Conn's handbook of models for human aging [2 ed.] 9780128113530, 0128113537

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Conn’s Handbook of Models for Human Aging

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Conn’s Handbook of Models for Human Aging SECOND EDITION

Edited by Jeffrey L. Ram P. Michael Conn†

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2018 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-811353-0 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Sarah Tenney Senior Acquisition Editor: Stacy Masucci Editorial Project Manager: Samuel Young Production Project Manager: Poulouse Joseph Cover Designer: Christian J. Bilbow Typeset by TNQ Books and Journals

Contents List of Contributors Preface – P. Michael Conn Preface – Jeffrey L. Ram

xi xvii xix

3

21

Fabio Coppedè

3.  Models, Definitions, and Criteria for Frailty

35

45

5. Vulnerability and Experiential Health in Old Age—A Qualitative Perspective59 Anneli Sarvimäki

6. Body Composition in Older Adults69

109

Francesca E. Duncan, Rafael Confino and Mary Ellen Pavone

131

Peter J. Hornsby

11. What Sets Iceland Apart in Understanding Human Aging

Tamas Fülöp, Graham Pawelec, Jacek M. Witkowski, Alan A. Cohen, Carl Fortin, Aurelie Le Page, Hugo Garneau, Gilles Dupuis and Anis Larbi

M.A. Camina Martín, B. de Mateo Silleras and M.P. Redondo del Río

9. Female Reproductive Aging: From Consequences to Mechanisms, Markers, and Treatments

10. Adrenopause

David B. Hogan

4. Immunological Methods and the Concept of Inflammaging in the Study of Human Aging

Mark Mc Auley and Kathleen Mooney

Samantha M. Solon-Biet, Devin Wahl, David Raubenheimer, David G. Le Couteur, Stephen J. Simpson and Victoria C. Cogger

Junko Oshima, Fuki M. Hisama and Raymond J. Monnat, Jr.

2. Premature Aging Syndrome

79

8. A Framework for Uncovering the Roles of Calories and Macronutrients in Health and Aging 93

Section I Aging in Humans 1. Werner Syndrome as a Model of Human Aging

7. Using Computational Models to Study Aging

139

Adalsteinn Gudmundsson and Pálmi V. Jónsson

Section II Animal Models: Vertebrates 12. Reproductive Tract Lesions in Aged Nonhuman Primates

149

Beth K. Chaffee and Elizabeth R. Magden

13. Age-Related Changes to the Bony Structure and Musculature of the Shoulder in a Nonhuman Primate Model155 Anthony C. Santago II, Johannes F. Plate and Katherine R. Saul v

vi Contents

14. The Dog as a Model for Aging Research167 Audrey Jones, Elizabeth Head and Kimberly A. Greer

15. The Domestic Dog as a Model for Human Brain Aging and Alzheimer’s Disease

Susan E. Swanberg

177

Trine Schütt, Jan T. Pedersen and Mette Berendt

16. Determining Cause of Death and Contributing Causes of Death in Rodent Aging Studies

195

211

Joseph A. McQuail, Sarah A. Johnson, Sara N. Burke and Jennifer L. Bizon

18. Life Extension in Dwarf Mice

231

Andrzej Bartke, Justin Darcy and Rong Yuan

19. Extension of Life Span in Laboratory Mice

245

Archana Unnikrishnan, Sathyaseelan S. Deepa, Heather R. Herd and Arlan Richardson

20. Development and Validation of ECG Analysis Algorithm in Mice 271 Mari Merentie, Line Lottonen-Raikaslehto and Seppo Ylä-Herttuala

21. Old Gray Mouse Lemur Behavior, Cognition, and Neuropathology

Donna J. Holmes and James M. Harper

25. Modeling Aging and Age-Associated Pathology in Zebrafish

335

Jessie Van houcke, Ilse Bollaerts, Lies De Groef and Lieve Moons

26. The Use of Mature Zebrafish (Danio rerio) as a Model for Human Aging and Disease

351

Jill M. Keller and Evan T. Keller

27. Piscine Polemics; Small Tropical Fish Species as Models for Aging Research361 Hayley D. Ackerman and Glenn S. Gerhard

28. The Short-Lived African Turquoise Killifish (Nothobranchius furzeri): A New Model System for Research on Aging

377

Joscha Muck, Samuel Kean and Dario Riccardo Valenzano

287

Nadine Mestre-Frances, Stéphanie G. Trouche, Pascaline Fontes, Corinne Lautier, Gina Devau, Christelle Lasbleiz, Marc Dhenain and Jean-Michel Verdier

22. Birds as Models for the Biology of Aging and Aging-Related Disease: An Update

24. Zebrafish Model for Investigating the Integrated Control of Reproduction323 Marco Bonomi, Ivan Bassi and Luca Persani

Jessica M. Snyder, Alessandro Bitto and Piper M. Treuting

17. Rat Models of Cognitive Aging

23. Telomeres and Telomerase in Birds: Measuring Health, Environmental Stress, and Longevity313

Section III Cellular Models and Invertebrates 29. A Budding Topic: Modeling Aging and Longevity in Yeast Jessica Smith and Brandt L. Schneider

301

389

Contents  vii

30. The Budding and Fission Yeast Model Systems for Aging Biology: Rapid Advancement With New Technologies417

431

32. Invertebrates as Model Organisms for Research on Aging Biology 445 Jeffrey L. Ram and Anthony J. Costa II

453

461

Robert J. Wessells, Maryam Safdar and Alyson Sujkowski

35. The Virtues and Challenges of Multidimensional Analyses of Whole Brains During Aging With Single Cell Resolution

473

483

Quentin Schenkelaars, Szymon Tomczyk, Yvan Wenger, Kazadi Ekundayo, Victor Girard, Wanda Buzgariu, Steve Austad and Brigitte Galliot

42. Genotype and Sex Differences in Longevity in Transgenic Mouse Models of Alzheimer’s Disease

563

Richard E. Brown, Sooyoun Shin, Nicole Woodland and Eric A. Rae

43. Animal Models of Vascular Cognitive Impairment and Dementia577 44. Alzheimer’s Dementia Drug Discovery: Targeting Synaptic Glutamate Uptake

587

45. A Transgenic Monkey Model of Huntington’s Disease

593

In K. Cho and Anthony W.S. Chan

497

David B. Mark Welch

38.  Hydra, a Model System for Deciphering the Mechanisms of Aging and Resistance to Aging

551

Markku Kurkinen

Kristin E. Gribble and Terry W. Snell

37. The Potential of Comparative Biology to Reveal Mechanisms of Aging in Rotifers

41. Approaches to the Assessment of Frailty in Animal Models

Donna M. Wilcock

Rui Sousa-Neves and Claudia M. Mizutani

36. Rotifers as a Model for the Biology of Aging

Section IV Disease Models

Alice E. Kane and Susan E. Howlett

Benjamin A. Eaton

34. Impact of Chronic Exercise on Invertebrate Functional Aging

533

Tyler P. Quigley, Gro V. Amdam and Olav Rueppell

Andrea Hamann and Heinz D. Osiewacz

33. Invertebrate Models for the Study of the Effects of Age on Neurotransmitter Release

William R. Jeffery

40. Honeybee Workers as Models of Aging

Kurt W. Runge and Haitao Zhang

31.  Podospora anserina: A Filamentous Fungus With a Strong Mitochondrial Etiology of Aging

39. Regeneration and Aging in the Tunicate Ciona intestinalis521

46. Parkinson’s Disease in Humans and in Nonhuman Primate Aging and Neurotoxin Models

617

Jeanette M. Metzger, Corinne A. Jones and Marina E. Emborg

507

47. Genetic Models of Parkinson’s Disease and Their Study in Nonhuman Primates Corinne A. Jones, Jeanette M. Metzger and Marina E. Emborg

641

viii Contents

48. Impact of the Aged Brain Environment on Gene Therapy for Parkinson’s Disease

647

Nicole K. Polinski, D. Luke Fischer, Megan F. Duffy, Fredric P. Manfredsson, Christopher J. Kemp, Kathy Steece-Collier and Caryl E. Sortwell

Zhiguo Chen

673

Ken S.K. Wong and Zhongjun Zhou

51. Progeria Mouse Models

689

703

Jane F. Reckelhoff, Licy L. Yanes Cardozo and Maria Lourdes Alarcon Fortepiani

53. Osteoporosis and Cardiovascular Disease in the Elderly

721

735

751

Michael R. Hamblin

Georgios Nikolakis, Evgenia Makrantonaki and Christos C. Zouboulis

David C. Gibson and Melanie R. Gubbels Bupp

60. Rodent Models of Ovarian Failure 831

61. Role of Sex and Aging in Mucosal Health845

62. Leydig Cell Development and Aging in the Brown Norway Rat: Mechanisms and Consequences

853

63. Models of Aging Kidney: Implications on Kidney Health and Disease

863

Brendan T. Bowman and Emaad M. Abdel-Rahman

Section V Organ Systems

56. Experimental Models of Human Skin Aging

59. Sex and the Aging Immune System803

Barry R. Zirkin, Haolin Chen and Vassilios Papadopoulos

Anne Steins, Maarten F. Bijlsma and Hanneke W.M. van Laarhoven

55. Alopecia

José-Enrique O’Connor, Guadalupe Herrera, Beatriz Jávega and Alicia Martínez-Romero

Chantelle Dills, Ronald Hart, Jovy Rex-Al Panem Orbon and Sumathi Sankaran-Walters

Ranganath Muniyappa and Sri Harsha Tella

54. The Role of the Tumor Microenvironment in Pancreatic Ductal Adenocarcinoma and Preclinical Models to Study It

783

Jose Marques-Lopes, Tracey A. Van Kempen and Teresa A. Milner

Pablo Mayoral, Clea Bárcena and Carlos López-Otín

52. Models of Hypertension in Aging

769

Thomas Brioche, Guillaume Py and Angèle Chopard

58. Models of Immune Aging

49. Cell Therapy for Parkinson’s Disease659 50. Genetics of Progeria and Aging

57. Muscle Deconditioning and Aging: Experimental Models

64. Age-Associated Changes in Structure and Function of the Aging Human Lung

873

Keith C. Meyer

763

65. Glucose, Insulin, and Human Brain Aging Abimbola Akintola and Diana van Heemst

889

Contents  ix

66. Pathology of Brain Aging and Animal Models of Neurodegenerative Diseases

73. Genetics of Human Aging 899

67. Leptin and Aging in Animal Models 909

68. Age-Related Changes to Bone Structure and Quality in Rodent Models919 Jeffry S. Nyman

939

76. Super DNAging—New Insights Into DNA Integrity, Genome Stability, and Telomeres in the Oldest Old

1083

1095

Jan O. Nehlin and Jens Krøll

78. Chaperone-Mediated Autophagy 1117

70. Experimental Models of Tau Aggregation953 Kerstin Buck, Thomas R. Jahn and Laura Gasparini

Esther Wong

79. Resveratrol in Aging and Age-Related Diseases

1133

Michael Rouse and Josephine M. Egan

975

Hang Lin, He Shen and Rocky S. Tuan

Jack D. Crouch, Taraswi Banerjee, Sanket Awate, Sanjay Kumar Bharti and Robert M. Brosh

Ka Yi Hui and Jürgen A. Ripperger

77. Model of Chaperones in Aging

Camelia Gabriel and Azadeh Peyman

72. Helicases and Their Relevance to Aging

1067

Karl-Heinz Wagner, Bernhard Franzke and Oliver Neubauer

Section VI Mechanisms

71. Aging of Human Mesenchymal Stem Cells

1041

Ramón Cacabelos and Oscar Teijido

75. The Circadian Clock and the Aging Process

Márta Balaskó, Ildikó Rostás, Judit Tenk, Szilvia Soós and Erika Pétervári

69. Dielectric Properties of Biological Tissues; Variation With Age

Gil Atzmon and David Karasik

74. Epigenetics of Brain Aging

Sameh A. Youssef

1025

995

80. Resveratrol in Experimental Models and Humans

1143

Juan Gambini, Lucia Gimeno-Mallench, Cristina Mas-Bargues, Gonzalo Perez-Lopez, Consuelo Borras and Jose Viña Index

1157

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List of Contributors Emaad M. Abdel-Rahman University of Virginia, Charlottesville, VA, United States

Brendan T. Bowman University of Virginia, Charlottesville, VA, United States

Hayley D. Ackerman The Lewis Katz School of Medicine at Temple University, Philadelphia, PA, United States

Thomas Brioche Université de Montpellier, Montpellier, France

Abimbola Akintola Leiden University Medical Centre, Leiden, The Netherlands

Robert M. Brosh National Institute on Aging, National Institutes of Health, NIH Biomedical Research Center, Baltimore, MD, United States

Gro V. Amdam Arizona State University, Tempe, AZ, United States; Norwegian University of Life Sciences, Aas, Norway Gil Atzmon University of Haifa, Haifa, Israel; Albert Einstein College of Medicine, Bronx, NY, United States Steve Austad University of Alabama at Birmingham, Birmingham, AL, United States Sanket Awate National Institute on Aging, National Institutes of Health, NIH Biomedical Research Center, Baltimore, MD, United States Márta Balaskó University of Pécs, Pécs, Hungary Taraswi Banerjee National Institute on Aging, National Institutes of Health, NIH Biomedical Research Center, Baltimore, MD, United States

