA, which then produce prostaglandins (PGs), thromboxanes (TXs), and leukotrienes (LTs), through the cyclooxygenase (COX) pathway, a series of inflammatory mediators with pleiotropic functions (Ma et al. 2019). Among others, AA produces LTB4 and the 2-series of PGs (such as PGE2), a powerful mediator of inflammation and cell growth. It has been observed that dietary supplements rich in ω-3 PUFA decrease PGE2 concentrations and increase the production of 3-series PG (such as PGE3) and LTB5, which are less inflammatory since prevent the production of ω-6 PUFA-derived pro-inflammatory eicosanoids. Moreover, two recently identified lipid mediators directly derived from ω-3 PUFAs, resolvins and protectins, have been shown to have powerful anti-inflammatory properties since they decrease the production of the inflammatory cytokines TNFα and IL-6. Long-chain triglycerides (LCT) are another type of lipids involved in increasing the risk of IBD onset and development. It has been shown that they increase intestinal lymphocyte proliferation and upregulate the expression of pro-inflammatory mediators, hence promoting inflammation. Therefore, lowering LCT levels might be beneficial in inducing IBD remission. Another class of lipids, namely medium-chain triglycerides (MCT), are antiinflammatory lipids, as they have been shown to reduce the expression of IL-8, a neutrophil attractant chemokine, which resulted to be overexpressed in the mucosa of IBD patients. In addition, an experimental study has shown that in a TNBS rat model, dietary MCT reduced the intestinal mucosal inflammation and the content of inflammatory mediators such as TNF-α and LTB4.

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The Role of Proteins in IBD Epidemiological studies revealed a strong association between an increased intake of animal proteins and the incidence of IBD, highlighting not only the source of proteins but also the importance of quantity, as high protein intake could increase the risk of developing IBD (Jantchou et al. 2010). According to a prospective study in UC patients in remission, a high consumption of animal protein (mainly red and processed meat) significantly increased the risk of UC relapse. This might be ascribed to a variable amount of animal proteins (peptides and amino acids), which can escape digestion in the small intestine and reach the colonic lumen where they are metabolized by the GM, with the production of putatively harmful compounds, such as hydrogen sulfide (H2S), nitric oxide (NO) and N-nitroso compounds, phenolic and indole compounds, biogenic amines, ammonia, branched chain FAs (BCFAs) (isobutyrate, isovalerate, and 2-methylbutyrate), and a concomitant reduced production of SCFAs. Isobutyrate, isovalerate, and 2-methylbutyrate have been described to be important for normal mucosal development and function of the intestinal barrier. Thus, they are important for cell proliferation, barrier permeability, gut motility, and mucus production (Mentella et al. 2020). The other compounds (ammonia, phenols, sulfide, and amines) may be toxic to the colon. Therefore, metabolites produced during protein fermentation play a role in the development and severity of IBD. It has been proposed that H2S may change the intestinal epithelial cell membrane, resulting in loss of barrier function and immunological reaction in UC. An increase in dietary protein intake results in increase fecal ammonia concentrations since ammonia derives from amino acids deamination by intestinal microbiota or urea hydrolysis. The negative effect of ammonia on gut health has been mainly ascribed to its dependent reduction of butyrate absorption in the colon. Being butyrate the energy source for epithelial cells, ammonia reduces the energy supply to the intestinal cells causing negative effect on intestinal health. A research study conducted in UC patients demonstrated clinical improvement of severe UC when dietary sulfur amino acids of animal and plant origin were reduced. This results in a reduced formation of bacteria-derived sulfide from dietary sulfur amino acids which leads to colonic epithelial cells damage when in excess. Also, phenol seems to reduce cell viability in human colonic epithelial cells, increase paracellular permeability, and reduce epithelial barrier function, probably contributing to severity of IBD. It has been observed that in the human enterocyte cell line HCT-8, the exposure to indole improved the mucosal barrier and mitigated inflammatory responses through downregulation of genes involved in inflammation and oxidative stress (Wlodarska et al. 2017). It has been reported that feces of patients with active UC presented a small amount of indole (Nemoto et al. 2012). Conversely, fecal concentrations of ammonia (Nemoto et al. 2012) and sulfide were higher in UC patients compared to control subjects. H2S is extremely toxic and specifically prevents colonocytes from oxidizing butyrate. Defect in amino acid-derived metabolite detoxification systems results in mucosal damage, inflammation, and CRC. It has been observed that the expression

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of H2S detoxifying enzyme thiosulfate sulfurtransferase (TST) was focally lost in UC colon sections and markedly reduced in advanced colon cancer. In the same study, it has been reported that in HT-29 cell line, HT-29 cell differentiation significantly increased TST activity and expression. It has also been reported that in endoscopic-derived colonic mucosal biopsies from patients with active CD, the expression of the TST is decreased. Similarly, UC patients have impaired mucosal sulfation of phenolic compounds. For all these reasons, consuming excessive animal proteins leads to dysbiosis and increased inflammation (Jantchou et al. 2010). A series of interventional and observational studies have shown that high protein intake also changes the GM composition. It is commonly accepted that certain metabolites, deriving from protein fermentation, may serve as substrates for the expansion of pathobionts. Particularly, Faecalibacterium prausnitzii, known for its antiinflammatory properties, was significantly decreased in the colon of rats administered with a protein-enriched diet. Its abundance has been shown to be also reduced in IBD patients. In a study involving healthy obese male volunteers, it has been observed that a high-protein and low-fiber diet decreased the health-promoting Roseburia and Eubacterium bacteria in fecal samples. Such decrease is maybe due to their capacity of producing butyrate. Anyway, dietary amino acids play an important role in cellular and microbial metabolic pathways. One of the features of IBD patients is the altered amino-acids metabolism of their GM. In IBD, amino acids are strongly required by both host cells and GM (Sugihara et al. 2019). In particular, tryptophan is a common constituent of protein-based foods such as fish, meat, and cheese and its metabolites are critical regulator of immunity, inflammation, and mucosal homeostasis. Given the strong relationship between the GM and the IBD susceptibility gene CARD9, it has been reported that Card9 / mice have an altered GM since it fails to metabolize tryptophan into metabolites that act as aryl hydrocarbon receptor (AHR) ligands, such as indole derivatives, regulating the production of IL-22. It has been observed that Card9 / mouse GM has decreased levels of bacteria with tryptophancatabolizing functions, such as Lactobacillus reuteri. The inoculation of three strains of Lactobacillus, which are capable to catabolize tryptophan, was able to attenuate intestinal inflammation. Patients with IBD have reduced microbial production of AHR ligands, which is correlated with an IBD-associated SNP within CARD9. The tryptophan catabolites derived from GM could be used as biomarkers for dysbiosis, therefore treatment with such compounds or probiotic able to generate them, could benefit IBD patients. Glutamine is one of the most important sources of energy for enterocytes and immune cells. It plays a significant immunoregulatory function in the intestinal tissue and it also regulates epithelial integrity through the maintenance of epithelial TJs and modulation of paracellular permeability. Consequently, cells need glutamine, especially during inflammation. Indeed, glutamine levels were decreased in the colonic tissue and serum of both UC and CD patients compared to control subjects. Also, it has been reported that oral or rectal supplementation of glutamine attenuates intestinal inflammation and colitis-associated colon tumorigenesis in male Sprague Dawley rats model in which the experimental ileitis was induced.

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Arginine has an important role in inflammation and intestinal epithelial integrity. It has also been suggested to influence IBD progression since changes in arginine metabolism have been seen in IBD patients. Research studies reported alterations in arginine metabolism as well as low colonic arginine levels in UC patients, correlating with disease severity. Arginine supplementation has been proven to improve experimental colitis in DSS-induced colitis in mice, alleviating intestinal inflammation and promoting mucosal healing thus, suggesting its potential role as an adjuvant therapy for IBD patients. Metabolome studies have shown a lower amount of histidine and threonine and higher amount of glycine in the serum and intestinal tissues of UC patients, the latter as probably a compensatory mechanism, since it was observed that dietary glycine prevents inflammation in a rat TNBS model of colitis and reduces the expression of pro-inflammatory cytokines. Reported low histidine plasma levels in UC patients in remission suggest its biomarker/predictive role of disease recurrence risk. Analysis of serum protein levels in IBD patients showed decreased levels of histidine, suggesting its potential use as novel and non-invasive biomarkers for IBD diagnosis. Threonine has an important role in intestinal homeostasis, regulating mucosal barrier function and, together with serine and proline, is a component of mucins. It has been reported that threonine levels are reduced in colonic biopsies of UC patients resulting in impaired intestinal mucosal barrier function, which allows gut bacteria to translocate. Further studies are needed to uncover the physiopathological significance of changes in amino acid metabolism because although their effect have been observed in experimental models, there are no clear evidence on their influence on the clinical progression of the disease.

The Role of Carbohydrates in IBD Several studies have identified excessive sugar consumption and low dietary fiber as risk factors for IBD (Khademi et al. 2021). Like dietary lipids, the type of carbohydrates consumed has a crucial role on IBD pathogenesis and/or its relapse. Based on their degree of polymerization, carbohydrates may be classified into monosaccharides (simple sugars: glucose, fructose, and galactose), disaccharides (simple sugars: lactose, saccharose, and maltose), oligosaccharides (i.e., fructooligosaccharides and galacto-oligosaccharides), and polysaccharides (i.e., starch, cellulose, and inulin). They can also be classified according to their availability for metabolism in the small intestine. Available carbohydrates, such as simple sugars and starch, are hydrolyzed and absorbed in the small intestine. It has been reported that a high consumption of available carbohydrates, such as glucose, sucrose, lactose, or fructose, overwhelm the absorptive ability of the intestine, resulting in increased luminal sugar concentrations which are used by the GM as energy source. As a result, patients with IBD often manifest fructose malabsorption or lactose intolerance. Two research studies conducted in transgenic CEABAC10 mice and

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ob/ob CD14 / mutant mice indicate that a consumption of a diet with an excess of readily available sugars promotes intestinal dysbiosis and bacterial overgrowth, respectively of Escherichia coli and Candida pathobionts, and simultaneously increases intestinal permeability and inflammation. It has also been reported that a high-sugar diet increases gut permeability and reduces microbial diversity and the production of SCFAs. This nutritional pattern also increased Verrucomicrobia while reducing Firmicutes and Tenericutes phyla. Conversely, unavailable carbohydrates, such as inulin, pectin, and cellulose, fructo-oligosaccharides and galactooligosaccharides are dietary fibers resistant to enzyme digestion and cannot be absorbed in the small intestine. Therefore, they reach the colon in an intact form, to be fermented by the GM. Those unavailable or nondigestible carbohydrates are known as prebiotics. Glenn Gibson and Marcel Roberfroid introduced the concept of prebiotic in 1995, describing it as “a non-digestible food ingredient that beneficially affects the host by selectively stimulating the growth and/or activity of one or a limited number of bacteria in the colon, and thus improves host health.” Then, in 2008, the 6th Meeting of the International Scientific Association of Probiotics and Prebiotics (ISAPP) defined dietary prebiotics as a selectively fermentable ingredient resulting in specific changes in the composition and/or activity of the gastrointestinal microbiota, thus conferring benefits to host health (Gibson et al. 2010). Fibers can be categorized into either soluble or insoluble, based on their ability to dissolve in water, or fermentable and non-fermentable, based on the bacterial ability to ferment them when they reach the colon. The majority of soluble fibers are fermentable, whereas insoluble fibers are less fermentable. The role of carbohydrates in IBD is controversial, as a meta-analysis suggested no association between total carbohydrate intake and risk of IBD (Khademi et al. 2021). However, this might be explained by the inclusion of dietary fibers in the overall amount of carbohydrates consumed. A higher intake of carbohydrates in the form of carbonated and sugar-sweetened beverages has been indicated in IBD patients (Opstelten et al. 2019), although no formal statistical association between sugar-sweetened beverages and the increased risk of IBD onset has been reported. Low dietary fiber intake is associated with an increased risk of IBD, thus suggesting their protective role against IBD. The nature and amount of fibers is, in fact, important for the fitness of the colonic mucus layer. A study in mice fed with either a fiber-rich or a fiber-free diet demonstrated that during chronic or intermittent dietary fiber deficiency, colonic mucus-degrading bacteria expanded and increased their activity. Such bacteria are forced to use secreted mucus glycoproteins as energy source, causing the degradation of the colonic mucus layer. The same study reported an altered abundance of the certain type of fecal bacteria such as Akkermansia muciniphila, a microorganism able to degrade mucin O-glycans (MOGs) using O-glycanases. Akkermansia muciniphila was isolated and characterized from human fecal samples from healthy adult volunteers. MOGs are polysaccharides that mostly composed the mucus layer. In the absence of fibers, the abundance of Akkermansia muciniphila rapidly increased with a consequence decrease of the fiber-degrading species. Two additional species sensitive to diet are Desulfovibrio piger and Marvinbryantia formatexigens which increased and decreased on fiber-

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free diet, respectively. As a result of a reduced mucus thickness, luminal bacteria were closer to the intestinal epithelium. Therefore, the thinned mucus layer contributes to enhance pathogens susceptibility. As fibers consumption also promotes the production of SCFAs, it has been suggested that they have a protective role on the development of intestinal inflammatory diseases, including IBD. According to data from the Nurses’ Health Study, long-term consumption of dietary fibers, especially from fruit, has been associated to a decreased risk of CD development. Moreover, increasing dietary fiber consumption improves symptoms and clinical conditions in individuals suffering CD. However, the EPIC-IBD study does not support the hypothesis that dietary fiber is involved in the development of UC (Andersen et al. 2018). Moreover, it is still to be clarified its involvement in protecting against the development of CD in certain groups and forms (Andersen et al. 2018). As fiber fermentation may trigger symptoms such as bloating, cramping, and abdominal distensions in specific groups of subjects with a sensitive gut, often IBD patients avoid fruits, vegetables, legumes, and whole grains for fear of triggering symptoms. The nature of fiber can indeed play a role in how well they are individually tolerated. As a general rule, adjusting quality and quantity of fibers can greatly help IBD subjects during periods of active inflammation. However, it is important to note that fiber consumption and supplementation, following experts’ advice, are effective in IBD patients to manage symptoms, reduce the risk of flares, and increase the time frame between flares.

Deficiency of Micronutrients in IBD Deficiency of micronutrients is more frequently observed in patients with CD than in patients with UC. Specific micronutrient deficiency occurs depending on the localization and extension of the disease and/or by surgical resection of part of the intestinal tract affected by inflammation (Park et al. 2021). Micronutrients deficiencies are also related to restrictive diets or treatments with specific drugs to which patients undergo. A diet with insufficient intake of dietary fibers reduces the intake of vitamin B9 (folic acid) and therapeutic treatment with sulfasalazine exacerbate such deficiency since it inhibits folic acid metabolism, limiting its absorption. Treatment with methotrexate, which is an antagonist of folic acid, may also contribute to folate deficiency. Thus, in this condition, folate supplementation is recommended. Unlike other water-soluble vitamins (i.e., B1, B2, B3, B5, B6, and C) absorbed in the proximal ileum, surgical terminal ileum resection of 20–60 cm, but also strictures and fistulas, cause vitamin B12 (cobalamin) deficiency. Since CD frequently affects the ileum, CD patients are at risk for developing vitamin B12 deficiency. Given the importance of vitamin B12 for erythrocytes synthesis, its deficiency may exacerbate anemia in these patients. An efficient method to avoid vitamin B12 insufficiency in CD patients, with or without ileal resection, is oral supplementation. As mentioned earlier, resection of proximal Ileum causes fat-soluble vitamin deficiencies. Vitamin D is involved in the regulation of immune system, and its lack has been associated with defective intestinal epithelial function. Therefore, it

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has been suggested that vitamin D is involved in the pathogenesis of IBD. Vitamin D deficiency has been assessed in 62% of IBD patients. Patients with IBD display low serum levels of 25-hydroxy (25-OH) vitamin, the main circulating form of vitamin D, especially in winter when UV-induced vitamin D production is lower. Low serum 25-OH vitamin D in patients with IBD may also be caused by dietary vitamin D malabsorption. A prospective cohort study among 72,719 women (age, 40–73 years) enrolled in the Nurses’ Health Study reported that increased plasma vitamin D levels reduce the risk of IBD onset, particularly CD. Although definitive data on vitamin D supplementation in IBD patients are lacking, its integration is strongly recommended. Oral vitamin D supplementation in IBD patients has been shown to be safe and successfully improved the level of serum 25-OH vitamin D, decreases the level of inflammatory markers such as ESR and serum C-reactive protein (CRP) levels, and simultaneously increases the expression of the LL37 gene, encoding for an important antimicrobial peptide. Further studies demonstrated its positive effect on disease activity index and relapse rates (Guo et al. 2021). In addition, in a research study conducted in Cyp knockout (KO) mice (mice that cannot produce the active form of vitamin D) and vitamin D receptor (VDR) KO mice (mice that cannot present the VDR), vitamin D supplementation has been associated to an enrichment of beneficial bacteria (e.g., Lactobacillus) and decreased abundance of pathogenic bacteria (e.g., Ruminococcus gnavus) in the feces compared with wild type. Low serum levels of vitamin A and E are common in young and adult IBD patients. The severity of disease is a predictor of risk for hypovitaminosis. Vitamin A is a crucial growth factor for epithelial cells and wound healing since it increases the number of macrophages and monocytes at the wound site and promotes collagen formation in fibroblasts. Supplementing IBD patients with levels of vitamin A for long periods of time may have toxic effects. Therefore, it is crucial to monitor any symptoms of vitamin A toxicity, such as headache, bone pain, liver toxicity, and bleeding (Masnadi Shirazi et al. 2018). Vitamin E has an antioxidant function and, as for all other liposoluble vitamins, its absorption could be impacted by fat malabsorption. There are limited evidence on vitamin E deficiency in IBD, and guidelines are missing. However, it should be considered for CD patients with severe fat malabsorption. Vitamin K deficiency is associated with low bone mineral density in CD, being a cofactor in the carboxylation of osteocalcin, an important protein for calcium binding to bone. However, there are limited evidence about vitamin K oral supplementation. Osteoporosis is a common complication of IBD, also due to hypocalcemia, as calcium is important for bone mineral density. Hypocalcemia is probably caused by restrictive diets or resection of the ileum tract. Calcium, zinc, and selenium deficiency have also been observed in IBD. A meta-analysis of IBD population revealed that zinc levels are often low in patients with chronic diarrhea and malabsorptive disorders. Zinc acts as a cofactor for enzymes involved in immune function and tissue repair. Therefore, zinc supplementation may reduce inflammation and improve mucosal healing and diarrhea symptoms.

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Proximal intestinal resection, restrictive diets, blood loss from the GI tract, or impaired absorption may cause iron deficiency. This is the main cause of anemia in IBD patients, which has been associated with impaired cardiac and renal function, fatigue, and decreased quality of life in IBD patients. For these reasons, iron supplementation is strongly recommended. Intravenous iron is considered the best method for treating its deficiency in IBD patients, especially patients with severe anemia since it does not require intestinal absorption. Selenium is important for protection against oxidative cellular damage and inflammation. In an experimental study conducted in DSS-mice models of experimental colitis, it has been observed that increasing dietary selenium intake may aid the resolution of inflammation, therefore suggesting a therapeutic role for selenium in IBD patients. In addition to macronutrients and micronutrients, it has been reported that other classes of substances might influence IBD pathogenesis and disease progression. For instance, food additives employed for long-term storage of food as well as for elevating the flavor of processed food have been suggested to play a role in IBD. Food additive includes thickeners, texturizers, emulsifiers, sweeteners, fillers, stabilizers, coating and coloring agents, or other compounds such as carrageenan, xanthan gum, maltodextrin, and carboxymethyl cellulose. Although they are considered safe by the Federal Food and Drug Administration, they could affect the intestinal barrier and promote bacterial overgrowth and alteration of the immune responses, thereby increasing the risk of IBD. The consumption of commercial foods thickened with xanthan gum has also been related to the recent increase in necrotizing enterocolitis (NEC) incidence in premature newborns. Maltodextrin, used as filler or thickener, reduces mucus secretion from intestinal GC and increases host susceptibility to colitis. NEC has also been experimentally induced by feeding preterm piglets with maltodextrin-enriched formula. Moreover, maltodextrin appears to be connected to the etiology of CD since a research has shown that it negatively impacts microbial phenotype, antibacterial defenses, and mucosal homeostasis (Zangara et al. 2022). Many processed foods contain emulsifiers, such as carboxymethylcellulose and polysorbate 80. They may cause damage to the mucus layer and change the GM (Chassaing et al. 2015). Sweeteners as well as inorganic nanoparticles, food colorants, and antimicrobial agents may cause dysbiosis and mucosal inflammation, thus contributing to the development of intestinal inflammatory disorders. It has been reported that a broad variety of organic substances, known as phytochemicals, which are present in plants, fruits, some vegetables, tea, coffee, cocoa, wine, herbs, and spices, have a positive effect on a wide range of chronic disorders. Polyphenols represent the more common group of phytochemicals. Among polyphenols, flavonoids, phenolic acids, stilbenes, and lignans have been found to prevent GI diseases, including IBD. Polyphenols, acting as free radical scavengers, promote the expression of proteins, which enhance antioxidant defense, and suppress inflammatory pathways, contributing to prevent the risk of IBD onset (Lu et al. 2017). Conversely, it is suggested to eliminate or at least reduce the consumption of foods with prooxidants activity, such as processed/cure meat,

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since they constantly expose the GI mucosa to oxidative stress, leading to damage and increasing the risk of IBD onset.

The Western-Style Diet or High-Fat Diet (HFD) The Western-style diet is characterized by an overall low food variety. It is high in fats and sugars and low in complex fibers. Particularly, it consists of higher amounts of refined, processed, and industrialized food, soft drinks, salt, red meat, dairy products, and a lower amount of vegetables, fruits, legumes, whole grains, and raw foods. For the pathogenesis of IBD, it is important to mention that the type of ingested lipids is more relevant than the total amount. Their contribution in the Western-diet is considerable and lipid types associated to a higher inflammatory potential such as SFAs, trans FAs, ω-6 PUFAs, and cholesterol forms, predominate over more beneficial types of fats (e.g., ω-3 PUFAs). The Western diet may create an intestinal inflammatory environment associated with intestinal mucosa dysbiosis, characterized by an increase of pro-inflammatory bacteria such as Escherichia coli, a decrease in protective bacteria, and a markedly reduced levels of SCFAs. Given its high fat content, a Western-like fat diet, has been used in several experimental models to assess its contribution to the pathogenesis of inflammatory diseases. In a research study conducted in C57BL/6 mice models, it has been reported that HFD feeding causes colonic inflammation, in turn increasing the expression of pro-inflammatory cytokines and intestinal permeability and aggravating intestinal inflammation. Moreover, it decreases GC differentiation and Muc2 production. The study reports that in Winnie mice models, which develop spontaneous ER stress-induced colitis, HFD feeding exacerbates colitis. An experimental study conducted in mice with DSS-induced chronic UC and fed a HFD to induce obesity demonstrated that although UC develops independently of obesity, obesity may worsen its pathogenesis. In the study, it has been observed that HFD prolongs and aggravates the inflammatory manifestations of the disease with increased colon and adipose tissue inflammation and increased infiltration of inflammatory cells which alter spleen, lymph nodes, and bloodstream functions. A study conducted in rats exhibiting either an obesity-prone (DIO-P) or obesity-resistant (DIO-R) phenotype and fed a HFD demonstrated that HFD in DIO-P rats leads to increased intestinal permeability. A further study using the obesity rat model OLEFT (Otsuka Long-Evans Tokushima Fatty) demonstrated that the increased small intestine permeability induced by HFD is due to decrease in the expression of TJ proteins, such as occludins, ZO1, and claudins. Moreover, in another murine, it was observed that the HFD correlated to changes of the GM including a decrease in Bacteroidetes and an increase in both Firmicutes and Proteobacteria. This was found for both genotypes causing of not obesity, indicating that alterations in GM were primarily caused by the HFD and not by obesity per se. In another recent murine study, it was observed that Western-style high-fat/high-sucrose diet (HFHSD) changed the GM composition with a significantly decrease of Bacteroidetes and an increase of Firmicutes compared to control mice. Evidence have revealed that the transplantation of the microbiota deriving

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from mice fed with a diet low in fats and high in fibers in mice fed with a Western diet prevents the mucus layer dysfunction. The impact of the Western-style diet on IBD pathogenesis is still under debate. However, it is rich in compounds that directly or indirectly cause gut inflammation and low in micronutrients and molecules with anti-inflammatory and antioxidant proprieties. It is commonly accepted that Western-like diets result in gut dysbiosis, which reduces the production of beneficial microbial metabolites, such as SCFAs, and promotes the growth of colonic mucusdegrading bacteria. All these aspects are strongly related to an increased risk of IBD pathogenesis. Thus, limiting the adherence to a Westernized diet might be promising to reduce the risk of IBD onset and progression.

The Low FODMAP Diet The FODMAP (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) group consists of a subset of oligosaccharides with fewer than 10 carbon atoms reaching the colon undigested or partially digested. In colon, they are fully or partially fermented by bacteria, and, in sensitive subjects, this may result in bloating, diarrhea, dysbiosis, and inflammation. The type, quantity, and molecular weight of carbohydrates ingested affect their fermentation rate. The low FODMAP diet was shown to reduce the digestive discomfort in specific subgroups of patients. A randomized controlled trial (RCT) has shown that dietary low FODMAP intake in clinically quiescent CD patients induced changes in the GM, which might have deleterious effects. In such study, it was reported that reducing FODMAD intake leads to a decrease in the number of bacteria producing butyrate, as well as numerous species that may have prebiotic features, such as Akkermansia muciniphila. It has been observed that butyrate enemas might be useful in lowering disease activity in UC patients and that Akkermansia muciniphila promotes the growth of beneficial bacteria to the epithelium, reducing the risk of inflammation. Another RCT has shown that a fermentable carbohydrate restriction decreases the relative quantity of Bifidobacteria, traditionally considered to have a probiotic activity in feces of irritable bowel syndrome (IBS) patients. The low FODMAP is anyway a restrictive dietary pattern and caution should be taken when prescribed: it should be performed for a short period of time and a dietitian should follow patients in all phases of this dietary regimen. The impact of low FODMAP diet on IBD patients’ inflammatory activity is still unclear; however, a study suggested that short-term low FODMAP diet in IBD patients, mainly in the quiescent phase, improved fecal inflammatory markers and quality of life (Bodini et al. 2019).

Anti-inflammatory Dietary Patterns The Anti-inflammatory Diet (AID) The AID consists in the elimination of pro-inflammatory foods containing saturated and hydrogenated fats, while increasing the consumption of anti-inflammatory food such as fruits, vegetables, legumes, fish, and spices, which contain – among other

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beneficial compounds – phytonutrients, minerals (i.e., zinc and magnesium), vitamins (i.e., A, B3, B6, C, and E), and ω-3 PUFAs. Strong evidence on AID in IBD is quite scattered. However, in a case series, 11 adult patients (8 with CD and 3 with UC) were subjected to AID for 4 weeks and results evaluated 1–3 months after the dietary treatment. The diet was shown to reduce symptoms (bloating, pain, diarrhea, urgency, bleeding, and fatigue, and consequently the medication need). Also, it has been demonstrated that the AID contains probiotics and prebiotics that can promote balanced GM composition and can confer benefits to patients. Indeed, in a doubleblind trial, UC patients were randomized to receive probiotic 200 mg capsules of Escherichia coli Nissle, and it was observed that these capsules had the capability of maintaining remission in UC patients (Kruis 2004). In addition, an adult patients’ population with quiescent UC was recruited for a controlled multicenter research study in which the control group received low-fiber wheat-based products corresponding to 5 g of dietary fiber per day, whereas the active group received 60 g of oat bran, corresponding to 20 g of dietary fiber per day. The oat bran group had considerably greater fecal butyrate concentrations after the 24-week of study, together with gastrointestinal symptoms amelioration. Another study reported that a pro-inflammatory diet increased the abundance of Ruminococcus torques, Eubacterium nodatum, Acidaminococcus intestini, and Clostridium leptum, while following an AID resulted in the enrichment of the beneficial strains and mucus producers, such as Akkermansia muciniphila (Zheng et al. 2020).

Semi-vegetarian Diet or Plant-Based Diet (PBD) The semi-vegetarian or ovolactovegetarian diet consists in the daily consumption of fruits, vegetables, legumes, and yogurt, including the consumption of fish once a week and meat every 2 weeks. The consumption of alcohol and sugary and processed products is not encompassed in this dietary pattern. It has been observed that the PDB leads to regular stools in individuals with diarrhea and normalizes bowel movements in constipated individuals, contributing to revert gut dysbiosis in the active phase and maintain gut symbiosis (Storz et al. 2022). The plant-based diet promotes microbial diversity, increases the abundance of helpful bacteria, such as Bifidobacterium, and produces helpful microbial metabolites like SCFAs, while suppressing the growth of pathogens. The increase of Bifidobacterium was observed in a randomized, doubleblind, crossover trial, in which participants were given formulas with fructooligosaccharides and fiber, both forms of carbohydrates abundantly present in plant foods. In a prospective single-group trial, CD patients were treated with infliximab combined with the plant-based diet and a remission rate of 96% was observed, suggesting that this diet is beneficial in the maintenance of remission phase in CD patients. In a further single-group trial, patients were admitted for induction therapy with PBD. The cumulative relapse rates at 1- and 5-year follow-up were 14% and 27%, respectively, for the initial episode cases, and 36% and 53%, respectively, for relapses (Chiba et al. 2019). Therefore, the study demonstrated that PBD is effective for preventing UC relapse. However, further dietary intervention trials on more patients are needed to make formal recommendation to IBD patients (Yan et al. 2022).

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The Mediterranean Diet (Med Diet) The Med diet pattern encompasses a high intake of fruits, vegetables, high-fiber whole grains, legumes, fish, in addition to olive oil and nuts, containing ω-3 FAs and phytonutrients. Conversely, it envisages a low intake of red meats, sugary and processed foods containing SFAs, trans FAs, and ω-6 FAs, which are associated with increased incidence of CD and UC (El Amrousy et al. 2022). The Med diet promotes simple culinary techniques, and it has been associated with decreased inflammatory markers. Moreover, the Med diet is associated with higher fecal SCFAs levels, probably due to an enrichment of bacteria belonging to Firmicutes and Bacteroidetes families capable of fermenting nondigestible carbohydrates, with the consequent production of beneficial metabolites. A pilot study of CD patients following the Med diet for 6 weeks revealed that the expression level of more than 3000 bacterial genes had changed, and that the beneficial effect of the diet was ascribed to the combination of affected genes which regulate canonical pathways, such as the interferon regulatory factor 2 (IRF2) which regulate NF-κB pathway and in turn, interact with the with signal transducer and activator of transcription 3 (STAT3), important in the JAK/STAT pathway. Taxonomic analysis showed an increase in the expression of Bacteroidetes, Clostridium cluster IV, and Clostridium cluster XIVa, and a decrease in the abundance of Proteobacteria and Bacillaceae. Thus, the diet appears to promote eubiosis and induce beneficial effects in CD patients. Contrarily to other diets, the Med diet does not expose patients to nutritional deficiencies. Benefits of the Mediterranean diet in IBD could be also ascribed to one of its main components, the extra-virgin olive oil (EVO) and its nutraceutical properties. Up to 83% of EVO total lipid composition is represented by oleic acid, a MUFA exhibiting a protective effect on gut and liver inflammation. We evaluated the effect of four EVO cultivars from the Apulian Region of Italy, namely Ogliarola (Cima di Bitonto), Coratina, Peranzana, and Cima di Mola in a mouse model of dextran sodium sulfate (DSS)-induced colitis (Cariello et al. 2020). We demonstrated that Apulian EVO reduces intestinal inflammation in DSS-treated mice, improves intestinal permeability and morphology, and downregulates pro-inflammatory cytokines levels such as IL-1β, TGF-β, and IL-6 (Cariello et al. 2020). Above all, the peculiar beneficial effect of EVO on human body is ascribed to phenolic substances (1–2% of the total). About 90% of the total phenolic components in EVO are secoiridoids, which include oleuropein, ligstroside, and oleocanthal, and their derivative phenolic alcohols, such as hydroxytyrosol and tyrosol. The remaining 10% are flavonoids and lignans. Polyphenols act as antioxidant mainly beneficially affecting the GI tract. They protect colon cells from oxidative damage by reducing of hydrogen peroxide formation. They also regulate immunity by interfering with immune cell regulation, pro-inflammatory cytokines synthesis, and gene expression. Overall, EVO has an important role in the reduction of chronic inflammation by interfering with the arachidonic acid and NF-κB signaling pathways, mainly responsible for promoting the inflammatory response. Since the incidence of IBD is lower in the Mediterranean area where EVO consumption is elevated, and given its antioxidant and anti-inflammatory properties, EVO supplementation may have an important role in the management of IBD. Thus, the regular consumption of EVO may improve the signs of IBD-related chronic inflammation and simultaneously avoid the onset and progression of the disease.

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Protein-Based Dietary Patterns The High-Protein Diet (HPD) The HPD is based on the consumption of high amount of animal protein, and it has been associated with a higher incidence of IBD. High levels of H2S, resulting from meat digestion, damaging the intestinal barrier, seem to have an important role in IBD pathogenesis. According to the E3N Prospective Study, the high intake of red meat, as source of animal protein, is associated with an increased risk of IBD in French middle-aged women (Jantchou et al. 2010). In a Large European Prospective Cohort Study, a direct correlation between meat and red meat consumptions and higher risks of UC was found, supporting the beneficial role of low consumption of meat in people at high-risk of IBD. A meta-analysis of cohort studies revealed that as total meat intake increases, also the risk of IBD increases (Talebi et al. 2023). Nevertheless, IBD patients have a higher protein requirement due to chronic inflammation resulting in tissue damage. Therefore, it is strongly recommended to choose a safe protein dietary source: red meat consumption should be reduced whereas the consumption of eggs and plant-based proteins should be increased.

The Paleolithic Diet The Paleolithic diet consists in the consumption of foods of the Paleolithic era, such as fruits, vegetables, and animal products (meat and fish), and excludes grains, dairy, beans/legumes, and all refined, manufactured, and processed foods on which the current eating habits are based. Moreover, complex cooking methods are not allowed. The underlying assumption of the Paleolithic diet is that the current eating habits have evolved more quickly than the genetics and digestive system of humans. Thus, processed food produces an excess of metabolites causing inflammation and metabolic disorders. It has been observed that restriction of refined carbohydrate encompassed in the Paleolithic diet may alleviate symptoms in IBD patients. Nevertheless, since it is an animal protein-based diet, encouraging the consumption of animal proteins, the effect of such foods may be detrimental to IBD patients (Serrano-Moreno et al. 2022). Studies about the effect of the Paleolithic diet on IBD patients are still not available, although some of them have reported a reduction of the inflammatory biomarkers.

Other Restrictive Dietary Patterns The Specific Carbohydrate Diet (SCD) SCD promotes monosaccharides intake and avoids complex carbohydrates. When complex carbohydrates reach the colon still undigested, their fermentation causes bacterial and yeasts overgrowth. This results in the GM shift toward a pro-inflammatory profile potentially leading to IBD onset or relapse. The SCD diet excludes a wide variety of foods, including dairy products, margarine, gluten-

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containing cereals, smoked meat and fish, maize and potato flour, snacks, fruit juices, chocolate, coffee, and alcohol. It has been demonstrated that SCD diet alleviates UC symptoms and causes a radical shift in the GM. In a study, a 2-week time period was enough to observe alterations in the GM. Before the SCD diet, the most prevalent species in IBD patients were the detrimental species Veillonella dispar and Fusobacterium ulcerans. Following the SCD, Fusobacterium dramatically decreased, however remaining dominant, together with Veillonella. Concomitantly, Enterobacteriaceae abundance strongly increased. In another small study, six CD patients were treated for 30 days with the SCD. A reduced microbial diversity was observed in CD patients: certain species of the phylum Bacteroidetes (i.e., Bacteroides fragilis) and the classes Clostridia and Gammaproteobacteria were increased, whereas Clostridium lactatifermentans was significantly decreased. After the SCD regimen, microbial diversity increased and the proportion of nonpathogenic clostridia family species was particularly high, although no significant clinical improvement was observed.

