Handbook of Obesity - Volume 1: Epidemiology, Etiology, and Physiopathology [4 ed.] 9781032558622, 9781003437673, 9781032047126, 9781003437734

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Handbook of Obesity Volume 1 of the Fourth Edition of the Handbook of Obesity, written by global experts, covers the basic science aspects under the broad topic areas of epidemiology, etiology, and pathophysiology of obesity. Divided into five parts and detailed in 66 chapters, this volume covers the important advances occurring over the past decades. With a focus on the science of obesity and factors contributing to the etiology of obesity, this topic is studied from biological, behavioral, and environmental perspectives. Volume 1 is structured into five parts: • Part 1 focuses on the history, definitions, and prevalence of obesity. It identifies the historical references to excess weight; obesity in art and literature; direct and surrogate measurements of adiposity and obesity-related traits; the epidemiology of obesity around the globe; and age, sex, and ethnic variation. • Part 2 explains the biological determinants of obesity. It explains the bioenergetics, energy dissipation mechanisms and exposure to experimental overfeeding, genetic and epigenetic evidence, metabolic rates, energy expenditure and energy partitioning, and the evidence on infections and adiposity. • Part 3 describes the behavioral determinants of obesity. It deals with chapters related to food, beverages, and ingestive behavior; smoking, breastfeeding, and sleep duration and pattern; and sedentary behavior, occupational work, and leisure-time physical activity and obesity. • Part 4 comprises chapters explaining the environmental, social, and cultural determinants of obesity. The chapters in this part focus on the role of agriculture and the food industry in the current obesity epidemic, social and economic aspects of obesity, ethnic and cultural differences, and environmental pollutants. • Part 5 discusses the health consequences of obesity. The chapters address important topics such as obesity and heart disease, lipoprotein metabolism, insulin resistance and diabetes, metabolic syndrome, cancer, hepatic biology, pulmonary functions, arthritis and gout, mental health and quality of life, growth and health disorders in pediatric populations, and bias and discrimination related to obesity.

Handbook of Obesity – Volume 1 Epidemiology, Etiology, and Physiopathology Fourth Edition

Edited by

George A. Bray, MD Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

Claude Bouchard, PhD Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

Associate Editors

Peter T. Katzmarzyk, PhD Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana

John P. Kirwan, PhD Integrated Physiology and Molecular Medicine Laboratory Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana

Leanne M. Redman, PhD Reproductive Endocrinology and Women’s Health Laboratory Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana

Philip R. Schauer, MD

Metamor Institute Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana

Designed cover image: Shutterstock Fourth edition published 2024 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2024 selection and editorial matter, George A. Bray, Claude Bouchard, John Kirwan, Peter Katzmarzyk, Leanne Redman, Philip Schauer; individual chapters, the contributors This book contains information obtained from authentic and highly regarded sources. While all reasonable efforts have been made to publish reliable data and information, neither the author[s] nor the publisher can accept any legal responsibility or liability for any errors or omissions that may be made. The publishers wish to make clear that any views or opinions expressed in this book by individual editors, authors or contributors are personal to them and do not necessarily reflect the views/ opinions of the publishers. The information or guidance contained in this book is intended for use by medical, scientific or health-care professionals and is provided strictly as a supplement to the medical or other professional’s own judgement, their knowledge of the patient’s medical history, relevant manufacturer’s instructions and the appropriate best practice guidelines. Because of the rapid advances in medical science, any information or advice on dosages, procedures or diagnoses should be independently verified. The reader is strongly urged to consult the relevant national drug formulary and the drug companies’ and device or material manufacturers’ printed instructions, and their websites, before administering or utilizing any of the drugs, devices or materials mentioned in this book. This book does not indicate whether a particular treatment is appropriate or suitable for a particular individual. Ultimately it is the sole responsibility of the medical professional to make his or her own professional judgements, so as to advise and treat patients appropriately. The authors and publishers have also attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www​.copyright​.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions​@tandf​.co​​.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. ISBN: 9781032558622 (hbk) ISBN: 9781003437673 (ebk) DOI: 10.1201/9781003437673 Set ISBN: 9781032047126 (hbk) Set ISBN: 9781003437734 (ebk) Set DOI: 10.1201/9781003437734 Typeset in Times by Deanta Global Publishing Services, Chennai, India

This edition of the Handbook of Obesity is dedicated to Claude Bernard “Doc” Pennington whose generous gift to Louisiana State University in 1980 made possible the initiation of the Pennington Biomedical Research Center, which has served all of the editors and associate editors of this book as an academic home and a site for internationally recognized research in nutrition and metabolic health.

Contents Preface xiii About the Editors xv Associate Editors xvii Contributors xix Introduction to Volume 1 xxix

Part 1  History, Definitions, and Prevalence

1

1 From Hippocrates to the Obesity Society: A Brief History George A. Bray and Marta Sumin´ska

3

2 Obesity in Art and Literature Fiona Haslam and David Haslam

17

3 Measurement of Total Adiposity, Regional Fat Depots, and Ectopic Fat Steven B. Heymsfield, Brooke Smith, and Matthew D. Robson

28

4 Anthropometric Indicators of Adiposity in Relation to the Gold Standards Peter T. Katzmarzyk

38

5 Worldwide and Regional Prevalence of Obesity Jacob C. Seidell

48

6 Age, Sex, Ethnicity, and Other Sources of Variation in the Prevalence of Obesity Sohyun Park and Heidi M. Blanck

53

Part 2  Biological Determinants of Obesity

67

7 Bioenergetics and Obesity Matthew D. Lynes and Yu-Hua Tseng

69

8 Mitochondrial Metabolic Inefficiencies in Skeletal Muscle: Implications for Obesity and Weight Loss 79 Mary-Ellen Harper, Ruth McPherson, and Robert Dent 9 Human Experimental Overfeeding Claude Bouchard and George A. Bray

86

10 Biology of Calorie Restriction and Intermittent Fasting Leonie Heilbronn, Kai Liu, and Luigi Fontana

95

11 Genetic Epidemiology and Obesity Karri Silventoinen

103

12 Genetic Variation and Obesity Ruth J.F. Loos

113

vii

viii Contents 13 Single-Gene Defects and Obesity Héléna Mosbah, Christine Poitou, and Karine Clément

123

14 Micro and Long RNAs as Regulators in Obesity Jean-Francois Landrier, Flavie Sicard, and Lourdes Mounien

133

15 Epigenetics and Obesity Charlotte Ling and Sonia Garcia-Calzon

139

16 The Role of Adipocyte Precursors in Development and Obesity Tammy Ying and Rebecca A. Simmons

148

17 Small Animal Models of Obesity Heike Münzberg, Christopher D. Morrison, and J. Michael Salbaum

155

18 Obesity in Nonhuman Primates Barbara C. Hansen, Daniel Harrison, Vernon Volante, Nana Gletsu-Miller, and Kai-Lin Catherine Jen

165

19 The Transcriptome, Proteome, and Metabolome of Obesity Monalisa Hota, Lijin Wang, Pratap Seshachalam, and Sujoy Ghosh

173

20 CNS Regulation of Energy Balance Justin L. Grobe and Matthew J. Potthoff

207

21 Gastrointestinal Regulation of Energy Balance Timothy Sean Kairupan, Nova Hellen Kapantow, and Akio Inui

216

22 Gut Microbiome and Obesity Kristina Martinez-Guryn and Eugene B. Chang

225

23 Sympathetic Nervous System and Obesity Elisabeth Lambert

233

24 Hypothalamic–Pituitary Hormones and Obesity Jonathan Q. Purnell, Mary Samuels, and Maria Fleseriu

241

25 Brown, Beige, and White Adipocyte Development Shingo Kajimura

250

26 Adipose Tissue Metabolism, Adipokines, and Obesity Ricardo J. Samms and Philipp E. Scherer

259

27 Hepatic, Visceral, Pancreatic, and Cardiac Fat Deposition Amalia Gastaldelli

267

28 Skeletal Muscle Metabolism and Obesity Polina M. Krassovskaia, Nicholas T. Broskey, and Joseph A. Houmard

276

29 Resting Metabolic Rate, Thermic Effect of Food, and Obesity Yves Schutz and Abdul Dulloo

286

30 Energy Cost of Exercise, Post-Exercise Metabolic Rates, and Obesity Robert Ross, Erin Miller, Emily John, and Laura Guzmán Caballero

296

Contents  ix 31 Energy Partitioning, Substrate Oxidation Rates, and Obesity Angelo Tremblay, Margriet Westerterp-Plantenga, and Patrick Schrauwen

312

32 COVID-19, Other Microbial Infections, and Obesity Md Akheruzzaman, Vijay Hegde, and Nikhil V. Dhurandhar

319

Part 3  Behavioral Determinants of Obesity

329

33 Obesity and Dietary Intake Eunjin Cheon, Lissa A. Davis, Vinicius M. Valicente, Yu Wang, and Richard D. Mattes

331

34 Beverages and Obesity: A Review of Current Scientific Evidence Miaobing Zheng, Sofus C. Larsen, Nanna J. Olsen, and Berit L. Heitmann

339

35 Obesity and Sedentary Time at Work and Home Stuart J. H. Biddle and Gregory J. H. Biddle

347

36 Leisure Time Physical Activity and Obesity Julianne G. Clina, R. Drew Sayer, and James O. Hill

356

37 Role of Early Life Nutrition and Breastfeeding on Obesity Development Maryam Kebbe, Kelsey Goynes, and Leanne M. Redman

363

38 Tobacco Use, Marijuana Use, Vaping, and Obesity Bernard F. Fuemmeler, Kristina L. Tatum, and D. Jeremy Barsell

371

39 Sleep and Obesity Silvia Cerolini and Caterina Lombardo

378

40 Eating Disorders and Obesity Jonathan M. Mond and Timothy Gill

384

Part 4  Environmental, Social, and Cultural Determinants of Obesity

393

41 The Role of the Food Industry in Obesity Martin Binks

395

42 Transportation Policies and Obesity James E. Peterman and David R. Bassett, Jr.

406

43 Urban Environment and Obesity Katherine R. White and Lawrence D. Frank

415

4 4 Social and Economic Factors Related to Obesity across the Globe Penny Gordon-Larsen and Pasquale E. Rummo

423

45 Influence of Culture on Obesity Alison Tovar, Peter T. Katzmarzyk, and Aviva Must

433

46 Environmental Chemicals and Obesity Sumira Phatak, Amanda S. Janesick, Thaddeus T. Schug, Jerrold J. Heindel, and Bruce Blumberg

441

x Contents

Part 5  Consequences of Obesity

449

47 Obesity and Mortality Thorkild I.A. Sørensen, Justin C. Brown, and Terese Sara Høj Jørgensen

451

48 Obesity and Heart Disease Alexandra M. Sanchez, Nadia I. Abelhad, Andrew Elagizi, Sergey Kachur, Hector O. Ventura, and Carl J. Lavie

461

49 Obesity and Hypertension John E. Hall, Ana C. M. Omoto, Jussara M. do Carmo, Alexandre A. da Silva, Zhen Wang, Alan J. Mouton, Xuan Li, and Michael E. Hall

469

50 Obesity and Lipoprotein Metabolism Sally Chiu and Ronald M. Krauss

481

51 Obesity and Insulin Resistance John P. Kirwan and Christopher L. Axelrod

488

52 Obesity and Type 2 Diabetes Norbert Stefan

496

53 Obesity and Metabolic Syndrome André Tchernof and Jean-Pierre Després

503

54 Obesity and Cancer Alpa V. Patel and Lauren R. Teras

511

55 Obesity and Inflammation Sierra Nance, Ramiah Jacks, and Carey Lumeng

519

56 Obesity, Gallbladder, and Gastrointestinal Diseases Michael Camilleri and Andres Acosta

527

57 Obesity and Liver Diseases Münevver Demir, Josephine Frohme, and Frank Tacke

537

58 Obesity, Lung Function, and Lung Disease Anne E. Dixon and Swati A. Bhatawadekar

548

59 Obesity and Gout Lorraine Watson and Edward Roddy

556

60 Obesity, Osteoarthritis, and Bone Disorders Sanjay Mediwala and Dennis T. Villareal

565

61 Obesity and Cognitive Decline Adam Tabak, Vince Fazekas-Pongor, Stefano Tarantini, Priya Balasubramanian, Anna Csiszar, and Zoltan Ungvari

572

62 Obesity, Mental Health, and Health-Related Quality of Life Osnat C. Melamed, Peter Selby, and Valerie H. Taylor

581

63 Obesity and Reproductive Dysfunction Ronald Swerdloff, Waleed Butt, and Christina Wang

588

Contents  xi 64 Obesity in Pregnancy Complications and Outcomes Amy M. Valent and Aaron B. Caughey

597

65 Obesity, Growth, Development, Metabolic Disorder, and Insulin Resistance in Pediatrics Nicola Santoro, Alfonso Galderisi, and Sonia Caprio

608

66 Bias, Discrimination, and Obesity Rebecca M. Puhl

617

Index 627

Preface Welcome to the fourth edition of the Handbook of Obesity. The first edition was published in 1998 with subsequent editions in 2004, 2010, and 2014. The intervening years have seen enormous progress in our understanding of the etiology of obesity as well as innovative approaches to its prevention and treatment. The previous editions of the Handbook of Obesity were edited by George A. Bray and Claude Bouchard, both of whom are subject to the laws of biology. For this reason, we invited four colleagues from the Pennington Biomedical Research Center to join us as associate editors in selecting and editing chapters for the two volumes that make up this edition. We are thus pleased to publish this update of the Handbook of Obesity in the form of two coordinated and comprehensive volumes. Shortly after the first edition, translational research took hold and it became evident that the “therapeutic” strategies for the treatment of obesity would need more space, and this was accomplished by splitting the second edition into two volumes. Subsequently, the therapeutic volume was updated and published separately without the basic science volume. Between the publication of the last edition in 2014 and today, the growth of the science underlying the increase in the prevalence of obesity and the emphasis on translational research led to the need for a new edition of each volume with a high number of new authors and completely renewed content. During the 2 years that it took from inception to completion of this work, we have been confronted with the SARS-CoV-2 pandemic. Obesity is a risk factor for COVID-19, which highlights the challenges of this and other diseases for patients with obesity. This pandemic even affected the Handbook of Obesity in other ways

too. Several of the editors had been infected along with many of the authors, and one author, Dr. David Haslam, even succumbed to COVID-19 before publication. In brief, Volume 1 covers the epidemiology, etiology, and physiopathology of obesity, and Volume 2 covers the clinical applications associated with the translation of basic science into treatment strategies for obesity. Mechanistic studies relevant to the regulation of energy balance have become increasingly powerful with the incorporation of genomic sequencing, multi-omics, computational biology, and bioinformatics. Many drugs that were available in 1998 are no longer available, and new and more potent ones have come on the market. With these two volumes, we believe the reader has access to the latest research and clinical practice in the field. We are indebted to the authors for maintaining a tight writing schedule so that all chapters would appear in a reasonably short time after submission. We are also deeply indebted to Melanie Peterson for her tireless hard work as the editorial assistant for this publication. She has made extensive editorial suggestions and facilitated editing of the chapters in a timely fashion. Without her diligent and continuing support, this book might never have seen the light of day—thank you from the bottom of our hearts. We also thank the publisher, especially Shivangi Pramanik, commissioning editor at CRC Press/Taylor & Francis Group, and her staff for helping us rapidly move this book from manuscript to published book. Their timely help has allowed us to get the book assembled during the COVID-19 pandemic.

xiii

About the Editors George A. Bray, MD is recognized for his research on obesity and is among the 1000 most cited scientists. In 1989, he became the first executive director of the Pennington Biomedical Research Center of Louisiana State University in Baton Rouge, Louisiana, and is now a Boyd Professor Emeritus. He was a principal investigator for the Diabetes Prevention Program Study and the Look AHEAD study, two multicenter trials funded by the National Institutes of Health. He is a member of many professional societies, including the Obesity Society. In 1977, he cofounded the International Journal of Obesity. In 1982, he founded the North American Association for the Study of Obesity (now the Obesity Society), and in 1993 founded its journal Obesity, and he was a founding editor of Endocrine Practice in 1995. Bray has received the Goldberger Award from the American Medical Association; was elected to the Society of Scholars at Johns Hopkins University; and received the Osborne–Mendel Award from the American Society for Nutrition, the McCollum Award from the American Society of Clinical Nutrition, the Mead Johnson Award, the Tops Award, Stunkard Award, the Presidential Medal from the Obesity Society, and in 2019, the W.O. Atwater Award from the U.S. Department of Agriculture and the American Society for Nutrition.

Claude Bouchard, PhD is Boyd Professor Emeritus at the Pennington Biomedical Research Center. He served as the executive director of the Pennington Center for a decade and was the director of the Human Genomics Laboratory of the center for 20 years. His research deals with the genetics of adaptation to exercise and nutritional challenges, as well as the genetics of obesity and its comorbidities. He is a past president of the Obesity Society and of the International Society for the Study of Obesity. He has authored or coauthored more than 1,000 scientific papers and has written or edited 35 books. He has been a foreign member of the Royal Academy of Medicine of Belgium since 1996. He became an officer of the Order of Leopold II of Belgium in 1994, a member of the Order of Canada in 2001, and a chevalier in the Ordre National du Quebec in 2005. Bouchard has received seven honoris causa doctorates. Early in his career, from 1965 to 1999, he was on the Kinesiology Faculty at Laval University, Quebec City, and he was made a professor emeritus, Faculty of Medicine, upon his retirement. He is a fellow of the American Association for the Advancement of Science and six other scientific societies.

xv

Associate Editors Peter T. Katzmarzyk, PhD is professor and associate executive director for Population and Public Health Sciences at the Pennington Biomedical Research Center where he holds the Marie Edana Corcoran Endowed Chair in Pediatric Obesity and Diabetes. Katzmarzyk is an internationally recognized leader in the field of physical activity and obesity, with a special emphasis on pediatrics and ethnic health disparities. He has over 2 decades of experience in conducting large clinical and population-based studies in children and adults. Katzmarzyk has a special interest in global health and has a record of building research capacity in physical activity and obesity research in developing countries. He has published his research in more than 625 scholarly journals and books and has delivered more than 235 invited lectures in 16 countries. He is associate editor-in-chief for Medicine and Science in Sports and Exercise, associate editor for American Journal of Human Biology, and an editorial board member for Metabolic Syndrome and Related Disorders and Pediatric Exercise Science. Katzmarzyk served on the 2018 U.S. Physical Activity Guidelines Advisory Committee for the U.S. Department of Health and Human Services and the World Health Organization Guideline Development Group for the WHO 2020 Guidelines on Physical Activity and Sedentary Behaviour. John P. Kirwan, PhD is executive director of the Pennington Biomedical Research Center and holds the George A. Bray, Jr. Endowed Super Chair in Nutrition. He also leads the Integrated Physiology and Molecular Metabolism Laboratory at Pennington Biomedical and is the director/principal investigator of the Louisiana Clinical and Translational Science Center.

His professional expertise includes over 30 years of research, teaching, and service in the obesity and diabetes fields. He received his clinical physiology training at Washington University School of Medicine in St. Louis, Missouri; his PhD in human bioenergetics at Ball State University, Muncie, Indiana; his MSc in exercise biochemistry from the University of Massachusetts, Amherst, Massachusetts; and his BA (Hons) from the University of Limerick, Ireland. Kirwan leads an internationally acclaimed biomedical research program focused on diabetes, obesity, nutrition, and exercise. To date, he has generated more than $60 million in research funding, most of which has come from the National Institutes of Health and the food, pharmaceutical, and medical device industries. He has published more than 275 scientific papers related to diabetes and metabolism in prestigious peer-reviewed journals, including the New England Journal of Medicine, JAMA, Diabetes, and Diabetes Care. Leanne M. Redman, PhD is a professor in the Division of Clinical Sciences and associate executive director for Scientific Education at Pennington Biomedical Research Center in Baton Rouge, Louisiana. Her expertise is in human physiology as it relates to the quantification of energy balance, in both controlled and free-living conditions in humans. Her research is conducted with the goal to understand the mechanisms of body weight regulation to promote healthy aging across the life span, as well as to develop and test inventions for effective prevention and treatment of obesity and its comorbidities. Redman directs the Reproductive Endocrinology and Women’s Health Laboratory. The lab conducts extramurally funded studies in pregnant women (and their infants) and women with infertility with the goal to understand the impact of obesity and metabolic health on women and obesity risk in offspring. Redman is also involved in the scientific development, training, and mentoring of postdoctoral fellows and early career investigators. She has published more than 200 research articles, reviews, and book chapters concerning energy metabolism, insulin sensitivity, obesity, calorie restriction, exercise, and maternal/infant physiology.

xvii

xviii Associate Editors Philip R. Schauer, MD is the United Companies Life Insurance Co./Mary Kay and Terrell Brown endowed Professor of Metabolic Surgery at Pennington Biomedical Research Center of Louisiana State University in Baton Rouge, Louisiana. He is also director of the Metamor Metabolic Institute at Pennington Biomedical. He is past president of the American Society for Metabolic & Bariatric Surgery. He is the former chair and

founder of Obesity Week. Schauer’s clinical interests include obesity, diabetes, and metabolic surgery. He has performed more than 8,000 operations for severe obesity and diabetes and has trained more than 100 fellows in metabolic surgery. His research interests include the pathophysiology of obesity and type 2 diabetes, and outcomes of metabolic surgery. He has an H-index of 90 and has published more than 400 peerreviewed articles in high-impact journals such as the New England Journal of Medicine, Nature, JAMA, Lancet, Journal of the American College of Cardiology, Diabetes Care, Annals of Surgery, and others. He is the principal investigator of the STAMPEDE trial, which showed that surgery can put type 2 diabetes into long-term remission.

Contributors Nadia I. Abelhad Department of Cardiovascular Disease John Ochsner Heart & Vascular Institute Ochsner Clinical School New Orleans, Louisiana The University of Queensland School of Medicine Brisbane, Australia Andres Acosta Clinical Enteric Neuroscience Translational and Epidemiological Research (C.E.N.T.E.R.) Division of Gastroenterology and Hepatology Department of Medicine Mayo Clinic Rochester, Minnesota Md Akheruzzaman Department of Nutritional Sciences Texas Tech University Lubbock, Texas Christopher L. Axelrod Integrated Physiology and Molecular Medicine Laboratory Pennington Biomedical Research Center Baton Rouge, Louisiana Priya Balasubramanian Vascular Cognitive Impairment and Neurodegeneration Program Center for Geroscience and Healthy Brain Aging/Reynolds Oklahoma Center on Aging Department of Biochemistry and Molecular Biology The Peggy and Charles Stephenson Cancer Center University of Oklahoma Health Sciences Center Oklahoma City, Oklahoma D. Jeremy Barsell Department of Health Behavior and Policy Massey Cancer Center Virginia Commonwealth University Richmond, Virginia David R. Bassett, Jr. University of Tennessee Knoxville, Tennessee

Swati A. Bhatawadekar Larner College of Medicine University of Vermont Burlington, Vermont Gregory J. H. Biddle Loughborough University Loughborough, United Kingdom Stuart J. H. Biddle Diabetes Research Centre College of Life Sciences University of Leicester and NIHR Leicester Biomedical Research Centre Leicester, United Kingdom Martin Binks Nutrition & Metabolic Health Initiative (NMHI) Department of Nutritional Sciences College of Human Sciences Texas Tech University Lubbock, Texas Heidi M. Blanck Division of Nutrition, Physical Activity and Obesity National Center for Chronic Disease Prevention and Health Promotion Centers for Disease Control and Prevention Atlanta, Georgia Bruce Blumberg Department of Developmental and Cell Biology Department of Pharmaceutical Sciences Department of Biomedical Engineering University of California, Irvine Irvine, California Claude Bouchard Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana George A. Bray Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

xix

xx Contributors Nicholas T. Broskey Department of Kinesiology Human Performance Laboratory, School of Health and Human Performance East Carolina Diabetes and Obesity Institute East Carolina University Greenville, North Carolina

Sally Chiu Department of Pediatrics University of California, San Francisco San Francisco, California and Touro University California Vallejo, California

Justin C. Brown Pennington Biomedical Research Center Baton Rouge, Louisiana Waleed Butt Lundquist Institute at Harbor-UCLA Medical Center Los Angeles, California

Karine Clément Nutrition Department Pitié-Salpêtrière Hospital Assistance Publique-Hôpitaux de Paris Sorbonne University/INSERM, Nutrition and Obesities Systemic Approaches, NutriOmics, Research Unit Paris, France

Laura Guzmán Caballero School of Kinesiology and Health Studies Queen’s University Kingston, Ontario, Canada

Julianne G. Clina Department of Nutrition Sciences University of Alabama at Birmingham Birmingham, Alabama

Michael Camilleri Clinical Enteric Neuroscience Translational and Epidemiological Research (C.E.N.T.E.R.) Division of Gastroenterology and Hepatology Department of Medicine Mayo Clinic Rochester, Minnesota

Anna Csiszar Vascular Cognitive Impairment and Neurodegeneration Program Center for Geroscience and Healthy Brain Aging/Reynolds Oklahoma Center on Aging Department of Biochemistry and Molecular Biology The Peggy and Charles Stephenson Cancer Center University of Oklahoma Health Sciences Center Oklahoma City, Oklahoma

Sonia Caprio Department of Pediatrics Yale School of Medicine New Haven, Connecticut Aaron B. Caughey Oregon Health & Science University Portland, Oregon

Alexandre A. da Silva Department of Physiology & Biophysics Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center University of Mississippi Medical Center Jackson, Mississippi

Silvia Cerolini Department of Psychology Sapienza University of Rome Rome, Italy

Lissa A. Davis Department of Nutrition Science Purdue University West Lafayette, Indiana

Eugene B. Chang Section of Gastroenterology, Hepatology and Nutrition Department of Medicine University of Chicago Chicago, Illinois

Münevver Demir Department of Hepatology and Gastroenterology Charité Universitätsmedizin and Campus Virchow Clinic and Campus Charité Mitte Berlin, Germany

Eunjin Cheon Department of Nutrition Science Purdue University West Lafayette, Indiana

Robert Dent Department of Medicine Division of Endocrinology University of Ottawa Ottawa, Ontario, Canada

Contributors  xxi Jean-Pierre Després Quebec Heart and Lung Institute and Department of Kinesiology, Laval University and VITAM – Centre de recherche en santé durable, CIUSSS de la Capitale-Nationale Québec City, Canada Nikhil V. Dhurandhar Department of Nutritional Sciences Texas Tech University Lubbock, Texas Anne E. Dixon Pulmonary Disease and Critical Care Medicine University of Vermont Medical Center Larner College of Medicine University of Vermont Burlington, Vermont Jussara M. do Carmo Department of Physiology & Biophysics Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center University of Mississippi Medical Center Jackson, Mississippi Abdul Dulloo University of Fribourg Fribourg, Switzerland Andrew Elagizi Department of Cardiovascular Disease John Ochsner Heart & Vascular Institute Ochsner Clinical School New Orleans, Louisiana The University of Queensland School of Medicine Brisbane, Australia Vince Fazekas-Pongor Department of Public Health Semmelweis University Faculty of Medicine Budapest, Hungary Maria Fleseriu Pituitary Center and Departments of Medicine, and Neurological Surgery Oregon Health & Science University Portland, Oregon

Luigi Fontana Charles Perkins Centre Faculty of Medicine and Health, University of Sydney Department of Endocrinology, Royal Prince Alfred Hospital Sydney, New South Wales, Australia and Department of Clinical and Experimental Sciences, Brescia University Brescia, Italy Lawrence D. Frank Urban Studies and Planning University of California, San Diego San Diego, California Josephine Frohme Department of Hepatology and Gastroenterology Charité Universitätsmedizin and Campus Virchow Clinic and Campus Charité Mitte Berlin, Germany Bernard F. Fuemmeler Department of Health Behavior and Policy Massey Cancer Center Virginia Commonwealth University Richmond, Virginia Alfonso Galderisi Department of Pediatric Diabetes, Endocrinology and Gynecology Hôpital Necker-Enfants Malades AP-HP Paris, France Sonia Garcia-Calzon Epigenetics and Diabetes Unit Department of Clinical Sciences Lund University Diabetes Centre, Lund University Scania University Hospital Malmö, Sweden Amalia Gastaldelli Cardiometabolic Risk Group Institute of Clinical Physiology, National Research Council Pisa, Italy Sujoy Ghosh Centre for Computational Biology Duke-NUS Medical School Singapore and Laboratory of Computational Biology Pennington Biomedical Research Center Baton Rouge, Louisiana

xxii Contributors Timothy Gill Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders School of Public Health University of Sydney Sydney, New South Wales, Australia Nana Gletsu-Miller School of Public Health Indiana University Bloomington, Indiana Penny Gordon-Larsen Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill, North Carolina Kelsey Goynes Reproductive Endocrinology and Women’s Health Laboratory Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana Justin L. Grobe Department of Physiology Department of Biomedical Engineering Comprehensive Rodent Metabolic Phenotyping Core Cardiovascular Center Neuroscience Research Center Medical College of Wisconsin Milwaukee, Wisconsin John E. Hall Department of Physiology & Biophysics Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center Mississippi Center for Clinical and Translational Research University of Mississippi Medical Center Jackson, Mississippi

Mary-Ellen Harper Department of Biochemistry, Microbiology and Immunology Faculty of Medicine and Ottawa Institute of Systems Biology University of Ottawa Ottawa, Canada Daniel Harrison University of South Florida Morsani College of Medicine Tampa, Florida Fiona Haslam Historian of Art Bedfordshire, United Kingdom David Haslam (Deceased) Aberdeen University; Chester University Obesity Research at Luton & Dunstable Hospital Bedforshire, United Kingdom Vijay Hegde Department of Nutritional Sciences Texas Tech University Lubbock, Texas Leonie Heilbronn Adelaide Medical School The University of Adelaide Lifelong Health Theme South Australia Health and Medical Research Institute Adelaide, Australia Jerrold J. Heindel Healthy Environment and Endocrine Disruptor Strategies Durham, North Carolina

Michael E. Hall Department of Physiology & Biophysics Department of Medicine Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center Mississippi Center for Clinical and Translational Research University of Mississippi Medical Center Jackson, Mississippi

Berit L. Heitmann The Parker Institute, Research Unit for Dietary Studies Bispebjerg and Frederiksberg Hospital Frederiksberg, Denmark and The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders The University of Sydney Sydney, Australia and Section for General Practise, Department of Public Health University of Copenhagen Copenhagen, Denmark

Barbara C. Hansen Department of Internal Medicine University of South Florida Morsani College of Medicine Tampa, Florida

Steven B. Heymsfield Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

Contributors  xxiii James O. Hill Department of Nutrition Sciences University of Alabama at Birmingham Birmingham, Alabama

Timothy Sean Kairupan Faculty of Medicine Sam Ratulangi University Manado, Indonesia

Monalisa Hota Centre for Computational Biology Duke-NUS Medical School Singapore

Shingo Kajimura Division of Endocrinology, Diabetes and Metabolism Beth Israel Deaconess Medical Center Department of Medicine Harvard Medical School Boston, Massachusetts and Howard Hughes Medical Institute Chevy Chase, Maryland

Joseph A. Houmard Department of Kinesiology Human Performance Laboratory, School of Health and Human Performance East Carolina Diabetes and Obesity Institute East Carolina University Greenville, North Carolina Akio Inui Pharmacological Department of Herbal Medicine Kagoshima University Graduate School of Medical & Dental Sciences Kagoshima, Japan Ramiah Jacks Department of Pediatrics University of Michigan Medical School Ann Arbor, Michigan Amanda S. Janesick Department of Developmental and Cell Biology Biological Sciences University of California, Irvine Irvine, California Kai-Lin Catherine Jen Department of Nutrition and Food Science Wayne State University Detroit, Michigan Emily John School of Kinesiology and Health Studies Queen’s University Kingston, Ontario, Canada Terese Sara Høj Jørgensen Section of Social Medicine, Department of Public Health Faculty of Health and Medical Sciences University of Copenhagen Copenhagen, Denmark Sergey Kachur Department of Cardiovascular Disease Ascension Sacred Heart Pensacola, Florida

Nova Hellen Kapantow Faculty of Public Health Sam Ratulangi University Manado, Indonesia Peter T. Katzmarzyk Pennington Biomedical Research Center Louisiana State University System and Population and Public Health Sciences Pennington Biomedical Research Center Baton Rouge, Louisiana Maryam Kebbe Reproductive Endocrinology and Women’s Health Laboratory Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana John P. Kirwan Integrated Physiology and Molecular Medicine Laboratory Pennington Biomedical Research Center Baton Rouge, Louisiana Polina M. Krassovskaia Department of Kinesiology Human Performance Laboratory, School of Health and Human Performance East Carolina Diabetes and Obesity Institute East Carolina University Greenville, North Carolina Ronald M. Krauss Department of Pediatrics and Department of Medicine University of California San Francisco San Francisco, California Elisabeth Lambert Iverson Health Innovation Research Institute and School of Health Science Swinburne University of Technology Hawthorn, Victoria, Australia

xxiv Contributors Jean-Francois Landrier Aix Marseille University INSERM (National Institute of Health and Medical Research) INRAE (French National Research Institute for Agriculture, Food and Environment) C2VN (Center for CardioVascular and Nutrition Research) Marseille, France Sofus C. Larsen The Parker Institute Research Unit for Dietary Studies Bispebjerg and Frederiksberg Hospital Frederiksberg, Denmark Carl J. Lavie Department of Cardiovascular Disease John Ochsner Heart & Vascular Institute Ochsner Clinical School New Orleans, Louisiana The University of Queensland School of Medicine Brisbane, Australia Xuan Li Department of Physiology & Biophysics Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center University of Mississippi Medical Center Jackson, Mississippi

Carey Lumeng University of Michigan Medical School Department of Pediatrics Ann Arbor, Michigan Matthew D. Lynes Maine Medical Center Research Institute Scarborough, Maine and Joslin Diabetes Center Harvard Medical School Boston, Massachusetts Kristina Martinez-Guryn Biomedical Sciences Department College of Graduate Studies Midwestern University Downers Grove, Illinois Richard D. Mattes Department of Nutrition Science Purdue University West Lafayette, Indiana Ruth McPherson Atherogenomics Laboratory University of Ottawa Heart Institute Ottawa, Ontario, Canada

Charlotte Ling Epigenetics and Diabetes Unit Department of Clinical Sciences Lund University Diabetes Centre, Lund University Scania University Hospital Malmö, Sweden

Sanjay Mediwala Division of Endocrinology, Diabetes, and Metabolism Baylor College of Medicine Center for Translational Research on Inflammatory Diseases Michael E DeBakey VA Medical Center Houston, Texas

Kai Liu Adelaide Medical School The University of Adelaide Lifelong Health Theme South Australia Health and Medical Research Institute Adelaide, South Australia, Australia

Osnat C. Melamed Centre for Addiction and Mental Health Department of Family and Community Medicine University of Toronto Toronto, Canada

Caterina Lombardo Department of Psychology Sapienza University of Rome Rome, Italy Ruth J. F. Loos Novo Nordisk Foundation Center for Basic Metabolic Research University of Copenhagen Copenhagen, Denmark and The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York, New York

Erin Miller School of Kinesiology and Health Studies Queen’s University Kingston, Canada Jonathan M. Mond Centre for Rural Health University of Tasmania, Australia School of Medicine Western Sydney University Sydney, Australia Christopher D. Morrison Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

Contributors  xxv Héléna Mosbah Nutrition Department Pitié-Salpêtrière Hospital Assistance Publique-Hôpitaux de Paris Paris, France Lourdes Mounien Aix Marseille University INSERM (National Institute of Health and Medical Research) INRAE (French National Research Institute for Agriculture, Food and Environment) C2VN (Center for CardioVascular and Nutrition Research) Marseille, France Alan Mouton Department of Physiology & Biophysics Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center University of Mississippi Medical Center Jackson, Mississippi Heike Münzberg Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

Alpa V. Patel Department of Population Science American Cancer Society Kennesaw, Georgia James E. Peterman Fisher Institute of Health and Well-Being Ball State University Muncie, Indiana Sumira Phatak Department of Developmental and Cell Biology Biological Sciences University of California, Irvine Irvine, California Christine Poitou Nutrition Department Pitié-Salpêtrière Hospital Assistance Publique-Hôpitaux de Paris Sorbonne University/INSERM, Nutrition and Obesities Systemic Approaches, NutriOmics, Research Unit Paris, France

Aviva Must Department of Public Health & Community Medicine Tufts University School of Medicine Boston, Massachusetts

Matthew J. Potthoff Department of Neuroscience and Pharmacology Fraternal Order of Eagles Diabetes Research Center Iowa Neuroscience Institute University of Iowa Carver College of Medicine Iowa City, Iowa

Sierra Nance Department of Pediatrics Department of Molecular and Integrative Physiology University of Michigan Medical School Ann Arbor, Michigan

Rebecca M. Puhl Department of Human Development & Family Sciences Rudd Center for Food Policy & Health University of Connecticut Hartford, Connecticut

Nanna J. Olsen The Parker Institute Research Unit for Dietary Studies Bispebjerg and Frederiksberg Hospital Frederiksberg, Denmark

Jonathan Q. Purnell Department of Medicine Knight Cardiovascular Institute and Division of Endocrinology, Metabolism, and Clinical Nutrition Oregon Health & Science University Portland, Oregon

Ana C. M. Omoto Department of Physiology & Biophysics Mississippi Center for Obesity Research Cardiorenal and Metabolic Diseases Research Center University of Mississippi Medical Center Jackson, Mississippi

Leanne M. Redman Reproductive Endocrinology and Women’s Health Laboratory Pennington Biomedical Research Center Louisiana State University Baton Rouge, Louisiana

Sohyun Park Division of Nutrition, Physical Activity and Obesity National Center for Chronic Disease Prevention and Health Promotion Centers for Disease Control and Prevention Atlanta, Georgia

Matthew D. Robson The Oxford Centre for Clinical Magnetic Resonance Research University of Oxford Perspectum, Gemini One Oxford, United Kingdom

xxvi Contributors Edward Roddy Primary Care Centre Versus Arthritis School of Medicine Keele University and Haywood Academic Rheumatology Centre Midlands Partnership NHS Foundation Trust Keele, United Kingdom Robert Ross School of Kinesiology and Health Studies School of Medicine, Department of Endocrinology and Metabolism Queen’s University Kingston, Ontario, Canada

Philipp E. Scherer Touchstone Diabetes Center Department of Internal Medicine University of Texas Southwestern Medical Center Dallas, Texas Patrick Schrauwen Faculty of Health, Medicine and Life Sciences Maastricht University Maastricht, the Netherlands Thaddeus T. Schug National Institute of Environmental Health Sciences Division of Extramural Research and Training Cellular, Organ and Systems Pathobiology Branch Research Triangle Park, North Carolina

Pasquale E. Rummo Department of Population Health New York University Grossman School of Medicine New York, New York