Richard E. Brown Dalhousie University, Halifax, NS, Canada Kerstin Buck AbbVie Deutschland GmbH & Co., Ludwigshafen, Germany Sara N. Burke University of Florida, Gainesville, FL, United States Wanda Buzgariu Switzerland

University

of

Geneva,

Geneva,

Ramón Cacabelos Institute of Medical Science and Genomic Medicine, Corunna, Spain; Continental University Medical School, Huancayo, Peru M.A. Camina Martín University of Valladolid, Valladolid, Spain

Clea Bárcena Universidad de Oviedo, Oviedo, Spain

Beth K. Chaffee The University of Texas M. D. Anderson Cancer Center, Bastrop, TX, United States

Andrzej Bartke Southern Illinois University School of Medicine, Springfield, IL, United States

Anthony W.S. Chan Emory University School of Medicine, Atlanta, GA, United States

Ivan Bassi IRCCS Istituto Auxologico Italiano, Milan, Italy

Haolin Chen Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China

Mette Berendt University of Copenhagen, Copenhagen, Denmark Maarten F. Bijlsma Academic Medical Center, Amsterdam, The Netherlands Alessandro Bitto University of Washington, Seattle, WA, United States Jennifer L. Bizon University of Florida, Gainesville, FL, United States

Zhiguo Chen Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Institute for Brain Disorders, Beijing, China In K. Cho Emory University School of Medicine, Atlanta, GA, United States

Ilse Bollaerts KU Leuven, Leuven, Belgium

Angèle Chopard Université de Montpellier, Montpellier, France

Marco Bonomi University of Milan, Milan, Italy; IRCCS Istituto Auxologico Italiano, Milan, Italy

Victoria C. Cogger The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia

Consuelo Borras Freshage Research Group, Department of Physiology, University of Valencia, CIBERFES, INCLIVA, Valencia, Spain

Alan A. Cohen University of Sherbrooke, Sherbrooke, QC, Canada xi

xii  List of Contributors

Rafael Confino Northwestern University, Chicago, IL, United States

Carl Fortin University of Sherbrooke, Sherbrooke, QC, Canada

Fabio Coppedè University of Pisa, Pisa, Italy

Bernhard Franzke University of Vienna, Vienna, Austria

Anthony J. Costa II Wayne State University, Detroit, MI, United States

Tamas Fülöp University of Sherbrooke, Sherbrooke, QC, Canada

Jack D. Crouch National Institute on Aging, National Institutes of Health, NIH Biomedical Research Center, Baltimore, MD, United States

Camelia Gabriel C Gabriel Consultants, San Diego, CA, United States

Justin Darcy Southern Illinois University School of Medicine, Springfield, IL, United States Lies De Groef KU Leuven, Leuven, Belgium

Juan Gambini Freshage Research Group, Department of Physiology, University of Valencia, CIBERFES, INCLIVA, Valencia, Spain

B. de Mateo Silleras University of Valladolid, Valladolid, Spain

Hugo Garneau University of Sherbrooke, Sherbrooke, QC, Canada

Sathyaseelan S. Deepa Oklahoma University Health Science Center, Oklahoma, OK, United States

Laura Gasparini AbbVie Deutschland GmbH & Co., Ludwigshafen, Germany

Gina Devau Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France

Glenn S. Gerhard The Lewis Katz School of Medicine at Temple University, Philadelphia, PA, United States

Marc Dhenain UMR CEA-CNRS-UPSud 9199 Molecular Imaging Research Center (MIRCen), Fontenay aux Roses, France

Lucia Gimeno-Mallench Freshage Research Group, Department of Physiology, University of Valencia, CIBERFES, INCLIVA, Valencia, Spain

Chantelle Dills University of California, Davis, CA, United States

Victor Girard University of Geneva, Geneva, Switzerland

Megan F. Duffy Michigan State University, Grand Rapids, MI, United States; Michigan State University, East Lansing, MI, United States Francesca E. Duncan Northwestern University, Chicago, IL, United States Gilles Dupuis University of Sherbrooke, Sherbrooke, QC, Canada Benjamin A. Eaton University of Texas Health Sciences Center at San Antonio, San Antonio, TX, United States Josephine M. Egan NIA/NIH, Baltimore, MD, United States

Brigitte Galliot University of Geneva, Geneva, Switzerland

David C. Gibson Randolph-Macon College, Ashland, VA, United States

Kimberly A. Greer Prairie View A&M University, Prairie View, TX, United States Kristin E. Gribble Marine Biological Laboratory, Woods Hole, MA, United States Melanie R. Gubbels Bupp Randolph-Macon College, Ashland, VA, United States Adalsteinn Gudmundsson Landspitali University Hospital, Reykjavík, Iceland; University of Iceland, Reykjavik, Iceland Andrea Hamann Goethe University, Frankfurt, Germany

Marina E. Emborg University of Wisconsin–Madison, Madison, WI, United States

Michael R. Hamblin Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States

D. Luke Fischer Michigan State University, Grand Rapids, MI, United States

James M. Harper Sam Houston State University, Huntsville, TX, United States

Pascaline Fontes Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France

Ronald Hart University of California, Davis, CA, United States

Kazadi Ekundayo University of Geneva, Geneva, Switzerland

Maria Lourdes Alarcon Fortepiani University of the Incarnate Word, San Antonio, TX, United States

Elizabeth Head University of Kentucky, Lexington, KY, United States Heather R. Herd Oklahoma University Health Science Center, Oklahoma, OK, United States

List of Contributors  xiii

Guadalupe Herrera The University of Valencia, Valencia, Spain; University Clinical Hospital, Valencia, Spain Fuki M. Hisama University of Washington School of Medicine, Seattle, WA, United States David B. Hogan University of Calgary, Calgary, AB, Canada Donna J. Holmes University of Idaho, Moscow, ID, United States Peter J. Hornsby University of Texas Health Science Center, San Antonio, TX, United States Susan E. Howlett Dalhousie University, Halifax, NS, Canada Ka Yi Hui University of Fribourg, Fribourg, Switzerland Thomas R. Jahn AbbVie Deutschland GmbH & Co., Ludwigshafen, Germany Beatriz Jávega The University of Valencia, Valencia, Spain; Principe Felipe Research Center, Valencia, Spain William R. Jeffery Marine Biological Laboratory, Woods Hole, MA, United States; University of Maryland, College Park, MD, United States Sarah A. Johnson University of Florida, Gainesville, FL, United States

Sanjay Kumar Bharti National Institute on Aging, National Institutes of Health, NIH Biomedical Research Center, Baltimore, MD, United States Markku Kurkinen Wayne State University, Detroit, MI, United States Anis Larbi Agency for Science Technology and Research, Singapore, Singapore Christelle Lasbleiz Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France Corinne Lautier Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France David G. Le Couteur The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia Aurelie Le Page University of Sherbrooke, Sherbrooke, QC, Canada Hang Lin University of Pittsburgh School of Medicine, Pittsburgh, PA, United States Carlos López-Otín Universidad de Oviedo, Oviedo, Spain

Audrey Jones University of St. Thomas, Houston, TX, United States

Line Lottonen-Raikaslehto University of Eastern Finland, Kuopio, Finland

Corinne A. Jones University of Wisconsin–Madison, Madison, WI, United States

Elizabeth R. Magden The University of Texas M. D. Anderson Cancer Center, Bastrop, TX, United States

Pálmi V. Jónsson Landspitali University Hospital, Reykjavík, Iceland; University of Iceland, Reykjavik, Iceland

Evgenia Makrantonaki Dessau Medical Center, Dessau, Germany; University Clinic of Ulm, Ulm, Germany

Alice E. Kane Dalhousie University, Halifax, NS, Canada David Karasik Bar-Ilan University, Safed, Israel; Hebrew SeniorLife, Boston, MA, United States Samuel Kean Max Planck Institute for Biology of Ageing, Cologne, Germany; CECAD, University of Cologne, Cologne, Germany Evan T. Keller University of Michigan, Ann Arbor, MI, United States Jill M. Keller University of Michigan, Ann Arbor, MI, United States Christopher J. Kemp Michigan State University, Grand Rapids, MI, United States Ken S.K. Wong The University of Hong Kong, Hong Kong, People’s Republic of China Jens Krøll Hafnia Unit of Biogerontology, Frederiksberg, Denmark

Fredric P. Manfredsson Michigan State University, Grand Rapids, MI, United States; Mercy Health Saint Mary’s, Grand Rapids, MI, United States David B. Mark Welch Marine Biological Laboratory, Woods Hole, MA, United States Jose Marques-Lopes The University of Western Ontario, London, ON, Canada Alicia Martínez-Romero The University of Valencia, Valencia, Spain; Principe Felipe Research Center, Valencia, Spain Cristina Mas-Bargues Freshage Research Group, Department of Physiology, University of Valencia, CIBERFES, INCLIVA, Valencia, Spain Pablo Mayoral Universidad de Oviedo, Oviedo, Spain Mark Mc Auley University of Chester, Chester, United Kingdom

xiv  List of Contributors

Joseph A. McQuail University of Florida, Gainesville, FL, United States

Mary Ellen Pavone Northwestern University, Chicago, IL, United States

Mari Merentie University of Eastern Finland, Kuopio, Finland

Graham Pawelec University of Tübingen, Tübingen, Germany

Nadine Mestre-Frances Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France

Jan T. Pedersen H. Lundbeck A/S, Copenhagen, Denmark

Jeanette M. Metzger University of Wisconsin–Madison, Madison, WI, United States Keith C. Meyer University of Wisconsin School of Medicine and Public Health, Madison, WI, United States Teresa A. Milner Weill Cornell Medicine, New York, NY, United States; The Rockefeller University, New York, NY, United States Claudia M. Mizutani Case Western Reserve University, Cleveland, OH, United States Raymond J. Monnat, Jr. University of Washington School of Medicine, Seattle, WA, United States Kathleen Mooney Edge Hill University, Ormskirk, United Kingdom Lieve Moons KU Leuven, Leuven, Belgium Joscha Muck Max Planck Institute for Biology of Ageing, Cologne, Germany; CECAD, University of Cologne, Cologne, Germany

Gonzalo Perez-Lopez European University of Valencia, Valencia, Spain Luca Persani University of Milan, Milan, Italy; IRCCS Istituto Auxologico Italiano, Milan, Italy Erika Pétervári University of Pécs, Pécs, Hungary Azadeh Peyman Public Health England, Oxfordshire, United Kingdom Johannes F. Plate Department of Orthopaedic Surgery Wake Forest Baptist Health, Winston–Salem, NC, United States Nicole K. Polinski Michigan State University, Grand Rapids, MI, United States; Michigan State University, East Lansing, MI, United States Guillaume Py Université de Montpellier, Montpellier, France Tyler P. Quigley Arizona State University, Tempe, AZ, United States Eric A. Rae Dalhousie University, Halifax, NS, Canada Jeffrey L. Ram Wayne State University, Detroit, MI, United States David Raubenheimer The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia

Ranganath Muniyappa National Institutes of Health, Bethesda, MD, United States

Jane F. Reckelhoff University of Mississippi Medical Center, Jackson, MS, United States

Jan O. Nehlin Odense University Hospital & University of Southern, Denmark, Odense, Denmark

M.P. Redondo del Río University of Valladolid, Valladolid, Spain

Oliver Neubauer University of Vienna, Vienna, Austria; Queensland University of Technology, Brisbane, QLD, Australia

Jovy Rex-Al Panem Orbon University of California, Davis, CA, United States

Georgios Nikolakis Dessau Medical Center, Dessau, Germany

Arlan Richardson Oklahoma University Health Science Center, Oklahoma, OK, United States; Oklahoma City VA Medical Center, Oklahoma, OK, United States

Jeffry S. Nyman Vanderbilt University Medical Center, Nashville, TN, United States

Jürgen A. Ripperger University of Fribourg, Fribourg, Switzerland

José-Enrique O’Connor The University of Valencia, Valencia, Spain; Principe Felipe Research Center, Valencia, Spain

Ildikó Rostás University of Pécs, Pécs, Hungary

Junko Oshima University of Washington School of Medicine, Seattle, WA, United States; Chiba University School of Medicine, Chiba, Japan Heinz D. Osiewacz Goethe University, Frankfurt, Germany Vassilios Papadopoulos McGill University, Montreal, QC, Canada; University of Southern California, Los Angeles, CA, United States

Michael Rouse NIA/NIH, Baltimore, MD, United States Olav Rueppell The University of North Carolina at Greensboro, Greensboro, NC, United States Kurt W. Runge Lerner College of Medicine at CWRU, Cleveland Clinic, Cleveland, OH, United States Maryam Safdar Wayne State University School of Medicine, Detroit, MI, United States Sumathi Sankaran-Walters University of California, Davis, CA, United States

List of Contributors  xv

Anthony C. Santago II The MITRE Corporation, McLean, VA, United States

Piper M. Treuting University of Washington, Seattle, WA, United States

Anneli Sarvimäki The Age Institute, Helsinki, Finland

Stéphanie G. Trouche Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France

Katherine R. Saul Department of Mechanical and Aerospace Engineering North Carolina State University, Raleigh, NC, United States Quentin Schenkelaars University of Geneva, Geneva, Switzerland Brandt L. Schneider Texas Tech University Health Sciences Center, Lubbock, TX, United States Trine Schütt University of Copenhagen, Copenhagen, Denmark He Shen University of Pittsburgh School of Medicine, Pittsburgh, PA, United States