The Gluten-Free Diet (GFD) The term gluten refers to a class of proteins, primarily gliadin and glutenin present in various cereals (wheat, barley, rye, and oats). In certain percentage of the general population, gluten intolerance may occur. Gluten intolerance refers to three different conditions: coeliac disease (CeD), non-coeliac gluten sensitivity (NCGS), and wheat allergy (WA). IBD patients have a low prevalence of CeD, ranging from 0.5% to 5%, whereas the prevalence of NCGS is higher among IBD patients (up to 27%) (Limketkai et al. 2018). GFD is strongly recommended for patients suffering CeD, non-NCGS, and WA. Moreover, a cross-sectional study reported that IBD patients with concomitant CeD and NCGS who have been attempted the GFD showed an improvement of symptoms and a reduction in the number of flares. It is still unknown whether the improvement of symptoms is due to the elimination of gluten or the elimination of concomitant FODMAP, such as fructans. A possible explanation about the benefits of the GFD in IBD patients could also be attributed to the reduced intestinal permeability, with a reduced translocation of bacteria and consequent lower activation of the immune response. Furthermore, another important aspect related to the GFD is its effects on the GM composition. In healthy individuals, the GFD causes a reduction of beneficial bacterial strains, such as Bifidobacterium and Lactobacillus, and increased abundance of detrimental strains belonging to Escherichia coli and total Enterobacteriaceae, possibly due to a relevant lack of fibers occurring in these subjects. Thus, individuals following a GFD might favor bacterial overgrowth of opportunistic pathogens, which results in a higher susceptibility to infection and chronic inflammation, as well as micronutrients deficiency and an inadequate caloric intake. Therefore, patients following a GFD should ensure an adequate caloric, micronutrient, and dietary fiber intake.

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The Lactose-Free Diet (LFD) The incidence of lactose intolerance is increased among CD patients, and it appears to be associated with the severity of the condition. Lactose intolerance is caused by a reduced or lack of synthesis of the enzyme lactase resulting in a decreased or absent digestion of lactose, a disaccharide presents in milk and derivatives. Partially digested or undigested, lactose reaches the colon where it is fermented by intestinal bacteria. This causes increased gas, thus leading to symptoms such as bloating, meteorism, abdominal pain, and diarrhea. Severity of symptoms depends on the amount of lactose ingested. However, although milk and milk products both contain a comparable amount of lactose, studies have shown that lacto-fermented foods, such as yogurt, are more tolerable since they contain microorganisms with endogenous activity lactase such as Lactobacillus bulgaricus and Streptococcus thermophilus. The exclusion of lactose from the diet is strongly recommended in IBD patients with lactose intolerance, and the LFD may help to reduce symptoms (Nardone et al. 2021). Caution is anyway required by IBD patients with lactose intolerance following LFD, since decreased dairy consumption reduces calcium levels with possible deleterious consequences on bone health. A summary of the effects of the different dietary patterns in IBD described in the text is shown in Fig. 2.

Fig. 2 Summary of the effects of the different dietary patterns in IBD

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Current Therapeutic Approaches in IBD and Their Impact on the GM Traditional IBD treatments, such as glucocorticoid therapy (corticosteroids), antiinflammatory drugs (sulfasalazine and mesalamine) (Lim et al. 2016), immunomodulators (methotrexate, azathioprine, and 6-mercaptopurine) (Sandborn et al. 1998; Alfadhli et al. 2004), and immunosuppressive drugs (calcineurin inhibitors such as cyclosporine and tacrolimus), are effective in reducing inflammation and inducing prolonged remission, although their long-term effects are still topic of ongoing research. Anyhow, all these treatments may have negative effects on the nutritional status of IBD patients. Moreover, there is still a high number of patients who are unresponsive to these therapies. One of the most innovative strategy for IBD patients is the use of biological therapies and biologic agents (monoclonal antibodies against TNF-α such as infliximab, adalimumab, and certolizumab). The first biological authorized drug to treat CD and UC has been infliximab, which is still utilized. TNF-α antagonists improve the management of patients with IBD resulting in a decrease in their need for surgery and hospitalizations. Nevertheless, the effectiveness of these therapies declines over time because of immunogenic mechanisms. To tackle this problem, clinicians can increase the dose over time or switch class of drugs or add immunomodulators (Marsal et al. 2022). To make available therapies more effective and try to reduce the percentage of patients’ unresponsiveness over time, some innovative safe and efficient agents have been or are currently being tested as adjuvant therapies, to work in synergy and potentiate canonic treatment (Barberio et al. 2022b). Curcumin, a hydrophobic polyphenol produced by the Curcuma longa plant, has a wide range of biological properties, including antiinflammatory, anticancer, antioxidant, and immunomodulatory effects. Research studies have reported that, combined with standard treatments, curcumin supplementation might be a safe and effective option for induction and maintenance of remission in UC patients, as well as amelioration of CD symptoms. Due to its poor absorption in the small intestine, curcumin as well as other polyphenols and polyphenol complexes have a low bioavailability and reach the colon where they interact with the GM. In fact, emerging evidence shows that dietary polyphenols and their metabolites modulate the composition and activity of the GM, encouraging the growth of beneficial bacteria and inhibiting the growth of pathogenic ones, similarly to prebiotics. Prebiotics are nondigestible dietary components which promote bacterial diversity, improve the composition of GM through fermentation, regulate the mucosal and systemic immune response, and improve the function of intestinal barrier. According to animal and human studies, prebiotics increase the number of protective bacteria, such as Bifidobacteria and Lactobacilli, in spite of other diseasecausing bacteria. Bifidobacterium strains are commonly used as probiotics, characterized by a mucus-modulating action. It has been observed that supplementation with Bifidobacterium longum restored mucus growth rate in mice fed a Western diet without influencing the penetrability rate, whereas a single type of dietary fiber (inulin) restored the mucus barrier function while having no impact on mucus growth rate. It has been observed that Bifidobacterium strains prevent

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DSS-induced colitis and associated dysbiosis in mice, inhibiting the abundances of pathobionts. The observed beneficial effects seem to be related to the reduction of inflammatory cytokines expression in the intestine (e.g., TNF-α, IL-1β, IL-8, and IL-6). This suggests that IBD patients can benefit from supplementation of Bifidobacterium strains as a preventive or adjuvant measure. A randomized placebo-controlled trial assessing the effect of Bifidobacteria-fermented milk on active UC showed an improvement of SCFA levels. A clinical trial conducted in Japanese patients with active UC revealed that Bifidobacterium longum 536 (BB536) supplementation was well tolerated and lead to clinical remission (Tamaki et al. 2016). A randomized double-blind placebo-controlled study in active CD has also shown positive benefits of symbiotic treatment, consisting in a combination formula of prebiotics and the probiotic Bifidobacterium longum in improving clinical symptoms in patients with active CD. The study showed that symbiotic treatment was successful in changing the GM composition of CD patients by introducing beneficial bacteria into the Gl tract. In another clinical trial, 30 patients with moderate-to-severe ulcerative colitis received a single daily oral administration of mesalazine 1200 mg and a double daily administration of a probiotic blend of Lactobacillus salivarius, Lactobacillus acidophilus, and Bifidobacterium bifidum strain BGN4. After 2 years of treatment, the study showed a better improvement of the disease suggesting a beneficial effect of probiotics together with antiinflammatory drugs. The results demonstrate how the manipulation of the GM by probiotics supplementation acts in synergy with pharmacological therapy and represents a promising strategy to improve the clinical outcomes in IBD patients. Beside prebiotics, also probiotics appear to be able to change the clinical course of IBD patients. Probiotics contain microorganisms which have a beneficial effect on the health of their host. According to a study with favorable results for CD patients, individuals receiving mesalamine together with Saccharomyces boulardii, a nonpathogenic yeast, saw considerably fewer relapses than those receiving mesalamine alone. Moreover, probiotic treatment, in combination with standard therapies, results in improvement of disease and achievement of remission in patients with active UC (Lorentz and Müller 2022). All the probiotics used in the research studies include Lactobacillus spp., which is associated to a healthier mucosa. Indeed, UC patients frequently have few Lactobacillus species. Moreover, in patients who are allergic to traditional treatments, the use of probiotics in IBD therapy may represent an alternative since they seem to be more tolerated. Furthermore, in individuals with an inactive state of UC, probiotic therapy appears to be as effective as conventional therapy (Lorentz and Müller 2022). Recently, probiotic treatment with Escherichia coli Nissle 1917 or VSL#3 has been recommended by the European Society of Parenteral and Enteral Nutrition (ESPEN) for the induction and maintenance of remission in individuals with mildto-moderate UC, but not in active CD. Thus, further studies are required to better evaluate the efficacy of probiotics in IBD management. Since protein-energy malnutrition and micronutrient deficiency are critical in IBD patients, nutritional support is recommended as an adjuvant strategy as well as firstline treatment for IBD. Nutritional management is important to ameliorate nutritional

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deficiencies as well as to improve symptoms and clinical course of IBD. Besides macronutrient and micronutrient supplementation, also enteral nutrition (EN) and parenteral nutrition (PN) are strongly studied in IBD patients. Differently from steroids, nutritional treatment has been found to improve nutritional status, reducing intestinal mucosa inflammation, inducing mucosal healing, and restoring the epithelial barrier function with a decreasing in intestinal permeability and antigen exposition. EN consists in administration of liquid o powder diet composed of elemental, semielemental, or polymeric formula via a feeding tube or orally (De Sire et al. 2021). This type of diet includes micronutrients such as vitamins and minerals in addition to macronutrients such as simple carbohydrates, lipids, and proteins. Depending on the protein structure, elemental formulas are based on amino acids; semi-elemental formulas are based on oligopeptides or hydrolyzed proteins; and polymeric formulas are based on whole proteins. Carbohydrates, proteins, and lipids are absorbed in the duodenum and jejunum, preventing them from reaching the ileum or the colon (De Sire et al. 2021). For mild to moderate pediatric CD suffering malnutrition, EN represents the first-line therapy because steroids are avoided for their toxicity. It has been reported that 80% of pediatric CD patients receiving EN therapy had CD remission, although the specific mechanism is not clearly understood (Herman et al. 2021). In adult CD, the primary therapy remains the treatment with steroids that are the gold standard therapy for CD. EN may have a supportive role, and it has not yet been proven as a first-line therapy. The potential long-term benefits of EN are currently still investigated, as well as the benefits of EN in term of remission in UC patients. Although there are no evidences supporting the benefits of EN for severe acute UC patients, it may be practiced since it has been reported that EN is as effective as PN, with lower costs and fewer side effects. PN refers to the intravenous injection of a combination of macronutrients (lipid emulsions, dextrose monohydrate, and essential and nonessential amino acids), micronutrients, electrolytes (sodium, magnesium, calcium, potassium, and phosphorus), and trace elements. A continuous infusion is performed in the most compromised patients, and once they become more stable, PN can be discontinued. PN is recommended as a supplementary therapy for individuals in whom EN had failed or is contraindicated due to intestinal obstruction, ischemia, intractable vomiting, severe diarrhea, or with surgical resection to assure excellent nutritional status and reduce inflammatory response. Indeed, main goals of PN are promotion of intestinal rest, reduction of nutritional deficiencies, and elimination of mucosal antigenic stimuli in intestine, thus reducing inflammatory reactions and decreasing disease activity in patients with IBD (Comeche et al. 2019). PN support may be also indicated in situations of severe malnutrition as well as for pre- and postoperative nutritional support in CD and UC patients. Preoperative PN in CD patients resulted in fewer postoperative problems, an improved clinical outcome, and a reduction in the length of bowel which require resection. Rates of remission between PN and EN do not differ significantly. The initial effects of PN, mainly in the form of total parenteral nutrition (TPN), lead to a remission rate of more than 80%, although delayed recurrence is frequently experienced after interruption of TPN. Unfortunately, TPN is more expensive than EN, and some frequent complications are infection and thrombosis due to venous catheter and hepatobiliary ones.

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Conclusion Nutrition and the GM have a strong influence on IBD onset and progression. Recent data have highlighted the network complexity of the diet and the gut microbiota cross talk in intestinal fitness and homeostasis. The nutritional status and habits of patients affected by IBD have fundamental effects on their quality of life, immune system, and flares versus remission timeframes, as well as on the GM composition. Disturbance of this interaction may also be co-responsible of treatment efficacy for IBD patients. Emerging evidence clearly indicates that dysbiosis is associated with IBD pathogenesis and that the manipulation of the GM via nutrition and pro- and prebiotic administration holds promise as an adjuvant strategy in these patients. Currently, there is no specific nutritional regimen that has proven its efficacy in IBD patients; but this is intuitive given the extreme heterogeneity of clinical and molecular features in these patients. Anyhow, despite all the experimental challenges, there is a clear indication that dietetic protocols and GM manipulation both have the potential to considerably contribute to the maintenance of intestinal homeostasis and alleviating abdominal and inflammatory symptoms. As discussed in this chapter, a great number of studies have indicated that specific diets can negatively or positively influence symptoms, and this can be also associated to a modification of the intestinal microorganism composition. Next-generation sequencing techniques, global molecular and microbial profiling, and quantitative technologies are emerging as incredible tools not only to keep dissecting IBD molecular pathophysiology but also identify novel biomarkers useful for diagnosis, patients’ stratification, and treatment monitoring. The application of these techniques in novel well-designed clinical trials will inform both patients and clinician about novel personalized treatment frameworks and advance the field of precision nutrition.

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Gut Microbiota and Diabetic Kidney Diseases

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Alessandra Stasi, Francesca Conserva, Maria Teresa Cimmarusti, Gianvito Caggiano, Paola Pontrelli, and Loreto Gesualdo

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DKD Pathophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advanced Glycation End Products in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Polyol Pathway in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Hexosamine Pathway in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The PKC Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hemodynamic Changes in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epigenetics and Noncoding RNA in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Impact of the Microbiome on Host Immune Response in DKD Progression . . . . . . . . . Dysbiosis in DKD: Clinical and Experimental Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dysbiosis-Driven Inflammation in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiome as Therapeutic Target in DKD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Diabetic kidney disease (DKD) is a well-known risk factor for cardiovascular morbidity and mortality, as well as the first cause of end-stage renal disease. The need for renal replacement therapy in the form of dialysis or kidney transplant at this stage still points to DKD as a major public health challenge worldwide. The onset and progression of DKD are driven by a great number of molecular mechanisms, among them, the most characterized include: alterations of the cellular metabolism due to increased glucose uptake; renal hypoxia and ROS production with subsequent tissue inflammation and fibrosis; changes in the renal and systemic hemodynamics, and, importantly, dysbiosis of the gut microbiota. A. Stasi · F. Conserva · M. T. Cimmarusti · G. Caggiano · P. Pontrelli (*) · L. Gesualdo (*) Department of Precision and Regenerative Medicine and Ionian Area (DIMEPRE-J), University of Bari Aldo Moro, Bari, Italy e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_15

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There is growing evidence for a bidirectional microbiota-kidney interaction in the setting of DKD. Indeed, alterations within the gut microbiome composition encourage the expansion of microbial pathobionts and is associated with a significant increase in uremic solutes and inflammatory mediators that gradually intensify kidney damage, creating a vicious cycle in which dysbiosis and renal dysfunction are progressively worsened. Novel strategies aimed at targeting the intestinal microbiome in the setting of DKD have been suggested. Expanding our knowledge regarding the mechanisms through which the intestinal microbiota promotes renal damage in diabetes will be crucial in the clinical management of DKD as it could result in immediate applications in the daily approach to patients with DKD. Keywords

Diabetic kidney disease · Gut microbiota · Uremic toxins · Inflammatory mediators · Therapeutic strategies

Introduction Diabetic kidney disease (DKD) is a complex multifactorial disease whose clinical and histological manifestations can be very heterogeneous (Gesualdo and Di Paolo 2015). Importantly, a number of completely different triggers can promote CKD in patients with diabetes, such as previous acute kidney injury (AKI), autoimmune reactions, and persistent inflammation (Hapca et al. 2021). The most widely described form of DKD is diabetic nephropathy (DN), characterized by a thickening of the glomerular basement membrane (GBM). In the setting of type 2 diabetes, it was abundantly demonstrated that, apart from altering hormone production and cellular metabolism, high blood glucose and hypertension affect renal hemodynamics, increase oxidative stress and promote inflammation. At the cellular level, the loss of homeostasis within podocytes, tubular, endothelial, and mesangial cells can be driven by ROS-induced injury of the mitochondria, leading to cell death and ultimately tissue damage and fibrosis. Changes in the chromatin condensation have also been described, leading to dysregulation of gene expression. Importantly, along with the deregulation of protein-coding genes, miRNA expression can also be affected, with unpredictable consequences in terms of posttranscriptional regulation (Conserva et al. 2019). Molecular changes within the extracellular space have also been reported, these involve for instance the release of extracellular vesicles with subsequent alteration of the extracellular signaling, as well as changes in the glycocalyx composition and structure, leading to protein leakage in the urine (Satchell 2012). Therefore, it is important to identify the specific pathogenic mechanisms for better management of DKD. The “second genome” that regulates human health has been referred to as the human gut microbiota. Through genes, metabolic intermediates, and metabolic activity, the human gut microbiota has an impact on human immune and metabolic

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processes. Recent research suggests that changes in the genetic makeup of all microbes along with the release of harmful mediators and the subsequent breakdown of mucosal barriers lead to the activation and infiltration of inflammatory cells in the islets of Langerhans with subsequent degeneration and atrophy of islet cells, which could increase the onset of type 1 diabetes (T1D) in people with genetic predisposition (Kostic et al. 2015). In addition, gut microbial taxa interact with nutritional components, metabolizing them, in order to modulate inflammation and affect gut permeability, glucose and lipid metabolism, insulin sensitivity, and overall energy homeostasis, involving in the pathophysiology of type 2 diabetes (T2D) (Gurung et al. 2020). As previously underlined, one of the most prevalent and dangerous complications of diabetes is DKD. The gut microbiota is now receiving a lot of attention and is thought to play a significant role in the occurrence and development of DKD. This concept is supported by several evidence which demonstrated the ability of the gut bacteria to spread to the mesenteric lymphnodes and peripheral circulation, activating Th17 and effector T cells promoting neutrophil infiltration and activation of inflammatory response (Levy et al. 2017) both locally and systemically, exacerbating renal dysfunction in DKD patients. Therefore, the gut microbiota composition and their metabolites could serve as a biomarker for disease diagnosis and predicting remission. Finally, understanding how the diet affects the microbiome composition and the mechanisms linking gut-kidney connection will hopefully provide novel strategies to shape the ideal, therapeutic gut microbiota in the treatment of DKD and diabetes in general.

DKD Pathophysiology In this paragraph, we will describe the metabolic pathways affected by persistent hyperglycemia and what is their effect on cell metabolism. In parallel, we will describe the important hemodynamic alterations that characterize patients with DKD. Then we will briefly review the role of high glucose on posttranscriptional regulation, describing the main discoveries on the role of noncoding RNA and epigenetics. Finally, we will describe the detrimental effects of high-glucose-induced persistent inflammation in DKD and the contribution of the gut microbiota to these processes. With respect to the role of hyperglycemia in the alteration of specific cellular biochemical pathways, firstly, it is important to understand that the cellular populations that are more susceptible to high glucose are those that cannot reduce glucose influx through downregulation its receptor, with endothelial cells probably being the most sensitive. Secondly, according to the elegant description by Browlee (Brownlee 2001), it is possible to prove that an overproduction of ROS, a physiological response to increased glucose degradation within mitochondria, is concurrently responsible for the hyperactivation of at least four different biochemical pathways, namely: the polyol pathway, the hexosamine pathway, the PKC pathway, and the AGEs pathway.

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The general concept is that ROS increase causes direct damage to the DNA in the form of DNA strand breaks; this event activates the DNA damage response through activation of the enzyme PARP1 (poly[ADP-ribose] polymerase 1 [PARP-1]) to promote DNA repair. The activity of this enzyme, however, chemically modifies and inhibits the key glycolytic enzyme GAPDH preventing the whole glycolytic pathway to proceed. The accumulation of specific intermediates of the glycolytic pathway alters cellular metabolism since these intermediates are channeled into the four pathogenic routes mentioned above, driving pathogenesis (Du et al. 2003).

Advanced Glycation End Products in DKD Advanced glycation end products (AGEs) are very stable, long-lasting chemical intermediates derived from the nonenzymatic attachment of sugar molecules (such as glucose or fructose) to lipids or proteins. Many studies have linked a constant high influx of glucose inside the cell to accumulation of AGEs (Singh et al. 2014). In the extracellular space, AGEs can bind proteins of the extracellular matrix (ECM) such as laminin, fibronectin, and collagen preventing their degradation, thus leading to their accumulation outside the cell (Schmidt et al. 1995). Importantly, these events lead to decreased blood perfusion and gas exchange in different districts of the organism such as the heart, retinal vessels, peripheral arteries, and nervous tissue. AGEs can also bind their specific receptor (RAGE) expressed by certain types of immune cells. The binding of AGEs to their receptors initiates a signaling cascade that activates other proteins. One of them is the enzyme NADPH oxidase (NOX), whose function is to convert molecular oxygen (O2) to superoxide (O2-); the destiny of superoxide can vary, however, a small part is converted to hydrogen peroxide (H2O2), a reactive oxygen species that can damage a variety of cellular components, from DNA to phospholipids, proteins, etc. (Cepas et al. 2020). Importantly, low-density lipoproteins (LDL) can also be oxidized and their oxidation activates the immune response, increasing the risk of atherosclerosis and promoting tissue inflammation. The binding of AGEs to their receptor also causes activation of the transcription factor NF-kB inside the cell, with consequent nuclear translocation and transcriptional activation of many inflammatory cytokines and growth factors, all these events exacerbate inflammation and fibrosis.

The Polyol Pathway in DKD An overactivation of the polyol pathway following high intracellular glucose uptake has been observed both in mice and in vitro. This pathway leads to the production of fructose starting from glucose and involves the formation of the intermediate sorbitol. When the glucose concentration exceeds the metabolic demand of the cell, a portion of this excess is converted to sorbitol, in a reaction catalyzed by the enzyme aldose reductase. Aldose reductase requires the cofactor NADPH to

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promote this conversion and it was shown that NADPH progressive depletion can be detrimental for the regeneration of reduced glutathione (GSH), an important antioxidant tripeptide. It was largely proven that low levels of reduced GSH expose cells to oxidative stress. The conversion of sorbitol to fructose is promoted by another enzyme, known as sorbitol dehydrogenase; interestingly, specific tissues such as the kidney, the retina, and the nervous system have low levels of this enzyme, and are thus more exposed to damage induced by hyperactivation of the polyol pathway (Srikanth and Orrick 2022).

The Hexosamine Pathway in DKD The hexosamine pathway is physiologically involved in the synthesis of amino sugars through a sequence of well-coordinated enzymatic reactions that ultimately lead to the production of proteoglycans, glycoproteins, and glycolipids. The first step of these reactions is catalyzed by the enzyme glutamine fructose-6-phosphateamidotransferase (GFAT) that converts fructose-6-phosphate (produced through glycolysis) to glucosamine-6-phosphate in the presence of glutamine. The production of glucosamine-6-phosphate is thus precisely regulated through the activity of the GFAT enzyme. Interestingly, cells control GFAT activity and synthesis through many different mechanisms, suggesting that a tight regulation of GFAT activity is important to preserve cellular homeostasis. Experimental evidence showed that cells are physiologically capable of internalizing glucosamine through the glucose transporter [38], this allows to bypass the reaction catalyzed by the enzyme GFAT since, once in the cell, glucosamine is rapidly converted to glucosamine-6-phosphate. Although the levels of glucosamine are usually negligible in the extracellular space, the in vitro addition of glucosamine in the cell culture media was able to mimic overactivation of the hexosamine pathway as seen during hyperglycemia. Interestingly, increased hexosamine pathway activation was associated to increased production of the profibrotic cytokine transforming growth factor-β1 (TGF-β1), activation of protein kinase C (PKC) as well as to a reduced expression of the endothelial nitric oxide synthase (eNOS), with detrimental effects on cell viability (Schleicher and Weigert 2000). These and other experimental evidences were able to suggest that, in the presence of hyperglycemia, the increase in glucose metabolism and the subsequent channeling of bigger quantities of fructose-6-phosphate (produced through glycolysis) to the hexosamine pathway could contribute to fibrosis, inflammation, and ROS production.

The PKC Pathway Intracellular hyperglycemia stimulates the production of the lipid second messenger DAG diacylglycerol (DAG) which in turn activates protein kinase C (PKC). When active, PKC can induce the activation of mitogen-activated protein kinase (MAPK)

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and promote the phosphorylation of several transcription factors responsible for the activation of genes that contribute to endothelial dysfunction and microangiopathy such as vascular endothelial growth factor (VEGF), tissue growth factor b (TGF-b), and plasminogen activator inhibitor-1 (PAI-1) (Zhang et al. 2021b).

Hemodynamic Changes in DKD Clinical and experimental evidence clearly support alterations of the reninangiotensin-aldosterone system (RAAS) in the pathogenesis of DKD. This system is responsible for the regulation of both the systemic and the intraglomerular blood pressure, with the latter being consistently increased during DKD and the former being frequently altered in patients with diabetes even in the absence or renal impairment. In addition, the beneficial effects of drugs targeting the RAAS system, such as ACE inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs), on delaying end-stage renal disease (ESRD) is well established. In the early phases of DKD, it is possible to detect an increase of the intraglomerular pressure and consequent hyperfiltration. It was suggested that these early events lead to sustained mechanical stress and ultimately damage of the glomerular and peritubular capillaries. One of the proposed pathobiological mechanism responsible for glomerular hyperfiltration implies a substantial alteration of the tubuloglomerular feedback due to a chronic increased glucose reabsorption via the sodium glucose linked transporter 2 (SGLT2) in the proximal tubule. This symporter operates coupling the cellular internalization of glucose to that of sodium and when these two molecules are internalized for reabsorption by the proximal tubular cells, low sodium delivery occurs to the cells of the macula densa located in the distal tubule. Decreased delivery of sodium to these cells activates an autoregulatory response that induces stimulation of the adjacent juxtaglomerular cells to release renin, a precursor of angiotensin II whose effect is the vasoconstriction of the efferent arteriole with consequent renal hyperperfusion. As a result, the glomerular hydrostatic pressure increases and along with it, the glomerular filtration rate (GFR). Notably eGFR increase is an early sign of DKD. In the long run, this positive feedback loop causes damage not only to the endothelial cells but also to podocytes and mesangial cells, with consequent eGFR decline. Notably, the recent drugs known as “gliflozins” exert beneficial effects by inhibiting the SGLT2 cotransporter, this allows glycosuria, restores Na þ delivery to the distal tubule, and thus tubuloglomerular feedback (Fioretto et al. 2016).

Epigenetics and Noncoding RNA in DKD Over the last decades, many research studies have tried to shed light on the mechanisms of epigenetics, posttranscriptional (e.g., noncoding RNA) and posttranslational regulation in relation to diabetes, and its associated complications (Conserva et al. 2016).

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There are possibly several reasons for the growing interest in these areas of research: primarily, the genetic factors that predispose to DKD remain still largely unknown; and secondly, despite the recent advances in treatments, it is still not possible to effectively block progression to ESRD. Another aspect is linked to the available diagnostics, in order to gain a clear picture of each individual DKD phenotype invasive techniques, such as renal biopsy, that are still required. Lastly, the possibility to exploit the power of the diet and the gut microbiota in order to modulate epigenetic mechanisms and ameliorate DKD progression is very attractive both for the nephrologist and for patients.

The Impact of the Microbiome on Host Immune Response in DKD Progression The mutualistic relationship between the human host and its microbiota evolves throughout human life. The physiological roles played by the dynamic community of microorganisms living in the digestive tract help regulate various metabolic, endocrine, and immune functions, as an added organ to the body. It is estimated that this microbe population includes more than 100 trillion microorganisms, dominated by more than 1500 species of bacteria, but also viruses, fungi, and archaea. Bacteria are certainly the best studied population and species belonging to six phyla are the most abundant in the human gastrointestinal tract, namely Bacillota, Bacteroidota, Actinomycetota, Pseudomonadota, Actinobacteria, and Verrucomicrobia (Qin et al. 2010). Among them, Bacillota and Bacteroidota represent around 90% of the gut microbiota. The composition of the human gut microbiota remains relatively unchanged during acute perturbations, due to the known plasticity of the gut microbiota in rapidly reorganizing itself (Candela et al. 2012). However, age, diet, antibiotics, prebiotics and probiotics, geographic living areas, host genetic background, and chronic exposure to harmful stressors can profoundly alter the symbiotic relationship between microorganisms. This unbalanced equilibrium of gut microbial community known as dysbiosis is often involved in pathogenesis and progression of several diseases, such as type 1 diabetes (T1DM), type 2 diabetes (T2DM), DKD, and end-stage renal disease (ESRD). Gut microbiota encodes specific patterns of enzymes involved in the metabolization of exogenous and endogenous substrates that contribute to human metabolism and synthesizes a range of metabolic products involved in bidirectional dialogue with the host. The host immune system and the gut microbiota interact in a complex and welltuned dynamic process, which is important for maintaining body homeostasis and human health by regulating immune responses and immunological memory formation. Gut microbiota contribute to the production of various metabolites, generally divided into three main groups: metabolites produced by gut bacteria from dietary

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components, metabolites produced by the host and modified by gut bacteria, and metabolites synthesized de novo by gut bacteria. These metabolites can act locally in the intestine or exert their effects on other organs, as well as regulating immune responses and chronic immune-related inflammatory diseases. Microbial metabolism of dietary proteins and complex carbohydrates can modulate the progression of DKD through the production of proteolytic and saccharolytic end products, which in turn can exacerbate renal injury and dysfunction (Mosterd et al. 2021). Therefore, DKD patients must limit the consumption of sugar, to avoid hyperglycemia, and reduce the intake of proteins to preserve residual renal function. Differences in gut bacterial microbiota populations and richness between DKD patients and healthy controls have been observed in several studies, although the results have been highly controversial in identifying cause-and-effect relationships between microbial populations. It is also observed that the gut microbiota changes profoundly during the progression of DKD. Bacillota and Bacteroidota were the most abundant phyla in predialysis and dialysis DKD patients (Zhang et al. 2022). The phylum Actinobacteria was found to be enriched in DKD subjects compared to healthy controls. A marked increase in the phylum Pseudomonadota is observed in DKD patients compared to healthy people and diabetic patients without kidney disease, and is characterized by the expansion of the genus Escherichia, Citrobacter, and Klebsiella. The reduction in the population of Bifidobacterium, belonging to the phylum Bacillota, observed in DKD patients has been associated with diets rich in fat, which contribute to the inflammatory state caused by the increased plasma concentration of lipopolysaccharide (LPS), determined by the reduction of expressions of tight junction proteins in the intestinal epithelium. LPSs are the major component of the outer membrane of gram-negative bacteria that act as a ligand of Toll-like receptors (TLRs), an important component of the innate immune system, which in turn promote the expression of inflammatory factors. Therefore, inflammatory responses started with the binding of LPS at TLR2/4 in macrophages and endothelial cells, thus mediating the activation of nuclear transcription factor κB (NFκB) and leads to the secretion of pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin1 (IL-1), and IL-6, favoring the recruitment of other leukocytes. Macrophages influx may drive kidney damage by attracting T lymphocytes in diabetic kidneys exacerbating diabetes-related complications by producing pro-inflammatory cytokines, such as interferon-ɣ (IFN-ɣ), thus promoting cellmediated immunity and macrophage activation. In fact, elevated concentration of IFN-ɣ have been found in blood and urine of DKD patients (Mosterd et al. 2021). The human microbiota is normally capable of partially fermenting dietary polysaccharides such as resistant starch, dietary fiber, and other poorly digestible polysaccharides into short-chain fatty acids (SCFAs). SCFAs are capable of reducing inflammation, regulate intestinal epithelial cells functions modulating their proliferation and differentiation, impact gut motility, and strengthen the gut barrier functions as well as host metabolism.

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Acetate and propionate were mainly produced by Bacteroidota, while Bacillota synthesizes butyrate. Patients with DKD showed lower levels of serum acetate, propionate, butyrate, and total SCFAs compared to healthy subjects (Zhong et al. 2021). The decreasing of SCFAs production, especially acetate and butyrate, caused by the imbalanced equilibrium of gut microorganisms, cannot alleviate oxidative stress and inflammation on the intestinal epithelial cells. Dysbiosis can cause an accumulation of microbial substances, normally excreted by healthy kidneys, that can negatively interact with biologic functions contributing to the development of uremia; these compounds are known as uremic toxins (UTs) (Mishima and Abe 2022). In patients with kidney diseases and/or in case of dysbiosis the increase of these substances was due to reduced renal excretion, and excessive production by gut microbiota. This increase contributes to dysbiosis which in turn may further damage renal tubular cells, and vice versa. The UTs production by indigenous gut microbiota has been correlated with the abundance of bacterial species belonging to Bacillota and Pseudomonadota phyla. Several species of Clostridia, Enterobacteria, and Enterococci possess tyrosine phenol-lyase and tryptophan indole-lyase catalytic activities, which may have a role in the synthesis of UTs. Impaired accumulation of endotoxins and bacterial products begins in the intestine, reducing colonic epithelial cell viability and decreasing epithelial barrier function, disrupting colonic tight junction proteins (Vaziri et al. 2013). Urea, derived from amino acids decomposition in the liver, and converted into ammonia by intestinal microbiota, generates large amounts of ammonium hydroxide in the intestine, and has been associated with the loss of renal function. Urea accumulation increases the intestinal pH causing mucosal irritation and negatively impacting the growth of commensal bacteria, thus favoring the maintenance of intestinal dysbiosis (Kang 1993). Therefore, changes in lifestyle and diet cause the imbalance of the intestinal flora, increasing the level of gut-derived toxins in the blood, compromising gut homeostasis, and promoting intestinal disorders. The reduced filtering capacity of the kidney, combined with dysbiosis, causes the deposition and accumulation of end products of protein fermentation in the blood, and their renal clearance is further reduced during the progression of renal damage. UTs, such as indoxyl sulfate (IS), p-cresyl sulfate (pCS), and indole 3-acetic acid (IAA), are produced during the intestinal microbial metabolism of aromatic amino acids. Tyrosine and phenylalanine are converted into p-cresol, and tryptophan into indole and IAA. Further, p-cresol and indole are partly detoxified by the host through sulfation in the colon mucosa and liver into, respectively, the uremic toxins pCS and indoxyl sulfate (IS) (Edamatsu et al. 2018). Meanwhile, fermentation by the gut microbiota of dietary nutrients such as choline and carnitine originate trimethylamine (TMA), which is converted into trimethylamine N-oxide (TMAO) in the liver. IS and pCS exert their toxic effects in renal disease through several ways which include among others: damage to the endothelium, renal proximal tubular cells,

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induction of oxidative stress and ROS production, and stimulation of inflammation and immune responses in the renal tubular cell; downregulation of klotho; and increased angiotensinogen expression in proximal tubular cells (Vanholder et al. 2014). In healthy kidneys, aryl hydrocarbon receptors (AhRs) bind IS to modulate podocyte functionality. Prolonged activation of AhRs by continuous exposure to IS results in progressive damage of podocytes and glomeruli including altered cell morphology, declined podocyte differentiation, and reduced expression of cytoskeletal proteins. Further, IS and IAA interact with AhR, upregulating proinflammatory cytokines expression via NF-κB pathway, in uremic conditions. IAA induces endothelial oxidative stress, and stimulates glomerular sclerosis and interstitial fibrosis in the kidneys due to its prooxidant and proinflammatory effects (Zhang et al. 2021a). TMAO enhanced M1 macrophage polarization via activation of the NLRP3 inflammasome, which in turn activates inflammatory pathway with a strong T helper type 1 (Th1) and Th17 response; in addition, TMAO promotes oxidative stress and renal fibrosis (Castillo-Rodriguez et al. 2018). Increasing IS and pCS levels correlate with changes in albuminuria, and, together with IAA and TMAO levels, correlate with decreased estimated glomerular filtration rate (eGFR), and are all associated with renal dysfunction and DKD progression in patients (Poesen et al. 2016). The inflammatory responses, as well as the increased UTs production and decreased SCFAs levels play a central role in the progression of DKD. Further studies on the correlation between gut microbiota and kidney function may help in understanding the mechanisms of pathogenesis and discovery of treatment for DKD.

Dysbiosis in DKD: Clinical and Experimental Evidence In recent years, emerging studies have shown a significant role of gut microbiota in DKD pathogenesis. Indeed, the alterations of the gut microbiota composition and associated changes in metabolites, with an increase of damaging mediators, influence the gut-kidney connection, amplifying renal dysfunction in patients with DKD. This dysbiosis increases the number of harmful bacteria that release proinflammatory mediators, causing the activation of the immune system and a severe injury of intestinal epithelial cells, with consequent disruption of intestinal barrier that may result in a “leaky gut syndrome.” This condition enables the leakage of these pro-inflammatory and damage-associated mediators that contribute to insulin resistance and accelerate renal mechanisms of injury (Fig. 1) (Mosterd et al. 2021). Recently, He et al. performed metagenomic analysis to compare the composition and functional profiles of gut microbiota in T2DM patients with or without DKD. The authors demonstrated that the increase of Proteobacteria in gut microbiota of DKD patients could contribute to the pathogenesis of DKD via the dysregulation of LPS and SCFA biosynthesis and carbohydrate metabolism (He et al. 2022).