Yves Schutz University of Lausanne Lausanne, Switzerland

J. Michael Salbaum Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana

Jacob C. Seidell Department of Health Sciences Vrije Universiteit Amsterdam Amsterdam, the Netherlands

Ricardo J. Samms Diabetes, Obesity and Complications Lilly Research Laboratories Eli Lilly and Company Indianapolis, Indiana

Peter Selby Centre for Addiction and Mental Health Department of Family and Community Medicine Department of Psychiatry Dalla Lana School of Public Health University of Toronto Toronto, Canada

Mary Samuels Department of Medicine Division of Endocrinology, Metabolism, and Clinical Nutrition Oregon Clinical and Translational Research Institute Oregon Health & Science University Portland, Oregon Alexandra M. Sanchez Department of Cardiovascular Disease John Ochsner Heart & Vascular Institute Ochsner Clinical School New Orleans, Louisiana University of Queensland School of Medicine Brisbane, Australia Nicola Santoro Department of Pediatrics Yale School of Medicine New Haven, Connecticut R. Drew Sayer Department of Nutrition Sciences University of Alabama at Birmingham Birmingham, Alabama

Pratap Seshachalam Centre for Computational Biology Duke-NUS Medical School Singapore Flavie Sicard CRIBIOM (Criblage BIOlogique Marseille)/PhenoMars Marseille, France Karri Silventoinen Population Research Unit Faculty of Social Sciences University of Helsinki Helsinki, Finland Rebecca A. Simmons Department of Pediatrics Center for Research on Reproduction and Women’s Health Perelman School of Medicine at the University of Pennsylvania Philadelphia, Pennsylvania

Contributors  xxvii Brooke Smith Pennington Biomedical Research Center Louisiana State University System Baton Rouge, Louisiana and Texas Children’s Hospital Houston, Texas Thorkild I. A. Sørensen Section of Epidemiology, Department of Public Health Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen, Denmark Norbert Stefan Department of Internal Medicine IV, University Hospital Tübingen Institute of Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen German Center for Diabetes Research (DZD) Tübingen, Germany Marta Sumińska Poznań University of Medical Sciences Poznań, Poland Ronald Swerdloff David Geffen School of Medicine at UCLA Lundquist Institute at Harbor-UCLA Medical Center Los Angeles, California Adam Tabak Department of Public Health Department of Internal Medicine and Oncology Semmelweis University Faculty of Medicine Budapest, Hungary and Department of Epidemiology and Public Health University College London London, United Kingdom Frank Tacke Department of Hepatology and Gastroenterology Charité Universitätsmedizin and Campus Virchow Clinic and Campus Charité Mitte Berlin, Germany

Stefano Tarantini Vascular Cognitive Impairment and Neurodegeneration Program Center for Geroscience and Healthy Brain Aging/Reynolds Oklahoma Center on Aging Department of Biochemistry and Molecular Biology The Peggy and Charles Stephenson Cancer Center Department of Health Promotion Sciences, the Hudson College of Public Health University of Oklahoma Health Sciences Center Oklahoma City, Oklahoma Kristina L. Tatum Department of Pediatrics Virginia Commonwealth University Richmond, Virginia Valerie H. Taylor Department of Psychiatry University of Calgary Calgary, Canada André Tchernof School of Nutrition and Quebec Heart and Lung Institute Laval University Québec City, Canada Lauren R. Teras Department of Population Science American Cancer Society Kennesaw, Georgia Alison Tovar School of Public Health Brown University Providence, Rhode Island Angelo Tremblay Department of Kinesiology Faculty of Medicine Laval University Québec City, Québec, Canada Yu-Hua Tseng Joslin Diabetes Center Harvard Medical School Boston, Massachusetts

xxviii Contributors Zoltan Ungvari Vascular Cognitive Impairment and Neurodegeneration Program Center for Geroscience and Healthy Brain Aging/Reynolds Oklahoma Center on Aging Department of Biochemistry and Molecular Biology The Peggy and Charles Stephenson Cancer Center Department of Health Promotion Sciences, the Hudson College of Public Health University of Oklahoma Health Sciences Center Oklahoma City, Oklahoma Amy M. Valent Oregon Health & Science University Portland, Oregon Vinicius M. Valicente Department of Nutrition Science Purdue University West Lafayette, Indiana Hector O. Ventura Department of Cardiovascular Disease John Ochsner Heart & Vascular Institute Ochsner Clinical School New Orleans, Louisiana The University of Queensland School of Medicine Brisbane, Australia Dennis T. Villareal Division of Endocrinology, Diabetes, and Metabolism Baylor College of Medicine Center for Translational Research on Inflammatory Diseases Michael E. DeBakey VA Medical Center Houston, Texas

Lijin Wang Centre for Computational Biology Duke-NUS Medical School Singapore Yu Wang Department of Nutrition Science Purdue University West Lafayette, Indiana Zhen Wang Department of Physiology & Biophysics Mississippi Center for Obesity Research and Cardiorenal and Metabolic Diseases Research Center University of Mississippi Medical Center Jackson, Mississippi Lorraine Watson Primary Care Centre Versus Arthritis School of Medicine Keele University Keele, United Kingdom Margriet Westerterp-Plantenga Faculty of Health, Medicine and Life Sciences Maastricht University Maastricht, the Netherlands Katherine R. White University of British Columbia School of Population and Public Health Vancouver, Canada

Vernon Volante University of South Florida Morsani College of Medicine Tampa, Florida

Tammy Ying The Cell and Molecular Biology Graduate Group Perelman School of Medicine at the University of Pennsylvania Philadelphia, Pennsylvania

Christina Wang David Geffen School of Medicine at UCLA Clinical and Translational Research Center at the Lundquist Institute, Los Angeles, California

Miaobing Zheng Institute for Physical Activity and Nutrition Research School of Exercise and Nutrition Sciences Deakin University Geelong, Australia

Introduction to Volume 1 Volume 1 of the fourth edition of the Handbook of Obesity covers the basic science aspects under the broad topic areas of epidemiology, etiology, and pathophysiology of obesity. The content of the volume is detailed in 66 chapters organized into five parts. Important advances have occurred over the past decade, and the reader will notice that some of the early topics have been merged and are now addressed together in a single chapter. Some areas have enjoyed significant growth and are now dealt with in multiple chapters. The attentive reader will also appreciate that there are many chapters devoted to basic science topics that were not considered in prior editions. The focus of Volume 1 is on two fundamental questions: What is the science of obesity and related fields telling us about the factors participating in the etiology of obesity? This global topic is examined from biological, behavioral, and environmental perspectives. What are the consequences of obesity for the individual and society? This is addressed in multiple chapters. It is always a challenge for a publication of the magnitude of the Handbook of Obesity to be as current as possible at the time of publication. We believe that we have successfully met this challenge in this new edition. However, due to the size limit imposed on each chapter and the number of references to be cited, it should be kept in mind that not all the published work related to relevant topics could be cited for some chapters. When this occurs, it is by no means the result of an intentional omission on the part of the authors. The content of the volume is organized as described next.

the epidemiology of obesity around the world, while the final chapter on age, sex, and ethnic variation completes this part of the volume.

PART 1: HISTORY, DEFINITIONS, AND PREVALENCE

PART 3: BEHAVIORAL DETERMINANTS OF OBESITY

Part 1 is organized into six chapters. The first chapter identifies the historical references to excess weight or corpulence and the changes surrounding the notion of obesity that have taken place from the early days of Homo sapiens to our current era. This is complemented by a chapter on obesity in art and literature. Two chapters deal with direct and surrogate measurements of adiposity and obesity-related traits. One chapter focuses on

Eight chapters are devoted to the behavioral determinants of obesity. Among them, three chapters relate to food, beverages, and ingestive behavior. They are followed by chapters dealing with smoking, smoking cessation and vaping, breastfeeding, and sleep duration and pattern. To complete this part of the volume, there are chapters on sedentary behavior, occupational work, and leisure-time physical activity and obesity.

PART 2: BIOLOGICAL DETERMINANTS OF OBESITY There are 26 chapters in this part of the volume. The part begins with chapters on bioenergetics, energy dissipation mechanisms, and exposure to experimental overfeeding. Five chapters are devoted to genetic and epigenetic evidence, and they are followed by a chapter on fetal life and early postnatal influences on obesity. Other chapters provide information on nonhuman primates and rodents as models for obesity research and discuss how to address mechanistic questions using these resources. They are followed by chapters on the regulation of energy balance at multiple levels. The role of the sympathetic nervous system and endocrine determinants in the etiology of obesity is addressed. Various aspects of adipogenesis and adipose tissue metabolism are examined in other chapters. The topics of skeletal muscle biology and molecular aspects of bioenergetics are subsequently addressed. In addition, three chapters cover metabolic rates, energy expenditure, and energy partitioning. The last chapter reviews the evidence on infections and adiposity.

xxix

xxx Introduction to Volume 1

PART 4: ENVIRONMENTAL, SOCIAL, AND CULTURAL DETERMINANTS OF OBESITY Part 4 comprises six chapters. The opening chapter discusses the role of agriculture and the food industry in the current obesity epidemic. It is followed by chapters on transportation modes, the urban environment, social and economic aspects of obesity, and ethnic and cultural differences. A final chapter is devoted to the complex question of environmental pollutants and how they can lead to enhanced fat deposition and obesity.

PART 5: CONSEQUENCES OF OBESITY Part 5 contains 20 chapters dealing with the health consequences of obesity. The first chapter focuses on obesity and mortality rates. It is followed by chapters addressing the topics of obesity and heart disease, hypertension, lipoprotein metabolism, insulin resistance and diabetes, metabolic syndrome, cancer, and inflammation and immunity. The topics of obesity and gallbladder disease, hepatic biology, pulmonary functions, and arthritis and gout are covered in the next series of chapters. Two chapters are focused on mental health and quality of life. Obesity, reproductive functions, and pregnancy outcomes are addressed next. Volume 1 concludes with two chapters on growth and health disorders in pediatric populations, and on bias and discrimination affecting persons with obesity.

PART 1

History, Definitions, and Prevalence

From Hippocrates to the Obesity Society A Brief History

1

George A. Bray and Marta Sumin´ska We do not live in our own time alone; we carry our history within us. Gaarder (1995) [1] The history of truth is neither linear nor monotone. Canguilhem (1988) [2]

1.1 INTRODUCTION This chapter presents an overview of historical events related to obesity. The curious reader can refer to other sources [3–14]. These historical events will be viewed against the recent surge in the prevalence of obesity [15] and a background of expanding knowledge about obesity. From the time when Homo sapiens may have begun speaking 75,000 years ago, when the first figurine showing obesity was made some 35,000 years ago, to the time of the first agricultural revolution 10,000 years ago, and the scientific revolution began in 1500 CE, there has been a progressive march toward improved understanding of the world around us and its implications for obesity. We begin with the Greeks and Romans, then look backward at early representations of obesity and finally turn to changes after the scientific revolution beginning about 1500 CE. We end with a discussion of organizations and scholarly journals focusing on obesity.

1.2  OBESITY IN THE TIME OF HIPPOCRATES Hippocrates (460–377 BCE) was an astute observer of human illness, an ethical voice for the protection of patients, and a DOI: 10.1201/9781003437673-2

beacon of light in the Greek medical tradition. He was an anchor point for the medical profession beginning in Greece and lasting through the downfall of Rome some 800 years later. Hippocrates stated that “sudden death is more common in those who are naturally fat than in the lean.” His approach to treating the patient with obesity is described next: Obese people and those desiring to lose weight should perform hard work before food. Meals should be taken after exertion and while still panting from fatigue and with no other refreshment before meals except only wine, diluted and slightly cold. Their meals should be prepared with sesame or seasoning and other similar substances and be of a fatty nature as people get thus, satiated with little food. They should, moreover, eat only once a day and take no baths and sleep on a hard bed and walk naked as long as possible. [16]

Both Greek and Roman physicians treated patients with obesity. Soranus of Ephesus, an early 2nd-century CE Greek physician, described obesity as a disease resulting from eating too much. Within the Greek humoral model of disease, treatment was needed to rebalance what was out of balance [17]. Soranus’s regimen consisted of laxatives and purgatives along with exercise, heat, and massage. “Fat individuals should vomit in the middle of the day, after a running or marching exercise and before taking any food.” The emetic for this purpose was half a cup of the hyssop plant (0.15 L) ground with 3 L of water to which vinegar and salt were added. Vinegar was another favorite for use in the context of humoral medicine. Since obesity was considered as a “moist and cool” condition, dry and warm vinegar would be an appropriate balancing agent. Galen (circa 130–200 CE), another leading Roman physician distinguished, “moderate” and “immoderate” forms of obesity, the latter perhaps anticipating the “morbid” category of obesity used today by some physicians. 3

4  Handbook of Obesity

1.3  OBESITY PRIOR TO HIPPOCRATES For thousands of years before Hippocrates, human beings had made statues depicting obesity, beginning in the Upper Paleolithic age about 40,000 years ago. Among the 97 known figures from this period, 51 were obese [18]. The Venus of Hohle Fels, which dates from 35,000 years ago [19], is the oldest, but the Venus of Willendorf from Germany, standing 11 cm tall, is the most famous [20]. These figurines were found along a 2000 km band from Southwestern France to Southern Russia [21; see map in Reference 22]. They are made of ivory, limestone, or terracotta, leading Beller [3] to suggest that “obesity was already a fact of life” for Paleolithic humans. Recently, Johnson et al. analyzed the waist circumference and waist-to-hip ratio of 40 or so of these figurines and argued that the further they were from the glacial front, the less obese they were, suggesting that obesity and the extra fat may have survival value [23]. The Neolithic or New Stone Age, 8000–5500 BCE, saw the first agricultural revolution, the domestication of animals, and the establishment of human settlements. It also produced numerous figurines appearing to be obese found in the Mediterranean basin. Although hunter-gatherers are typically lean [24], “thrifty genes” might enhance the ability of these people to store food energy as fat and thus enhance survival in the face of scarcity.

1.4  OBESITY FROM HIPPOCRATES TO THE INDUSTRIAL REVOLUTION 1.4.1 Obesity in the Islamic Tradition The teachings of Muhammad in the 7th century spread rapidly across the Middle East, North Africa, and into the Iberian Peninsula. Baghdad in the east and Cordoba on the Iberian Peninsula became centers of excellence. Among the many men who flourished during this period was Mohamed ibn Zakaria Al-Razi (841–926). In his book Al-Hawi FitTibb (An Encyclopedia of Medicine), which summarized all the available knowledge on obesity at that time, he noted in detail treatments including diet, drugs, exercises, massage, hydrotherapy, and lifestyle changes. Years later, Ibn Hubal Al-Baghdady (1121–1213) also reported that “hugely obese persons” fell ill quickly. He managed them with heavy exercises on an empty stomach and stressed the importance of a gradually increasing schedule of exercise rather than abruptly moving to heavy activities. Another Arab physician, Ibn el Nefis (1207–1288), in his book Al Mujaz Fit-Tibb (The Concise Book of Medicine), reported on the association

between excessive obesity and cardiovascular and cerebrovascular accidents, saying: Excessive obesity is a constraint on the human being limiting his freedom of actions constricting his pneuma (vitality) which may vanish and may also become disordered as air may not be able to reach it. They [excessively obese persons] run the risk of a fatal vessel rupture causing sudden death or bleeding into a body cavity.

This doctor also distinguished a special type of excessive obesity in those who are “obese by birth” (congenitally obese). He recognized that “they are usually cool-tempered, slendervesselled, subfertile, could not endure hunger or thirst, and medicaments hardly reach their organs except with difficulty and after a long time” [25]. In addition to these physicians, we will highlight two others, Avicenna (Ibn Sina) (980–1037 CE) from Baghdad, and the Jewish physician Hasdai ibn Shaprut (917–970 ACE) from Cordoba on the Iberian Peninsula.

1.4.1.1 Abu Ali ibn Sina (Avicenna) Abu Ali ibn Sina (Avicenna) was a prolific and influential author who published the most influential medical textbook of the Middle Ages. Avicenna published works on religion, law, metaphysics, mathematics, astronomy, and medicine, but his masterpiece is the five-volume work on medicine called The Canon on Medicine (Kitab al-Qanun). It was translated into Latin in the 11th century and used in Western medical education until at least the 17th century [26]. Avicenna describes four stages in the management of the patient with obesity:

1. Produce a rapid descent of the food from the stomach and intestines, in order to prevent completion of absorption by the mesentery 2. Take food that is bulky but feebly nutritious 3. Take a bath before food, often 4. Hard exercise

1.4.1.2 Hasdai ibn Shaprut The physician Hasdai ibn Shaprut from Cordoba on the Iberian Peninsula is famous because he treated King Sancho the Fat. Sancho I (aka Sancho the Fat) (935?–966 CE) of Leon (Pamplona, Spain) became king of Leon in 958 CE when his elder brother King Ordono III died. “The nobility of Leon thought Sancho was weak-willed because of his obesity” and he was dethroned [27]. Sancho’s grandmother Toda Arnez was determined to help Sancho regain his throne. First, she took him to local physicians who were unsuccessful in helping him. Although she loathed the Muslims in the southern Iberian Peninsula (Spain) in the 10th century, Toda and Sancho sought the help of Hasdai ibn Shaprut, a brilliant and learned Jewish physician from Cordoba. Shaprut was a physician to the Muslim Caliph in Cordoba, a city in the 10th century that occupied a place similar to Rome in the 1st century or New York City in the 20th

1  •  From Hippocrates to the Obesity Society  5 century. In a day when house calls were still in fashion, Shaprut went to Pamplona to evaluate Sancho. He agreed to take him as a patient but advised him to relocate to Cordoba, and so he did. The medicine that Sancho received was “Theriaca,” a mixture of ingredients that are said to have originated with Mithridates VI in Grecian times. It may contain up to 64 or more ingredients, with the principal ones being opium, ginger, cinnamon, myrrh, saffron, and castor oil, a well-known laxative. Over time, Sancho gradually lost weight. After losing weight, he could mount his horse and regained his kingdom [27].

1.5  FROM THE SCIENTIFIC REVOLUTION TO THE PRESENT From our vantage point, most of the key developments in understanding and treating obesity came as the result of new knowledge generated during the scientific revolution beginning around 1500 AD. Table 1.1 presents a summary of some key events during the last 5 centuries [28].

TABLE 1.1  Short Summary of Events in the History of Science and Obesity since 1500 AD WORLD SCENE

SCIENCE AND TECHNOLOGY

BIOLOGY AND MEDICINE

15th century

Printing

16th century

Copernicus (heliocentric theory)

Vesalius (anatomy)

OBESITY

17th century

1649 English Civil War

Galileo (telescope) Boyle (laws of temperature and pressure)

Harvey (blood circulation) Malpighi (pulmonary circulation) Hooke (micrographia)

Santorio (metabolic balance) Benvieni (fat dissection)

18th century

1776 American Revolution 1789 French Revolution Napoleon

Watt (steam engine) Photography Electrical cell Atomic theory Electromagnetism

Morgagni (1st pathology text) Lind (On Scurvy) 1780 Cullen (Classification) Oxygen and hydrogen discovered

1727 Short (1st monograph on corpulency) 1760 Flemyng (2nd monograph on corpulency) 1780s Lavoisier (Oxygen Theory of Metabolism)

19th century

1861 US Civil War SpanishAmerican War

Wohler (urea synthesized from inorganic molecules) Mendeleev (periodic table of elements) Bernard (liver glycogen) Morphine, cocaine, quinine, amphetamine Ions hypothesized Internal combustion engine

Jenner and vaccination Laennec (stethoscope) Schwann (cell theory) Morton (ether anesthesia) Helmholtz (ophthalmoscope) Darwin (Origin of the Species) Semmelweis (puerperal fever) Lister (antiseptic surgery Mendelian genetics Sherrington and reflex arc 1897 Pavlov identified conditioned reflexes

1810 Wadd (On Corpulency) 1826 Brillat-Savarin (Physiologie du gout) 1835 Quetelet (body mass index) 1848 Helmholtz (conservation of energy) 1849 Hassall describes fat cell 1863 Banting (1st diet book) 1866 Sleep apnea described 1870 Fat cell identified 1896 Atwater (room calorimeter) 1900 Babinski and 1901 Fröhlich describe hypothalamic obesity

20th century

World War I World War II Vietnam War World Trade Center 9/11/2001 Iraq War Afghanistan War

Salvarsan Wright Brothers’ flight Vacuum tube Theory of relativity Sulfonamides Nylon DDT Vitamins E = mc2, atomic bomb Quantum physics Rockets Transistor Laser Computed tomography Magnetic resonance imaging scans Internet CRISPR-Cas9

1902 Secretin (1st hormone) 1921 Banting isolates Insulin 1928 Fleming finds penicillin Operant conditioning Salk and Sabin polio vaccines Watson Crick DNA hypothesis AIDS Genetic engineering Human genome

1912 Cushing syndrome 1914 Gastric contractions and hunger 1932 Garrod (Inborn Errors of Metabolism) Family study of obesity Genetically obese animals Amphetamine used to treat obesity CT/MRI scans for visceral fat DXA and density for body fat Doubly labeled water 1994 Leptin Laparoscopic surgery Combination therapy Glucagon-like peptide agonists

Genome wide scans Functional brain imagining

Setmelanotide First SGLT drug 2012 to 2020 Lorcaserin Guidelines for Rx obesity

21st century

Source: Adapted and extended from Prentice AM et al., Proc Nutr Soc (2005), PMID: 15960860, DOI: 10.1079/pns2005421 [28].

6  Handbook of Obesity

1.5.1 16th Century Figure 1.1 presents a timeline for scientific and historical events related to obesity, science, and politics in the 16th century. The year 1543 was a seminal year with the publication of the heliocentric theory of the solar system by Copernicus (1473–1543) and De Humani Corporis Fabrica by Vesalius

FIGURE 1.1  Timeline of events in the 16th century.

(1514–1564), the first modern anatomy book based on his own dissections. The practice of medicine and treatment of patients with obesity was still governed by the Grecian humoral theory developed 2000 years earlier. Interestingly, the first doctoral dissertation with fatness in its title was published in 1595 [4].

1  •  From Hippocrates to the Obesity Society  7

1.5.2 17th Century Figure 1.2 provides a timeline depicting some of the key scientific and historical events in the 17th century. Anatomic dissections of obese individuals occurred in the 17th century and some 3000 were collected into a work

FIGURE 1.2  Timeline of events in the 17th century.

by Theophile Bonnet (Bonetus) (1620–1689). Tobias Venner (1577–1660) used the word “obesity” for the first time in 1660 [27]. The Italian physician Santorio Santorio (1561–1636), a professor of medicine at Padua, Italy, developed a balance beam on which he weighed himself for 30 years recording data on changes in his weight when eating and evacuating. He

8  Handbook of Obesity discovered the concept of “insensible” loss of water and can aptly be called the “Father of Metabolic Obesity” [5]. The theme of “metabolism” was expanded by Robert Boyle (1627–1691), who demonstrated that when a lighted candle burned out in a closed chamber, a mouse in the same chamber rapidly died. This was the result of the depletion of oxygen, but oxygen wouldn’t be discovered for another century. Circulation of blood was discovered by William Harvey (1578–1657) in 1628 [5].

1.5.3 18th Century The oxygen theory of metabolism, formulated by Antoine Lavoisier (1743–1794), overthrew the older phlogiston theory and opened up the field of energy metabolism [5]. These and other events related to science, medicine, and obesity in the 18th century are depicted in Figure 1.3. The first monographs devoted to obesity were published in 1727 by Thomas Short (1690–1772) [29] and in 1760 by Malcolm Flemyng (d. 1764) [30]. Short opens his book by stating: “I believe no age did ever afford more instances of corpulency than our own.” He believed that obesity was the result of an imbalance in bodily humors, and treatment required restoration of the body’s natural balance, ideally by living where the air was not too moist or soggy and avoiding flat, wet countries, cities, and woodlands. Short considered exercise to be important for the obese and diet should be “moderate, spare and of the more detergent kind.” Flemyng believed obesity could be called a disease. He listed four causes of corpulency:

1) “The taking in of too large a quantity of food, especially of the rich and oily kind.” 2) People with obesity had “too lax a texture of the cellular or fatty membrane … whereby its cells or vesicles are liable to be too easily distended.” 3) An abnormal state of the blood facilitated the storage of fat in the vesicles. 4) “Defective evacuation.” Flemyng believed that sweat, urine, and feces all contained “oil”, and obesity could be treated by eliminating this oil through the administration of laxatives, diaphoretics, or diuretics. He was a strong proponent of the use of mild soap in treatment of obesity [30].

1.5.4 19th Century There were many monographs and scholarly dissertations about obesity published in the 19th century [4]. Two monographs appeared in 1811, one in French by Ange Maccary (dates unknown) [31] and one in English by William Wadd (1776–1829) [32]. Wadd, a surgeon, and noted Member of the Royal College of Surgeons said:

If the increase of wealth and the refinement of modern times, have tended to banish plague and pestilence from our cities, they have probably introduced the whole train of nervous disorder, and increased the frequency of corpulence.

[30, p. 3; 3333] He went on to say: It has been conjectured by some, that for one fat person in France or Spain, there are an hundred in England. I shall leave others to determine the fairness of such a calculation. [30, p. 5; 32]

The concept of energy balance expanded in the 19th century. In 1848 Hermann von Helmholtz (1821–1894) published the laws of the conservation of mass and of energy [5]. The question of whether the first law of thermodynamics—the conservation of energy—applied to humans, was settled by Wilbur Olin Atwater (1847–1907) when he and Edward D. Rosa constructed the first human calorimeter in 1899. By measuring the oxygen consumed by a subject sealed in this “metabolic” chamber, they proved that humans, like all other animals, obeyed the first law of thermodynamics [33]. Studies in the 18th century established that digestion was a chemical process different from fermentation and putrefaction. The elegant experiments by William Beaumont (1785–1853) in the 19th century were conducted on a volunteer who, through a bullet wound, ended up with an opening, or fistula, into his stomach. By observing the stomach, as well as by introducing and then removing food from the stomach, Beaumont established the true nature of digestion as a chemical process [5]. In France, Claude Bernard (1813–1878) established the idea of “homeostasis,” or the stability of the internal milieu surrounding the cells in the body. He also showed that the liver could release glucose into the circulation. The low carbohydrate diet that was given to William Banting by his physician William Harvey, who had read Bernard’s work, allowed Banting to lose nearly 50 pounds in one year [34]. He was so excited by his success, that Banting self-published his “miracle diet” in a pamphlet titled Letter on Corpulence Addressed to the Public in 1863. In the 19th century, the Belgian statistician and astronomer, Lambert Adolphe Francois Quetelet (1796–1874), based on his attempts to validate a mathematical way to estimate human size independent of height, developed the concept of the body mass index (body weight [kg] divided by the square of height [m2]), which has become a major way of assessing weight status in populations [5]. Following the recognition by Adolph Schwann (1810– 1882) in 1839 that the “cell” was the basic unit of biology, it was a small step for Arthur Hill Hassall (1817–1894) to identify the adipocyte as the storage cell for fat [5]. These and other events in the 19th century related to obesity and science are presented in a timeline in Figure 1.4.

1  •  From Hippocrates to the Obesity Society  9

FIGURE 1.3  Timeline for events in the 18th century.

10  Handbook of Obesity

FIGURE 1.4  Timeline of events for the 19th century.

1.5.5 20th Century Figure 1.5 is a timeline showing key developments during the 20th century. At the beginningg of the 20th century, it became clear that obesity had more than one cause [5]. Babinski

(1857–1932) in Paris and Fröhlich (1871–1953) in Vienna each described a patient with obesity caused by hypothalamic damage. A decade later, a different etiology was published by the American neurosurgeon Harvey Cushing (1869–1939), who described the association of a basophilic adenoma of the pituitary with obesity. Other distinctive

1  •  From Hippocrates to the Obesity Society  11

FIGURE 1.5  Timeline of events for the 20th century.

types of obesity were due to genetic defects in both animals and human beings. Bardet–Biedl and Alström syndrome are recessively inherited, whereas Prader–Willi syndrome, described in 1956 by Andras Prader and colleagues, results from a chromosomal alteration. The discovery of leptin in 1994 by Jeffrey Friedman (1954–) was a milestone in

providing evidence for a specific genetic lesion that was invariably associated with obesity [35]. When deficient in leptin, individuals become very fat, and this can be reversed by treatment. Sadly, the discovery of leptin did not provide a cure for obesity, but did show that obesity was more than “weak willpower.”

12  Handbook of Obesity Therapeutic advances in the treatment of obesity during the 20th and 21st centuries occurred in three areas: lifestyle, new and better medications, and surgery. Lifestyle or behavioral strategies can be dated to the seminal work of Ivan Pavlov (1849–1936), who showed that dogs could learn to associate the ringing of a bell with the availability of food, i.e., conditioned behavior. An alternative strategy, called operant conditioning, was developed half a century later by Burrhus Frederic Skinner (1904–1990), who used the principle that rewarding spontaneous activities would increase the frequency of their occurrence. Richard B. Stuart applied the principles of behavioral techniques to people with obesity. His initial results were astounding: an average 19.1% weight loss over 1 year, a result that has never been surpassed. His paper initiated a tidal wave of behavioral studies that are now a standard part of almost all programs for weight management [5]. Three medications dominated the first half of the 20th century: thyroid hormone, which entered clinical practice in 1893; 2,4-dinitrophenol, which made its entry into clinical medicine in 1933 before being withdrawn by the US Food and Drug Administration (FDA) in 1938; and amphetamine, which began in the 1930s and continued as part of the rainbow pill treatment programs of bariatric physicians [34]. The righthand side Figure 1.6 shows a time frame for various pharmacological strategies. Amphetamine was effective in reducing food intake, but it also produced addiction, which led to the search for drugs with less addictive potential. Phentermine arose from this search and is currently the most widely used antiobesity drug, even though it is only approved for use for a few weeks. Fenfluramine, another amphetamine-like drug, turned out to work on the serotonin neurotransmitter system, not the noradrenergic neurotransmitter system like most other sympathomimetic drugs. Clinical use of fenfluramine was short-lived because it produced cardiac valvopathy and pulmonary hypertension. The discovery that glucagon-like peptide-1

reduces food intake led to the development of analogs that appear to open a bright new future [5]. The 20th century saw significant refinements in the measurement of energy expenditure using doubly labeled water [5]. When water with both hydrogen and oxygen-carrying isotopic labels is consumed, it is possible to measure energy expenditure by exploiting differences in the ways that the hydrogen and oxygen atoms are metabolized in the body. Application of this technique has confirmed the fundamental principle that people with obesity expend more energy than people who are not obese and must therefore consume more energy to maintain their higher body weight, let alone gain weight. This technique has also shown that people who are overweight underestimate their food intake more than people of normal weight, which has challenged the validity of a large body of research based on conventional dietary records and has important implications for the practical management of obesity [5]. The importance of central adiposity was identified in actuarial data from 1901, which showed that excess weight, especially around the abdomen, was associated with shortened life expectancy. It was the studies of Jean Vague (1911–2002), working in Marseille, France, that clearly established the overriding importance of abdominal (central) adiposity. His studies led to the popular apple-shaped (upper body obesity or android obesity) and pear-shaped (lower body or gynoid obesity) body descriptions [5]. Surgical techniques have provided the largest and most durable effects on body weight and are reviewed elsewhere in this volume.

1.5.6 21st Century Figure 1.6 shows many of the events during the first 20 years of the 21st century. At the time of this writing, the worldwide

FIGURE 1.6  An inverted pyramid showing the expanding base of knowledge during the 20th century. The left side shows major events in the history of obesity. The right side shows in brackets the periods when several drugs were used for the treatment of obesity.

1  •  From Hippocrates to the Obesity Society  13 pandemic of SARS-CoV-19 was raging and people with obesity were at higher risk for getting the disease and its complications. Guidelines for the management and evaluation of patients with obesity were provided by the Edmonton Staging System in 2009, the American College of Cardiology and collaborators in 2013, and the American Association of Clinical Endocrinologists in 2014. On the genetic front, the fat and obesity-related gene (FTO) was consistently identified as one among multiple markers of increased risk for obesity and diabetes. Beiging of white adipose tissue was identified and offered the hope that activation of this “thermogenic” pathway might provide new therapeutic approaches to treat obesity. Advances in the use of smartphones to gather and deliver information from patients with obesity added to the clinician’s armamentarium. Bariatric–metabolic surgery was shown to reverse diabetes and delay its onset. Pharmacotherapy for obesity produced both good and bad news. The bad news was the continuation into the 21st century of a trend for approved drugs to run into post-marketing problems and be withdrawn. This happened to dinitrophenol, methamphetamine, phenylpropanolamine, and fenfluramine [35] in the 20th century, and continued into the 21st century with the withdrawal of sibutramine in 2010 (approved by the FDA in 1997) and the removal of lorcaserin in 2020 (approved by the FDA in 2012). A third drug, rimonabant, which had been approved in Europe in 2006, was withdrawn in 2008 due to enhanced suicidality. The good news was that new and better medications continued to appear. The first were the glucagon-like peptide-1 receptor agonists. With the arrival of semaglutide, which was approved for the management of diabetes in 2017, a new threshold was crossed. In clinical trials for obesity, weight losses of nearly 15% were observed, putting this class of drugs into competition with bariatric–metabolic surgery for successful weight loss.

1.6  OBESITY COMES OF AGE: FORMATION OF ORGANIZATIONS AND JOURNALS FOCUSING ON OBESITY, 1950–2021 1.6.1 Scientific Organizations Historically, “scientific” societies began to appear in the 17th century, with the British Royal Society being founded in 1660 and the French Academie des Sciences in 1666 [36]. In North America, organizations in science and medicine began before the revolutionary war with the Massachusetts Medical Society being founded in 1781 by John Warren. A pivotal point was the founding of the American Medical Association in 1846– 1847 [37]. The American Association for the Advancement of Science was formed in 1848 and marked the emergence of a

national scientific community in the United States. Gradually, this splitting of scientific interest in societies representing focused scientific interest reached the field of obesity. Two American monographs on obesity can be used to frame the onset of the organized interest in obesity. The first, Obesity and Leanness by Hugo Rony (1888–1972), was published in 1939 [38] and the second “Obesity…” 10 years later in 1949 by Edward N. Rynearson (1901–1987) and Clifford Gastineau (1920–2018), two Mayo Clinic physicians [39]. Just as Rony’s book was published in 1940, Western Research Laboratories, a company making rainbow pills to treat obesity, held its first Symposium on Obesity. In 1950, 10 years later, when Rynearson and Gastineau published their monograph, bariatric physicians formed the National Glandular Society, which became the American Association of Bariatric Physicians and then the Obesity Medicine Association in 2015 (Table 1.2). The years 1966 to 1968 were important for obesity [41]. In 1967, a group of physicians in the United Kingdom formed the Obesity Society, which soon changed its name to the Association for the Study of Obesity (ASO) [43]. It held a scientific symposium in London in 1968, and its proceedings were edited by Ian McClean Baird and Alan Howard [40]. On the American side of the Atlantic, the multicolored rainbow pills, containing amphetamine, thyroid extract, digitalis, diuretics, and laxatives, among other things, had proliferated after World War II. When deaths associated with their use began to be reported a Senate Select Committee of the US Congress was empaneled in 1968 to hold hearings on alleged misuse of these medications [43]. In this same year, Western Research Laboratories held its 28th Annual Symposium on Obesity in Denver, Colorado. Finally, in 1968, the US Congress approved the formation of the John E. Fogarty International Center at the National Institutes of Health (NIH). Among its first activities was to organize an international conference on obesity. George Bray chaired this meeting, which was held at the NIH on October 1–3, 1973 [44]. As the Fogarty Center conference was coming together in 1972, Bray had the opportunity to meet with Alan Howard, one of the editors of the 1968 publication by the Association for the Study of Obesity in the UK. During their meeting, they looked down the road at the future of obesity [6]. Out of this meeting came the idea of organizing additional International Congresses on Obesity to follow the Fogarty Center meeting, the need for a journal focused on publishing articles about obesity and obesity research, and the need for more regional associations focused on obesity, an idea from which the North American Association for the Study of Obesity (NAASO), the precursor to The Obesity Society (TOS), would arise (see later) [45]. The British ASO organized the First International Congress on Obesity held at the Royal College of Physicians on October 9–11, 1974. The Second International Congress on Obesity was held in Washington DC 4 years later with help from the National Council on Obesity, which had been formed in Los Angeles in 1975, and the Nutrition Foundation as two of its sponsors along with the Fogarty Center, which held a second

14  Handbook of Obesity TABLE 1.2  Formation of Obesity Associations ASSOCIATION NAME

YEAR

FOUNDER(S)

COMMENTS

National Glandular Society (Then: American Society of Bariatric Physicians and now Obesity Medicine Association) Obesity Association (Now: Association for the Study of Obesity)

1950

Argentinian Obesity Association North American Association for the Study of Obesity (Now: The Obesity Society)

1979 1981

Irving Fisher JM Shreve Ted Alexander Philip Lebon John Butterfield Trevor Silverstone Jorge Braguinsky George A. Bray

American Society of Bariatric Surgeons (Now: The American Society of Metabolic and Bariatric Surgeons) International Association for the Study of Obesity (IASO) (Now: World Obesity) European Association for the Study of Obesity

1983

Edward E. Mason

1986

Barbara C. Hansen

Selects sites for meetings, select award winners

1986

Associated with World Obesity

1994 1995

Per Bjorntorp and Stephan Rossner Jorge Braguinsky and others W. Philip James Nicola Scopinaro

1998

13 members in 2020

Associated with World Obesity

2006

Arya M. Sharma

2006

Nicolas Christou Mehran Anvari Daniel Birch Robert Kushner

Federacion Latinomericana de Sociedades de Obesidad (FLASO) International Obesity Task Force International Federation for the Surgery of Obesity (Now: International Federation for the Surgery of Obesity and Metabolic Disorders) Asia-Oceania Association for the Study of Obesity Canadian Obesity Network (Now: Obesity Canada) Canadian Association of Bariatric Physicians and Surgeons American Board of Obesity Medicine

1967

1990

2011

conference at the NIH [46]. Other International Congresses on Obesity followed at intervals of 3 or 4 years [47]. Interest in the formation of an association similar to the British ASO was stimulated in North America by the Second International Congress on Obesity in Washington DC and by regular discussions between George Bray and John Brunzell. NAASO was founded in 1981 by Bray, along with Brunzell, C. Wayne Callaway, M.R.C. Greenwood, and Judith Stern [38]. It was also in 1981 that NAASO was asked to form a Joint Publication Committee with ASO in England to select a new editor for the International Journal of Obesity [47]. Greenwood hosted the first meeting of NAASO at Vassar College in the fall of 1982 with the title “Types of Obesity: Animal Models and Clinical Applications” [6, 40]. There were 37 abstracts of scientific work submitted for the NAASO meeting [40]. As part of this meeting, there was also a conference funded by the NIH with the intriguing title “Methods of Characterizing Obesity,” based on a grant obtained by Callaway, who was on leave from the Mayo Clinic [48]. The second NAASO meeting was held in 1983 during the Fourth International Congress on Obesity in New York City. At this meeting, Barbara Hansen

Physicians treating patients with obesity

Held 1968 Symposium in London Hosted First International Congress, 1974 London Holds first meeting Held first meeting in 1982. Joint Publication Committee with ASO to select new editor for International Journal of Obesity

Associated with World Obesity Incorporated into IASO/World Obesity

was elected president, Stern as secretary, and Anne Sullivan as treasurer. In 1984, Brunzell hosted the third NAASO meeting at the University of Washington in Seattle on October 6–7, focusing on the role of exercise in obesity. The fourth NAASO meeting was organized by Hansen as a Joint Conference of the American Diabetes Association and NAASO held in Toronto, Canada, in 1985 [40]. Obesity associations continued to proliferate into the 21st century. The American Society for Bariatric Surgery was founded in 1983 by Edward Mason, a pioneering bariatric– metabolic surgeon from Iowa. Its name was changed to the American Society for Bariatric and Metabolic Surgeons to reflect the consequences of their surgical procedure on patients with obesity. In 2006, the Canadian Obesity Network was established and soon changed its name to Obesity Canada. The Canadian Association of Bariatric Physicians and Surgeons was also founded in 2006. Regional associations representing several countries also emerged, including the International Association for the Study of Obesity (IASO; now World Obesity) (1986), the Federation for the Surgery of Obesity (IFSO) (1986), the Federation of Latin American Societies

1  •  From Hippocrates to the Obesity Society  15 of Obesity (FLASO) (1990), the European Association for the Study of Obesity (EASO) (1993), and the Asia-Oceania Association for the Study of Obesity (AOASO) (1998). To provide evidence of training in the care of patients with obesity, the American Board of Bariatric Medicine began a certifying examination in 1997. In 2011 it joined the American Board of Obesity Medicine to provide a single North American certifying organization. World Obesity initiated a similar program called SCOPE (Strategic Centre for Obesity Professional Education) in 2003 to provide evidence that physicians and other healthcare professionals had a background base of knowledge in the management of obesity.