Rocky S. Tuan University of Pittsburgh School of Medicine, Pittsburgh, PA, United States Archana Unnikrishnan Oklahoma University Health Science Center, Oklahoma, OK, United States Dario Riccardo Valenzano Max Planck Institute for Biology of Ageing, Cologne, Germany; CECAD, University of Cologne, Cologne, Germany

Sooyoun Shin Dalhousie University, Halifax, NS, Canada

Diana van Heemst Leiden University Medical Centre, Leiden, The Netherlands

Stephen J. Simpson The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia

Jessie Van houcke KU Leuven, Leuven, Belgium

Jessica Smith Texas Tech University Health Sciences Center, Lubbock, TX, United States Terry W. Snell Georgia Institute of Technology, Atlanta, GA, United States Jessica M. Snyder University of Washington, Seattle, WA, United States Samantha M. Solon-Biet The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia Szilvia Soós University of Pécs, Pécs, Hungary Caryl E. Sortwell Michigan State University, Grand Rapids, MI, United States; Mercy Health Saint Mary’s, Grand Rapids, MI, United States

Tracey A. Van Kempen Weill Cornell Medicine, New York, NY, United States Hanneke W.M. van Laarhoven Academic Medical Center, Amsterdam, The Netherlands Jean-Michel Verdier Université de Montpellier, Montpellier, France; Inserm, Molecular Mechanisms in Neurodegenerative Diseases (MMDN), Montpellier, France; EPHE, Paris, France; PSL Research University, Paris, France Jose Viña Freshage Research Group, Department of Physiology, University of Valencia, CIBERFES, INCLIVA, Valencia, Spain Karl-Heinz Wagner University of Vienna, Vienna, Austria

Rui Sousa-Neves Case Western Reserve University, Cleveland, OH, United States

Devin Wahl The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia

Kathy Steece-Collier Michigan State University, Grand Rapids, MI, United States; Mercy Health Saint Mary’s, Grand Rapids, MI, United States

Yvan Wenger University of Geneva, Geneva, Switzerland

Anne Steins Academic Medical Center, Amsterdam, The Netherlands

Donna M. Wilcock University of Kentucky, Lexington, KY, United States

Alyson Sujkowski Wayne State University School of Medicine, Detroit, MI, United States

Jacek M. Witkowski Medical University of Gdansk, Gdansk, Poland

Susan E. Swanberg University of Arizona, Tucson, AZ, United States

Esther Wong Nanyang Singapore, Singapore

Oscar Teijido Institute of Medical Science and Genomic Medicine, Corunna, Spain

Nicole Woodland Dalhousie University, Halifax, NS, Canada

Sri Harsha Tella National Institutes of Health, Bethesda, MD, United States

Licy L. Yanes Cardozo University of Mississippi Medical Center, Jackson, MS, United States

Judit Tenk University of Pécs, Pécs, Hungary

Seppo Ylä-Herttuala University of Eastern Finland, Kuopio, Finland; Heart Center, Kuopio University Hospital, Kuopio, Finland

Szymon Tomczyk University of Geneva, Geneva, Switzerland

Robert J. Wessells Wayne State University School of Medicine, Detroit, MI, United States

Technological

University,

xvi  List of Contributors

Sameh A. Youssef Janssen Pharmaceutica Research and Development, Beerse, Belgium

Zhongjun Zhou The University of Hong Kong, Hong Kong, People’s Republic of China

Rong Yuan Southern Illinois University School of Medicine, Springfield, IL, United States

Barry R. Zirkin Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States

Haitao Zhang Lerner College of Medicine at CWRU, Cleveland Clinic, Cleveland, OH, United States

Christos C. Zouboulis Dessau Medical Center, Dessau, Germany

Preface The mere fact that aging impacts all of us has made this topic attractive for research. The very nature of this topic means that aging research transcends all areas of physiology and causes it to rely on biological, mathematical, and chemical tools for its study. Accordingly, putting together a volume addressing the large number of models of human aging was a daunting task. The effort was made to cast a large net and we included many topics that were not included in the first edition, 10 years ago. Some areas will have been overlooked and not every viewpoint has been included for reasons of space limitation. The editor thanks the authors, selected for their prominence in the field and their areas of contribution, for timely submission of well-circumscribed overviews and for including tips and hints for their individual disciplines that have not appeared previously in print. I also appreciate the efforts of colleagues at Elsevier for embracing the significance of the project and for providing the printed and electronic space to complete it. P. Michael Conn (November 4, 2016)

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Preface Some cells are seemingly immortal, continually dividing, growing, and dividing again, in a continuous line from the first primordial cell to the present day. Other cells have limits, beginning as undifferentiated multipotent units, but then differentiating into specialized cells, performing tasks appropriate to their environments and then, eventually, progressively losing their capacities to metabolize, repair, reproduce, and survive: they get old. So it is with humans and many, though not all, multicellular organisms. Some organisms have a more-or-less fixed life cycle, dying not of old age but of death-dealing mechanisms turned on by biological or seasonal clocks, and others have impressive powers of regeneration and repair; however, for humans and many of their close and also quite distant relatives, the inevitable end is in a decreasingly able condition of aging. Or is it? If seemingly immortal cells and efficient regeneration exist, why has aging evolved? And can the mechanisms that mediate senescence be stopped or reversed both to lengthen life, and to create a healthier life span? Scientific studies often advance through the use of models, which are both ethically and practically more accessible for experimentation than humans. Models can be other types of animals, models of diseases, organ system models, or for probing cellular mechanisms of even single-celled organisms or cultured cells. Advantages of one model or another may depend on close genetic and physiological relationships to humans (e.g., nonhuman primates), relative ease of maintenance (rats and mice, dogs, birds, fish), availability of genomes and genetic tools (originally, the advantage of fruit flies and nematodes), short life spans allowing the full process of senescence to be studied repeatedly in much less time than the human life span, extraordinary powers of regeneration that can counteract senescence (tunicates), similarities of the models to specific human conditions, and so on. Much has been learned about possible mechanisms of aging at the cellular, tissue, organ, and whole organism level. The first edition of this Handbook was edited by Dr. P. Michael Conn, who gathered together authors who could cover this broad range of models and mechanisms. Realizing that in the 10 years since publication of the first edition, many advances had been made and new model organisms and new technologies had been developed, he recruited a largely new set of authors to review the advances in this critical field of research for this Second Edition of Conn’s Handbook of Models for Human Aging. Unfortunately, in the midst of this editorial process, he passed away. This second edition is dedicated to his memory. P. Michael Conn had a long-time interest in the biology of aging, having published several papers on aging in rats in the 1980s, a long scientific career that focused on endocrine mechanisms that change throughout life, and most recently developing molecules with chaperone-like functions that could help repair misdirected misfolded receptors. The role of chaperones in aging mechanisms, included in this second edition, undoubtedly reflects this interest. For additional remembrances of Michael Conn, see the obituary by the Endocrine Society, for which he served as president in 1996–1997, at https://www.endocrine.org/aboutus/societyhistory/inmemoriam/pmichaelconn. As one of the invited authors of the second edition and the coeditor of a 2015 journal issue on invertebrate models for the study of aging (Invertebrate Reproduction and Development, volume 59, Supplement 1), I was able to resume the editorial process that Michael Conn had begun for the Second Edition. However, I also have to thank Dr. Mahadev Murthy, who, in 2010 recruited me into the field of invertebrate models and aging. The special journal issue arose from a symposium that we organized in Detroit, and we were planning a reprise of that symposium for another conference when he, too, passed away. Dr. Murthy served as a Program Director in the Division of Aging Biology of the National Institute on Aging, joining NIA in 2007 after a distinguished career in academia. As noted in his NIA obituary (https://www.nia.nih. gov/research/dab/mahadevmurthymemoriam), “His passion was interacting with investigators, established and new, and going to meetings to interface with new research communities and promote the biology of aging. He was very successful at bringing established investigators from other fields into the study of aging.” His interest in how invertebrate model systems could contribute to the study of aging was deep, and his enthusiasm for the types of studies described in this Second Edition would have been boundless. The symposium that we had begun planning took place in August 2017 in Naples, Regione Campania, Italy, and several of the presenters at that symposium (Wessells, Sousa-Neves, Gribble, Mark Welch, Jeffery, Kurkinen, and Ram) have contributed chapters to this edition. This edition of Conn’s Handbook is also dedicated to the memory of Mahadev Murthy. xix

xx Preface

This volume would also not have been brought to fruition without the encouragement of the editorial staff at Elsevier. I especially thank Samuel Young, Editorial Project Manager at Elsevier, with whom I worked most closely throughout the review and production process to make this publication possible. We also thank the organizational assistance at Wayne State University by Anthony J. Costa II. Jeffrey L. Ram (September 11, 2017)

Section I

Aging in Humans

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Chapter 1

Werner Syndrome as a Model of Human Aging Junko Oshima1,2, Fuki M. Hisama1, Raymond J. Monnat, Jr.1 1University

of Washington School of Medicine, Seattle, WA, United States; 2Chiba University School of Medicine, Chiba, Japan

INTRODUCTION AND HISTORICAL OVERVIEW Werner syndrome (WS; OMIM record #27770) is an uncommon, autosomal recessive human disease that displays clinical features suggestive of rapid or premature aging. The initial description of WS was by a German medical student, Otto Werner, in 1904 (Werner, 1985). He saw a family consisting of four adult siblings, ages 31–40, who shared common features including short stature, premature graying of the hair, bilateral cataracts, skin changes (hyperkeratosis, sclerodermalike changes, and ulceration) that were most severe on the feet and ankles, atrophy of the extremities and, in females, an early cessation of menstruation. He noted that one of the siblings, a 36-year-old man, gave “…the impression of extreme senility.” Werner published these observations as part of his doctoral thesis, though he did not further study these or similar patients. He practiced general medicine in Eddelak, a small village on the North Sea near the Danish border, where he died in 1936 (Pehmoeller, 2001). The eponym Werner’s syndrome was first used in 1934 by Oppenheimer and Kugel. Their report of findings in a patient, together with the more comprehensive study of five additional cases published by Thannhauser in 1945, provided an accurate clinical description of WS and distinguished WS from Rothmund (now Rothmund–Thomson) syndrome. The diagnosis of WS in three Japanese American sisters early in the 1960s led to further, detailed clinical and pathological analyses of WS, and a formal genetic analysis that established an autosomal recessive mode of inheritance. These observations, together with a critical analysis of 122 additional cases, were published in 1966 (Epstein et al., 1966). This landmark paper remains accessible as a key source of information for investigators interested in WS (see Resources section). The modern clinical and biological investigation of WS has been an international effort. This reflects the worldwide occurrence of WS (Goto, 1997), and collaborative efforts by investigators in Japan (where WS is prevalent), the United States and Western Europe to understand the WS clinical phenotype and underlying biology. The first major gathering of investigators as part of this effort was in Kobe, Japan, in 1982 at the “United States–Japan Cooperative Seminar on Werner’s Syndrome and Human Aging.” Proceedings from this US National Science Foundation–Japan Society for the Promotion of Science funded workshop were subsequently published, together with a selection of key prior publications including a translation of Otto Werner’s thesis (Salk et al., 1985). Subsequent small workshops sponsored by the United States–Japan Cooperative Cancer Research Program were held in 1994, 1996, and 1997. These focused on WS as a cancer predisposition syndrome (1994); on clinical and biological aspects of WS (1996); and on WS as one of several pediatric cancer predisposition syndromes (1997). A US–Japan Workshop on “Cancer in Human RecQ Helicase Gene Disorders” was held in 2002 as was the first Keystone Symposium on “DNA Helicases, Cancer, and Aging,” followed in 2003 by an NIH-sponsored “International Workshop on Werner Syndrome.” Regrettably, only one of these very productive workshops was captured in a meeting report (Bohr, 2003). Subsequent meetings have focused on the RECQ helicase deficiency syndromes: “Molecular and Clinical Mechanisms in Bloom’s Syndrome and Related Disorders” (2008) (Ellis et al., 2008), and “RECQ2016—The third International Meeting on RECQ Helicases in Biology and Medicine” (2016). This progressive broadening of focus reflects the recognition that WS, Bloom syndrome, and Rothmund–Thomson syndrome all result from loss-of-function mutations in different members of the 5-member human RECQ helicase gene family. Early attempts to identify the affected gene in WS took advantage of cellular phenotypes for complementation such as a severe in vitro cell proliferation defect and chromosomal instability. These efforts failed for at least two important reasons: the scarcity and poor growth properties of primary cells from WS patients, and the large size of the WRN gene. Linkage mapping by the then-new technique of “homozygosity mapping” led, in 1992, to assignment of the WRN locus to the Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00001-4 Copyright © 2018 Elsevier Inc. All rights reserved.

3

4  SECTION | I  Aging in Humans

proximal short arm of chromosome 8 (Goto et al., 1992), followed by the positional cloning and identification of pathogenic mutations in the WRN locus in 1996 (Yu et al., 1996). The positional cloning of WRN provided a powerful stimulus for subsequent work on WS. Several laboratories rapidly confirmed the predicted helicase and exonuclease activities of WRN and identified likely nucleic acid substrates for these activities. There was also renewed speculation on how the loss of WRN function leads to WS cellular and clinical phenotypes, and whether genetic variation in WRN might contribute to disease risk in the general population. Each of these areas is discussed in greater detail below.