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Fig. 1 The pathophysiological mechanism involved in the gut-kidney axis. Briefly, the massive accumulation of uremic catabolites into the intestinal lumen lead to the intestinal permeability and foster the proliferation of uremic toxins-producing bacteria. The subsequent translocation of such toxins lead to systemic inflammation, endothelial dysfunction, and exacerbate the kidney damage

Similarly, a recent study analyzed fecal samples collected from age/gendermatched DKD, T2DM, and healthy patients by 16sRNA microbial profiling approach. This study showed that gut microbiota of healthy subjects was more enriched with Prevotella, which could synthesize SCFA, known for its properties in decreasing inflammation in acute kidney injury (AKI). In addition, healthy patients presented higher levels of Firmicutes that are typically involved in butyrate synthesis and amelioration of inflammation. In T2DM patients there was a strong increase in Bacteroides that released LPS. Interestingly, Proteobacteria were more enriched in DN patients and were also effective in increasing the LPS level in circulation. The increase of endotoxin in gut lumen led to the activation of neutrophil and macrophages causing inflammation and deterioration of intestinal barrier.

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Therefore, the prevalence of Escherichia-Shigella in gut microbiota of DKD patients could explain the typical gut leakiness, the increase of ethanol, and the alterations of fatty acid metabolism at liver level (Tao et al. 2019). Accordingly, Kai et al. demonstrated a significant decrease in SCFAs-producing bacteria and low levels of sera SCFA in DKD patients. In addition, oral butyrate supplementation in DM rats protected renal parenchyma, inhibiting glomerular area expansion and tubule-interstitial fibrosis (Cai et al. 2022). In another study, Du and colleagues analyzed fecal samples of 37 healthy donors and 43 patients with DKD showing a significant decrease in gut bacterial richness and diversity in the DKD group. In particular, the authors hypothesized that several genera like Megasphaera, Veillonella, Escherichia-Shigella, Anaerostipes, and Haemophilus might be considered the potential microbial hallmarks of DKD. In addition, gender and BMI had minor effect on the gut microbiota composition, but the major difference still came from the disease (Du et al. 2021). Several studies provided evidence of the strict association between changes in microbes-derived metabolites and DKD progression. Kikuchi et al. found in a cohort of 362 diabetic patients increased serum levels of the uremic toxin phenyl sulfate (PS) that were associated with increased albumin-creatinine ratio (ACR) especially in DKD patients. In addition, the authors demonstrated in a mouse model of mild and severe DKD that PS induced podocytes injury and loss and exerted pro-fibrotic effects. The inhibition of the enzyme responsible for PS synthesis induced a decrease of albuminuria and serum creatinine in diabetic mice, revealing this mediator as a possible therapeutic target in DKD (Kikuchi et al. 2019). Finally, Yu et al. analyzed the composition of the gut microbiota of DKD and membranous nephropathy (MN) patients and demonstrated considerable differences between the two groups (Yu et al. 2020). These clinical and experimental studies showed how the altered composition of gut microbiota and derived metabolites in DKD patients reshape a pathogenetic microbiome able to cause gut leakage and systemic inflammation, worsening renal damage (Table 1). Moreover, gut microbiota analysis could be a noninvasive tool based on intestinal flora to distinguish DKD from other renal diseases.

Dysbiosis-Driven Inflammation in DKD The loss of beneficial microbes in gut microbiota of DKD patients could favor the expansion of microbial pathobionts, such as Enterobacteriaceae. As previously described, the contribution of Enterobacteriaceae to DKD pathogenesis was widely demonstrated. The expansion of gram-negative bacteria is associated with a significant increase in the serum levels of the endotoxin that consequently increases the release of proinflammatory mediators, such as tumor necrosis factor-a (TNFα), IL-6, and CRP. In addition, the expansion of bacteria with proteolytic activities induces the synthesis and release of the UTs, IS, PCS, indole-3-acetic acid (IAA), and TMAO that could enhance inflammatory and oxidative response. The overgrowth of bacteria with

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Table 1 Studies of gut dysbiosis in DKD Authors He et al.

Study design Metagenomic sequencing of fecal samples of ten DKD patients and ten T2DM patients

Tao et al.

Analysis by 16sRNA microbial profiling approach of fecal samples of 14 DKD, 14 age/ gender-matched T2DMs without renal diseases (DM), 14 age- and gender-matched healthy controls (HC), and household contacts (HH) of DM group

Cai et al.

Gut microbiota and serum SCFA levels were measured by 16S rDNA and GC-MS in DKD patients. Rat model of DM (DM rats) and treated DM rats with 300 mg/kg sodium butyrate for 12 weeks (DM þ BU rats). Gut microbiota, serum, and fecal butyrate levels were measured in animal model Gut microbiota 16S rDNA V3-V4 regions analysis performed in fecal samples from 37 healthy volunteers (HG) and 43 DKD patients (PG)

Du et al.

Kikuchi et al.

Diabetes was induced in 8-week-old SLCO4C1-Tg rats by an intraperitoneal injection of STZ (rat model of mild and

Study findings Gut microbiota of DKD group was characterized by a marked increase in phylum genus Selenomonadales, Neosynechococcus, Shigella, Bilophila, Acidaminococcus species, Escherichia coli, Bacteroides plebeius, Megasphaera elsdenii, Acidaminococcus unclassified, and Bilophila wadsworthia. Dysregulation of LPS and SCFA biosynthesis and carbohydrate metabolism DM could be distinguished from HC by detecting g_Prevotella_9 level in feces; DKD was different from DM by the variables of g_Escherichia-Shigella and g_Prevotella_9 which potentially contributed to the physiopathological diagnosis of DKD from DM In gut microbiota of DKD patients there was a significant decrease in SCFAs-producing bacteria and low levels of sera SCFA. Oral butyrate supplementation in DM rats protected renal parenchyma, inhibiting glomerular area expansion and tubuleinterstitial fibrosis The gender and BMI had some impact on the gut microbiota profile. DKD patients showed dysbiosis and a decrease in gut bacterial richness and diversity compared with HG. Several characterized genera like Megasphaera, Veillonella, Escherichia-Shigella, Anaerostipes, and Haemophilus might be the new potential microbial biomarkers of DKD In experimental models, PS administration induces albuminuria and podocyte damage. In DKD patintes, PS

References https://doi. org/10.2147/ DMSO. S347805

https://doi. org/10.1007/ s00592-01901316-7

https://doi. org/10.1007/ s12020-02203002-1

https://doi. org/10.1007/ s12020-02102721-1

https://doi. org/10.1038/ s41467-01909735-4 (continued)

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Table 1 (continued) Authors

Yu et al.

Study design

Study findings

severe DKD). The genomic DNA of gut microbiota was extracted from murine feces and analyzed using a MiSeq sequencer. Enrollment of 362 DKD patients and analysis of sera samples

levels significantly correlate with basal and predicted two-year progression of albuminuria in patients with microalbuminuria. The inhibition of the enzyme responsible for PS synthesis induced a decrease of albuminuria and serum creatinine in diabetic mice, revealing this mediator as a possible therapeutic target in DKD Overexpression of several amino acid metabolic pathways, carbohydrate metabolism, and lipid metabolism was found in DKD, while interconversion of pentose/glucoronate and membrane transport in relation to ABC transporters and the phosphotransferase system were increased in MN

16S rRNA gene sequencing was performed on 271 fecal samples (DKD ¼ 129 and MN ¼ 142), and taxonomic annotation of microbial composition and function was completed

References

doi.org/10. 1080/ 0886022X. 2020. 1837869

urease activities induces an increased release of ammonia that augmented intestinal permeability (Vaziri et al. 2013). This leaky-gut syndrome is a typical feature of CKD and end-stage renal disease. The release of bacteria-derived immunostimulant and pro-inflammatory mediators can activate, after TLR recognition, the downstream pro-inflammatory pathways in renal and endothelial cells, accelerating CKD progression and vascular complications (Szeto et al. 2008). The impairment of intestinal barrier is also a hallmark of diabetic and obese subjects, underlying the central role of gut dysbiosis and systemic inflammation in DKD onset and progression. In DKD, TLR2 and TLR4 are the principal regulators of the inflammatory response and have been associated with many cellular processes activated in renal parenchyma. Indeed, the increased expression of TLR2 and TLR4 in renal cells under hyperglycemic conditions might be a molecular link between inflammation and progressive glomerular and tubule-interstitial fibrosis. Nonetheless, the increase of harmful bacteria-derived products in bloodstream circulation, due to the loss of intestinal barrier integrity, could induce renal cell activation via TLR signaling, contributing to the progression of systemic inflammation and diabetic kidney damage (Mosterd et al. 2021). The depletion of short-chain fatty acid (SCFA)–producing bacteria contributes to CKD and DKD progression. The dietary modifications with high fiber content increased SCFAs levels and could attenuate systemic and renal inflammation

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delaying the development of diabetic nephropathy. Indeed, a high-fiber diet suppressed the production of pathogenic proinflammatory cytokines and the expression of the innate immune receptors TLR2 and TLR4 within diabetic kidneys (Li et al. 2020). Accordingly, the infusion of the SCFA acetate in a mouse model of renal ischemia reperfusion injury induced a decreased expression of TLR4 within renal parenchyma. Moreover, in in vitro studies, SCFA, mainly acetate, propionate, and butyrate, showed relivable effects in preserving the immune homeostasis, affecting the differentiation and functions of dendritic cells (DC) and inhibiting their capacity to induce effector T cell proliferation, favoring the increase of Treg amount (Nastasi et al. 2017). Since DC and lymphocytes have crucial roles in the maintenance of kidney physiology and in response to injury, coordinating immune cell differentiation, activation and function by SCFA administration could be a new therapeutic strategy to treat immunity-driven kidney diseases such as DKD. Interestingly, Huang et al. provided new insight on the molecular mechanisms by which SCFAs mediate antioxidant and anti-inflammatory effects ameliorating renal impairment in DKD context. In particular, the authors analyzed the effects of the three main SCFAs (acetate, propionate, and butyrate) on high-fat diet (HFD) and streptozotocin (STZ)-induced T2D and DN mouse models. Their results showed the involvement of SCFAs in GPR43-β-arrestin-2 signaling, mediating the upregulation of GPR43 expression in renal cells, along with inhibition of oxidative stress and NF-κB signaling (Huang et al. 2020). In addition, SCFAs, especially butyrate, partially improved T2D-induced kidney injury, preventing proteinuria and the increase of serum creatinine, urea nitrogen, and markers of tubular damage and reducing the accumulation of mesangial matrix and tubule-interstitial fibrosis. Unlike the positive effects of SCFAs on DKD, the therapeutic potential of SCFAs on CKD seems to be more controversial. Indeed, Park et al. observed that higher doses of SCFAs could lead to kidney hydronephrosis, then it is clear that it depends on the concentration infused (Park et al. 2016). Among the molecular and pathophysiological mechanisms associated with the development of DKD, NLRP3 inflammasome triggered by hyperglycemia could play a crucial role. In diabetic context, the production of mitochondrial ROS has been shown to enhance the activation of NLRP3 inflammasomes underlining the correlation between NLRP3 inflammasome and DKD pathogenesis. In line with these findings, Wu et al. demonstrated that NLRP3 knockout (KO) STZ-induced diabetic mice presented improved renal function, and attenuated glomerulosclerosis and interstitial fibrosis, compared to wild-type animals (Wu et al. 2018). In addition, the uremic metabolites have been shown to induce oxidative stress and proinflammatory responses in immune, renal parenchymal cells, probably through the activation of NLRP3 inflammasome. In addition, SCFAs proved highly effective for the protection against kidney disease in a podocyte injury model, probably due to their inhibitory effects on the NLRP3 inflammasome cascade. Interestingly, Devlin et al. demonstrated that the genetic modulation of gut microbiota to lower the amount of indole and then the circulating levels of IS could slow down the progression of renal damage in the course of CKD and DKD (Devlin et al. 2016).

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Lastly, hyperactivation of the complement C5a-C5aR axis has been identified as one of the most important mechanisms involved in the progression of DKD and in the development of gut microbiota dysbiosis and dysfunctional intestinal barrier. Interestingly, recent findings demonstrated that elevated levels of C5a caused gut microbiota dysbiosis with decreased SCFAs production and affected the gut permeability, increasing the intestinal inflammatory milieu and facilitating systemic inflammation. In addition, C5 blockade reduced renal inflammation and dysfunction in db/db mice and partly restored the gut microbiota (Li et al. 2021). Overall, the connection between gut microbiota and inflammation-mediated injury in DKD has been proven by the antioxidant effects of SCFA and the deleterious effects of uremic toxins in renal parenchyma. Therefore, interventions aimed at modulating gut microbiota may represent a therapeutic strategy.

Gut Microbiome as Therapeutic Target in DKD Manipulation of gut microbiota through dietary approaches, the use of prebiotics, probiotics, and synbiotics, could ameliorate renal diseases. Healthy diet associated with functional foods can improve glycemic and lipid profile, reducing inflammation and oxidation, favoring the growth of nonindole bacteria in the gut and then determining improvements in the endothelial function and renal parenchyma (Li et al. 2017). As previously underlined, a high-fiber diet and the increase of SCFA production decreased blood urea and creatinine levels and improved eGFR in CKD patients. Therefore, strategies aimed at improving the SCFA synthesis may induce several benefits for prevention and management in DKD. Until now, there are conflicting results about the effects of prebiotics, probiotics, and synbiotics on renal function. Prebiotics are defined as substrates, or nondigestible dietary substances, mostly oligosaccharide carbohydrates that are fermented by host microorganisms in order to favor the growth of healthy bacteria. Indeed, host microbiota metabolized prebiotics and produced SCFA with positive effects on immune system and the pH of colon favoring butyrate formation of Firmicutes. Probiotics are defined as live microorganisms, which confer health benefits when administered in adequate amounts; these living bacteria modulate the composition of host microbiota ameliorating immune system and preventing inflammatory state. Finally, symbiotics are the combination of prebiotics and probiotics in the one treatment in order to obtain stronger effects. It is believed through growing research that the use of synbiotics, prebiotics, and probiotics preserve intestinal epithelial barrier function, modifying the population of the host microorganisms, reducing uremic toxicity and pro-inflammatory mediators, and delaying the progression of renal disease. Recently, the findings of an extensive systematic review demonstrate the benefits of probiotic supplementation on the reduction of inflammation, oxidative stress, and on the amelioration of renal function biomarkers. In particular, the authors observed that

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the microorganisms belonging to the Lactobacillus and Bifidobacterium genus had significant effects in amelioration of glycemic state and diabetic renal injury (Vlachou et al. 2020). Accordingly, another meta-analysis in CKD patients indicated that microbial therapies have significant beneficial effect on serum levels of C-reactive protein (CRP), total glutathione (GSH), malondialdehyde (MDA), and total antioxidant capacity (TAC) (Zheng et al. 2021). Another meta-analysis showed that probiotics treatment modulated inflammation and oxidative stress biomarkers in DKD patients but without beneficial effects on lipid profiles and NO release (Bohlouli et al. 2021). Despite some controversies derived from meta-analysis research, recent clinical studies conferred more precise understanding of the effects of these approaches in reducing renal damage and ameliorating glucose and lipid metabolism, inflammation, and oxidative stress in patients with DKD. The Synbiotics Easing Renal Failure by Improving Gut Microbiology (SYNERGY) study was a single-center, double-blind, placebo-controlled, randomized crossover trial investigating the effects of synbiotics on serum PCS and IS in patients with moderate to severe CKD (Rossi et al. 2016). Overall, the findings of this study demonstrated that synbiotics supplementation reduced serum concentrations of uremic toxin, PCS, with positive appreciable modulation of host microbiome. Similarly, Nakabayashi et al. reported a decrease in PCS generation rate and serum concentration after four-week symbiotic therapy (Nakabayashi et al. 2011). Although most studies have focused on nutritional strategy and synbiotics approaches, recent findings report considerable advantages in the use of fecal microbial transplantation (FMT) in preclinical models of CKD. Barba et al. showed that FMT from healthy mice in CKD animals improved glucose tolerance and albuminuria and modulated dysbiosis, increasing alpha diversity microbiome. There were no relevant results on the effectiveness of FMT in renal function (Barba et al. 2020). In an elegant study, Bastos et al. investigated the benefits of FMT on functional and morphological parameters in a preclinical model of type 2 DM, obesity, and DKD using a mouse model that develop hyperglycemia and albuminuria in a time-dependent manner, mimicking DKD in humans (BTBRob/ob mice). The authors demonstrated a decrease in body weight, albuminuria, and inflammation within the ileum and ascending colon and a partial recovery of insulin resistance (Bastos et al. 2022). Since accumulating evidence coming from a number of clinical trials indicate that FMT exerted relevant effects not only in Clostridioides difficile infection but in a wide range of pathological conditions, from gastrointestinal to liver diseases, and from cancer to inflammatory, infectious, autoimmune diseases and brain disorders, obesity, and metabolic syndrome, the reestablishment of a “healthy microbiota” in DKD and CKD patients open new therapeutic perspectives in nephrology field.

Conclusions The diagnosis of DKD is mainly based on clinical evidence through the assessment of urinary protein loss (albuminuria) and sustained renal function decline (eGFR). Patients with diabetes, however, can have different underlying causes of DKD, with

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the two main drivers being metabolic syndrome and hypertension. The occurrence of these events is often underestimated, especially in the absence of a detailed renal pathology assessment; this might explain the present lack of effective therapeutics against DKD. This early histological feature, however, can only be detected through electron microscopy observation of the biotic specimen. In the early stages of DN, proliferation of the mesangial cells and accumulation of extracellular matrix within the glomerulus become visible; when DN progression cannot be arrested promptly, ECM continues to accumulate in the glomerular compartment, with nodular lesions becoming clearly distinguishable (Tervaert et al. 2010). At this stage, kidney function is compromised and many patients will require renal replacement therapy through either dialysis or kidney transplant. According to the observations and data published by different research teams including ours, when renal histology is investigated in all patients with diabetes and compromised renal function, even in the early stages of disease, specific DN lesions can only be found in a subset of patients, usually also affected by diabetic retinopathy (Fiorentino et al. 2017; Fiorentino et al. 2016; Di Paolo et al. 2020). This correlation suggests that the underlying molecular alterations might be shared. A substantial group of patients with diabetes and renal impairment, however, completely lack GBM thickening and glomerular ECM deposition, instead they present with severe vascular damage and nephroangiosclerosis. To complicate this picture even further, DN lesions and nephroangiosclerosis can either coexist in some patients, or be totally absent in favor of other glomerulonephritis such as IgA nephropathy, membranous nephropathy, minimal change disease, and focal segmental glomerulosclerosis (Fiorentino et al. 2017; Mazzucco et al. 2002). Recent studies demonstrated the importance of the gut microbiota in the pathogenesis of DKD. Indeed, changes in the genetic makeup of all microbes along with the release of harmful mediators and the subsequent breakdown of mucosal barriers have an impact on the gut-kidney connection and exacerbate renal dysfunction in DKD patients. Due to their proximity to the immune environment within the gastrointestinal tract, microbes in the human gut have a significant impact on immune response, with widespread effects on several organs such as the kidney. Further, gut microbiome dysbiosis can result in the thickened glomerular basement membrane and podocyte dysfunction, and tubulointerstitial injury through the loss of cholesterol homeostasis (Hu et al. 2020). Unfortunately, at the present time, the recognition of these cellular events often requires invasive diagnostic methods and/or a broad consensus to ensure reproducibility of the results. In addition, diabetes may remain undiagnosed for several years, and renal damage can progress unnoticed. To overcome the limitations of the therapeutic strategies currently used to tackle DKD, which are mainly based on the control of blood glucose and blood pressure, many DKD consortia were created over the last decades. Their goal is to address crucial questions that still remain unanswered, such as: (i) what are the genetic elements that confer susceptibility to DKD? (ii) Is it possible to use noninvasive techniques such as renal imaging, circulating biomarkers, and targeting specific microbial taxa to better stratify the several DKD phenotypes? (iii) Is there a link between the diet, the diabetic gut microbiota, and metabolic memory in DKD progression?

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A major obstacle to overcome when researching on the genetic risk factors of DKD is the heterogeneity of the DKD cohorts described within the different consortia and research studies. Noninvasive imaging techniques such as ultrasound (US) and magnetic resonance imaging (MRI) are thus gaining increasing interest to measure different structural and functional characteristics of the kidney tissues such as glomerular filtration, tubular flow, renal perfusion, organ oxygenation, etc., with the goal to derive a multiparametric diagnostic score for DKD. Biofluid collection and processing, performed according to standardized methods for biomarker discovery, is also fundamental when aiming to identify molecular features that can potentially discriminate progressors from nonprogressors at an early stage (Gooding et al. 2020). Finally, the identification of the crucial microbial taxa that are closely related to DKD could provide potential bacterial targets for the diagnosis/prevention and treatment of DKD breaking the vicious cycle of DKD-gut dysbiosis that leads to ESKD. Acknowledgments PP and LG received funds from the PNRR-PE10 ON Foods: Research and innovation network on food and nutrition Sustainability, Safety and Security – Working ON Foods.

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Aging and Gut Dysbiosis

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Federica D’Amico, Marco Fabbrini, Monica Barone, Patrizia Brigidi, and Silvia Turroni

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Human Gut Microbiome Through Aging and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age-Related Compositional and Functional Changes in the Gut Microbiome . . . . . . . . . . . . The Gut Microbiome and Longevity: A Focus on Centenarians . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiome Dysbiosis Is Associated with Several Age-Related Disorders . . . . . . . . . . . . . . Hypertension and Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ischemic Stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chronic Kidney Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Type 2 Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonalcoholic Fatty Liver Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sex Hormone-Related Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gut Microbiome Metabolites Along Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolites Produced by the Gut Microbiome from Dietary Components . . . . . . . . . . . . . . . . Metabolites Produced De Novo by the Gut Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolites Shared by the Host and the Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Host Metabolites Converted by the Gut Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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F. D’Amico · M. Barone · P. Brigidi (*) Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy e-mail: [email protected]; [email protected]; [email protected] M. Fabbrini Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy e-mail: [email protected] S. Turroni Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy e-mail: [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_16

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Abstract

Aging is a complex phenomenon, driven by tangled interactions between biotic and environmental factors. In recent decades, mounting scientific evidence has laid the gut microbiome at the core of many age-related changes, including susceptibility to cardiometabolic diseases and immune system dysregulation. The gut microbiome undergoes considerable compositional and functional changes across the lifespan, and aging-related processes may be responsible for – and due to – its alteration in elderhood. In people who achieved successful aging (i.e., centenarians and semisupercentenarians), peculiar microbial signatures have been detected, hinting the ability of the gut microbiome to adapt to aging-related stresses, improving overall host health. This book chapter aims to describe the gut microbiome in aging and successful aging, focusing on its close relationship with onset and progression or vice versa protection from aging-related diseases, especially cardiometabolic ones. Finally, readers will find a broad description of microbiota-derived metabolites affecting health status during aging, on the thin ice that is the fragile microbiotahost homeostasis in the last stages of life. Keywords

Aging · Inflammaging · Age-related disease · Gut microbiome · Dysbiosis · Centenarians · Microbial metabolites

Introduction As far as we know, the gut microbiome – i.e., the whole microbial community residing in the gastrointestinal tract – is our fundamental side-organ involved in several aspects of human homeostasis. In this context, maintaining an eubiotic gut ecosystem is strategic across lifespan for immune system education and development, metabolic regulation, and protection against pathogens, just to name a few aspects. During lifespan, the gut microbiome faces an enormous and various number of internal and external stimuli (e.g., diet, drugs, physical activity, and lifestyle in general) leading to fluctuations toward many configurations. Alongside all these factors, aging has been noted as one of the most important elements involved in gut microbiome reorganization throughout life. In this scenario, a strong and persistent deviation of the gut microbial profile from healthy-like configurations can be defined as dysbiosis, mostly characterized by decrease of intraindividual diversity (i.e., alpha diversity) and reduction of heath-associated taxa, as well as overabundance of pathobionts. In this regard, research is still ongoing on whether variations in the gut microbiome in elderly subjects are to be considered dysbiosis or adaptation to aged conditions, also considering the recent identification of potential longevity signatures in the microbiome profile. Over the past few years, gut microbial

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imbalances have been linked to the onset and progression of several types of disorders, including age-related ones (e.g., cardiometabolic diseases). In this chapter we performed a pointy overview of the gut microbial ecosystem from the healthy adult-like profile up to the one associated with the extreme limit of human life. Then, we discussed the potential involvement of the gut microbiome in age-related disorders, namely, cardiovascular disorders and stroke, metabolic diseases (i.e., chronic kidney failure, nonalcoholic fatty liver disease, type 2 diabetes), as well as endocrine diseases (e.g., postmenopausal osteoporosis, ovarian cancer). For each of the aforementioned disorders, we provided some examples of microbiome-based interventions through prebiotics, probiotics, and even fecal microbiota transplantation (FMT), in order to restore an eubiotic profile of the gut microbiome. Lastly, we provided an overview of the relationship between age-related disorders and microbial molecules derived from diet, such as short-chain fatty acids (SCFAs), gases, phenolic acids, and vitamins, as well as bacterial metabolites produced de novo (e.g., exopolysaccharides, lipids, neurotransmitters). We also focused on molecules shared between host and gut microbial members (e.g., polyamines), as well as secondary bile acids and trimethylamine N-oxide (TMAO) as molecules converted by gut microbial members with a strong relationship with age-related cardiometabolic diseases.

The Human Gut Microbiome Through Aging and Beyond Age-Related Compositional and Functional Changes in the Gut Microbiome The human gut microbiome, i.e., the large ensemble of over 10 trillion microbes (mainly bacteria but also archaea, fungi, and viruses) that inhabit our gut, is unquestionably an integral component of our physiology, capable of supporting health or vice versa contributing to disease (Turroni et al. 2018). The discovery of such an impact (which ranges from the regulation of metabolism to the modulation of the immune system and central nervous system, to the metabolism of xenobiotics) has prompted research in recent decades to try to unravel the changes in the gut microbiome throughout life and in particular to identify the drivers of variation, to possibly be exploited in microbiome-based preventive and therapeutic strategies in the context of a multitude of intestinal and extra-intestinal disorders. In this regard, the scientific community agrees that age is a major microbiome-associated confounding factor (Vujkovic-Cvijin et al. 2020), whose components, both endogenous and exogenous ones, are known to have a predictable impact on the gut microbiome compositional structure and functionality, as will be detailed below (Kundu et al. 2017). What is still up for debate is the adaptive or maladaptive nature of age-related gut microbiome changes, although in the absence of serious health issues, researchers are leaning more toward the former. Likewise, it is still impossible to say whether these changes merely reflect secondary biological changes occurring at distinct stages of life or whether they contribute to at least some of

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the age-related physiological transitions. Regardless, it is a fact that gut microbiome eubiosis and, not least, the eubiotic trajectory of the gut microbiome need to be ensured throughout the lifespan to promote healthy aging and possibly longevity. As for the features of an aged-type gut microbiome, the available studies that have compared the gut microbiome structure between people of different ages, such as adults and the elderly, have consistently highlighted the following: (i) reduction in alpha diversity (or biodiversity), a parameter considered as a hallmark of a healthy intestine and good health in general; (ii) reduced relative abundance of microbes from Lachnospiraceae and Ruminococcaceae, dominant families of the adult gut microbiome, known to include major health-associated, fiber-degrading and shortchain fatty acid (SCFA)-producing bacteria; (iii) increased proportions of generally subdominant taxa, including opportunistic pathogens or pathobionts, such as taxa belonging to the Enterobacteriaceae family; and (iv) alterations of microbial metabolic pathways, with an increased propensity for proteolytic metabolism to the detriment of saccharolytic metabolism (Barone et al. 2022). It should be remembered that SCFAs are metabolites resulting from microbial fermentation of complex polysaccharides or fibers (otherwise known as microbiota-accessible carbohydrates), which in turn play a key and multifactorial role in host physiology, being important for energy extraction and storage, serving as an energy source for intestinal epithelial cells, modulating appetite, acting as potent immunomodulators (promoting overall anti-inflammatory activities), and even exerting functions relevant to neuronal health (including microglia maturation) (Turroni et al. 2018). It should also be noted that these age-related gut microbiome changes were found to be independent of the so-called geographical effect (meaning lifestyle, socioeconomic status, environmental exposure, etc.) or medication burden, perhaps representing universal microbial signatures of aging. As expected, such changes were more pronounced in older adults with compromised health status, primarily those living in long-term residential care and those approaching the extreme limits of the human lifespan, i.e., centenarians and supercentenarians (aged over 105 years), as will be discussed in the next paragraph. In particular, associations have been established over the years between unbalanced (i.e., dysbiotic) aged-type gut microbiome profiles and the degree of frailty, decreased bone health, and loss of cognitive health (please, see the next sections). Most of the gut microbiome modifications listed above can easily be linked to the inevitable physiological and lifestyle changes associated with aging; for example, tooth loss and sensory changes in taste and smell may lead to changes in eating habits, i.e., less consumption of high-fiber foods, with an obvious reduction in fiber-degrading (and SCFA-producing) bacteria within the gut microbiome, thus a reduced representation of undoubtedly beneficial taxa. In addition, older people are typically increasingly sedentary, which contributes to the age-associated decrease in intestinal motility, i.e., to reduced bacterial excretion via the stool and thus to increased chances of proliferation of opportunistic pathogens or pathobionts. Not least, some features of the aged-type gut microbiome are fueled by and fuel inflammaging, i.e., the low-grade chronic inflammation that characterizes age (Franceschi et al. 2000), as well as immunosenescence (i.e., the progressive deterioration of the immune system associated with aging, induced by inflammaging and

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vice versa), probably also through increased intestinal permeability (or leaky gut), with transfer of microbial components (or microbe-associated molecular patterns) or even microbes into the mesenteric lymph nodes and then into the peripheral circulation, with all the resulting downstream repercussions. Exactly when all of this happens is again hard to say. Probably, the transition to an elderly-type gut microbiome is a progressive and individual-specific multistep process, as it is closely linked to personal characteristics such as host genetics, diet, lifestyle, or, more generally, the exposome, i.e., the totality of endogenous and exogenous exposures that each of us experiences in the course of our existence. In this regard, a very interesting concept that has recently emerged is precisely the loss of uniqueness of the gut microbiota that seems to distinguish elderly subjects whose health worsens (Wilmanski et al. 2021). In other words, in the last decades of life, healthy elderly people continue to show a unique gut microbiota profile, while the loss of this pattern is associated with a reduced survival in the following years. Despite this uniqueness, the gut microbiota of 200 elderly Italians was recently found to cluster into three groups based on compositional profiles, which variously correlated with body composition (particularly visceral adipose tissue) and other health-related parameters (including cardiovascular risk factors, renal function markers, adiponectin, and circulating levels of minerals, amino acids, fatty acids, and bile acids) (Tavella et al. 2021). Notably, the elderly gut microbiota cluster that was specifically associated with improved metabolic health showed high diversity and was enriched in three families, Christensenellaceae, Rikenellaceae, and Porphyromonadaceae. Among these, it should be noted that Christensenellaceae have been found to be particularly abundant even in centenarians and semi-supercentenarians and shown to be significantly associated with host genetics, potentially representing a heritable component of longevity (Biagi et al. 2016). It is therefore not surprising that Christensenella species have been proposed as next-generation probiotic candidates, also for immunomodulatory activities, to promote healthy aging. As previously anticipated and mirroring the compositional changes, the aged-type gut microbiome also shows a progressive functional rearrangement in its major metabolic pathways, with decreased proportions of genes involved in carbohydrate metabolism and, in parallel, a higher abundance of genes involved in protein metabolism, particularly in the metabolism of aromatic amino acids (tryptophan, tyrosine, and phenylalanine) (Rampelli et al. 2020). Functional changes also concern the metabolism of xenobiotics, with the elderly-type gut microbiome being particularly rich in genes for the degradation of toluene, ethylbenzene, caprolactam, chlorocyclohexane, and chlorobenzene, i.e., pervasive chemicals in Western societies, deriving mainly from industrial manufacturing, municipal waste, and indoor/consumer products. As hypothesized by the authors, this peculiar enrichment could reflect lifelong habits, i.e., living in environments under strong anthropogenic pressure with continuous and constant exposure to xenobiotics. This could create the appropriate conditions for the host to select bacteria endowed with metabolic capacity toward these chemical substances, thus being the result of an adaptive process, likely inherent to the physiology of human aging in modern urban societies. Similarly, the gut microbiome of older people has been shown to undergo

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progressive changes in its resistome, i.e., the collection of acquired and intrinsic antimicrobial-resistance genes, including potential resistance mechanisms (Tavella et al. 2021). In particular, aging is associated with an increased burden of some antimicrobial-resistance genes, especially proteobacterial genes encoding multidrug efflux pumps. Again, this may simply be the result of an adaptive process toward the multitude of antimicrobials used through the food chain, for health reasons and house cleaning, but it clearly warns of potentially serious health implications.

The Gut Microbiome and Longevity: A Focus on Centenarians Longevity is a complex, multifactorial, and dynamic phenomenon, which can be considered as the positive side of aging, resulting from the cumulative effect of distinctive mutual interactions between host genetics, epigenetics, environmental factors, and also stochasticity. In this scenario, centenarians, i.e., those who reach >100 years of age, can be seen as individuals endowed with the ability to withstand and adapt to physical and chemical agents, stressors, and other biological stimuli, who have been able to delay or even escape the onset of life-threatening chronic diseases. By surviving to the extreme limit of human lifespan, centenarians therefore represent the best model for healthy aging and longevity (Santoro et al. 2021). As anticipated above, over the past few years, the gut microbiome has gained increasing attention as a key mediator of healthy aging. From a co-evolutionary point of view, the relationship between the gut microbiome and extreme aging could be seen as a successful adaptive process of the human superorganism. In other words, the gut microbiome of extremely long-lived individuals has probably managed to maintain a mutualistic relationship with a continuously adapting host, adapting itself to the progressive changes to which the host is subjected. Studying the gut microbiome of centenarians could therefore provide valuable insights into the mechanisms by which gut microorganisms contribute to health maintenance and survival. To date, several studies have been conducted to characterize the gut microbiome of centenarians from all over the world, in particular from Italy (Biagi et al. 2016), China (Kong et al. 2019), Japan (Odamaki et al. 2016), South Korea (Park et al. 2015), India (Tuikhar et al. 2019), and Russia (Kashtanova et al. 2020). As expected, these studies broadly confirmed the age-related changes in gut microbiome composition and functionality already described in older people, i.e., the depletion of a substantial portion of the core microbiome compared to what was observed in younger individuals, with reduced proportions of bacterial taxa with healthpromoting activities such as the synthesis of SCFAs and other key anti-inflammatory mediators. More specifically, the researchers found rearrangements in the relative abundances of microorganisms belonging to the phylum Firmicutes and an overall prevalence of Proteobacteria. In particular, the Ruminococcaceae family (especially Faecalibacterium) and the Bacteroides genus, typically dominant in the adult gut microbiome, were largely underrepresented, while at the same time some generally subdominant potential pathogens, such as Desulfovibrionaceae (sulfate-reducing bacteria capable of releasing hydrogen sulfide, a cytotoxic, genotoxic and

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pro-inflammatory agent) and Enterobacteriaceae, increased along with Methanobrevibacter, the most prevalent methane-producing archaea in healthy individuals. On the other hand, the gut microbiome of centenarians was found to show some distinctive and basically unexpected characteristics, including higher biodiversity and higher prevalence and/or enrichment of keystone taxa such as Bifidobacterium and Akkermansia. It should be remembered that bifidobacteria are well-known probiotics with a long history of use due to their well-known anti-inflammatory and immunomodulatory properties, while Akkermansia has recently been proposed as a next-generation probiotic candidate for obesity and related complications, and its use has recently been approved by the European Food Safety Authority in pasteurized (or rather postbiotic) form (EFSA Panel on Nutrition, Novel Foods and Food Allergens (NDA) et al. 2021). Despite the common gut microbiome signatures of longevity identified so far, specific compositional characteristics closely related to geography have also been highlighted, stressing once again the relevance of diet, lifestyle, and, generally, exposome in shaping the gut microbiome profile and its long-life trajectories. For example, high levels of Bifidobacterium longum subsp. longum and Bifidobacterium adolescentis were observed in the cohort of Italian centenarians analyzed by Biagi et al. (2016), as well as in Russian centenarians, who were found to be particularly abundant in Lactobacillus as well, while depleted in the pathobiont Desulfovibrio (Kashtanova et al. 2020). Lactobacillus along with Enterobacteriaceae and Ruminococcaceae were found to be overrepresented even in long-lived Indian populations (Tuikhar et al. 2019). Furthermore, in the very first longitudinal sampling of the gut microbiome of Chinese centenarians over a 1-year period, Luan et al. (2020) observed a major rearrangement approximately 7 months before their deaths, with an increase in B. longum subsp. longum and a decreasing trend of Akkermansia. On a functional scale, researchers hypothesized that centenarians retain a high central metabolic capacity, albeit with a general deficiency of carbohydratedegrading genes, especially those involved in glycolysis and the generation of SCFAs, particularly butyrate (Rampelli et al. 2020). As already described in the previous paragraph in relation to the elderly, an over-representation of genes involved in the metabolism of xenobiotics as well as of genes conferring antibiotic resistance has also emerged in the gut microbiome of centenarians compared to younger individuals (Rampelli et al. 2020; Tavella et al. 2021). Another functional feature recently found in the centenarian-type gut microbiome is the presence of genes coding for bile acid-metabolizing enzymes, which resulted in increased level of the secondary bile acids isolitocholic acid (iso-LCA), 3-oxo-LCA, allo-LCA, 3-oxoallo-LCA, and isoallo-LCA, compared to the other age cohorts (Sato et al. 2021). Through a combination of in vitro and in vivo assays, including mouse models, the authors demonstrated that bacterial strains belonging to the Odoribacteraceae family expressed genes encoding the key enzymes 5α-reductase and 3β-hydroxysteroid dehydrogenase, both essential for isoallo-LCA biosynthesis in vivo. Importantly, isoallo-LCA exerts antimicrobial effects against multidrugresistant Gram-positive pathogens (e.g., Clostridioides difficile), potentially contributing to the reduction of infection risk and the maintenance of gut homeostasis.