1.6.2 Formation of Scientific Journals Focusing on Obesity Accompanying the formation of scholarly associations was the publication of scientific journals to disseminate the findings of the “new science.” There were scientific and medical journals founded by professional societies and others founded without the support of a scientific society [45, 48]. Many journals failed to survive more than 10 years, and those that did were much more likely to have a scientific or medical society supporting them. The number of scientific journals has grown logarithmically since their inception in the 17th century [49]. The doubling time has been between 10 and 20 years. The International Journal of Obesity is the longest continuously published journal with “obesity” in its title. The earlier Journal of Obesity ceased publication after 1 year and Obesity and Bariatric Medicine survived for 12 years. During the publication of the Proceedings from the First International Congress on Obesity in 1974, Howard and Bray interested Newman Publishing in undertaking the publication of the International Journal of Obesity. Other journals with obesity in their title began to appear in expanding numbers [42]. It was in 1991 at the tenth-anniversary celebration of the founding of NAASO that the NAASO Council gave approval for a new journal [50]. There was a steady logarithmic increase in the number of journals with “obesity” in their title until the advent of electronic journals open the floodgates for the market. Where the expansion will stop is unclear, with one, Pillars of Obesity, being launched by Obesity Medicine in 2021. More detail on these developments can be found in Reference 50.

REFERENCES 1. Gaarder J et al. Sophie’s world: A novel about the history of philosophy. New York: Berkley Books; 1996. xii, 523 pp. 2. Canguilhem G. Ideology and rationality in the history of the life sciences. Cambridge, MA: MIT Press; 1988. xi, 160 pp. 3. Beller AS. Fat & thin: A natural history of obesity. 1st ed. New York: Farrar, Straus and Giroux; 1977. 310 pp.



4. Bray GA. Int J Obes (1990). PMID: 2276853. 5. Bray GA. The battle of the bulge: A history of obesity research. Pittsburgh, PA: Dorrance Publishing; 2007. 6. Bray GA. Annu Rev Nutr (2015). PMID: 26185976/DOI: 10.1146/annurev-nutr-121214-104412. 7. Foxcroft L. Calories & corsets: A history of dieting over 2,000 years. London: Profile Books; 2011. 232 pp. 8. Gilman SL. Obesity: The biography. Oxford and New York: Oxford University Press; 2010. xvi, 214 pp. 9. Guerrini A. Obesity and depression in the enlightenment: The life and times of George Cheyne. Norman, OK: University of Oklahoma Press; 2000. xx, 283 pp. 10. Haslam D, Haslam F. Fat, gluttony and sloth: Obesity in medicine, art and literature. Liverpool: Liverpool University Press; 2009. 326 pp. 11. Karasu SR. Of epidemic proportions: The art and science of obesity. Expanded Edition. San Francisco, CA: Blurb; 2019. 12. Power ML, Schulkin J. The evolution of obesity. Baltimore, MD: Johns Hopkins University Press; 2009. ix, 392 pp. 13. Schwartz H. Never satisfied: A cultural history of diets, fantasies, and fat. New York and London: Free Press; Collier Macmillan; 1986. vii, 468 pp., 16 p. of plates p. 14. Stearns PN. Fat history: Bodies and beauty in the modern West. New York: New York University Press; 1997. xvi, 294 pp. 15. Rodgers A et al. Lancet Public Health (2018). PMID: 29501260/ DOI: 10.1016/S2468-2667(18)30021-5. 16. Hippocrates, PJ. Hippocrates on diet and hygiene. London: Zeno; 1952. 207 pp. 17. Papavramidou N, Christopoulou-Aletra H. Obes Surg (2008). PMID: 18386109/DOI: 10.1007/s11695-007-9362-1. 18. Jozsa LG. Hormones (Athens) (2011). PMID: 22001136/DOI: 10.14310/horm.2002.1315. 19. Conard NJ. Nature (2009). PMID: 19444215/DOI: 10.1038/ nature07995. 20. Angell W. Die Venus von Willendorf. Wien: Edition Wien; 1989. 79 pp. 21. Hitchcock D. Don’s maps: What was the purpose of the Paleolithic Venus figures? [updated June 25, 2021; cited 2021 September 30]. Available from: https://www​.donsmaps​.com​/ venus​.html. 22. Editors of the national geographic. Icons of the stone age. History 2021; December pp 18–31. 23. Johnson RJ et al. Obesity (Silver Spring) (2021). PMID: 33258218/DOI: 10.1002/oby.23028. 24. Prentice AM et al. Proc Nutr Soc (2005). PMID: 15960860/ DOI: 10.1079/pns2005421. 25. Abdel-Halim RE. Lancet. PMID: 16023509/DOI: 10.1016/ S0140-6736(05)66907-3. 26. Gruner OC. A treatise on the canon of medicine of Avicenna incorporating a translation of the first book. Birmingham: The Classics of Medicine Library; 1984. 27. Hopkins KD, Lehmann ED. Lancet (1995). PMID: 7623606/ DOI: 10.1016/s0140-6736(95)92830-8. 28. Bray GA. History of obesity. In: Obesity science to practice. Williams, G., Fruhbeck, G., editors. Chichester: WileyBlackwell; 2009. 29. Short T. A discourse concerning the causes and effects of corpulency together with the method for its prevention and cure. London: J. Robert; 1927. 79 pp. 30. Flemyng M. A discourse on the nature, causes and cure of corpulency. London: L. Davis and C. Reymers; 1760. 31. Maccary A. Traite sur la polysarcie. Paris: Crochard; 1811. 192 pp. 32. Wadd W. On corpulence; or obesity considered as a disease with a critical examination of ancient and modern opinions, relative to its causes and cure. London: J. Callow; 1816.

16  Handbook of Obesity 33. Atwater WO, Rosa EB. Description of a new respiration calorimeter and experiments on the conservation of energy in the human body experiment station bulletin. U.S. Department of Agriculture Office of the Experimental Station; 1899. 34. Bray GA, Obes Res (1993). PMID: 16350570/DOI: 10.1002/ j.1550-8528.1993.tb00604.x. 35. Zhang Y et al. Nature (1994). PMID: 7984236/DOI: 10.1038/ 372425a0. 36. Major RH. A history of medicine. Springfield: Charles C. Thomas; 1954. 37. Marks G, Beatty WK. The story of medicine in America. New York: Scribner; 1973. xi, 416 pp. 38. Rony HR. Obesity and leanness. Philadelphia, PA: Lea & Febiger; 1940. 300 pp. 39. Rynearson EH, Gastineau CF. Obesity. 1st ed. Springfield, IL: C. C. Thomas; 1949. viii, 134 pp. 40. Bray GA et al. Obesity (Silver Spring) (2021) PMID: 34813174/ DOI: 10.1002/oby.23319. 41. Howard AN. Int J Obes Relat Metab Disord (1992). PMID: 1335982. 42. McLean Baird I et al. Obesity: Medical and scientific aspects, Proceedings of the First Symposium of the Obesity Association of Great Britain held in London, October 1968. Edinburgh: E. & S. Livingstone; 1969.

43. Diet pill industry: Hearings before the Subcommittee on Antitrust and Monopoly of the Committee on the Judiciary, United States Senate, Ninetieth Congress, Second Session Pursuant to S. Res. 26 (January 23, 24, 26, 30, and 31 and February 2, 1968). 44. Bray GA, John E. Fogarty international center for advanced study in the health sciences. Obesity in perspective: Fogarty international center series on preventive medicine. Washington, DC: U.S. Govt. Print. Off.; 1976. 45. Bray GA Howard AN. Founding of the international journal of obesity: A journey in medical journalism. Int J Obes (Lond). 2015 Jan;39(1):75–9. DOI: 10.1038/ijo.2014.77. 46. Bray GA. Obesity in America. Washington, DC: United States Government Printing Office; 1979. 47. Bray GA, Hansen BC. IASO Newsletter (2003). 48. Bray GA. Obes Res (1995). PMID: 7712362/DOI: 10.1002/ j.1550-8528.1995.tb00123.x. 49. Ziman JM. The force of knowledge: The scientific dimension of society. Cambridge and New York: Cambridge University Press; 1976. ix, 374 pp. 50. Bray G. The Founding of Obesity Research/Obesity: A brief history. Obesity (Silver Spring) 2022 Nov;30(11):2100–2102. doi: 10.1002/oby.23426.

Obesity in Art and Literature Fiona Haslam and David Haslam

2.1  INTRODUCTION TO OBESITY IN ART AND WESTERN LITERATURE People with obesity have often provoked mixed reactions, with fashion and culture playing their part. A range of artistic and literary images on various topics may be of some value in assessing public perceptions of obesity as reflected by members of the general public, past and present, since characters with obesity in literature mirror those of people with obesity in society as a whole. Some people with obesity have been regarded as jolly and pleasure-loving but weak-willed; others are seen as lazy and self-indulgent, but their problems are those of real people, and their presence is usually of some significance to the plot, where one exists. This chapter will examine early literary and artistic views, followed by several examples from more recent literature. Examples of obesity in politics, obesity and disease in artistic writings, and finally obesity as seen in children will also be described.

2.2  LITERARY EXAMPLES OF OBESITY BEFORE THE 18TH CENTURY 2.2.1 Chaucer and His Work, Including Canterbury Tales Chaucer, an English writer from the 14th century, held up a mirror to the England of his time in The Canterbury Tales, which he began writing in 1387 and covered a period of 13 years until his death. William Blake said that the characters among Chaucer’s pilgrims were those which compose all ages and nations: “they are the physiognomies or lineaments of universal human life.” In “The Pardoner’s Tale,” for example, Chaucer describes the gluttony among them this way: DOI: 10.1201/9781003437673-3

2

O, if men knew how many a malady Proceeds from gluttony and from excess, They’d be so much more moderate and frugal With what they eat when they sit down at table

[1] Gluttony at that time was not always associated with fatness, corpulence, or obesity. A thin person might be a glutton; an act of overindulgence might be the sin. Being fat per se was not an issue.

2.2.2 Hieronymus Bosch and the Seven Deadly Sins Gluttony is considered one of the seven deadly sins. The painting The Seven Deadly Sins by Hieronymus Bosch, c. 1475– 1485, (Figure 2.1) is in the form of an eye in the center of which is an image of Christ—an all-seeing being—surrounded by images of perceived sins. (“Cave, cave, Dominus vides— Beware, beware the Master sees.”). “Gula,” or gluttony, is one of the sinful actions, echoing Chaucer’s condemnation. Food, drink, and its consumption are the central themes in a scene of disorder and overindulgence. Even a child begs for more. A glutton’s fate is depicted by a hat pierced by an arrow hanging on a wall, the arrow pointing to the glutton as a symbol of his deathly fate [2].

2.2.3 William Shakespeare’s Falstaff Arguably, Falstaff, in Shakespeare’s Henry IV and The Merry Wives of Windsor, is the epitome of British obesity—a benchmark for the literal portrayal of a corpulent male body. He is introduced as a buffoon, a character who led Henry astray as a youth—a gross, fat fellow, a boaster and a coward, a glutton, “fat-witted and slothful,” a thief, always ready to cheat the weak and prey upon the poor. He had made himself indispensable to Prince Hal by his perpetual gaiety and power to excite laughter. But Falstaff is set up as a warning: Who was to be the prince’s role model in life—the chivalrous Hotspur or the decadent Falstaff? Shakespeare likened Falstaff’s bodily 17

18  Handbook of Obesity

FIGURE 2.1  Hieronymus Bosch, The Seven Deadly Sins, painting, 1475–1485. (Original in Prado Museum, Madrid, Spain.)

corruption with the disruption he could cause to the body politic if he continued to hold influence. When the prince becomes king, he rejects Falstaff and his lifestyle saying: There is a devil haunts thee in the likeness of a fat old man: a tun of man is thy companion. Why dost thou converse with that trunk of humours, that bolting-hutch of beastliness that swoln parcel of dropsies, that huge bombard of sack, that stuffed cloak-bag of guts, that roasted Manningtree ox with a pudding in his belly … worthy in nothing?

[3] From the bragging soldier of his youth, Falstaff becomes an aged, fat, sexually depleted male, perceived as a comic old man in The Merry Wives of Windsor (see Figure 2.2). The happy corpulence of his youth had become the melancholic obesity of old age.

2.2.4 Ben Jonson’s Ursula from Bartholomew Fair (1614) A second example is Ursula—a pig-woman in Ben Jonson’s play Bartholomew Fair, who was also an “earthy,” low-life character [4]. She describes herself as “all fire and fat.” She had a booth at the fair where she roasted pigs. She perspired copiously—“watering the ground like a great garden pot”— quenched her thirst with ale and taught her colleagues how

FIGURE 2.2  Image of Falstaff statue outside the Royal Shakespeare Theatre in Stratford-upon-Avon, England, Shakespeare’s birthplace.

2  •  Obesity in Art and Literature  19 to cheat customers by mixing colt’s foot with tobacco “to itch it out” and to “froth” their ale well. “Be ever busy and mistake away the bottles and cans in haste before they be half drunk—never heed a call till you have brought fresh.” With these tricks, she prospered.

2.3  OBESITY IN LITERARY CHARACTERS AFTER THE 17TH CENTURY Characters with obesity in 18th- and 19th-century literature mirror those of people with obesity in society as a whole. Some have been regarded as jolly, pleasure-loving but weakwilled; others are seen as lazy, greedy, and self-indulgent, even repulsive, but their problems are those of real people and their presence is usually of some significance to the plot. Literary examples from all walks of life and all levels of society have been described, and the narrative gives a view into the perceptions and attitudes held toward obesity at the time.

2.3.1 Charles Dickens’s Mrs. Gamp from Martin Chuzzlewit Mrs. Gamp also qualifies as a character from the lower side of life (see Figure 2.3). She was one of Dickens’s characters in Martin Chuzzlewit (1843–1844) [5]. She was a “fat old woman.” She was not comely, ample, broad, or stout— descriptions, which might be applied to those of a more “polite” maternal variety; she was just fat with a somewhat red and swollen nose and breath decidedly perfumed with spirits. She was a low comic character who provided some relief in times of trouble—as a monthly nurse or midwife for a “lying in” or “with equal zest and relish” at a “laying out.”

2.3.2 Oscar Wilde’s Lady Bracknell A certain stoutness can be a positive property for both men and women. Lady Bracknell in Oscar Wilde’s The Importance of Being Earnest (1895) needs only her embonpoint to signify her own importance and stature. Portly, elderly widows, aunts, and mothers inhabit many 19th-century novels. Their size denotes their significance in the unfolding drama; their physical inactivity and afternoon teas go some way to explain their size. Victorian literature seems to accept that women will become overweight with age. Fair, fat, and fifty equated with ample, comely, and stout. Research bears this out to some extent showing that body fat in elderly women is about 5% greater than in young women [6].

FIGURE 2.3  Mrs. Sairey Gamp, Charles Dickens’s character from Martin Chuzzlewit.

2.3.3 Mrs. Tulliver from Mary Ann Evans (pen name George Eliot) George Eliot’s chubby Mrs. Tulliver from The Mill on the Floss had, for a woman of 50, a very comely face and figure, and had been left “sitting stout and helpless” when the child, Maggie, whose hair she was brushing, escaped her reach [7].

2.3.4 Edith Wharton’s Mrs. Mingott Mrs. Manson Mingott is a more extreme and unfortunate example—“a vision to behold.” She is the literal embodiment of woman-made flesh designed by Edith Wharton in The Age of Innocence (1920). The immense accretion of flesh which had descended on her in middle life like a flood of lava in a doomed city had changed her from a plump active little woman with a neatly turned ankle into something as vast and august as a natural phenomenon. [8]

In spite of her will and determination, Mrs. Mingott succumbed to a stroke, one of the comorbidities associated with obesity and compounded with inactivity. She was found early one morning “with a crooked smile on her face and one little hand hanging limp from its huge arm.”

20  Handbook of Obesity

2.3.5 Father Thomas Murray in Erin McGraw’s “Ax of the Apostles”

2.3.7 Charles Dickens’s Mr. Bumble from Oliver Twist

In modern times, overeating is perceived as a psychological disorder rather than as a crime against the divine order. This is demonstrated in a short story written in 2003 by Erin McGraw, “Ax of the Apostles.” Father Thomas Murray, a philosophy lecturer in a seminary, had been warned by his doctor that the rise in his blood sugar levels, his expanding weight and waistline, and his family history warranted serious consideration. Initial attempts to lose weight by staving off hunger by drinking glasses of water no longer worked. “One night he awoke and let his hunger propel him to the kitchen. He ate two and a half pieces of cheese cake, went back to bed and slept as if pole-axed.” Afterward, almost every night he stole down to the kitchen for some snack—whatever was left in the fridge. He stored what he could in a plastic bag that he kept in his desk drawer, available for those hunger periods between classes before dinner. His hunger was becoming a kind of insanity. Food never left his mind [9].

Mr. Bumble in Oliver Twist by Dickens was fat and choleric. He perspired as he walked and had to “mop his brow.” He also had a great idea of his oratorical powers and his importance; he had, in other words, swollen into a public character. “Please, sir, I want some more.” Oliver had requested more food and was conducted to the board room of the Parish Workhouse to account for his audacity:

2.3.6 William Makepeace Thackeray’s Becky Sharp It would seem that, in many instances, in literary terms, for men to be “big” is commendable, powerful, rich, and influential, but to be grossly big, obese, or corpulent is to be set apart from the norm, and such men’s habits and other attributes are more gross. Becky Sharp, a wily young lady of the day, according to Thackeray in his novel Vanity Fair (1848), fixed her eye on the rich and unmarried Joseph Sedley. Her first sight of him was of “a very stout, puffy man in buckskins and Hessian boots, with several immense neckcloths, that rose almost to his nose, with red-striped waistcoat and an apple-green coat with steel buttons almost as large as crown pieces.” In other words, dressed in the morning costume of a dandy of the day. His bulk, however, caused Joseph some alarm and he made many attempts to get rid of his superabundant fat. He had tried every girth, stay and waistband then invented to give himself a waist and like most fat men he would have his clothes too tight, and took care that they should be of the most brilliant colors and youthful cut.

Poor Joseph; his outward appearance belied the weak character, lack of courage, and loss of moral control behind the facade. On a visit to Vauxhall, Becky is expecting him to propose marriage, but his trepidation allied with the consumption of the contents of a bowl of rack punch led to his inebriation, and applause from a surrounding audience encouraged him in his singing and jesting. The result was an inability to rise to the occasion of proposing marriage [10].

The members of the board were very sage, deep, philosophical men. Eight or ten fat gentlemen were sitting round a table. At the top of the table seated in an armchair rather higher than the rest, was a particularly fat gentleman with a very round, red face.

Oliver’s diminutive size and wants highlighted the distance between the orphans and their superiors [11].

2.4  OBESITY AND POLITICS 2.4.1 A History of John Bull An individual’s constitution is considered to be the very foundation of his well-being, but the term can also be a useful political analogy with a country’s constitution. Consider the fate of John Bull, for example, who came into being during the 18th century and whose stereotyped figure came to represent the true-born Englishman. Bull was conceived in 1712 by the Scottish physician, writer, and mathematician John Arbuthnot, who was a close friend of Jonathan Swift with whom he formed the Scriblerus Club—a group of satirical writers. As a contribution to this genre, Arbuthnot wrote five allegorical political pamphlets, titled collectively “The History of John Bull” [12] to promote the end of the prevailing war with France. In this history, the nations at war are represented as tradesmen involved in a lawsuit. Tradesman John Bull, representing the English, and his linen draper friend Nicholas Frog, representing the Dutch, compiled a lawsuit against Lewis Baboon, representing the French in the name of Louis XIV, for interfering with trade. The various events of the war and the political intrigues involved are symbolized by the stages in the progress of the suit, the tricks of the lawyers, and the devices of the principal attorney (Duke of Marlborough) to prolong the struggle. The images and perceived characteristics of those taking part in the lawsuit offer the satirists’ view of the politics of the period and the psychosomatic effects of the proceedings on the health and constitution of John Bull (aka the nation).

2  •  Obesity in Art and Literature  21 John Bull was described by his creator as: In the main, an honest plain-dealing Fellow, choleric, Bold, and of a very uncertain Temper, … but then he was very apt to quarrel with his Best Friends, especially if they pretended to govern him; if you flatter’d him, you might lead him like a child.”

Arbuthnot went on to say: John’s Temper depended very much upon the Air, his Spirits rose and fell with the Weather-glass. John was quick, and understood his business very well, but no man alive was more careless in looking into his Accounts, or more cheated by his Partners, Apprentices, and Servants: This was occasioned by his being a Boon-Companion, loving his Bottle and Diversion; for to say the Truth, no Man kept a better House than John, nor spent his Money more generously.

This somewhat gullible true-born Englishman was proud of his freedom to eat his own beef and pudding and drink his own beer. These could be seen as patriotic activities with corpulence often the result. Stoutness at this time was usually associated with prosperity. Hogarth alluded to this national trait in his painting Calais Gate, or The Roast Beef of Old England (1748) (Figure 2.4). This illustrates scrawny French soldiers carrying a cauldron of soup for lunch while a rotund monk rubs his stomach in anticipation of a meal of English roast beef.

The French were generally thought by the English to have affected manners and undernourished bodies. This image was still prevalent in 1793 as an etching, French Happiness/English Misery, by Isaac Cruikshank illustrates (Figure 2.5). In this work, starving oppressed “happy” Frenchmen fight over their dinner—a frog. A miniature “Tree of Liberty”' wilts in the background. Meanwhile “English Misery” portrays a scene of overfed Englishmen enjoying their beef, pudding, and ale. The bulldog gorging by their feet is considered to be a breed of great courage, formerly used for baiting bulls. What better dog to represent a typical English character—one possessed of obstinate courage? Size signified perceived self-importance. How did foreigners see the English? In the 18th century, they were considered by the French as undignified, unstylish, gluttonous, and morose. John Bull fitted their bill. Although conceived in 1712, John Bull did not appear overtly in visual images aligned with Arbuthnot’s character reference until later. Then, in the middle of the 18th century, he appeared in work by the artist Gillray as a stout, good-natured, and big-hearted individual who became a familiar figure as the personification of Old England. He even acquired a Union Jack waistcoat. On October 24, 1797, following news of successive naval victories against the French, including Nelson’s victory on the Nile, Gillray produced a caricature titled John Bull Taking a Luncheon (see Figure 2.6). In this, John is almost overwhelmed by the attention of naval cooks offering him such delicacies as “fricassee a la Nelson”—a large dish of battleships surplus to

FIGURE 2.4  William Hogarth, The Gate of Calais, or O, The Roast Beef of Old England, 1748. The painting was engraved in 1749 and sold as prints. Scrawny French soldiers carry a cauldron of soup-meagre, while a rotund monk rubs his stomach in anticipation of a meal of English roast beef.

22  Handbook of Obesity

FIGURE 2.5  Isaac Cruikshank, French Happiness/English Misery, etching, 1793.

FIGURE 2.6  James Gillray, John Bull taking a Luncheon: or British Cooks, cramming Old Grumble-Gizzard, with Bonne-Chere, print, 1798. Naval cooks, headed by chief chef, Nelson, are feeding John with battleships surplus to requirements following the naval Victory of the Nile in 1797.

2  •  Obesity in Art and Literature  23 requirements. John’s dimensions increased on his diet of ships washed down with good old British beer. Other artists contributed to John Bull’s rapid gain in weight and self-esteem. However, a slimmed-down version soon followed. The financial cost of victory against Napoleon in the early 19th century was high, and John became downtrodden, reduced to poverty, and almost of cadaverous proportions by a diet of debt, taxation, and oppression (see Figure 2.7), but his courage remained high and he maintained his patriotism symbolized by his stick of English oak and his clay pipe. He underwent changes at the hands of satirists to whom he had become a convenient character whose bodily size and constitution lent continuity to their prints and who was instantly recognizable. He was transformed artistically by the use of prevailing medical regimes of physicians, apothecaries, surgeons, quacks, and politicians. Treatments such as bleeding, purging, cupping, and leeching mirrored those suffered by society as a whole. Many of the caricaturists’ themes were political in design, and Bull was “treated” vicariously for the good of his constitution by political doctors. John Bull became underfed and emaciated; his own and the country’s constitutions were at stake. Images of him live on in various guises, his size dependent upon his country’s circumstances, politics, and social attitudes of the day.

FIGURE 2.7  Impoverished John Bull thinned down. Etching by George Moutard Woodward at the time of the Napoleonic wars when John Bull (aka the general public) was taxed heavily for the costs incurred.

Historically, corpulence indicated that one was successful and could afford to eat. In contrast, thinness was derided as a mark of penury.

2.4.2 Future King George IV as Prince Regent Bodily size is a useful political indicator, and the varying attributes of politicians lend themselves to artists for satirical comment. The stoutness that is acceptable for an important, upright, philanthropic individual such as American President William Howard Taft (1908–1912) can turn into an image of a lazy, gluttonous, and selfish person under other circumstances. Satirists made use of this distinction. For example, in 1797, Gillray portrayed George, Prince of Wales in A Voluptuary Under the Horrors of Digestion (Figure 2.8) as a gross individual (weighing 111 kg) with a bursting waistcoat, inelegantly sprawled in his chair, picking his teeth with a dinner fork, surrounded by empty wine bottles, bills, dice, and other indicators of his dissolute lifestyle. He was known to be weak-willed, a gambler, a glutton, lustful, and lazy. On the wall above his head is an image of the 16th century Luigi Cornaro, a Venetian nobleman who had written a book titled De Vita Sobria or The Temperate Life, advocating a restricted diet with which Cornaro had himself slimmed. This was still

FIGURE 2.8  James Gillray, print of Prince Regent in A Voluptuary under the Horrors of Digestion, 1797. This caricature of the Prince Regent illustrates his gluttony in all aspects of his dissolute lifestyle.

24  Handbook of Obesity in print during the prince regent’s lifetime and often quoted. The prince has his back turned to the nobleman—a signal of rejection? A portrait of him painted by Sir Thomas Lawrence in 1816 illustrates an upright imperial figure of stature and importance, his girth girded by a corset for a waist of 50 cm hidden beneath his robes along with his many vices.

barber-surgeon. Bleeding was a recognized form of treatment at this time, but his ministrations appear to be in vain—no blood flows. Was this the mayor’s reward for gluttony or for misguided political ideas [13]?

2.4.3 William Hogarth’s Election Entertainment

A number of medical problems can occur in people with obesity, some of which have been the subject of artistic representation from antiquity to the present.

William Hogarth’s series of paintings known as “Modern Moral Histories” has been described as novels in paint. These were narrative paintings that reflected social attitudes of the day including those of bodily size. Prints were sold by subscription to a mainly educated clientele. The first part of his “Election” series (1755–1758) was based on a rowdy and often corrupt Oxfordshire election campaign of 1754. Hogarth satirized the occasion in An Election Entertainment, during which feasting played a major role (see Figure 2.9). The obese mayor has succumbed to a fit of apoplexy following a surfeit of oysters and is being bled by the

2.5 ART, OBESITY, AND HEALTH

2.5.1 Obesity and Sleep Apnea Sleep apnea is a sleep disorder in which there are periods of shallow breathing or short periods with no breath, followed by deep gasps of breath. It is often associated with snoring and with sleepiness during the day. One of the earliest descriptions of sleep apnea is from Grecian times. Dionysius, tyrant of Heracles in Pontus in the fourth century BC at the time of Alexander the Great,

FIGURE 2.9  William Hogarth, An Election Entertainment, first part of the “Election” series of prints (1755–1758). The overweight and overfed mayor suffers from an attack of “apoplexy.” He is being bled by a barber-surgeon whose ministrations appear to be in vain, as “no blood flows.”

2  •  Obesity in Art and Literature  25 through daily gluttony and intemperance, increased to an extraordinary degree of Corpulence and Fatness and lived in fear of suffocating whilst asleep. His physicians recommended employment of attendants to thrust long, thin needles through his sides into his belly through the fat and into the flesh to awaken him every time he fell asleep, enabling him to breathe. [14]

Shakespeare provided another early example of sleep apnea in Falstaff whom he described as “snorting like a horse” to which Hal remarks, “Hark, how hard he fetches breath.” Later, in 1829, William Wadd, surgeon to the king, described contemporary cases of sleepiness associated with obesity, including a man who weighed 145 kg and “was withal so lethargic that he frequently fell asleep in company.” His condition improved with dieting during which he reduced his weight to 95 kg [15]. Fortunately, treatment for sleep apnea has much improved since that used on Dionysius.

2.5.2 Artistic Depictions of Fat Distribution: Apples and Pears In the 1940s, French physician Jean Vague differentiated between gender-specific aspects of obesity, which he called “android obesity” versus “gynoid obesity.” Men tended to android obesity reflected in fat around the abdomen and in the omentum—now considered a major endocrine organ—releasing many hormonal substances resulting in an apple-shaped figure. Women had fat around the buttocks, hips, and thighs forming a pear-shaped body configuration. Fat in these locations is metabolically less active; the greater danger there lay in the weight stress on bones and joints. The importance of fat distribution had been suggested earlier by two surgeons, Morgagni and Wadd. Different locations of fat represented different levels of risk for disease as opposed to simply the total amount of body fat [16]. Saucy seaside postcards, which became popular in the 20th century, brought the android and gynoid images with their double entendres into many homes—raising a smile, but little concern. They do, however, illustrate well the gender differentiation (see Figure 2.10) [17].

2.6  LITERARY EXAMPLES OF OBESITY IN CHILDHOOD 2.6.1 Charles Dickens’s Joe in The Pickwick Papers Joe is the fat, red-faced boy in Charles Dickens’s The Pickwick Papers (1836), whose duties entailed opening the carriage

FIGURE 2.10  “Saucy seaside postcards” illustrating android and gynoid obesity. These illustrate gynoid and android fat distribution in adults. First produced in the 20th century, they are still popular today.

door, letting down the steps, and dispensing lunch from a large hamper at the appropriate time. ‘Joe!’, said his master- ‘damn that boy, he’s gone to sleep again! Be good enough to pinch him, sir—in the leg, if you please; nothing else wakes him.’ … ‘Very extraordinary boy, that,’ said Mr. Pickwick. ‘Does he always sleep in this way?’ ‘Sleep!’ said the old gentleman. ‘He’s always asleep. Goes on errands fast asleep and snores as he waits at table.’ The fat boy rose, opened his eyes, swallowed the huge piece of pie he had been in the act of masticating when he last fell asleep, and slowly obeyed his master’s orders.

[18] Joe’s utter vacancy and bloated countenance contrasted with that of Mr. Pickwick, who was also fat but a cheery, plump man “charged with energy” (see Figure 2.11). It is alleged that Dickens had modeled Joe on one James Budden, who had bullied Dickens in his childhood. In 1956 C. Sidney Burwell and colleagues coined the term “Pickwickian Syndrome” because a patient of theirs had similar breathing and sleep problems as Joe. The obese patient had apparently sought advice for his condition after falling asleep during a game of poker when he had been dealt a full house; he had thus failed to take advantage of his situation! The term was subsequently used to describe “obstructive sleep apnea” in which the sufferer stops breathing for 10 seconds or more for over 30 times a night during their normal sleep periods. The oxygen levels in the blood fall, resulting in sleepiness or even mental confusion. We have already seen examples of sleep apnea in the description of Dionysius, the tyrant of Heraclea. Gross obesity is often associated with a reduction in chest wall movement, which causes hypoventilation, hypoxia, and hypercapnia. The

26  Handbook of Obesity

FIGURE 2.11 Charles Dickens’s character Mr. Pickwick. Illustration from The Posthumous Papers of the Pickwick Club. Mr. Pickwick is giving a speech to members of the club.

body tries to compensate for the lack of oxygen by increasing the percentage of red blood cells in the blood (polycythemia) causing an associated red face and ruddy cheeks and a risk of venous thrombosis and pulmonary edema. The chronic shortage of oxygen leads to pulmonary hypertension, right-sided heart failure, and peripheral edema. Nighttime sleep is disturbed, and the sufferer nods off uncontrollably during the day. Burwell’s patient had many of these features, which improved after he lost 18 kg in weight. The term “sleep apnea” is often used, with Pickwickian Syndrome being reserved for the more severe late stage of the condition with signs of impending heart failure [19].

2.6.2 Charles Hamilton’s (pen name Frank Richards) William “Billy” George Bunter Created in 1908 and making his debut in the first issue of The Magnet comic set at Greyfriars School, Billy Bunter is possibly childhood’s most blatant stereotypical character with obesity —a popular antihero of juvenile comic fiction (see

FIGURE 2.12  Billy Bunter on the cover of The Magnet comic, circa early 20th century.

Figure 2.12). He is portrayed as an inept, short-sighted, bumbling child, becoming massively obese. Food was his main concern: its quantity, its frequency, and its consistency—“juicy, jammy, sticky things”—interspersed with periods of somnolence. Mr. Quelch, the schoolmaster describes Billy: “lazy, idle, greedy, undutiful, slack in class, and slack at games—in no respect whatsoever a credit to this school.” Less complimentary descriptions came from his classmates including “fat, lazy, slack little porker,” “frabjous footling,” “lazy toad,” “a slacking fat frog,” and “a prevaricating fat porker.” Several comorbidities of childhood obesity are fairly accurately depicted in the stories. Bunter frequently gets short of breath on minimal exertion; he is a snorer with daytime fatigue, somnolence, and sleep apnea. He suffers from other effects of obesity, such as joint pains (the presence of which could be an excuse from playing games), and he had frequent accidents and falls from broken tree branches. Bunter, though, remained phlegmatic and resisted many attempts to lose weight

2.6.3 William Golding’s Lord of the Flies The Lord of the Flies (1954) focuses on a group of British boys stranded on an uninhabited island and their disastrous

2  •  Obesity in Art and Literature  27 attempt to govern themselves. One of the boys called Piggy “was very fat, wore glasses and had asthma.” He had a protective aunt who kept a sweet shop and let him eat as many sweets as he wanted, and he was not allowed to swim. The very name “Piggy” was an emblem of greed and grossness. Association with this plump, greedy being was obvious, even to young children, and illustrates some of the causes of obesity and its ramifications; it exemplified the psychological trauma suffered by an obese child—the isolation, the disability, the inability to take part in activities, the derision caused by being fat, and the ignominy of being called “Piggy.” Unlike Billy Bunter, William Golding’s Piggy reveals circumstances that are real, and the consequences of his obesity are stark and uncomfortable. Piggy had worn glasses since he was three; they connected him with a blurred and hazy world, with a society in which he was isolated because he was fat and different from the others, but he took some pride in being the only one in his school to have asthma—unaware of any possible association between diet, decreased physical activity, and asthma. His glasses, however, gave him some value on the island on which the children had become stranded because they could be used to start a fire, but without them, Piggy was vulnerable and unable to function. Finally, this “bag of fat” was felled by rocks cast by children turned savages: “his arms and legs twitched a bit, like a pig’s after it has been killed” [20]. Young children grow up surrounded by images of fat figures depicted for fun. One might deplore the exploitation of obesity in the form of entertainment, but who could be kinder, jollier, funnier, and plumper than Santa Claus, the reliable bearer of gifts? What about Winnie the Pooh—that cuddly, undemanding, simple bear of very little brain; Humpty Dumpty; boastful Toad from Toad Hall, with his courageous exploits with a caravan and motor car; and the Fat Controller in charge of trains? Gradually, though, the narrative changes. Billy Bunter and other comic figures carry their baggage with them, but they never grow up. Golding illustrates the threatening prospect ahead for those who do.

2.7 CONCLUSION It is not possible in one chapter to reflect a complete picture of attitudes held toward obesity in society over centuries and decades; some selection of material is inevitable. Nevertheless, it is clear that literary and artistic imagery is culturally determined and can be seen as part of social history. “Probing the history of how fat has been perceived and imagined may provide richer insights into the stuff our stereotypes are made of” [21].

ACKNOWLEDGMENTS This chapter is dedicated to the memory of David Haslam.