WERNER SYNDROME AS A CLINICAL DISEASE The key clinical features of WS, first recognized by Otto Werner and confirmed and elaborated by Oppenheimer and Kugel (1934), Thannhauser (1945) and Epstein et al. (1966) are observed in nearly all patients (Epstein et al., 1966; Goto, 1997; Tollefsbol and Cohen, 1984; Huang et al., 2006; Takemoto et al., 2013; Oshima et al., 2014). These findings have been used to develop clinical diagnostic criteria for WS and a scoring system of diagnostic confidence (Table 1.1). WS is a heritable disease with first apparent onset in adolescence or young adulthood. This key feature—delayed clinical expression—distinguishes WS from nearly all other progeroid syndromes and disorders that are often apparent in infancy or childhood. This delay in the appearance of findings can be seen in pairs of photos of WS patients in the second and later decades of life (Fig. 1.1). Among the most consistent and earliest clinical signs of WS are short stature, bilateral cataracts, the early graying and loss of hair, and scleroderma-like skin changes. Short stature: The short stature of WS patients results from a failure to undergo an adolescent or pubertal growth spurt. Short stature, together with progressive thinning and atrophy of the limbs, and a stocky trunk give patients a “cushingoid” appearance that is readily apparent in full body clinical photographs (see patient photos in Goto, 2001). Graying and loss of hair: Early graying and hair loss (alopecia) are, together with short stature, among the earliest and most consistent findings in WS patients. Hair graying and loss start in the second decade of life, and first affect the scalp and eyebrows. The loss of hair pigmentation is progressive and may eventually be complete. Premature graying and loss of hair may extend to other areas of the body, although these changes usually start later and may not be as extensive as those observed in the scalp and eyebrows. Cataracts: Bilateral ocular cataracts appear in many patients by the second or third decade of life. The cataracts consist of posterior cortical and subcapsular opacification, and less consistent vacuoles and small punctate opacifications in other parts of the lens—a type of cataract often referred to as “juvenile,” to distinguish these from the more common opacification of the lens nucleus observed in the “senile” cataracts of normal aged individuals. Vision is otherwise unimpaired and can be restored by cataract removal. Scleroderma-like skin changes: Skin changes in WS were first clearly detailed by Thannhauser, who distinguished the changes in WS from those observed in individuals with scleroderma or Rothmund–Thomson syndrome (Thannhauser, 1945). The histologic appearance of skin biopsies from WS patients reveals an interesting mix of atrophic and proliferative changes: epidermal atrophy extends to include skin appendages (hair follicles, sweat, and sebaceous glands), in conjunction with focal hyperkeratosis and basal hypermelanosis. Dermal subcutaneous connective tissue atrophy is common, often in conjunction with dermal fibrosis and underlying atrophy of muscle, adipose and connective tissue (Epstein et al., 1966). This constellation of changes gives skin a “tight, white, and shiny” appearance, with loss of normal elasticity. Cutaneous changes, often seen first in the face and extremities, lead to a progressive sharpening of facial features. Many patients eventually develop a “pinched,” “beaked,” or “birdlike” appearance (see patient photos in Goto, 2001). Lower extremities, especially the feet, are often affected with foot deformation, ulceration of nonpressure–bearing portions of the foot and ankles, and calcification of soft tissue and tendons (Hatamochi, 2001; Fig. 1.2). Indolent foot and ankle ulcers are highly characteristic of WS and are significant source of morbidity for many patients as they often lead to amputation. Laryngeal changes include a mix of proliferative and atrophic changes that result in a thin, high-pitched voice. The associated laryngeal pathology has not been well studied. Other prominent clinical features of Werner syndrome: Several less consistent clinical findings have also been reported in WS patients (Table 1.1). These more variable findings include several clinically important, age-associated disease states: atherosclerosis and its cardiovascular and cerebral sequelae; a mix of nonepithelial and epithelial neoplasms (see below); osteoporosis that is typically most severe in the distal phalanges; diabetes mellitus; and hypogonadism affecting both men and women. As discussed below, these associated disease states may all in part be explained by proliferative and atrophic changes at the tissue level.

Werner Syndrome as a Model of Human Aging Chapter | 1  5

TABLE 1.1  Diagnostic Criteria for Werner Syndrome (WS) International Registry of Werner Syndrome Criteria 1. Cardinal signs and symptoms (onset over 10 years old) a. Cataracts (bilateral) b. Characteristic dermatological pathology (tight skin, atrophic skin, pigmentary alterations, ulceration, hyperkeratosis, regional ­subcutaneous atrophy), and characteristic facies (“bird” facies) c. Short stature d. Premature graying and/or thinning of scalp hair 2. Additional Signs and Symptoms a. Diabetes mellitus. b. Hypogonadism (secondary sexual underdevelopment, diminished fertility, testicular ovarian atrophy) c. Osteoporosis. d. Osteosclerosis of distal phalanges e. Soft tissue calcification f. Evidence of premature atherosclerosis (e.g., history of myocardial infarction) g. Neoplasia: including mesenchymal, rare, or multiple neoplasms h. Voice changes (high pitched, squeaky, or hoarse voice) i. Flat feet 3. WS Diagnostic Confidence Definite: All the cardinal signs and two others. Probable: The first three cardinal signs and any two others. Possible: Either cataracts or dermatological alterations and any four other signs/symptoms. Exclusion: Onset of signs/symptoms before adolescence (except stature). Japanese Registry Criteria 1. Cardinal Signs and Symptoms (Onset 10–40 years) a. Progeroid changes of hair (graying, loss of hair) b. Cataracts (bilateral) c. Changes of skin, intractable skin ulcers (atrophic skin, tight skin, clavus, callus) d. Soft tissue calcification (Achilles tendon, etc) e. Birdlike face f. Abnormal voice (high pitched, squeaky, hoarse voice) 2. Additional Signs and Symptoms a. Abnormal glucose and/or lipid metabolism b. Bone deformation/abnormality (e.g., osteoporosis) c. Malignant tumors (e.g., nonepithelial tumors, thyroid cancer) d. Parental consanguinity e. Premature atherosclerosis (angina pectoris, myocardial infarction) f. Hypogonadism g. Short stature/low bodyweight 3. WS Diagnostic Assessment Confirmed: All cardinal signs present or pathogenic WRN mutations + ≥3 cardinal signs. Suspected: ≥2 cardinal signs or 1–2 cardinal signs + ≥1 additional sign/symptom. These two sets of diagnostic criteria were most recently reported or updated in Takemoto et al. (2013) and Oshima et al. (2014). Addendum: mental retardation is seldom found in WS, and cognitive function is often appropriate for chronological age.

CANCER IN WERNER SYNDROME WS patients are at increased risk of developing cancer, as are patients with the other RECQ helicase deficiency syndromes (Goto et al., 1996; Monnat, 2001). Among the many different histopathologic tumor types reported in WS, patients are at clearly elevated risk of developing only a small subset of neoplasms (Lauper et al., 2013; Table 1.2). “Elevated risk” neoplasms, representing 2/3 of all reports, are thyroid epithelial neoplasms, malignant melanoma, meningioma, soft tissue sarcomas, leukemia and preleukemic conditions of the bone marrow, and primary bone neoplasms. Cancer risk in Japan-resident WS patients, as defined by standardized incidence ratios, ranged from a 53.5-fold for melanoma (95% CI: 24.5, 101.6) to 8.9-fold (95% CI: 4.9, 15.0) for thyroid neoplasms. Leukemia and preleukemic conditions were nearly statistically significant, with a 1.5-fold elevated risk (Lauper et al., 2013). Several pathologic and clinical features of neoplasia identify WS as a cancer predisposition syndrome. For example, WS patients develop neoplasms at a comparatively early age and often display unusual sites of presentation (e.g., osteosarcoma

6  SECTION | I  Aging in Humans

(A)

(C)

(B)

(D)

FIGURE 1.1  Clinical progression and features of Werner syndrome. (A and B) Photographs of a Werner syndrome patient reported by Epstein et al. (1966) as Case 1, at ages 15 (A) and 48 (B). (C and D) Photographs of a second patient at ages ∼13 (C) and 56 (D). Note in both instances the rounded face, sharp features, graying, thinning, and loss of scalp and eyebrow hair and, in (D), the thin, atrophic forearms. (Panels (A and B) are courtesy of Drs. George Martin and Nancy Hanson of the International Registry of Werner Syndrome, and Lippincott Williams & Wilkins (panel B). The patient photographs in panels (C and D) are reproduced from Martin, G.M., 2005. Genetic modulation of senescent phenotypes in Homo sapiens. Cell 120, 523–532, and are used here courtesy of the patient’s spouse, with informed consent of the patient, Drs. George Martin and Nancy Hanson, and Elsevier Press.)

(A)

(B)

FIGURE 1.2  Skin and soft tissue changes in Werner syndrome (WS) patients. (A) Scleroderma-like skin changes, hyperpigmentation, and ulceration of the lateral border of the left foot in a male Japanese WS patient age 37. (B) Localized calcification of the Achilles tendon and plantar tendon insertion site calcification in a Japanese female WS patient age 54. (Photos are reproduced from Hatamochi, A., 2001. Dermatological features and collagen metabolism in Werner syndrome. GANN Monogr Cancer Res, 51–9, and used here with kind permission of Dr. Atsushi Hatamochi, Professor of Dermatology, Dokkyo University School of Medicine and the Japanese Scientific Societies Press.)

Werner Syndrome as a Model of Human Aging Chapter | 1  7

TABLE 1.2  Spectrum of Neoplasms Reported in Werner Syndrome Patients Frequent Neoplasms (67% of Total, n = 167)

Less Common Neoplasms (33% of Total, n = 81)

Thyroid neoplasms (16.1%, n = 40)

Nonmelanoma skin cancer (4.8%, n = 12)

  Follicular thyroid carcinoma

  Squamous cell carcinoma

  Papillary thyroid carcinoma

  Basal cell carcinoma

  Anaplastic thyroid carcinoma

Gastrointestinal (4.4%, n = 11)

  Thyroid adenoma

  Esophageal carcinoma

Malignant melanoma (13.3%, n = 33)

  Gastric carcinoma

  Acral lentiginous melanoma (ALM)

  Pancreatic carcinoma

  Malignant mucosal melanoma

Uterus/ovary (4.0%, n = 10)

  Malignant melanoma non-ALM

  Ovarian cystadenocarcinoma

Meningioma (10.9%, n = 27)

  Uterine carcinoma

Soft tissue sarcomas (10.1%, n = 25)

  Uterine leiomyoma

 Undifferentiated pleomorphic sarcoma (malignant fibrous histiocytoma)

Hepatobiliary (4.0%, n = 10)

 Leiomyosarcoma

  Hepatocellular carcinoma

 Fibrosarcoma

Genitourinary (3.6%, n = 9)

  Malignant peripheral nerve sheath tumor

  Ureteral transitional cell carcinoma

 Rhabdomyosarcoma

  Bladder transitional cell carcinoma

  Synovial sarcoma

  Vulvar carcinoma

Hematologic/lymphoid (9.3%, n = 23)

  Prostate carcinoma

  Acute myelogenous leukemia (M1–M5, M6, M7)

Head and neck neoplasms (3.2%, n = 8)

  Preleukemic marrow disorders

  Nasal carcinoma NOS

   Myelofibrosis

  Hard/soft palate squamous cell carcinoma

   Myelodysplasia

  Tongue squamous cell carcinoma

   Refractory anemia with excess blasts

  Laryngeal carcinoma

  T cell leukemia

Breast carcinoma (2.8%, n = 7)

 Plasmacytoma

Lung (2.0%, n = 5)

Osteosarcoma/bone (7.7%, n = 19)

  Squamous cell carcinoma

  Osteoblastic osteosarcoma

 Adenocarcinoma

  Fibroblastic osteosarcoma

  Bronchioloalveolar carcinoma

  Extra-skeletal/soft tissue osteosarcoma

 Carcinoid

 Osteochondroma

CNS (2.0%, n = 5)

 Cholangiocarcinoma

 Astrocytoma   Spinal cord hemangiolipoma Adrenal cortical (1.6%, n = 4)  Carcinoma  Pheochromocytoma Compiled from 248 well-documented neoplasms by histopathologic type in individuals with a convincing diagnosis of WS (Monnat Jr, 2001; Lauper et al., 2013). Among the most frequently reported neoplasms (left column), the risk of all except hematologic/lymphoid neoplasms is clearly elevated over population controls. Among all reports, the ratio of epithelial to nonepithelial neoplasms is roughly equivalent—45% versus 55%.