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Given the obvious difficulty of conducting longitudinal studies throughout people’s lives, it obviously remains impossible to establish whether all the compositional and functional characteristics mentioned above have always been present in the gut microbiome of centenarians or whether they were acquired later and are somehow linked to their lifestyle. Hand in hand goes the inability to establish the causal role of the gut microbiome in healthy aging and longevity, but the fact remains that the possible contribution of gut microbial communities in supporting aging and promoting longevity is not only fascinating but of great value for potential clinical repercussions, so it definitely deserves further study.

Gut Microbiome Dysbiosis Is Associated with Several Age-Related Disorders Given the multifactorial role of the gut microbiome in human physiology, it is not surprising that short- and long-term fluctuations in gut microbiome structure have been shown to contribute to the onset and progression of numerous diseases throughout our lives, not just confined to the gastrointestinal tract (Duvallet et al. 2017). Below we summarize the main changes observed in the composition and functionality of the human gut microbiome in the context of some age-related diseases, specifically cardiovascular diseases, such as hypertension and stroke, metabolic diseases (i.e., chronic kidney failure, nonalcoholic fatty liver disease, type 2 diabetes), as well as endocrine diseases (e.g., postmenopausal osteoporosis, ovarian cancer) (Fig. 1). Moreover, microbiome-based intervention strategies for prophylactic and therapeutic purposes are also discussed.

Hypertension and Cardiovascular Disease Cardiovascular diseases represent one of the leading causes of death worldwide with an average age of first heart attack of 65 years for men and 72 years for women. In recent years, these disorders have received more and more scientific attention regarding their pathophysiology. Indeed, cardiovascular diseases and atherosclerosis – i.e., the key pathophysiological mechanism of its development – are commonly linked to several risk factors such as aging, smoking, diabetes, altered lipid metabolism, dysregulated blood pressure, and, last but not least, gut microbiome dysbiosis. Atherosclerosis processes have an inflammatory basis, with microbial infections exerting a major contribution to vascular inflammation through direct or indirect mechanisms. Infections caused by one of the over 50 bacterial traces found to date in atherosclerotic plaques, mostly derived from the oral or gut microenvironment, can in fact directly trigger the vascular inflammatory state (Koren et al. 2011). As for hypertension, a recent work from Silveira-Nunes et al. (2020) has highlighted in hypertensive subjects, including the elderly, the overabundance of Lactobacillus, Eggerthella, and Bacteroides plebeius, as well as the lack of SCFA-producing microorganisms. Beyond the well-known anti-inflammatory effect, SCFAs could

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Fig. 1 Gut microbiome dysbiosis can be associated with several age-related diseases. Gut microbiome dysbiosis has recently been correlated with several age-related diseases, specifically hypertension, stroke, cardiovascular disease (CVD), metabolic diseases (i.e., chronic kidney disease (CKD), nonalcoholic fatty liver disease (NAFLD), and type 2 diabetes), as well as endocrine diseases (e.g., postmenopausal osteoporosis and ovarian cancer). In contrast, a healthy gut microbiome has been linked to longer life and successful aging. The figure was generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license

regulate blood pressure via the Olfr78 and GPR41 receptors, and exert relaxant effects on resistance arteries, thereby improving microcirculation (Pluznick 2014). Some microorganisms, known to be SCFA producers, such as Roseburia, are also capable of producing conjugated linoleic acid, which has been shown to reduce blood pressure. On the other hand, several studies have linked Eggerthella and B. plebeius to hypertension, along with other pathobionts, such as Klebsiella and Desulfovibrio, but the underlying mechanisms are not yet known (Palmu et al. 2020). Conflicting data are available regarding Lactobacillus, as some reports support hypotensive effects for probiotic species (e.g., Lactobacillus coryniformis),

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while other species (e.g., L. salivarius) have been associated with cardiovascular disease (Jie et al. 2017). Notably, gut microbiome dysbiosis in stroke-prone spontaneously hypertensive rats was related to pathophysiological changes in the gastrointestinal tract, in particular impairment of intestinal barrier integrity. Moreover, after FMT from stroke-prone spontaneously hypertensive rats to controls, the latter showed an increase in systolic blood pressure (Adnan et al. 2017). Regarding cardiovascular disease, the overabundance of Collinsella, Streptococcus spp. and Enterobacteriaceae has been shown in several studies to contribute to a chronic inflammatory state (Jie et al. 2017). Indeed, stimulation of toll-like receptors (TLRs) by bacterial components, including peptidoglycan and lipopolysaccharide (LPS), triggers the production of pro-inflammatory effectors (Rocha et al. 2016). On the other hand, dietary saturated fatty acids can promote the growth of Gramnegative bacteria (such as enterobacteria), further enhancing LPS biosynthesis. This pro-inflammatory loop may favor the translocation of endotoxins and even microorganisms into the bloodstream due to increased gut permeability, further favoring TLR activation (Rocha et al. 2016). In this regard, the gastrointestinal tract has been hypothesized to be a source of microbes associated with atherosclerotic plaques, which could therefore directly affect the pathogenesis of atherosclerosis. Gut microbiome dysbiosis could also lead to altered production of potentially harmful metabolites, such as TMAO. It is known that choline and carnitine derived from the diet are converted by gut commensals into trimethylamine (TMA) that, once absorbed, circulates in the liver where it is oxidized by host enzymes into TMAO (please, see more details in the “Trimethylamine-N-oxide” section). TMAO has been identified as a cardiovascular risk factor, as it is pro-atherogenic, it increases platelet hyperreactivity and therefore the risk of thrombosis. On the other hand, Hoyles et al. (2021) have recently demonstrated that physiologically relevant concentrations of TMAO improve the integrity of the blood–brain barrier in murine models, exerting a protective effect in counteracting pro-inflammatory states through an annexin A1-mediated mechanism. Although still debated, the involvement of gut microbiome-derived methylamines in cerebrovascular and cognitive function cannot be ignored. Based on all this evidence, several microbiome-based interventions have been performed in both animal models and humans. Starting from in vitro experiments, some Bifidobacterium spp. have shown the ability to remove cholesterol from the surrounding environment in the presence of bile acids. Lactobacillus plantarum, mainly present in fermented plant foods and milk, has also been shown to reduce circulating cholesterol as well as triglycerides in hypercholesterolemic murine models. Moreover, in such models, L. plantarum reduced TMAO levels thus inhibiting TMAO-induced atherosclerosis, while in individuals with normal or mildly elevated cholesterol levels, its intake was associated with reduced low-density lipoprotein (LDL)-cholesterol and triglycerides. Similar results were found upon administration of L. rhamnosus to obesogenic animal models on a highfat diet. Indeed, L. rhamnosus was involved in reducing oxidative stress, chronic inflammation, and ultimately the development of atherosclerosis. Another example is given by the intake of Akkermansia muciniphila that reversed some atherosclerotic risk factors, such as adipose tissue inflammation, fat mass, insulin resistance,

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metabolic endotoxemia, and even atherosclerotic lesions, in mice with type 2 diabetes and obesogenic murine models. Interestingly, this beneficial effect on most atherosclerotic risk factors was even increased after pasteurization of A. muciniphila. The administration of the so-called next-generation probiotic (or postbiotic) A. muciniphila has therefore proved to be effective in animal model and also safe in humans, although further studies are still needed in patients at atherosclerotic risk. In this scenario, the last frontier of microbiome-based interventions is FMT, although studies on FMT in cardiovascular disease have been limited so far. In a recent study, animal models with autoimmune myocarditis that received FMT from healthy models had less myocardial injury due to reduced inflammatory infiltration. However, most studies were conducted in mouse models, stressing the urgent need for translational research, especially in older adults, to confirm the beneficial effects of FMT or single/multi-strain probiotics.

Ischemic Stroke Since 1970, the World Health Organization has defined stroke episodes as “rapidly developed clinical signs of focal (or global) disturbance of cerebral function, lasting more than 24 hours or leading to death, with no apparent cause other than of vascular origin.” Most stroke episodes occur in people 65 years of age and older, defining this disease as one of the burdens of aging. In addition to brain damage, strokes can also lead to gastrointestinal alterations, including loss of epithelial barrier integrity and gut microbiome dysbiosis. On the other hand, a growing body of evidence supports the role of the gut microbiome in stroke prognosis, recovery and, more recently, stroke onset. An example of gut microbiome dysbiosis caused by stroke events is related to the development of dysphagia, difficulty swallowing. The latter in turn may be associated with an increased risk of developing pneumonia, malnutrition, and even mortality, thus significantly affecting stroke prognosis. Due to the close interplay between the gut microbiome community and dietary intake in stroke patients, the microbiome profiling could lead to the identification of therapeutic targets to safeguard brain function during post-stroke recovery. Moreover, recent studies have shown that mice treated with quintuple antibiotics (i.e., ampicillin, vancomycin, ciprofloxacin, imipenem, and metronidazole) had an increased poststroke mortality rate (Benakis et al. 2020). Another example of gut microbiome involvement in stroke recovery is the development of infections, especially pneumonia and urinary tract infections. Recently, it has been proposed that these types of infections may be due to post-stroke disruption of the gut epithelial barrier, leading to systemic translocation of microbes. After examining post-stroke lung samples from both animal models and patients, the researchers detected microorganisms of intestinal origin, thus confirming the gut-lung route of such microbes and their involvement in infection development. Indeed, the analysis of human lung samples from stroke patients showed that most of these microorganisms were symbionts or potential pathobionts commonly present in the gastrointestinal tract, such as Enterococcus spp., Escherichia coli, and Morganella morganii (Stanley et al. 2016). On the

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other hand, studies on germ-free mice have shown the absence of pneumonia episodes, supporting the hypothesis of bacterial translocation from the gastrointestinal tract to the lungs. Another factor linking stroke and gut microbiome dysbiosis is aging. Since aging is one of the major factors contributing to stroke development and since the gut microbiome ecosystem changes throughout life (please, see the paragraph on “the human gut microbiome through aging and beyond”), several studies have been conducted on this topic (Crapser et al. 2016). In particular, it has been shown that young mice had a better post-stroke prognosis than aged mice, and the latter showed both gut microbiome alterations and gut epithelial barrier disruption, resulting in microbial translocation and increased risk of pneumonia. Furthermore, young mice receiving FMT from healthy aged ones suffered from local and systemic inflammation due to increased production of pro-inflammatory cytokines, with higher mortality due to middle cerebral artery occlusion. On the other hand, after FMT from healthy young mice to aged ones, improved post-stroke recovery and survival was observed (Spychala et al. 2018). As for humans, the gut microbiome was profiled in post-stroke patients and compared with that of healthy subjects. While most studies involved a small cohort of Northeast Asia populations (i.e., Chinese, Japanese), several microbial features have been identified. Briefly, an overall decrease in alpha diversity, as well as 62 upregulated and 29 downregulated microbial taxa characterized the post-stroke cohort. In particular, a high prevalence of Bacteroides, Escherichia/Shigella, Lactobacillus, Prevotella, Ruminococcus, and Streptococcus characterized patients who recently had a stroke, along with a lower prevalence of Eubacterium, Faecalibacterium, and Roseburia. However, some taxa were both upregulated and downregulated depending on the cohort/study, including Bacteroides, Coprococcus, Faecalibacterium, Gemmiger, Odoribacter, Prevotella, Roseburia, and Ruminococcus (Peh et al. 2022). Most of these microorganisms are known to be SCFA producers; several studies have shown that fecal SCFA levels are lower in acute ischemic stroke patients than in healthy controls and that reduced acetate levels are associated with increased risk of poor functional outcomes at 90 days. Based on all this evidence, Xia et al. (2019) developed an index to measure gut microbiome dysbiosis in acute stroke patients that correlated with early stroke outcomes. The so-called Stroke Dysbiosis Index was in fact positively correlated with stroke severity and poor functional outcomes. A higher index occurred in case of a greater abundance of pathogenic bacteria such as Enterobacteriaceae and decreased abundance of the health-associated taxon Faecalibacterium. Furthermore, mice receiving FMT from patients with a high Stroke Dysbiosis Index had worse stroke outcomes and larger infarct volumes than mice transplanted from patients with a low Stroke Dysbiosis Index. Current data therefore strongly support a complex relationship between gut microbiome and stroke. However, once again, more work is needed to determine how these microbes and their metabolites regulate central nervous system processes and participate in gut–brain interactions, as well as the mechanisms that shape behavior and cognitive function. Together with additional knowledge on the mechanisms of action of microbes/metabolites, future studies are key to support the development of new cost-effective, accessible, and potent gut microbiome-based treatments for stroke.

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Chronic Kidney Disease Chronic kidney disease is a growing healthcare burden affecting about 13.4% of the population worldwide. In the last few decades, the number of patients with chronic kidney disease has steadily increased especially in the elderly population, with hypertension and diabetes being the leading causes of chronic kidney disease in adults. Several factors contribute to the progression of chronic kidney disease including activation of the renin–angiotensin–aldosterone system, proteinuria, as well as a chronic inflammatory state and repetitive acute kidney injury. Chronic kidney disease is also associated with the development of serious health conditions such as cardiovascular disease, neurological complications, adverse pregnancy outcomes, and hyperkalemia. Recently, associations between chronic kidney disease and the human gut microbiome and its metabolites have been demonstrated. Dysbiosis has been observed in patients with chronic kidney disease and the relationship has been defined as bidirectional, with gut microbiome-derived metabolites and toxins affecting the progression of chronic kidney disease, and the uremic milieu affecting in turn the gut microbiome homeostasis. Indeed, accumulation of harmful metabolites (e.g., TMAO) and toxins, such as p-cresyl sulfate and indoxyl sulfate – well-known gut-derived uremic toxins – are linked to loss of kidney function and increased risk of mortality (Wehedy et al. 2022). On the other hand, progressive renal failure results in higher blood urea concentrations, and gastrointestinal exposure to urea can lead to conversion to ammonia via microbial ureases. Higher urea levels may be linked to the overgrowth of urease-producing bacterial taxa. The expansion of gut microbiome members that produce uricase and indoleand p-cresyl-forming enzymes is in fact observed in patients with end-stage renal disease compared with healthy controls. Conversely, renoprotective microbial metabolites such as SCFAs and bile acids may help restore kidney function and increase the survival rate in chronic kidney disease patients. Several clinical studies have consistently identified some microbial signatures of chronic kidney disease, such as the overabundance of Alistipes, Coriobacteriaceae members (Eggerthella, Collinsella), and Bacteroides, and the decrease in health-associated microbial taxa (e.g., Faecalibacterium, Blautia, and Dorea) (Hobby et al. 2019; Wehedy et al. 2022). To date, several microbiome-based interventions have been studied to ameliorate the symptoms of patients affected by chronic kidney disease. In animal models, some molecules have been shown to adsorb gut-derived uremic toxins and thereby restore epithelial tight junction proteins and reduce endotoxin levels and markers of oxidative stress and inflammation. In clinical trials, coadministration of pre- and probiotic supplements reduced serum levels of p-cresyl sulfate and favorably altered the gut microbiome profile, while oral administration of B. longum reduced serum levels of indoxyl sulfate. Furthermore, dietary intervention based mainly on increasing the intake of resistant starch slowed down the progression of chronic kidney disease. Resistant starch may increase the proportion of SCFA producers, thus promoting intestinal epithelial cell health, and reduce indoxyl sulfate and p-cresyl sulfate levels in both serum and urine. The β-glucosidase inhibitor acarbose has also been explored as a way to increase the concentration of

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polysaccharides reaching the colonic lumen. Serum levels of p-cresyl sulfate and indoxyl sulfate were decreased after acarbose treatment, while the excretion of these two uremic toxins increased. This is also a potential mechanism to enhance the fermentation of polysaccharides to produce SCFAs. Finally, probiotics have been examined for potential recovery of gut microbiome eubiotic traits in patients with chronic kidney disease, with promising results (Hobby et al. 2019).

Type 2 Diabetes On a global scale, diabetes is a burden that causes a significant negative impact on the human health status. The growing prevalence of diabetes is a worldwide urbanization phenomenon caused by changes in diet and the development of increasingly sedentary lifestyles. A 2019 report highlighted that about 463 million adults worldwide currently have diabetes and future projections indicate that the number of diabetic patients will reach 700 million by 2045. Being a common chronic endocrine and metabolic disease, type 2 diabetes is more common in advancing age compared to type 1 diabetes, which is more prevalent in children and adolescents. Indeed, type 2 diabetes is caused by a combination of insulin resistance and insulin deficiency that develop throughout life. Several risk factors have been identified for type 2 diabetes development, such as family history of diabetes, unhealthy eating habits, and obesity. Based on the close connection between dietary and lifestyle habits and diabetes diagnosis, it is not surprising that several correlations have been made with the gut microbiome. So far, we know that there is a body of evidence even leading to some support for the potential causal role of the gut microbiome in several aspects of diabetic disease. As far as diet is concerned, several studies have shown that the incidence of type 2 diabetes is inversely associated with the total amount of dietary fiber consumed. In the literature it has been reported that soluble fiber has a direct blood glucose lowering effect and increases the viscosity of gastric juices (i.e., more viscous fiber leads to longer gastric emptying times). Additionally, these changes may lead to slower small intestine transit time and increased starch digestion, which is associated with a reduced rate of glucose absorption, with consequent changes in blood glucose and cholesterol concentrations (Gurung et al. 2020). From the gut microbiome standpoint, increased fiber levels are well-known to be associated with stable, diverse, and healthy ecosystems, featured by increased proportions of healthassociated microorganisms with SCFA-producing capability. As mentioned above, acetate, propionate, and butyrate – the main SCFAs – are indisputably beneficial for health, acting as local (butyrate) and peripheral (acetate and propionate) energy sources, inflammation modulators, vasodilators and regulators of gut motility, wound healing, metabolism, and epigenetics. Indeed, type 2 diabetes patients show fewer butyrate-producing microbes compared to healthy subjects. Patients are also characterized by the loss of Bacteroides, Faecalibacterium, Akkermansia, and Roseburia, while higher concentrations of [Ruminococcus] (a well-known pro-inflammatory mucus degrader), Blautia and Fusobacterium have been reported. The loss of beneficial microbes counterbalanced by an overabundance of

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pro-inflammatory taxa is a typical microbial feature of type 2 diabetes. Overall, type 2 diabetes is associated with elevated levels of pro-inflammatory cytokines, chemokines, and inflammatory proteins. In particular, Roseburia intestinalis, B. fragilis, A. muciniphila and Lactobacillus spp. have been found to be associated directly with anti-inflammatory effectors (i.e., IL-10, IL-22), while inversely with pro-inflammatory cytokines (i.e., TNF-α, IL-17). On the other hand, potentially detrimental microbes in type 2 diabetes, like Fusobacterium nucleatum and [Ruminococcus] gnavus can increase several inflammatory cytokines. All these findings that tightly link type 2 diabetes and the gut microbiome suggest that it is plausible to use microbiome-tailored interventions, including diet, probiotics, prebiotics, or even FMT, as prevention tools for the general population and therapeutics for type 2 diabetes patients (Candela et al. 2016). In this context, probiotics appear to have a broad range of effects on the host, including improved regulation of insulin sensitivity, host metabolism and intestinal permeability, and reduced levels of pro-inflammatory cytokines, also mediated by the gut microbiome. Numerous experiments in both animal models and patients have confirmed that probiotic intake, mainly Lactobacillus spp., can reduce insulin resistance by affecting the gut microbiome, and thus ameliorate diabetes symptoms. Recently, A. muciniphila has received a crescendo of attention because of its ability to reduce insulin resistance and limit the destruction of the gastrointestinal barrier. Indeed, it has been shown that the administration of A. muciniphila-derived extracellular vesicles to diabetic mice was associated with decreased fat content and increased glucose tolerance. Additional animal studies on A. muciniphila have shown a decrease in low-grade inflammatory responses and metabolic disorders. Moreover, its abundance has been found to positively correlate with glucose tolerance and fat accumulation in mouse models. As for humans, clinical trials are increasing in frequency and the new results are particularly encouraging for using novel microbiome-based approaches even in the diabetes context (Li et al. 2020; Gurung et al. 2020; Zhou et al. 2022).

Nonalcoholic Fatty Liver Disease The global burden of nonalcoholic fatty liver disease (NAFLD), and age-related and liver fatty degenerative disorders including steatosis, steatohepatitis, and fibrosis, is growing at an alarming rate, and has an estimated prevalence of 24–45% worldwide. Along with escalating morbidity, overall mortality among the NAFLD population is also a major concern, with NAFLD deaths accounting for 23–29% of total deaths, and the ratio is expected to be even higher in 2030. Despite two decades of research in trying to understand the pathophysiology of NAFLD, the actual underlying factors are still elusive, including impaired metabolic functions that facilitate fat storage in hepatocytes leading to steatosis and lipotoxicity. Steatosis increases oxidative stress and mitochondrial disfunctions, which cause constant hepatocytic injuries, promoting the activation of inflammatory cascades. It is well-known that there is a direct anatomical and physiological connection between the liver and the

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gut via the hepatic portal vein. This connection facilitates bidirectional communications between the liver (and its by-products) and the gut microbiome (and its metabolites). NAFLD is strongly linked to metabolic syndrome and shares common pathways involved in obesity, type 2 diabetes, insulin resistance, hyperlipidemia, and atherosclerosis. Against this backdrop, inflammation is a hallmark of the pathological features of a broad array of chronic conditions, including NAFLD. Inflammation can also be involved in intestinal barrier disruption leading to the translocation of microbes and its metabolites into the systemic circulation, which can reach different body parts. However, hepatocytes have an integrated response system to cope with such stressors, including inflammation, pathogen invasion, and nutrient fluctuations, but when this adaptive mechanism is overloaded with metabolic and microbial stresses, the promotion of immunometabolic dysregulation leads to NAFLD progression. Besides direct hepatic microbial invasion that can promote and/or brace local inflammation, gut microbiome dysbiosis has been observed in NAFLD patients compared to healthy controls. The latest published study identified a decrease in alpha diversity – a common intestinal dysbiotic trait – in moderate stage NAFLD, while even lower diversity values were achieved in severe stage disease. The gut microbiome profile of NAFLD patients was characterized by the overabundance of certain pathobionts, such as Fusobacteria, and by the loss of health-associated Oscillospira, Ruminococcus, and Coprococcus. Further microbial signatures included increased Proteobacteria, Escherichia, and other Enterobacteriaceae members. A dysbiotic gut microbiome, its metabolites, and endotoxins might be involved in NAFLD progression through numerous mechanisms (Khan et al. 2021). Below are some of the major mechanisms with a significant role in the development of NAFLD. An example is given by bile acids, produced in the liver from cholesterol and secreted in the biliary tract then reaching the small intestine via the duodenum, where they enable the emulsification, digestion, absorption of dietary fat, cholesterol, as well as fat-soluble vitamins. Bile acids are an important regulator of lipid metabolism, and glucose and energy homeostasis. It has been shown that almost 95% of bile acids are reabsorbed in the last part of ileum, then transferred back to the liver. The residual 5% bile acids are deconjugated, dehydroxylated, and dehydrogenated by the gut microbiome to generate secondary bile acids (please, see more details in the “Secondary Bile Acids” section). Deconjugated bile acids are less effective in micelle formation and emulsification of ingested lipids, so fat absorption is reduced. Gut microbiome members can therefore affect the pool of bile acids and downstream signaling. Another example is choline, an essential phospholipid part of the cell membrane, which shows a key role in lipid metabolism in the liver. Choline halts abnormal lipid accumulation in the liver, while its deficiency produces abnormal phospholipids and defective very low-density lipoproteins and causes alteration in the circulation of bile acids, thus leading to hepatic steatosis. Several factors affect the bioavailability of choline, including food intake, estrogen status, and singlenucleotide polymorphism variations in genes for de novo choline metabolism. As anticipated above, the gut microbiome is able to metabolize dietary choline into a variety of metabolic products, such as TMAO, thus decreasing its bioavailability. Several studies have shown that choline-deficient food stimulates liver steatosis,

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which is reversible through choline infusion. Due to the importance of the gut microbiome in liver functionality and the close interplay between these two actors, several intervention studies based on probiotic intake have been conducted in both animal models and NAFLD patients. For example, oral supplementation of B. longum along with fructo-oligosaccharides and modification of lifestyle in patients with NAFLD improved hepatic health, cholesterol, TNF-α, lipoprotein, endotoxins in serum, insulin resistance, and the hepatic steatosis index. Moreover, a meta-analysis confirmed that probiotic treatment effectively improved the liver enzyme profile, although the effects were not significant. Other studies have failed to support the effects of probiotics in this setting, showing conflicting clinical findings for several probiotic strains and their formulations, particularly in the treatment of gut barrier dysfunction, as indicated in patients with abdominal surgery. Large-scale clinical studies are therefore still required to assess the benefits of microbiome-based approaches in the treatment of NAFLD (Gupta et al. 2022).

Sex Hormone-Related Diseases Another interesting connection with gut microbiome dysbiosis has been made in the context of hormonal homeostasis. Several cross-sectional studies are present nowadays in the literature but most of the results are inevitably affected by confounding factors, including genetics and the environment, and only prove the existence of a correlation between sex hormones and the gut microbiome instead of a causal relationship (Shirvani Rad et al. 2020; Régnier et al. 2021).

Ovarian Cancer The etiology of ovarian cancer is unknown and may be related to environmental, reproductive behavioral, and genetic factors and the accumulation of mutations during life. Among them, dysfunctions of estrogen levels and estrogen activity play a crucial role. The gut microbiome has been shown to be involved in ovarian development and also in the early onset of ovarian cancer in animal models by affecting estrogen levels. Indeed, the effect of estrogens is directly related to the expression of estrogen receptors and estrogens enhance cell adhesion and migration, which contribute to the ovarian cancer metastasis and colonization. In this context, bacteria with β-glucuronidase activity can mediate estrogen deconjugation, thus affecting the amount of active estrogens in the circulation. The gut microbiome also plays a role in influencing the response to ovarian cancer chemotherapy as shown in both patients and animal models. An example is cyclophosphamide, one of the first-line chemotherapy drugs for the treatment of ovarian cancer that interferes with the synthesis of DNA and RNA. The efficacy of cyclophosphamide treatment is related to some gut microbial species, although the specific mechanisms are still unclear. In particular, Enterococcus hirae has shown the ability to translocate from the small intestine to secondary lymphoid organs and increase the antitumor activity of cyclophosphamide. In contrast, control groups with a destroyed gut microbial community showed resistance to

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cyclophosphamide (He et al. 2021; Docimo et al. 2020). Another recent study on ovarian cancer found that platinum-resistant patients showed a marked temporal reduction in gut microbial diversity with loss of health-associated taxa and increased proportions of lactate producers, such as Bifidobacterium and Coriobacteriaceae members. On the other hand, the gut microbiome of platinum-sensitive patients appeared overall more diverse and stable and enriched in lactate utilizers from the Veillonellaceae family. Interestingly, these potential gut microbiome signatures of therapeutic success/failure were detectable within the first half of chemotherapy cycles, suggesting that early treatments also aimed at modulating the gut microbiome could influence the therapeutic outcome (D’Amico et al. 2021). Finally, some researchers have considered bacterial β-glucuronidases as possible drug targets for estrogen-related cancer, just like ovarian cancer. In particular, the identification of a conserved motif including asparagine and lysine residues from bacterial β-glucuronidase has paved the way for the development of β-glucuronidase inhibitors to improve therapeutic outcomes.

Postmenopausal Osteoporosis Osteoporosis is a metabolic bone disease taking the form of bone loss and structural destruction during life. Postmenopausal osteoporosis is a form of osteoporosis induced by estrogen deficiency that leads to an increased frequency of fractures in postmenopausal women. Current studies have suggested the potential and close relationship between gut microbiome and bone remodeling, as well as between gut microbiome and metabolic bone diseases. For example, in germ-free mice, induced sex steroid deficiency failed to promote osteoclastogenic cytokine expression, activation of bone resorption, and trabecular bone loss. In the intestine, an increase in epithelial permeability (a factor significantly contributing to inflammation and microbe translocation) has been noted during the menopausal transition. By analyzing fecal samples from postmenopausal women, several changes in the gut microbiome linked to endocrine disorders and osteoporosis have been observed, including a decrease in Firmicutes and Roseburia spp. and an increase in Bacteroidetes and toluene-producing Tolumonas. Recent experiments have also shown that many species of the gut microbiome, such as belonging to the genus Clostridium, L. acidophilus, and Bacillus clausii, can modulate the pro- versus. Antiinflammatory balance, inhibit bone loss, and increase bone heterogeneity in osteoporotic mice because of the secretion of TNF-α, a key cytokine for osteoclast formation involved in bone resorption. Microbiome-based interventions showed that sex steroid-deficient mice treated twice weekly with the probiotic L. rhamnosus or VSL#3 were characterized by averted bone loss. Moreover, after probiotic intake, the reduction of gut permeability, the inhibition of intestinal and bone marrow inflammation was observed. In contrast, supplementation with a non-probiotic strain of Escherichia coli or a mutant L. rhamnosus did not show protection against bone loss. Probiotic treatments can also decrease gut permeability and enhance the intestinal epithelial barrier function, thus reducing the invasion of pathogenic bacteria and immune responses caused by inflammation (He et al. 2021; Régnier et al. 2021; Han et al. 2022).

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Gut Microbiome Metabolites Along Aging Gut microbes brew large numbers of microbial metabolites via de novo metabolism or by introducing secondary modifications in host-derived molecules. Through active secretion and/or microbial death and lysis, the host gut is exposed to these metabolites, which mediate local intestinal homeostasis as well as signaling from the gut to other body districts constituting the so-called axes of gut interaction (e.g., gut-brain, gut-immune, gut-liver, gut-lung). Considering the relevance of gut microbiome-derived metabolites on human health, it is not surprising that such metabolites are of great interest also in the context of aging. Generally speaking, gut microbiome metabolites can induce a series of physiological and pathological functions on the host and other microbial members, and such microbiota-host and microorganism-microorganism metabolic ties are essential for a full understanding of the roles of the gut microbiome in healthy successful aging (Fig. 2). According to their origin, microbial metabolites can be divided into four categories, all of which encompass broad classes of metabolites whose production depends on the gut microbial configuration and functionality: (i) metabolites that are directly produced by gut microbes from dietary components; (ii) metabolites produced by de novo metabolism of gut microbiome members; (iii) metabolites that are generated by concerted actions of the host and the gut microbiome; and (iv) host metabolites that are further converted into secondary metabolites by gut microorganisms. It should be noted that because these compounds have various chemical structures and biosynthetic routes, which sometimes are shared between different categories, the classification here is not very strict and shall be considered as purely conceptional. Below, each category is described and the effects on the host are discussed, with particularly reference to the elderly.

Metabolites Produced by the Gut Microbiome from Dietary Components This category of metabolites includes a series of molecules that are introduced as a part of our daily dietary intake and undergo microbial-mediated metabolism yielding functional end products that act on the host and/or on the microbial community itself.

Short-Chain Fatty Acids SCFAs are fatty acids with a chemical backbone made of up to five carbon atoms, including formic acid, acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, and 2-methylbutyric acid. As mentioned above, such molecules are derived from intestinal microbial fermentation of indigestible fibers and possess pleiotropic effects over the host. The first effect of SCFAs is on epithelial intestinal cells, either acting on surface G-protein-coupled receptors (GPCRs) such as GPR41 (encoded by the FFAR3 human gene), GPR43 (FFAR2), and GPR109A (HCAR2) or entering the epithelial cells. The intestinal epithelial cell entrance is regulated by passive diffusion and carrier-mediated transport through the

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Fig. 2 Aging-Relevant Gut Microbiota Metabolites. Gut microbiota (GM)-derived metabolites can be broadly encompassed in four categories based on their origin, i.e., metabolites (I) directly produced by GM from dietary components, (II) produced de novo by GM, (III) produced by both GM and the host, and (IV) host metabolites further converted by GM into secondary metabolites. The main functions provided by these metabolites – that might be relevant for a successful aging – are detailed in the right part of the figure and are mainly oriented toward gut epithelial and blood– brain barrier maintenance, inflammation control, blood vessel regulation, and tissue oxidative stress relief. Higher concentrations of all these metabolites have been associated with the proposed effects, except for TMAO, for which lower circulating levels are desirable. AhR, aryl hydrocarbon receptor; BBB, blood–brain barrier; EPS, exopolysaccharide; GM, gut microbiota; MPL, metalloproteinase; ROS, reactive oxygen species; SCFA, short-chain fatty acid; TMAO, trimethylamine N-oxide. The figure was generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license

H+-coupled monocarboxylate transporter 1 known as MCT1 and encoded by the SLC16A1 gene, and the sodium-coupled monocarboxylate transporter 1 or SMCT1 encoded by the SLC5A8 gene. The latter is expressed mainly in distal colon epithelial cells, while MCT1 is found in colonocytes, as well as in monocytes, granulocytes, and lymphocytes. Intestinal epithelial cells are the first line of defense against harmful lumen pathogens and their toxins. These cells are tightly connected by several intercellular junctions, namely, tight junctions, adherent junctions, gap junctions, and desmosomes, which are fundamental for the integrity of the gut barrier function. The genetic expression of these components is regulated by several proteins such as claudin-1, zonulin-1, and occludin. SCFAs have been shown to exert a positive influence over the expression of these proteins, upregulating tight

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junctions via activation of the AMP-activated protein kinase (AMPK), promoting the proper maintenance of the gastrointestinal barrier. As discussed above, the integrity of the intestinal epithelial barrier is critical for health status preservation and disease onset and progression. Anomalies in barrier function might lead to chronic immune activation contributing to the development of local and systemic disorders (e.g., cardiometabolic diseases) and increasing the overall host inflammatory status. Thus, SCFAs might exert a protective effect toward the onset of a vast array of age-related diseases. Importantly, the impairment of the intestinal epithelial barrier is also thought to contribute to the pathogenesis of several central nervous systems age-related disorders, such as Parkinson’s and Alzheimer’s diseases, and multiple sclerosis. Another key factor for the maintenance of a healthy intestinal epithelium is represented by the mucus layer covering all over the gut epithelial surface. Mucus is secreted by goblet cells and contains several major components including mucins, which are heavily O-glycosylated molecules. This thick mucus layer helps to create a gradient of microbes and metabolites from the gut epithelium to the intestinal lumen and exerts a crucial role in the maintenance of gut microbiotahost homeostasis. During aging the mucus thickness appears not to be influenced, while its glycosylation pattern has been shown to be altered. Several microorganisms are known to adhere to mucin O-glycans via mucus-binding proteins, thus age-related modification of mucus glycosylation pattern might be related to the onset of age-related dysbiosis. In this context microbiota-derived metabolites, such as SCFAs, can counterbalance those age-related modifications of the mucus layer by stimulating epithelial mucin 2 expression through differential effects on myofibroblasts-derived prostaglandins E1 and E2. In addition, carbon dioxide (CO2) generated from β-oxidation of SCFAs in colonocytes is transformed into bicarbonate, which in turn determines the correct polar layering of the mucus from the epithelium toward the lumen. In addition, SCFAs can also enter the bloodstream as they are active in the form of metabolized moieties (such as the hydroxylated form of butyrate, β-hydroxybutyrate) and systemically act on the host. The first systemic site of action are certainly the blood vessels, where SCFAs can activate GPR41, GPR43, GPR109A, and the olfactory receptor OR51E2, leading to the regulation of blood pressure through vasodilation and vasoconstriction as needed. Interestingly, SCFA binding to different receptors plays an opposite role in blood pressure regulation, suggesting the evolutionary adaptation of a fine-tuned mechanism. GPR41 and GPR43 are generally located in vascular smooth muscle cells and endothelial cells and they are selective for acetate, propionate, and butyrate, while butyrate and β-hydroxybutyrate bind GPR109A. In animal models, the activation of these GPCRs results in vasodilation and has shown to reduce hypertension and hypertensive risks, such as vascular fibrosis and vessel thickening. On the other hand, OR51E2, generally distributed in kidney blood vessels, selects for acetate and propionate and increase the blood pressure in renal afferent arterioles and peripheral blood vessels. The renal afferent arteriole is the main site for renin secretion and storage, and SCFAs have been shown to induce the stimulation of cyclic adenosine monophosphate (cAMP) production in glomerular cells, resulting in renin release and consequent increase in blood pressure through the renin-angiotensin-aldosterone

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system. Thus, the reduction of SCFAs often associated with age-related dysbiosis might play a role in the onset and progression on cardiometabolic diseases, such as hypertension and chronic kidney disease. The anti-inflammatory effect of SCFAs is not limited just to the maintenance of epithelial cell integrity. Indeed, through binding to GPCRs and inhibition of histone deacetylases (HDAC), SCFAs exert an immunomodulatory effect, stimulating tolerogenic and anti-inflammatory responses, and increasing the differentiation of fork head box P3 (FOXP3+) Treg cells through the inhibition of HDAC9. Exposure of peripheral blood mononuclear cells and neutrophils to microbial SCFAs, similar to their exposure to global HDAC inhibitors, inactivates nuclear factor-κB (NF-κB) and downregulates the production of the pro-inflammatory cytokine TNF-α. Additional studies have shown the SCFAsmediated anti-inflammatory effects due to HDAC inhibition in macrophages. Like other gut microbial metabolites, SCFAs can pass through the blood–brain barrier and interact with the microglia. High levels of microbial SCFAs in the central nervous system have shown to modulate the blood–brain barrier integrity and inhibit the inflammatory response of peripheral monocytes, while low levels, as reported in the elderly, are related with systemic central nervous system inflammation and the onset of several disorders, such as Alzheimer’s and Parkinson’s diseases. Although the precise mechanisms involved in the action of SCFAs on the central nervous system are still unknown, plenty of animal studies have shown that they exert a broad influence on key neurological and behavioral processes and might be involved in critical phases of neurodevelopment and in the onset of neurodegenerative disorders. Neutrophils are characterized by the presence of the GPR43 receptor that, when activated by SCFAs, stimulates chemotaxis, favoring the immune response against viral and bacterial pathogens. Local and systemic reduced levels of SCFAs are commonly associated with chronic autoimmune and inflammatory diseases. Inducing the differentiation and proliferation of Treg cells and the production of antiinflammatory cytokines, SCFAs may tackle the onset and progression of type 1 diabetes. In addition, acetate has shown to alter the frequency of autoimmune T effector cells in a type 1 diabetes mouse model via the alteration of the surface phenotype of B cells, making them less reactive and more tolerogenic. These types of B cells have a pathogenic role in autoantibody production and in the transition from insulitis to clinical diabetes, and serve as antigen-presenting cells for the isletantigen-reactive T cells. In conclusion, the presence of cell-specific and tissuespecific GPCRs that are able to recognize SCFAs allows the host to regulate inflammatory response, control infection or injuries, and maintain host homeostasis. The levels of SCFAs tend to decrease across aging thus exposing the human body to cardiometabolic and neurological diseases, while they remain abundant in those people who reach a successful aging, i.e., centenarians (Zhou et al. 2021).