REFERENCES

1. Chaucer Geoffrey, ‘The Pardoner’s Tale’, Quoted from the Canterbury Tales Taken from the Oxford World Classics Edition (New York: Oxford University Press, 2003), p.399. 2. Haslam David & Haslam Fiona, Fat, Gluttony and Sloth (Liverpool: Liverpool University Press, 2009), pp.151–152. 3. Shakespeare William, King Henry IV Pt 1, Act 2, Scene 4. 4. Jonson Ben, ‘Bartholomew Fair’, 1614. 5. Dickens Charles, The Life and Adventures of Martin Chuzzlewit (Harmondsworth, Middlesex: Penguin Classics, Penguin Books Ltd, 1986), ch.19. p.378. 6. Bedogni. Obes Res (2001) PMID: 11346663/DOI: 1038/ oby.2001.3. 7. Eliot George, The Mill on the Floss (London: William Blackwood and Sons, 1860). 8. Wharton Edith, The Age of Innocence (London: Chancellor Press, Great Classic Library, 1994), pp.15–20. 9. McGraw Erin, ‘Ax of the Apostles’, in Donna Jarrell & Ira Sukrungruang, eds., ‘What Are You Looking At? The First Fat Fiction Anthology (London: Harcourt, 2003), p.102. 10. Thackery William Makepeace, Vanity Fair, 1848 (London: Penguin, 1985), p.55. 11. Dickens Charles, Oliver Twist, Autograph edition (London: Chapman & Hall, no date), p.8. 12. Arbuthnot John, The History of John Bull. Eds Alan W Bower & Robert A Erickson (Oxford: Clarendon Press, 1976), p.lv1. 13. Haslam Fiona, From Hogarth to Rowlandson – Medicine in Art in Eighteenth-Century Britain (Liverpool: Liverpool University Press, 1996), pp.270–272. 14. Smith W, ed., A Dictionary of Greek and Roman Biography and Mythology (London: John Murray, 1880) Quoted in Meir H Kryger, ‘Sleep Apnoea, From the Needles of Dionysius to Continuous Positive Airway Pressure,’ Archives of Internal Medicine (1983), vol. 143, pp. 2301–3. 15. Wadd William, ‘Cursory Remarks on Corpulence; or Obesity as a Disease’, 1816. 16. Vague Jean, ‘Sexual Differentiation, a Factor Affecting the Forms of Obesity’, La Presse Médicale (1947), vol. 30, pp.339–40. 17. Orwell George, ‘The Art of Donald McGill’, in The Collected Essays, Journalism and Letters of George Orwell, Eds. Sonia Orwell & Ian Angus, vol. 2 (London: Secker & Warburg, 1968). 18. Dickens Charles, ‘The Pickwick Papers’ Penguin Popular Classics (Harmondsworth: Penguin Books Ltd., 1994), pp.75,80,81. 19. Burwell C S, et al.,’ Extreme Obesity Associated with Alveolar Hypoventilation – A Pickwickian Syndrome’, American Journal of Medicine (1956), vol. 21, pp.811–818. Quoted in Neil J. Douglas, Clinicians Guide to Sleep Medicine (London: Arnold, 2002), p.25. 20. Golding William, Lord of the Flies (London: Faber & Faber, 1954), pp.1, 8. 21. Forth Christopher E, Fat, A Cultural History of the Stuff of Life (London: Reaktion Books Ltd, 2019), p.16.

3

Measurement of Total Adiposity, Regional Fat Depots, and Ectopic Fat Steven B. Heymsfield, Brooke Smith, and Matthew D. Robson

3.1 INTRODUCTION Overweight and obesity represent the long-term effects of positive energy balance with excessive fat accumulation that presents a health risk [1]. This chapter reviews current methods for quantifying total body, regional, and intracellular fat in research, clinical, and field settings. A confusing and misleading array of terms are often used when referring to body lipids, fat, and adipose tissue. Chemists have advanced standard nomenclature for body lipids, hydrocarbon chemical compounds that have limited solubility in water [2]. Eight groups of lipids are recognized that have wide-ranging functions in vivo: fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterols, prenols, saccharolipids, and polyketides. The glycerolipids include triacylglycerols, or triglycerides, the water-insoluble molecules often referred to collectively as “fat” and that are stored mainly in intracellular vesicles. Triacylglycerols are found widely throughout the human body, primarily in adipocytes, and with glycogen serve as a main metabolic energy reserve. Triglycerides account for about 80% of the total lipid content present in normally nourished rodent models [3], although comparable data in humans is lacking. The critical distinctions between lipid subgroups arise when developing explicit body composition methods and models. For example, computed tomography (CT) quantifies adipose tissue volume, the typical triglyceride content of which is about 80% of the mass, the remainder accounted for by water (15%), protein (4%), and minerals (85th percentile) from WHtR were 0.89 in males and 0.91 in females. These values were not improved by using sex- and age-specific exponents for WHtR, suggesting that a single threshold may be suitable for all ages.

4.6 ANTHROPOMETRIC PREDICTORS OF ABDOMINAL VISCERAL ADIPOSE TISSUE Interest in the relationship between anthropometry and imaging-derived measures of abdominal VAT began in the 1980s

4  •  Anthropometric Indicators of Adiposity  43 [72, 73], and many studies have subsequently investigated these relationships.

4.6.1 Adults Data from the Atherosclerosis Risk in Communities Study (ARIC) provided good evidence of a relationship between anthropometry and VAT in a sample of 60 women and 97 men [74]. Quadratic functions of BMI, waist circumference, weight, and subscapular skinfold were the best predictors in men, explaining 32.2%, 35.2%, 20.4%, and 20.2% of the variance in VAT, respectively. Linear functions of BMI, waist circumference, weight, and subscapular skinfold were the best predictors in women, explaining 45.8%, 45.8%, 44.3%, and 33.8% of the variance in VAT, respectively. The association with WHR was best explained by a linear function in both men and women, explaining 32.9% and 33% of the variance, respectively [74]. Age-adjusted correlations between anthropometry and VAT were investigated in another sample of 71 men and 34 women from the Netherlands [75]. Among men, BMI (r = 0.81), waist circumference (r = 0.85), and WHtR (r = 0.87) had the highest correlations with VAT, whereas WHR had a somewhat lower correlation (r = 0.78). Similar results were found for women: correlations were highest for BMI (r = 0.77), waist circumference (r = 0.72), and WHtR (r = 0.71), while they were lower for WHR (r = 0.40). The correlations between subcutaneous skinfolds and VAT were also moderate in this sample (r = 0.72 in men and 0.65 in women) [75]. Similar results were obtained in a sample of 76 White adults; the correlations with VAT were highest for waist circumference and SAD (r = 0.84–0.93) and were lower for WHR (r = 0.64–0.75) [76].

Several studies have provided evidence of the clinical utility of anthropometric measurements in identifying high levels of VAT. Pouliot and colleagues demonstrated that waist circumference and SAD were better correlates of VAT than WHR in a sample of 70 women and 81 men [23]. Rankinen and colleagues used receiver operating characteristic curve analyses in a large sample of 458 women and 331 men to examine the clinical utility of anthropometry in identifying those with elevated levels of VAT [77]. The results indicated that waist circumference was the best overall predictor of VAT (compared to BMI, percentage body fat, and WHR), whereas WHR was a poor indicator (especially in women). This work was confirmed in a sample of 690 Chinese adults, in which waist circumference, BMI, and WHR were all associated with VAT levels. However, waist circumference exhibited the highest sensitivity and specificity in identifying those with high VAT [78]. A recent study of 900 White and African American adults compared correlations between several anthropometric indicators of abdominal VAT [79]. Figure 4.3 presents the correlations from that study, and it is evident that the correlations with VAT are lower than those with total body fat in Figure 4.2. With the exception of height, the correlations ranged from about 0.40 to 0.80, and there was not one marker of obesity that stood out as superior to the others [79]. The question of whether BMI or waist circumference is the better predictor of VAT was addressed in a study of 341 White men and women [80]. Regression analysis was used to determine the independent associations of BMI and waist circumference with VAT; waist circumference was the strongest correlate of VAT (R2 = 0.76 in women and 0.55 in men), and adding BMI to the model did not explain additional variance [80]. More recently, researchers pooled data from several

FIGURE 4.3  Correlations between anthropometric measures and abdominal VAT in a large sample (n = 900) of White and African American adults. Error bars indicate 95% confidence intervals. AAM, African American men; AAW, African American women; BAI, body adiposity index; BMI, body mass index; VAT, visceral adipose tissue; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WM, White men; WW, White women. (Adapted from Barreira et al. [79].)

44  Handbook of Obesity research sites to investigate the relative contributions of BMI and waist circumference to VAT [81]. Waist circumference explained 51% of the variance in VAT, and the addition of BMI to the model added 1% [81]. The results of this study, as well as the others described in this section, suggest that waist circumference is the best anthropometric predictor of VAT; however, waist circumference explains only about half of the variance. Further work is required to determine the best approach for using anthropometry as a clinical marker of abdominal VAT.

4.6.2 Children VAT is present in humans at birth, and it increases throughout childhood into adulthood [82, 83]. But on average, children have low levels of VAT compared to adults, which sometimes makes measurement challenging. Several studies have examined the associations between anthropometric measurements and more precise measurements of VAT in children. Based on the results of several small, cross-sectional studies, VAT demonstrates consistent, moderate correlations with trunk skinfolds in children (0.47– 0.88) [84–87]. In a similar manner, VAT is moderately correlated with other anthropometric measurements such as waist circumference (r = 0.42–0.84) and BMI (r = 0.52–0.83) [84–86, 88, 89], while correlations with WHR are lower (r = 0.16–0.52) [84, 85, 88]. Brambilla and colleagues pooled data from several laboratories to analyze the association between anthropometry and VAT in 407 White and Hispanic children aged 7–16 years [90]. Regression analysis revealed that waist circumference (64.8% of the variance) and BMI (56.3% of the variance) were the best predictors of VAT. A more recent study among 6- and 7-year-olds (n = 31) reported that the highest correlations with VAT were observed for the abdominal skinfold (r = 0.72), waist circumference (r = 0.70), BMI (r = 0.69), and hip circumference (r = 0.65), whereas the correlation with WHR was much lower (r = 0.11) [91].

Taken together, the evidence suggests that VAT is moderately correlated with anthropometric measurements in children and adolescents. The highest correlations are with trunk skinfolds, waist circumference, and BMI. The correlations between VAT and WHR were consistently lower, further emphasizing that WHR is a marker of relative fat distribution (trunk vs. extremity) and may not be a good predictor of body fat stored in the visceral compartment in children. Although waist circumference is often reported to be the best anthropometric marker of VAT, the weighted average variance in VAT explained by waist circumference in the seven studies in children just described (that reported a correlation) was 52.7%. Further research is required to identify ways in which anthropometric measurements can be combined with other simple clinical measures to improve the prediction of VAT.

4.6.3 Summary of Associations between Anthropometry and VAT Given the location of VAT within the abdominal cavity, and the wide variability in abdominal subcutaneous adiposity in the population, it is difficult to imagine that anthropometric predictors of VAT will be identified that demonstrate an adequate level of precision. Figure 4.4 presents the results from two studies that considered the correlations among BMI, waist circumference, and several measures of adiposity in adults [92] and children [93]. In both samples, BMI and waist circumference are highly correlated with each other (r >0.93), while they also demonstrated similar correlations with fat mass (>0.92) and VAT (0.72–0.84). These results reinforce the notion that BMI and waist circumference are highly correlated with measures of total adiposity and less so with VAT.

FIGURE 4.4  Correlations among body composition variables in (a) adults and (b) children and adolescents. The estimates for adults represent the weighted mean of six correlations computed in samples of Black men, Black women, White men, and White women from the HERITAGE Family Study, and of White men and women from the Quebec Family Study [92]. The estimates in children are age-adjusted partial correlations (weighted mean of four age-by-race groups) in a sample of 380 White and Black children and adolescents 5–18 years of age [93].

4  •  Anthropometric Indicators of Adiposity  45

4.7 ANTHROPOMETRIC PREDICTORS OF ECTOPIC FAT Ectopic fat can be defined as body fat stored in locations and organs not normally associated with adipose tissue storage [94, 95]. There is surging interest in the metabolic health consequences of ectopic fat accumulation, particularly of fat stored in the liver, heart, muscle, and pancreas [95, 96]. Ectopic fat can be measured using imaging methods such as ultrasound, multidetector CT, MRI, and magnetic resonance spectroscopy methods [95]. The degree to which anthropometry can be used to assess ectopic fat is potentially of interest, but it has been explored in only a few studies to date. In general, the correlations between BMI and measures of ectopic fat measured in the pericardium, liver, and muscle range from ~0.30 to 0.70 [97–100]. Individuals with higher BMI (and total adiposity) tend to show higher levels of ectopic fat in several organs. However, given the weak association between anthropometry and ectopic fat observed to date, it is unlikely that precise anthropometric methods will be developed to estimate ectopic fat distribution in specific organs. Most of this association is likely explained by the joint relationships between total adiposity, adipose tissue metabolism, and ectopic body fat accumulation.

4.8 CONCLUSION Overall, there are significant associations between anthropometric measurements and more direct measures of total and regional body fat. BMI is currently recommended for screening for obesity in children and adults; nevertheless, care should be used when interpreting BMI in clinical settings. A wide range of anthropometric indicators (BMI, waist circumference, etc.) are associated with body fat levels, and in large samples, these indicators have been shown to have similar correlations with total fat mass. Waist circumference is a significant predictor of VAT and is typically a stronger predictor than other anthropometric measures. However, waist circumference explains only 50% of the variance in VAT and is more highly correlated with total fatness [92]. Care should be taken when using waist circumference to make inferences about the amount of abdominal VAT.

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Worldwide and Regional Prevalence of Obesity

5

Jacob C. Seidell

5.1 INTRODUCTION This chapter describes the global prevalence of overweight and obesity in children and adolescents as well as in adults. Specific regions and countries are highlighted with more specific information about prevalence and trends with attention to deviating criteria for the assessment of overweight and obesity in specific ethnic groups.

5.2  GLOBAL ESTIMATES OF OBESITY IN CHILDREN AND ADOLESCENTS One of the complicated aspects of assessing the prevalence of obesity in children is that body fatness is not very reliably determined using data derived from routine measurements of weight and height. This is particularly true for phases during growth and development when there are rapid changes in growth and body composition such as during the growth spurt in adolescence [1]. But usually only weight and height are available for epidemiological purposes and therefore most estimates of overweight and obesity are based on these measurements. The criteria for overweight and obesity based on weight and height have resulted in several different definitions. General concerns have been expressed about using categories of body mass index (BMI) to assess overweight and obesity in individual children and adolescents as well as in populations. For instance, Reilly et al. [2] argued that the use of BMI to define obesity (the degree of excess body fat) is highly specific but has low to moderate sensitivity. As a result, BMI-based estimates of obesity prevalence are highly conservative for all ages and both sexes. They estimated that at least 25%–50% of children and adolescents defined as having a healthy BMI-forage would also have excess body fat [2]. 48

The most important classifications are as follows: World Health Organization (WHO) standard [3, 4] • Healthy weight (standard deviations [SDs] were ageand sex-specific): Median to 1 SD above the median. • Overweight: 1–2 SD above the median. • Obesity: More than 2 SD above the median. Cole (International Obesity Task Force) classification [5] • Age- and sex-specific cutpoints based on the LMS method using data from six countries corresponding to adult BMI cutoff values for overweight (BMI 25), obesity (BMI 30), and severe obesity (BMI 35). The LMS method is a way of summarizing growth standards and monitors the changing skewness of the distribution during childhood. One of the most comprehensive recent studies on the global prevalence of obesity is based on data from the Noncommunicable Disease Risk Factor Collaboration (NCD-RisC) [6]. They used the WHO reference data to define overweight and obesity. They pooled 2416 population-based studies with measurements of height and weight on 128.9 million participants aged 5 years and older, including 31.5 million aged 5–19 years. Regional change in age-standardized mean BMI in girls from 1975 to 2016 ranged from virtually no change in eastern Europe to an increase of 1.00 kg/m2 per decade in central Latin America and an increase of 0.95 kg/m2 per decade in Polynesia and Micronesia. The range for boys was from a nonsignificant increase of 0.09 kg/m2 per decade in eastern Europe to an increase of 0.77 kg/m2 per decade in Polynesia and Micronesia. Trends in mean BMI have recently flattened in northwestern Europe and the high-income Englishspeaking and Asia-Pacific regions for both sexes, southwestern Europe for boys, and central and Andean Latin America for girls. By contrast, the rise in BMI has accelerated in east and south Asia for both sexes and in southeast Asia for boys. Global age-standardized prevalence of obesity increased from 0.7% in 1975 to 5.6% in 2016 in girls, and from 0.9% in 1975 to 7.8% in 2016 in boys. Prevalence of obesity was more DOI: 10.1201/9781003437673-6

5  •  Worldwide and Regional Prevalence of Obesity  49 than 30% in girls in Nauru, the Cook Islands, and Palau; and boys in the Cook Islands, Nauru, Palau, Niue, and American Samoa in 2016. The prevalence of obesity was about 20% or more in several countries in Polynesia and Micronesia, the Middle East and North Africa, the Caribbean, and the US. In 2016, 75 million girls and 117 million boys worldwide were moderately or severely underweight. In the same year, 50 million girls and 74 million boys worldwide had obesity [6].

5.3  PREVALENCE AND TIME TRENDS OF OVERWEIGHT AND OBESITY IN CHILDREN IN SELECTED COUNTRIES 5.3.1 United States of America Long-term trend data based on measured heights and weights in surveys are relatively rare. But some countries have collected nationally representative BMI data over a long period. One example is the United States. Figure 5.1 shows the time trends of obesity for different age groups in the United States [7].

5.3.2 United Kingdom In the United Kingdom, routine standardized measured heights and weights are collected in the National Child Measurement

Programme (NCMP) [8, 9]. The NCMP is an annual program that measures the height and weight of over 1 million children when they enter primary school (aged 4 to 5 years) and year 6 (aged 10 to 11 years). In the NCMP, obesity is defined as above the 95th percentile of the national UK 1990 age- and sex-specific growth reference. The most recent data showed that in 2019/2020, in the first year of primary school, 10.1% of boys had obesity compared to 9.7% of girls. In the sixth year of primary school, 23.6% of boys had obesity compared to 18.4% of girls. Children living in the most deprived areas were more than twice as likely to have obesity than those living in the least deprived areas. In addition, from 2006 to 2007, the prevalence of severe obesity among children in their first school year remained stable at around 2.3% to 2.4%. The prevalence of severe obesity among children in their sixth school year has shown an increase from 3.2% in 2006 to 2007 to 4.2% in 2017 to 2018. Also for severe obesity the inequalities in child obesity in England are widening among children in both the first and last year of primary school [8, 9].

5.3.3 Asia There are concerns about using global criteria for the BMI to define overweight and obesity in people from Asian backgrounds (see Section 5.4). It has been proposed that BMI cutpoints should be based on BMI centile curves passing through the adult BMI cutoff points for Asian populations: 23 (overweight), 25 to 27.5 kg/m2 (obesity) [10].

FIGURE 5.1  Time trends in the prevalence of obesity (defined as a body mass index [BMI] at or above the 95th percentile from the sex-specific BMI-for-age 2000 CDC Growth Charts) in children aged 2–19 years of age in the United States of America. (Adapted from [7].)

50  Handbook of Obesity National prevalence data in Asian countries using ethnicspecific criteria to define overweight and obesity in children and adolescents are not available. Prevalence data based on the WHO standards almost certainly lead to substantial underestimations of unhealthy levels of body fatness. Using the WHO criteria to assess overweight and obesity in Asian countries, it has been estimated that the prevalence of overweight and obesity among preschool children was 3.2% in 1990, increasing to 4.9% in 2010, and to 6.8% in 2020 [11]. Translated into actual numbers, in 1990 there were 12.4 million children in Asia who had obesity, which increased to 18 million children with obesity in 2010. By 2020, there were 24 million children in Asia with obesity. For southeast Asia, the number of children with obesity increased from 1.2 million to 2.5 million over 20 years (1990–2010).

5.4  OBESITY IN ADULTS 5.4.1 Global Prevalence The most comprehensive overview of global trends and prevalence of obesity in adults is from the NCD-RisC [6, 12]. Their data on the global trends in the prevalence of obesity (BMI ≥30 kg/m2) in men and women in 1980, 2008, and 2016 from select regions of the world showed an increase in obesity across the world. The worldwide number of adult women with obesity increased from 69 (57–83) million in 1975 to 390 (363–418) million in 2016; the number of men with obesity increased from 31 (24–39) million in 1975 to 281 (257–307) million in 2016. That corresponds to a 6.7-fold increase. That is 3.7 times greater than the increase in the growth of the world’s population in that time (4.1 to 7.4 billion, a factor of 1.8). In all regions of the world, an increase in the prevalence of obesity was seen in that period. The highest prevalence of obesity observed in 2016 was in men in high-income Western countries and in women in Central Asia, the Middle East, and North Africa. Another study from the NCD-RisC found that BMI has risen at the same rate or faster in rural areas compared with cities in many low- to middle-income countries, challenging the paradigm of urbanization as a key driver of the global obesity epidemic [13].

5.4.2 Obesity in Adults in Selected Regions and Countries 5.4.2.1 India The NCD-RisC used BMI ≥30 kg/m2 as a cutpoint for adult obesity. That is also the definition of the WHO. It is expected that the prevalence of overweight (BMI 25–30) and obesity (BMI 30 or higher) will reach 30.5% (27.4%–34.4%) and 9.5%

(5.4%–13.3%) among men, and 27.4% (24.5%–30.6%) and 13.9% (10.1%–16.9%) among women, respectively, by 2040. That definition of obesity (BMI ≥30 kg/m2) has, however, been challenged in many Asian countries where the risk of complications tends to increase rapidly at much lower BMIs than in Caucasian populations [14–16]. This is particularly true in India. In 2009, more than 100 Indian medical experts representing reputable medical institutions, hospitals, government-funded research institutions, and policy-making bodies participated to develop Asian Indian-specific guidelines for defining and managing overweight and obesity. The consensus guidelines defined overweight as those with BMI between 23.0 and 24.9 kg/m2 and obesity as those having BMI ≥25.0 kg/m2. This classification would imply that an additional 10%–15% of the Indian population would be labeled as having overweight/ obesity and would require appropriate management.

5.4.2.2 China Also in China, there have been country-specific definitions for overweight and obesity. The Working Group on Obesity in China recommended BMI cutoffs of 24.0 kg/m2 to define overweight and 28.0 kg/m2 to define obesity [17]. In national surveys, the prevalence of overweight and obesity, respectively, in adults increased substantially from 16.4% and 3.6% in 1992 to 22.8% and 7.1% in 2002, and to 30.1% and 11.9% in 2010–2012 based on the Chinese criteria. The corresponding prevalence rose from 18.9% and 2.9% in 2002 to 27.1% and 5.2% in 2010–2012 based on the WHO criteria. The WHO criteria, therefore, led to estimates of obesity that are considerably lower by more than half based on the national criteria. The most recent nationwide prevalence estimates for 2015–2019 based on Chinese criteria were 34.3% for overweight and 16.4% for obesity in adults (≥18 years) [17]. The prevalence of overweight and obesity differed by sex, age group, and geographical location but was substantial in all subpopulations.

5.4.2.3 Latin America Obesity has become a major health challenge in Latin America [18]. Around 57% (302 million) of the region’s adult population (54% of men and 70% of women) have overweight, and 19% (100.8 million) have obesity (14.6% in men and 24% in women). Similar to other low- to middle-income countries, overweight (61% in women and 54% in men) and obesity (24% in women and 14.6% in men) are more prevalent in women than in men. The highest prevalence of obesity in the adult population is found in El Salvador (33%) and Paraguay (30.1%) for women and in Uruguay (23.3%) and Chile (22.0%) for men (19).

5.4.2.4 United States of America According to a study that reported national survey data from 2000 to 2018 in the US, obesity prevalence has increased from

5  •  Worldwide and Regional Prevalence of Obesity  51 TABLE 5.1  Global Prevalence of Overweight/Obesity in Different Age Groups AGE GROUP Preschool children (180 minutes of screen time [67]. Sigmund et al. found that reaching the 10,000 steps per day recommendation reduces the odds of having overweight or obesity [68]. One systematic review examining seven longitudinal studies found agreement among six of these studies that there is a negative relationship between LTPA participation and adiposity in children [69].

36.4.2 Studies in Adults Sarma et al. found that higher LTPA (such as walking for one hour a day) reduced the probability of obesity by 5 percentage points, which increases to 11 percentage points if work-related PA is added [70]. One study found that those who participated in ≥300 min/week of PA had the lowest occurrence of obesity when compared to groups participating in varying amounts of PA [71]. Chun-Xiao and colleagues found that men with more than 150 min/week of LTPA had a reduced risk of obesity, and those with less than 2.5 hours a day of sitting time also had a

360  Handbook of Obesity lower risk of obesity when compared to those who sat for more than 4 hours per day. A cross-sectional analysis of adults in the International Physical Activity and Environment Network (IPEN) study measuring PA with accelerometers found that increased PA was associated with lower BMI [72]. The intensity of activity performed also seems to have an impact on the development of overweight and obesity. For example, in a longitudinal study with an average follow-up of 11.6 years, data collected between 1992 and 2004 demonstrated that total LTPA showed an inverse relationship with the development of obesity, and greater LTPA time spent performing vigorous-intensity activity was associated with an even lower risk of developing overweight or obesity [73]. DiPietro et al. estimated that weight gain would occur in men who had a decrease in PA level over 5 years with modest weight loss occurring in men who increased their activity from low (1.60 METs per 24 hours) to high [74]. An analysis of longitudinal data from Canada’s National Population Health Survey over 16 years demonstrated that LTPA has a negative effect on BMI, and, more specifically, at least 30 minutes of walking per day reduces BMI by 0.11–0.14 points in males and 0.20 points in females [75]. Similar to data with children, most studies have found an inverse relationship between the amount of PA and obesity.

36.5  INTERVENTION STUDIES OF LTPA AND WEIGHT LOSS With the rise in the prevalence of obesity in adults and children, many studies have aimed to increase LTPA engagement. A review of PA interventions in Europe demonstrated that these interventions are successful at increasing time spent in PA but are less effective at reducing obesity [76]. Another review substantiated these findings and concluded that at best, interventions to increase PA can produce only modest weight loss of 2%–3% [77]. Taking a closer look at the effect of PA intensity, one intervention randomized participants to one of three PA groups, either a sedentary group, a moderate-intensity group, or a high-intensity group, and found that both exercise groups had a reduction in body weight when compared to the sedentary group. Moreover, while there were no differences in body weight between the exercise groups, those in the intense activity group had lower body fat and higher lean mass when compared to the moderate-intensity group [78]. This study demonstrates that while PA alone is not sufficient to produce significant weight loss, the changes in body composition are significant. Due to the lack of weight loss seen in many of these interventions, there has been an increase in the number of combined interventions, which include both a PA and dietary component. These studies have demonstrated that combined interventions are more effective at reducing obesity than interventions that include just a dietary or exercise component [79].

In studies aiming to prevent weight gain, as opposed to weight loss, PA remains a key intervention component. To prevent the transition from overweight to obesity, one study found that 45–60 minutes of MVPA/day are required as opposed to the standard recommendation of 150 minutes per week [80].

36.6  LTPA AND MAINTENANCE OF REDUCED WEIGHT While several studies have demonstrated that LTPA alone is not sufficient to produce significant weight loss, data have demonstrated that it is a major component of maintaining weight once it is lost [81–84]. A review performed by Fogelholm et al. concluded that an EEPA of at least 1500–2000 kcal/week is needed to prevent weight regain [81]. Previously, the American College of Sports Medicine created a distinction between the amount of PA recommended to maintain health (150 min/week) and the minimum recommendation to prevent weight regain (200 min/week) [85]. A review substantiated this finding, with data from epidemiologic studies and clinical trials indicating that a higher amount of PA is required, and at a higher intensity, to maintain a reduced body weight [86]. Jakicic et al. reported a dose–response relationship between the amount of exercise performed following weight loss and the level of success with weight loss maintenance [87]. Research with the National Weight Control Registry (NWCR), a registry of people who have been successful in long-term weight loss, suggests that engaging in high levels of PA is important for the long-term maintenance of weight loss [88, 89]. In this group of over 10,000 participants, the average self-reported amount of PA is about 60 min/day. Not only do participants report substantial increases in LTPA, but they also increase other aspects of physical activity. They tend to choose more active transport and insert more movement into activities of daily living. Additionally, many even choose to change occupations to ones involving more physical activity. Only 9% of participants report long-term success with no intentional increase in physical activity.

36.7  SUMMARY AND CONCLUSION While LTPA is a small percentage of total energy expenditure, it is particularly important since it is the component of energy expenditure most under voluntary control. Overall, the PA of humans declined substantially during the latter half of the 20th century, but most of this decline appears to be due to lower requirements for PA in work and transportation. Available data suggest that the average LTPA in the population has remained relatively constant during the period, making it difficult to

36  •  Leisure Time Physical Activity and Obesity  361 attribute increases in obesity to decreases in LTPA. However, some research suggests that those with lower amounts of LTPA are more susceptible to weight gain. Importantly, as the amount of total leisure time has increased, LTPA has not, with most of the extra time being spent on sedentary activities. LTPA is an important target for increasing PA since it will be extremely difficult to increase PA in work and transportation. Even small increases in LTPA could be important for preventing obesity. LTPA seems to result in only modest—but highly variable—weight loss, which may partially be explained by the variable increase in appetite reported by some as a result of increased LTPA. Still, LTPA engagement provides additional benefits to body composition and cardiovascular health compared to diet-only interventions. While weight loss is primarily driven by reductions in energy intake, increases in LTPA appear to be critical for long-term success in weight loss maintenance by improving metabolic flexibility.

19. King NA et al., Am J Clin Nutr (2009). PMID: 19675105 / DOI: 10.3945/ajcn.2009.27706 20. Beaulieu K et al., Obes Rev (2021) PMID: 33949089 / DOI: 10.1111/obr.13251 21. King NA et al., Int J Obes (Lond) (2008). PMID: 17848941 / DOI: 10.1038/sj​.ijo​.0803​712 22. Herrmann SD et al., Obesity (2015). PMID: 26193059 / DOI: 10.1002/oby.21073 23. Huffman WE. Rev Agric Econ (2005). DOI: 10.1111 /j.1467-9353.2005.00229.x 24. McMenamin TM. Monthly Lab Rev, 2007. 130, pp. 3-15. https:// heinonline​.org​/ HOL​/ Page​? handle​= hein​.journals​/month130​ &div​=96​&g​_sent​=1​&casa ​_token=​&collection​=journals 25. Ng SW, Popkin BM. Obes Rev (2012). PMID: 22694051 / DOI: 10.1111/j.1467-789X.2011.00982.x 26. Aguiar M, Hurst E. Q J Econ (2007). DOI 10.1162/ qjec.122.3.969 27. U.S Bureau of Labor Statistics. American Time Use Survey Summary, 2021 [cited 2021]. https://www​.bls​.gov​/news​.release​ /atus​.nr0​.htm 28. Dollman J et al., Br J Sports Med (2005). PMID: 16306494 / DOI: 10.1136/bjsm.2004.016675 29. Clark W. Kids’ sports. Canadian Social Trends, 2008. 85, pp. 54–61. 30. Seippel Ø et al., Ungdom og trening. Endring over tid og sosiale skillelinjer [Youth and physical activity. Changes over time and social divides] (Report No. 3). Oslo: NOVA; 2011, p. 3. 1. Caspersen CJ et al., Public Health Rep. (1985). PMID: 3920711 31. Bassett DR et al., J Phys Act Health (2015). PMID: 25347913 / 2. U.S. Bureau of Labor Statistics. American Time Use Surveys, DOI: 10.1123/jpah.2014-0050 2020 [cited 2021]. https://www​.bls​.gov​/tus/ 32. Cameron C et al., Prev Med (2016). PMID: 26757400 / DOI: 3. Craig CL et al., Med Sci Sports Exerc (2003). PMID: 12900694 10.1016/j.ypmed.2015.12.020 / DOI: 10.1249/01.MSS.0000078924.61453.FB 33. Venetsanou F et al., Int J Environ Res Public Health (2020). 4. Donahoo WT et al., Curr Opin Clin Nutr Metab Care (2004). PMID: 32138370 / DOI: 10.3390/ijerph17051645 PMID: 15534426 / DOI: 10.1097/00075197-200411000-00003 34. Kalman M et al., Eur J Public Health (2015). PMID: 25805785 5. Levine JA. Public Health Nutr (2005). PMID: 16277824 / DOI: / DOI: 10.1093/eurpub/ckv024 10.1079/phn2005800 35. Tremblay MS et al., J Phys Act Health (2014). PMID: 25426906 6. Westerterp KR. Physiol Behav (2008) PMID: 18308349 / DOI / DOI: 10.1123/jpah.2014-0177 10.1016/j.physbeh.2008.01.021 36. Ryu S et al., Am J Health Promot (2019). PMID: 31185728 / 7. Tappy L et al., Proc Nutr Soc (2003) PMID: 14692602 / DOI: DOI: 10.1177/0890117119854043 10.1079/PNS2003280 37. Pate RR et al., Br J Sports Med (2011). PMID: 21836174 / DOI: 8. Myers J. Exercise and Cardiovascular Health. Circulation 10.1136/bjsports-2011-090192 (2003). PMID: 12515760 / DOI: 10.1161/01​ .cir​ .0000048890​ 38. Cui Z et al., Int J Behav Nutr Phys Act (2011). PMID: 21867565 .5938​3​.8d / DOI: 10.1186/1479-5868-8-93 9. Kelley DE et al., Am J Physiol (1999). PMID: 10600804 / DOI: 39. Melkevik O et al., Int J Behav Nutr Phys Act (2010). PMID: 10.1152/ajpendo.1999.277.6.E1130 20492643 / DOI: 10.1186/1479-5868-7-46 10. Kelley DE et al., Diabetes (2002). PMID: 12351431 / DOI: 40. Anderson YC et al., Sci Rep (2017). PMID: 28157185 / DOI: 10.2337/diabetes.51.10.2944 10.1038/srep41822 11. Blaak EE et al., Diabetes (2000). PMID: 11118013 / DOI: 41. Vale S et al., J Pediatr (2015). PMID: 25962928 / DOI: 10.2337/diabetes.49.12.2102 10.1016/j.jpeds.2015.04.031 12. Stein T, Wade C. J Nutr (2005) PMID: 15987873 / DOI: 42. Brownson RC et al., Annu Rev Public Health (2005). PMID: 10.1093/jn/135.7.1824S 15760296 / DOI: 10.1146/annurev.publhealth.26.02130​4.144​437 13. Bergouignan A et al., J Appl Physiol (2011). PMID: 21836047 / 43. Keadle SK et al., Prev Med (2016). PMID: 27196146 / DOI: DOI: 10.1152/japplphysiol.00698.2011 10.1016/j.ypmed.2016.05.009 14. Bergouignan A et al., J Appl Physiol (2012). PMID: 23239872 44. Whitfield GP et al., MMWR Morb Mortal Wkly Rep (2019). / DOI: 10.1152/japplphysiol.00458.2012 PMID: 31194722 / DOI: 10.15585/mmwr.mm6823a1 15. Bergouignan A et al., Diabetes (2009). PMID: 19017764 / DOI: 45. Morseth B et al., BMC Public Health (2016). PMID: 27912742 10.2337/db08-0263 / DOI: 10.1186/s12889-016-3886-z 16. Saltin B, Gollnick PD. Skeletal muscle adaptability: 46. Borodulin K et al., Eur J of Public Health (2008). PMID: Significance for metabolism and performance. In Handbook of 17875578 / DOI: 10.1093/eurpub/ckm092 Physiology Skeletal Muscle. Bethesda, MD: Am. Physiol. Soc., 47. Stamatakis E et al., Prev Med (2007). PMID: 17316777 / DOI: 1983, sect. 10, chap. 19, pp. 555–632. 10.1016/j.ypmed.2006.12.014 17. Battaglia GM et al., Am J Physiol Endocrinol Metab (2012). 48. Juneau CE, Potvin L. Prev Med (2010). PMID: 20832417 / PMID: 23047988 / DOI: 10.1152/ajpendo.00355.2012 DOI: 10.1016/j.ypmed.2010.09.002 18. Miles JL et al., Endocrinology (2009). PMID: 18772230 / DOI: 49. Hallal PC et al., J Phys Act Health (2014). PMID: 24905186 / 10.1210/en.2008-1035 DOI: 10.1123/jpah.2013-0031

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Role of Early Life Nutrition and Breastfeeding on Obesity Development

37

Maryam Kebbe, Kelsey Goynes, and Leanne M. Redman

37.1 NUTRITION RECOMMENDATIONS FROM BIRTH TO 2 YEARS Optimal nutrition during the first 2 years of life is essential for growth, health, and the prevention of obesity [1]. Hence, international and national recommendations exist to support optimal nutrition (Figure 37.1). From birth, exclusive intake of breastmilk through 6 months of age is universally recommended by regulatory bodies including the World Health Organization (WHO) and the American Academy of Pediatrics [2, 3]. Furthermore, to encourage exclusive breastfeeding, the WHO recommends initiation of breastfeeding within 1 hour of birth [2]. However, breastfeeding is not always possible due to a variety of contraindications and challenges. When breastfeeding is not possible, provision of an iron-fortified infant formula is advised [4]. From 6 months to 2 years, the energy and nutritional needs of an infant begin to exceed what can be provided by breastmilk or infant formula alone. At this approximate age, table foods become necessary [2, 4]. Due to the small amount of food consumed relative to the nutrient requirements of infants at this age, it is recommended that table foods complementing breastmilk or formula are nutrient-rich and contain no added sugar [4]. As taste preferences are beginning to develop during this time period, low- or no-calorie sweeteners are not recommended [4]. Consistently introducing healthy foods with a variety of tastes and textures, including different varieties

DOI: 10.1201/9781003437673-40

of fruits and vegetables, is essential given that an infant may require eight to ten exposures to a new taste before accepting it into the diet [1, 5]. Consumption of a greater variety of vegetables during the period of complementary food introduction leads to children consuming a greater variety up to 6 years later [1]. Energy intake for healthy infants from birth to 12 months of age should be approximately 100 kcal per kg per day [6]. During the first 6 months, energy is supplied to the infant by breastmilk and/or formula, followed by a combination of breastmilk and/or formula and complementary food from 6 months to 1 year. The exact composition of complementary food intake may vary depending on the infant’s main source of milk at the time [1]. From 6 months to 1 year, the recommended dietary allowance (RDA) of protein is 11 g/day, the adequate intake for carbohydrates is 95 g/day, and there is no applicable recommendation for total lipids per day in this age group [4]. During the second year of life, infant formula is not recommended. Although breastmilk may still be consumed, it does not satisfy the majority of nutrient and energy requirements [4]. Thus, from 12–23 months of age, it is recommended that a healthy dietary pattern consisting of fruits, vegetables, protein, grains, dairy, and a limited amount of oils be established. The RDA of protein is 13 g/day, the RDA for carbohydrates is 130 g/day, and the acceptable macronutrient distribution range of lipids is 30%–40% of calorie intake [4]. All food choices should be nutrient dense with minimal added sugars and saturated fats [4]. Meeting these recommendations can help establish healthy eating patterns from infancy through adulthood, which may prevent the development of obesity.

363

364  Handbook of Obesity Birth to 6 months

6 months to 1 year

1 year to 2 years

Energy from breastmilk (%)

100

50 Exclusive breastfeeding

Complementary food introduction

Establishment of a healthy dietary pattern

Continued breastfeeding

May be supplemented by breastmilk

0 Timeline

FIGURE 37.1  Breastfeeding and nutrition recommendations in early life.