8  SECTION | I  Aging in Humans

of the patella) or less common histopathologic subtypes (e.g., follicular, as opposed to the more common papillary thyroid carcinoma). Melanoma in WS provides a striking example: these are almost exclusively acral lentiginous melanomas (ALM), a comparatively rare subtype that arises on the palms and soles or in mucosa of the nasal cavity or esophagus. The risk of ALM is most clearly elevated in Japanese WS patients, suggesting potential population-specific modifiers of risk. Diabetes mellitus, a common, comorbid condition in many WS patients, has been suggested as a tumor-promoting covariable, though this suggestion remains controversial (Onishi et al., 2012; Lauper and Monnat, 2014). Multiple neoplasms are remarkably common: up to five different primary neoplasms having been reported in individual WS patients, and nearly a quarter (22%) of patients in our recent analysis had ≥2 different histopathologic types of neoplasm (Lauper et al., 2013). Few neoplasms arising in WS patients have received molecular characterization. To address this lack of information, we recently used targeted DNA capture and sequencing to determine genomic features of three neoplasms in two WS patients. Each patient had a metastatic pancreatic adenocarcinoma, and one had an incidental pulmonary neuroendocrine carcinoid tumor found at autopsy. Among the 234 cancer-associated genes included in our analysis, we identified nonsynonymous somatic single nucleotide variants (SNVs) in KRAS and TP53 in both pancreatic carcinomas, together with a stop-gain SMAD4 SNV in one patient. Of note, these genes are also recurrently mutated in sporadic pancreatic carcinoma (Waddell et al., 2015), a tumor type for which WS patients are not at elevated risk. We identified no somatic SNV variants in the pulmonary carcinoid or other genomic features that distinguished these neoplasms from comparable neoplasms arising in the general population (Tokita et al., 2016). Additional work is clearly required on this issue.

CLINICAL PROGRESSION OF WERNER SYNDROME One aspect of WS not well conveyed by Table 1.1 is the progressive development of the WS clinical phenotype: findings may develop over two or three decades, after first beginning in the second decade of life. A sense of this progressive development of findings can be gleaned, as noted above, from pairs of patient photos taken in early adulthood and later in midlife (Fig. 1.1). Indeed, many WS patients appear remarkably normal until the time of puberty. The progressive development of WS clinical findings can be thought of as three overlapping phases. The first is the absence of an adolescent growth spurt, followed over the subsequent decade by the appearance of graying and loss of hair, the development of skin changes and cataracts. A second phase, often first seen late in the third decade, includes skin ulceration, hypogonadism, and reproductive failure, together with further progression of primary changes. A third phase includes rapidly rising risk for clinically important age-associated diseases such as atherosclerosis, osteoporosis, diabetes mellitus, and cancer. A major source of morbidity in many patients, deep, chronic ulceration of the medial or lateral malleolus or Achilles tendon are now ubiquitous in Japanese-ancestry patients. These later acquired complications or concurrent disease states develop proportionately earlier, or are more severe, in WS patients than in comparably aged normal individuals. The two leading causes of death in WS patients are atherosclerotic cardiovascular disease and neoplasia, followed by a smaller number of patients who die of infection. The mean age at death in recent reports is 54–55 years (Huang et al., 2006; Goto et al., 2013), or approximately 7 years later than reports from the previous century (see, e.g., Goto, 1997). This difference in longevity likely reflects improved supportive care, as opposed to a changing natural history: e.g., no comparable change has been observed over this period in the mean age of onset of cataracts (31 years; Huang et al., 2006). WS patients remain responsive to therapeutic intervention, e.g., pharmacologic management of diabetes mellitus or other metabolic abnormalities, which may provide longer life with fewer complications (Ide et al., 2016). Better control of diabetes may also reduce the risk of epithelial neoplasms, though as noted above this remains controversial. Some well-documented WS patients have lived into the seventh decade of life (Goto, 1997).

DIAGNOSTIC CRITERIA AND DIFFERENTIAL DIAGNOSIS The WS clinical phenotype is progressive, though variable, and may involve multiple organ systems. This temporal and anatomic variability make clinical diagnosis challenging, especially in young adults where there may be as yet few convincing signs or changes or a family history to raise suspicion. To aid clinical recognition and diagnosis, diagnostic criteria, and scoring systems have been developed by investigators at the International Registry for WS and the Japanese Registry (Table 1.1) (Takemoto et al., 2013). A suspicion of WS based on clinical findings and history, together with molecular testing, can be used in most instances to confirm or exclude a diagnosis of WS. The advent of genomic and molecular testing has markedly improved the diagnosis of WS and many other rare heritable disorders. Sequence-based genomic approaches also have the potential to identify other medically significant genetic variation in genes unrelated to the reason for testing. For example, diagnostic labs in the United States may test for and report pathogenic variants in well-studied disease-causing genes such as BRCA1 and BRCA2 to individuals who have provided written, informed consent for testing for and disclosing these results (Kalia et al., 2017). The ability to couple clinical and

Werner Syndrome as a Model of Human Aging Chapter | 1  9

molecular diagnostic approaches now allows the unambiguous identification of heterozygous carriers in families, as well as homozygous mutant individuals regardless of age or clinical findings.

THE WRN GENE AND DISEASE-ASSOCIATED MUTATIONS The chromosome 8p12 WRN gene encodes a 1432 amino acid member of the human RECQ helicase family (Yu et al., 1996). WRN is one of five human RECQ helicase genes that each encodes a protein with 3′-to- 5′ helicase and ATPase activities. WRN is unique among the human RECQ helicases in possessing an additional 3′-to-5′ exonuclease activity. Other WRN protein domains include a C-terminal nuclear localization signal, “RECQ conserved/consensus,” and “helicase, RNaseD, C-terminal conserved region” domains that play roles, respectively, in substrate DNA binding and WRN recruitment to DNA double strand breaks (Fig. 1.3). WRN protein also possesses single strand DNA annealing activity. All of these properties are discussed in recent reviews, where the reader is directed for additional detail (Croteau et al., 2014; Sidorova and Monnat, 2015). The 163 kDa WRN protein can be detected by Western blot analysis in cell lines and tissue samples from normal individuals, and at reduced level in heterozygous carriers of single pathogenic alleles of WRN (Moser et al., 2000b; Muftuoglu et al., 2008). WRN expression is ubiquitous, though there has not been as yet a systematic study of tissue or cell typespecific expression during development or adult life. Eighty-four different pathogenic WRN mutations have been identified in WS patients. These variants have, with rare exceptions, the same predicted consequence: WRN protein truncation and loss from patient cells (Moser et al., 2000b; Muftuoglu et al., 2008). Additional rare missense mutations within the WRN exonuclease domain and genomic rearrangements all appear to confer the same biochemical consequence (Huang et al., 2006; Friedrich et al., 2010; Yokote et al., 2016). Missense mutations that selectively inactivate the WRN helicase have been identified in compound heterozygous mutation carriers (Uhrhammer et al., 2006; Tadokoro et al., 2013), suggesting that impaired helicase activity alone may be sufficient to promote WS pathogenesis. However, the cellular phenotype of WS is only fully developed in exonuclease- and helicase-deficient cells, in the absence of detectable dominant-negative effects if expression of the catalytically inactive WRN protein is retained (Swanson et al., 2004). Moreover, recent analyses of individuals homozygous for a WRN missense variant that largely abolishes helicase activity (R834C substitution; see Kamath-Loeb et al., 2004) also suggests that the clinical and cellular phenotype of WS requires the loss of both WRN catalytic activities from patient cells. Among clinically ascertained pathogenic WRN mutations, the most frequent is a nonsense mutation in exon 8 (c.1105C > T, rs17847577, p.R369*), which accounts for ∼20% of pathogenic alleles with an allele frequency of 1.8 or 3.0

FIGURE 1.3  WRN gene structure and pathogenic variation ascertained in Werner syndrome patients. The 35 exons of WRN gene locus and WRN protein are shown. Exon 1 and part of 35 are noncoding. The relationship between exons and functional domains are indicated by extending a shaded region from the gene region to the protein functional domains. Known functional domains are indicated by darker shades, including the exonuclease and helicase domains, RecQ helicase conserved region (RQC), helicase RNaseD C-terminal conserved region (HRDC), and the nuclear localization signal (NLS). Mutations are grouped by the types shown in the left. Large genomic rearrangements are shown on top using horizontal brackets, labeled “Dup” for duplication or “Del” for deletion. Mutations that affect splicing are indicated with inverted triangles above the WRN locus. Small genomic changes, such as insertions and deletions (Indels) are indicated below the gene locus, using a black-filled diamond shape (♦), while stop codons are indicated with a closed triangle (▲). Missense mutations are indicated with black-filled circles (●). (Modified from Yokote, K.K., Chanprasert, S.S., Lee, L.L., Eirich, K.K., Takemoto, M.M., Watanabe, A.A., et al., WRN mutation update: mutation spectrum, patient registries, and translational prospects. Hum Mutat September 26, 2016 (Epub ahead of print). https://doi.org/10.1002/humu.23128.)

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per 10,000 in the Exome Aggregation Consortium and Exome Variant Server data, respectively (Yokote et al., 2016). One Japanese founder mutation (c.3139-1G > C, leading to an exon 26 skip) accounts for ∼70% of the mutant alleles identified in Japanese WS patients, where the aggregate carrier frequency for pathogenic variants has been estimated to be 1/167 (Satoh et al., 1999). Other prevalent founder mutations include c.2089-3024A > G (which alters splicing with the inclusion of a new exon between exons 18 and 19) in Sardinian WS patients with a reported allele frequency of ∼1/120 (Masala et al., 2007); c.561A > G (which alters exon 6 splicing) in WS patients originally from India/Pakistan; and c.3460-2A > G (leading to exon 30 skipping) identified in Turkish WS patients (Friedrich et al., 2010; Saha et al., 2013). We anticipate other founder mutations will eventually be identified. The human WRN gene displays abundant genetic variation at the base pair level. In a recent analysis we identified 91 putative pathogenic variants and 759 population variants among the nearly 62,000 individuals included in 1000 Genomes, Exome Sequencing, and Exome Aggregation Consortium Project databases (Fu et al., 2017). Clinically ascertained variants were significantly enriched in mutation types predicted to lead to loss of function (e.g., insertion–deletion and stop-gain variants), whereas nearly all of the remaining population variants (95.6%) were single base changes (SNPs or SNVs) of unknown functional significance. We found no individuals in population data whose genotype suggested they might have a compound RECQ deficiency syndrome (e.g., WS + BS or WS + RTS). Among known pathogenic variants of WRN only a small fraction appear to be good candidates for mutation-specific therapies such as exon skipping or stop codon read-through. This analysis of population-level WRN variation also allowed us to use direct counting to determine a population carrier frequency of 2.3% for known pathogenic WRN variants. This frequency is substantially higher than previous estimates based on case counting or consanguinity-based analyses (0.0067–0.001; see above and Schellenberg et al., 2001), and supports the long-held suspicion that WS is clinically underrecognized and underdiagnosed. These results also emphasize the ongoing need to determine whether heterozygous carriers of known pathogenic WRN alleles have any WS-related or other, clinically relevant phenotype. Of note, heterozygous carriers of several pathogenic WRN mutations display in vivo genetic instability (Moser et al., 2000a), and their cell lines display intermediate sensitivity to killing by DNA damaging agents that selectively kill WRN-deficient cells (camptothecin, mitomycin-C, and cis-Pt) (Ogburn et al., 1997; Okada et al., 1998; Poot et al., 2001).

ATYPICAL WS/OTHER HERITABLE PROGEROID SYNDROMES DISTINCT FROM WS The availability of molecular diagnostic criteria for WS has provided a sharper definition of WS as a disease entity, together with a way to distinguish WS from diseases or syndromes that may mimic WS. For example, individuals ascertained with “atypical Werner syndrome (AWS)” share some clinical features of WS (Table 1.1), though lack mutations in the WRN gene. Exome sequencing, linkage analysis, array CGH (comparative genome hybridization), and candidate gene sequencing have revealed several genes causally associated with AWS (Oshima and Hisama, 2014). A subset of AWS patients carries heterozygous mutations in the LMNA gene encoding the nuclear lamina proteins lamin A/C (Chen et al., 2003). Mutations in LMNA were previously identified as causal for Hutchinson–Gilford progeria (HGPS), a rapidly progressive rare disease often referred to simply as “progeria” or “childhood progeria.” Other mutations that alter LMNA splicing or the properties of lamin A/C have also been associated with at least eight additional, clinically distinguishable diseases collectively referred to as “laminopathies.” While the LMNA mutations found in HGPS are unique splicing mutations that generate a mutant protein called progerin (Eriksson et al., 2003), mutations in AWS patients are either heterozygous missense mutations mainly localized in the heptad repeat region of lamin A/C, or splice-altering mutations that generate very small amounts of progerin (Hisama et al., 2011). Another group of AWS patients carry heterozygous mutations in the POLD1 gene (Lessel et al., 2015). The POLD1 gene encodes the catalytic subunit of the DNA replication polymerase δ, a key protein that interacts with and modifies WRN activities (Kamath-Loeb et al., 2012). Unlike cancer-associated POLD1 variants, mutations found in AWS abolish the exonuclease function of POLD1. Affected individuals display progeroid features, together with mandibular hypoplasia, deafness, and lipodystrophy, though no apparent increase in cancer (mandibular hypoplasia, deafness, progeroid features, and lipodystrophy syndrome; Weedon et al., 2013). Two AWS individuals have also been found with biallelic mutations in the SPRTN (Lessel et al., 2015), which encodes a nuclear adaptor protein with roles in DNA replication and replication-related G2/M-checkpoint regulation (Hiom, 2014). SPRTN mutation carriers display developmental delay, progeroid features and may develop hepatocarcinoma, a triad originally recognized as Ruijs–Aalfs syndrome (Ruijs et al., 2003). A single AWS individual homozygous for a SAMHD1 mutation known to cause Aicardi–Goutieres syndrome has also been identified (Lessel et al., 2015). SAMHD1 encodes a dNTP pool regulator with exonuclease and ribonuclease activities that has been implicated in the DNA damage response (Clifford et al., 2014). This same patient also carried a heterozygous WRN pathogenic variant, raising the possibility of allelic synergy of the two mutations. Despite their rarity AWS and other WS “look-alikes” are of considerable clinical and biological interest, as they may identify additional proteins that act with—or act on—WRN to modify or target function in vivo (Prince et al., 1999).