Gases Microbial fermentation of dietary compounds also yields various gases, among which are hydrogen (H2), methane (CH4), carbon dioxide and monoxide (CO2, CO), hydrogen sulfide (H2S), nitric oxide (NO), and sulfur-containing compounds. Intestinal gases are in equilibrium between their dissolved form in the liquid content

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of the gut lumen and in their gaseous form as headspace gas. Also, oxygen and nitrogen are present in the gastrointestinal tract, albeit they are mostly derived from swallowed air, while a small amount of nitrogen is produced via microbial denitrification of proteins in the colon. The gastrointestinal tract becomes increasingly anaerobic throughout its length, with progressive decease in oxygen concentration and increase in abundance of luminal microorganisms, as well as their resulting gas. CO2 is first derived from the reaction of bicarbonate secreted by the pancreas and hydrochloric acid contained in the gastric chyme, which takes place in the duodenum. Secondly, CO2 is produced together with H2 during the microbial-derived fermentation of carbohydrates and, to lesser extent, endogenous and dietary proteins. CH4 is produced from metabolism of CO2 and H2 by archaea in the colonic part. Lastly, H2S and sulfur-containing gas traces are produced during the fermentation of proteins and by sulfur-reducing microbes (metabolizing sulfates and sulfites). Differences in gas production have been associated with gut microbiome composition, its functional potential, as well as to substrates available from the dietary intake. Also, intestinal permeability is responsible of how efficiently the content of the gastrointestinal lumen and body fluids are exchanged among each other. Producer gas are often referred to as gasotransmitters, i.e., small, labile, and endogenously generated gaseous transmitters that are involved in host physiology. The most studied gasotransmitters are NO, CO, and H2S. Among these, age-dependent decline in plasma H2S levels has been clearly detected in humans between 50 and 80 years of age, suggesting a link between H2S and aging, although its role is still controversial. High genotoxic H2S concentration in the gut, possibly due to the production by commensal sulfate-reducing bacteria, might play a role in the genomic instability and the acquisition of mutations, thus favoring colorectal cancer development. At the same time, H2S has shown to attenuate DNA damage in human endothelial cells and fibroblasts, leading to the activation of DNA damage repair mechanisms and protection from cellular senescence. In addition, H2S has shown to be involved in epigenetic modifications of the chromatin and DNA methylation, with evidence of anti-inflammatory, proapoptotic, and proteostatic effects (Perridon et al. 2016).

Phenolic Acids and Bioactive Phytoderivates Phenolic acids are non-flavonoid phenolic compounds widespread in plants and usually introduced with diet. In addition, complex polyphenolic molecules contained in food (as vegetables, fruits, and grains) are metabolized by the gut microbiome in smaller molecules, precisely phenolic acids. Phenolic acids exert radical scavenging capacity, resulting in a beneficial effect against cancer development, cardiovascular disease, and other cardiometabolic disorders. Phenolic acids as gut microbiomederived end products can be absorbed by the intestinal epithelium and enter the portal circulation, where they are metabolized by human phase II metabolism enzymes, undergoing methylation, glucuronidation, and sulfation with derivates that may be (more) biologically active and have been shown to possess intrinsic anti-inflammatory properties, as well as synergistic anti-inflammatory effects with SCFAs. In murine models, long-term intake of phenolic compounds has shown to attenuate age-related plasma inflammatory and fibrotic markers, ameliorating

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extracellular matrix remodeling and reducing interstitial inflammation through the downregulation of the p38 pathway. Moreover, myocardia of these rats presented less fibrosis and a suppression of the profibrotic transforming growth factor-β1 Smad pathway, which has been associated with age-related ventricular stiffness and cardiac dysfunction. Circulating phenolic-derived microbial metabolites have also been linked to reduction in cardiovascular risk factors due to antithrombotic effects in humans, as well as to a lower risk of type 2 diabetes and osteoporosis. The mechanism through which these molecules mediate such effects, aside from their powerful antioxidant properties, has yet to be described. However, it has been clearly shown that phytoderivates introduced with diet have an impact on cardiometabolic biomarkers and risk factors, and the gut microbiome members have proven to exert a crucial role in the yield of biologically active molecules (Fabbrini et al. 2022).

Tryptophan/Indole Metabolites: Aryl Hydrocarbon Receptor Ligands Amino acids are crucial macronutrients in mammalian diets and are essential for the biosynthesis of hormones and neurotransmitters. Gut resident bacteria can utilize amino acids derived from the host or dietary sources for the synthesis of various microbialderived metabolites. Several nutritionally essential amino acids can be synthetized de novo by gut microbial members. An example is tryptophan, an aromatic amino acid mainly derived from protein-rich food. Once produced by gut microbiome members from the metabolism of dietary proteins, tryptophan enters the blood, is transported across the blood–brain barrier, and taken up by neurotransmitter-producing cells in the central nervous system, regulating neurotransmission balance in the brain. Furthermore, bacterial-derived tryptophan metabolism contributes to the synthesis of 5-hydroxytrypanim (serotonin), tryptamine, and kynurenines, all involved in the bidirectional communication of the gut–brain axis. Tryptophan metabolites also include indole, indole ethanol, indolepropionic acid, indole lactic acid, indoleacetic acid, indolealdehyde, indoleacrylic acid, and skatole, which are generally good ligands for aryl hydrocarbon receptor (AhR). AhR is a transcription factor that regulates gene expression, immunity, and cellular differentiation and is expressed in various body tissues. Several aromatic hydrocarbons, as well as natural plant polyphenols and indole compounds can bind to this receptor, activating its translocation in the nucleus, where it affects the expression of several target genes as Cyp1a1, Cyp1b1, AhRR, and IL-10. AhR activation directly on the gut epithelium exerts immunomodulatory effects on the intestinal dendritic cells, intraepithelial lymphocytes, and innate lymphoid cells through the AhR/IL-22 axis. In turn, IL-22 regulates the epithelial integrity via phosphorylation of STAT3 to accelerate intestinal epithelial proliferation, thereby restoring damaged intestinal mucosa. Additionally, its activation regulates the immune response at the interface with the intestinal lumen, preserving the antimicrobial defense capability, overall favoring intestinal homeostasis. Indole derivates shape CD4+CD8αα+ doublepositive intraepithelial lymphocytes to have regulatory and tolerogenic functions. Indole and its derivatives are absorbed by the epithelium due to the ability to freely diffuse through lipid membranes and enter the portal circulation, through which they afflux into the liver and undergo further metabolism. Initially, indole is absorbed and oxidized by microsomal CYP450 isozymes (especially the CYP2E1 isoform) to indoxyl and

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indican. Hepatic phase II drug metabolism of indoxyl mediates its sulfurylation to generate indoxyl-3-sulfate (I3S), which is finally excreted by the kidney. In the liver, these metabolites can reduce inflammation and regulate lipid metabolism, counteracting steatohepatitis, and glucose metabolism, ameliorating type 2 diabetes. Another role of AhR is to mediate antigen presentation by dendritic cells and induction of T cell activation. Therefore, microbial-derived AhR ligands could have the potential to prevent and dampen autoimmune disorders as type 1 diabetes. Like dendritic cells, macrophages also play a predominant role in type 1 diabetes initiation and progression by means of antigen presentation or production of inflammatory cytokines to destroy pancreatic β cells. Generally, upon activation, AhR reduces IL-6 expression in macrophages to suppress immoderate inflammatory responses and induce tolerance. Thus, AhR ligands produced by the gut microbiome might have a role in positively regulating the immune homeostasis during aging. In fact, in centenarians and especially semisupercentenarians (i.e., people reaching the age of 105), a significant increase in tryptophan metabolism by the gut microbiome was found, in parallel with a decrease in tryptophan serum bioavailability (Rampelli et al. 2020; Brinkmann et al. 2020).

Bacterial-Derived Vitamins Vitamins are essential nutrients for gut microorganisms and for maintaining host metabolism and are either obtained from the diet or synthesized directly by the gut microbiome itself. Dietary vitamins are mostly absorbed in the small intestine, while microbiome-derived vitamins undergo absorption in the colon. Members of the family of vitamins K2 and B are the major vitamins produced by gut microbes (i.e., thiamin B1, riboflavin B2, nicotinate B3, pantothenate B4, pyridoxine B5, biotin B6, folates B9, and cobalamin B12). During aging, the calorie needs usually decrease due to the lack of physical activity, changes in metabolism, and age-related loss of bone and muscle mass. Nonetheless, the need of nutrients is comparable to that of younger adults, thus the possible deficiencies in the dietary intake along with the reduced capability of nutrient absorption might be partially counterbalanced by endogenous production of vitamins from a healthy gut microbiome. In this scenario, centenarians have shown signs of increased gut-derived vitamin B2 and K2 biosynthesis compared to adults and some of them did not report the typical age-related changes in appetite. Moreover, vitamin B1 and K2 have been reported to maintain host immunity, promote bone health, and reduce the risk of cardiovascular diseases (Liu et al. 2022).

Metabolites Produced De Novo by the Gut Microbiome This category of metabolites encompasses metabolites that are structural parts of the microbial cells or are synthetized de novo by the gut microbiome without the need to utilize any preexistent scaffold or dietary intake moieties.

Exopolysaccharides Microbes do not only catabolize carbohydrates coming from the diet, but also synthetize their own, mostly in the form of polysaccharides. Such complex

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carbohydrate polymers can be divided in capsular polysaccharides, exopolysaccharides, LPS, teichoic acids, and peptidoglycans. Many of these compounds are attached to the outer surface of microbes, constituting a shield against toxins and pathogens. Exopolysaccharides are instead secreted into the environment and influence the interaction between different bacterial species (activating quorum sensing mechanisms). Several commensals have also been shown to promote the production of SCFAs starting from certain exopolysaccharides. Although the mechanism of action of exopolysaccharides in aging is not clearly outlined, evidence is available suggesting a role of these microbial components in this context. For example, colanoic acid is a specific exopolysaccharide secreted by E. coli that was identified for its pro-longevity effect through a genomic screen for microbial regulators of aging in Caenorhabditis elegans. Supplementation with colanoic acid led to lifespan extension through regulation of mitochondrial dynamics and protected against amyloid-β accumulation and germline tumor progression, suggesting a protective effect of such microbial metabolite toward inflammation, central nervous system disorders, and cancer development. Furthermore, specific exopolysaccharides from L. plantarum HY7714 have shown a protective effect against skin aging, reducing ultraviolet-B-induced cytotoxicity, modulating the hydration status of skin cells, downregulating the production of metalloproteinases, and tackling the onset of reactive oxygen species. As metalloproteinases are involved in solid tumor metastasis through the basal lamina, exopolysaccharides might also help in counteracting tumor infiltration. Finally, exopolysaccharides produced by Leuconostoc pseudomesenteroides XG5 have demonstrated a protective effect against type 1 diabetes in mice via stimulation of glucagon-like-peptide 1 secretion (Zhou et al. 2021).

Lipids LPS is one of the major classes of lipid metabolites produced by gut microbial members, along with phosphatidylcholines, cholesterol, and other conjugated fatty acids. LPS is a crucial component of the outer membrane of Gram-negative bacteria. Structurally, these molecules are amphipathic glycoconjugates, made up of an oligosaccharide core and a distal polysaccharide (often referred to as the O-antigen) linked to a hydrophobic lipid domain (lipid A). The main function of LPS is to act as a protective barrier against toxic molecules and pathogens. As part of the outer surface of bacteria, LPS is the first antigen that the host immune system is able to recognize, promoting strong innate inflammatory reactions with pro-inflammatory cytokine cascades through TLR4 signaling. In particular, high circulating levels of LPS are associated with endotoxemia (a condition in which alterations in the gut epithelial barrier allow the microbiome-derived LPS to enter the bloodstream) and septic shock, while low persistent concentrations are associated with low-grade inflammation. Such low-grade inflammation has been linked to many different diseases, such as type 1 diabetes, obesity, NAFLD, chronic kidney disease, and cardiovascular disease. Moreover, gut microbiome dysbiosis, due to diseased conditions or aging, might result in alteration of the gut epithelium permeability thus mediating loads of LPS to diffuse in the bloodstream. It is also well known that aging can be associated with higher blood–brain barrier permeability,

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allowing circulating pro-inflammatory LPS to enter the brain tissue. Once there, the binding to TLR4 activates the production of pro-inflammatory cytokines from the microglia. It has been demonstrated that LPS binding to microglia induces the production of lipid droplets within microglia cells. These structures have a hydrophobic coat of neutral lipids, primarily triglycerides and cholesteryl esters, surrounding a phospholipid monolayer enriched with proteins known to govern lipid droplet function. The droplet-accumulating microglia is characteristic of aged brains, showing phagocytosis abnormalities with the presence of more reactive oxygen species and releasing higher levels of pro-inflammatory cytokines with possible consequent adverse effects over the neurons. It has been shown that the combined effect of aging and LPS induces Parkinson’s disease in animal models, with the decline of dopaminergic neurons and increased neuroinflammation and oxidative stress in the brain. Aside from the effect over the central nervous system, LPS has shown to accelerate inflammaging in murine models in a TLR4-dependent manner. However, LPS – when produced in adequate amounts by a healthy gut microbiome – is essential for the establishment and maintenance of a homeostatic tolerance between the immune system and the microbiome itself. In this regard, centenarians and semisupercentenarians tend to show higher LPS biosynthetic potential than elderly people, with the latter having a biosynthetic potential comparable to that of young adults (Rampelli et al. 2020). Another microbial-derived lipid class is constituted by conjugated fatty acids, such as polyunsaturated fatty acids (PUFAs) that can be generally found as free fatty acids or as moieties on complex lipid species (e.g., glycerophospholipids, sphingolipids, and triglycerides). PUFAs are quite sensible to oxidation, more than monounsaturated fatty acids (MUFAs) and saturated fatty acids. Oxidation of PUFAs present in membrane phospholipids can be involved in the production of reactive oxygen species and lead to further oxidative chain reactions on nearby PUFAs, damaging membrane proteins. The ratio of MUFAs and PUFAs in host cell membranes has been linked with aging, with the latter increasing during the aging process. Several studies evaluating the supplementation of MUFAs (e.g., oleic acid, palmitoleic acid, and cis-vaccenic acid) in C. elegans detected longevity-improving responses. The mechanism is complex and further studies are needed to elucidate the precise molecular roles of MUFAs and PUFAs in aging. In fact, although PUFA peroxidation causes oxidative damage to the cellular membrane, oxidation products generated from this process may serve as signaling molecules to activate specific longevity-promoting pathways in C. elegans. The human gut microbiome produces MUFA and PUFA metabolites, possibly regulating host oxidative responses that might be connected to longevity and aging, thus age-related dysbiosis might hamper the host-microbiota crosstalk also at this level (Hasavci and Blank 2022).

Neurotransmitters Some gut metabolites can influence – triggering or waning – neuron transmission from and toward the nervous system, serving as full-fledged neurotransmitters and orchestrating the so-called gut–brain axis. The best-known gut microbiome-derived neurotransmitters are dopamine, 5-HT, glutamate, and gamma amino-butyric acid

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(GABA). Those metabolites are not able to penetrate the blood–brain barrier, thus the effect mediated by the gut microbiome mostly concerns direct effects toward the enteric nervous system, and indirect actions toward the central nervous system via vagal signaling. Dopamine is the main catecholaminergic neurotransmitter and is synthesized in both the central and peripheral nervous system, playing crucial roles in multiple psychological processes, including food intake, anxiety, and depression. Several studies have linked the configuration of the gut microbiome to levels of peripheral and central dopamine, whose levels are also altered during aging due to the reduction of receptors and the increase of dopamine. Aging-related cognitive decline is thought to be dependent on dopamine levels as well, thus the gut microbiome might have a role in counteracting or vice versa favoring the natural decay of dopaminergic transmission during aging. Serotonin is one of the microbiome-derived neurotransmitters impacting on the gut homeostasis itself, given its role in regulating gastrointestinal secretions, peristalsis, as well as localto-global vasoconstriction. It should be remembered that the majority of body serotonin resides in the gastrointestinal tract, especially in epithelial enterochromaffin cells. It is not yet clear whether the gut microbiome directly produces serotonin or triggers its production in enterochromaffin cells via the action of small molecules, however there is evidence that the gut microbiome plays a major role in serotonin regulation versus dysregulation in pathological conditions. While serotonin is the most abundant neurotransmitter in the gastrointestinal tract, glutamate achieves similar abundances in the central nervous system, with glutamatergic receptors being present on more than 90% of neurons and 40% of neuronal synapses. Glutamatergic homeostasis is crucial for healthy aging. During physiological aging, there is a gradual loss of glutamatergic neurons, while longevity models showed that a preservation of glutamate signaling delayed cognitive decline. When disruption of glutamate neurotransmission occurs in aging, the mechanisms of healthy aging are no longer preserved and may promote the onset of neurodegenerative disorders. Aside from dietary sources of glutamate such as cheese, seafood, some vegetables and flavor-enriched food additives, which constitute the major source for glutamate in the gastrointestinal tract, glutamate is produced by several bacterial strains such as those belonging to the genus Corynebacterium. In the gastrointestinal lumen, glutamate regulates the glutamatergic neurotransmitter machinery of the enteric nervous system that in turn is involved in intestinal motility, secretion, and visceral pain perception. Glutamate receptors are also present in vagal and spinal extrinsic pathways, sending sensory information to the central nervous system. Indeed, glutamate introduced with the diet and produced by gut microbial members also exerts an effect on the brain, constituting one of the interaction paths forming the gut–brain axis. Even in this context, age-related gut microbiome dysbiosis might have a role in successful aging through the maintenance of glutamate levels and regulation of glutamatergic transmission, acting at both local (gut motility and secretion) and global (whole body, central nervous system) levels. While glutamate is the main excitatory neurotransmitter, GABA is the main inhibitory counterpart. During aging, a decline in GABAergic transmission in the human colon and in certain areas of the brain has been reported, with less evident reductions

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in centenarians, which have also been linked to the gut microbial community. Additional studies are needed to better elucidate the mechanisms of neurotransmitter production and regulation by the gut microbiome in older adults. However, recent evidence is suggesting a novel “microbiota-gut-brain axis,” given that the neurotransmitter production and modulation is likely to be carried out by a close crosstalk between the gut microbiota and the enteric nervous system rather than by the single components themselves. What should be clear, however, is that the gut microbiome has a relevant role in the maintenance and regulation of neurotransmission homeostasis in the gastrointestinal tract and indirectly in the central nervous system, and that dysbiosis, whether related to disease conditions or aging, could hamper such finely regulated balance (Zhou et al. 2021).

Metabolites Shared by the Host and the Gut Microbiota This section includes metabolites that are synthetized by both host and microbial cells, and specifically focuses on polyamines, a well-known class of compounds matching such a definition.

Polyamines Polyamines are small polycationic molecules mostly represented by spermine, spermidine, and putrescine. Polyamines are produced by human cells starting from S-adenosyl-methionine (spermine, spermidine) or ornithine (putrescine). Ingested food is the major source of polyamines in the gut lumen that are absorbed in the upper intestinal tract. The gut microbiome is the main responsible for the production of polyamine in the lower part of the intestine, which are absorbed by the colonic mucosa and transferred into the bloodstream. Consequently, host intracellular polyamine levels are regulated by both endogenous biosynthesis and degradation, as well as exogenous transport. Polyamines exert pleiotropic effects on both human and microbial cells, including gene regulation, stress resistance, cell proliferation and differentiation, membrane stability, ion channel regulation, and free radical scavenging. Their action spans from the microbial and host cell metabolism regulation to gastrointestinal mucosa homeostasis – whose relevance has been previously described – modulating cell division and apoptosis. Bacterial polyamines also include homospermidine, norspermidine, cadaverine, and 1,3-diaminopropane, which along with the more common spermine, spermidine, and putrescine are also relevant for bacterial cell-to-cell communication. The concentration of polyamines in the gastrointestinal tract depends on the presence of polyamine-producing microbes, as well as polyamine-absorbing ones, thus the homeostatic balance in polyamine content throughout the body is strongly related to host diet and gut microbiome composition. Evidence has reported increased serum levels of polyamines in the blood of centenarians compared to adults and the elderly, meaning that such molecules might be involved in successful aging. On the other hand, gut microbiome dysbiosis has been associated with functional shifts in polyamine biosynthetic pathways, resulting in lower polyamine production. In addition, higher

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levels of polyamines have been shown to protect against the onset and progression of type 1 and type 2 diabetes, and cardiovascular disease (Sagar et al. 2021).

Host Metabolites Converted by the Gut Microbiome In this section, two of the most-known microbial metabolites derived from the modification of host molecules are going to be discussed, i.e., secondary bile acids and TMAO.

Secondary Bile Acids As mentioned above, primary bile acids – generally referred as bile acids – are cholesterol derivates such as cholate and chenodeoxycholate synthetized by human hepatocytes. Bile acids are commonly surfactants in the gastrointestinal lumen, aiding the digestion, transport, and absorption of nutrients, and are generally conjugated with glycine or taurine. Most of the bile acids are re-absorbed in the small intestine and transported back to the liver, but around 5% of bile acids reach the lower intestinal tract including the colon, where they are substrate for the resident microbiota. Primary bile acids are converted into secondary bile acids by a limited pool of microbial species, such as Clostridium scindens. Deoxycholate and lithocholic acid represent the major types of secondary bile acids generated through the deconjugation and 7a-dehydroxylation of cholate and chenodeoxycholate, respectively. Secondary bile acids directly affect microorganisms as well as intestinal epithelial cells, binding to nuclear hormone receptors, such as farnesoid X receptor, pregnane X receptor, vitamin D receptor, and constitutive androstane receptor as well as to the cell-surface G-proteincoupled bile acid receptor 1. An increased activation of farnesoid X receptor mediated by secondary bile acids has been detected in a longevity murine model, resulting in an upregulation of xenobiotic detoxification genes, as observed in the gut microbiome of centenarians and semi-supercentenarians (Rampelli et al. 2020). In addition, a high concentration of the taurine-cholic acid conjugate taurocholate strongly correlated with human longevity, and supplementation of primary and secondary bile acids extended lifespan in several in vivo models (i.e., yeast, fruit flies, and mice). In humans, changes in microbial-derived secondary bile acids have been associated with obesity, metabolic disorders, cardiovascular disease, and other age-related complications. Moreover, as discussed above (see section “The Gut Microbiome and Longevity: A Focus on Centenarians”), several types of lithocholic acid have been found at elevated levels in centenarians with distinct gut microbial signatures, which exhibited antimicrobial activities against widespread pathobionts. These results strongly support the role of bile acids as contributors to longevity, a role that is tightly connected to the gut microbiome composition and function, thus subjected to alterations in dysbiotic settings, such the age-related one (Liu et al. 2022; Sato et al. 2021). Trimethylamine N-Oxide TMAO is an amine oxide and osmolyte, particularly enriched in foods such as marine crustaceans and fish. Aside from dietary intake, TMAO can be synthesized

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by gut microbial members as its precursor TMA. Gut microbes are able to process TMA from choline, lecithin, and carnitine derived from dietary intake – mostly present in red meat and other animal sources – which undergoes absorption across intestinal mucosa and transport through the portal circulation. As previously mentioned, once transferred to the liver, TMA is converted into TMAO by host hepatic flavin monooxygenases. Therefore, TMAO circulating levels depend on the amount of TMA produced by the gut microbiome. In particular, Desulfovibrio desulfuricans and D. alaskensis are characterized by the choline-utilization gene cluster involved in the gastrointestinal conversion of choline to TMA. Moreover, several other taxa, such as Acinetobacter and Serratia, can efficiently generate TMA from L-carnitine. TMAO activates human amine-associated receptors (TAARs), a class of GPCRs, and the protein kinase R-like endoplasmic reticulum kinase (PERK), a key sensor of intracellular stress. TMAO has also shown to induce macrophage M1 polarization, a pro-inflammatory state often associated with cardiometabolic diseases, contributing, for instance, to adipose tissue inflammation and insulin resistance in type 2 diabetes. Impaired high-fat-induced insulin resistance increased by TMAO might be mediated by this metabolite binding to PERK and activating downstream FoxO1 responses to drive metabolic diseases. Administration of TMAO-reducing agents, such as 3,30 diindolylmethane and 3,3-dimethyl-1-butanol, induced positive effects partially recovering insulin-resistance in mice, thus placing TMAO as a potential therapeutic target for treating type 2 diabetes. Generally, elevated levels of TMAO have been associated with cardiovascular disease (including myocardial infarction, hypertension, and myocardial fibrosis) and other age-related pathologies including arteriosclerosis, Alzheimer’s disease, and cancer onset. TMAO can also impair endothelial self-repair capacity, enhancing monocyte adhesion via the activation of NF-kB, protein kinase C, and nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome. Increased levels of circulating TMAO have been detected in aged humans, suggesting a correlation between age-related dysbiosis and TMAO production. Several studies in animal models showed a negative correlation between TMAO and cognition and working memory. A possible mechanism to explain this phenomenon is that TMAO can promote neuronal senescence and synaptic damage while downregulating the expression of proteins related to synaptic plasticity and inhibiting the mTOR signaling pathway, all of which might partially contribute to age-related brain and cognitive function deterioration (Hasavci and Blank 2022).

Conclusion The recent involvement of the gut microbiome in several aspects of health and disease has paved the way for a new frontier of research regarding this intricate – but rather interesting – relationship. Maintaining an eubiotic gut microbial ecosystem, with all its challenges, has turned out to be one important aspect for aging as healthy as possible. In this scenario, analyzing the gut microbial profile across lifespan has received a crescendo of attention, above all to develop new intervention strategies

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aimed at reducing the burden of age-related diseases, exerting overall geroprotective effects. Indeed, gut microbes and the plethora of their metabolites have been identified as a valid option for the rational design of precision microbiome-based interventions as adjuvants to currently available drug therapies for several disorders, including age-related ones. Prebiotics, probiotics, and other bacterial strains, as well as FMT were identified as novel and intriguing strategies to ameliorate symptoms in several different disorders. It must be said that in this context, the research is still uphill due to the importance of maintaining stability, safety, and efficacy of such strategies in each therapeutic usage, as well as the still limited knowledge for application in age-related disorders. For these reasons, microbial metabolites or postbiotics – rather than alive microorganisms – can be helpful to overcome some of these problems, opening the way to a new window of research in the field. Only these new achievements are likely to actually contribute to healthy aging and hopefully promote longevity.

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Gut Microbiota and Specific Response to Diet

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Asma Amamou, Cian O’Mahony, Maria Antonia Lopis-Grimalt, Gaston Cruzel, Noel Caplice, Florence Herisson, and Subrata Ghosh

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diet and Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diet Component and Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pattern Diet and Gut Microbiota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiovascular Disease and Gut Dysbiosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbiota-Directed Therapeutics for Cardiometabolic Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Gut microbiota is now considered as a “metabolic organ” that exerts several functions affecting the host metabolism and physiology. Specifically, intestinal bacteria are a highly dynamic component of the gut microbiota that are shaped by both endogenous factors (i.e., host genes), and exogenous factors (i.e., host exposome). The latter mainly referring to the environmental factors, which the host is exposed throughout life. Diet is one of the strongest factors that modulates the composition and the function of the gut bacteria and is therefore considered as a pivotal determinant of pathophysiological mechanisms. Over the past half century, the adoption of modern dietary habits has become a growing health concern – as these habits are strongly associated with both obesity and related metabolic diseases. This change especially drives inflammation and gives rise to A. Amamou (*) · C. O’Mahony · S. Ghosh APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland e-mail: [email protected]; [email protected]; [email protected] M. A. Lopis-Grimalt · G. Cruzel · N. Caplice · F. Herisson Centre for Research in Vascular Biology, Biosciences Institute, University College Cork, Cork, Ireland e-mail: [email protected]; [email protected]; fl[email protected]; [email protected] © Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1_17

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both structural and behavioral changes in the gut microbiota. This chapter highlights the major mechanisms by which dietary components (e.g., macronutrients, micronutrients, . . .), as well as dietary patterns (e.g., Western diet, Mediterranean diet, . . .) modulate the composition of the gut bacteria and their metabolic activity. Based on observational, interventional, and experimental studies, we highlight the chief concepts relating to the crosstalk between diet and gut microbiota, and how these current insights can generate promising perspectives on the treatment of cardiometabolic diseases. Keywords

Diet · Western Diet · Mediterranean Diet · Gut microbiota · Metabolites-derived bacteria · Metabolic disorders · Cardiovascular diseases · Probiotic · Prebiotic

Introduction The idea of an intimate link between what we eat and our gut health it not new. In 1907, Elie Metchnikoff was the first to hypothesize that reducing harmful bacteria in the gut or replacing them with lactic acid bacteria from fermented foods could prolong life through gut health. In the twenty-first century, we have learned that in addition to the hereditary components associated with the gut microbiota, both environmental factors related to diet and medication intake also have a strong influence on our gut microbiota. The intestinal microbiota is defined as all the microorganisms present in our gastrointestinal tract. The latter is composed of more than 40 trillion bacteria, viruses, and fungi. More than 1500 species are present there, mainly belonging to five major phyla: Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, and Verrucomicrobia. The composition of the intestinal microbiota can vastly differ from one individual to another. This is explained by factors that are intrinsic to the host: host genetics, immune system, metabolic activity, but also extrinsic factors such as lifestyle, physical activity, or xenobiotics. It is becoming increasingly apparent that extrinsic factors play critical roles in shaping the gut microbiota. Among these extrinsic factors, the most determining factor is undoubtedly the diet. It is estimated that 20% of the composition of our microbiota is shaped by food, and this extends all the way from childhood into adulthood. In recent decades, a growing number of clinical and experimental studies have highlighted the bidirectional link between the intestinal microbiota and food. However, the underlying mechanisms are only beginning to be understood. The intestinal microbiota remains globally stable throughout an individual’s life but the different dietary patterns and eating habits can significantly influence the composition of the microbiota. It is estimated that 30–40% of the composition of the intestinal microbiota may change over the lifetime of an individual as a result of their dietary habits (Leeming et al. 2019). In some cases, these changes in the intestinal microbiota may lead to dysbiosis, characterized by the loss of beneficial bacteria,

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Fig. 1 Diet, microbiota, and host interactions. Food components play a crucial role in shaping the composition and the function of the gut microbiota. For instance, diets rich in fats and carbohydrates strongly affect the gut microbial composition, which ultimately alter the gut microbiota’s communication with other organs via a dysregulated production of bacteria-derived metabolites. Food-based microbiota alterations have been linked to a range of health outcomes that includes obesity and cardiometabolic diseases and, thus, highlight the importance of maintaining a healthy gut microbiota through a balanced diet and probiotics supplementations

expansion of potentially harmful bacteria, and an overall decrease in species diversity. In some instances, this dysbiosis can be deleterious to the health of the individual, primarily by impacting the physiology of the host and participating in the development of inflammatory, neurological, or even metabolic diseases. The industrialization of the food and the introduction of processed products in our eating habits have impacted the incidence of numerous pathologies in Western countries. Therefore, research involving diet and their impact on the intestinal microbiota is particularly intense. In this chapter we explore how the food we consume can positively or negatively impact the gut microbiota behavior. We will also discuss how targeting the gut microbiota through diet can not only be of interest for healthy people but can also serve as an “adjuvant” for medical treatments for persons with metabolic cardiovascular disorders (Fig. 1).

Diet and Gut Microbiota Food and the different food components, nutrients and additives affect both the composition of the gut microbiota and its functions. These effects of food on the intestinal microbiota begin very early after birth, with the oligosaccharides contained in human breast milk participating in the maturation of the intestinal microbiota (Jost

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et al. 2015). The influence of diet extends beyond weaning, with an increase in the diversity of the microbiota with the introduction of solid foods. Eventually, these influences being to deteriorate in aging populations, likely due to a decrease in food diversity.

Diet Component and Gut Microbiota The composition of the foods we eat, particularly in terms of macronutrients (carbohydrates, proteins, lipids) and micronutrients, play a critical role in remodeling the intestinal microbiota. Even over short periods, these can significantly impact the composition of the microbiota and affect the metabolism and functions of the host. Indeed, certain nutrients can directly interact with bacteria, thus causing their expansion or regression. This will mainly depend on the ability of these microorganisms to extract energy from food. Thus, the composition of the food consumed confers a selective advantage for a beneficial or harmful microbial community.