37.2 BREASTFEEDING, BREASTMILK, AND RISK OF OBESITY Breastfeeding is the process by which breastmilk is fed to an infant. Breastmilk may be consumed directly from the breast, expressed from the breast and then fed to the infant in a bottle, or a combination of both. Exclusive breastfeeding means that the infant receives only breastmilk and no other liquids or solids, including water and excluding oral rehydration solution or drops/syrups (vitamins, minerals, or medicines), is particularly dynamic and bioactive as it undergoes compositional changes in response to the changing needs of the infant from the beginning (foremilk) to the end (hindmilk) of a feeding session. Recommendations to exclusively breastfeed for the first 6 months can be explained by its health-protective effects for the infant and mother. In the mother, a longer duration of breastfeeding reduces the risk of maternal ovarian cancer, breast cancer, and type 2 diabetes and is associated with postpartum weight loss, although the mechanisms are not well studied [7, 8]. In the infant, there is strong evidence that exclusive breastmilk feeding aids in the long-term prevention of gastrointestinal infections, upper- and lower-respiratory tract infections, and sudden infant death syndrome. These benefits contrast with milder evidence for reduced risks of cognitive development, atopic allergies, asthma, pediatric cancers, and obesity [8]. Despite recommendations and benefits, between 2015 and 2020, breastfeeding was only initiated in 40% of infants worldwide in the first hour of life, and only 44% of infants aged 0–6 months were exclusively breastfed [9]. In the U.S., rates of exclusive breastfeeding are significantly lower, with only 25.6% of infants exclusively breastfed at 6 months of age [10]. Women with overweight and obesity are less likely to intend to breastfeed, initiate breastfeeding, and exclusively breastfeed, and they are more likely to breastfeed for a shorter duration compared to women of normal weight [11]. Physiologically, a less adequate milk supply and delayed onset

of lactogenesis contribute to low breastfeeding rates among women with obesity [12]. These disparities occur even after adjusting for psychosocial and demographic factors, highlighting the need to create novel solutions that support women of all weights to initiate breastfeeding and continue this practice beyond the first days. Weight gain from birth to 2 years of age should reflect normal growth. When a positive energy imbalance occurs, characterized by energy intake exceeding energy expenditure, excess weight gain in adipose tissue typically results. Interestingly, independent of energy expenditure, energy intake has been positively related to body size (weight-for-length, body fat, fatfree mass, and skinfold thickness) in a cohort of lean infants at 1 year [13]. Compared with exclusive formula feeding, current evidence from observational studies suggest that exclusive breastfeeding is associated with lower odds of obesity in children (follow-up age between 6 months and 18 years) expressed as body mass index (BMI) [14–17] and high body fat defined as ≥90th percentile for age and sex [14]. Observational studies point to a longer duration of exclusive and partial breastfeeding being associated with lower weight-for-age z-scores at 1 year of age in developed countries only (based on the Human Development Index) [18]. A dose-dependent effect of breastfeeding duration (infants of any given age) and obesity was found, whereby a 4% reduction in obesity risk (any definition of overweight or obesity) was observed for each month of breastfeeding [19, 20]. Pre-gravid BMI is independently and positively associated with infant birth weight [21]. In a model including maternal pre-gravid BMI and categorical durations of any breastfeeding, each 1-unit increase in maternal pregravid BMI and 30 kg/m2 [1]. Over the last few decades, the incidence of obesity has been increasing at an alarming rate and has resulted in a significant financial burden and health utilization from morbidity and mortality stemming from increased obesity-related cardiovascular disease (CVD) [1, 2]. Although the mechanisms through which obesity results in cardiomyopathy are not fully understood, it is believed that the excessive accumulation of adipose tissue is associated with greater lean mass or fat-free or non-fat mass. This would result in increased circulating blood volume, cardiac output, cardiac work, systolic blood pressure, lipotoxicity, and myocardial lipid accumulation [3, 4]. Even in the absence of other risk factors, obesity has proven to be a strong independent predictor of CVD. However, a phenomenon termed the “obesity paradox” has revealed that the relationship between a higher BMI and clinical outcomes in the setting of chronic disease is a dynamic one [1]. Several retrospective and prospective studies have implicated a potentially protective effect of obesity when coexisting with CVD, including heart failure (HF), coronary heart disease (CHD), hypertension (HTN), atrial fibrillation (AF), and pulmonary artery hypertension [4–7].

48.2 PATHOPHYSIOLOGY Obesity has been associated with hemodynamic modifications that predispose to changes in cardiac structure and ventricular dysfunction. The impact of obesity on hemodynamics plus cardiac structure and function are reviewed in Table 48.1 and Figure 48.1 [8]. While these changes can be seen to a lesser degree in the population with mild-tomoderate obesity, they are more noticeable in patients with

DOI: 10.1201/9781003437673-53

severe obesity. As excessive adipose tissue accumulates, there is an increase in lean body mass that leads to more blood volume (total and central) and augments left ventricular (LV) stroke volume and cardiac output. Increases in cardiac output can lead to LV enlargement and LV hypertrophy [9, 10]. If stress on the LV wall is significant, LV diastolic dysfunction may arise. Furthermore, if the LV wall cannot keep up with LV hypertrophy, concomitant LV systolic dysfunction may ensue along with adverse effects, such as increased left atrial pressure and volume and increased pulmonary capillary (wedge) pressure. These changes, along with obesity-related obstructive sleep apnea, often lead to right-sided structural abnormalities that culminate in right ventricular heart failure [9]. A pro-inflammatory state resulting in adverse outcomes is often linked with obesity. High-sensitivity C-reactive protein (hs-CRP) has been used as a biomarker of inflammation given its effects on vascular endothelium and is an established risk factor for CHD and CHD events [11]. Obesity has adverse effects on cardiometabolic parameters, including raising levels of arterial pressure, worsening plasma lipids by raising triglycerides, reducing the cardioprotective high-density lipoprotein cholesterol, and changing the low-density lipoprotein cholesterol to a smaller, denser form that is more easily oxidized and more atherogenic. Furthermore, it increases glucose levels and insulin resistance, raising the risk of metabolic syndrome and type 2 diabetes mellitus (T2DM), all ultimately increasing the risk of CVD [12]. Sleep apnea and obesity hypoventilation may lead to tissue hypoxia and subsequent activation of the sympathetic nervous system through a catecholamine surge and/or increased inflammatory cytokines driven by oxidative stress [11]. In addition, other mechanisms may contribute to the negative prognostic impact of obesity and CVD, such as neurohormonal activation and metabolic alteration (i.e., activation of the renin–angiotensin–aldosterone system, hyperinsulinemia, hyperleptinemia) [9]. Ultimately, these mechanisms promote structural, atherogenic, and electrical cardiac remodeling of the heart contributing to the development of HF, CHD, and arrhythmias—most commonly AF [9, 11].

461

462  Handbook of Obesity TABLE 48.1  Effects of Obesity on Hemodynamics and Cardiac Structure and Function A. Hemodynamics 1. Increased blood volume (total and central) 2. Increased stroke volume 3. Increased atrial pressure 4. Increased LV wall strain 5. Pulmonary artery hypertension B. Cellular 1. Hypertrophy 2. Fibrosis C. Cardiac structure 1. LV remodeling/hypertrophy (concentric and eccentric) 2. Left atrial enlargement 3. RV hypertrophy D. Cardiac function 1. LV diastolic dysfunction 2. LV systolic dysfunction 3. RV failure E. Inflammation 1. Elevated CRP 2. Tumor necrosis factor overexpression F. Neurohumoral 1. Insulin resistance, hyperinsulinemia 2. Leptin insensitivity, hyperleptinemia 3. Reduced adiponectin 4. Activation of sympathetic nervous system 5. Activation of renin–angiotensin–aldosterone system Source: Reproduced with permission from Lavie et al. [8]. Abbreviations:  LV, left ventricle, RV, right ventricle, CRP, C-reactive protein.

48.3  OBESITY AND CARDIOVASCULAR DISEASE MORBIDITY AND MORTALITY Two recent large studies, one comprising ten cohorts from the United States (U.S.) and the other from nearly 300,000 White Europeans, clearly demonstrate that overall CVD morbidity and mortality are increased with obesity. The first by Khan and colleagues from ten large U.S. cohorts with 3.2 million person-years of follow-up showed that obesity was associated with shorter longevity and considerably higher CVD morbidity and mortality, especially increased risk of HF. Although individuals with overweight did not have increased mortality, they had a markedly increased risk of prolonged CVD-related morbidity [13]. In the UK Biobank study by Iliodromiti of 296,535 White Europeans, there was a J-shaped association with increased CVD in the underweight BMI, the lowest risk with BMI 22–23 that increased proportionally with higher

BMI [14]. In addition, other obesity measures, including waist circumference (WC), waist-to-hip ratio, and waist-to-height ratio, as well as body fat, all had direct relationships with CVD morbidity and mortality. [14] Together, these studies illustrate that obesity is associated with an increased risk of CVD as well as overall CVD morbidity and mortality.

48.4  OBESITY, HYPERTENSION, AND THE OBESITY PARADOX In addition to increasing risks of arterial pressure, obesity increases cardiometabolic abnormalities in HTN. These increase the risk of CHD resulting in more concentric remodeling and LV hypertrophy. Interestingly, despite these phenotypic changes, as HTN becomes manifest, several studies show that individuals with obesity and HTN may have a better prognosis than leaner individuals with HTN. One of the largest of these studies was the INVEST study, which included 22,576 patients with HTN and showed that even though individuals with obesity had less controlled levels of blood pressure, they had a 30% lower risk of mortality, with essentially all the major types of CVD being lower in both men and women with obesity compared with the lean patients with HTN [15]. Although some studies show more of a J-shaped association between BMI and morbidity and mortality from HTN, almost all HTN studies show lower risk in patients with overweight/mild obesity and HTN. The reasons for this paradox are unknown, but individuals with obesity and HTN tend to have lower plasma renin activity, which may lower their risk.

48.5  OBESITY, HEART FAILURE, AND THE OBESITY PARADOX IN HEART FAILURE More than 6 million adults in the U.S. have HF; however, that number is projected to increase by almost 50% by 2030 due to the aging population and increased prevalence of comorbidities, including obesity [16]. Indeed, obesity is a major risk factor for the development of almost all CVD, especially HF and AF. This is in part due to the numerous adverse effects on CHD, including HTN, along with structural changes and cardiac dysfunction. Certainly, CHD leading to myocardial infarction (MI) and HTN are leading causes of HF. As seen in the Framingham Heart Study, there is increasing evidence that any level of obesity increases the risk of HF, although a higher BMI was shown to be a stronger predictor for HF with preserved ejection fraction (HFpEF) compared to HF with reduced ejection fraction (HFrEF). Moreover, the increased risk for HF in the setting of obesity seems to be mediated by

48  •  Obesity and Heart Disease  463

FIGURE 48.1  Cardiac morphology and ventricular function changes associated with obesity. LA, left atrial; LV, left ventricular; RV, right ventricular. (From Lavie CJ, Laddu D, Arena R, et al., Healthy weight and obesity prevention: JACC health promotion series, J Am Coll Cardiol. 2018;72(13):1510. With permission.)

low physical activity (PA) and cardiorespiratory fitness (CRF), again more so for HFpEF than HFrEF [1]. As depicted in Figure 48.1, excess adipose tissue seen with obesity leads to changes in cardiac morphology and ventricular function; however, despite these adverse effects, various studies in both HFpEF and HFrEF have exhibited a strong obesity paradox, wherein patients who have overweight or obesity with HF have a better clinical prognosis than patients with HF who are underweight (BMI 40 kg/m2 (stillbirth RR 2.19 [95% CI 2.03–2.36]) [8]. The relative risk for stillbirth per 5 BMI unit increase was 1.24 [95% CI 1.18–1.30]. The absolute risk per 10,000 pregnancies for stillbirth increased with rising BMI: BMI 20 kg/m2 (40; referent), BMI 25 kg/m2 (48 [95% 46–51]), and BMI 30 kg/m2 (59 [95% CI 55–63]). The etiologies for perinatal deaths associated with obesity are poorly understood. However, placental dysfunction, intrapartum complications, and umbilical cord complications are more commonly reported among persons with obesity [9]. In a small cohort of pregnancies complicated by stillbirths, women with obesity demonstrated a higher prevalence of placental lesions, including infarcts, decidual arteriopathy, and fetal vascular malperfusion [10]. Although the efficacy of

64.3.2 Congenital Anomalies Congenital malformations (20.6%) were the leading cause of infant death in the U.S. in 2019 [20]. Obesity before pregnancy is associated with an increased risk of major congenital anomalies including central nervous system, cardiac, genitourinary, orofacial, and limb abnormalities (Table 64.1) [17]. Persons with obesity are faced with potential challenges in obtaining high-quality images necessary to prenatally detect congenital anomalies [21]. Ultrasonography is commonly used at 18–20 week gestation to screen for congenital malformations. Rates of suboptimal views of the major organ systems are greater among pregnant persons with obesity ranging between 30% and 50% of second-trimester ultrasound evaluations [22]. The mechanisms responsible for the associations between obesity and birth defects remain unclear. Prior studies have shown elevated random plasma glucose values during early pregnancy to be independently correlated with an increased risk for congenital heart disease in offspring [23]. Even under controlled diet conditions, glucose-tolerant persons with

TABLE 64.1  Adverse Early Pregnancy Outcomes and Congenital Anomalies in Pregnant Persons with Obesity versus Normal Weight OUTCOME Pregnancy Loss Recurrent miscarriage Recurrent miscarriage Spontaneous abortion Live birth rate after IVF Fetal death Stillbirth Congenital Anomalies Neural tube defect Neural tube defect Cardiac malformation Cardiac malformation

Any major congenital anomaly

SAMPLE POPULATION (OBESE/NORMAL WEIGHT) 3,800/17,146 168/635 5,545/11,151 138,605/425,703 — 865,524 8,493 2,093/495,522 9,349/629,634 6,467,422

1.2 million

RR OR OR (95% CI)

TYPE OF STUDY

OR 1.31 (1.18–1.46) OR 1.75 (1.24–2.47) OR 1.67 (1.25–2.25) RR 0.86 (0.84–0.87) RR per 5 BMI units 1.16 (1.07–1.26) OR 2.04 (1.30–3.17)

Systematic review [7] Meta-analysis [12] Meta-analysis [13] Meta-analysis [14] Meta-analysis [8]

OR 1.4 (1.2–1.7) OR 1.84 (1.60–2.12) OR 1.24 (1.06–1.44) OR 1.32 (1.21–1.43) all classes OR 1.15 (1.11–1.20) Class 1 OR 1.26 (1.18–1.34) Class 2 OR 1.42 (1.33–1.51) Class 3 RR 1.12 (1.08–1.15) Class 1 RR 1.23 (1.17–1.30) Class 2 RR 1.37 (1.26–1.49) Class 3

Quantitative bias analysis [16] Meta-analysis [17] Meta-analysis [17] Meta-analysis [18]

Meta-analysis [15]

Population-based cohort study [19]

600  Handbook of Obesity obesity demonstrate higher daytime and nocturnal glucose profiles than persons with prepregnancy weights in the normal range [24]. The mediation of dysglycemia at any degree with the association of adverse fetal outcomes is complex and not well understood. In the early embryo, fetal glucagon and insulin can be detected by 8 weeks with functional fetal islet cells demonstrated by 12–13 weeks gestation [25]. Thus, during critical organ development, the fetus may be exposed to relative increases in maternal glycemia without developed systems able to regulate the excess fuels. This risk can be further magnified for individuals consuming restricted carbohydrate intakes (≤5th percentile [95 g/d]), which has been shown to be associated with increased rates of neural tube defects, independent of folic acid consumption [26]. Animal studies have provided evidence that hyperglycemia is teratogenic to the developing embryo [27]. Additionally, evidence suggests that inflammation and oxygen radical toxicity may be contributing etiologies for congenital anomalies through alterations in gene expression and apoptosis during cell differentiation and growth [28].

adipogenesis, which correlates with neonatal percent fat mass at birth, suggesting intrauterine programming that perpetuates later obesity [35]. Although prepregnancy weight is most strongly associated with fetal macrosomia [36], excessive GWG is also associated with increased neonatal birth weight. Fetal macrosomia, as defined by birth weight ≥4,000 g, is associated with maternal genitourinary and perineal trauma, shoulder dystocia, and infant birth trauma, including clavicle and humerus fractures and brachial or facial paralysis [37]. The risk for neonatal morbidity and mortality increases with rising birth weight, particularly ≥4,500 g [38]. Persons with obesity and excessive GWG are associated with increased infant and childhood adiposity and metabolic dysfunction as demonstrated by less favorable lipid profiles [39]. However, the relationship between GWG and birth weight is strengthened with decreasing prepregnancy BMI, suggesting that GWG has a lower impact on birth weight in persons with obesity and/or diabetes [40]. Independent of postnatal factors, maternal obesity correlates with obesity across the life span in the offspring, highlighting both immediate and long-term consequences for the parent and child [41].

64.3.3 Abnormal Fetal Growth Prepregnancy obesity plays a large role in the metabolic factors that influence the intrauterine environment and subsequently fetal growth trajectories [29]. Birth weight, neonatal fat mass, infants who are large for gestational age (LGA), macrosomia, and offspring who become overweight later in childhood are positively associated with high maternal prepregnancy BMI, independent of diabetes status, excessive gestational weight gain (GWG), or history of fetal overgrowth [30, 31]. Additionally, low birth weight is associated with later central obesity, which presents a paradoxical but consistent association with long-term health consequences associated with poor fetal growth and overgrowth. Persons with higher prepregnancy BMI are associated with increased rates of infants who are small for gestational age (SGA) and more severe fetal growth restriction compared to persons without obesity complicating pregnancy, particularly during adolescence [32]. The in utero factors directly contributing to obesity in the offspring are largely unknown, but potential mechanisms related to intrauterine overnutrition or stress exposures persist. The longstanding concept of “fuel-mediated teratogenesis” reflects maternal fuels available to the fetus, increased umbilical cord insulin and leptin levels, fetal growth and fat mass, and risk of long-term dysmetabolism among offspring. Other factors include epigenetic changes and lifelong adaptations in body composition (i.e., lean and fat mass), central nervous appetite control, insulin metabolism, and liver function. Additionally, maternal triglycerides are one of the strongest predictors of infant fat mass [33], which could explain the inconsistent findings of fasting blood glucose and fetal overgrowth in glucose-tolerant persons with obesity [34]. Umbilical cord-derived mesenchymal stem cells from offspring of persons with obesity have been shown to have greater potential for

64.4 CARDIOMETABOLIC CONSEQUENCES OF OBESITY IN PREGNANCY 64.4.1 Gestational Weight Gain GWG has been correlated with fetal macrosomia, LGA infants, and cesarean deliveries [42]. The National Academy of Medicine (NAM) recommends that persons with prepregnancy obesity (BMI ≥30 kg/m2) maintain weight gain during pregnancy between 11–20 lb (5–9 kg) [35]. However, prepregnancy BMI is more strongly associated with adverse perinatal and infant outcomes compared with GWG, highlighting the limitation of GWG as a predictive tool for adverse outcomes [43]. Additionally, limiting GWG specifically with diet or lifestyle interventions has not been shown to decrease the majority of adverse perinatal (gestational diabetes mellitus [GDM] or preeclampsia) or fetal outcomes [44]. Some experts advocate for GWG less than the NAM lower limit or at least individualized, particularly for higher obesity classes [45]. Retrospective studies have suggested a potential reduction in adverse perinatal outcomes among individuals with obesity who acquire less GWG than recommended by NAM [46, 47]. Physical activity and dietary interventions are effective in reducing GWG among persons with obesity [48–50]. Providers are recommended to offer behavioral counseling interventions aimed to promote healthy weight gain and prevent excess gestational weight gain in pregnancy. However, more comprehensive maternal and offspring data from well-designed prospective or randomized controlled

64  •  Obesity in Pregnancy Complications and Outcomes  601 studies are needed to determine safe weight recommendations in pregnancy.

64.4.2 Hypertensive Spectrum Disorder Rates of hypertensive spectrum disorder (HSD; gestational hypertension, preeclampsia, eclampsia, and HELLP syndrome [hemolysis, elevated liver function, low platelets]) have steadily increased over the last 20 years from 529 to 912 in 10,000 deliveries in the U.S. Among the leading causes of pregnancy-related deaths, HSD and cardiovascular conditions are among the most common that disproportionately affect

non-Hispanic Black persons [51]. Preeclampsia is a complex, multi-organ, pregnancy-specific disorder that affects approximately 4% of all pregnancies and is associated with high perinatal morbidity and mortality and long-term health sequelae [52]. Preeclampsia is associated with long-term heart failure (RR 4.19 [95% 2.09–8.38]), cardiovascular disease (RR 2.50 [95% 1.43–4.37]), stroke (RR 1.81 [95% CI 1.29–2.55]), and diabetes risks (RR 2.36 [95% CI 1.94–2.88]) [53, 54]. Although there are no evidence-based tools or strategies for predicting or preventing preeclampsia, there are several well-recognized risk factors [55]. Prepregnancy BMI is strongly associated with an increased risk for preeclampsia in a dose-dependent fashion (Table 64.2). In a large review including over 1.4 million women, the risk for preeclampsia doubled with each 5–7 kg/m2 increase in prepregnancy BMI

TABLE 64.2  Adverse Perinatal Outcomes in Persons with Obesity Compared to Normal-Weight Pregnancies OUTCOME Fetal Overgrowth LGA LGA Class 1 (30–34.9 kg/) Class 2 (35–39.9 kg/m2) Class 3 (≥40 kg/m2) Macrosomia (BW ≥4,000 g) Hypertensive Spectrum Disorder Preeclampsia Preeclampsia (prepregnancy BMI >30) Preeclampsia Class 1 (30–34.9 kg/) Class 2 (35–39.9 kg/m2) Class 3 (≥40 kg/m2) Preeclampsia (low- to middle-income countries) Gestational hypertension (low- to middle-income countries) Gestational hypertension Class 1 (30–34.9 kg/m2) Class 2 (35–39.9 kg/m2) Class 3 (≥40 kg/m2) Gestational diabetes GDM Class 1 (30–34.9 kg/ m2) Class 2 (35–39.9 kg/m2) Class 3 (≥40 kg/m2) GDM (by phenotype) General obesity Central obesity Visceral adiposity GDM BMI ≥30 kg/m2 BMI 30–35 kg/m2 BMI >35 kg/m2

SAMPLE POPULATION (OBESE/NORMAL WEIGHT)

RR OR OR (95% CI)

162,183/1,072,397 3,929/20,369 2,725/14,853 888/4,205 316/1,311 20693/110696

2.54 (2.22–2.92) 2.28 (2.19–2.37) 2.15 (2.05–2.25) 2.56 (2.37–2.77) 3.06 (2.69–3.49) 2.17 (1.92–2.45)

Meta-analysis [30]

1,387,599 5,921,559

3.15 (2.96–3.36) 2.7 (2.5–2.9)

Meta-analysis [73] Meta-analysis [74]

1,621/18,797 1,047/13,811 410/3,810 164/1,176 492,745

3.70 (3.48–3.93) 3.20 (2.98–3.44) 4.81 (4.31–5.37) 6.50 (5.48–7.73) 3.87 (3.48–4.29)

Meta-analysis of 39 cohorts [72]

Meta-analysis [75]

492745

5.61 (4.86–6.46)

Meta-analysis [75]

1687/18,863 1136/13,900 412/3,812 139/1,151

3.68 (3.46–3.91) 3.31 (3.08–3.55) 4.66 (4.17–5.20) 5.40 (4.47–6.51)

Meta-analysis of 39 cohorts [72]

1020/21,148 636/15,405 271/4,386 113/1,357 (10 studies)

4.59 (4.22–4.99) 3.97 (3.61–4.37) 5.85 (5.09–6.73) 7.59 (6.14–9.38) 2.73 (2.20–3.38) 2.53 (2.04–3.14) 3.25 (2.01–5.26)

Meta-analysis of 39 cohorts [72]

364,668

3.76 (3.31–4.28) 3.01 (2.34–3.87) 5.55 (4.27–7.21)

TYPE OF STUDY Meta-analysis [30] Meta-analysis of 39 cohorts [72]

Meta-analysis [93]

Meta-analysis [94]

602  Handbook of Obesity [56]. Obesity is commonly accompanied by higher rates of insulin resistance, endothelial dysfunction, metabolic syndrome, and hypertension, which are all known risk factors for preeclampsia. However, not all persons with obesity develop preeclampsia, which emphasizes the metabolic heterogeneity and our poor understanding of the contributing mechanisms of obesity [57]. Low-dose aspirin has been demonstrated to have a modest risk reduction in preeclampsia when started before 16 weeks gestation, particularly among high-risk individuals with a history of severe, preterm preeclampsia [58]. The current guidelines recommend low-dose aspirin (81 mg) for high-risk individuals or persons with more than one moderate risk factor for preeclampsia [55, 59]. Although the optimal dose of aspirin for preeclampsia prevention is debatable, persons with higher class obesity have been shown to have lower rates of complete inhibition of thromboxane A2 with low-dose aspirin compared to lower-weight pregnant persons, suggesting further studies are needed in populations with obesity to determine appropriate preventive aspirin dosing [60].

64.4.3 Glucose Metabolism and Gestational Diabetes Mellitus Pregnant persons with obesity have overall higher baseline glycemia compared to individuals within the normal weight range, even under controlled meal conditions and normal glucose tolerance [24, 61]. GDM is defined as abnormal glucose metabolism first recognized in pregnancy generally between 24 and 28 weeks. Although impaired glucose metabolism generally resolves after delivery, GDM is often associated with progression to type 2 diabetes mellitus (T2DM). Prepregnancy obesity of any phenotype is significantly associated with GDM, but the highest association is appreciated with the visceral adiposity phenotype (OR 3.25 [95% 2.01–5.26]) (see Table 64.2) [62]. For every unit increase in BMI, the risk for GDM increases by 0.92% (95% CI 0.73–1.10). For every increase in BMI class, the prevalence of GDM is calculated to increase by 4.6% [63]. GDM and obesity are both independently associated with higher rates of LGA, preeclampsia, primary cesarean deliveries, and long-term, offspring cardiometabolic health conditions, but in combination have significantly greater risks for these adverse outcomes, particularly with increasing BMI and dysglycemia [64]. Interventions of nutrition and lifestyle modifications that begin after the first trimester have not demonstrated a reduction in rates of LGA or GDM among persons with obesity [65]. Interpregnancy weight loss is associated with lower rates of LGA, GDM, and HSD in a subsequent pregnancy, emphasizing the importance of pregravid weight on the risk reduction for adverse perinatal outcomes [66]. In a population-based cohort, weight loss of at least 10 lb (4.5 kg) between pregnancies decreased the risk of developing GDM in a subsequent pregnancy (RR 0.63 [95% CI 0.38–1.02]), while weight gain of at least 10 lb (4.5 kg) was associated with significantly increased

GDM risk (RR 1.47 [95% CI 1.05–2.04]) [67]. Persons who gain or retain postpartum weight have higher rates of cesarean deliveries, HSD, GDM, and LGA, particularly among persons who had a prepregnancy weight within the normal range in the initial pregnancy [68]. These data emphasize the importance of obesity prevention strategies and weight management before and after pregnancies to improve long-term and perinatal health. Pharmacologic agents have inconsistently shown a risk reduction for GDM among pregnancies complicated by obesity. Metformin has not been shown to reduce rates of GDM or LGA among persons with obesity [69]. Myo-inositol is a carbocyclic sugar that when taken as a supplement has shown potential for risk reduction for GDM [70]. One study demonstrated lower rates of GDM among persons with obesity taking myo-inositol from the first trimester of pregnancy compared to placebo (OR 0.34 [95% CI 0.17–0.68]) [71]. Pharmacologic therapy addresses one facet of management for persons with obesity but requires further studies to determine its safety and role during pregnancy.

64.5 PERIPARTUM COMPLICATIONS 64.5.1 Failed Induction and Slow Labor Progression Persons with obesity are more likely to have medical, surgical, and obstetric complications that include higher rates of induction, dysfunctional labor patterns, increased cesarean delivery, and a greater incidence of postoperative complications of surgery (Figure 64.1; Table 64.3). Obesity in pregnancy has been associated with longer gestation and elevated risk for postterm delivery [76]. Arrowsmith et al. found that women with severe obesity had a greater than twofold risk of late-term and postterm pregnancies compared to normal-weight women (aOR 2.27 [95% CI 1.78–2.89]). The association of obesity with prolonged pregnancy may be related to a reduction in myometrial contractility that has been demonstrated in persons with obesity by both clinical and laboratory markers [77]. However, overdiagnosis of postterm pregnancies may be higher among individuals with obesity who are oligoovulatory secondary to misdating the pregnancy. Population-based studies suggest that approximately a third of individuals with obesity require induction of labor compared to a quarter of pregnancies among persons with normal weight [78]. Even when labor occurs spontaneously, dysfunctional labor has been more commonly observed among parturients with obesity. In one study, the median duration of labor from 4 to 10 cm cervical dilation increased from 6.2 hours for women with normal weight to 7.9 hours for women with obesity, after adjustment for a number of confounding factors including

64  •  Obesity in Pregnancy Complications and Outcomes  603 TABLE 64.3  Adverse Labor Outcomes in Pregnant Persons with Obesity Compared to Persons with Normal Weight OUTCOME Prolonged Pregnancy ≥42 weeks gestation ≥290 days gestation BMI 30–34.9 kg/m2 BMI 35–39.9 kg/m2 BMI >40 kg/m2 Induction of labor Class 1 Class 2 Class 3 Induction of labor Cesarean Delivery Cesarean Obese Severe obesity Cesarean (nulliparous) Cesarean

SAMPLE POPULATION (OBESE/NORMAL WEIGHT)

RR OR OR (95% CI)

31,276/176,923 3,061/9,530

1.72 (1.23–2.42) 1.52 (1.37–1.70) 1.75 (1.48–2.07) 2.27 (1.78–2.89)

Single cohort—retrospective [82] Single cohort—retrospective [76]

860/2,275

1.73 (1.46–2.06) 1.58 (1.19–2.11) 1.73 (1.13–2.66)

Single cohort—retrospective [83]

22,328,777

1.55 (1.36–1.77)

Meta-analysis [84]

(29 studies) (7 studies)

2.05 (1.86–2.27) 2.89 (2.28–3.79)

Meta-analysis [85]

— 22,293/143,875

1.50 (1.05–2.00) 2.36 (2.15–2.59)

Meta-analysis [79] Meta-analysis [81]

1.23 (1.04–1.45) 2.20 (1.29–3.78)

Population cohort [86] Multicenter cohort secondary analysis [87]

1.38 (1.25–1.54) 1.14 (0.97–1.35) 1.10 (0.90–1.34) 0.99 (0.73–1.34)

Meta-analysis [84] Meta-analysis [88]

38,229

1.12 (1.03–1.22) 3.77 (2.60–5.46)

Multicenter cohort secondary analysis [89]

2,444

1.4 (0.99–2.0) 2.6 (1.7–3.8) 3.0 (1.9–4.9)

Single-center— retrospective [90]

Postpartum Hemorrhage (PPH) PPH (Class 3) 1,114,071 PPH (Class 2 and Class 3 7,189 nulliparous) PPH 22,328,777 PPH 3,722,477 Class 1 Class 2 Class 3 Wound Infection Wound infection BMI 30–45 kg/m2 BMI >45 kg/m2 Wound complication BMI 30–39.9 kg/m2 BMI 40–49.9 kg/m2 BMI ≥50 kg/m2

TYPE OF STUDY

labor induction, membrane rupture, oxytocin use, epidural analgesia, GWG, and fetal size [79]. Both term and postterm pregnancies complicated by obesity are twice as likely to experience a failed induction of labor compared to pregnancies by women with normal weight [80]. Fetal weight, parity, and provider bias likely play a role in the higher rates of cesarean deliveries in the setting of induction of labor in this population.

64.5.2 Mode of Delivery/ Cesarean Delivery Pregnant persons with obesity face a greater likelihood of undergoing either an elective or emergent cesarean delivery [81]. A meta-analysis of 11 studies reported the risk of cesarean

delivery in nulliparous women with singleton pregnancies was 1.5 times higher in those with overweight, 2.25 times higher in those with obesity, and 3.4 times higher in women with severe obesity (or BMI >40 kg/m2) compared to women with a normal BMI [81]. This risk is likely augmented by obesity-related pregnancy complications including fetal macrosomia, gestational hypertension, and diabetes but conveys independent risk for operative deliveries after accounting for factors including HSD, diabetes, and fetal growth [81]. The biological pathways associated with dysfunctional labor and increased cesarean risks in persons with obesity have been proposed to include increased deposition of pelvic fat, narrowing the diameter of the birth canal, causing obstruction to the birth passage, and possibly increasing the need for an operative vaginal delivery [80].

604  Handbook of Obesity

64.5.3 Postoperative Complications Operative risks in patients with obesity include increased operative time, excessive blood loss (>1,000 mL), and increased postoperative complications such as wound infection and dehiscence [90]. A course of perioperative antibiotics has been shown to reduce postoperative infection and endometritis rates by up to 75% and is recommended for all patients undergoing cesarean delivery, with some authors suggesting the use of higher doses in patients with obesity [91]. A postoperative course of prophylactic oral antibiotics has been shown to decrease surgical site infections, particularly among higher BMI classes [92]. Negative pressure dressings have not been consistently found to improve wound complications after cesarean delivery [93]. Venous thromboembolism (VTE) is one of the leading causes of maternal death in the U.S. and occurs more often among persons with obesity. All patients undergoing a cesarean should receive VTE prophylaxis with compression stockings [94]. Because the risk for postpartum deep vein thrombophlebitis is increased with higher BMI, chemoprophylaxis with low molecular weight heparin for patients with obesity after cesarean delivery should be considered [95]. Primary cesarean delivery has implications for the clinical management of subsequent deliveries. The success of vaginal birth after cesarean (VBAC) in future pregnancies is inversely related to BMI. In a prospective multicenter trial including over 4,000 VBAC attempts, women who did not have obesity had a VBAC failure rate of 15% compared to 30% and 39% for persons with obesity and severe obesity (BMI >40 kg/m2), respectively [96]. However, one study found no difference in the rate of VBAC success between parturients with differing BMI classes [97], suggesting that some of the cesarean risk differences seen in people with obesity may be due to clinician biases as opposed to actual physiologic or anatomic differences. The increased operative risks associated with cesarean delivery after failed VBAC (e.g., infection, uterine rupture and hemorrhage, and the need for blood transfusion) must be considered in subsequent pregnancies. Furthermore, severe obesity may impede adequate monitoring of uterine activity and fetal heart rate, which may further compromise the safety of VBAC in persons with obesity.

64.6  MATERNAL OBESITY AFTER PREGNANCY Postpartum weight retention is a large contributor to future obesity. Differences in socioeconomic status, race and ethnicity, and adverse behavioral factors have been associated with greater postpartum weight retention including but not limited to consuming ≤3 servings of fruit or vegetables, 10% from 2017 [107]. Obesity surgery is considered an appropriate intervention for (1) persons with a BMI ≥40 kg/m2; (2) persons with a BMI ≥35 kg/m2 with coexisting medical conditions, such as diabetes mellitus, coronary artery disease, and severe sleep apnea; or (3) persons who have prior efforts and interventions for weight loss without success. Weight reduction following obesity surgery is associated with increased fertility rates and decreased GDM, gestational hypertension, LGA, and cesarean deliveries [108]. The risk for SGA, fetal growth restriction, and preterm deliveries are higher among pregnancies after bariatric surgery, especially if the pregnancy is conceived within a year of the obesity surgery or during active weight fluctuations following surgery.

Patients desiring obesity surgery should avoid pregnancy for at least a year following the operation or until at least 3 months of weight stability to avoid increasing risks for adverse perinatal outcomes secondary to malnutrition and weight instability [109]. Therefore, long-acting reversible contraception is recommended to avoid unintended pregnancies and contraception failure from oral contraceptives. Nutritional requirements should be individualized and monitored during preconception and throughout pregnancy. Postprandial dumping syndrome is a common effect following gastric bypass surgery that occurs within 60 minutes of ingestion of simple carbohydrates, resulting in vasomotor symptoms (i.e., dizziness, flushing, and palpitations). Persons that experience this during pregnancy may not tolerate the oral glucose tolerance test for GDM screening and can alternatively monitor with self-capillary blood glucose checks at home with or without a continuous glucose monitor. Other complications that have been documented in pregnancies following obesity surgery include internal hernia formation and bowel ischemia, band slippage and excessive nausea, bowel obstructions, staple line strictures, vitamins A and B12 and folate deficiency, and chronic diarrhea [109]. Recognizing the potential risks associated with obesity surgery, specialized care and close monitoring (dietitian, nutritional assessments, fetal growth monitoring, and prevention of surgical complications) are recommended.

64.8 SUMMARY Prepregnancy obesity is independently associated with several significant perinatal and long-term offspring health risks. Because adverse effects of obesity can occur during the early prenatal period, preconception health education and optimization is beneficial as a risk-reduction strategy. The metabolic heterogeneity of obesity, effects of gestational weight gain, and other pregnancy complications that persons with obesity are at higher risk for such as gestational diabetes complicate the strength of the association of prepregnancy obesity with adverse perinatal outcomes. Therefore, pregnancy is a motivating and opportune time to encourage and support healthy dietary patterns and lifestyle changes to optimize overall metabolic health that should be encouraged to continue long after pregnancy. Future research is needed to metabolically characterize pregnant persons with obesity with higher pregnancy risks and develop targeted interventions to improve adverse perinatal and offspring health outcomes.

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Obesity, Growth, Development, Metabolic Disorder, and Insulin Resistance in Pediatrics

65

Nicola Santoro, Alfonso Galderisi, and Sonia Caprio

65.1  PREVALENCE OF ADOLESCENT OBESITY AND ITS ASSOCIATED METABOLIC HEALTH RISKS Pediatric obesity is a worldwide phenomenon that has grown over the past 40 years. Epidemiological studies show that the increase in the prevalence of childhood obesity in the U.S. began during the early 1980s and now affects 20% of the American population under age 19 (Figure 65.1) [1–3]. The development of childhood obesity is influenced by several genetic and nongenetic factors, including maternal programming, socioeconomic status, and common gene variants [4–6]. Almost 20% of those aged 2–19 years are obese, [7] and one out of four is expected to have impaired glucose metabolism. Childhood obesity dramatically increases the risk for noncommunicable diseases in adulthood, including cardiovascular disease, type 2 diabetes (T2D), sleep and behavioral disorders, and liver disease [8, 9].