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WRN PROTEIN FUNCTION IN HUMAN CELLS Purified WRN protein unwinds and/or degrades many of the types of DNA substrates that are common intermediates in DNA replication, recombination, repair, or telomere maintenance (Croteau et al., 2014; Sidorova and Monnat, 2015). DNA replication and recombination are tightly linked and interdependent on one another in many or all organisms. In human and other mammalian cells, this close interrelationship is indicated by the importance of homology-dependent recombination in DNA break repair; in the rescue of stalled replication forks; and in mammalian development (reviewed in Jasin and Rothstein, 2013). Consistent with these observations, aberrant replication, and recombination have been mechanistically linked to the loss of WRN function (Prince et al., 2001; Saintigny et al., 2002; Sidorova et al., 2008, 2013). Several lines of evidence point to telomeres as physiologic substrates for WRN function in vivo: WRN can be found at telomeres, and WRN interacts physically and/or functionally with key telomeric proteins such as TRF1 and TRF2 (Opresko et al., 2004). Moreover, the T-loop structure of telomeres resembles D-loop intermediates involved in recombination and replication, and telomeres are transiently converted by replication into another postulated WRN substrate, single-ended DNA double strand breaks. A final line of evidence, albeit less direct, comes from the observation that telomerase expression can extend the life span and modify the DNA damage response of primary WS fibroblasts (Hisama et al., 2000; Wyllie et al., 2000). WS-like changes have also been observed in mouse models in which telomerase and Wrn function have been simultaneously ablated (see below; Chang et al., 2004; Du et al., 2004). Given these functional roles, how does the loss of WRN lead to the characteristic molecular, cellular and organismal phenotype of WS? A simple model can be constructed from the above observations, in which the absence of WRN leads to failed or aberrant replication, recombination or repair, with the generation of potentially toxic DNA intermediates that promote genomic instability, nucleate chromosome rearrangements, and trigger a DNA damage response and apoptosis (Fig. 1.4). The consequences of WRN loss of function in many cell lineages may include elevated levels of apoptosis, mitotic death, or genomic instability that can be readily visualized in the karyotype of surviving cells (Fig. 1.5). Additional new information suggests that WRN loss also perturbs the expression of several hundred genes that are rich in sequences with the potential to form G quadruplex (G4) DNA structures (Tang et al., 2016). WRN appears to facilitate the expression of these genes by binding a distinct subpopulation of G4 motifs in human cells that differ from those bound by the Bloom syndrome RECQ helicase. These results provide strong evidence that WRN and the related BLM RECQ helicase protein bind G4 DNA structures in vivo, at many chromosomal sites, to modulate gene expression (Tang et al., 2016). Functional annotation of the genes and miRNAs whose expression is altered in WS fibroblasts has provided several new insights into WS disease pathogenesis. Altered genes are enriched in multiple, mechanistically distinct, senescenceassociated gene expression programs; altered miRNAs have several prominent disease associations as do their targets; and canonical pathways that regulate cell signaling, genome stability, and tumorigenesis are all perturbed. WS fibroblasts also displayed an unusual and highly statistically significant gene expression signature, in which nearly all of the cytoplasmic tRNA synthetases and associated AIMP genes are coordinately upregulated. These changes have the potential to alter both the canonical (i.e., tRNA charging) and “noncanonical” functions of many tRNA synthetases to promote WS disease pathogenesis (Guo and Schimmel, 2013). One persistent question, given the above observations, is why dividing cell lineages are not preferentially or selectively affected in WS. A corollary question is why some organs, such as the CNS, are spared the consequences of WRN loss. A partial answer will likely involve a deeper understanding of the both the organization and developmental history of

FIGURE 1.4  Model of Werner protein function in recombination in human cells. A requirement for WRN function can be initiated by DNA damage that leads to DNA strand breaks or that stalls or disrupts DNA replication. Damage to chromosomal DNA, replication forks or telomeres can initiate homologous recombination repair (HR repair) that, in the presence of WRN, is successfully resolved to insure high cell viability and genetic stability (WRN+ arrow). In the absence of WRN (WRN− arrow), HR resolution and/or replication restart fail, leading to mitotic arrest, cell death, and genetic instability. Two of the experimental tests of this model are shown by the ovals: reexpressing WRN protein (+wt WRN) improves both cell survival and the recovery of viable mitotic recombinants, as does expression of the bacterial resolvase protein RusA (+RusA) (for more details see Saintigny et al., 2002).

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FIGURE 1.5  Variegated translocation mosaicism in primary fibroblasts from a Werner syndrome patient. The Figure is a spectral or SKY karyotype of the chromosomal complement of a skin fibroblast obtained from one of the male WS patients reported in Melcher et al. (2000). Note the many reciprocal translocations, e.g., involving chromosomes 1 and 8, together with translocations that are not obviously reciprocal in nature and may be accompanied by deletions (e.g., the translocation of material from chromosome 1 to chromosomes 7 and 17). (This original spectral karyotype is courtesy of Dr. Holger Hoehn, University of Würzburg, Würzburg, Germany.)

specific cell lineages. For example, continuously dividing lineages (e.g., skin, gut, and bone marrow) may be tolerant of the loss of WRN function by virtue of mutation expansion-limiting lineage architecture; normally stringent cell editing by a combination of apoptosis and terminal differentiation; and potentially large reserves of stem or lineage repopulating cells. Conversely, cell lineages or tissues that are largely postmitotic following development (e.g., many CNS cell lineages) may undergo adaptations to compensate for increased cell loss or dysfunction. This type of response can be glimpsed in the altered transcription of genes and miRNAs in primary WS fibroblasts described above (Tang et al., 2016). Fibroblasts and other mesoderm-derived cell lineages can be linked to clinical findings in WS and may be selectively affected by the loss of WRN function for reasons in addition to those mentioned above. Many fibroblast populations retain conditional cell division potential throughout life. They have the potential to accumulate genetic damage with altered gene expression and are comparatively resistant to damage-induced apoptosis. Connective tissue is also largely lacking in compartmentalized tissue architecture that may antagonize neoplastic outgrowth. These features may also predispose mesenchymal lineages to the progressive accumulation of mutant, dysfunctional, and/or senescent cells, with a progressive disruption of trophic or regulatory interactions required to maintain adjacent epithelial or stromal cells and tissue architecture (Fig. 1.6) (reviewed in Tchkonia et al., 2013; Campisi and Robert, 2014). Despite the attractiveness of this model, a recent analysis of dermal fibroblasts in skin samples from autopsied WS patients failed to reveal the predicted high frequency of senescent cells (Tokita et al., 2016). Nor could we find clear evidence of disproportionately short telomeres or of disproportionate mtDNA mutation accumulation, two other popular postulated mechanisms by which WRN loss might promote WS pathogenesis (Tokita et al., 2016). Thus key variables that determine the outcome of WRN loss are likely to include the number of cell divisions required to generate a mature lineage; how much cell editing occurs during and after development; and what quantitative or functional redundancy can be invoked to compensate for cell loss or dysfunction. We clearly need to know more about the “normal biology” of specific human cell lineages over the human life span, and need better ways to study cell and lineage-specific functions of WRN.

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FIGURE 1.6  Model for pathogenesis of disease in the absence of WRN function. WRN loss as a result of inherited germline mutations (see Fig. 1.3) leads from the beginning of development to genetic instability and cell loss in many or all cell lineages. These changes following the completion of development can be perpetuated or amplified in specific cell lineages or tissues where division potential is retained. How the intermediate consequences or phenotypes of the absence of WRN function, i.e., mutation accumulation, cell dysfunction, and cell loss, affect specific lineages or tissues to lead to the emergence of either neoplastic or atrophic or progeroid outcomes is heavily conditioned by normal lineage biology (see text for additional discussion). Two different timelines above the figure indicate the progressive nature of cell and cell lineage defects, and their origins during development.

THE RELATIONSHIP OF WERNER SYNDROME TO NORMAL AGING The similarities between WS and what might be expected in accelerated or premature aging were first noted by Otto Werner over a century ago. These similarities include the striking clinical appearance of many WS patients, where changes beginning in the second decade lead to a consistent appearance of advanced chronological age by the fourth or fifth decade of life (Fig. 1.1). These findings and disease progression led to the idea that WS is a premature or accelerated aging syndrome, and by extension to the idea that there must be deep mechanistic links between WS and normal aging. However, careful clinical and pathologic examination of WS patients have pointed out differences in both the nature or degree of change observed in WS, as opposed to normal aging (Epstein et al., 1966; Tokita et al., 2016). Quantitative differences in WS patients include the greater extent or severity of loss of hair and hair color; the severity of calcification of heart valve leaflets, atherosclerosis and osteoporosis; and the more rapid loss of fecundity. Qualitative differences include the unusual cataracts observed in WS patients; the skin and subcutaneous connective tissue changes in the face and extremities, leading to a scleroderma-like appearance and nontrophic ulcers; the unusual spectrum of neoplasms; and the location and extent of soft tissue and arterial calcification. Epstein et al. (1966) concluded that despite similarities, these many differences indicate that WS “…may be better considered a ‘caricature’ of aging, exaggerating, although not necessarily by the same mechanisms, some of the clinical and pathologic changes which connote aging.” This conclusion remains largely sound after a half-century of additional work on WS, despite the large number of references to WS in both the popular and scientific press as a “premature aging syndrome.” This seemingly philosophical issue has some practical importance: diseases such as WS may be phenocopies of accelerated normal aging that are mechanistic “blind alleys,” the study of which may reveal little about the mechanisms underlying either normal aging or clinically important, age-associated diseases (Miller, 2004). A more optimistic view, to which we subscribe, is that WS has substantial mechanistic overlap with pathways and general mechanisms that contribute to human aging and age-associated disease pathogenesis (Hisama et al., 2016). This view was reinforced by our recent analysis of tissue and tumors from autopsied WS patients, which suggested WS may represent an acceleration, rather than a qualitative change in, the molecular, cellular and tissue-level changes that drive normal aging (Tokita et al., 2016).

EXPERIMENTAL MODELS OF WERNER SYNDROME Cells from WS patients continue to provide valuable insight into the molecular and cellular defects that drive WS disease pathogenesis. Primary dermal WS fibroblasts and their derivatives have been the most extensively studied, following the initial demonstration of their limited proliferative or cell division potential (Martin et al., 1970) in conjunction with chromosomal abnormalities (Hoehn et al., 1975). The utility of primary fibroblasts was extended when it was found that SV40 transformation—and later telomerase reexpression—largely abrogated the proliferative defect observed in primary WS fibroblasts (Hisama et al., 2000; Wyllie et al., 2000; Matsumura et al., 1985; Huschtscha et al., 1986). Recent extensions of this work have taken full advantage of advances in stem cell and reprogramming technologies. For example, induced pluripotent stem cells (iPSCs) generated from WS fibroblasts regain proliferative potential with the suppression of cellular senescence, telomere loss, and chromosomal instability (Cheung et al., 2014; Shimamoto et al., 2014). Despite the genomic instability of WS fibroblasts, karyotypically stable WS fibroblast-derived iPSCs have been