Carbohydrates Carbohydrates are the main source of energy in the human diet. These are mainly found in plant-based diet and constitute a powerful modulator of our intestinal microbiota. These components may also be known colloquially as carbs or saccharides and are primarily found in sugars, and starches, yet are also found in milk (Lactose). There are two commonly described categories of carbohydrates with distinct effects on the intestinal microbiota, namely – simple and complex carbohydrates. Indeed, simple, and digestible carbohydrates, such as fructose or sucrose, are distinguished from complex and indigestible carbohydrates, such as fructooligosaccharides (FOS) and galacto-oligosaccharides (GOS). While simple carbohydrates are known to induce rapid alteration of the intestinal microbiota, leading to alterations in host metabolism, many studies have shown the beneficial role of certain complex carbohydrates (Gentile and Weir 2018). These are referred to as “microbiota-accessible carbohydrates” or MACs, and are mainly found in dietary fibers, fruits, and vegetables. Indeed, MACs are known to favor Bifidobacterium and Lactobacillus, bacteria commonly associated with a healthy status of the host. The mechanisms underpinning the “healthy nature” of these bacteria likely result from their importance in the maturation of innate immunity and the maintenance of tolerance (eubiosis) between the microbiota and host. Dietary fiber, commonly found in a plant-based diet, contains indigestible polysaccharides and oligosaccharides such as lignin, inulin, pectin, cellulose, and fructooligosaccharides (FOS), which are difficult to absorb in the intestine. Indeed, humans only produce a very limited number of carbohydrate-active enzymes, commonly called CAZymes, which are in charge of digesting polysaccharides. Hence, these macronutrients, pass through the small intestine into the large intestine largely undigested. The bacteria in the intestinal microbiota have a considerable number of CAZymes, allowing them to degrade these glycans and extract energy from them in order to proliferate. This process is called fermentation. Many bacterial

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metabolites are produced during fermentation: acetate, propionate, formate, and butyrate (short-chain fatty acids, SCFAs) and other intermediaries such as lactate and succinate. These SCFAs are associated with numerous beneficial effects. Butyrate is mainly produced by Firmicutes and plays a critical role in the renewal of monocytes and the maintenance of the integrity of the intestinal barrier, while propionate promotes intestinal gluconeogenesis. SCFAs are important metabolites known for their ability to negatively regulate the pro-inflammatory nuclear factorKappa B (NF-κB) signaling pathway. Dietary fiber appears to confer benefits to various aspects of human health: cardiovascular disease, body weight management, immunity, and intestinal health including colorectal cancer prevention, laxation, regularity, and appetite control (satiation, satiety) (Gentile and Weir 2018). Studies have shown that regular fiber consumption of at least 30 g per day promotes the expansion of many butyrate-producing bacteria such as Faecalibacterium prausnitzii, Roseburia, and Ruminococcaceae (Makki et al. 2018). Therefore, a diet lacking in MACs may contribute to impoverishment of the microbiome richness, particularly of the SCFA-producing bacteria. This decrease may lead to disturbances in energy balance, particularly involving the metabolism of carbohydrates and lipids. In a diet excluding all traces of polysaccharides, the intestinal microbiota instead employs glycans constituting the mucus layer in intestine of the intestinal barrier as an alternative energy source, this can lead to erosion of the intestinal barrier and promote both inflammation and enhanced susceptibility to pathogens.

Proteins Proteins are another essential macronutrient provided by diet, representing 20% of our daily energy source. In accordance with the recommendations of the Centers for Disease Control and Prevention, 46 g of protein (for women) to 56 g (for men) of protein are requires per day to maintain optimum health. The gut microbiota is involved in the metabolism and transformation of food-derived proteins into a large group of compounds such as: SCFAs, indoles, amines, phenols, thiols, hydrogen sulfide, carbon dioxide (CO2), and dihydrogen (H2). In general, studies show that protein consumption is associated with a greater diversity of the intestinal microbiota. However, the impact of proteins on the intestinal microbiota is dependent on the source, concentration, and amino acid composition of these proteins. Dietary proteins are transformed by peptidases and proteases at the colon level into amino acids, peptides, and tripeptides. These products are predominantly absorbed by the intestinal (small intestine) cells (enterocytes). However, a portion of these proteins is not digested by the intestine and ends up in the colon (large intestine) where they are fermented by bacteria to produce amino acid-derived metabolites or hydrogen sulfate, indole, and ammonia. Some of these bacterial products can be absorbed by the epithelial cells of the colon itself or be transported to the periphery and exert beneficial or deleterious effects on the host, depending on their concentrations (Cai et al. 2022). The predominant sources of protein in humans are of plant or animal origin. These two types of protein are digested, and their products may then interact and affect bacterial patterns differently. These divergences are largely explained by differences in amino acid composition. Animal proteins are considered as “complete

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protein,” containing all 20 amino acids, unlike vegetable proteins, which may have deficits in one or more amino acids. The first studies describing the effects of proteins on the modulation of the intestinal microbiota appeared in the 1970s with Henges et al. (Cai et al. 2022). In these cultured-based studies, individuals consuming low amounts of animal protein showed a specific bacterial signature with a decrease in Bifidobacterium adolescent and an increase in Bacteroides and Clostridia bacteria compared to individuals consuming larger quantities of meat. The concentration of dietary protein is a primary factor affecting protein fermentation and intestinal microbe composition. Indeed, a study comparing the effects of various diets containing different amounts of protein and carbohydrates revealed that consumption of a diet rich in animal protein and low in carbohydrates led to weight loss (Russell et al. 2011). However, this negatively impacted the composition of the intestinal microbiota by increasing the number of Streptococcus, Escherichia coli/Shigella, and Enterococcus to the detriment of Butyrate-producing bacteria such as Roseburia or Faecalibacterium prausnitzii. Excessive consumption of animal protein also causes a shift in gut microbiota metabolism toward the utilization of dietary and endogenously supplied proteins, causing elevated levels of cytotoxic and pro-inflammatory metabolites (such as branched-chain fatty acids, ammonia, amines, N-nitroso compounds, p-cresol, sulfides, indolic compounds, and hydrogen sulfide) (Cai et al. 2022). The gut microbiota altered by dietary protein influences host metabolism by regulation of the intestinal barrier function, gut motility, and the immune system. The main advantage of eating proteins from animal sources is that they contain all 20 amino acids. In contrast, proteins from vegetables are generally combined with animal proteins to satisfy the complete need for each amino acid. However, people consuming protein from animal sources only, have a higher risk of cardiovascular diseases, a phenomenon that is not observed with consumption of plant proteins. Numerous epidemiological studies highlighted that the risk of disease and colorectal cancer are greater following the frequent consumption of red meat, particularly beef and pork (Aykan 2015). Plant-based proteins are poorly digestible compared to animal-based proteins. However, several proteins of plant origin display favorable effects on the function of the intestinal microbiota. Indeed, it has been observed that protein extract increases the abundance of beneficial gut bacteria such as Bifidobacterium and Lactobacillus and decreases deleterious intestinal microbes like Bacteroides fragilis and Clostridium perfringens (Sakkas et al. 2020). Moreover, the increased intestinal levels of SCFA induced by the consumption of pea protein, may act as anti-inflammatory agents and maintain the mucosal barrier. The proteins found in soy milk – a beverage consumed in large quantities in Asia and the United States, is a good example of a plant-based protein whose effects on the intestinal microbiota have been well characterized. Indeed, the consumption of soy milk leads to an increase in the total number of intestinal bacteria, which accompanies an increase in the abundance of Bacteroidetes and reducing the populations of Bifidobacteria and Firmicutes. This decrease in the Firmicutes/Bacteroidetes ratio is associated with a reduced risk of obesity and other metabolic syndromes (Singh et al. 2017).

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Finally, the proteins of algal extracts promote growth and gut health by altering the composition of intestinal bacteria and improve the digestibility and nutrients absorption. Moreover, proteins from seaweed beneficially modify the gut microbiota, modulates immune response, thus strengthening the gut barrier function. Arguably the most significant observation is that the mortality rate is lower in individuals that consume plant-derived proteins than in those who consume primarily animal-derived proteins.

Lipids Lipids are a category of fatty compounds, including fatty acids and their derivatives (mono-,di, triglyceride), along with phospholipids, and sterols such as cholesterol. Dietary lipids are provided by both animal products (fish, eggs, cheese, meat) and plant products (oil, seeds, etc.). Intake of lipids is particularly higher in persons who consume animal-based diets, which also affects the composition of microbes. Among dietary lipids, fatty acids have been the subject of extensive research, including for their role in modulating the intestinal microbiota. Generally, these have distinct saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). Each of these fatty acids impact the composition and function of the intestinal microbiota. A diet rich in MUFA and PUFA leads to an increase in the Bacteroidetes/Firmicutes ratio, as well as an increasing lactic acid bacterium such as Bifidobacteria and Akkermansia muciniphila (Moszak et al. 2020). Conversely, a diet richer in SFA leads to an increase in Bilophila and Faecalibacterium prausnitzii to the detriment of Bifidobacterium, Bacteroidetes, Bacteroides, Prevotella, Lactobacillus ssp. (Moszak et al. 2020). The lipid part of bacterial lipopolysaccharide (LPS), a pro-inflammatory cell wall component produced by gram-negative bacteria, is partly made up of SFA. This SFA-LPS combination is a common signature in metabolic and inflammatory pathologies, a pro-inflammatory state that is termed “metabolic endotoxemia.” Metabolic endotoxemia results from an increase of intestinal permeability, which is associated with reduced expression of tight junction proteins. Each of these gastrointestinal alterations were found to be reversible upon antibiotic treatment. In addition, SFAs act on the production of bile acids in the intestine, causing an increase in the production of deoxycholic acid (DCA). Elevated levels of DCA were associated with dysbiosis of the intestinal microbiota, characterized by a decrease in the anti-inflammatory bacteria Blautia and Rumminococcaceae (Hoshino et al. 1999). Additionally, elevated levels of DCA have been associated with promoting atherosclerosis, diabetes, and other cardiometabolic diseases. Interestingly, these adverse effects seem to be specific to intake of saturated fat. Comparative studies in mice have shown that mice fed a diet rich in SFA had an intestinal microbiota rich in Bacteroides, Turicibacter, and Bilophila spp., which promots inflammation, adipose tissue expansion, and insulin resistance (Devkota et al. 2012). On the contrary, mice that had received fish oil rich in unsaturated fatty acids had an increase in Bifidobacterium, Akkermansia, and Lactobacillus.spp. and showed no metabolic impairments. Unsaturated fatty acids have been the subject of numerous observational and experimental studies. In addition to the amount of fatty acids ingested, the number of

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bonds of these fatty acids has a different impact on the composition of the intestinal microbiota. MUFA (palmitoleic (C16:1 n-7), oleic (C18:1 n-9), and eicosenoic (C20: 1 n-9) acids) are provided by certain foods and have many benefits. Together with eicosanoic acid, oleic acid is the most representative MUFA. They are found in safflower, sesame, pumpkin seed, rice bran, human milk, rapeseed, olive, or peanut for oleic acid and in wheat germ, rapeseed, and hemp for eicosanoic acid. The consumption of a diet rich in MUFA such as extra-virgin olive oil leads to a greater microbial richness, with an increase in the Bacteroidetes/Firmicutes ratio, prevents obesity and reduces the risk of metabolic disorders in humans (Martínez-González et al. 2019). PUFAs are essential fatty acids exclusively provided by food. Much attention has been given to the omega-3 (α-linolenic acid (ALA)) and omega-6 (linoleic acid (LA)). Among the omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), present in marine-derived sources, such as fish, have been the subject of several studies on the intestinal microbiota (MartínezGonzález et al. 2019). Intriguingly, it has been shown that the consumption of nuts rich in ALA can promote an enrichment of the intestinal microbiota with beneficial bacteria (enhancing Ruminococcaceae and Bifidobacteria and decreasing Clostridium sp. cluster XIVa species) of the probiotic type, associated with a decrease in more deleterious bacteria. The joint intake of ALA (via the consumption of flaxseed oil), EPA, and DHA (via the consumption of fish oil) have similarly shown consistent beneficial effects. Omega-6 PUFAs on the other hand, are better known for their pro-inflammatory effects, yet few studies have studied their impact on the composition of the intestinal microbiota (Caesar et al. 2015). Consumption of a high-fat diet containing corn oil, a source of LA, induces a microbiota enrichment with bacterial species known for their involvement in various inflammatory processes. Consumption of fish oil supplements, which are a source of both EPA and DHA corrects this dysbiosis, primarily by stimulating the growth of probiotic bacteria such as Bifidobacterium species (Caesar et al. 2015). LA can similarly be converted by the gut microbiota species Roseburia spp. into vaccenic acid – a derivative considered to be beneficial for health (Caesar et al. 2015). This synthesis would support a detoxification mechanism for the bacteria present in the intestine.

Micronutrients Dietary intake of minerals and vitamins are required in smaller amounts compared to macronutrients (generally in the milligram range). However, its intake remains essential for the regulation of many biological processes, and several can also influence the function of gut microbiota. Both the presence or absence of certain micronutrients have been found to trigger distinct patterns of microbiota structural alterations in humans, mice, rats, and piglets. Noteworthy examples include iron, magnesium, zinc, selenium, nitrite or nitrate, vitamin A, vitamin D, and flavonoids. Several other compounds have been shown to display properties that counteract the deleterious effects of modern diets. These have emerged as potential candidates for the prophylaxis, diagnosis, and

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treatment of diet-induced obesity and metabolic syndrome. For example, cranberry extract increased the abundance of Akkermancia muciniphila in mice consuming a high fat high sugar diet (HFHSD) improving the metabolic syndrome symptoms (Sakkas et al. 2020). Vitamins are organic compounds that are water-soluble or fat-soluble and can be essential micronutrients. Although the intestinal microbiota can synthesize several B vitamins and vitamin K, other micronutrients provided by food can impact the composition of the intestinal microbiota. This is the case for vitamin D – which is a fat-soluble vitamin extracted from food and absorbed in the colon. It has been shown by several studies that vitamin D has a positive effect on the composition of the intestinal microbiota, mainly by increasing beneficial bacteria such as Lachnobacterium, which has been associated with the modulation of immune responses (Sakkas et al. 2020). In vitamin D-deficient pre-diabetic subjects, supplementation with exogen vitamin D, leads to increased serum 25(OH) D. These levels were negatively correlated with the abundance of harmful bacteria such as Proteobacteria, and positively associated with Bacteroidetes abundance (Singh et al. 2020). A randomized clinical trial in vitamin D-deficient overweight or obese adults demonstrated that vitamin D supplementation was associated with higher levels of Coprococcus genera and lower abundance of the Firmicutes phylum. Additionally, vitamin D intake in humans was associated with decreased levels of circulatory pro-inflammatory LPS, decreased abundance of Coprococcus, and increased abundance of Prevotella (Singh et al. 2020). On the other hand, while sufficient levels of iron and zinc contribute to immune function, an excess of these minerals seems to favor the colonization of pathogenic bacteria in the intestine, such as Clostridium difficile (Barra et al. 2021). Iron and zinc are found in high abundance in red meat and oysters. Zinc is involved in many cellular processes including cell division, protein and DNA synthesis, wound healing, and immune function. In the intestine, zinc import into Paneth cells is critical to produce functional antimicrobial granules with bactericidal activity against microorganism’s invasion. Of note, low levels of zinc are associated with gut inflammatory conditions, which strengthen the pivotal role of zinc in the gut. Furthermore, zinc deficiency promotes the colonization and bacterial persistence of the pathogen Shigella flexneri in mice, while supplementation allows resolution of the pathogen-induced inflammation (Barra et al. 2021). However, whether zinc and/or zinc-induced antimicrobial peptide production by Paneth cells provide protection against these pathogens in supplemented states remains unclear. While supplementation may limit some pathogens, excessive zinc intake can promote the growth of Clostridium difficile in gut mice, which is associated with a significant reduction of gut microbiota diversity, and a bloom of Enterococcus and Clostridium bacteria (Barra et al. 2021). Few studies have explored the effects of dietary zinc on gut microbiota modulation in human. Studies in mice have shown some discrepancies since dietary zinc restriction can either increase microbial diversity or have no effect on gut microbiota composition (Barra et al. 2021). In broiler chickens, zinc-deficient diets lower cecal microbial species richness along with a lower relative abundance of Firmicutes, and

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increased Proteobacteria and Enterobacteriaceae (Barra et al. 2021). Zinc deficiency altered gut bacterial pathways involved in mineral (i.e., zinc) absorption and carbohydrate breakdown thus promoting the decrease of SCFA production. Changes in gut microbiota induced by zinc deficiency increased intestinal permeability in pregnant mice (Barra et al. 2021). In Crohn’s disease patients, zinc supplementation improved gut barrier function (Barra et al. 2021). Overall, these studies underline that dietary zinc deficiency may disrupt intestinal barrier, modify gut microbiome composition and function (carbohydrates digestion), and ultimately alter SCFAs production. Altogether, these results suggest that zinc intake may have a pivotal role in host metabolism.

Pattern Diet and Gut Microbiota Although numerous studies have evaluated the effects of different macro and micronutrients on the composition and behavior of the gut microbiota, the impact of diet on the modulation of the intestinal microbiota must indeed be taken into consideration also. Especially relating to the complex interactions of the complete repertoire of dietary compounds. Therefore, dietary patterns have been broadly investigated with the postulation that the striking surge in metabolic diseases and other sequelae in modernized societies can be attributed to changing dietary trends throughout the past century. Dissimilarities in the microbiomes of populations consuming disparate diets can be robustly inferred from studies in modern-urban populations versus agrarian cohorts and in herbivores versus omnivores. The gut microbiome of hunter-gatherers, as well as of rural and agricultural populations around the world, shows increased bacterial richness when compared with those of modernized societies. These findings suggest that agricultural populations require a greater functional repertoire to maximize their energy intake from dietary fibers than those from modernized societies. These observations may be resulting from increased consumption of mostly processed food by modern populaces, although such causality needs to be formally proven.

Mediterranean Diet The first discussions relating to the ‘Mediterranean diet’ started with the publication in 1980 of the Keys et al. study involving seven countries: the United States, Finland, the Netherlands, Italy, Yugoslavia, Japan, and Greece (Russo et al. 2021). This epidemiological study aimed to measure the relationship between diet and human health. The study clearly demonstrated that subjects originating from the Greek isle of Crete, were by far the least affected by cardiovascular disease, despite a moderate to high consumption of fat (but this was mainly from olive oil). A key finding of the study was that cardiovascular disease is preventable and is strongly influenced by the fat composition of the diet. In the intervening years, the “Lyon Diet Heart Study,” published in the 1990s, involved 605 people who recently had suffered a heart attack (Kris-Etherton et al. 2001). Two groups were defined: the first followed a classic diet recommended for such cardiac pathologies; the second

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followed a Mediterranean diet enriched with rapeseed oil (rich in omega 3). This study demonstrated a dramatic drop( 70%) in all-cause mortality in the group following the Mediterranean diet, mainly due to a 73% decrease in cardiovascular mortality in this population who had previously suffered a heart attack. Stemming from this significant outcome measure of the dietary intervention being met early, the study was terminated prematurely. The traditional Mediterranean diet has its origins in the countries of the Mediterranean basin, where the sunny and mild climate favors the production of many fruits and vegetables all year round. It is therefore characterized by the abundant consumption of fruits and vegetables, using virgin olive oil as the main source of fat, the consumption of legumes, whole grains, nuts, seeds, and aromatic herbs. There is a moderate consumption of dairy products, mainly in the form of fermented products (yogurt and cheese), eggs, fish, and small amounts of red wine with meals. Meat consumption is very low (once a week on average), favoring lean meats (rabbit, chicken, and turkey). Numerous studies have shown the beneficial role of the Mediterranean diet on the richness and composition of the gut microbiota (Gotsis et al. 2015). The traditional Mediterranean diet is composed of a high fatty acid content rich in MUFA and PUFA, and a very low amount of SFA, which as previously mentioned are harmful in the long term toward the microbiota and host metabolism. Another great characteristic of the Mediterranean diet is that this dietary pattern is rich in fiber and polyphenols found in fruits, vegetables, and wine. Indeed, it has been shown that people consuming a Mediterranean diet style display decreased Escherichia coli and proliferation of Bifidobacteria and Candida albicans, associated with higher circulating levels of acetate (SCFA) compared to people who ate a non-Mediterranean diet (Moszak et al. 2020). In addition, the high consumption of plant-food associated with Mediterranean diet has been associated with a beneficial metabolic profile of the gut microbiota characterized by an expansion of certain fiber-degrading Firmicutes profiles, and higher levels of SCFAs. The increased intestinal SCFAs level in subjects on the Mediterranean diet is determined by high consumption of vegetables and fruits, which are rich sources of complex fiber. An interventional study conducted in 2017, “CARDIOPREV” showed that patients suffering from metabolic syndrome placed on the Mediterranean diet had a partial restoration of their microbial dysbiosis, characterized by a restoration of butryrate-producing bacteria such as Faecalibacterium prausnitzii and Clostridium cluster XIVa (Haro et al. 2017). Similarly, strong evidence has accrued that the Mediterranean diet beneficially modulates gut microbiota by increasing the abundance of Bacteroidetes, Clostridium cluster XIVa, Faecalibacterium prausnitzii, Lactobacillus, and Bifidobacteria and decreasing the abundance of Firmicutes. This diet pattern positively affects the diversity and activity of various gut bacteria and increases the levels of SCFAs, and hence improves host metabolism (Gotsis et al. 2015). The predominant mechanisms involved in establishing a beneficial gut microbiota in response to the Mediterranean diet involves the effect of prebiotics on dietary fibers, the positive effect of polyphenols and n-3 PUFA, and a low intake of processed food.

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Vegetarian and Vegan Diet Over the past decade, an increasing number of people have embraced vegetarianism or veganism. Considering the explosion of this dietary trend, numerous studies have focused on vegetarian and vegan diets, particularly focusing on their effects on the health of the host and their impact on the composition of the intestinal microbiota. Interestingly, these studies show that the microbial signature between vegan and vegetarian people is very different from the one of omnivores people (Sakkas et al. 2020). These differences are directly explained by numerous factors, including different dietary components, the variation of the surrounding pH, the transit time, and the quantity of fermentable products by the microbiota provided by the diet (MACs). Indeed, the sources of nutrients for the gut microbiota are very different in these diets, conferring heterogeneous effects on both abundance and diversity of the gut microbiota. The vegan and vegetarian microbiota may not show any major differences (Sakkas et al. 2020).The vegan and vegetarian diet are plant-based diet, meaning these are naturally rich in fiber, particularly in MACs, helping to induce a richer and more stable microbiome repertoire. One approach to characterize microbial diversity in humans is by the Prevotella to Bacteroides ratio (P/B). This ratio is particularly high in persons whose diet is mainly plant-based, in the circumstances where the gut microbiota is dominated by Prevotella (responsible for cellulose and xylan fermentation) producing 2–3 times more propionate than the Bacteroides-dominated microbiota (Moszak et al. 2020). Among the critical factors involved in the modulation of the gut microbiota is the surrounding pH, which plays a critical role in the proliferation or inhibition of certain bacterial classes. A comparative study of the intestinal microbiota of children living in Burkina Faso with a traditional plant-based diet (rich in cereals, legumes, and vegetables) and the intestinal microbiota of children in Europe consuming a Western Diet style showed vast differences (De Filippo et al. 2010). Interestingly, they witnessed a high abundance of certain pathogenic bacteria, such as Shigella and E. Coli in European children, accompanied by an absence of Prevotella. A lower abundance of butyrate-producing bacteria in persons consuming a low-fiber and high meat diet can occur through negative changes to colonic pH, promoting the growth of pathogenic bacteria. Longterm consumption of an animal-based diet positively correlates with the abundance of bile-tolerant microorganisms (Bacteroides, Alistipes, Bilophilia) and negatively correlated with Firmicutes, which can degrade dietary plant polysaccharides (Roseburia, Eubacterium rectale, and Ruminococcus) (Devkota et al. 2012). An abundance of Bacteroides in response to a Western diet/high in animal products and low in fiber from fruits and vegetables has been previously described (Cai et al. 2022). Ruminococcus are an anaerobic bacterial genus playing an important role in the degradation of complex CHO (cellulose, RS) and are associated with higher circulating levels of butyrate, having a preventive effect on endotoxemia, arterial stiffness, and obesity (Tomova et al. 2019). Indeed, several dietary intervention studies on obese populations, suffering from metabolic disorders or at high cardiovascular risk have been conducted. The introduction of fruits and vegetables in subject’s dietary habits leads to an improvement in microbial diversity (Martínez-

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González et al. 2019). Evidence also suggests that gut microbiota established by a plant-based diet promotes lower production of secondary bile acids (DCA) and trimethylamine (TMA) and, therefore, reduces the risk of intestinal barrier dysfunction, inflammation, and liver cancer (Tomova et al. 2019). Polyphenols are another form of micronutrient that naturally occur in plants; accordingly these are abundant in vegan diets. Consumption of polyphenols increases both Bifidobacterium spp. and Lactobacillus spp., providing cardiovascular protection as well as antibacterial and anti-inflammatory effects. Most of these compounds are structurally complex and include diverse molecules such as flavonoids, phenolic acids, stilbenes, and lignans. Polyphenols transit into the colon where they are metabolized by resident bacteria. Fruits like grapes, blueberries, mango, and citrus, vegetables, herbs, seeds, cereals, and beverages including coffee, tea, and red wine are rich in polyphenols. The effect of the tea or soy isoflavones consumption beneficially shape intestinal microbiota. For instance, wild blueberries have been shown to increase Bifidobacterium and Lactobacillus species (Tomova et al. 2019). In addition, Tomova et al. recently reviewed the positive effects of dietary polyphenols intake on decreasing pathogenic Clostridium perfringens and Clostridium histolyticum levels. They also underline that proanthocyanidin-rich extract from grape seeds increased the number of Bifidobacterium spp. significantly, while decreasing Enterobacteriaceae family (Tomova et al. 2019). The protein-energy status in vegans is lower compared to omnivores. Studies examining the impact of these specific dietary proteins on the gut bacteria composition attest that both Bifidobacterium and Lactobacillus species as well as the intestinal SCFA productions were greatly upregulated after pea protein consumption. While both pathogenic Clostridium perfringens and Bacteroides fragilis were decreased (Sakkas et al. 2020). Furthermore, a positive effect of walnuts consumption on gut microbiota composition by increasing Ruminococcus spp. and Bifidobacterium spp. and decreasing Clostridium spp. has been observed. Vegetarian and vegan diets are effective in promoting the optimal diversity and richness of beneficial bacteria (e.g., by increasing Prevotella taxa) and reduction in harmful metabolites (DCA, TMA), hence supporting both human gut microbiota and overall health.

Western Diet The typical Western Diet is a diet high in saturated fat, sugar, salt, red meat, and processed food, while remaining low in fiber, vegetables, and fruit. Numerous studies have shown the negative impact of adherence to the Western diet on the function and composition of the intestinal microbiota, with a direct impact on the health of the host – in particular cardiovascular disorders (Roth et al. 2020). The lack of vegetables and fruits consumption in favor of a diet high in animal-derived protein, directly impacts microbial richness and diversity. A decrease in the total number of bacteria and a drastic decrease in the number of commensal bacteria such as Bifidobacterium and Eubacterium was accompanied by an increase in Firmicutes

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and Proteobacteria (Leeming et al. 2019). Processed red meat associated with Western diet is highly associated with colorectal-cancer, which is explained by the production of carcinogenic heterocyclic amines and elevated endogenous production of carcinogenic N-nitroso compounds during fermentation. Lactic acid-producing bacteria such as Lactobacillus can complex heterocyclics, thus allowing protection to the host by avoiding DNA damage. Observational studies have highlighted the possible definitive impact on the intestinal microbiota by the long-term consumption of these foods (Leeming et al. 2019). This is mainly explained by the low content of MACs such as fiber, leading directly to the extinction of certain bacterial partners. The lack of nutrients favorable to microbial fermentation leads to a decrease in circulating SCFAs resulting in a reduced growth rate of the inner mucus layer and gut barrier hyperpermeability, increasing the susceptibility to infections. The negative effects of a Western Diet on the intestinal microbiome can not only be attributed to poor intake of dietary fiber, high intake of saturated fat and animal-derived proteins, but also to a high content of ultra-processed food and harmful food additives (e.g., emulsifiers, noncaloric artificial sweeteners NAS). Indeed, an important characteristic of the Western Diet associated with highly industrialized modern societies is the large amount of processed product, rich in food additives and emulsifiers. A growing number of experimental studies have shown the deleterious effects of this hyper-processed food on the intestinal microbiota. Emulsifiers are very common additives in Western Diet used to maintain oil-water emulsions. Carboxymethylcellulose and polysorbate-80, which are two commonly used emulsifiers, cause dysbiosis in mice associated with low-grade inflammation and facilitate the development of metabolic syndrome(Chassaing et al. 2015). In humans, these emulsifiers cause an increase in flagellin levels, which form the filament in a bacterial flagellum, and is an inflammatory molecule often associated with a dysbiotic microbiota (Chassaing et al. 2015). Another commonly consumed group of food additives are noncaloric artificial sweeteners (NAS, saccharin, sucralose, aspartame, cyclamate, neotame, and acesulfame-potassium), which are often promoted as a low-calorie substitute for sugary beverages. Studies regarding NAS are particularly controversial. Indeed, while a portion of the studies shows the beneficial role of the use of NAS on body weight loss, others on the contrary show the impact of these NAS on metabolic disorders, stemming from alterations to the microbiota function, leading to glucose intolerance in some people (Moszak et al. 2020). However, the mechanisms involved in these metabolic and microbial disturbances are unclear. To conclude, there is strong evidence that Western diet leads to gut dysbiosis, enhances chronic inflammatory process, and consequently promotes the development of metabolic disorders and cardiovascular diseases.

Dietary Restrictions Caloric Restriction Pattern Many other factors related to our eating habits can influence the composition and function of the intestinal microbiota. Among these factors, food quantity can

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significantly modify the microbiota. Calorie restrictions, for example, affect all bacterial phyla. Indeed, a calorie restriction of 25–40% based on reduced carbohydrate intake in the absence of malnutrition in humans results in an increase in Lactobacillaceae such as Lactobacillus spp, Erysipelotrichaceae, and Ruminococcaceae (Duncan et al. 2007). A study conducted on a cohort of obese women shows that after 4 weeks of caloric restriction (800 Kcal per day) an increase in Ruminococcus spp., Anaerostipes hadrus, and Bifidobacterium spp (Duncan et al. 2007) was found. Moreover, a decrease in Proteobacteria within the microbiota was witnessed. Extending this restriction to 10 weeks resulted in an increase in Bacteroides fragilis and a decrease in butyrateproducing bacteria, such as Blautia coccoides. Other interventional studies show that caloric restriction over a longer term (1 year) are further evidence supporting this increase in Bacteroidetes. This was accompanied by a decrease in pro-inflammatory bacteria such as Actinobacteria observations that were not initially apparent within a shorter time frame (Santacruz et al. 2009). Fasting Fasting is a dieting pattern consisting of abstinence from all solid food for a defined period. It has been practiced for millennia, especially as a religious observance, and likely involuntarily for much longer (millions of years) periods during human evolution, in this way becoming a defining factor in shaping human metabolic flexibility. Numerous scientific reports studying the effect of short-term fasting show several beneficial effects on the host metabolism associated with a remodeling of the intestinal microbiota. Indeed, in different murine models of obesity, diabetes, or metabolic syndrome, the fasting of these animals (16 h per day over 1 month) in addition to partially lowering body weight, helped to correct the metabolic disorder (Rinninella et al. 2020). These effects were accompanied by an increased beneficial bacterial pattern, such as Bifidobacteria and Blautia. For instance, every-other-day fasting (EODF), which is a type of intermittent fasting, led to a shift in the gut microbiota composition, increasing the levels of Firmicutes, while decreasing most other phyla (Rinninella et al. 2020). Consequently, these changes led to increased production of SCFAs as compared to control animals fed ad libitum. Conversely, Bilophila abundance decreased in all fasting groups plus Bacteroides, Enterococcus spp., and Lactococcus spp. Studies on the effect of fasting on the gut microbiota in humans is more limited. A pilot study conducted on overweight people experiencing intermittent fasting for 1-week followed by a 6-week probiotic intervention showed that after the week of fasting there were no significant changes in total bacteria of the species Bacteroidetes, Prevotella, Clostridium cluster XIVa, or Clostridium cluster IV (Remely et al. 2015). However, an increase of Faecalibacterium prausnitzii, A. muciniphila, and Bifidobacteria spp. abundance was shown. Recently, the wellknown Ramadan fasting, corresponding to 17 h of fasting per day during 29-day period, was investigated (Özkul et al. 2019). A significantly increased abundance of Akkermansia muciniphila and Bacteroides fragilis was found after the Islamic fasting period.

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Further studies on the effects of these nutrient restrictions are needed to elucidate the mechanism and the long-term repercussions on the metabolism and the health of the host. However, it is tempting to speculate that these calorie-restricted diets could promote health and lifespan. One of the most significant effects of calorie restriction intervention in overweight or obese individuals concerns fecal Akkermansia muciniphila abundance, which is well known to be greatly associated with improved metabolic outcomes. Nowadays, limiting the quantity of nutrients in the diet, and more specifically energy intake, is becoming an increasingly popular weight-loss strategy. Nonetheless, considering microbial features, such as gene richness or a “post-obesity microbiome signature,” might complement the current nutritional practice to better contend with the worldwide obesity epidemic.

Cardiovascular Disease and Gut Dysbiosis Cardiovascular diseases (CVD), including hypertension, coronary disease, heart failure, and atrial fibrillation have emerged as the leading causes of death worldwide, resulting in almost twice as many deaths as cancer and representing a third of global deaths. These diseases disproportionally affect low- and middle-income countries. As a leading cause of mortality, CVD have emerged as a public health care priority for the World Health Organization (WHO). Current research identifies the gut microbiota as an important mediator in the development and outcome of CVD, and this area could potentially provide novel therapeutic targets. Indeed, decreases in microbiota diversity can lead to physiological changes in released metabolites causing a low-grade inflammatory state, which is increasingly viewed as a basic pathological process within CVD. Targeting the gut microbiota and its metabolites therefore provides new and promising strategies for the treatment of CVD.

Hypertension Hypertension is a major risk factor for CVD, contributing to pathological situations such as strokes, myocardial infarction, coronary heart diseases, kidney failure, and premature death, among others. It affects more than a billion people across the globe and is responsible for about 40% of CVD-related deaths (Chockalingam 2007). Hypertension is a complex multifactorial process influenced by host genetic and environmental risk factors. While 901 loci have been identified in the latest genomewide association study, altogether this can only explain 20 ng/ml has been assessed to represent the condition to define low-grade emdotoxiemia. This helps distinguish this condition from sepsis, which involves a combination of systemic inflammation associated to widespread organ damage and infection, along with symptoms and clinical signs and at least a twofold or threefold increase in LPS serum levels (Carnevale et al. 2020). A condition of low-grade endotoxemia has been associated with the occurrence of small amounts of endotoxins after food intake. In particular, LPS, which is formed by the combination of lipids and carbohydrates entering the outer membrane of gram-negative bacteria populating the intestine, is involved in the formation of chilomicrons immediately after food ingestion via mobilization of B48 apolipoprotein generated in the intestinal cells. The process of chylomicron formation represents the driving mechanism by which LPS absorption occurs in small amounts, getting the systemic circulation via the lymphatic system. Then, LPS is transported via a specific binding protein. However, LPS is rapidly cleared during the degradation of chylomicrons and the formation of other lipoproteins including LDL, VLDL, and HDL. HDL is the main lipoprotein implicated in LPS transport and clearance by hepatic bile, and the HDL–LPS interaction directly protects against the toxic effect of LPS, as shown in vitro and in vivo. In normal settings, LPS is transported to the liver, where it undergoes degradation by specific liver enzymes (such as acyloxyacyl hydroxylase and alkaline phosphatase) or excretion into the bile via scavenger receptors. The inability of liver cells to completely metabolize or excrete LPS into the bile might have consequences not only in the establishment of low-grade endotoxemia but also in inducing liver damage, such as nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) (Levels et al. 2005).