65.2  GROWTH AND DEVELOPMENT IN CHILDREN WITH OBESITY The onset of obesity during childhood may affect critical milestones of somatic and psychological growth. Earlier studies have shown that adolescents with obesity can experience 608

precocious puberty and that this is more pronounced among non-Hispanic Black girls. In fact, the age of thelarche (the growth of the mammary gland when puberty begins) is on average 3 months earlier in girls with obesity as compared to those without obesity [10, 11]. The earlier onset of puberty is accompanied by an acceleration of linear growth, although the final height of children with obesity is not greater than those without obesity and they usually reach a final height within their genetic target [12]. The mechanisms behind these phenomena are not completely understood but are probably the consequence of the hormonal modifications experienced by these patients. Hyperinsulinemia consequent to insulin resistance may play a role in the acceleration of the growth experienced by some children with obesity [13]. Insulin is a powerful growth factor, driving intra-uterine and postnatal growth [14, 15]. Interestingly, children with obesity experience an acceleration of linear growth despite showing lower peaks of growth hormone (GH) than children without obesity [16–19]. This is important because the area under the curve of GH excursions directly correlates with the linear growth in the general population [20, 21]. Early onset puberty might be triggered in children with obesity by the increase of leptin synthesis by the adipose tissue in subjects with obesity. Leptin represents a key hormone-triggering puberty [22–26] and since children with obesity experience high production rates of leptin, this can initiate an earlier activation of the gonadotropic axis in the hypothalamus [24, 27]. The effects of early onset obesity on the bone are not limited to earlier growth and development. One of the main problems with obesity early in life is bone deformity often due to excessive weight. The weight in excess in children with obesity, in fact, can cause bone deformity due to the relative immaturity of the bones [28]. One example is Blount disease, which is characterized by a deformity of the lower limbs occurring DOI: 10.1201/9781003437673-70

65  •  Obesity, Growth, Development, Metabolic Disorder  609

FIGURE 65.1  The trend of obesity in children, between the years 1963 and 2018. (From www​.cdc​.gov​/nchs​/data​/hestat​/obesity​ -child​-17- 18/obesity​- child​.h​tm.)

usually before the age of 4 years beginning after the child is able to start walking [29]. If left untreated, this can cause longterm deformity of the lower limbs [29, 30]. The bone composition at this age is important also because it affects the motor skills of children with obesity. Kojic et al. have recently shown that children with flat feet (a frequent condition among children with obesity) tend to have reduced motor skills as compared to a group of children with a similar, age, gender, and body mass index (BMI) but normal arch feet [31]. However, regardless of the shape of the foot, children with obesity tend to show impaired motor skills, including impaired motor coordination [32]. This affects their ability to increase physical activity favoring sedentary behaviors.

during childhood, including poverty [38], are a risk factor by themselves for early obesity onset with a more pronounced effect in girls than boys [39]. These conditions are sometimes worsened by the bullying experienced by children with obesity [40, 41]. These adverse experiences during childhood may lead some of these children to develop low self-esteem, which in turn may be the trigger for developing eating disorders [42, 43]. Therefore, the high prevalence of psychological comorbidities in children with obesity requires an attentive screening by primary care providers, preferably in a team-based approach environment [35].

65.3 PSYCHOLOGICAL CONSEQUENCES OF OBESITY IN YOUTH

65.4  INSULIN RESISTANCE AND THE DEVELOPMENT OF METABOLIC COMPLICATIONS IN YOUTH WITH OBESITY

From a psychological point of view, a child with obesity experiences an overall lower quality of life when compared to a peer with normal weight [33]. Youth with obesity face a greater risk of psychological comorbidities, including depression[34], anxiety, eating disorders, low self-esteem, and substance abuse [35, 36]. The determinants of the quality of life in this population include the degree of obesity, symptoms of depression, a social support network, and the economic status of the family [37]. On the other hand, adverse psychological experiences

When obesity occurs, lipids that cannot be stored in the adipocytes reach and accumulate in tissues other than adipose tissue, such as skeletal muscle and liver [44, 45]; this phenomenon known as ectopic fat accumulation plays a key role in the development of insulin resistance. The excess of fatty acids in the cells of the liver and muscle results in the formation and accumulation of molecules such as diacylglycerol (DAG) and ceramides [44, 45], compounds known to negatively affect insulin signaling through different pathways making these

610  Handbook of Obesity

FIGURE 65.2  Potential mechanisms leading to insulin resistance in youth with obesity. (a) In subjects with a normal ratio of visceral to subcutaneous abdominal fat (VAT/SAT), the free fatty acids are stored in the adipose tissue. (b) When obesity occurs, individuals with a high VAT/SAT experience a high flux of FFA from adipose tissue to muscle, liver, and other tissue. This causes an accumulation of FFA in those tissues (ectopic fat accumulation) with the production of some metabolites such as diacylglycerols (DAG) and ceramides that lead to insulin resistance in those tissues. The consequent increased production of glucose from the liver and reduced uptake from the muscle leads to hyperglycemia, which in turn fuels hepatic de novo lipogenesis (DNL) causing an increased production of FFA in the liver, perpetuating this cycle.

tissues resistant to the action of insulin [46]. To cope with insulin resistance, the pancreatic beta cell increases beta cell insulin secretion [47, 48] causing hyperinsulinemia, which in turn exacerbates intrahepatic fat synthesis and accumulation by upregulating genes in the pathway of hepatic lipogenesis thus favoring the conversion of the extra circulating glucose into fatty acids [48, 49]. When the beta cell response becomes insufficient to compensate for the degree of insulin resistance, hyperglycemia occurs [48]. The hyperglycemia further fuels hepatic de novo lipogenesis, worsening the production and accumulation of lipids, and further exacerbating insulin resistance. The exact mechanisms leading to insulin resistance in youth with obesity are still unknown. The distribution of body fat seems to play a key role in shaping the metabolic phenotype of individuals with obesity. Ectopic fat accumulation due to the inability of adipose tissue to store fat may be the consequence of an innate lower capability of subcutaneous fat to expand, directing the lipids from the subcutaneous to the visceral fat. Visceral fat accumulation would then result in an increased flux of free fatty acids (FFA) to the liver through the portal system and then to other tissues. This has been shown to be the case in youth with obesity. Kursawe et al. investigated abdominal subcutaneous adipose tissue from two groups of adolescents: one group with a high ratio of visceral adipose tissue to subcutaneous adipose tissue (VAT/SAT) and the other with a low VAT/SAT ratio [50]. The study showed that youth with a high VAT/SAT tend to have smaller adipose cells and lower adipogenic ability in the SAT than the group with low VAT/ SAT and concluded that the diminished ability for fat storage leads to lipid accumulation in VAT [50]. A follow-up study

showed that girls with obesity and a high VAT/SAT have high triglyceride turnover rates in SAT, which can result in higher FFA flux to the liver [51]. Moreover, it has been shown that, in the context of insulin resistance, SAT has a lower ability to internalize glucose because expression of the GLUT4 transporter is reduced [51]. In this context, the glucose that is not absorbed and processed in adipose tissue tends to be diverted to the liver where it becomes a substrate for hepatic de novo lipogenesis, further promoting intrahepatic fat synthesis and accumulation (Figure 65.2).

65.5  INSULIN RESISTANCE AS A NATURAL PHENOMENON DURING ADOLESCENCE A certain degree of insulin resistance is present during adolescence. This phenomenon, first described by Amiel et al., has been known since the mid-1980s [52]. In 1986, Amiel et al. published a study in which a group of prepubertal adolescents underwent the euglycemic clamp to determine the degree of insulin resistance expressed as insulin-stimulated glucose metabolism. They showed that insulin resistance was higher during puberty than in prepubertal children [52]. In a follow-up study, the authors showed that this could be due to an increase in anti-insulin hormones such as growth hormones during adolescence [53]. Moreover, insulin resistance during puberty could be the result of a

65  •  Obesity, Growth, Development, Metabolic Disorder  611 state of selective insulin resistance for glucose metabolism, in order to benefit from the anabolic effects of insulin during growth [53]. A study by Moran et al. showed that insulin resistance during puberty is usually more severe in girls than in boys and, it resolves at the end of puberty when maturity is reached [54]. Pubertal insulin resistance is key to understanding the pathogenesis of prediabetes and type 2 diabetes in youth. In youth with obesity, obesity-related insulin resistance compounds the insulin resistance associated with puberty. This combination entrains a perfect storm leading to a severe degree of insulin resistance. This explains why prediabetes and type 2 diabetes in pediatrics are present almost exclusively after the onset of puberty and why these two conditions are more prevalent among girls [8, 55].

65.6  PREDIABETES IN YOUTH WITH OBESITY In the U.S., the prevalence of T2D has increased in the last few years among youth with obesity. This is due to the occurrence of a severe degree of insulin resistance occurring in youth when they develop obesity. When insulin resistance occurs in youth as a consequence of obesity, the beta cell increases the rate of insulin secretion [56]. When the insulin secreted by the beta cell becomes insufficient to maintain plasma glucose concentrations within the normal values, then plasma glucose concentrations start rising [56, 57]. From a clinical standpoint, it is imperative to distinguish between prediabetes and overt T2D. In the context of obesity, three main clinical entities are defined as prediabetes: (1) impaired fasting glucose (IFG) characterized by a fasting glucose between 100 mg/dl and 125 mg/dl; (2) impaired glucose tolerance (IGT) defined as glucose concentrations 2 hours after the ingestion of 75 g of glucose between 140 mg/dl and 200 mg/dl; and (3) concentrations of HbA1c% in the blood between 5.7% and 6.4% [58, 59]. Similarly to adults [60], the HbA1c categories and glucose categories poorly overlap in youth with prediabetes [61]. Therefore, it is always useful to assess fasting, 2-hour glucose, and HbA1c when a diagnosis of prediabetes is suspected [61]. Studies in pediatric populations with prediabetes have clearly shown that prediabetes is highly reversible in youth, particularly in those of a White ethnic background, compared to what is seen in adults [62]. Longitudinal studies in youth with prediabetes have also shown that the progression from prediabetes to overt diabetes is five times faster in youth than in adults: it requires about 2 years in youth, whereas the transition usually happens over 10 years in adults [64] This is probably the consequence of the high severity of insulin resistance experienced by youth with obesity [63]. Moreover, in a recent study in which 364 youth with normal glucose tolerance (NGT) and 162 with IGT were followed up for about 2

years, individuals who develop IGT or T2D tended to have a lower ability to secrete insulin to start with [62]. Furthermore, ethnicity seems to play an important role in the development of T2D in youth [57]. In youth with prediabetes, being nonHispanic Black was shown to be the strongest predictor for the development of overt diabetes [65].

65.7 TYPE 2 DIABETES IN YOUTH WITH OBESITY: CLINICAL CHARACTERISTICS AND COMPLICATIONS T2D is defined as a fasting glucose greater than 125 mg/dl or 2-hour glucose greater than 200 mg/dl [59]. According to the SEARCH for Diabetes in Youth study, the incidence of T2D has been rising in youth, especially among nonHispanic Blacks and Native Americans [55]. The reasons for this phenomenon are multiple and are certainly, but not solely related to the rise in the prevalence of obesity [2]. The increase in prevalence of T2D in youth became evident in the late 1990s [66]. Since the initial report, many studies have focused on understanding the pathophysiology of T2D in youth [67]. T2D is often accompanied by other dysmetabolic characteristics, such as dyslipidemia, that define the metabolic syndrome. Not surprisingly, the prevalence of metabolic syndrome among youth with T2D is high with estimates in the range from 75.8% to 83.1% in females and 62.3% in males. [68]. When compared to youth with T1D (type 1 diabetes), youth with T2D tend to present with higher total cholesterol, LDL cholesterol, and triglycerides; and lower HDL cholesterol [69]. The higher rate of dyslipidemia translates into a higher adverse cardiovascular phenotype characterized by greater arterial stiffness, impaired function of the autonomic system, and pathological changes in cardiac structure and functions [70–72]. Retinopathy also exists in youth with T2D. Its prevalence has been estimated to be between 13.7% and 42% in adolescents with an average diabetes duration between 5 and 7 years [73, 74]. The unfavorable metabolic state predisposes youth with obesity and T2D to long-term adverse sequelae resulting in a greater rate of early onset end-stage renal disease and shorter survival rates [75–77]. It has been demonstrated that 60% of subjects with youth onset T2D have at least one microvascular complication before the age of 30 and the mean time from diabetes diagnosis to the first complication is 13.3 years [79], such a prevalence being more than two times that observed in T1D (25% for microalbuminuria after 10 years and ~30% for kidney disease after 25 years [80, 81]) and T2D (25% of increased albuminuria after 10 years [82]). The higher rate of complications and the early onset of the first organ damage pose a major challenge to T2D treatment and its prevention.

612  Handbook of Obesity

65.8  GENETIC PREDISPOSITION TO EARLY ONSET T2D Early onset T2D, like other complex diseases, has a genetic component to its pathogenesis. The majority of genome-wide association studies (GWAS) have been performed in adults and have uncovered many loci associated with T2D [83]. Only recently has the first GWAS in pediatric T2D been published [84]. This study confirmed some of the strongest associations previously established in adults [84]. Among them, the strongest signal was obtained with the transcription factor 7-like 2 (TCF7L2) rs7903146 [84]. This is an intronic variant characterized by a C to T substitution. The TCF7L2 gene is involved in Wnt/c-Jun signaling, but its functional role in insulin metabolism is still not completely clear [84]. Despite the lack of knowledge of the molecular implications of this mutation, clinical studies have shown that youth carrying the risk allele (T) have an elevated risk of prediabetes and T2D with obesity [85]. When insulin resistance is present, the individuals carrying the T allele experience an inadequate beta cell response, which may be due to an impaired incretin effect [86]. One study has shown that in the presence of the rs7903146 risk allele, the ability to secrete incretin (in particular GLP-1) is reduced by about 40% compared to subjects homozygous for the wild-type allele [86]. Carriers of the risk allele exhibited lower insulin secretion as well as reduced insulin clearance with respect to the wild-type genotype [86]. On the other hand, a study performed using the hyperinsulinemic euglycemic clamp showed that subjects carrying the at-risk allele also have a greater degree of hepatic insulin resistance as compared to those carrying the TCF7L2 not-at-risk allele [85]. These studies provide evidence that the TCF7L2 rs7903146 variant probably plays a role in the development of early onset T2D and that the TCF7L2 gene is important in modulating insulin secretion in the presence of glucose intake and maintaining hepatic insulin sensitivity (Figure 65.3).

65.9  LESSONS FROM STUDIES EXPLORING THERAPEUTIC STRATEGIES FOR PEDIATRIC T2D Despite the pervasiveness of T2D in youth, the number of drugs available to manage young patients with T2D is much lower than in adults mainly because there are fewer clinical trials in youth with T2D than in adults. The first clinical trial in youth with T2D was the TODAY a multicenter NIH/NIDDK-funded study involving 15 centers in the U.S.(mentioned above) [78]. The study, called the Treatment of Options for type 2 Diabetes in Adolescents and Youth (TODAY), was a three-arm study that sought to evaluate the effectiveness of metformin plus rosiglitazone versus metformin plus lifestyle intervention and metformin alone [78]. The investigators observed that metformin plus rosiglitazone was superior to metformin alone in achieving durable glycemic control during the study, while there was no difference between metformin alone and metformin plus lifestyle intervention [78]. The TODAY study has shed light on several aspects of the disease. One of the most remarkable observations was that in each treatment arm, about 50% of the subjects failed and that failure rates were higher in non-Hispanic Black youth [78]. To understand whether early intervention could stop the progression of beta cell failure, the RISE study, another NIH/ NIDDK-funded clinical trial, was designed [66]. The RISE trial had an adult and a pediatric component. In particular, the pediatric arm of the RISE trial was designed as a randomized open-label clinical trial comparing insulin glargine for 3 months followed by metformin for 9 months, against metformin alone for 12 months [87]. At the end of the study, there was no difference in insulin secretion between the groups after the treatment [87]. Despite the negative results, the RISE study provided the opportunity to compare the many features of T2D between adults and youth, adding critical information to our knowledge of the disease [63] and showing for the first time that diabetic youth are more insulin resistant than adults with the same degree of obesity.

65.10 NONALCOHOLIC FATTY LIVER DISEASE IN ADOLESCENTS WITH OBESITY

FIGURE 65.3  The risk allele (T) for the transcription factor 7-like 2 (TCF7L2) rs7903146 has been associated with reduced incretin secretion (gastrointestinal effect), increased insulin resistance, decreased hepatic insulin clearance (liver effect), and reduced insulin secretion due to both an impaired processing of proinsulin to insulin and a reduced incretin response (beta cell effect).

Nonalcoholic fatty liver disease (NAFLD) is the most common hepatic complication of pediatric obesity [88]. The term NAFLD defines a disease spectrum with different degrees of severity [89]. The first stage is characterized by intrahepatic fat accumulation (steatosis) without signs of inflammation or fibrosis [89]. The progressive accumulation of fat in the liver leads to an inflammatory state known as nonalcoholic steatohepatitis (NASH), which in turn may result in fibrosis, cirrhosis, endstage liver disease, or hepatocellular carcinoma (HCC) [89].

65  •  Obesity, Growth, Development, Metabolic Disorder  613 The progression of the disease can take years and has a different pace for each individual. Although the disease has been extensively studied in adults, it is well established that severe degrees of NAFLD are present already in youth and that the onset of NAFLD in childhood predisposes a person to develop liver failure during the second decade of life [89]. A seminal study by Feldstein and colleagues has shown that youth diagnosed with NAFLD at about 13 years of age have a 16 times higher risk of developing end-stage liver disease in their early 20s compared to a group of individuals of similar age and gender in the U.S. [90]. These data have been recently replicated in a large Swedish cohort in which the investigators reviewed clinical data of children and young adults (≤25 years) who were followed up for about 16 years [91]. The study showed that early onset NAFLD predisposed to higher rates of mortality than a similar population without early onset NAFLD in a 15-year follow-up period. In particular, intrahepatic fat per se increased the mortality rate by 5.3-fold and NASH was associated with an 11.5-fold increase in the rate of mortality [91].

65.11  NAFLD AND DYSGLYCEMIA IN YOUTH WITH OBESITY: EVIDENCE OF CLINICAL OVERLAP The clinical overlap between prediabetes and NAFLD has been shown in many studies. Youth with obesity and NAFLD have higher plasma concentrations of 2-hour glucose, fasting insulin and glucose, and a higher prevalence of IGT compared to obese youth without NAFLD [88]. Furthermore, in youth with biopsy-confirmed NASH, the prevalence of IGT with NAFLD parallels the severity of the disease, and increases with the growing amount of intrahepatic fat, inflammation, and liver fibrosis [92]. There is some evidence that fatty liver affects insulin resistance independent of fat accumulation in other depots [93]. More recently, a longitudinal study has shown that youth without fatty liver who develop NAFLD within 2 years display a greater degree of insulin resistance compared to youth who do not develop NAFLD in the same time frame [88]. This suggests that insulin resistance may trigger and aggravate NAFLD, leading to a progressive worsening of the metabolic phenotype of the individual. From a clinical standpoint, NAFLD and prediabetes share other features related to insulin resistance such as dyslipidemia [88]. This is due to both adipose tissue lipolysis consequent to adipose tissue insulin resistance and increased hepatic de novo lipogenesis, a key pathway in the pathogenesis of NAFLD [94]. Subjects with NAFLD, in fact, often have high triglycerides and LDL cholesterol and lower HDL cholesterol [88, 95]. The co-occurrence of these conditions has led some investigators to propose a new nomenclature for fatty liver that accounts also for dyslipidemia and prediabetes/type 2 diabetes. According to some investigators, the term NAFLD should be substituted with MAFLD, an acronym that stands for metabolic (dysfunction)-associated fatty liver disease [96].

65.12  GENETIC PREDISPOSITION TO NAFLD IN YOUTH Heritability studies have shown that NAFLD is a highly heritable trait. By studying pairs of identical twins with discordant phenotypes (one individual with NAFLD and the other without), the heritability of NAFLD heritability was estimated to be around 52% [97]. In 2008, the first GWAS reported that the rs738409 variant in the PNPLA3 gene was strongly associated with NAFLD [98]. These results have been widely replicated in adults and youth [99, 100]. In particular, subjects carrying the minor (G) allele were more likely to exhibit the features of NAFLD [99, 100]. Studies in adolescents showed that the minor allele of the rs738409 variant was not only associated with an increase in intrahepatic fat content but also with a higher susceptibility to developing liver fibrosis [100]. Data in the pediatric population also showed that adiposity distribution, in particular visceral fat, and dietary components, such as added sugars and omega 6 PUFA, may favor these associations [101–103]. Despite numerous studies, it is still unclear how this variant modulates the susceptibility to NAFLD. The rs738409 defines a genetic variant characterized by a C to G substitution resulting in an isoleucine to methionine substitution at the amino acid position 148 [98]. The PNPLA3 encodes for a protein, called adiponutrin, highly expressed in the liver and adipose tissue [98]. Adiponutrin contains both lipogenic and lipolytic activities [98]. Animal and in vitro studies suggest that, as a consequence of the mutation, the lipogenic activity is increased, while the lipolytic domain is impaired [98]. On the other hand, human studies suggest that the genetic variant may serve as a trigger for intrahepatic fat accumulation only in the presence of stressors, such as a diet rich in fructose or omega-6 fatty acids, alcohol intake, or liver infections [104–106]. Along with the PNPLA3 rs738409, other common variants have been shown to be associated with pediatric NAFLD, such as the rs1260326 in the GCKR gene [107], the rs58542926 in the TM6SF2 gene [108], the rs641738C in the MBOAT7 gene [109], and the rs72613567 in the HSD17B1 gene [110–112]. The GCKR gene encodes for the glucokinase regulatory protein, a protein that binds the glucokinase (GK) in the nucleus of hepatocytes [107]. The rs1260326 results in a C to T substitution at position 446 resulting in a leucine to proline substitution at codon 446. In vitro studies have shown that the mutation confers to the GK a lower susceptibility to bind the GCKR, making more GK available for the conversion of glucose in glucose-6-phosphate (G6P) [107, 113]. The increased G6P in the cytoplasm of hepatocytes results in enhanced glycolysis with increased synthesis of malonyl-CoA and hepatic de novo lipogenesis [113]. Studies in youth have shown that youth with obesity who are homozygous for the minor risk allele T have a greater degree of glycolysis and lipogenesis than subjects of similar age, gender, and degree of adiposity but who are homozygous for the common not-at-risk C allele [114].

614  Handbook of Obesity The rs58542926 variant in the transmembrane 6 superfamily member 2 (TM6SF2) gene is characterized by a C to T substitution in position 499, encoding a glutamate to lysine change at codon 167 [115]. The association between the variant in the TM6SF2 gene and NAFLD was discovered as a result of an exome-wide association study in the Dallas Heart Study [115]. Studies in youth have shown that this variant is associated with the degree of intrahepatic fat accumulation as well as liver inflammation [108]. The mutation seems to impair the function of TM6SF2 gene product in the liver, thus facilitating large VLDL secretion from hepatocytes. The phenotype of youth carrying the minor (at-risk T) allele is characterized by increased intrahepatic fat, but lower large VLDL as compared to those individuals homozygous for the common allele [108].

65.13 CONCLUSION Pediatric obesity is a worldwide growing burden and a major public health problem. Obesity occurring during the pediatric age has a detrimental impact on the physical and psychological growth of a child. Youth with obesity tend to experience earlier puberty onset and, in some cases, lower limb deformities. These structural changes affect their motor skills as well as self-esteem, which is further affected by the bullying experienced by some of these children and may result in higher rates of depression and other psychosocial conditions that affect the life of the child. Moreover, the early onset of obesity causes severe insulin resistance, especially during puberty, followed by early beta cell impairment. Clinical consequences are the development of prediabetes, T2D, NAFLD, and other cardiometabolic diseases. Thereby, early onset obesity impacts the developmental trajectory of youth with respect to their growth, psychosocial and metabolic development, and life expectancy, with an unprecedented social and individual cost.

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Bias, Discrimination, and Obesity

66

Rebecca M. Puhl

66.1 INTRODUCTION Epidemiological evidence linking obesity to health impairments and diseases has been well documented, triggering worldwide efforts to prioritize obesity prevention and treatment. A highly common consequence of obesity often neglected in these initiatives is pervasive societal devaluation and mistreatment, known as weight stigma. People with high body weight are vulnerable to societal weight stigma, which can be manifested in a range of ways including negative weight-based stereotypes (e.g., generalizations that people with obesity are lazy, lacking willpower and self-discipline, unmotivated), teasing, bullying, victimization, exclusion, unfair treatment, and discrimination [1]. These negative experiences occur across many societal settings and can result in structural disadvantages and inequities for people with obesity [2], reflecting a social injustice that persists globally [3]. Moreover, exposure to weight stigma incurs damaging consequences to health, reducing the quality of life for those affected [4]. This chapter summarizes the current literature to provide an overview of the prevalence, nature, and consequences of weight stigma facing youth and adults with obesity. Additionally, this chapter highlights key messages drawn from this literature and the importance of prioritizing multilevel stigma-reduction strategies.

66.2  PREVALENCE OF WEIGHT STIGMA The presence of societal weight stigma has been documented by researchers for more than 5 decades [5]. Despite reductions in other forms of societal bias throughout this time period, weight bias has persisted [6] and spread globally [3]. For example, reductions in population-level implicit biases have been documented for race and sexual orientation over the past 2 decades, but implicit biases have worsened for body weight [6]. DOI: 10.1201/9781003437673-71

Paradoxically, the increased prevalence of obesity throughout this time period has not been paralleled with increasing societal tolerance or acceptance of people with higher weight. Instead, weight stigma and discrimination remain prevalent. From 1995 to 2005, perceived weight discrimination among U.S. adults increased by 66% [7]. In 2008, national U.S. prevalence data documented weight discrimination to be the third most common form of discrimination reported by women and the fourth most common among men [8]. Furthermore, adults with a body mass index (BMI) in the obesity range reported weight discrimination at rates that were three times higher than individuals in lower weight categories [8]. While no differences were found across race or education level in links between BMI and weight discrimination, women were two times more likely than men to report weight discrimination [8]. These patterns have persisted in recent years. A 2016 meta-analysis of pooled prevalence data (primarily from U.S. studies) reported that prevalence rates of weight discrimination range from 19.2% for adults with grade I obesity to 41.8% for adults with “severe” obesity, and higher prevalence in women compared to men [9]. Other U.S. evidence shows that approximately 40% of adults report a history of experiencing weight-based teasing or mistreatment [10, 11]. Most recently, a 2020 international study of adults engaged in weight management in six Western countries (Australia, Canada, France, Germany, U.K., and U.S.) found more than half of participants (55%–61%) in each country experienced weight stigma [12]. Like prior evidence, weight-stigmatizing experiences were significantly more likely to be reported by individuals with higher BMI [12]. For youth, weight stigma is most often experienced as teasing, bullying, and victimization, and can involve verbal name-calling, cyberbullying, physical aggression, and social exclusion. Prevalence estimates suggest that approximately 25%–50% of youth have been bullied because of their body weight, [13, 14]. Rates of weight bullying are higher for children with overweight or obesity, who have a 32% higher likelihood of being verbally bullied by peers than youth with lower BMIs [15]. Weight stigma persists through adolescence; those whose weight remains high throughout this time period report 617

618  Handbook of Obesity the highest levels of weight victimization [16]. Among ethnically diverse adolescents in the U.S., girls and boys are more likely to be bullied because of their weight or physical appearance than because of their sexual orientation, race/ethnicity, or disability status [13]. Moreover, reports from teachers, parents, and adults in different countries suggest that body weight is the most common reason that youth are teased or bullied [17]. While gender differences have been examined in this literature, findings are somewhat mixed. Some evidence has found that girls report higher rates of weight teasing compared to boys [18, 19], whereas other studies report more frequent weight teasing among boys [20] or no gender differences [21]. Studies have begun to examine rates of weight stigma among individuals with intersecting stigmatized identities, such as those who identify as LGBQ (lesbian, gay, bisexual, questioning) and individuals with racial/ethnic minority backgrounds. Among adults, weight stigma is just as common among sexual minority adults as heterosexual adults [22] and is similarly present among Black, Hispanic, Asian, and White adults in the U.S. [11]. In contrast, among youth and adolescents, those who identify as LGBQ are more likely to be targets of weight bullying than heterosexual peers, regardless of their weight status [13], with more than 50% of sexual and/ or gender minority (SGM) adolescents reporting weight-based victimization from peers and family members [23]. Less work has compared weight stigma across different racial/ethnic groups of youth. Some evidence shows similar rates of weight mistreatment across racial/ethnic groups [21], while other studies have documented differences. In particular, data from over 18,000 youth in the Early Childhood Longitudinal Study found that White children with overweight or obesity were more likely to experience bullying than their Hispanic peers [15]. Other research has found that Hmong adolescents report more weight teasing from family than their Latinx, Somali,

and White peers [24]. Collectively, this evidence underscores the need for additional research to clarify differences in youth experiences of weight stigma across gender and race/ethnicity.

66.3  SOCIETAL SETTINGS AND INTERPERSONAL SOURCES OF WEIGHT STIGMA 66.3.1 The Workplace Numerous studies have reported weight-based inequities in the workplace, ranging from unfair hiring practices to wage penalties to wrongful termination of employment [25, 26]. Evidence from experimental research shows that job applicants with higher weight status are less likely to be recommended for hiring and receive worse ratings of potential leadership and success than thinner applicants [27, 28]. Workplace discrimination is more commonly reported by adults with higher levels of obesity compared to those with lower levels of obesity [9], and some evidence indicates that negative weight-based employment outcomes may be more common and worse for women than men [9, 25].

66.3.2 Healthcare Healthcare is another societal setting where weight stigma is common (Figure 66.1). Several decades of evidence have documented weight-biased attitudes among medical and healthcare professionals spanning a wide range of specialty areas

FIGURE 66.1  A summary of contributors to weight stigma within a social-ecological framework.

66  •  Bias, Discrimination, and Obesity  619 [29], even among obesity specialists [30]. Levels of weight bias among physicians are similar to those expressed in the general population [31], and negative weight-based stereotypes expressed by healthcare professionals include assumptions that patients with obesity are lazy, unmotivated to improve their health, and noncompliant with treatment [29]. Studies have also documented that healthcare providers report having less respect for patients with obesity and engage in poorer patient– provider communication with these patients compared to thinner patients [29]. Patients with obesity appear to be very aware of provider biases, reporting physicians to be a common source of weight stigma [12], and that they experience less respect and listening from their healthcare providers and lower quality of healthcare [32]. These experiences can have concerning consequences for healthcare utilization; among women, weight stigma and associated body-related guilt and shame contribute to healthcare stress and avoidance [33]. Recent multinational evidence found that weight stigma was associated with greater healthcare avoidance, lower frequency of obtaining routine checkups, and poorer healthcare experiences; these findings were consistent across six Western countries and persisted after accounting for demographic factors and BMI [32].

66.3.3 Schools A 2019 systematic review of 45 studies showed that students commonly experience multiple forms of weight victimization (e.g., teasing and bullying) from peers in K–12 school settings and that weight-biased attitudes are present among classroom teachers, physical education teachers, and preservice teachers [34]. Views of teachers included perceptions that students with obesity have inferior academic and physical abilities and more academic challenges compared to thinner students. Recent experimental evidence indicates that weight stigma (e.g., weight stereotype threats)—not weight status—decreases working memory in children [35], and weight discrimination is associated with a twofold increased risk of poor performance in cognitive functions in adults after controlling for their BMI and demographic factors [36]. Thus, weight stigma (rather than body weight) may impact students’ educational experiences in ways that are harmful to their academic performance. Weight stigma may continue to negatively impact academic prospects at the postsecondary level, as experimental evidence shows that qualified applicants with high BMI receive fewer offers of admission to graduate school than those with lower BMI [37].

66.3.4 Mass Media The mass media serves as a powerful source of messages that perpetuate weight stigma. In both child-targeted and adulttargeted television shows and movies, characters with larger body sizes are negatively stereotyped and portrayed in stigmatizing ways [38]. Among the top-grossing children’s movies from 2012 to 2015, 84% contained weight stigma such as insults about body size or weight [39]. Weight stigma is also

reinforced through fat shaming, derogatory comments about weight, negative weight-based stereotypes, and idealization of thinness posted on social media platforms [40]. Even news media coverage of obesity contributes to weight stigma, through frequent use of images and videos that portray people with obesity in a dehumanizing manner and engaging in stereotypical unhealthy behaviors [41]. Not surprisingly, exposure to stigmatizing content in the media increases viewers’ negative attitudes toward people with obesity and implicit weight bias [42].

66.3.5 Home Environment Weight stigma can also occur in one’s home setting and family environment. Studies with youth and adult samples show that family members, including parents, are reported to be common sources of weight-based teasing, name-calling, judgment, and mistreatment [18, 43]. Evidence also suggests that parents exhibit both implicit and explicit weight bias against children with obesity [44]. Across U.S. studies of youth, reports of family weight teasing range from 13.5% to 70%; evidence suggests that girls, youth with higher weight, and SGM youth are especially vulnerable to family weight stigma [18, 45, 46]. For example, in a large study of 9,383 sexual and gender minority youth, 44%–70% reported being teased or made fun of about their weight by family members [23]. Weight-stigmatizing experiences from family members continue into adulthood, with over one-third of women and more than 20% of men reporting hurtful weight comments from family members [46]. As discussed later, these negative familial experiences have long-lasting consequences on health.

66.3.6 Common Interpersonal Sources of Stigma Several studies, using samples of adults engaged in weight management, have compared weight stigma experiences across different interpersonal sources [12, 43, 47]. Consistently, family members and healthcare providers were the most common sources of weight stigma, with at least 50% of participants reporting these sources of stigma. Classmates were common interpersonal sources of weight stigma, aligning with youth reports of high levels of weight teasing from peers in the school setting [15, 17]. This pattern of findings is consistent across different Western countries. Specifically, in a study of 13,996 adults engaged in weight management residing in Australia, Canada, France, Germany, U.K., or U.S., among those who reported a history of weight stigma approximately 80% in each country reported weight stigma from family members, 75% reported weight stigma from classmates, and two-thirds reported being stigmatized by doctors [12]. These findings collectively highlight that people commonly experience weight stigma in multiple close interpersonal relationships and across different societal settings. These contributors to weight stigma

620  Handbook of Obesity can be summarized within a social-ecological framework (Figure 66.1). Taken together, the literature indicates that there may be no societal domain where one can be protected from or immune to weight stigma. This will likely remain true while there continues to be an absence of federal policies prohibiting weight mistreatment and discrimination. While public support for policies to prohibit weight discrimination has been established [48], to date it remains legal to discriminate against people because of their weight almost everywhere in the world. Perhaps the most unexpected source of weight stigma is oneself. The negative societal judgment and stigma that people face because of their weight can become an internalized process of negative self-judgment and self-blame. Weight bias internalization (WBI) happens when people are aware of negative weight-based stereotypes, apply these beliefs to themselves, and engage in self-directed stigma and personal blame for their weight [49]. Among U.S. adults with overweight or obesity, initial evidence indicates that as many as 40% have internalized weight bias, and 20%–24% express high levels of WBI [50, 51]. Higher levels of WBI were reported among individuals with higher BMI, women, those who are currently trying to lose weight [50], and those who believe that obesity is under personal control [51]. International research in Western countries shows that WBI follows this pattern among adults engaged in weight management [52]. Among youth, emerging evidence suggests that WBI is present in children and adolescents with overweight and obesity [49]. As summarized next,

both experienced weight stigma and internalized weight bias have concerning implications for health.

66.4 CONSEQUENCES OF WEIGHT STIGMA FOR EMOTIONAL WELL-BEING AND PHYSICAL HEALTH Weight stigma has been linked with many adverse consequences for psychological and physical health. Key and recent findings from this literature are summarized in the following, highlighting the damaging health effects of weight stigma (Figure 66.2). Importantly, many studies in this literature control for BMI in study analyses, demonstrating that weight stigma is associated with adverse health behaviors and outcomes independent of weight status. This evidence underscores that weight stigma itself is a public health issue.

66.4.1 Psychological Distress Two recent systematic reviews show consistent associations between both experienced and internalized weight stigma

Unhealthy eating behaviors: • Disordered eating behaviors • Binge eating • Eating as a coping strategy • Increased caloric consumption • Unhealthy weight control

Lower physical activity (PA): • Less self-efficacy for PA • Lower motivation for PA • Reduced engagement in PA • Avoidance of PA

Exposure to Weight Stigma (manifested as stereotypes, prejudice, teasing, bullying, victimization, exclusion, unfair treatment, inequities, and/or discrimination)

Distress & Stress

Poor psychological wellbeing: • Depression • Anxiety • Low self-esteem • Body dissatisfaction • Substance abuse • Suicidality

Weight gain: Worse healthcare experiences: • Stigmatizing communication • Less trust of healthcare providers • Healthcare avoidance • Less frequent routine checkups • Delay in preventive health screenings

• Obesity • Poorer weight loss maintenance

Physiological/cardiometabolic risk factors: • Elevated cortisol, HbA1c levels C-reactive protein • Increased blood pressure • Poorer glycemic control • Metabolic syndrome

FIGURE 66.2  Emotional and physical health consequences of weight stigma.