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generated, from which differentiated progeny can be isolated that regain a senescent cellular phenotype. Of interest, this reacquisition of senescence differs by cell type or lineage: mesenchymal stem cells (MSCs) and embryoid body-derived cells regain senescence that can be resuppressed by telomerase expression or TP53 depletion, whereas differentiated WS neural progenitors appear to escape senescence (Cheung et al., 2014; Shimamoto et al., 2014). WS patient-derived iPSCs may provide a useful new resource to analyze WRN function during lineage-specific differentiation and form the basis for potential cellular therapies to treat, e.g., the common, intractable skin ulcers observed in many WS patients. Similar analyses of a WRN knockout human embryonal stem cell (ESC) model have further suggested a role for WRN in maintaining heterochromatin state (Zhang et al., 2015). WS ESCs show continuous proliferation in culture, and, when differentiated, can generate MSCs that reexpress cellular phenotypes and epigenetic changes associated with cellular aging. The identification of a role for WRN in chromatin remodeling, with reduced expression of heterochromatin protein 1α and suppressor of variegation 3–9 homolog 1 (SUV39H1), indicates that chromatin disorganization may be a general mechanism contributing to the pathogenesis of WS (Zhang et al., 2015). Of note, the WRN locus itself may be epigenetically modified during reprogramming and organoid development (Weeber et al., 2015). Despite the novelty of these findings, the gene expression profile of WS ESC-derived MSCs does not resemble primary WS fibroblasts (Tang et al., 2016). Thus primary fibroblasts and MSCs might best be considered complementary cellular resources for the study of WS. Lymphoblastoid cell lines (LCLs) derived from WS patients served as an important cellular resource that enabled positional cloning of the WRN locus (Goto et al., 1992; Yu et al., 1996). WS LCLs have also been used to study proliferation, telomere dynamics, and drug sensitivity (Okada et al., 1998; Poot et al., 2002; Tahara et al., 1997). These cells are more widely available via cell repositories, although users are cautioned to independently verify both the WRN mutation and WRN protein expression status of any putative WS cell source prior to beginning experiments (see cautionary note in Tang et al., 2016). The only mammalian models of WS developed thus far have been in mice, with three different models published to date: a complete knockout, with the loss of Wrn protein expression in all mouse tissues (Lombard et al., 2000); an in-frame deletion of the helicase domain of murine Wrn, leading to expression of a truncated protein that retains exonuclease though lacks helicase activity (Lebel and Leder, 1998); and transgenic expression of a human K577M WRN variant protein that lacks helicase activity, in the presence of normal murine Wrn (Wang et al., 2000). Only the first of these models faithfully recapitulates the genetic and biochemical defect observed in WS patients (Yu et al., 1996; Lombard et al., 2000). However, despite faithfully recapitulating the biochemical defect observed in WS patients, Wrn knockout mice do not have an obvious aging, genetic instability, or cancer phenotype even when ≥2 years of age (Lombard et al., 2000). Moreover, fibroblasts derived from these mice do not display prominent cellular phenotypes of primary human WS fibroblasts (Dhillon et al., 2007, 2008). Wrn helicase-deficient mice are phenotypically normal, at least during the first year of life (Lebel and Leder, 1998; Labbé et al., 2011), though are slightly shorter-lived than Wrn null mice (19 mo for Wrn helicase–deficient mice vs. 21 mo for Wrn null and 23 mo for littermate controls) (Aumailley et al., 2015b). Inflammatory cytokines and autophagy are increased in Wrn helicase-deleted mice, which may in part reflect mislocalization of mutant Wrn to the cytosol as opposed to the nucleus (Aumailley et al., 2015a, 2015b). Three caveats in interpreting the above models are that the phenotyping of Wrn mouse models has been comparatively modest to date; there has not as yet been a careful aging cohort study taken to completion; and the different models have not been systematically challenged with DNA damaging agents such as cross-linkers that may reveal defects in known Wrn-dependent functional pathways. One way to further test these models is to impose additional stress on the replication, HR repair or telomere maintenance pathways where Wrn is likely to function. One example of this sensitized background approach has been to mate a murine telomerase RNA template-deficient (or Terc-deficient) mouse with either a Wrn null mouse (Chang et al., 2004) or to a Wrn/Blm double mutant mouse. The resulting offspring of these crosses display progressive age-dependent changes similar to those observed in WS patients (Du et al., 2004), including the graying and loss of hair, osteoporosis, diabetes mellitus, and cataract formation, all changes that appear to depend on critically short telomeres as a primary driver of phenotype. Later generation Terc-deficiency leads to substantial, though variable, telomere erosion, which may explain the variability of findings in individual double- or triple-mutant mice. Mouse embryonic fibroblasts derived from Wrn-deficient/Tercdeficient mice also display chromosomal aberrations and elevated telomere sister chromatid exchanges, which are also seen in human WRN-deficient cells (Laud et al., 2005).

POTENTIAL THERAPEUTIC TARGETS AND STRATEGIES The clinical management of WS patients remains largely symptomatic: the treatment of skin ulcers, control of diabetes mellitus, removal of cataracts, and the early detection and treatment of cancers (Oshima et al., 2014). Several different classes of pharmacologic agent are being explored for their potential to delay the onset and progression of these signs of WS. These include molecules that suppress senescence cellular phenotypes, and may extend the replicative life span of WS cells. One

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class are p38 mitogen-activated protein kinase (MAPK) inhibitors that suppress NF-κB and a senescence-associated secretory phenotype (SASP) (Freund et al., 2011) in part by altering Ras signaling that activates p53/p21WAF1 (Collado et al., 2007; Sharpless and Sherr, 2015). Selective inhibitors of p38MAPK have been shown to prevent accumulation of p21WAF1 and to restore the replicative capacity of WS fibroblasts (Davis et al., 2005, 2016). Another potentially useful class of inhibitors targets the mTOR (mammalian Target of Rapamycin) pathway and includes rapamycin and metformin. mTOR is a key modulator of life span and health span in many species (Kennedy and Lamming, 2016). Long-term treatment of WS cells with rapamycin has been shown to reduce the accumulation of DNA damage and restore replicative capacity (Saha et al., 2014). These beneficial effects may in part reflect the more efficient removal of oxidatively damaged macromolecules (Talaei et al., 2013). Metformin is particularly attractive in light of its well-vetted safety profile: it is in very wide use to treat diabetes mellitus. Metformin is also being investigated for its ability to prevent or attenuate aging phenotypes in otherwise normal, aged individuals (Barzilai et al., 2016). Widely available dietary supplements have also been tested in WS model systems. For example, vitamin C has been shown to reduce senescence in WS fibroblasts; in WS MSCs differentiated from WS ESCs; and in a helicase-deficient mouse model of WS, with significant improvements in several disease-relevant endpoints (Aumailley et al., 2015a; Massip et al., 2010) that include telomere shortening (Kashino et al., 2003), suppression of SASP (Li et al., 2016), and the normalization of metabolic parameters (Aumailley et al., 2015a). Resveratrol, a dietary supplement with potent antioxidant activity, appears to mediate its activities by stimulating sirtuins, the physiological effectors of caloric restriction (Sinclair and Guarente, 2014). WRN helicase-deficient mice showed improvements in hyperglycemia and insulin resistance following resveratrol supplementation (Labbé et al., 2011). There also is a single case report of Japanese WS patients, in whom treatment with a keto-cartinoid antioxidant, astaxanthin, markedly improved fatty liver (Takemoto et al., 2015). Caloric restriction (reduced total caloric intake without malnutrition) has been shown to extend the life span of a wide variety of organisms, through mechanisms that remain to be fully understood. Caloric restriction appears to overlap with the mTOR pathway (Kennedy and Lamming, 2016) and was recently shown to significantly reduce the accumulation of DNA damage and extend life span in mouse models of genomic instability with accelerated aging caused by a nuclear excision repair defect (Vermeij et al., 2016). This study provides one rationale for a clinical trial of modest dietary restriction and/or the administration of pharmacomimetics of caloric restriction, in progeroid syndromes such as WS that have a component of defective DNA repair. A final therapeutic approach is the direct targeting of pathogenic mutations in WRN to promote exon skipping or stop codon read-through. In 2016, the US Food and Drug Administration approved eteplirsen (Exondsy51) for treatment of Duchenne muscular dystrophy (DMD) patients with mutations amenable to exon 51 skipping therapy (approximately 13% of DMD patients). However, our recent analysis identified only a small fraction of pathogenic WRN mutations that might be amenable to either type of mutation-specific therapy (Fu et al., 2017; Agrelo et al., 2015).

CONCLUDING REMARKS WS is one of a growing number of human diseases that reveal the importance of genetic instability, replicative senescence, and cell death as intermediate phenotypes that shape human disease risk and pathogenesis. WS is further distinguished as one of the growing number of human cancer predispositions that appear to result from defects in homologous recombination (Prakash et al., 2015). There is profitable work that can be done at all levels—clinical, organismal, cellular, and molecular—to improve our understanding of WS. WRN clearly modulates many important aspects of human biology, physiology, and age-dependent disease risk and pathogenesis in WS patients and may contribute to disease in otherwise unaffected members of the general population. Thus additional work on WS will improve our understanding of this fascinating human syndrome, and better delineate the growing roles for WRN function in human health and disease.

PRINT RESOURCES Croteau, D.L., Popuri, V., Opresko, P.L., Bohr, V.A., 2014. Human RecQ helicases in DNA repair, recombination, and replication. Annu Rev Biochem 83, 519–552. Sidorova, J.M., Monnat Jr., R.J., 2015. Human RECQ helicases: roles in cancer, aging, and inherited disease. Adv Genom Genet 5, 19–33 (the above two comprehensive reviews of biochemical and cellular functions of RECQ helicases provide additional perspective and references to primary literature). Epstein, C.J., Martin, G.M., Schultz, A.L., Motulsky, A.G., 1966. Werner’s syndrome: a review of its symptomatology, natural history, pathologic features, genetics and relationship to the natural aging process. Medicine 45, 177–221 (A modern classic: detailed clinical, pathologic and formal genetic analysis of WS cases reported through 1966).

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Werner’s syndrome and human aging. In: Salk, D., Fujiwara, Y., Martin, G.M., (Eds.), 1985. Advances in experimental medicine and biology, vol. 190. Plenum Press, New York. (Proceedings of the 1982 Kobe, Japan meeting that also includes reprints of important primary references beginning with a translation of Otto Werner’s 1904 thesis). From premature gray hair to Helicase–Werner syndrome: implications for aging and cancer. In: Goto, M., Miller, R.W., (Eds.), 2001. GANN monograph cancer research, vol. 49. (This monograph compiles reviews covering historical, clinical and biological features of WS).

WEB RESOURCES “On-Line Mendelian Inheritance in Man” Werner Syndrome record: http://omim.org/entry/277700. International Registry of Werner Syndrome: http://www.wernersyndrome.org/registry/registry.html. WRN Locus-Specific Mutational Database: http://www.pathology.washington.edu/research/werner/database/. note: refer to recently published update reports, e.g., (Yokote et al., 2016) for more recently described pathogenic variation in WRN. GeneReview of Werner Syndrome (authoritative, up-to-date review includes information on counseling and molecular diagnostics): http://www.ncbi.nlm.nih.gov/books/NBK1514/. Gene Testing Registries (GTR) resource (list of WRN gene testing sites). http://www.ncbi.nlm.nih.gov/gtr/conditions/ C0043119/. Clinical Utility Gene Card for Werner Syndrome (overview of WRN gene testing). http://www.nature.com/ejhg/journal/ v23/n6/full/ejhg2014171a.html. Genetics Home References for Werner Syndrome. https://ghr.nlm.nih.gov/condition/werner-syndrome. National Organization of Rare Diseases Werner Syndrome record (NORD). http://rarediseases.org/rare-diseases/ werner-syndrome/.

ACKNOWLEDGMENTS This work supported by grants from the US National Institutes of Health (NIA R24 AG42328 and NCI R01CA210916 and P01CA077852). We dedicate this chapter to our colleague Dr. George M. Martin, who continues to provide inspiration, ideas, and energy to all engaged in Werner syndrome research, patient care, and the biology of aging.

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18  SECTION | I  Aging in Humans

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Chapter 2

Premature Aging Syndrome Fabio Coppedè University of Pisa, Pisa, Italy

INTRODUCTION Premature aging syndromes (progeroid syndromes) are a heterogeneous group of rare human conditions resembling features of accelerated aging (Navarro et al., 2006). The term “progeria” derives from Greek words meaning “prematurely old” and is commonly used to indicate a childhood condition named Hutchinson–Gilford Progeria Syndrome (HGPS) (Ullrich and Gordon, 2015). The disease was named after the English physicians Jonathan Hutchinson and Hastings Gilford who first described it at around the end of the 19th century (Hutchinson, 1886; Gilford, 1904). HGPS is clinically the most dramatic form of progeroid syndromes, an umbrella term used to describe a heterogeneous group of rare genetic disorders mimicking features of accelerated aging, mainly caused by mutations in DNA repair genes or in genes affecting the structure or posttranslational maturation of nuclear lamins (Navarro et al., 2006). HGPS and Werner syndrome (WS), also referred to as childhood- and adulthood-progeria respectively, are two of the best-characterized premature aging disorders; they are defined as segmental progeroid syndromes as multiple organs and tissues replicate phenotypes associated with physiological aging (Kudlow et al., 2007). The identification of their genetic basis and the creation of patient’s registers, databases, and/or foundations have revealed the existence of a disease spectrum ranging from moderate and mild–severe to very aggressive forms, known as atypical progeroid syndromes (Coppedè, 2013). The availability of cell culture models and mutant mice that recapitulate these conditions has been fundamental to provide insight into the genetics and cellular pathways that underlie these disorders, as well as for the design of preclinical studies of promising therapeutic molecules (Swahari and Nakamura, 2016). After a description of both HGPS and WS symptoms, genetics, and molecular mechanisms, the chapter will focus on available cell culture and mouse models highlighting some of their main applications.