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Several protective barriers in the human intestine prevent microorganisms from entering the bloodstream. This multilayer barrier forms the largest interface between the external environment and the host. The gut microbiota is separated from the intestinal wall by a layer of mucus and epithelial cells. The continuous barrier formed by intestinal epithelial cells controls the movement of water, ions, and organic molecules across the epithelium. The intestinal epithelial barrier is composed of a physical barrier formed by the apical plasma membrane of enterocytes, held together by tight junction proteins, adherens junction proteins, gap junction proteins, and desmosomes. Tight junction proteins regulate gut permeability and are crucial for maintaining cell-to-cell adhesion and gut barrier health. Transmembrane proteins like claudins, occludin, tricellulin, and junctional adhesion molecules are part of the tight junction complex, along with intracellular scaffold proteins ZO1, ZO2, and ZO3. The gut-vascular barrier, positioned below the epithelial barrier, helps regulate the movement of microorganisms into the portal vein (Guerville and Boudry 2016). The gut barrier’s permeability is controlled by both intrinsic and extrinsic mechanisms in the intestinal epithelial cells. It can be changed by external factors like excessive alcohol consumption and nonsteroidal anti-inflammatory drugs, as well as internal factors such as inflammation associated with systemic disease. The gut microbiota helps protect the digestive mucosa by maintaining the integrity of tight junction proteins. Various metabolites are produced by the gut microbiota from food, which play a crucial role in gut barrier function and immune responses. The gut barrier is protected by microbiota metabolites such as Short Chain Fatty Acids (SCFA), indole and its derivatives, bile acid metabolites, polyamines, and polyphenols. Moreover, an imbalanced gut microbiome is necessary for the alteration of gut barrier function and the movement of microorganisms or their by-products into the body’s circulation system. In animal models of diabetes and obesity, a broad range of antibiotics was found to decrease LPS levels in the bloodstream, simultaneously reducing gut permeability by increasing the expression of ZO1 and occluding tight junction proteins (Cani et al. 2008). Diet is an important factor affecting gut permeability. In rats on a high-fat diet, an imbalance in gut microbiota occurs, with a higher ratio of gram-negative to grampositive bacteria, which may lead to changes in gut barrier defense. Cani and colleagues first described the link between a high-fat diet, metabolic disease, and blood LPS levels. In mouse models, they showed that a 4-week high-fat diet led to a chronic increase in plasma LPS concentration, reaching levels two to five times higher than those seen with infections. The high-fat diet also impacted gut barrier integrity through a decrease in Bifidobacteria20, known for their protective properties, via upregulation of tight junction proteins such as ZO1 and occluding. Cani et al. demonstrated that chronic, experimental metabolic endotoxemia caused obesity, diabetes, and liver insulin resistance, resembling the effects of a high-fat diet alone (Cani et al. 2008). Gut barrier dysfunction can be promoted by aging, leading to low-grade systemic inflammation commonly seen in older individuals. The gut microbiota diversity decreases, and the balance between opportunistic and commensal bacteria is disrupted (with a rise in opportunistic bacteria like Enterobacteriaceae, Clostridium

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perfringens). Older individuals have been reported to have decreased numbers of commensal bacteria, such as Bacteroides, Bifidobacteria, and Lactobacilli, as well as Clostridium difficile. Age-related changes in the gut microbiome affect gut barrier function and allow harmful microbes to enter the bloodstream, causing inflammation and weakening the immune system (Violi et al. 2023). These data suggest that changes in gut microbiota caused by diet are the initial cause of a series of events involving increased gut permeability due to dysbiosis, where LPS plays a critical role in the breakdown of intestinal adhesion proteins. The binding of LPS to TLR4 in intestinal cells triggers inflammation and reduces tight junction protein levels, facilitating LPS translocation into the bloodstream. Multiple studies using TLR4 inhibitors or Tlr4-knockout animals have shown the important role of the LPS-TLR4 pathway in regulating gut barrier integrity. Nonetheless, the intestinal barrier contains protective factors against LPS-induced damage. One example is how intestinal alkaline phosphatase (IAP) removes a phosphate group from the LPS lipid A, causing LPS degradation to monophosphoryl LPS, which can still bind to TLR4 but has an antagonistic effect. In animals on a high-fat diet, increased IAP expression improved intestinal function, maintaining mucosal integrity and reducing translocation of LPS into the bloodstream and lipid accumulation in the liver, resulting in reduced atherosclerotic plaque burden (Mollace et al. 2023). The biosynthesis of HDL subspecies by intestinal cells can counteract LPS proinflammatory activity and protect against liver damage (Fig. 2).

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Fig. 2 The link between dysbiosis and microbiota-related cardiovascular disorders. Here are displayed pathways that have been found to affect vascular regulation via the release of metabolites, which lead to vascular impairment and hypertension. In particular, SCAFAs are associated with normal blood pressure, while TMAO and LPS produce direct damage in endothelial cells and oxidative stress associated with an early inflammatory response. FMO3 flavin-containing monooxygenase 3, iNOS inducible nitric oxide synthase, LPS lipopolysaccharides, MD2 myeloid differentiation 2, NF-kB nuclear factor kB, NO nitric oxide, SCFAs short-chain fatty acid, TLR4 toll-like receptor 4, and TMAO trimethylamine N-oxide

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Dysbiosis and Cardiometabolic Risk Emerging evidence indicates that gut dysbiosis is a risk factor for cardiovascular disease in mice with type 1 diabetes mellitus, obesity, or hypertension. Most studies on assessing gut permeability in at-risk or coronary heart disease patients have relied on measuring zonulin levels in the serum, an indirect marker of gut permeability. Zonulin is a 47-kDa protein released by epithelial cells of the small intestine after stimulation by gliadin or gut dysbiosis. The zonulin signaling pathway in interstitial epithelial cells causes protein kinase C phosphorylation, leading to the disassembly of tight junction proteins like ZO1 (Fasano 2012). People with type 2 diabetes, obesity, or cardiovascular disease have higher levels of zonulin and LPS in their blood compared to healthy individuals. Patients with nonseptic pneumonia or myocardial infarction have shown increased gut permeability and low-grade LPS endotoxemia due to gut dysbiosis. It is worth mentioning that since analyzing serum zonulin levels indirectly measures gut permeability, other tests should be utilized to evaluate changes in gut permeability in humans. Considering this, examining d-lactate levels could be a viable option since elevated levels indicate intestinal permeability linked to bacterial infection or induced gut injury. Yet, analyzing gut barrier dysfunction through urinary excretion of dextrose and mannitol after oral ingestion is considered a more effective method, although it is complex and requires expertise. Thus, vascular impairment and hypertension appear to be affected by gut microbiota alterations; even further investigation is needed to understand the connection between gut permeability and low-grade endotoxemia in patients at risk of or with cardiovascular disease.

LPS and Vascular Impairment A special mechanism that combines dysbiosis, dyslipidemia, ECs cell impairment, and oxidative stress occurs when LDLs accumulate in the vascular wall and are oxidized to produce smooth muscle cell proliferation and ECs apoptotic cell death. Macrophages, dendritic cells, and lymphocytes are recruited due to LDL accumulation. If tissue repair is absent or inflammation is not resolved, atherosclerotic lesions progress, resulting in a necrotic core and inflammatory cell presence. The continuous buildup of LDL in the arterial wall, mostly associated to low-grade endotoxiemia and an inflammatory plaque phenotype, results in plaque instability and eventual thrombosis. Interventional studies in the past decade have shown that the use of antiinflammatory therapies only partially can attenuate the risk of cardiovascular disease, supporting the central role of inflammation in the atherosclerotic process. The mechanism linking low-grade endotoxiemia, oxidation of LDL, impaired autophagy, and dysregulation of NO-mediated vasodilatation has been recently clarified by our and other groups. In particular, studies carried out in vitro confirmed that oxyLDL leads to apoptotic cell death of Bovine Aortic ECs (BAEC) via the overproduction of free radical species. In particular, it has been found that the incubation of oxyLDL

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with BAEC was accompanied by an early impairment of constitutive eNOS functionality and overproduction of inducible, proinflammatory iNOS and caspase 3 being both effects accompanied by EC apoptotic cell death. This involves an overexpression of the scavenger receptor LOX-1 in endothelial cells which seems to play a pivotal role in attenuating protective autophagy which counteracts apoptotic ECs death. Indeed, silencing LOX-1 receptor via ShRNA restores autophagy and protects against oxyLDL-induced apoptotic cell death, thus suggesting the essential role of LOX-1 in mediating oxyLDL-dependent impairment of protective autophagy in ECs. Similar results have recently been found in animal models of atherosclerotic disorders in carotid arteries. Oxidation of LDL has been strongly implicated in the pathogenesis of atherosclerosis; indeed, it has been demonstrated that oxidized LDL uptake through LOX-1 contributes to inducing endothelial dysfunction observed in the early stages of this pathology. In particular, an increased production of ROS has been observed, such as superoxide anions (O2 ), that is also directly produced after LOX-1-induced NADPH oxidase activation (Li and Mehta 2009). Furthermore, recent evidence also shows that ROS overproduction blocks PI-3-kinase/Akt pathway causing an early impairment of constitutive endothelial NOS (eNOS) activity through the inhibition of its phosphorylation/activation, an effect due to LOX-1 activation. This event was clearly associated with oxyLDL and elicited a time-dependent decrease in serine 1179 phosphorylation of eNOS. This finding suggests that its inactivation affects physiological NO-operated suppression of iNOS gene expression normally caused by inactivated transcriptional factor Nuclear Factor kB (NF-kB) (Janda et al. 2011). ONOO overproduction, derived by high O2 levels, on the one hand, and by iNOSinduced NO overproduction, on the other hand, has been correlated with endothelial cell death via apoptosis. We also observed that increased iNOS expression was accompanied by an enhancement of caspase-3 levels and that oxyLDL-induced apoptosis was associated with the generation of free radical species. In fact, cell mortality was counteracted by pretreatment with N-acetylcysteine (NAC), a thiol-containing radical scavenger and glutathione precursor. Moreover, recovering physiological NO levels via pretreatment of BAECs with the NO donor S-nitroso-Nacetylpenicillamine (SNAP) was shown to produce significant protection against oxyLDL-induced endothelial apoptosis. Moreover, recent evidence proposes a direct correlation between LOX-1 activation and free radical-induced apoptosis of endothelial cells. On the other hand, the modulation of oxidative stress via both endogenous as well as exogenous antioxidants prevents apoptotic cell death, an effect that occurs via concomitant modulation of LOX-1. This has been proven by using pterostilbene, a naturally occurring analog of the antioxidant resveratrol, which has been shown to inhibit oxyLDL-induced apoptosis of endothelial cells from the umbilical vein by downregulating LOX-1 expression and thereby suppress intracellular oxidative stress. Novel findings hypothesize a more complex mechanism responsible for EC death via apoptosis, supporting previous data reported here. Indeed, Lu et al. showed that LOX-1 activation by L5, an electronegative component of LDL abundant in dyslipidemic but not in normolipidemic human plasma, selectively inhibited

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Bcl-xL expression, suppressed expression of Bcl-2, attenuated Akt and endothelial nitric oxide synthase phosphorylation, and increased synthesis of proapoptotic factors Bax and Bad. In addition, L5 has been shown to induce activation of caspase-3 and mitochondrial release of cytochrome c (Maiuri et al. 2007). Bcl-2 family proteins have been divided into subgroups based on the presence of their Bcl-2 homology (BH) domain(s): antiapoptotic proteins as Bcl-2 and Bcl-xL and proapoptotic proteins as Bad and Bax. Bcl-2 family proteins also regulate autophagy, a type of cell death, occurring under both basal conditions and conditions of stress (i.e., starvation), which represents a cellular defensive mechanism able to eliminate ROS-induced damaged proteins. Beclin 1 is an important effector of autophagy belonging to Bcl-2 family proteins. It binds to a hydrophobic groove in Bcl-2/Bcl-xL similar to proapoptotic proteins of Bcl-2 family. Bcl-2/Bcl-xL Beclin-1 binding, in turn, impairs autophagy, and its inhibitory effect can be suppressed after Beclin-1 dissociation from this complex by proapoptotic proteins. Thus, apoptosis and autophagy may be coregulated in the same direction. However, when cell survival is impaired by a stimulus that downregulates Bcl-2/ Bcl-xL and enhances activation and expression of proapoptotic protein levels as in the case of Bad (i.e., LOX-1 activation by L5 or oxyLDL), it is conceivable that the switch between autophagy and apoptosis is regulated through an alternative mechanism (Mollace and Gliozzi 2015). It has been demonstrated that Beclin-1 is also a direct caspase substrate that, after cleavage, loses its autophagy-inducing capacity; indeed, after the direct interaction of its C-terminal fragment with mitochondria, it causes the release of proapoptotic factors enhancing the mechanisms underlying apoptotic cell death. Under starvation conditions, we observed a reduced expression of oxyLDL-induced full-length Beclin-1. This effect was correlated to an increased expression of iNOS and caspase-3 suggesting that after the oxidative stimulus, when physiologic NO levels were not early restored, free radical overproduction and consequent caspase activation induce Beclin-1 cleavage-suppressing autophagy in favor of apoptosis. Thus, from these studies it is clear that the balance between protective autophagy and apoptotic cell death is mediated by LOX-1 activation as clearly demonstrated by the modulation of another marker of autophagy, LC3 II (microtubule-associated protein 1A/1B-light chain 3 type II). Various types of stressors upregulate LC3 and promote the conjugation of its cytosolic form (LC3 I) to phosphatidylethanolamine to constitute the autophagosome-specific LC3 II. OxyLDL incubation downregulated LC3 II expression after starvation, and the involvement of LOX-1 in the negative regulation of protective autophagy was confirmed by the restoration of LC3 II levels after LOX-1 silencing, via LOX-1 shRNA (Thevaranjan et al. 2017). Similar mechanisms have been identified in dysbiosis-related progression of vascular impairment and high blood pressure. In fact, recent studies have indicated that gut dysbiosis plays a role in the mentioned mechanisms of atherosclerosis by promoting metabolic diseases that contribute to arterial inflammation. Patients with symptomatic atherosclerosis have a higher occurrence of pathogenic gut microbiota compared to those with asymptomatic atherosclerosis, and this is a predictor of

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coronary heart disease risk. Studies in germ-free mouse models have provided additional evidence linking gut microbiota and cardiovascular disease, showing a causal relationship between gut dysbiosis, hypertension, vascular dysfunction, systemic inflammation, and atherothrombosis (Carnevale et al. 2017). In this scenario, data emerging on the potential role of LPS in atherosclerosis could shed light on how gut microbiota relates to the disease. LPS promotes atherosclerosis due to its prooxidant properties, activating NOX2, a key enzyme for generating reactive oxygen species (ROS). In individuals without sepsis, LPS concentrations boost platelet responses through TLR4-mediated, NOX2-derived ROS formation, promoting LDL oxidation. Patients with impaired fasting glucose show a significant correlation between low-grade endotoxemia and elevated levels of oxLDL, indirectly indicating the prooxidant properties of LPS. Immunohistochemistry analysis of carotid atherosclerotic plaques from patients undergoing endarterectomy supports the putative role of LPS in atherosclerosis, as it showed the presence of LPS near plaque macrophages with high TLR4 levels. By contrast, LPS was not detected in atherosclerosis-free thyroid arteries from the same patients. Lipoproteins in humans predominantly bind circulating LPS (80–97%), with LDL having the highest concentration (35.7%) and VLDL the lowest (13.9%). On the other hand, VLDL particles transport a greater amount of LPS molecules. The transport of circulating LPS by proatherogenic lipoproteins like VLDL and LDL could be significant in atherosclerosis development. The binding of LPS to proatherogenic lipoproteins in the arterial wall could enhance LDL oxidation, thereby contributing to arterial inflammation (Lehr et al. 2001). Another factor that contributes to the proatherogenic process is the transfer of LPS from HDL to LDL through LPS-binding protein. Experiments in animal models have been conducted to substantiate the role of LPS as a trigger of atherosclerosis. According to one study, a single LPS infusion triggered a significant inflammatory response throughout the body but did not impact the atherosclerotic plaque. Nevertheless, other studies consistently demonstrate a link between LPS infusion and arterial damage. In animals, daily intravenous or intraperitoneal LPS infusions sped up atherosclerosis in the aorta (Rice et al. 2003; Ding et al. 2012). This was accompanied by higher production of proinflammatory cytokines like IL-8 and TNF, as well as autoantibodies against oxLDL. Additionally, there was an increased buildup of activated lymphocytes and deposition of IgG and IgM in the arterial intima (Grunfeld et al. 1995). In isolated human saphenous vein samples, even at concentrations as low as 0.1 ng/ml, LPS enhanced ROSsc production and chemotactic cytokine release, such as IL-8 and CCL2, through TLR4 interaction. The administration of statins helped mitigate these changes, thanks to their antioxidant properties in addition to reducing LDL levels. Studies in mice further support the relevance of the LPS-TLR4 axis in atherogenesis. In mice prone to atherosclerosis and high cholesterol, activating TLR4 with LPS increased neointima formation, while the absence of MyD88 or TLR4 decreased atherosclerotic burden. Ldlr / mice fed a carbohydrate-rich diet or a control diet had significantly smaller aortic atherosclerotic lesions compared to Tlr4 / Ldlr / mice. Based on these experiments, the analysis of atherosclerotic plaques in humans showed overexpression of

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TLR4 in various cell types, such as macrophages, vascular smooth muscle cells, and dendritic cells. It is uncertain if TLR4 overexpression is caused by LPS or other TLR4 ligands like oxLDL, cleaved fibrinogen, or heparin sulfate proteoglycan. Furthermore, inconclusive results have been obtained from studies on patients with the Asp299Gly variant in the TLR4 region on chromosome 9, which is linked to impaired TLR4 signaling. LPS has demonstrated the ability to destabilize atherosclerotic lesions, making them more prone to rupture or erosion. The injection of LPS in Apoe / mice fed a Western diet caused a transition from stable to unstable phenotypes in aortic arch atherosclerotic plaques. In accordance with these results, hypercholesterolemic mice showed a significant increase in the size of atherosclerotic plaques following a single LPS infusion, which imitates the acute release of DNA, histones, and neutrophil stimulation via pattern-recognition receptor activation or chemokines. ROS formation and calcium mobilization trigger the release of NETs by activating PAD4. In vitro, LPS induces NET formation in a dose-dependent manner, relying on TLR4 binding and increased ROS from NOX. This is evident from the inhibition of LPS-induced NETosis by TAK242 and diphenyleneiodonium (Carnevale et al. 2020). To investigate if the prothrombotic effect of LPS could be replicated in vivo, a mouse model of low-grade endotoxemia has been created. This involved injecting LPS (0.5 mg/kg) intraperitoneally at a concentration equivalent to the LPS level found in human thrombus (40 pg/ml). Thrombus growth was accelerated in animals treated with LPS, and it was linked to higher levels of platelet activation biomarkers in the system. The coadministration of a TLR4 inhibitor prevented both changes, supporting the hypothesis that TLR4 plays a crucial role in the prothrombotic effect of LPS. The finding supports the strong correlation between platelet TLR4 upregulation and low-grade endotoxemia observed in patients with coronary thrombosis. Other components of the TLR family are also implicated in the thrombotic process mediated by gut microbiota. TLR2 detects and is triggered by lipoprotein parts of either gram-positive or gram-negative bacteria, resulting in direct prothrombotic effects. Mice without germs or TLR2 deficiency had less thrombus growth after carotid artery injury compared to controls, and this was reversed by colonization with intestinal microbiota. The relationship between LPS and TLR2 in this context still needs to be determined. LPS levels have been measured in various populations, including the general population, patients at risk of cardiovascular events, and patients with metabolic diseases or acute infections. The relationship between circulating LPS levels and atherosclerotic burden and the clinical consequences of atherosclerosis were analyzed. In a study involving 516 individuals at risk for atherosclerotic disease, Wiedermann and colleagues were pioneers in exploring the impact of low-grade endotoxemia on atherosclerosis risk. Individuals with LPS levels above 50 pg/ml at baseline had three times the risk of carotid artery atherosclerosis after 5 years of follow-up compared to those with LPS levels below 50 pg/ml (Wiedermann et al. 1999). A study involving 2568 individuals without previous cardiovascular disease supported and expanded upon these findings. Following 10 years of observation, initial serum levels of LPS-binding protein were found to be significantly linked to the development of cardiovascular disease, even

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after accounting for conventional cardiovascular risk factors. A study analyzed circulating LPS levels in 7927 individuals from a population-based chronic disease risk factor survey, corroborating this finding. The study identified a meaningful connection between the LPS to HDL cholesterol ratio and cardiovascular events after 10 years of observation (Jäckel et al. 2017; Asada et al. 2019). Additionally, a noteworthy link between the genetic risk score of endotoxemia and venous thromboembolism was discovered. Cross-sectional and prospective studies have examined low-grade endotoxemia in patients with stable or unstable cardiovascular disease. Patients in the early phase of acute myocardial infarction have reported elevated levels of LPS and d-lactate, indicating gut permeability issues. A study in an animal model of coronary ischemia supported this hypothesis, showing increased gut permeability and decreased tight junction proteins. Additionally, a notable correlation was found between LPS levels during the acute phase of myocardial infarction and subsequent major adverse cardiovascular events after 3 years. Finally, patients with myocardial infarction have shown higher levels of circulating LPS in both peripheral and coronary circulation compared to those with stable cardiovascular disease and healthy individuals (Asada et al. 2019). Notably, the levels of circulating LPS were found to correlate with serum levels of zonulin and various markers of inflammation, such as IL-1β and TNF, supporting the idea that there is a connection between coronary ischemia and dysfunction of the intestinal barrier. Similar pathophysiological features have been found in additional clinical settings. In particular, studies suggest that patients with metabolic diseases like type 2 diabetes, obesity, NAFLD, and NASH experience low-grade endotoxemia. A significant association was discovered in patients with diabetes and obesity. The levels of LPS are linked to triglyceride and total cholesterol levels, fasting glycemia, insulinemia, HbA1c levels, and C-reactive protein levels. Patients with macroalbuminuria had a greater increase in circulating LPS levels, while those taking hypoglycemic drugs showed a reduction. In a study of 3781 type 1 diabetes patients, antibiotic purchases and high LPS activity were linked to cardiovascular events over 15 years. Higher serum LPS levels and LPS hepatocyte localization have been detected in patients with NAFLD or NASH and in mouse models of these conditions compared with the levels in controls (Amedei and Morbidelli 2019). The immunohistochemistry analysis found a potential causal relationship between liver steatosis, fibrosis, and TLR4 overexpression in macrophages and platelets. Platelet TLR4 levels were found to be significantly associated with circulating LPS levels, indicating that LPS may contribute to increased platelet activation. This discovery could offer insights into the involvement of platelets in liver inflammation and cardiovascular events in liver disease patients. Finally, the role of LPS as a trigger of arrhythmia or cardiovascular events has been explored in the clinical setting of atrial fibrillation. In fact, in patients with atrial fibrillation a gradual rise in LPS levels as they age was found, suggesting its role in the development of this arrhythmic disorder. The relationship between endotoxemia and atrial fibrillation was also studied in experimental models, revealing a connection between gut dysbiosis and atrial fibrillation in old rats. Increased TLR4 expression and NLRP3 inflammasome activation in the atria contributed to atrial fibrosis

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and heightened vulnerability to atrial fibrillation. Higher levels of blood LPS (>100 pg/ml) are associated with a greater risk of major adverse cardiovascular events in atrial fibrillation patients.

Gut Microbiota, Endothelial Dysfunction, and Cardiovascular Injury Endothelial function is also maintained through interaction with endogenous mediators. Consequently, the microbiota’s metabolic derivatives can influence organism physiology by regulating homeostasis or triggering diseases. Research has demonstrated that these metabolites can have various effects on endothelium function (Amedei and Morbidelli 2019). Intestinal bacteria can impact the circulatory system’s endothelium through two pathways: by stimulating the enteric nervous system and brain centers controlling the cardiovascular system. Therefore, maintaining a healthy microbial composition is suggested as a strategy to reduce endothelial and vascular dysfunction. SCFAs, as previously mentioned, have beneficial effects on the endothelium and blood vessel control. Conversely, some harmful metabolites will be investigated below. Trimethylamine (TMA), an organic compound with the formula N(CH3)3, is a tertiary amine produced in humans following the ingestion of foods from certain plants and meats containing choline, phosphatidylcholine, glycerol-phosphocholine, carnitine, betaine, lecithin, and L-carnitine. The gut microbiota uses these substrates to produce TMA, which is then absorbed into the bloodstream and converted to trimethylamine-N-Oxide (TMAO) in the liver. TMAO is a cardiac risk biomarker with proatherogenic properties and predictive capabilities for heart attack, stroke, or death. The mechanism of action includes endothelial dysfunction and platelet aggregation, causing a prothrombotic effect. Endothelial dysfunction caused by TMAO occurs due to NF-kB activation, which increases inflammatory signals and leukocyte adhesion (Ahmad et al. 2019). Additionally, studies have shown a correlation between elevated TMAO levels, endothelial dysfunction, and atherosclerosis (Ren et al. 2016). Furthermore, mice fed a choline-rich diet and exhibiting high TMAO levels experienced significant endothelial damage, dyslipidemia, and hyperglycemia (Chen et al. 2019). Moreover, an intriguing clinical study revealed a correlation between high TMAO levels and inflammation, as well as reduced endothelial progenitor cells in individuals with cardiovascular conditions (Kirichenko et al. 2020). TMAO not only downregulates IL-10, an anti-inflammatory cytokine, but also leads to ROS generation and reduction of nitric oxide, both of which can negatively impact vascular function. In a significant study, Matsumoto et al. emphasized an additional mechanism through which TMAO can modify vascular endothelial function. The impact of TMAO on endothelial-dependent relaxation has been investigated in the upper mesenteric and femoral arteries, revealing that TMAO can inhibit EDH and induce arterial relaxation. It is worth noting that this phenomenon is not present in any vascular bed but rather selectively affects the femoral arteries while leaving the upper mesenteric artery untouched (Matsumoto et al. 2020).

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In humans and animals, a connection was discovered between blood levels of TMAO, increased mortality risk, and renal insufficiency. This was observed through renal tubulointerstitial fibrosis, elevated levels of the early renal injury marker KIM-1, enhanced phosphorylation of Smad3, and renal dysfunction indicated by elevated cystatin C values after choline intake. Finally, TMAO impairs the selfhealing ability of damaged endothelial cells, leading to irreversible endothelial dysfunction. The gut microbiota forms metabolites known as uremic toxins through the metabolism of aromatic amino acids such as tyrosine, phenylalanine, and tryptophan. When amino acids are metabolized by the gut microbiota in the host liver, toxins such as indoxyl sulfate and p-cresyl sulfate are produced. These circulating nitrogen metabolites are considered to be a predictive biomarker of coronary atherosclerosis. Activation of NF-kB transcription factor signaling by uremic toxins disrupts endothelial balance and function, overriding ICAM-1 and MCP-1. Moreover, these toxins hinder the production of NO and enhance the buildup of ROS. The role of oxidative stress is indicated by the fact that antioxidants like N-acetylene and apocynin can reduce the proapoptotic effect of p-cresyl sulfate in the endothelium. In addition, the treatment with caffeic acid, a polyphenol present in white wine with antioxidant properties, was able to restore NO production and reduce ROS (Migliori et al. 2015). In fact, 3-hydroxyphenylacetic acid (3-HPAA) and other metabolites produced by the gut microbiota after the intake of polyphenol-rich foods, and in particular quercetin, have been shown to be potentially beneficial in hypertension. In spontaneous hypertensive rats, the administration of 3-HPAA resulted in a dosedependent reduction in mean systolic and diastolic pressure, with no impact on heart rate. This effect was observed through bolus or slow intravenous infusions but not through intravenous injection, indicating that it was solely due to peripheral relaxation. The vasodilatory response in porcine coronary arteries treated with 3-HPAA was dose-dependent and mediated by endothelium-derived NO (Dias et al. 2022). Furthermore, it is known that intestinal microorganisms release proteins and peptides that affect both bacteria and the body. Pathogenic bacteria can release peptides that destroy the blood-intestinal barrier, leading to bacterial spread, increased inflammation, and changes in intestinal cells. On the other hand, chronic kidney disease in cats is linked to reduced diversity of fecal bacteria and increased blood indoxyl sulfate levels. Similarly, in cats and dogs with chronic kidney disease and persistent azotemia, the presence of elevated indoxyl sulfate levels was associated with higher serum phosphorous levels, decreased renal function, and smaller kidneys compared to nonazotemic cats. Additionally, the rise in uremic toxins correlated with the elevation of fibroblast growth factor and with the increase in blood urea nitrogen, serum creatinine phosphate, and the decrease in hematocrit. While research on the direct link between gut microbiota and hypertension in animals like dogs and cats is in its early stages, intriguing studies have been done on other cardiovascular diseases. One common form of cardiovascular disease in dogs is canine-degenerative mitral valve disease (DMVD), which has molecular and pathophysiological similarities to humans. Recent studies have shown that high circulating concentrations of

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TMAO and its nutrient precursors, including choline and L-carnitine, phosphatidylcholine, betaine, and trimethyl-lysine, together with uremic toxins, such as guanidino compounds and urea, were recorded in dogs with DMVD and Congestive Heart Failure (CHF) compared to asymptomatic or healthy dogs. Interestingly, a targeted dietary intervention primarily utilizing medium-chain triglycerides, fish oil, and antioxidants resulted in reduced concentrations of certain short-chain and longchain acyl-carnitines (Li et al. 2019). In this context, the assessment of the role of the increased concentrations of TMAO and its precursors in the development and progression of endothelial dysfunction and cardiovascular impairment. A recent pilot study found clear evidence of quantifiable dysbiosis in dogs with CHF, showing increased levels of Proteobacteria, including Escherichia coli and an unidentified Enterobacteriaceae species (Li et al. 2020). This suggests a similar pattern to what has been observed in human patients. Consistent with prior research, the authors linked higher Escherichia coli levels to increased TMAO concentrations in dogs with CHF. Furthermore, they pointed out the opportunistic nature of these bacteria; indeed, while some strains of E. coli are benign, some others are compatible with pathobionts inducing inflammation and contributing to inappetence, malnutrition, and cachexia. Further evidence has been provided assessing, for the first time, the relationship between gut microbial dysbiosis and circulating gut-derived metabolites in dogs with preclinical myxomatous mitral valve disease (MMVD) or with CHF secondary to MMVD, compared to healthy dogs (Li et al. 2019). Specifically, the researchers found higher alpha and beta diversities in the gut of healthy dogs compared to those with MMVD. They discovered alterations in five genera and six species of bacteria and convincingly proved that the dysbiosis index increased as the severity of MMVD worsened. Additionally, the dysbiosis index showed a negative correlation with Clostridium hiranosis, an important bile acid converter in the gut, as secondary bile acids support the growth of beneficial bacteria and suppress the growth of harmful bacteria. A positive correlation was found between long-chain fatty acid intermediates, short-chain acyl-carnitines, and gut bacteria Lactobacillus and Megamonas in MMVD dogs. Thanks to these and other studies, the so-called “gut hypothesis” for the development of cardiovascular disorders has been confirmed. Gut dysbiosis develops during the preclinical stages of the disease before symptoms of cardiac remodeling appear, setting the stage for a future targeted diagnostic and therapeutic approach.

Gut Microbiota and Hypertension The correlation between microbiota and hypertension has been studied experimentally using numerous animal models, including SHR, Dahl-sensitive rats, angiotensin-II-induced hypertensive rats, and deoxycorticosterone acetate (DOCA)-salt mice (Marques et al. 2017). The results obtained showed that hypertension is accompanied by marked differences in the composition of the microbiota

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and their metabolites. In particular, there is less abundance of SCFA-producing bacteria, less abundance of Bacteroidetes, more abundance of lactate-producing bacteria, and more abundance of proteobacteria and cyanobacteria. Hypertension has been associated with lower gut microbial alpha diversity in several crosssectional studies; in fact, a greater abundance of gram-negative bacteria has been appreciated, such as Klebsiella, Parabacteroides, Desulfovibrio, and Prevotella. Gram-negative bacteria are a source of endotoxins, such as LPS, which are proinflammatory molecules. The potential mechanisms contributing to hypertension development linked to dysbiosis involve the following: (1) metabolism-dependent pathways, consisting in a decrease in SCFA and TMAO production; (2) metabolismindependent pathways: such as the ones deriving from high circulating levels of LPS as well as peptidoglycan translocation (Lau et al. 2017). Lower gut microbial alpha diversity in hypertension leads to intestinal dysbiosis with impaired integrity of the intestinal barrier resulting in the entry of LPS into the blood stream. In this way, LPS can advance intestinal dysregulation creating positive feedback damage. In particular, evidence exists that when bacterial LPS binds to TLR4, this complex activates NF-κB, as previously described, and promotes the subsequent inflammasome activation. The inflammasome serves to promote autoproteolysis and activation of caspase-1, which, in turn, cleaves pro-IL-1β and pro-IL-18 (Soares et al. 2010). The occurrence of a possible correlation between gut microbiota composition, inflammasome modifications, and BP regulation has also been confirmed by interventional studies. In fact, the use of prebiotics has determined the reduction of BP in hypertensive patients. Despite these favorable outcomes, it is still not clear how gut microbiota affects BP under normal and hypertensive conditions, and additional and specific studies should be organized. The gut microbiota metabolites appear to be a key mechanism linking BP changes and dysbiosis. In fact, it has been shown that microbiota is able to produce unique metabolites that are potentially important in blood pressure control. These bacteria are essential for the production of SCFAs from dietary fibers and undigested carbohydrates in the gut microbiota, which are vital for the body. SCFAs are fatty acids belonging to a family of fatty acids containing less than six carbon atoms, which include acetic acid, propionic acid, butyric acid, valeric acid, and caproic acid. Bacteria produce SCFAs by going through sequential steps, starting from glucose glycolysis and ending with acetic acid, propionic acid, and butyric acid. SCFAs have been found to be beneficial metabolites for regulating blood vessels and can impact the immune, epithelial, nervous, and circulatory systems, leading to reduced hypertension risk. A correlation exists between hypertension and a decline in intestinal microbial diversity, as well as SCFA-producing bacteria. Furthermore, high levels of SCFA-producing bacteria in pregnant women are associated with lower blood pressure. SCFAs have been demonstrated to activate intracellular signaling in different cell types by binding to a G protein mechanistically, as well as influencing blood pressure. A relationship was discovered between Gpr41 and SCFAs produced by the gut microbiota. The interaction between SCFAs and Gpr41 specifically activates renin secretion, leading to an elevation in blood pressure. Moreover, the presence of Grp41 affects vasodilation by influencing the endothelium. KO mice

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lacking this G protein display isolated systolic hypertension and elevated pulse wave velocity compared to wild-type mice, as observed through telemetry measurement. These effects suggest that the gut microbiota, through SCFAs, can influence BP regulation (Natarajan et al. 2016). Many sensory receptors, such as olfactory and taste receptors, also mediate the modulation of BP through SCAFAs. In addition to their role in sensory tissues, these receptors also act as selective and sensitive chemoreceptors in other regions. Olfactory receptors (OR) are found in various tissues in mice, humans, and other primates, and their ligands are typically produced through physiological or metabolic processes. Olfr78 is found in the olfactory epithelium and the renal afferent arteriole, where renin is stored for potential release into the blood, impacting blood pressure control and tissue blood flow regulation. In both in vitro and in vivo studies, it has been demonstrated that Olfr78 functions as a receptor for SCFAs, particularly acetate and propionate. Furthermore, SCFAs have an epigenetic impact on epithelial cells through histone deacetylase activation and by enhancing the expression of interleukin-10, an antiinflammatory cytokine with immunosuppressive properties produced by various mammalian cell types. This cytokine can decrease inflammation and inhibit the production of proinflammatory cytokines like IFN-γ, IL-2, IL-3, and TNFα. Interleukin-10 can effectively suppress the antigen-presenting-capability of antigenpresenting cells. Interleukin-10 knockout studies imply it plays a crucial role in regulating the immune system in the intestines. In fact, patients with Crohn’s disease respond well to treatment involving bacteria that produce recombinant interleukin-10. Dogs with chronic enteropathy have demonstrated a correlation between SCFA concentrations, inflammatory status, and dysbiosis. This is evidenced by lower fecal concentration and altered SCFA patterns associated with fecal microbiota modifications. Dysbiosis caused by inflammatory bowel disease resulted in a reduction of SCFA-producing bacteria and alterations in ileal and colon mucosal bacteria in dogs, as observed by the increase in adherent bacteria, such as total bacteria, Enterobacteriaceae, E. coli, and the presence of invasive bacteria, such as Enterobacteriaceae, E. coli, and Bacteroides in the sites of intestinal mucosa. In general, these effects result in the deterioration of the clinical condition. SCFAs help maintain the epithelial barrier to lower inflammation, and their decrease is linked to increased blood pressure in obese pregnant women (Richards et al. 2017). Overall, these findings support the assumption that a healthy gut microbiota reduces the risk of hypertension. In the case of intestinal dysbiosis, it is worth noting that specific by-products of intestinal bacteria can have adverse effects on blood pressure. The gut microbiota composition is always changing and influenced by various factors like diet, intestinal mucosa, drugs, immune system, and microbiota. “Intestinal eubiosis” occurs when a proper balance between gut microbiota and its genetic heritage (microbiome) exists. Conversely, when there are reductions in microbial diversity with the expansion of specific bacterial taxa, a state of dysbiosis occurs. Thus, dysbiosis is a state of microbial imbalance resulting from an overgrowth of “harmful” bacteria in the gut, leading to irritation and increasing the risk of numerous diseases such as ulcerative colitis, Crohn’s disease, necrotizing enterocolitis, colorectal cancer, autoimmune diseases, and neurological disorders.

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This confirms the strong link between intestinal dysbiosis and hypertension. In the case of intestinal dysbiosis, certain by-products of intestinal bacteria can trigger the onset of diseases, as described earlier, through the induction of systemic inflammation. Microbiota-derived metabolites can cross the blood-brain barrier, impacting brain inflammation and causing disease states, including neurological disorders and hypertension, and this should represent a consistent basis for microbiota-targeting therapeutic strategies.