Reduced quality of life Poorer overall health Increased disease risk

66  •  Bias, Discrimination, and Obesity  621 and psychological distress, including depression, anxiety, low self-esteem, and poor body image [53, 54]. In their 2020 meta-analysis of 33 studies (25 studies focusing on correlates of internalized stigma and 8 studies on experienced stigma), Alimoradi and colleagues found that pooled associations were moderate for experienced weight stigma and depression and moderate for internalized weight bias and depression and anxiety [53]. Likewise, Wu and Berry’s 2018 review of 33 studies found that greater weight stigma was related to greater depressive symptoms, higher levels of anxiety, and lower self-esteem in people with overweight or obesity, regardless of how weight stigma was measured [54]. Importantly, these findings persist independently of weight status. For example, findings from a large population sample of adults with overweight/obesity (N = 22,231) documented associations between perceived weight discrimination and psychiatric morbidity and comorbidity even after accounting for perceived stress and BMI [55]. Experiences of weight stigma may mediate the relationship between depressive symptoms and weight status or BMI [56, 57], and positive associations between weight stigma and depression are present in both youth [58] and adults [59]. Similar mediation relationships have been documented for the role of weight stigma in associations between BMI and selfesteem and anxiety [56, 57]. For youth, low self-esteem and body dissatisfaction may exacerbate the experience of weight teasing, contributing to depression [58]. Furthermore, a 2018 review on WBI and health suggests that WBI may play a particularly important role in depressive symptoms. Among 30 studies examining these links, 28 studies documented positive associations between WBI and greater depressive symptoms, with 25 studies showing moderate or strong correlations in both community and clinical samples and among individuals of different weight categories [49]. Additionally, WBI remained significantly associated with depressive symptoms after controlling for BMI. A similar pattern of findings has been documented for links between WBI and anxiety [49]. Recent evidence suggests that people who experience weight stigma and in turn engage in internalization of stigma may be at increased risk for psychological distress through a higher likelihood of using maladaptive coping strategies to deal with stigma, such as disengagement or self-blame [60]. Body dissatisfaction is also common among people who have experienced or internalized weight stigma. Studies with community and clinical samples of adults show that weight stigma is associated with poorer body image and higher body dissatisfaction [4]. In particular, body dissatisfaction worsens with more frequent experiences of weight stigma [61] and when weight teasing starts in youth and adolescence [62]. Associations between weight stigma and poor body image persist into adulthood [18], and experiences of weight stigma have been found to mediate the relationship between BMI and body dissatisfaction [63]. As with findings for depressive symptoms and anxiety, WBI is also important for body image. Among 27 studies examining the relationship between WBI and body image, WBI was consistently associated with worse body image in individuals across weight categories, gender, and in both clinical and community samples, and

associations persisted after accounting for BMI and psychological distress [49]. Substance use is an increasing focus of research examining the psychological health implications of weight stigma. In a large study of adults with overweight or obesity from the general population, those who reported weight discrimination were more likely to have a substance use diagnosis [55], and associations remained after controlling for BMI. Recent evidence from a national consensus-matched sample of adults similarly documented associations between weight stigma and increased alcohol use, controlling for BMI [64]. Similar patterns are emerging in youth. Longitudinal evidence has found that adolescent girls who experience weight stigma and its perceived harms are more likely to engage in substance use in emerging adulthood [65]. Among 9,838 sexual and gender minority youth, weight-based teasing was associated with increased odds of alcohol use, binge drinking, and marijuana and cigarette use, regardless of their race, age, BMI, or sexual or gender identity [66]. Collectively, this initial evidence suggests that substance use is important to examine as an adverse health correlate of weight stigma. Finally, recent evidence of links between weight stigma and suicidality illustrates the psychological turmoil of stigma and bullying. Nationally representative data of U.S. adolescents from the Youth Risk Behavior survey show that from 1999 to 2017, perceived overweight predicted a 7% increase in suicidality, after accounting for BMI, age, sex, and ethnicity; this risk grew considerably from 2009 to 2017 [67]. Similarly, a 2019 meta-analysis of observational studies indicated that identifying oneself as “overweight” explains the relationship between BMI leading to increased suicidality [68]. Evidence in adolescents has illustrated specific links between weight stigma and suicidal behavior. Using data from the Longitudinal Study of Australian Children, perceived weight discrimination was associated with an increased risk of self-harm and suicidal thoughts and behaviors, even after controlling for BMI, sociodemographic characteristics, and depressive symptoms [69].

66.4.2 Maladaptive Eating Behaviors Several published reviews of the literature confirm consistent associations between experiences of weight stigma and maladaptive eating behaviors including disordered eating, binge eating, unhealthy weight control, emotional eating [54, 70], and using food as a coping strategy, distraction, or way to comfort oneself. These associations have been documented in community and treatment-seeking samples [70] and persist after accounting for BMI [64]. Additionally, experimental studies have demonstrated that exposing participants to stigmatizing stimuli may induce maladaptive eating responses, independent of BMI [70]. While study samples have been typically comprised of more females than males, evidence indicates that links between weight stigma and disordered eating are common in young men [71]. Stigmatizing experiences may also have long-lasting implications for eating behaviors. For

622  Handbook of Obesity example, findings from Project EAT, a longitudinal cohort study, demonstrate that early experiences of weight teasing in adolescence are longitudinally associated with maladaptive eating behaviors like disordered eating, unhealthy weight control, binge eating, and eating as a coping strategy; weight teasing predicted these behaviors as much as 15 years later in adulthood, controlling for baseline weight status, race/ethnicity, socioeconomic status, and age [18, 72]. Sociodemographic differences in the literature are somewhat mixed; while some evidence has documented similar associations between weight discrimination and overeating across characteristics like age, sex, and race [73], other research has found racial differences in eating-related responses to weight stigma, particularly among women [11]. Researchers have increasingly examined possible mechanisms that may explain the relationship between weight stigma and maladaptive eating behaviors. Evidence has pointed to the role of anticipated stigma [74], poor inhibitory control [75], and stress [76] as factors that induce eating responses following stigmatizing experiences. Other evidence has highlighted the role of WBI in mediating [77] or moderating [78] the relationship between weight stigma and eating behaviors. Indeed, over 30 studies have examined WBI in relation to disordered eating, binge eating, and addictive eating, with moderate-tostrong correlations across studies that remain significant after controlling for BMI and other eating/weight-related variables [49]. As relatively few studies have examined differences such as race/ethnicity or gender in links between WBI and eating, future research in this area is needed to identify how stigma affects eating responses across diverse populations.

66.4.3 Physical Activity In contrast to eating behaviors, studies linking weight stigma and physical activity are fewer in number, and findings are mixed. Research with national panels of adults 50 years and older show that perceived weight discrimination is associated with 59% higher odds of being inactive and 30% lower odds of engaging in moderate activity (independent of BMI) [79], and mediates the relationship between BMI and engagement in moderate and vigorous physical activity [80]. Some evidence has documented gender differences, with weight stigma linked to lower motivation and engagement in physical activity for women but not men [81]. Among adults who have had bariatric surgery, exercise avoidance may mediate the associations between both experienced and internalized weight stigma and physical activity [82]. However, studies linking WBI and engagement in physical activity are less consistent; some studies have demonstrated WBI to be a significant mediator or moderator of physical activity, while others have found only small effects or no associations between WBI and frequency of physical activity [49]. In contrast, associations are stronger between WBI and motivational or attitudinal aspects of physical activity, such as lower exercise self-efficacy, reduced motivation for exercise, negative attitudes about going to the gym, and avoidance of physical activity [49]. As one example, a

2020 multinational study documented consistent associations between WBI and gym avoidance and lower physical activity self-efficacy across six Western countries, controlling for demographic characteristics [52].

66.4.4 Weight Gain and Weight Loss Maintenance Prospective evidence from the Health and Retirement Study [83] and the English Longitudinal Study of Ageing [84] show that weight discrimination (but not other forms of discrimination) [83] increases the risk for obesity and weight gain in adults over time, independent of baseline BMI and demographic factors. Similar findings have emerged in longitudinal studies with adolescents. Data from the National Heart, Lung, and Blood Institute Growth and Health Study showed that girls who were called “too fat” by peers or family had higher odds of obesity 9 years later, regardless of their initial weight status and adjusting for race and SES [85]. Findings from Project EAT demonstrated 15-year longitudinal associations between weight teasing in adolescence and elevated risk of obesity in adulthood for both women and men [18]. More recently, longitudinal evidence has found that youth with obesity who report frequent weight teasing at baseline experience a 33% increased gain in BMI over 8 years compared to peers who do not experience weight teasing [86]. Internalized weight bias may also interfere with weight loss and/or weight loss maintenance. Recent studies have demonstrated associations between WBI and poorer weight loss outcomes among adults with obesity [87], and associations between WBI and poorer weight loss maintenance in community samples of adults [88, 89]. Recent multinational evidence has shown consistent associations between WBI and greater weight gain in the past year among adults engaged in weight management in six countries [52]. Mechanisms underlying these relationships have not yet been adequately studied, but recent evidence suggests that while exposure to weight stigma might initially motivate weight loss efforts, the distress resulting from weight stigma undermines people’s perceived capacity to control their food intake, in turn contributing to weight gain [90].

66.4.5 Physiological Stress Physiological consequences of weight stigma have received increasing attention in the past decade. U.S. national studies have documented an increased risk of high allostatic load [91] and higher levels of C-reactive protein [92] in adults with obesity who experience weight discrimination compared to those with no history of stigmatizing experiences, and that weight discrimination worsens the negative effects of weightto-hip ratio on glycemic control in nondiabetic adults [93]. U.K. studies using data from the English Longitudinal Study of Ageing have documented positive associations between

66  •  Bias, Discrimination, and Obesity  623 weight discrimination and cortisol levels [94], with recent prospective evidence showing that weight discrimination predicted an increase in physiological dysregulation 4 years later and explained more than 25% of the longitudinal association between obesity and health deterioration [95]. In addition, experimental studies have demonstrated a causal role of weight stigma in physiological stress, showing that participants exposed to weight stigma in laboratory conditions

exhibit sustained elevated cortisol [96] and blunted cortisol responses [97] compared to participants in control conditions. Emerging evidence has begun to examine WBI in this context, showing that acute stress in individuals with moderate or high levels of WBI appears to blunt hypothalamic–pituitary– adrenal (HPA) axis reactions to acute psychosocial stress, compared to individuals with low WBI who show typical cortisol responses [98].

TABLE 66.1  Key Evidence-Based Messages Drawn from the Weight Stigma Literature TOPIC AREA Prevalence

Employment Education

Healthcare

Media

Home Interpersonal sources Health consequences

Psychological health Physical health

Policy

Stigma reduction

KEY MESSAGE 19%–40% of U.S. adults with obesity report weight discrimination; prevalence rates increase with BMI, and among women compared to men. U.S. studies with parents, teachers, and students suggest that weight-based bullying is the most common form of bullying youth face at school. As many as 40% of U.S. adults internalize weight stigma, engaging in self-devaluation and self-stigma because of their weight. Weight discrimination exists in the workplace, contributing to unfair hiring practices, lower performance ratings, job termination, and stigma from co-workers. Students with higher weight commonly experience weight victimization from peers in the school setting. Educators express weight-biased attitudes and lower expectations of students with higher weight that can adversely affect their academic experiences. Many healthcare professionals hold negative attitudes and stereotypes toward patients with obesity. Quality of health care is adversely affected by weight stigma. Patients with obesity perceive negative healthcare experiences due to weight bias in the healthcare setting. Weight stigma contributes to avoidance of healthcare and less frequent routine medical checkups in some patients with obesity. Entertainment media, news media, and social media platforms communicate content that reinforces negative weight stereotypes and societal weight stigma. Exposure to stigmatizing media content can increase negative attitudes toward people with obesity and implicit bias. Weight stigma is present in the home environment from family members, who are commonly reported as sources of weight-based teasing. Family members, healthcare providers, and classmates are typically reported to be the most common sources of weight stigma. Experiences of weight stigma and internalization of weight bias both contribute to negative emotional and physical health outcomes. Many studies control for BMI, indicating that stigma rather than body weight itself contributes to poor health. Health consequences of weight stigma have been documented in childhood, adolescence, and adulthood and reflect a public health issue. Weight stigma contributes to depression, anxiety, low self-esteem, poor body image, substance use, and suicidality. Weight stigma contributes to maladaptive eating behaviors, lower physical activity, increased physiological stress, and weight gain. Evidence of the health harms of weight stigma show that it contributes to poor weight-related health, including obesity. This directly challenges societal misperceptions that stigmatizing or shaming people about their weight will motivate weight loss. Weight stigma creates structural disadvantages and inequities; it is a legitimate form of social injustice that requires policy remedies. To date, there are no federal laws that prohibit weight discrimination. Public support is present for policies and legislation to protect people from weight discrimination and bullying. Stigma reduction interventions have focused on trying to change individual attitudes and biases with little success or sustainability. As weight stigma is present in multiple societal settings, multilevel and broad efforts are needed to effectively address this problem.

624  Handbook of Obesity

FIGURE 66.3  Priorities for multilevel stigma reduction initiatives.

66.5 CONCLUSION AND PRIORITIES FOR STIGMA REDUCTION Weight stigma is a key aspect of the lived experience of obesity, common in multiple societal domains from diverse interpersonal sources and with adverse implications for emotional and physical health. There are a number of evidence-based messages that can be drawn from this literature, which are important to emphasize in efforts to increase awareness of weight stigma. Table 66.1 summarizes these key messages that can help inform researchers, clinicians, and other health professionals in the obesity field and more broadly in the medical community. Despite substantial literature documenting weight stigma and its consequences, comparatively little is known about ways to eradicate this social injustice. Interventions to reduce weight stigma have demonstrated limited success, even among healthcare professionals [99]. Most interventions reflect brief, shortterm, educational initiatives targeting individual attitudes, with some studies reporting shifts in knowledge but little, if any, improvement in attitudes [99]. Given that existing evidence of effectiveness is poor and that weight stigma remains pervasive across societal settings, stigma-reduction efforts require research and intervention at multiple and broader levels. Figure 66.3 highlights key priorities to reduce weight stigma and its harmful health effects using a multilevel approach, aligning with the social-ecological framework presented earlier in this

chapter. As emphasized in a 2020 international consensus statement to eradicate weight stigma [100], these efforts will require multiple stakeholders to work collaboratively across diverse settings and disciplines to implement concrete actions that can help eliminate structural disadvantages and ensure that people of all body sizes are afforded equitable treatment, dignity, and respect. Within the medical community, these efforts must include formal teaching on obesity and sensitivity training for weight stigma in academic institutions and professional bodies, as well as improved infrastructure for the care of patients with obesity and standards for stigma-free skills and practices. More broadly, however, meaningful change will necessitate educational, regulatory, and legal initiatives to effectively tackle weight stigma and discrimination in our society.

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Index AAC, see ADP/ATP carrier AASHTO, see American Association of State Highway and Transportation Officials ACC, see Acetyl-CoA carboxylase ACE, see Angiotensin-converting enzyme Acetyl-CoA carboxylase (ACC), 98 ACLF, see Acute-on-chronic liver failure ACTH, see Adrenocorticotropic hormone Activity space, 417 Acute insulin response (AIR), 171 Acute-on-chronic liver failure (ACLF), 537 Acute pancreatitis, 534 Acyl-ghrelin, 217 ADA, see American Diabetes Association Adaptive immune cells B cells, 522–523 CD8+ T cells, 522 regulatory T Cells (Tregs), 522 T helper 1 (Th1) cells, 521–522 T helper 2 (Th2) cells, 522 Adaptive thermogenesis, 70, 292–293 Adenosine monophosphate (AMP), 97 Adenosine triphosphate (ATP), 72 Adenovirus 36 animal studies, 322–324 cell culture studies, 324 human studies, 324–325 ADF, see Alternate-day fasting Adipocytes beige adipocyte development beige fat maintenance, 255 de novo differentiation, 255 developmental origins adipocyte progenitor cells, 253–254 brown adipocyte development, 254–255 dipocyte heterogeneity, 255 functional characteristics de novo lipogenesis, 252 lipolysis, 252 UCP1-dependent thermogenesis, 252–253 UCP1-independent thermogenesis, 253 location, 250–252 molecular regulation signaling pathway, 256 transcriptional regulation, 255–256 morphology, 250 Adipogenesis, 135, 506 Adipogenic microbes, 322, 323 Adipokines, 235, 270–271 Adiponectin, 264 Adiponutrin, 541, 613 Adipose tissue adipocyte precursors in human health, 152–153 BAT, 148 developmental vs. adult adipocyte precursors, 149–150 developmental vs. adult adipogenesis, 149 distribution, 35–36 epigenetic regulation, 152 fat tissue development, 149

metabolism (see Adipose tissue metabolism) multilevel adipogenesis, 151 neonatal, 149 regulation of adipogenesis, 151–152 unique adipocyte precursors, 150–151 WAT, 148 Adipose tissue macrophages (ATM), 520–521 Adipose tissue metabolism energy balance, 259 energy-wasting, 261–262 hormones for treatment of obesity additional adipokines, 264 adiponectin, 264 FGF21, 263 leptin sensitization, 262–263 negative energy balance, 259–260 WAT dysfunction, 260–261 Adiposity evaluations anthropometry adipose tissue distribution, 35–36 body weight and stature, 34–35 circumferences, 35 skinfold, 35 BIA, 34 body density methods ADP technique, 33 hydrodensitometry, 33 hydrometry, 33–34 imaging methods CT system, 32–33 DXA technology, 33 MRI, 29–32 multicomponent models, 34 optical methods, 34 ultrasound, 34 ADP, see Air-displacement plethysmography ADP/ATP carrier (AAC), 73 Adrenal insufficiency, 245 Adrenocorticotropic hormone (ACTH), 127, 209 AF, see Atrial fibrillation Afferent signals dipose-derived signals, 210–211 gut-derived signals, 210 liver-derived signals, 211 pancreas-derived signals, 211 vagal afferent transmission, 211 The Age of Innocence (Wharton), 19 Aging, 98 Agouti-related peptide (AgRP), 124, 125, 158, 209 AIR, see Acute insulin response Air-displacement plethysmography (ADP), 28, 33 Airway dysanapsis, 548 Akkermansia muciniphila, 227 Alpha-melanocyte-stimulating hormone (αMSH), 158 Alstrom syndrome, 11 Alternate-day fasting (ADF), 99 American Association of State Highway and Transportation Officials (AASHTO), 412 American Diabetes Association (ADA), 169, 501

American Institute for Cancer Research (AICR), 511 AMP, see Adenosine monophosphate Amphetamine, 12 Amylin, 220 AN, see Anorexia nervosa Analysis of covariance (ANCOVA), 162 Android obesity, 25 AngII receptor blockade (ARB), 472 Angiotensin-converting enzyme (ACE), 472 Anorexia nervosa (AN), 384 Anthropometric indices BMI, 39 body shape indices, 40 WHR, 40 WHtR, 40 Anthropometric measurements height and weight, 38 hip circumference, 39 SAD, 39 subcutaneous skinfolds, 39 waist circumference, 39 Anthropometric predictors abdominal VAT adults, 43–44 children, 44 adults, 40–41 ectopic fat, 45 fat mass adults, 40–41 children, 41–42 Anthropometry adipose tissue distribution, 35–36 body weight and stature, 34–35 circumferences, 35 measurements (see Anthropometric measurements) skinfold, 35 Apolipoprotein CIII, 483–484 Appetitive sensations desire to eat, 333 fullness, 333 hunger, 333 prospective consumption, 333 ARB, see AngII receptor blockade Arcuate nucleus (ARC), 158 ARIC, see Atherosclerosis Risk in Communities Art and western literature after 17th century Dickens, C., 19 Eliot, G., 19 McGraw, E., 20 Wharton, E., 19 before 18th century Bartholomew Fair, 18–19 The Canterbury Tales, 17 Falstaff, 17–18 gluttony, 17 seven deadly sins, 17 The Seven Deadly Sins, 17 Arterial stiffness, 238

627

628 Index Artificially sweetened beverages (ASBs), 342 Assisted reproductive technologies (ART), 593 Asthma, 550–551 ATG6, see Autophagy-related gene ATGL, see Triglyceride lipase Atherosclerosis, 575 Atherosclerosis Risk in Communities (ARIC), 167 ATM, see Adipose tissue macrophages ATP, see Adenosine triphosphate Atrial fibrillation (AF), 466–468 Autophagy, 98 Autophagy-related gene (ATG6), 98 Ax of the Apostles, 20 BAI, see Body adiposity index Bardet–Biedl syndrome (BBS), 11, 126 Bariatric physicians, 12, 13 Bariatric surgery, 238 Barrett’s esophagus, 530 Bartholomew Fair (Johnson), 18–19 Basal metabolic rate (BMR), 79, 80, 286–287 Basophilic adenoma, 10 BAT, see Brown adipose tissue BBB, see Brain-blood barrier BBS, see Bardet–Biedl syndrome BCAA, see Branch-chain amino acid BD, see Body dissatisfaction BDNF, see Brain-derived neurotrophic factor BED, see Binge eating disorder Behavioral Risk Factor Surveillance System (BRFSS), 53 β-cells, 220 Beta-less mice, 256 β-melanocyte-stimulating hormone (MSH), 125 Beverages, 339 100% fruit juice, 343 alcoholic drinks, 343 ASB, 342 coffee and tea, 342 dairy products, 342–343 substitution, 343–344 water, 342 BIA, see Bioimpedance analysis Bifidobacterium longum, 230 Biguanides, 493 Binge eating disorder (BED), 384 Bioenergetic processes, 69, 71 Bioimpedance analysis (BIA), 34 Blood–brain barrier (BBB), 213, 576 BMI, see Body mass index BMR, see Basal metabolic rate BN, see Bulimia nervosa Body adiposity index (BAI), 40 Body composition organization DXA, 29 five-level model, 28 six-component body density model, 29 two molecular-level body composition, 29 Body density methods ADP technique, 33 hydrodensitometry, 33 Body dissatisfaction (BD), 385 Body fat distribution, 456 Body fat mass, 455–456 Body mass index (BMI), 8, 48, 74, 89, 103, 104 Body roundness index (BRI), 40 Body shape indices, 40 Bone disorders evaluation, 567 management, 567–568 mechanisms, 566–567

OA adipokines, 568 interventions, 568–569 osteoporosis BMI, 565–566 fat depots, 566 frailty, 566 lean mass vs. fat mass, 566 management targets, 567 screening DXA, 567 TJA, 569–570 Bosch, H., 17 Brain–blood barrier (BBB), 213, 219, 576–577 Brain-derived neurotrophic factor (BDNF), 124 Branch-chain amino acid (BCAA), 100, 188 Breastfeeding breastmilk components, 365–367 risk of obesity, 364–365 BRFSS, see Behavioral Risk Factor Surveillance System BRI, see Body roundness index Brown adipocytes, 148 Brown adipose tissue (BAT), 76, 136, 148, 233 Browning, 261 Built environment, 415; see also Urban built environment Bulimia nervosa (BN), 384 Bulk-RNA-Seq, 160 Calorie restriction (CR) ameliorates oxidative stress, 98 autophagy, 98 cellular senescence, 98 fasting lengths, 100–101 humans, 96 inflammation, 98 long-term sustainability, 99 macronutrient composition, 100 mitochondrial efficiency, 98 nonhuman primates, 96 nutrient-sensing pathways, 97–98 rodents, 95–96 safety considerations, 99–100 Cancer anthropometric measures abdominal obesity, 515 weight gain, 515 weight loss, 515–516 attributable risk, 516 menopausal status, 513 mortality, 516 post-diagnosis, 516 postmenopausal exogenous hormone use, 513–514 smoking, 514 Candidate gene studies, 113, 114, 140 Cannabis, see Marijuana Cannabis sativa, 372 The Canon on Medicine (Kitab al-Qanun), 4 Carbohydrate metabolism insulin receptors, 278 insulin signal transduction, 277–278 phosphatases, 278–280 serine/threonine kinases, 278–280 Cardiac fat, 273 Cardiovascular disease (CVD), 39, 461 CHD, 464–466 heart failure, 462–464 hemodynamics, 462 hypertension (see Hypertension) morbidity and mortality, 462

pathophysiology, 461–462 prevention, 467 treatment, 467 CART, see Cocaine-and amphetamine-regulated transcript Case fatality, 453 Catabolic processes, 71 Catabolic reactions, 72 Cause-specific mortality, 453 CCK, see Cholecystokinin CCL11, see Chemokine ligand 11 CDC, see Centers for Disease Control and Prevention CDI, see Clostridium difficile infection CEACAM1, see Carcinoembryonic antigenrelated cell adhesion molecule 1 Celiac disease, 531 Cellular bioenergetics bioenergetic sources, 71–72 cellular work, 72–73 covalent bonds, 74 ion leak, 73–74 mammals, 73 targeting, 76 thermogenesis, 74–76 Centers for Disease Control and Prevention (CDC), 53 Central nervous system (CNS), 119, 155 afferent signals dipose-derived signals, 210–211 gut-derived signals, 210 liver-derived signals, 211 pancreas-derived signals, 211 vagal afferent transmission, 211 efferent signals autonomic mechanisms, 212 hypophyseal (pituitary) mechanisms, 211–212 energy balance, 207 obesity-induced changes, 212–213 blood–brain barrier (BBB), 213 leptin resistance, 213 signal integration, 207–209 hindbrain, 210 hypothalamic angiotensin, 209–210 melanocortin system, 209 Cerebromicrovascular impairment BBB, 576–577 dysregulation, 575–576 neuronal dysfunction, 577 adipose tissue dysfunction, 577 gut microbiome, 578 hippocampal insulin signaling, 577–578 Chemokine ligand 11 (CCL11), 263 ChIP, see Chromatin immunoprecipitation CHIP, see Clonal hematopoiesis of indeterminate potential ChIP-seq, see Chromatin immunoprecipitation combined with sequencing Cholecystokinin (CCK), 216, 218–219 Chromatin immunoprecipitation (ChIP), 143 Chromatin immunoprecipitation combined with sequencing (ChIP-seq), 152 Chronic intracerebroventricular (ICV), 218 Chronic kidney disease (CKD), 469, 477–478, 556 Chronic obstructive pulmonary disease (COPD), 552 Chronic overnutrition, 279 CI, see Conicity index Cidec, 250 Circadian rhythms, 446

Index  629 Citric acid cycle (TCA), 73 CKD, see Chronic kidney disease Clonal hematopoiesis of indeterminate potential (CHIP), 523 Clostridium difficile infection (CDI), 533 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), 159 CNS, see Central nervous system CNV, see Copy number variants Cocaine-and amphetamine-regulated transcript (CART), 125, 209 Coefficient of variation (CV), 33, 84 Cognitive decline cognitive functioning, 572–573 dementia, 573 follow-up time, 574–575 genetic effects, 574 incident dementia, 574 lifestyle interventions, 578 non-lifestyle interventions, 578 pathophysiological mechanisms atherosclerosis, 575 cerebromicrovascular impairment, 575–577 white matter damage, 577 risk of dementia, 575 Comorbidity, 385 Computed tomography (CT), 28, 32–33 Congenital anomalies, 599–600 Conicity index (CI), 40 Copy number variants (CNV), 119 Coronary heart disease (CHD), 464–466 Corpulency causes, 8 Covalent bonds, 74 COVID-19 pandemic, 319, 320 molecular mechanism, 321–322 physiological mechanism, 321 C-reactive protein (CRP), 96 CRISPR, see Clustered Regularly Interspaced Short Palindromic Repeats CRP, see C-reactive protein CT, see Computed tomography Culture acculturation, 433–435 definition, 433 food, 436–437 interventions, 437–438 migration, 433–435 norms, 435–436 physical activity, 436–437 sedentary behavior, 436–437 CV, see Coefficient of variation CVD, see Cardiovascular disease Cytokine storm, 321 DALYs, see Disability-adjusted life years DASH, see Dietary Approaches to Stop Hypertension Defective evacuation, 8 De humani corporis fabrica (Vesalius), 6 Dehydroepiandrosterone sulfate (DHEAS), 96, 246 De novo lipid lipogenesis (DNL), 268, 491 Developmental origins of health and disease (DOHaD) hypothesis, 441 De Vita Sobria/The Temperate Life (Cornaro), 23 DHEAS, see Dehydroepiandrosterone sulfate DHT, see Dihydrotestosterone Diabetes Remission Clinical Trial (DiRECT), 501 Diacylglycerols (DAG), 252 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), 384

Dicer, 134 Dickens, C., 19 Dietary Approaches to Stop Hypertension (DASH), 557 Dietary strategies, sensory function taste hedonics, 332 taste sensitivity, 331–332 Diet beverages, 342 Diet-induced obesity (DIO), 155 Diet-resistant (DR), 83 Diet-sensitive (DS), 83 Dihydrotestosterone (DHT), 245, 590 Dinitrophenol (DNP), 76 DIO, see Diet-induced obesity Dipeptidyl peptidase 4 (DPP4), 322 DiRECT, see Diabetes Remission Clinical Trial Direct calorimetry, 70 Disability, 385 Disability-adjusted life years (DALYs), 428 DIT, see Diet-induced thermogenesis DIXON technique, 32 DLW, see Doubly labeled water DNA methylation (DNAme), 83 blood vs. other tissues, 140–142 consequence of obesity, 142–143 gastric bypass, 143–144 genome-wide, 141–142 DNL, see de novo lipid lipogenesis DNP, see Dinitrophenol DOHaD, see Developmental origins of health and disease hypothesis Double burden, 59 Doubly labeled water (DLW), 70 DPP4, see Dipeptidyl peptidase 4 DR, see Diet-resistant Drp1, see Dynamin-related protein 1 DS, see Diet-sensitive DSM-IV, see Diagnostic and Statistical Manual of Mental Disorders Dual-emission X-ray (DXA), 515 Dual X-ray absorptiometry (DXA), 29, 33 DXA, see Dual X-ray absorptiometry; Dualemission X-ray Dynamin-related protein 1 (Drp1), 491 Dyslipidemia, 168–171 apolipoprotein CIII, 483–484 atherogenic, 483 carbohydrate quality, 484–485 carbohydrate quantity, 484 features, 481 fibrates, 485 high-density lipoproteins, 482 LDL receptor pathway, 485 low-density lipoproteins, 481–482 niacin, 485–486 plasma triglycerides, 482–483 EASO, see European Association for the Study of Obesity EAT, see Epicardial adipose tissue Eating disorders (ED) associations, 385–386 BED, 384 epidemiology, 385 interventions, 386–387 NES, 384 ECM, see Extracellular matrix Ectopic fat, 45 Ectopic fat accumulation, 267 ED, see Eating disorders EDCs, see Endocrine-disrupting chemicals

Edmonton Staging System, 13 EHIS, see European Health Interview Survey Electronic databases, 296 Electronic nicotine delivery systems (ENDS), 371 11β-hydroxysteroid dehydrogenase (HSD) type 1, 316 Eliot, G., 19 Embryonic stem (ES), 158 Endocrine-disrupting chemicals (EDCs), 436–437 Endocrine function LEP/LEPR, 126–127 MC4R, 127–128 PCSK1, 127 POMC, 127 Endoplasmic reticulum (ER), 226 Endothelial dysfunction, 237–238 ENDS, see Electronic nicotine delivery systems End stage kidney disease (ESKD), 469 Energy balance, 69, 79 CNS regulation, 207–213 Energy expenditure (EE), 69–70 Energy metabolism, 89–90 Energy partitioning alcohol, 313 BAT vs. VAT, 315 carbohydrate balance, 313 fat balance, 313–314 fat–lean tissue, 315 lifestyle, 316–317 protein metabolism, 312–313 respiratory quotient (RQ)–food quotient (FQ) concept, 312, 314 visceral vs. subcutaneous fat, 315–316 Energy transformation energy balance, 69 energy expenditure, 69–70 human energy expenditure, 70–71 laws of thermodynamics, 69 E4orf1, 325–326 Epicardial adipose tissue (EAT), 273 Epigenetics diet, 144–145 DNA methylation blood vs. other tissues, 140–142 consequence of obesity, 142–143 gastric bypass, 143–144 genome-wide, 141–142 exercise interventions, 144–145 histone modifications, 143 modifications, 139 Epigenome-wide association studies (EWAS), 140 EPOC, see Post-exercise oxygen consumption Erosive esophagitis, 529–530 ERV, see Expiratory reserve volume ES, see Embryonic stem ESKD, see End stage kidney disease Esmolol, 91 Esophageal adenocarcinoma, 530 Esophagus Barrett’s esophagus, 530 erosive esophagitis, 529–530 esophageal adenocarcinoma, 530 esophageal dysmotility, 529 GERD, 529 Estrogens, 590 European Association for the Study of Obesity (EASO), 15 European Health Interview Survey (EHIS), 51 Evidence-based treatments, 386 EWAS, see Epigenome-wide association studies

630 Index Exendin-4, 221 Exercise-associated energy expenditure, 82–83 Exercise-induced energy balance abdominal adipose tissue, 303–304 dose–response associations, 298, 302–303 limitations, 297–298 RCT, 299–301 reduction in body weight, 298, 302 trial designs, 308–310 waist circumference, 304, 306 Expiratory reserve volume (ERV), 321 Extracellular matrix (ECM), 174 Facultative energy expenditure exercise-associated thermogenesis, 82–83 NEAT, 82 non-shivering thermogenesis, 80–82 shivering thermogenesis, 80–82 Familial effect, 103 FAO, see Fatty acid oxidation Fasting inducible adipocyte factor (FIAF), 230 Fast-spin echo (FSE), 32 Fat and obesity-related gene (FTO), 13 Fat distribution, 88–89 Fat-free mass (FFM), 335, 455–456 Father of Metabolic Obesity, 8 FATP1-6, see Fatty acid transport proteins Fat patterning, 35 Fat saturation, 31 Fatty acid oxidation (FAO), 84 Fatty acid transport proteins (FATP1-6), 252 Fat–water separation, 31 F-cells, 219 FDA, see Food and Drug Administration FDG, see F-fluorodeoxyglucose Fecal microbiota transplantation (FMT), 225, 227, 230–231 Federation of Latin American Societies of Obesity (FLASO), 14–15 Fenfluramine, 12 FFA, see Free fatty acids FFAR3, see Free fatty acid receptor 3 18F-FDG, see 18F-fluorodeoxyglucose F-fluorodeoxyglucose (FDG), 234 18F-fluorodeoxyglucose (18F-FDG), 71 FFM, see Fat-free mass FGF21, see Fibroblast growth factor 21 FIAF, see Fasting inducible adipocyte factor Fiber photometry, 159 Fibrates, 485 Fibroblast growth factor 21 (FGF21), 211 Fiscal policy interventions, 341 Five-level body composition model, 28 FMT, see Fecal microbiota transplantation Follicle-stimulating hormone (FSH), 245 Food addiction, 384 Food and Drug Administration (FDA), 12, 220 Food deserts, 402 Food industry agricultural and manufacturing innovations, 400–401 agricultural subsidies, 400 food deserts, 402 marketing, 401 multidetermined disease, 395, 399 potential contributors, 396–399 retail food environments, 401–402 treatment vs. prevention, 400 Food insecurity state of the science, 402–403 ultra-processed foods, 403

Food science, 400–401 Food swamps, 402 Fork head box protein (FoxO1), 97 Formula feeding future research, 368–369 infant formula, composition/advances, 367 infant formula, effects on risk, 367–368 FoxO1, see Fork head box protein FRC, see Functional residual capacity Free fatty acid receptor 3 (FFAR3), 230 Free fatty acids (FFA), 233, 236 Free triiodothyronine (fT3), 242 French Happiness/English Misery (Cruikshank), 22 FSE, see Fast-spin echo FSH, see Follicle-stimulating hormone FT3, see Free triiodothyronine FTO, see Fat and obesity-related gene Functional residual capacity (FRC), 321 Gamma aminobutyric acid (GABA), 125 Gastroesophageal reflux disease (GERD), 529 Gastrointestinal tract (GIT) amylin, 220 anal canal and pelvic floor dyssynergic defecation, 533 fecal incontinence, 533 CCK, 218–219 cleavage products of proglucagon GLP-1, 221–222 GLP-2, 222 glucagon, 220–221 OXM, 222 colon and rectum CDI, 533 colonic polyps, 532 colorectal cancer, 533 constipation, 532 diverticular disease, 532 esophagus Barrett’s esophagus, 530 erosive esophagitis, 529–530 esophageal adenocarcinoma, 530 esophageal dysmotility, 529 GERD, 529 gallbladder, 534 gastric leptin, 218 ghrelin, 216–218 hepatic morbidity, 527 insulin, 219–220 liver HCC, 534 NAFLD, 533–534 pancreas, 534 physiological functions, 217 PP, 219 PYY, 219 risk ratios, 528 small intestine celiac disease, 531 diarrhea, 531 IBD, 531–532 stomach erosive gastritis, 530 gastric cancer, 530–531 gastric motor physiology, 530 GCaMP, see Genetically encoded calcium sensor GCTA, see Genome-wide complex trait analysis GDM, see Gestational diabetes mellitus Gene-environment interactions family environment, 108–110 parental education, 108–110

social position, 110 societal factors, 108 Gene Expression Omnibus (GEO), 160 Gene expression profiling, see Transcriptomics Generally recognized as safe (GRAS), 230 Genetically encoded calcium sensor (GCaMP), 159 Genetic obesity, 123 Genome-based restricted maximum likelihood (GREML), 83 Genome-wide association studies (GWAS), 158, 490 adiposity-related traits, 117 alternative variants CNV, 119 rare variants, 118 BMI, 114–116 body fat percentage, 116–117 children and adolescents, 117–118 interactions, 117 non-European ancestry, 118 thinness, 116 VAT, 117 WHR, 116 Genome-wide complex trait analysis (GCTA), 83 Genome-wide linkage studies, 113–114 Genome-wide screening approach, 114 GEO, see Gene Expression Omnibus GERD, see Gastroesophageal reflux disease Germ-free (GF) mice, 226 Gestational diabetes mellitus (GDM), 600, 602 Gestational weight gain (GWG), 600–601 GF, see Germ-free mice GFR, see Glomerular filtration rate GH deficiency (GHD), 241–242 Ghrelin, 216–218 Ghrelin and glucagon-like peptide 1 (GLP-1), 210 Ghrelin O-acyltransferase (GOAT), 217 Ghrelin receptor (GHSR), 210 Gillray, J., 22, 23 GIT, see Gastrointestinal tract Global adult obesity, 64 Global food supply chain, 395 Glomerular filtration rate (GFR), 470 GLP-1, see Ghrelin and glucagon-like peptide 1 Glucagon, 220–221 Glucagon-like peptide-1 (GLP-1), 127, 221–222, 493–494 Glucagon-like peptide-2 (GLP-2), 222 Glucose metabolism, 602 Glucose tolerance tests (GTTs), 490 Gluttony, 17 Glycerolipids, 28 Glyceroneogenesis (GlycNG), 268–269 GnRH, see Gonadotropin-releasing hormones GOAT, see Ghrelin O-acyltransferase Gold standard measurements, 40 Gold standard methods, 38 Gonadal WAT (gWAT), 153 Gonadotropin-releasing hormones (GnRH), 245 Gorillas, 165 Gout comorbidities, 556 dose-dependent associations, 558 flares, 561 pathophysiological development, 557 potential underlying mechanisms adipose tissue, 559–560 dietary factors, 560 insulin resistance, 559 serum urate, 559 triglycerides, 560

Index  631 xanthine oxidase activity, 560 risk, 556–558 weight change, 560–561 weight loss bariatric surgery, 562–563 dietary interventions, 562 management, 562 GPCR, see G protein-coupled receptor GPR120, see G-protein-coupled receptor 120 G protein-coupled receptor (GPCR), 125 G-protein-coupled receptor 120 (GPR120), 227 GRAS, see Generally recognized as safe Grazing, 353 Grecian humoral theory, 6 Green-colored packaging, 401 GREML, see Genome-based restricted maximum likelihood GTTs, see Glucose tolerance tests Gut microbiome bioinformatic methods, 225–226 characteristics, 226 host–microbe interactions, 225–226 microbe-mediated development altered circadian rhythms, 229 energy harvest, 229–230 gut–adipose tissue cross talk, 230 gut–liver axis, 229 lipid digestion, 228–229 oral microbiome, 227–228 sensory perception of food, 227–228 small intestinal microbiota, 228–229 obesity-associated microbiome maternal health, 226 microbiota in animal, 226–227 targeting therapeutically fiber supplementation, 230 FMT, 230–231 postbiotics, 231 prebiotics, 230 GWAS, see Genome-wide association studies GWAT, see Gonadal WAT GWG, see Gestational weight gain Gynoid obesity, 25 H19, 134 H1N1, see Influenza virus A HATs, see Histone acetyltransferases HCC, see Hepatocellular carcinoma HDAC, see Histone deacetylases HDL, see High-density lipoprotein HDL-C, see High-density lipoprotein cholesterol HDM, see Histone demethylases Health Resources and Services Administration (HRSA), 53 Healthy Weight Commitment Foundation (HWCF), 403 Heart rate reserve (HRR) method, 298 Hedonic hunger, 332 Hepatic fat, 89 Hepatic lipids, 271–272 Hepatic steatosis, 267 Hepatocellular carcinoma (HCC), 534, 537–538 Hepatokines, 270–271 Heterotrophic organisms, 71, 72 HFD, see High-fat diet HGH, see Human growth hormone High-density lipoprotein (HDL), 242, 560 High-density lipoprotein cholesterol (HDL-C), 481 High-fat diet (HFD), 443 Hindbrain, 210 Hippocampal insulin signaling, 577–578