High Salt Intake, Hypertension, and Gut Microbiota An additional mechanism that may lead to a further connection between gut microbiota and elevated blood pressure involves salt intake and food consumption. In particular, it is known that, in order to maintain the balance of liquids and cellular homeostasis, the human body needs a very small amount of salt. Over time, however, salt consumption has increased exponentially both because of a diet based on the “emphasis of flavour” (diet developed in Western countries), and the development of food technologies that use salt as a preservative in many foods. The result has been a consumption of a quantity of salt that exceeds by approximately 20 times the real requirement. Since the human body is not adapted to expel this large amount of salt, multiple repercussions on our health have occurred, causing millions of deaths per year. To date, it is known that excess salt in the diet is an important risk factor for hypertension and the onset of cardiovascular disease; for this reason, the American Heart Association has recommended the correct amount of salt to be taken. The salt should not exceed 2300 mg per day, although less than 10% of the US population observes this recommendation. In addition, large numbers of individuals are hypersensitive to salt changes and develop BP alterations even if they are normotensive subjects. An excess of salt involves organ damage in the kidney, vasculature, and central nervous system, although it has recently been discovered that even the intestinal microbiota and immune cells can perceive excesses of Na+ and contribute to inflammation and hypertension. The involvement of the gut microbiota has been demonstrated with some experimental evidence: First of all, the transplantation of the intestinal microbiome of hypertensive subjects causes increased blood pressure in germ-free mice (Guo et al. 2022). In addition, germ-free mice are resistant to hypertension, and vascular dysfunction, and have less renal and vascular infiltration of immune cells after infusion of angiotensin II. Both examples of evidence suggest a causal role of the intestinal microbiome in the development of hypertension. A high salt intake in the diet modulates both the composition and function of the microbiota in rodent models and in humans. Several bacterial taxa were observed to be different between hypertensive and normotensive groups, for example, the gut microbiome of both hypertensive rats and humans is characterized by an increase in the Firmicutes/ Bacteroidetes ratio (Robles-Vera et al. 2021). High salt administration also reduces the prevalence of Lactobacillus murinus by increasing the count of splenic proinflammatory Th17 cells. Daily administration of Lactobacillus murinus, as a probiotic therapy, leads to the reduction of Th17 cells and improves blood pressure in treated

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rats. Therefore, it can be deduced that the high salt intake and the reduced abundance of species Lactobacillus generates a mechanism that causes the interruption of intestinal homeostasis, as well as hypertension. Since the excessive intake of salt causes an alteration that also involves the gut microbiota, it would be desirable, in this condition, to take pre- and probiotics, which regulate immune function, improve the intestinal environment, tend to decrease inflammation, increase levels of SCFAs, Bacteroidetes, and Bifidobacterium, and decrease Firmicutes.

Effect of Prebiotics and Natural Antioxidants on LPS Circulating Levels The potential application for prebiotics and natural antioxidants polyphenols in modulating gut microbiota, lipidemic metabolism regulation, and cardiovascular performance. In particular, a high-fat diet (HFD) in rats leads to alterations of gut microbiota which resulted in a significant increase in the abundance of Firmicutes and Proteobacteria and a decreased abundance of Bacteroidetes compared to rats fed a normal-fat diet (NFD). This effect was associated with increased LPS levels in rats with HFD, thereby confirming that changes in gut microbiota subsequent to HFD lead to an inflammatory state combined with oxidative stress as detected by means of MDA measurements (Mollace et al. 2023). The changes in gut microbiota found after 4 weeks of HFD are associated with increased body weight and metabolic alterations represented by increased plasma glucose, cholesterol, and triglycerides and changes in lipoprotein size and concentration compared with rats fed a normolipidemic diet (NFD). Moreover, oxidative stress biomarker such as MDA was found to be elevated in the blood of HFD rats, as previously shown by our and other groups (Oppedisano et al. 2020). These effects were counteracted by treating rats with BPE alone or in combination with BMF. In fact, in rats fed an HFD in which diet was supplemented with BPE, BMP, or a combination of both, the normal pattern of gut microbiota was restored, this effect being associated with reduction of body weight and plasma levels of glucose, cholesterol, and triglycerides. Interestingly, the improvement of lipidemic profile in rats receiving bergamot extracts alone or in combination with fibers was accompanied by an increased size of lipoproteins, mostly LDL, which are known to play a key role in atherosclerosis development. On the other hand, the effect of natural antioxidants combined with BMF was associated with reduced LPS levels and a consistent reduction of MDA, thereby leading to an overall improvement of cardiometabolic risk profile in rats with HFD. These data are consistent with previous observations showing that gut microbiota is a key player in modulating dietary lipid metabolism, affecting almost all the steps involved in the regulation of lipid digestion and absorption, being also involved in the generation of lipoproteins occurring at the intestinal level (Marzullo et al. 2020). In particular, it has been shown that changes in gut microbiota composition, as the one obtained by means of supplementation with gram-negative bacteria, lead to obesity with increased LDL lipoproteins, an effect accompanied by elevation of

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plasma cholesterol and modulation of the lipid transfer protein system. On the other hand, germ-free mice develop an obesity-resistant phenotype in animals fed an HFD, an effect that involves decreased fasting triglycerides and VLDL production when compared to conventionally reared mice. Moreover, this evidence confirms previous data showing that HFD leads to modifications of gut microbiota. On the other hand, these results confirm that restoring the equilibrium among several intestinal bacteria, as the one found when animals are fed an NFD, is accompanied by normalization of lipidemic profile, by lipoprotein rearrangement, and finally by attenuated inflammation which is associated to the altered lipidemic profile. The rationale of these responses is still to be better clarified. However, clear evidence exists that LPS affects the integrity of the intestinal mucosa by altering tight junctions and thereby impairing intestinal permeability. In particular, evidence exists that alteration of gut microbiota induced by HFD leads to overproduction of LPS by gramnegative bacteria which is followed by impairment of the tight junction proteins such as occludin, claudin-1, and ZO-1 which leads LPS to enter the portal circulation and thereby producing at least two systemic responses: One is mediated by liver inflammation via TLR4 activation and cytokine release which represents the key mechanisms of imbalanced packaging and release of lipoproteins from the liver (Ghosh et al. 2020); the second one is represented by a condition of systemic inflammation and oxidative stress which leads to enhanced atherosclerosis and cardiometabolic risk. These pathophysiological events are counteracted by bergamot extract and fibers. This is also consistent with previous data showing that BPE, a powerful antioxidant in vitro and in vivo, leads to significant protection of vasculature against oxidative damage subsequent to dislipidemia in both diet-induced Metabolic Syndrome and in patients undergoing increased cardiometabolic risk (Carresi et al. 2020). On the other hand, natural antioxidants have been found to produce relevant protection under conditions of liver inflammation, thereby preventing NASH and its deleterious effects in cardiometabolic risk, mostly due to its antioxidant properties. Thus, there is a synergistic response when prebiotic fibers are associated with natural polyphenols. Previous data have shown that bergamot fibers may produce a significant inhibition of postprandial insulin response in patients and that this could account for the role of bergamot fibers in maintaining a normal metabolic balance in subjects suffering from Metabolic Syndrome. On the other hand, the use of prebiotics is consistent with gut microbiota normalization able to reduce cardiometabolic risk (Nicolucci et al. 2017). Thus, it is likely that a combination of both antioxidant and prebiotic bergamot fibers may better target inflammatory/oxidative damage and dysbiosis subsequent to HFD with a sequential response occurring via reduction of LPS release and subsequently by attenuating endotoxin-related systemic consequences. In conclusion, our data confirm that HFD-related changes of gut microbiota are accompanied by increased body weight, and alteration of lipoprotein size, an effect which is associated to the modification of lipidemic profile and imbalanced glucose levels. On the other hand, dysbiosis produced by alterations of gut microbiota and the subsequent alteration in lipidemic profile and lipoprotein packaging contributes

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to the development of HFD-associated imbalance occurring in the mechanisms of lipid regulation and transport, occurring both at the intestinal and systemic level. This is also expressed by enhanced oxidative stress and increased LPS levels, thereby representing key mechanisms in systemic inflammation which may be found in animals fed HFD. Thus, the alterations induced by HFD may be counteracted by supplementing rats with polyphenols and bergamot fibers or a combination of both which restored gut microbiota and produced a rearrangement of lipoprotein size, reduction of both LPS and MDA levels, and, finally, leading to the antagonism of diet-induced dyslipidemia and metabolic imbalance. This suggests that combining antioxidants and prebiotics leads to sequential responses for better counteracting diet-induced alterations of gut microbiota and its deleterious effects on cardiometabolic risk profile and arterial hypertension.

Conclusion The development of arterial hypertension and vascular injury, as the one found in the atherothrombotic process, is tightly connected to the maintenance of a “healthy” condition of vascular endothelium which contributes to the regulation of vascular tone and counteracting endogenous as well as exogenous substances which are the drivers of inflammation of blood vessels and subsequent oxidative stress. This compromises vascular integrity, leading to altered blood pressure and prothrombotic states. In particular, it has been found that both arterial hypertension and atherothrombosis are associated with progressive and unpredictable development which appears independent of simple dysregulation of classical mechanisms of vascular regulation and neurohumoral control. Indeed, the amount of subjects with normal or high-normal blood pressure migrates into one of the three classes of overt hypertension in a way that seems to depend on systemic inflammation and oxidative stress which are generated by endothelial dysfunction and lead, at the end stages, to organ dysfunction generated by meta-arteriole smooth muscle cell proliferation (mostly in the kidney). Similarly, the atherosclerotic plaque formation and progression as well as its rupture, which leads to arterial thrombosis, are unpredictable and appear as a consequence of a hypothetical infective/inflammatory process which we have been looking for over the last 30 years. The reason for this huge connection is still unknown. However, the evidence described in this chapter shed new light on the understanding of the causative processes that are associated with the progressive inability to regulate the vascular tone and to maintain the blood vessels’ integrity. Specifically, it is now clear that altered intestinal microbiota represents the true driver of the mechanisms linking endothelial dysfunction, vascular injury, and high blood pressure. In fact, the altered gut microbiota has been found under conditions of diabetes mellitus, hyperlipidemia, and Metabolic Syndrome, which are associated with high cardiometabolic risk and the development of high blood pressure. This relationship is now clearly related to a precise sequence of events that justify the so-called “gut hypothesis” which takes

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into account experimental and clinical evidence supporting the role of gut microbiota as a key player in the maintenance of a healthy condition of blood vessels. In particular, we describe here that altered microbiota is accompanied by abnormal release in the bloodstream of endotoxins, mostly LPS, which produces inflammatory/prooxidant effects in blood vessels via hyperactivation of the TLR4/cytokine/ NFkB pathway. This is associated with attenuated protective autophagic response and enhanced apoptosis of endothelial cells leading to a “frail endothelium” condition which is the major consequence of dysbiosis and low-grade endotoxemia. The abnormal release of endotoxins during the dysbiosis process, which needs to be better quantified and related to the entire process of development of vascular injury and arterial hypertension development, is frequently associated with other direct or indirect consequences of gut dysbiosis. In fact, altered microbiota has been found to contribute to the dysregulation of vascular endothelium and blood vessel control via the release of metabolites which may have a significant pathophysiological role. In detail, evidence exists that the generation and release of SCAFAs from intestinal bacteria serve to maintain an antihypertensive state. This process is defective in hypertensive subjects or under conditions of altered microbiota. On the other hand, an overproduction of TMAO is associated with hypertensive state and atherosclerosis development, thereby reinforcing the hypothesis that dysbiosis may significantly contribute to blood vessels being compromised. In this context, a substantial contribution of food intake and salt concentration in foods appears to play a considerable role in the maintenance of a healthy state of blood vessels. In fact, alongside the traditional policy to restrict salt consumption in order to reduce blood vessel hyperreactivity, recent data reported in this chapter documented that an exaggerated salt intake is accompanied by altered microbiota which may amplify dietary salt effect in blood pressure management. In contrast, data exists that supplementation with natural antioxidants and prebiotic fibers contributes to regulating gut microbiota, counteracting dysbiosis and its dangerous consequences in the cardiovascular system. Further clinical studies are required to define the role of nutritional supplementation with natural antioxidants and prebiotics able to modulate gut microbiota and to produce beneficial effects in blood pressure management. Acknowledgments The authors declare that no conflict of interest exists in the data included in the manuscript. All authors have read and agreed to the published version of the manuscript. The work was supported by public resources from the Italian Ministry of Research: PON-MIUR 03PE000_78_1 and PONMIUR 03PE000_78_2. POR Calabria FESR FSE 2014–2020 Asse 12-Azioni 10.5.6 e 10.5.12. and Agrinfra Project granted by Regione Calabria (Italy).

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Index

A Acetitomaculum ruminis, 294 Actinomycetota, 181 Acute kidney injury (AKI), 374 Acylcarnitine, 48 Adaptive immune system, 205 Advanced glycation end products (AGEs), 376 Akkermansia sp., 112, 136, 147, 163, 165, 167, 170, 208, 210, 242, 287, 288, 401, 408, 437, 455 A. muciniphila, 38, 115, 137, 141, 143, 146, 147, 162, 167, 205, 209, 218, 404, 439, 451 Alcohol-related liver disease (ALD), 178 Amino acid-derived metabolites branched-chain fatty acids, 100–101 hydrogen sulfide, 91–94 phenolic and indolic compounds, 94–98 polyamines, 98–100 Amino acid metabolites, 267–268 Amino acids, 31 in brain signaling, 44 essential, 37–42 insulinotropic, 43–44 Amplicon sequencing, 11 archaeome and parasitome, 15–16 bacterial 16S rRNA sequencing, 12–14 internal transcribed spacer sequencing, 14 Antidiabetic therapy, 223 Anti-inflammatory dietary patterns, 358–360 Aromatic amino acids, 244–247 Arterial hypertension, 465, 469 Atherosclerosis, 78, 219–220, 232 aortic, 253 and CHIP, 250 gut microbiota composition in, 235–236

mouse model of, 245 reduced bacterial diversity, 236 risk factors for, 232 Atherothrombosis, 474, 491 Atrial fibrillation, 449

B Bacillota, 181, 287, 288, 301, 379–381 Bacteremia, 321 Bacteria, 233–236, 247, 249 Bacterial 16S rRNA sequencing, 12–14 Bacterial capture sequencing, 9 Bacterial translocation, 121–123 Bacteroides sp., 38, 39, 41, 42, 44, 74, 85, 95, 100, 103, 132, 134, 137, 145, 163, 167, 186–188, 208, 214, 219, 221, 274, 287, 293, 294, 296, 298, 299, 301, 343, 400, 406–408, 437, 442, 445, 447, 448, 476, 487 B. caccae, 207, 295, 298 Bacteroidetes, 135, 261 Bacteroidota, 181, 287 Bait-and-capture strategy, 8–11 Bariatric surgery, 145–147, 164, 224 Beclin-1, 479 Betaine, 45 Bifidobacterium sp., 148, 208 B. adolescentis, 167, 287, 288 Bile acids, 133, 160, 162–165, 167, 171, 215–217, 240–241, 266 atherosclerosis, 89 impact of host metabolism, 86–88 impact on metabolic disorders, 88–90 microbial interaction, 84–85 NAFLD, 90 obesity, 88 T2D and bariatric surgery, 88

© Springer Nature Switzerland AG 2024 M. Federici, R. Menghini (eds.), Gut Microbiome, Microbial Metabolites and Cardiometabolic Risk, Endocrinology, https://doi.org/10.1007/978-3-031-35064-1

497

498 Bile salt hydrolase (BSH) enzymes, 133 Biomarkers, 30 Blautia, 85, 137, 146, 163, 208, 269, 348, 407, 408, 437, 445, 455 Body wasting, 271 Bottom-up signaling, 290 Brain, 284 Branched-chain amino acids (BCAA), 37, 100–101, 267 Brown adipose tissue (BAT) bariatric surgery, 164 cold exposure, 162–163 gut microbiota depletion, 167–169 intermittent fasting and caloric restriction, 165, 166 intestinal AMPK, 169 plant extract-derived bioactive compounds, 163–164 probiotics, 166, 167

C Calcium, 469 Caloric restriction, 163, 165, 166, 171, 445 Carbohydrates, 352–354, 434 CARD-FISH technique, 120 Cardiac cachexia, 271–272 Cardiometabolic diseases (CMDs), 218, 308 and periodontitis, 319–325 Cardiorenal syndrome, 471 Cardiovascular disease (CVD), 73, 78, 94, 103–104, 233–235, 237, 241, 243, 245, 247, 250, 253, 260 atrial fibrillation, 449 coronary artery disease, 448 heart failure, 449 hypertension, 446 CATCH computational method, 10 Caudovirales levels, 295, 298 CAZymes, 434 Cell adhesion molecules (CAMs), 467 Cellulosilyticum ruminicola, 208 Chenodeoxycholic acid, 49 Cholesterol, 232, 238, 242 Cholic acid, 49 Choline and metabolites, 45–46 Christensenellaceae family, 136, 287, 399 Chromosome conformation capture (3C) technology, 17 Chronic kidney disease, 270–271 Chylomicron formation, 474 Clonal hematopoiesis of indeterminate potential (CHIP), 250

Index Clostridium sp. C. butyricum, 208 C. difficile, 439 C. hathewayi, 207 C. paraputrificum, 208 C. ramosum, 207 C. symbiosum, 207 Coeliac disease (CeD), 362 Cognition and brain structure, 284 and metabolic diseases, 284–286 Cold exposure, 162 Comprehensive antibiotics resistance database (CARD), 8 Coprococcus sp., 439 Coronary artery disease, 448 C-reactive protein (CRP), 247 4-cresol, 213 Crohn’s disease (CD), 120, 334, 335 α-cyclodextrins, 150

D Degenerative mitral valve disease (DMVD), 484 Dendritic cells, 114 Derivatization, 61–62 Desulfovibrio piger, 208 Diabetic kidney disease (DKD), 374, 389–391 advanced glycation end products, 376 dysbiosis, 382–388 epigenetics and non-coding RNA, 378–379 gut microbiome, 388–389 hemodynamic changes, 378 hexosamine pathway, 377 microbiome on host immune response, in DKD progression, 379–382 PKC pathway, 377 polyol pathway, 376–377 Dietary Approaches to Stop Hypertension (DASH) diet, 273, 453 Dietary fibers, 339, 342, 343, 352–354, 359, 362, 364 Dietary fructose, 222 Dietary patterns anti-inflammatory, 358–360 protein-based, 361 restrictive, 361–363 Diet-derived lipids, 223 DNA extraction protocols, 18 Dopamine, 285

Index Dysbiosis, 240, 406, 473, 485 and atrial fibrillation, 482 and cardiometabolic risk, 477 clinical and experimental evidence, 382–384 in dogs, 485 dysbiosis-driven inflammation, 384–388 intestinal, 487 and microbiota-related cardiovascular disorders, 476 role in atherosclerosis, 479 Dysbiotic microbiota, 117, 206, 310, 321, 444 Dyslipidemia, 116, 237, 240 definition, 232 gut microbiota composition, 234–235 reduced bacterial diversity, 236

E Eggerthella lenta, 208, 234, 235, 287, 452 Embden-Meyerhof-Parnas pathway, 74 Emulsifiers, 444 Endocannabinoid (eCB) system, 139 Endothelial dysfunction definition, 467 gut microbiota, cardiovascular injury and, 483–485 human hypertension, 470 Endotoxemia, 247–248, 321–322 Enterococcus faecalis, 164, 194 Enterohepatic circulation, 216 Enterosynes, 251 Erythrocyte sedimentation rate (ESR), 335 Escherichia coli, 207, 208 Eubacterium sp., 39, 41, 74, 91, 133, 137, 144, 208, 225, 282, 292, 295, 343, 351, 406, 443, 447 E. rectale, 146, 207, 223, 234, 287, 295, 344, 346, 442 European Association for the Study of the Liver (EASL), 181

F Faecalibacterium sp., 74, 133, 137, 145, 146, 188, 206–210, 219, 234, 235, 241, 242, 271, 287, 288, 295, 343, 344, 346, 351, 400, 406–408, 435–437, 441, 445, 447, 450 F. prausnitzii, 207 Faecal microbiota transplantation (FMT), 193 Farnesoid X receptor (FXR), 240, 266 Fasting, 445 Fatty acid amide hydrolase, 140

499 Fecal microbiota transplant, 276 Firmicutes, 170 Flagellin, 113, 124, 209, 210, 219, 341, 444 Fluxomics, 51 aminoacid turnover and protein synthesis, 53 analytical methodologies, 54–59 glucose fluxes, 52 lipid fluxes, 53–54 Food addiction, 286 Free fatty acid receptor (FFAR), 75 Fusobacterium sp., 39, 91, 208, 311, 312, 314, 315, 318, 321, 325, 343, 344, 346, 362, 408, 409, 473

G Gamma-aminobutyric acid (GABA), 44, 217 Gammaproteobacteria (Escherichia), 165, 221, 362 Gas chromatography mass spectrometry (GC-MS), 58 Gastrointestinal (GI) tract, 334 Genetic susceptibility, 201 Genome-wide association studies (GWAS), 337 Glucagon-like peptide-1 (GLP-1), 150 Glutamine fructose-6-phosphateamidotransferase (GFAT), 377 Gluten-free diet (GFD), 362 Glycine cleavage system (GCS), 297 Glycolysis, 46 Goblet cells, 115 GPR81, 75, 79–81 G-protein coupled receptor 1, 266 GreenChipPm, 7 Gut bacteria-derived metabolites, 38–41 Gut barrier, 204 Gut-brain-axis (GBA), 288–293 Gut hypothesis of heart failure, 262–263 Gut–liver axis, 185 Gut microbiome, 182, 388–390 contributions in pathophysiology of heart failure, 263 description, 261 healthy, 261–262 HF comorbidities, 270–272 physiologic consequences of microbial metabolites, 265 SLD, 188 Gut microbiome dysbiosis, 402 chronic kidney disease, 407 hypertension and cardiovascular disease, 402

500 Gut microbiome dysbiosis (cont.) ischemic stroke, 405 nonalcoholic fatty liver disease, 409 sex hormone-related diseases, 411, 412 Type 2 Diabetes, 408 Gut microbiome metabolites, 413 aryl hydrocarbon receptor ligands, 418 bacterial-derived vitamins, 419 exopolysaccharides, 419 gases, 416 host metabolites converted by the gut microbiome, 424 lipids, 420 neurotransmitters, 421 phenolic acids and bioactive phytoderivates, 417 polyamines, 423 short-chain fatty acids, 413 Gut microbiota, 181, 337, 339, 340, 367, 374, 375, 379–384, 388, 390 and adipose tissue thermogenesis in humans, 169–171 alterations in obesity, 134–136 amino acid metabolites, 211–213 attention and executive function, 293–298 and bariatric surgery, 145–147 bile acids, 215–217 and brain structure, 299–301 branched chain and aromatic amino acid, 213–214 carbohydrates, 434 composition in atherosclerosis, 235–236 composition in dyslipidemia, 234–235 coronary artery disease, 448 description, 131 and development of T2DM, 206 dietary lipids on, 239 dietary restrictions, 444–446 and diet composition, 434–446 ecology, 112–113 endothelial function, cardiovascular injury and, 483–485 evidence from animal studies, 134–135 evidence from human studies, 135–136 fermentation, 160 on glucose metabolism, fatty acid metabolism and energy expenditure, 218 and heart failure, 449 high salt intake, hypertension and, 488–489 in host metabolism, 132–134 and hypertension, 446–448, 485–488 in IBD, 342–346 ketogenic diet, 144 lipids, 437–438 lipopolysaccharide, 138–140

Index and Mediterranean diet, 144, 440–441 and memory processes, 298–299 and mental health in obesity, 300 microbial metabolites in development of obesity, 136–140 micronutrients, 438–440 modulation of, 147–151 and neurotransmitters, 290–293 and obesity, 287 prebiotics, 148–151 proteins, 435–437 short chain fatty acids, 137–138, 214–215 T1DM, 288 T2DM, 288 timing of food consumption, 141–144 vegetarian and vegan diets, 442–443 and western diet, 142–144, 443–444

H Healthy nutritional status, 222 Heart failure, 260, 449 amino acid metabolites, 267–268 bile acids, 266 and cardiac cachexia, 271–272 and chronic kidney disease, 270–271 dietary and lifestyle interventions, 272–274 fecal microbiota transplant, 276 gut dysbiosis patterns associated with, 263–264 gut hypothesis of, 262–263 and insulin resistance, 271 lipopolysaccharide, 269 microbial enzyme inhibition, 275 phenylacetylglutamine, 269 prebiotics, 274, 275 short chain fatty acids, 264–266 strategies to target gut microbiome to treat, 272–276 trimethylamine N-oxide, 268–269 Hepatic steatosis (HS) cardiometabolic risk factors, 179 defining and diagnosing, 178 Hexosamine pathway, 377 High fat diet (HFD), 357–358, 489 High-performance liquid chromatography mass spectrometry (HPLC-MS), 58 High protein diet (HPD), 361 Host–microbiota co-metabolism, 183 Human gut microbiome, 397 age-related compositional and functional changes in the gut microbiome, 397 the gut microbiome and longevity: a focus on centenarians, 400 Human microbiome project, 12

Index Human umbilical vein endothelial cells (HUVECs), 468 Hybridization capture, 8 Hydrogen sulfide, 91 impact on disease, 92 impact on metabolic disorders, 92–94 physiological roles in host metabolism, 92 production by gut microbiota, 91–92 12-hydroxyeicosatetraenoic acid (12-HETE), 251 3-hydroxyoctadecaenoic acid (3-HPAA), 252, 484 Hydroxyphenyl-acetic acid, 51 Hydroxyphenyl-propionic acid, 51 5-hydroxytryptamine (5-HT), 217 Hygiene hypothesis, 336 Hypertension, 446–448 gut microbiota, 485–489 oxidative stress, vascular injury and, 469–472 Hypertriglyceridemia, 285 Hypothalamic-pituitary-adrenal (HPA) axis, 288

I Imidazole propionate (ImP), 213 Immune system, 113, 115, 122, 123 Indole, 211 Indole-3-propionate (IPA), 267 Indolic compounds impact on metabolic disorders, 97–98 physiological roles, 96 production by gut microbiota, 95 Indolpropionic acid, 212 Inflammatory bowel disease (IBD), 334–336 epidemiology, 336–337 genetic aspects, 337–339 GM in, 342–346 low FODMAP diet, 358 macronutrients in, 347–354 micronutrients in, 354–357 pathophysiological aspects, intestinal mucosa and immune system, 340–342 protein-based dietary patterns, 361 restrictive dietary patterns, 361–363 therapeutic approaches and impact on GM, 364–366 Western-style diet/HFD, 357–358 Innate immune system, 205 Innate lymphoid cells (ILC), 203 Insulin-dependent tissues, 158 Insulinotropic amino acids, 43–44 Insulin resistance, 201, 202, 213, 220, 271 Intergenic spacer (IGS) sequence, 23

501 Intermittent fasting, 165 International Human Microbiome Project, 112 Intestinal AMPK, 169 Intestinal bacteria, 483 Intestinal dysbiosis, 487 Intestinal eubiosis, 117, 310, 360, 398, 434, 487 Intestinal inflammation, 342, 351, 352, 357, 360 Intestine, 233, 238, 243, 248

K Ketogenic diet, 144 Kynurenines, 43

L Lactate impact on metabolic disorders, 81 physiological roles in host metabolism, 80–81 production from dietary fiber, 79–80 Lactate shuttle concept, 78 Lactobacillus sp., 148, 208 L. acidophilus, 167 L. murinus, 273 L. reuteri J1, 166 Lactose-free diet (LFD), 363 L-carnitine, 48 Leaky gut syndrome, 114, 117, 121, 122, 248, 263 Lipidomics, 118 Lipids, 232, 239, 347–349, 437–438 Lipopolysaccharide (LPS), 138–140, 160, 166, 188, 210, 269 Lithocholic acid (LCA), 167 Long-chain triglycerides (LCT), 349 Low FODMAP diet, 358 Low-grade endotoxemia, 464, 465, 474, 477, 481, 482

M M1 macrophages, 202 M2 macrophages, 203 Macroalbuminuria, 482 Macronutrients, in IBD, 347 carbohydrates, 352–354 lipids, 347–349 proteins, 350–352 Major depressive disorder, 300 Mass spectrometry, 56–57 Mediterranean diet (Med Diet), 144, 273, 360, 440–441 and DASH diet, 453

502 Metabolic diseases, 45, 116–117, 121 and cognition, 284–286 and gut microbiota, 287–288 Metabolic dysfunction-Associated SteatoHepatitis (MASH), 178 Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), 178 Metabolic effects, 159, 162, 163, 171 Metabolic endotoxemia, 117, 139, 140, 166, 167, 210, 248, 322, 405, 437, 475 Metabolic fluxes, 51 Metabolic retroconversion, 184–186 Metabolic syndrome, 490 Metabolomics, 30 analytical methodologies, 54–59 data analysis, 63 derivatization, 61–62 profiles of biological samples, 31–51 quantitative vs. qualitative methods, 63 sample matrix, sample acquisition and storage, 59–60 sample preparation, 60–61 targeted vs. targeted protocols, 62–63 Metagenomic analysis, 343 Metagenomic multi-locus sequencing typing, 13 Metagenomics, 2, 12, 20 Meta-inflammation, 201 Metformin, 252 Methanobrevibacter smithii, 181 Microbial composition, 160, 171, 172 Microbial enzyme inhibition, 275 Microbial–host co-metabolism, 183 Microbial metabolism, 183 Microbial metabolites, 31 Microbial microarray analysis, 7–8 Microbiome, 181 amplicon sequencing, 11–16 atherothrombotic potential, 251 bacterial translocation, 249 bait-and-capture strategy, 8–11 CHIP, 250 drug impact on intestinal, 252–253 endocannabinoid system, 251 endotoxemia, 247–248 immune system modulation and influence on T-cell response, 249 leaky gut syndrome, 248 microbial microarray analysis, 7–8 shotgun sequencing, 16–20 third generation sequencing (see Third generation sequencing) Microbiome-targeted interventions, 191

Index Microbiota-accessible carbohydrates, 434 Microbiota-driven proteolysis, 184 Micronutrients, 354–357 Monocyte chemoattractant protein-1 (MCP1), 202 Mono-methyl-L-arginine (L-NMMA), 469 Myxomatous mitral valve disease (MMVD), 485

N N-acetyl-L-cysteine (NAC) treatment, 468 NAFLD-associated faecal microbiota, 190 Nanopore DNA sequencing, 22–23 Natural antioxidants, 489–491 Neurotransmitters, 217 Next-generation sequencing, see Third generation sequencing NF-kB, 465 Non-alcoholic fatty liver disease (NAFLD), 178, 221 Non-alcoholic HS, 178 Non-alcoholic steatohepatitis (NASH), 178 Non-coeliac gluten sensitivity (NCGS), 362 Non-digestible dietary carbohydrates, 132 Non-esterified fatty acids (NEFA), 47 Normal fat diet (NFD), 489 Normolipidemic diet, 489 Nuclear magnetic resonance (NMR) spectroscopy, 55–56

O Obesity, 158–162, 164, 166, 169, 220–221 and cognitive function, 285 dysbiosis, 287 global prevalence, 130 gut microbiota alterations, 134–136 gut microbiota and mental health in, 300 microbial metabolites in development of, 136–140 pathophysiology of, 131 probiotics, 148 and type 2 diabetes, 145, 282 Obesity-associated metabolic disturbances, 158, 163, 164, 166, 169, 170 Olfactory receptors, 487 Oral hygiene, 326 Oral-intestine axis, 328 Oral microbiota, 310–311 and cardiometabolic risk, 317–319 and CMDS, 319–325 dietary and lifestyle habits, 326

Index oral hygiene, 326 periodontal, 312–316 phytotherapy, 327 pre/probiotics treatment, 326 salivary, 312 vitamin D treatment, 327 Oxford nanopore technology, 22, 23

P Paleolithic diet, 361 Panax notoginseng saponins, 164 Parabacteroides distasonis, 163 Pathogen-associated molecular patterns (PAMPs), 341 P-cresyl sulfate (PCS), 95–97, 267, 381, 407, 408, 484 Pentose-phosphate pathway, 74 Peptostreptococcus sp., 91, 208 Periodontal health, 313 Periodontitis, 314–316 bacteremia, 321 and CMDs, 320–325 and CVD, 317–318 endotoxemia, 321 low-grade inflammation, 322–323 and metabolic diseases, 318 Periodontium, 312 Porphyromonas gingivalis, 219, 311, 312, 314, 315, 321 Phage therapy, 194 Pharmacokinetics, 452 Phenolic compounds impact on metabolic disorders, 96–97 physiological roles, 95 production by gut microbiota, 95 Phenol metabolites, 50 Phenylacetate, 190 Phenylacetylglutamine, 246, 269 Phenylalanine, 42 Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), 12 Piphillin algorithm, 12 Plant-based diet (PBD), 273, 359 Plant extract-derived bioactive compounds, 163 Polyamines, 43 impact on metabolic disorders, 99–100 physiological roles in host metabolism, 98–99 production, 98 Polyol pathway, 376–377 Polyphenols, 443

503 Postbiotics, 161, 169, 171, 191, 193, 401, 405, 426, 451, 452, 454 Postprandial inflammation, 210 Prebiotics, 149–151, 274, 441, 486 and natural antioxidants, 489–491 Prevotella, 208 Primary bile acids, 49 Probiotics, 148–149, 166, 167, 191, 274, 451 Pro-inflammatory mediators, 382, 384, 386, 388 Propionate, 133 Protein Kinase C (PKC), 377 Proteins, 350–352, 435–437 Proteobacteriaceae, 120 Proton pump inhibitors, 252 Pseudomonadota, 181, 379–381 Puerarine, 327

R Reactive oxygen species (ROS), 463, 467 Renovascular hypertension, 471 Resistome, 8 Roseburia sp., 208 R. intestinalis, 207 Roux-en Y gastric by-pass (RYGB), 89, 146 Ruminococcus sp., 208

S S-adenosylmethionine, 297 Sanger sequencing, 3, 5 Saturated fatty acids (SFAs), 348 Secondary bile acids, 49 Semi-vegetarian diet/plant-based diet (PBD), 359 Serotonin, 44 Short-chain fatty acids (SCFA), 47–48, 73, 133, 137–138, 171, 241–243, 264–266 atherosclerosis, 78 hypertension, 77 impact on metabolic disorders, 76–78 lactate, 79–81 NAFLD, 78 obesity, 76–77 physiological roles in host mechanism, 74–76 physiology and pathology, 75 precursors, 78–84 production from dietary fiber, 79–80 succinate, 81–84 T2D, 77 Short-term low-carbohydrate diets, 223 Shotgun sequencing, 16–20

504 Single molecule, real-time (SMRT ®) sequencing, 21–22 Sleeve gastrectomy, 145 Specific carbohydrate diet (SCD), 361–362 Steatotic liver disease (SLD), 178 Succinate, 81 in metabolic diseases, 83–84 physiological roles in host, 82–83 SUCNR1, 79, 82, 83 Synbiotics, 191 Systemic inflammatory biomarkers, 327

T Takeda-G-protein-receptor-5 (TGR5), 240 Targeted amplicon sequencing, 262 Targeted NGS approach, 10, 16 Target-enriched long-read sequencing (TELSeq), 22 TCA cycle, intermediates of, 46 T helper lymphocytes, 204 Therapeutic strategies, 387, 388, 390 Thermogenesis, 159, 162, 164–171 Third generation sequencing nanopore DNA sequencing, 22–23 SMRT ® sequencing, 21–22 Tight junction proteins, 475 Tissue microbiota, 118–121 TLR4 signaling, 323 Toll-like receptors (TLRs), 315, 341, 380 Translocation, 324 Trimethylamine N-oxide (TMAO), 45, 101, 184, 243–244, 268–269, 483 amino acids-derived metabolites and, 86 beneficial effects, 105 biosynthesis, 102 and CVD, 103 gut microbiota in, 102 impact on metabolic disorders, 103–105 NAFLD, 104 obesity, 104 T2D, 104 Trimethylamine (TMA), 212 Tryptophan, 211, 245 Tryptophan-kynurenine pathway, 42–43 Tumor necrosis factor alpha (TNFa), 203 Type 1 diabetes (T1DM), 288 Type 2 diabetes mellitus (T2DM), 287 bariatric surgery, 224 characterization, 200

Index definition, 200 environmental factors, 201 genetic susceptibility, 201 impaired gut barrier, 207 lifestyle modification, 222 meta-inflammation, 201 microbiota contribution, drug therapy for, 222–224 microbiota effects on metabolism in patients with, 205–218 and obesity, 220 pathophysiology of, 202 pharmacological treatment, 223 therapeutic options for, 222–224 TMA, 212 Tyramine, 213

U Ulcerative colitis (UC), 334, 335 Uncoupling protein 1 (UCP1) activity, 159 Unsaturated fatty acid (UFAs), 347 Untargeted approach, 5, 18, 24 Uremic toxins (UTs), 381, 382, 384, 484 Urinary microbiome, 473 Ursodeoxycholic acid (UDCA), 167

V Vascular endothelium growth factor (VEGF), 467 Vegetarian and vegan diets, 442–443 Verrucomicrobia (Akkermansia), 165 Verrucomicrobiota, 181 ViroCap, 9 Virome dysbiosis, 17 Vitamins, 50

W Western diet, 142–144, 443–444 Wheat allergy (WA), 362 White adipose tissue (WAT), 159, 161–168, 170, 171 Whole genomic shotgun sequencing, 262 Wood–Ljungdahl pathway, 74

Z Zonulin, 477