Hippocrates industrial revolution Abu Ali ibn Sina (Avicenna), 4 Hasdai ibn Shaprut, 4–5 history of science, 5 Islamic tradition, 4 obesity in time, 3 obesity prior to, 4 scientific revolution 16th century, 6 17th century, 7–8 18th century, 8, 9 19th century, 8, 10 20th century, 10–12 21th century, 12–13 Histone acetyltransferases (HAT), 139, 152 Histone deacetylases (HDAC), 139, 152 Histone demethylases (HDM), 152 Histone methyltransferases (HMT), 152 Hormone-sensitive lipase (HSL), 252 Hounsfield units (HU), 33 HPGH, see Hypothalamic-pituitary-growth hormone axis HRR, see Heart rate reserve method HRSA, see Health Resources and Services Administration HSD, see 11β-hydroxysteroid dehydrogenase type 1 HSkMC, see Human skeletal muscle cells HSL, see Hormone-sensitive lipase HU, see Hounsfield units Human growth hormone (hGH), 91 Human overfeeding body weight, 87 chronic, 89, 90 components, 86 effects of, 90 energy metabolism, 89–90 fat distribution, 88–89 hepatic fat, 89 human variability, 87–89 individual differences, 88 liver fat, 88–89 long-term, 89 metabolic indicators adipose tissue, 91–92 blood pressure, 91 glucose and insulin, 90–91 heart rate, 91 hormones, 91 inflammation, 92 lipids and lipoproteins, 91 lipogenesis, 91 skeletal muscle, 92 short-term, 90 visceral fat, 88–89 Human skeletal muscle cells (HSkMC), 277 Hunger hormone, 210 Hydrodensitometry, 28, 33 Hydrometry, 33–34 Hyperinsulinemia, 235, 476 Hypertension ACE, 472–473 ARB, 472–473 ectopic fat, 469–470 hemodynamic changes, 470 immune cell–mediated inflammation, 475–476 insulin resistance, 476 MHO, 470 neurohormonal mechanism, 470–471

renal mechanisms, 470–471 RSF, 471–472 SNS activation, 473–474 chemoreflex, 475 CNS melanocortin system, 475 hypoxemia, 475 impaired baroreflexes, 474 leptin activation, 475 Hypertensive spectrum disorder (HSD), 601–602 Hypertrophy, 253 Hyperuricemia, 556 Hypothalamic angiotensin, 209–210 Hypothalamic–pituitary-adrenal (HPA) axis body composition and metabolism, 244 cortisol insufficiency adrenal insufficiency, 245 hypercortisolemia, 245 testing, 245 Hypothalamic–pituitary-gonadal (HPGN) axis, 245 body composition and metabolism, 245–246 effect of deficiency states, 246 gonadal testing, 246 Hypothalamic–pituitary-growth hormone (HPGH) axis body composition acromegaly, 242 GHD, 241–242 testing for GH deficiency, 242 Hypothalamic–pituitary-thyroid (HPT) axis body composition euthyroid subjects, 242 thyroid function and body weight regulation, 243 thyroid hormone-treated subjects, 242 thyroid disease status exogenous thyrotoxicosis, 243–244 mild (subclinical) thyroid dysfunction, 243 overt thyroid dysfunction, 243 Hypothalamic–posterior pituitary axis, 246–247 IASO, see International Association for the Study of Obesity ICAM1+, see Intracellular adhesion molecular 1 expressing cells ICAM-1, see Intercellular adhesion molecule 1 ICSI, see Intracytoplasmic sperm injection ICV, see Chronic intracerebroventricular IF, see Intermittent fasting IFSO, see International Federation for the Surgery of Obesity IGF-1, see Insulin-like growth factor-1 IGF-1 binding proteins (IGFBP), 241 IKMC, see International Knockout Mouse Consortium ILC2, see Type 2 innate lymphoid cells Imaging methods CT system, 32–33 DXA technology, 33 MRI, 29–32 I-meta-iodobenzylguanidine (I-MIBG), 235 Immoderate obesity, 3 IMPC, see International Mouse Phenotyping Consortium Increased subcutaneous inguinal WAT (iWAT), 153 Incretin hormone, 221 Indirect calorimetry, 70, 286 Industrial revolution Abu Ali ibn Sina (Avicenna), 4 Hasdai ibn Shaprut, 4–5 history of science, 5

632 Index islamic tradition, 4 Inflammation cellular mediators adaptive immune cells, 521–523 innate immune cells, 520 clinical evidence, 519 COVID-19, 523–524 mechanisms, 523 Inflammatory bowel disease (IBD), 531–532 Influenza virus A (H1N1), 320 Ingestive patterns eating frequency, 334 meal replacement, 335 meal skipping, 334 portion sizes, 335 snacking, 334 timing, 334 Innate immune cells ATM, 520–521 eosinophils, 521 lymphoid cells, 521 Inositol trisphosphate receptors (IP3Rs), 253 Insensible loss, 8 Insensible perspiration, 70 Insulin-like growth factor-1 (IGF-1), 96 Insulin receptor (IR), 220 Insulin resistance adipose tissue, 491 causes and risk factors, 490 definition, 490 epidemiology, 490 glucose-stimulated insulin secretion, 488–489 hepatic glucose production, 489 insulin-stimulated glucose uptake, 489 liver, 491–492 pancreas, 492 skeletal muscle, 491 structure and synthesis, 488 treatment biguanides, 493 exercise, 493 GLP-1, 493–494 TZD, 493 weight loss, 493 Intercellular adhesion molecule 1 (ICAM-1), 98 Intermittent fasting (IF) fasting lengths, 100–101 long-term sustainability, 99 safety considerations, 99–100 International Association for the Study of Obesity (IASO), 14 International Federation for the Surgery of Obesity (IFSO), 14 International Journal of Obesity, 15 International Knockout Mouse Consortium (IKMC), 158 International Mouse Phenotyping Consortium (IMPC), 158 International Physical Activity Questionnaire (IPAQ), 356 Interoceptive signals, 155 Intersectional genetics, 162 Intestinal fat absorption, 229 Intracellular adhesion molecular 1 expressing cells (ICAM1+), 151 Intracytoplasmic sperm injection (ICSI), 595 Intrahepatic triglycerides (IH-TG), 267 Intravenous glucose tolerance test (IVGTT), 169 In vitro fertilization (IVF), 593 IP3Rs, see Inositol trisphosphate receptors

IPAQ, see International Physical Activity Questionnaire IR, see Insulin receptor IVF, see In vitro fertilization IVGTT, see Intravenous glucose tolerance test IWAT, see Increased subcutaneous inguinal WAT Jonson, B., 18–19 Kinase suppressor of Ras 2 (KSR2), 130 Kisspeptin neurons, 246 KSR2, see Kinase suppressor of Ras 2 Lactobacillus spp. L. acidophilus, 230 L. gasseri, 230 L. rhamnosus, 230 LAMP1, see Lysosomal-associated membrane proteins 1 Laws of thermodynamics, 69 LBM, see Lean body mass L-cells, 219 LDL-C, see Low-density lipoprotein cholesterol Lean body mass (LBM), 242 Leisure time physical activity (LTPA), 356 appetite and eating behavior, 357–358 reduced weight, 360 studies in adults, 359–360 studies in children, 359 TEE, 357 trends in adults, 358–359 trends in children, 358 trends in leisure time, 358 weight loss, 360 LEP-R, see Leptin receptor Leptin, 92, 235–236 Leptin–melanocortin activation, 476 Leptin-melanocortin pathway, 123, 124 cilia and genetic obesity, 125–126 food intake regulation, 125 leptin signaling, 125 melanocortin signaling, 125 Leptin receptor (LEP-R), 218 LES, see Lower esophageal sphincter Letter on Corpulence Addressed to the Public, 8 LH, see Luteinizing hormone Lifestyle/behavioral strategies, 12 Lipid digestion, 228–229 Lipid metabolism oxidation, 280–281 reduction in FAO, 282–283 Lipogenesis, 91 Lipolysis, 252 Lipopolysaccharide (LPS), 228, 230 Lipoprotein lipase (LPL), 230 Lipotoxicity, 271–272 Listeria monocytogenes, 320 Liver disease, influence of obesity, 537 Liver fat, 88–89 Long noncoding RNAs (lncRNAs) biogenesis, 134–135 discovery, 134–135 functions, 137 obesity, 136 Long-term potentiation (LTP), 577 Lord of the Flies (Golding), 26–27 Low-density lipoprotein cholesterol (LDL-C), 481 Lower esophageal sphincter (LES), 529 LPL, see Lipoprotein lipase LPS, see Lipopolysaccharide LTPA, see Leisure time physical activity

Lung function asthma, 550–551 central obesity, 548 effect of childhood, 548 ventilation, 549–550 Luteinizing hormone (LH), 245 Luxuskonsumption, 86 Lysosomal-associated membrane proteins 1 (LAMP1), 521 Macaca mulatta lasiotis, 165 Macromolecules, 72 Macronutrient composition, 100 Macronutrient proportions carbohydrates, 335–336 fat, 336 healthy eating patterns, 336 protein, 335 MAF, see Minor allele frequency MAFLD, see Metabolic dysfunction-associated fatty liver disease MAG, see Monoacylglycerols The Magnet comic, 26 Magnetic resonance imaging (MRI), 29–32 MAGs, see Metagenomic assembled genomes Marijuana clinical and population based studies, 374–375 preclinical studies, 372 Martin Chuzzlewit (Dickens), 19 MAT, see Mediastinal adipose tissue Maternal obesity, 604 MC3R, see Melanocortin-3-receptor MC4R, see Melanocortin 4 receptor McGraw, E., 20 MCRs, see Melanocortin receptors Meal skipping, 334 Mediastinal adipose tissue (MAT), 273 Medicalization, 388 Medications, 12 Melanocortin-3-receptor (MC3R), 125 Melanocortin receptor 4 (MC4R), 76, 123, 125, 133, 209 Melanocortin receptors (MCRs), 125 Melanocortin system, 209 Mendelian randomization (MR), 83, 559 Mental health disorders biological mechanisms, 583–584 evidence-based messages, 586–587 postnatal depression, 582–583 psychosocial mechanisms, 583 QoL employment, 585 measurement, 584 physical health, 584 sexual health, 585 social health, 584–585 risk, 581–582 treatment, 585–586 youth populations, 582 Mental health literacy, 387–388 MERS-CoV, see Middle East respiratory syndrome coronavirus Messenger RNAs (mRNAs), 133 MET, see Metabolic equivalents Metabolic adaptation, 213 Metabolically healthy normal weight (MHNW), 267 Metabolically healthy obesity (MHO), 267, 470 Metabolically unhealthy obesity (MUO), 267 Metabolic dysfunction-associated fatty liver disease (MAFLD), 267–268

Index  633 Metabolic equivalents (MET), 347, 406 Metabolic flexibility, 277 Metabolic indicators adipose tissue, 91–92 blood pressure, 91 glucose and insulin, 91 heart rate, 91 hormones, 91 inflammation, 92 lipids and lipoproteins, 91 lipogenesis, 91 skeletal muscle, 92 Metabolic inefficiencies, skeletal muscle, 83–84 Metabolic syndrome clinical management, 507–508 clinical tools, 504 etiology, 506–507 prevalence, 504 risk factors, 504–506 Metabolism, 71 Metabolomics depot-specific studies, 189 dietary, 189–190 exercise, 190 expression profiles, 200–201 general profiling studies, 188 interventions, 189–190 obesity studies (2010-2021), 191–199 pharmacological, 190 special populations, 188–189 surgical, 189 Metagenomic assembled genomes (MAGs), 226 Methods of Characterizing Obesity, 14 Methylation quantitative trait locus (mQTL), 143 Metropolitan planning offices (MPOs), 412 METs, see Metabolic equivalents MGL, see Monoacylglycerol lipase MHNW, see Metabolically healthy normal weight MHO, see Metabolically healthy obesity Microbe-mediated development altered circadian rhythms, 229 energy harvest, 229–230 gut–adipose tissue cross talk, 230 gut–liver axis, 229 lipid digestion, 228–229 oral microbiome, 227–228 sensory perception of food, 227–228 small intestinal microbiota, 228–229 Microbial infections, 319–320, 326–327 MicroRNAs (miRNAs) biogenesis, 133–134 discovery, 133–134 functions, 137 obesity, 135 Middle East respiratory syndrome coronavirus (MERS-CoV), 552 Mindless eating, 353 Minor allele frequency (MAF), 118 Miracle diet, 8 Mitochondrial uncoupling, 315 Moderate obesity, 3 Moderate-to-vigorous physical activity (MVPA), 353, 375 Modern Moral Histories, 24 Moist and cool condition, 3 Monoacylglycerol lipase (MGL), 252 Monoacylglycerols (MAG), 252 Monogenic obesity, 123 class A genes ADCY3, 128

BDNF, 128 MRAP2, 128 MYT1L, 130 NTRK2, 128 SH2B1, 130 class B genes KSR2, 130 MC3R, 130 NRP1-2, 130 PHIP, 130 PLXNA1-4, 130 SEMA3A-G, 130 SRC-1, 130 new genes, 129 Monosodium urate (MSU), 556 Monozygotic (MZ) twins, 103, 174 Mortality and BMI, 451–453 body weight, 455–458 mediators and moderators, 454–455 NADIR, 453–454 observed associations of obesity, 454 MPOs, see Metropolitan planning offices MR, see Mendelian randomization MRFIT, see Multiple Risk Factor Intervention Trial MRI, see Magnetic resonance imaging MRNAs, see Messenger RNAs MSH, see β-melanocyte-stimulating hormone MSNA, see Muscle sympathetic nerve activity MSU, see Monosodium urate mTORC1, see mTOR complex 1 mTORC2, see mTOR complex 2 mTOR complex 1 (mTORC1), 97 mTOR complex 2 (mTORC2), 97 Multi-omics general profiling studies, 190, 204 obesity studies (2010-2021), 202–203 Multiple Risk Factor Intervention Trial (MRFIT), 561 Munchies, 374 MUO, see Metabolically unhealthy obesity Muscle sympathetic nerve activity (MSNA), 234 MVPA, see Moderate-to-vigorous physical activity Mycobacterium abscessus, 320 Mycobacterium tuberculosis, 320 Myelin transcription factor-1 like (MYT1L), 130 MZ, see Monozygotic twin NAASO, see North American Association for the Study of Obesity NAD+, see Nicotinamide adenine dinucleotide NAFLD, see Nonalcoholic fatty liver disease NAMPT, see Nicotinamide phosphoribosyl transferase NASH, see Nonalcoholic steatohepatitis National Child Measurement Programme (NCMP), 49 National Health and Nutrition Examination Survey (NHANES), 53, 423, 481, 504 National Health Interview Survey (NHIS), 53, 433 National Highway Traffic Safety Administration (NHTSA), 412 National Institute on Aging (NIA), 96 National Institutes of Health (NIH), 13, 168, 402 National Survey of Children’s Health (NSCH), 53 NCD-RisC, see Noncommunicable Disease Risk Factor Collaboration NCMP, see National Child Measurement Programme NcRNAs, see Noncoding RNAs

NEAT, see Non-exercise activity thermogenesis NEFAs, see Nonesterified fatty acids Neonatal adipose tissue, 149 NES, see Night eating syndrome Neuroendocrine, 218 Neuronal dysfunction, 577 adipose tissue dysfunction, 577 gut microbiome, 578 hippocampal insulin signaling, 577–578 Neuropeptide Y (NPY), 209, 443 Neurovascular coupling (NVC), 575 New science, 15 New Zealand Obese (NZO) mouse, 158 Next-generation sequencing (NGS), 136 NFAT, see Nuclear factor of activated T cells NGS, see Next-generation sequencing NHANES, see National Health and Nutrition Examination Survey NHIS, see National Health Interview Survey NHTSA, see National Highway Traffic Safety Administration Niacin, 485–486 Nicotinamide adenine dinucleotide (NAD+), 97 Nicotinamide phosphoribosyl transferase (NAMPT), 98 Nicotine clinical and population based studies ENDS, 373–374 smoking cessation and weight gain, 372–373 preclinical studies, 371–372 Nicotinic acid, 485–486 Night eating syndrome (NES), 384 NIH, see National Institutes of Health NMR, see Nuclear magnetic resonance NOAEL, see No-observed adverse-effect level Nonalcoholic fatty liver disease (NAFLD), 96, 133, 225, 241, 612–613 diagnosis, 541–542 epidemiology, 538–539 HCC surveillance, 542 management and therapy bariatric procedures, 542, 544 diagnosis and risk stratification, 543 diagnostic care pathway, 544 drug therapy, 544, 545 lifestyle modification, 542 natural history, 539–540 pathogenesis, 540–541 prognosis and mortality cardiovascular and non-hepatic morbidity, 540 extrahepatic tumors, 540 liver-specific morbidity and mortality, 540 screening of patients, 542 Nonalcoholic steatohepatitis (NASH), 267, 533 Noncoding RNAs (ncRNAs), 133 Noncommunicable Disease Risk Factor Collaboration (NCD-RisC), 48 Nonesterified fatty acids (NEFAs), 188 Non-exercise activity thermogenesis (NEAT), 80, 82 Nonhuman primates, 96 assessment of adiposity, 166 diet/food intake, 166 energy expenditure, 166 experimental induction, 166 vs. humans, 166 metabolic disorders in humans, 165–166 T2DM dyslipidemia, 168–171 longitudinal vs. cross-sectional analysis, 167

634 Index metabolic and endocrine disturbances, 167–168 Non-shivering thermogenesis, 80–82 Non-syndromic monogenic obesity, 123 No-observed adverse-effect level (NOAEL), 443 Normotensive people, 469 North American Association for the Study of Obesity (NAASO), 13 NPY, see Neuropeptide Y NSCH, see National Survey of Children’s Health NTS, see Nucleus tractus solitarius Nuclear factor of activated T cells (NFAT), 125 Nuclear magnetic resonance (NMR), 29 Nucleus tractus solitarius (NTS), 207, 217 Nutrient-sensing pathways, 97–98 Nutrient warning, 401 Nutrition, 6 months to 2 years, 363 Nutritional Disease, 160 NVC, see Neurovascular coupling (NVC) NZO, see New Zealand Obese mouse OA, see Osteoarthritis Obesity childhood Lord of the Flies, 26–27 The Magnet comic, 26 The Pickwick Papers, 25–26 derangements, 86 gender-specific aspects, 25 genetic effects eating behavior, 106–107 PA, 107 RMR, 107–108 genetic factors/obesogenic environments (see Gene–environment interactions) genetic similarity, 103 heritability BMI, 104 indicators, 104–106 immoderate, 3 lncRNAs, 136 miRNAs, 135 moderate, 3 prediction models, 119–120 prior to Hippocrates, 4 quantitative genetics, 103–104 in time of Hippocrates, 3 variant-to-function translation, 119 Obesity-induced inflammation, 520–523 The Obesity Society (TOS), 13 Obesogenic environment, 434 Obesogens, 441, 442 adipogenic pathways, 444–445 brain, 443 circadian rhythms, 446 enzyme-substrate interaction, 444 epigenetic landscape, 445 impaired thermogenesis, 445 ligand, 444 metabolic dysfunction, 445 microbiome composition, 445 neurobehavioral outcomes, 446 non-ligand-dependent fashion, 444 Obligatory energy expenditure, 70, 79–80 Obstructive sleep apnea (OSA), 25, 551–552 Olfactory receptor 78 (Olfr78), 230 Olfr78, see Olfactory receptor 78 Oliver Twist (Dickens), 20 Omics studies, see Metabolomics; Proteomics; Transcriptomics Operant conditioning, 12

Optical methods, 34 Organic packaging, 401 Osteoarthritis (OA), 568–569 Osteoporosis BMI, 565–566 fat depots, 566 frailty, 566 lean mass vs. fat mass, 566 management targets, 567 Oxidative phosphorylation (OXPHOS), 83, 488 OXM, see Oxyntomodulin OXPHOS, see Oxidative phosphorylation Oxygen theory, 8 Oxyntomodulin (OXM), 216, 222 PA, see Physical activity Pancreatic Cancer, 534 Pancreatic fat, 272–273 Pancreatic polypeptide (PP), 219 Parasympathetic nervous system, 91 Paraventricular nucleus (PVN), 125, 158, 209 PCOS, see Polycystic ovarian syndrome PCSK1, see Prohormone convertase subtilisin/ kexin 1 PDFF, see Proton density fat fraction Pediatric obesity adolescence insulin resistance, 610–611 NAFLD, 612–613 genetic predisposition, 612 growth and development in children, 608–609 metabolic health risks, 608 pediatric T2D, 612 youth dysglycemia, 613 genetic predisposition, 613–614 insulin resistance, 609–610 metabolic complications, 609–610 NAFLD, 613 prediabetes, 611 psychological consequences, 609 type 2 diabetes, 611 Peptide tyrosine-tyrosine (PYY), 216, 219 Peroxisome proliferator-activated receptor gamma (Pparγ), 148 PET, see Positron-emission tomographic scans PGSs, see Polygenic scores Pharmacotherapy, 13 Phentermine, 12 PHIP, see Pleckstrin homology domain interacting protein Phosphatidylinositol 3-kinase (PI3K), 97 Phosphorylated STAT3 (pSTAT3), 130 Physical activity (PA), 107, 356 Physical inactivity, 347 PI3K, see Phosphatidylinositol 3-kinase Pickwickian Syndrome, 25 The Pickwick Papers (Dickens), 25–26 Piggy, 27 Pillars of Obesity, 15 PINK1, see PTEN-induced kinase 1 Pin-structured precursor (pre-miRNA), 134 PKC, see Protein kinase C Plasma cortisol, 91 Plasma triglycerides, 482–483 Pleckstrin homology domain interacting protein (PHIP), 130 Policy vs. self-directed change legacy of tension, 403 successful engagement, 403–404

Politics The History of John Bull, 20–23 King George IV, 23–24 William Hogarth’s election series, 24 Polycystic ovarian syndrome (PCOS), 592 Polygenic scores (PGSs), 120 POMC, see Proopiomelanocortin Positron-emission tomographic (PET) scans, 71 Post-exercise oxygen consumption (EPOC), 296, 303–307 limitations and assumptions, 297–298 selection criteria, 296–297 Postnatal depression, 582–583 PP, see Pancreatic polypeptide Pparγ, see Peroxisome proliferator-activated receptor gamma PPARγ, see Proliferator-activated receptor gamma Prader–Willi syndrome (PWS), 11, 123, 225 PRAMS, see Pregnancy Risk Assessment Monitoring System Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA), 296 Pregnancy obesity abnormal fetal growth, 600 adipose physiology, 598 after obesity surgery, 605 cardiometabolic consequences gestational diabetes mellitus, 602 glucose metabolism, 602 GWG, 600–601 HSD, 601–602 complications, 597 congenital anomalies, 599–600 contributing factors, 598 interventional strategies lifestyle interventions, 605 preconception, 604–605 loss and perinatal mortality, 599 periconception, 599 peripartum complications mode of delivery/cesarean delivery, 603 slow labor progression, 602–603 postoperative complications, 604 Prevalence of obesity adolescents, 48–49 United States of America, 55–57 adults Africa, 51 Australian, 51 China, 50 European Union (EU), 51 India, 50 Latin America, 50 United States of America, 50–51, 53–54, 57–59 children, 48–49 Asia, 49–50 high weight-for-length, 54–55 United Kingdom, 49 United States of America, 49, 55–57 global age-standardized, 48–49 global obesity, 59–64 PRISMA, see Preferred Reporting Item for Systematic Reviews and Meta-Analysis ProCCK, 218 Prohormone convertase subtilisin/kexin 1 (PCSK1), 123, 125 Proliferator-activated receptor gamma (PPARγ), 441

Index  635 Proopiomelanocortin peptide (POMC), 123, 158, 209 Proprotein convertase subtilisin/kesin 1 (PCSK1), 133 Prostate disorders, 595 Prostatitis, 595 Protein kinase A (PKA), 125 Protein kinase C (PKC), 492 Protein metabolism fat mass vs. lean mass, 276 muscle, 276–277 Proteomics depot-specific studies, 183 dietary, 183 expression profiles, 186–187 general profiling studies, 177 interventions, 183 limitations, 183 obesity studies (2010-2021), 184–185 special populations, 177, 183 surgical, 183 Proton density fat fraction (PDFF), 32 PSTAT3, see Phosphorylated STAT3 PTEN-induced kinase 1 (PINK1), 491 Public–private partnerships, 404 Pulmonary hypertension, 553 PVN, see Paraventricular nucleus PYY, see Peptide tyrosine-tyrosine Quality of life (QoL) employment, 585 measurement, 584 physical health, 584 sexual health, 585 social health, 584–585 Quantitative trait locus (QTL), 158 RAAS, see Renin-angiotensin-aldosterone system Rainbow pill treatment, 12 RAMPs, see Receptor activitymodifying proteins Randomized controlled trials (RCTs), 296 Reactive oxygen species (ROS), 98 Receptor activitymodifying proteins (RAMPs), 220 Recommended dietary allowance (RDA), 363 REE, see Resting energy expenditure Regulatory T Cells (Tregs), 522 Renal sinus fat (RSF), 469 Renin–angiotensin–aldosterone system (RAAS), 470 Reproductive dysfunction ART, 595 dysfunction in women abnormalities in menstrual cycles, 591–592 infertility, 592–593 miscarriage rates, 593 oocyte quality, 593 polycystic ovarian disease, 592 female hypothalamic–pituitary axis, 588–589 gonadal hormone, 590 leptin, 591 male hypogonadism, 593 male hypothalamic–pituitary axis, 588 male infertility, 594 penile erectile dysfunction, 593–594 SHBG, 590 spermatogenesis, 594 RER, see Respiratory exchange ratio Residual metabolic rate, 287 Resource scarcity hypothesis, 402

Respiratory exchange ratio (RER), 280 Respiratory infections, 552–553 Resting energy expenditure (REE), 242 Resting metabolic rate (RMR), 89, 107–108, 207, 216, 286–287 Retinoic acid receptor (RXR), 443 Rhesus monkeys, 165 Rimonabant, 13 RISC, see RNA-induced silencing complex RMR, see Resting metabolic rate RNA-induced silencing complex (RISC), 134 Rodent models, 155 ROS, see Reactive oxygen species Roux-en-Y gastric bypass (RYGB), 144, 176 RPOs, see Rural planning offices RSF, see Renal sinus fat Rural planning offices (RPOs), 412 RXR, see Retinoic acid receptor Ryanodine receptor (RyR), 74 RYGB, see Roux-en-Y gastric bypass RyR, see Ryanodine receptor S6K, see S6 kinase S6 kinase (S6K), 98 Sagittal abdominal diameter (SAD), 39 Salt intake, 477 Sarco/endoplasmic reticulum (SR/ER), 253 Sarcoplasmic reticulum Ca2+ ATPases (SERCA), 74, 81–82 SARS-CoV-2, see Severe acute respiratory syndrome coronavirus 2 SAT, see Subcutaneous adipose tissue Saucy seaside postcards, 25 SCFA, see Short-chain fatty acid Scientific journals, 15 Scientific organizations, 13–15 Scientific revolution 16th century, 6 17th century, 7–8 18th century, 8, 9 19th century, 8, 10 20th century, 10–12 21th century, 12–13 SCN, see Suprachiasmatic nucleus SCOPE, see Strategic Centre for Obesity Professional Education ScRNA-seq, see Single-cell RNA sequencing Sedentary behavior, 347–349 additional methodological issues, 354 adiposity in adults, 352 adiposity in youth, 351–352 coexisting and moderating, 352–353 contexts, 349–350 correlates and determinants adults, 350–351 youth, 350 Sedentary behavior complexity, 349 Selective leptin resistance, 213 SERCA, see Sarcoplasmic reticulum Ca2+ ATPases Serotonin neurotransmitter system, 12 Serum urate (SU), 556 SES, see Socioeconomic status Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 319 Sex hormone-binding globulin (SHBG), 91, 513, 590 Sexual health, 585 SGLT1, see Sodium-glucose transporter 1 SH2, see Src homology 2 Sh2b adaptor protein 1 (SH2B1), 124

Shakespeare, W., 17–18 Sharp, B., 20 SHBG, see Sex hormone-binding globulin Shivering thermogenesis, 80–82 Short-chain fatty acid (SCFA), 226 Signal integration, 207–209 hindbrain, 210 hypothalamic angiotensin, 209–210 melanocortin system, 209 SIM1, see Single-minded 1 transcription factor Single-cell RNA sequencing (scRNA-seq), 150 Single-minded 1 transcription factor (SIM1), 124 Single-minded homolog 1 (SIM1), 128, 133 Single-nucleotide polymorphisms (SNPs), 139, 158 Six-component body density model, 29 Skeletal muscle, 276–283 Skinfold, 35 Sleep adults and elders epidemiological studies, 380–381 SARS CoV2 pandemic, 381–382 short sleep, 381 children and adolescents epidemiological studies, 378–379 experimental studies, 380 objectively measured sleep, 379–380 Sleep apnea, 24–25 SMA+, see Smooth muscle actin cells Small animal models big data science metabolic diseases, 159–160 metabolic sequencing, 159–160 molecular phenotyping, 159–160 single-cell/nucleus RNA sequencing, 160–161 classic obesity models diet-induced weight changes, 155–156 polygenic mouse models, 158 seasonal hamsters, 156–157 spontaneous monogenic mutations, 157–158 conditional mutation models brain complexity, 158 CRISPR technology, 159 neuronal circuit dynamics, 158–159 silence neuronal activation, 159 improved technologies, 160 metabolic phenotyping, 161–162 monogenetic mouse models, 157 Small intestine celiac disease, 531 diarrhea, 531 IBD, 531–532 Smooth muscle actin (SMA+) cells, 150 SNA, see Sympathetic nerve activity Snacking, 334 SNAP, see Supplemental Nutrition Assistance Program SNPs, see Single-nucleotide polymorphisms SNS, see Sympathetic nervous system Socioeconomic status (SES), 402 CDC, 423 disparities causes, 427–428 individual-level, 424–426 neighborhood-level, 426–427 strategies to mitigate, 429–430 global, 428–429 potential bias, 427 Sodium–glucose transporter 1 (SGLT1), 227 Solid food, 367

636 Index Src homology 2 (SH2), 130 Src homology 2 (SH2) B adaptor protein 1 (SH2B1), 130 SREBP1, see Sterol regulatory element-binding protein 1 SR/ER, see Sarco/endoplasmic reticulum SSB, see Sugar-sweetened beverages Standard error of estimate (SEE), 40 Sterol regulatory element-binding protein 1 (SREBP1), 135 Stomach erosive gastritis, 530 gastric cancer, 530–531 gastric motor physiology, 530 Strategic Centre for Obesity Professional Education (SCOPE), 15 Stromal vascular fraction (SVF), 150, 174, 253 SU, see Serum urate Subcutaneous adipose tissue (SAT), 33, 176, 267, 469 Sudden death, 3 Sugar-sweetened beverages (SSB) consumption, 339 correlates, 340 genetics, 341 intervention studies, 340–341 observational studies, 340 reviews and meta-analyses, 340 underlying mechanism, 341 Summer hamsters, 157 Supplemental Nutrition Assistance Program (SNAP), 430 Suprachiasmatic nucleus (SCN), 229 Surgical techniques, 12 SVF, see Stromal vascular fraction; Stromalvascular fraction Sympathetic nerve activity (SNA), 474 Sympathetic nervous system (SNS), 91, 470 activation, 235, 473–474 adipose tissues, 233–234 assess in humans, 234 cardiovascular consequences, 236 heart, 237 renal function, 237 chemoreflex, 475 CNS melanocortin system, 475 hypoxemia, 475 impaired baroreflexes, 474 interventions bariatric surgery, 238 diet and exercise, 238 leptin activation, 475 liver, 234 metabolic implication adipokines, 235 hyperinsulinemia, 235 insulin resistance, 235 pancreas, 234 skeletal muscle, 234 vascular function, 237–238 visceral adiposity FFA, 236 inflammation, 236 leptin, 235–236 Syndromic obesity, 123 T2D, see Type 2 diabetes T2DM, see Type 2 diabetes mellitus TAG, see Triglycerides Tamoxifen, 149 TBS, see Trabecular Bone Score

TBT, see Tributyltin TBW, see Total body water TCA, see Citric acid cycle; Tricarboxylic acid cycle TDEE, see Total daily energy expenditure TEE, see Total energy expenditure TEF, see Thermic effect of food Testosterone, 590 T helper 1 (Th1) cells, 521–522 T helper 2 (Th2) cells, 522 Thermic effect of food (TEF), 89 BMR vs. RMR, 286–287 effect of obesity, 290–292 endogenous/exogenous interaction, 292 fat-free mass, 288 intrinsic and extrinsic difficulties, 290 postprandial conditions, 286 prediction of RMR, 288 substrates, 289–290 technical issues, 289 whole-body, 287–288 Thermogenesis, 148 Thermogenic adipocytes, 74 Thermogenic mechanisms, 72 Thermogenic pathway, 13 Thiazolidinediones (TZDs), 493 Thrifty genes, 4 Thyroid-stimulating hormone (TSH), 83, 88, 242 Thyrotropin-releasing hormone (TRH), 88 Tissue-nonspecific alkaline phosphatase (TNAP), 253 TJA, see Total joint arthroplasty TLR, see Toll-like receptor TNAP, see Tissue-nonspecific alkaline phosphatase TNFα, see Tumor necrosis factor-α TODAY, see Treatment of Options for type 2 Diabetes in Adolescents and Youth Toll-like receptor (TLR), 230 Too fat, 622 TOS, see The Obesity Society Total body water (TBW), 28–29 Total daily energy expenditure (TDEE), 89 Total energy expenditure (TEE), 79 Total joint arthroplasty (TJA), 569–570 Total peripheral vascular resistance (TPR), 470 Total testosterone (TT), 246 TPR, see Total peripheral vascular resistance Trabecular Bone Score (TBS), 567 Transcriptomics depot-specific studies, 174, 176 dietary, 177 exercise, 177 expression profiles, 181–182 general profiling studies, 174 human, 174 interventions, 176–177 limitations, 177 major technologies, 175–176 obesity studies (2000–2021), 178–180 special populations, 174 surgical, 176 Transportation built environment, 408 ecological studies, 408–409 health benefits, 406–407 individual-level studies, 409–410 longitudinal evidence, 410 walking and cycling federal policies, 411 local policies, 412 state policies, 411–412

Treatment of Options for type 2 Diabetes in Adolescents and Youth (TODAY), 612 Tregs, see Regulatory T Cells TRH, see Thyrotropin-releasing hormone Triacylglycerols, 28 Tributyltin (TBT), 442–443 Tricarboxylic acid (TCA) cycle, 176 Triglyceride lipase (ATGL), 252 Triglycerides (TAG), 28, 250, 560 Tropomyosin receptor kinase B (TrkB), 128 TSH, see Thyroid-stimulating hormone TT, see Total testosterone Tumor necrosis factor-α (TNFα), 96, 279 Two molecular-level body composition, 29 Type 2 diabetes (T2D), 96, 133 fat distribution, 497–498 fat mass, 497–498 pathogenesis, 498–500 prevalence, 496–497 prevention, 500–501 treatment, 501 Type 2 diabetes mellitus (T2DM), 461 Type 2 innate lymphoid cells (ILC2), 151 TZDs, see Thiazolidinediones UCP1, see Uncoupling protein-1 UCP3, see Uncoupling protein-3 ULT, see Urate-lowering therapy Ultra-processed foods, 403 Ultrasound measurements, 34 Uncoupling protein 1 (UCP1), 81, 148, 229, 252 Uncoupling protein-3 (UCP3), 81 Underwater weighing (UWW), 33 Unique expression of uncoupling protein 1 (UCP1), 72 Urate-lowering therapy (ULT), 556 Urban built environment, 415 air pollution, 418 behavior pathway food environments and dietary choice, 416–417 physical activity, 416 social interaction, 417–418 walkability, 417 components, 415 confounding variables, 421 exposure pathway, 418 feasible study designs, 420 vs. health, 416–419 limitations, 420 measuring, 419–420 noise exposure, 418 parks and greenspace crime and traffic safety, 419 heat, 418–419 residential self-selection, 420–421 Urinary catechol amines, 91 UWW, see Underwater weighing Vanity Fair (Sharp), 20 Vascular cognitive impairment and dementia (VCID), 572 Vascular endothelial growth factor (VEGF), 270 VAT, see Visceral adipose tissue VCID, see Vascular cognitive impairment and dementia Venous thromboembolism (VTE), 604 Vinegar, 3 Virus influencing metabolism, 325–326 Visceral adipose tissue (VAT), 33, 241, 469 cardiometabolic risk, 269

Index  637 IH-TG, 269–270 Visceral fat, 88–89 Voluntary energy expenditure, 70 VTE, see Venous thromboembolism Waist circumference (WC), 105 Waist-to-height ratio (WHtR), 40 Waist-to-hip circumference ratio (WHR), 40, 105 WAT, see White adipose tissue WBI, see Weight bias internalization WC, see Waist circumference Weak willpower, 11 Weight bias internalization (WBI), 620 Weight stigma emotional well-being/physical health maladaptive eating behaviors, 621–622 physical activity, 622 priorities, 624

psychological distress, 620–621 weight loss maintenance, 622–624 societal setting healthcare, 618–619 home environment, 619 interpersonal sources, 619–620 mass media, 619 prevalence, 617–618 schools, 619 workplace, 618 WES, see Whole-exome sequencing Wharton, E., 19 White adipocytes, 148 White adipose tissue (WAT), 135, 148, 233, 260 Whitening, 255 Whole-body energy expenditure facultative energy expenditure exercise-associated thermogenesis, 82–83

NEAT, 82 non-shivering thermogenesis, 80–82 shivering thermogenesis, 80–82 obligatory energy expenditure, 79–80 Whole-exome sequencing (WES), 118 WHR, see Waist-to-hip circumference ratio WIC, see Women, Infants, and Children Wilms tumor 1 (Wt1), 149 Winter hamsters, 157 Wisconsin National Primate Research Center (WNPRC), 96 Women, Infants, and Children (WIC), 54, 435 World Cancer Research Fund (WCRF), 511 Wt1, see Wilms tumor 1 Youth Risk Behavior Surveillance System (YRBSS), 53