HUTCHINSON–GILFORD PROGERIA AND WERNER SYNDROME Epidemiology HGPS and WS represent two of the most studied progeroid syndromes (Kudlow et al., 2007). Both are extremely rare human conditions and a summary and comparison of their main characteristics and clinical signs is provided in Table 2.1. Particularly, HGPS is a rare de novo genetic disorder with a prevalence of 1 in 4–8 million births, leading to an estimate of between 350 and 400 affected children worldwide at any one time, and with 139 identified cases as of September 2016 (http://www.progeriaresearch.org). The onset of the symptoms occurs during the first year of life, and the mean life expectancy is 14.6 years, with myocardial infarction or stroke resulting from vascular diseases as the main cause of death (Ullrich and Gordon, 2015). There are no reported genetic clusters of the disease, which affects both genders equally (Coppedè, 2013). By contrast, WS is a rare autosomal recessive disorder with identified founder mutations leading to genetic clusters in Japan (disease frequency ranging from 1 in 20.000 to 1 in 40.000) and Sardinia (disease frequency of 1 in 50.000). Disease prevalence in other populations is less clear, but estimated at 1 or a few cases per 1 million individuals (Oshima et al., 2016). WS was named after the dissertation of Otto Werner, the German physician that provided the first description of the disease in 1904 (Werner, 1904), and almost 1.500 cases have been described since that first report, mainly in Japan (Goto et al., 2013). The first symptoms of WS usually manifest during the second–third decade of life leading to a mean life expectancy of about 53–54 years, with cancer or myocardial infarction resulting from vascular disease as the main causes of death (Coppedè, 2013). As a consequence, WS is also known as “adult progeria” to distinguish it from HGPS, the “childhood progeria” (Kudlow et al., 2007). Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00002-6 Copyright © 2018 Elsevier Inc. All rights reserved.

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22  SECTION | I  Aging in Humans

TABLE 2.1  Comparison of the Main Characteristics of Hutchinson–Gilford Progeria and Werner Syndrome Classic HGPS

Typical WS

References

Disease

Segmental progeroid syndrome

Segmental progeroid syndrome

Ullrich and Gordon (2015), Oshima et al. (2016) and Gordon et al. (2015)

Causative gene

LMNA (De novo dominant mutation)

WRN (Autosomal recessive mutations)

De Sandre-Giovannoli et al. (2003), Eriksson et al. (2003) and Yu et al. (1996)

Inheritance

Autosomal dominant (de novo)

Autosomal recessive

De Sandre-Giovannoli et al. (2003), Eriksson et al. (2003) and Yu et al. (1996)

Prevalence

1 in 4–8 million births

1:20.000–40.000 (Japan)

Ullrich and Gordon (2015), Oshima et al. (2016) and Gordon et al. (2015)

1:50.000 (Sardinia) Less frequent elsewhere Onset of symptoms

First year of life (childhood progeria)

Second to third decade of life (adulthood progeria)

Ullrich and Gordon (2015), Oshima et al. (2016) and Gordon et al. (2015)

Mean final height

110 cm

142 cm

Ullrich and Gordon (2015), Oshima et al. (2016) and Coppedè (2012)

Mean final weight

14.5 kg

36 kg

Ullrich and Gordon (2015), Oshima et al. (2016) and Coppedè (2012)

Mean life expectancy

14.6 years

53–54 years

Ullrich and Gordon (2015), Oshima et al. (2016) and Coppedè (2012)

Primary cause of death

Myocardial infarction or stroke (from vascular diseases)

Cancer or myocardial infarction, the latter from vascular disease

Ullrich and Gordon (2015), Oshima et al. (2016) and Gordon et al. (2015)

Main symptoms

Severe failure to thrive in infancy. Progressive alopecia leading to total alopecia. Skin lesions. Characteristic facies. Loss of subcutaneous fat. Bone changes. Skeletal anomalies. Musculoskeletal degeneration. Hearing loss.

Lack of the pubertal growth spurt during early teen years. Graying or loss of hair. Scleroderma-like skin lesions. Characteristic facies.

Ullrich and Gordon (2015), Kudlow et al. (2007) and Coppedè (2016)

High-pitched voice. Delayed and crowded dentition. Atherosclerosis. Cerebrovascular disease

Bilateral cataracts. Type 2 diabetes mellitus. Hypogonadism. Skin ulcers. Osteoporosis. Arteriosclerosis

Cancer risk

Not reported

High risk of cancer, including thyroid neoplasms, melanoma, meningioma, soft tissue sarcomas, hematologic/lymphoid cancers, and osteosarcomas

Goto et al. (2013), De Sandre-Giovannoli et al. (2003)

Nervous system disorders

Normal cognitive and motor functions

A few reported cases of peripheral neuropathy or dementia. No clear increased risk for neurodegeneration

Coppedè (2012)

Fertility

Sexually immature—do not reproduce

Reduced fertility—can reproduce

Ullrich and Gordon (2015), Oshima et al. (2016) and Gordon et al. (2015)

HGPS, Hutchinson–Gilford progeria syndrome; WS, Werner syndrome.

Premature Aging Syndrome Chapter | 2  23

Main Symptoms of Hutchinson–Gilford Progeria Syndrome The main symptoms of HGPS include a severe failure to thrive in infancy, progressive alopecia leading to total alopecia, loss of subcutaneous fat, and skin lesions. Stiffness of joints, bone changes, skeletal anomalies, and musculoskeletal degeneration also occur. The main facial abnormalities include a large cranium in relation to the facial size and a small face with pinched nose, prominent eyes, and scalp veins. Receding mandible, high-pitched voice, and delayed/crowded dentition are common in HGPS children, as well as protruding ears that lack lobules. Furthermore, HGPS individuals are sexually immature and do not reproduce. However, they preserve normal cognitive and motor functions for their age, and there are no reports of neurodegeneration or increased risk of cancer. Cardiovascular and cerebrovascular complications culminate in mortality from myocardial infarction, stroke, or congestive cardiac failure between ages 6 and 20 years (Kudlow et al., 2007; Coppedè, 2016).

Main Symptoms of Werner Syndrome The lack of a pubertal growth spurt in teenage years is the first clinical sign in WS individuals, followed by graying or loss of hair and scleroderma-like skin lesions in their 20s, and bilateral cataracts, type 2 diabetes mellitus, hypogonadism, skin ulcers, and osteoporosis in their 30s. An aged face with beaked nose is common, and both genders show reduced fertility (Coppedè, 2012). In contrast to HGPS individuals, WS individuals show an unusual spectrum of cancers, including soft tissue sarcomas, thyroid carcinomas, malignant melanoma, benign meningioma, osteosarcoma, and other less frequent cancers (De Sandre-Giovannoli et al., 2003). Overall, cancer was reported in almost 25% of about 1.500 WS cases recorded from 1904 to 2008 and often manifests with early age of onset and with the presence of multiple tumors in the same individual (Goto et al., 2013; De Sandre-Giovannoli et al., 2003). Only a few cases of dementia or peripheral neuropathy have been recorded among WS individuals, but there is no clear evidence of increased risk for neurodegenerative diseases (Coppedè, 2012). Complications caused by cancer or coronary artery atherosclerosis are the major cause of death in WS subjects (Oshima et al., 2016; Goto et al., 2013).

CAUSATIVE GENES AND MOLECULAR MECHANISMS LMNA Mutations Causing Hutchinson–Gilford Progeria Syndrome or Atypical Progeroid Syndromes Despite that the disease was first described in 1886 (Hutchinson, 1886), it took more than 100 years to identify the gene causing HGPS, which was discovered only in 2003 by two independent research groups (De Sandre-Giovannoli et al., 2003; Eriksson et al., 2003). The LMNA gene codes for A-type lamins (lamins A and C), major components of the nuclear lamina, and a de novo dominant point mutation in exon 11 (c.1824C>T, p.G608G) was identified as the cause of the majority of HGPS cases (De Sandre-Giovannoli et al., 2003; Eriksson et al., 2003). The LMNA c.1824C>T mutation results in the activation of a cryptic donor splice site leading to the production of a lamin A isoform containing an internal deletion of 50 amino acids near its C-terminal end, called progerin, that retains its farnesyl moiety (Fig. 2.1). Most (85%–90%) of progerin-producing HGPS cases carry the heterozygous LMNA c.1824C>T mutation and are referred to as classic HGPS (Gordon et al., 2015). Less frequent progerin-producing mutations, found in the remaining cases, can lead to a less or more aggressive phenotype, depending on the resultant final amount of progerin protein that is produced (Gordon et al., 2015). Several atypical progeroid syndromes, also referred as to nonclassic progeria, atypical HGPS, or atypical WS, result from additional LMNA mutations that do not produce progerin but still cause damages in the nuclear lamina that overlap with those produced by progerin-producing alleles (Coppedè, 2013; Gordon et al., 2015). As a consequence, patients with atypical progeroid syndromes differ in age at onset and severity of the symptoms with respect to classic HGPS individuals (De Sandre-Giovannoli et al., 2003). A continuously updated list of LMNA mutations leading to classic HGPS or atypical progeroid syndromes can be found at the UMD-LMNA Mutations Database (http://www.umd.be/LMNA/).

ZMPSTE24 Mutations and Premature Aging Disorders Lamin A proteins require posttranslational maturation to become mature proteins, and the zinc metalloprotease ZMPSTE24 is required to yield mature lamin A (Fig. 2.1). Recessive mutations in ZMPSTE24 impairing lamin A maturation cause three distinct premature aging disorders with increasing severity. Particularly, complete loss-of-function alleles lead to restrictive

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FIGURE 2.1  Representation of prelamin A processing. Prelamin A requires maturation to become mature lamin A. First, the C-terminal end of prelamin A contains a CaaX motif, which is modified by farnesylation of the cysteine residue (1), mediated by an FTASE protein and is followed by the cleavage of the (2) –aaX terminal residue performed either by RCE1 or ZMPSTE24. (3) After the cleavage, the cysteine residue is carboxymethylated by ICMT. The last step removes the C-terminal 15 residues through cleavage by (4) ZMPSTE24, yielding mature lamin A. In HGPS cells bearing the common LMNA p.G608G mutation, the activation of the cryptic splice site results in the deletion of a 50aa region from prelamin A (indicated in red in the figure), which contains the ZMPSTE24 cleavage site (indicated in the figure). As a result, the p.G608G mutation leads to the production and the accumulation of a smaller prelamin A protein, which cannot undergo complete maturation and retains the farnesyl moiety, termed progerin (shown in the box). FTASE, farnesyltransferase; HGPS, Hutchinson–Gilford progeria syndrome; ICMT, isoprenylcysteine carboxyl methyltransferase; RCE1, Ras-converting enzyme 1; ZMPSTE24, zinc metalloprotease related to Ste24p. (Adapted from Coppedè, F., 2013. The epidemiology of premature aging and associated comorbidities. Clin Interv Aging 8, 1023–1032.)

dermopathy, a neonatal “extreme form” of progeria, while alleles resulting in residual protein activity are associated with either a severe nonclassical form of HGPS or with a mild progeroid disorder called mandibuloacral dysplasia; this latter characterized by postnatal growth retardation and craniofacial, skeletal, and skin abnormalities (Navarro et al., 2014). Navarro and coworkers have recently provided an updated list of premature aging-related ZMPSTE24 mutations (Navarro et al., 2014).

WRN Mutations Causing Typical Werner Syndrome Mutations of the WRN gene, coding for a protein (WRN) that plays a critical role in repairing damaged DNA, cause typical WS (Yu et al., 1996). More than 70 different WRN mutations have been identified in WS patients, most of which lead to the generation of a premature stop codon resulting in protein truncation that prevents its nuclear import (Friedrich et al., 2010; Oshima and Hisama, 2014). Among them, founder mutations are known in Japan (c.3139-1G>C) and Sardinia (c.2089–3024A>G), and common mutations are reported in other populations (Fig. 2.2) (Friedrich et al., 2010; Oshima and Hisama, 2014). The Werner Syndrome Mutational Database (http://www.pathology.washington.edu/research/werner/database/) is an online repository containing an updated list of WRN mutations found in WS patients. However, atypical forms of the disease (atypical WS) are known, often featuring early onset and accelerated progression, and including cases caused by LMNA mutations leading to a weak activation of the same cryptic splice site as in HGPS (Oshima and Hisama, 2014).

Premature Aging Syndrome Chapter | 2  25

FIGURE 2.2  Representation of the WRN protein and common WRN mutations leading to Werner syndrome. The diagram illustrates the functional domains of the WRN protein: exonuclease, helicase, RQC (RecQ C-terminal domain), HRDC (helicase and RNase D C-terminal domain), and the NLS (nuclear localization signal). Over 70 insertion/deletion (I,D), missense (M), and nonsense (N) mutations, as well as splice mutations have been found in the different domains of the WRN protein. The boxes show the most frequent mutations observed according to ethnicity.

Molecular Mechanisms of Lamin A-Related Progeroid Syndromes Lamins are the major components of the nuclear lamina, a filamentous structure that supports the inner nuclear membrane and are required for the formation and maintenance of a proper nuclear shape and structure (Gordon et al., 2015). Lamins are therefore essential for the organization of the chromatin structure and for the proper functioning of several nuclear processes including DNA repair and replication, transcription, epigenetic mechanisms, cell division and differentiation, all impaired in cells from HGPS individuals, ultimately contributing to the premature aging phenotype (Gordon et al., 2015; Goldman et al., 2004). Indeed, HGPS cells accumulate structural and mechanical defects of the nuclear lamina with passaging due to the accumulation of a nonmature lamin A, either resulting from LMNA or ZMPSTE24 mutations, which renders it permanently intercalated into the inner nuclear membrane (Gordon et al., 2015). As a consequence, nuclei of cells from HGPS individuals appear bigger and dysmorphic, with thickening of the nuclear lamina, lobulations, clustering of nuclear pores, and disorganization of chromatin (Goldman et al., 2004). Loss of peripheral heterochromatin and modifications of the overall chromatin architecture are coupled to changes in histone tail modifications and DNA methylation (Arancio et al., 2014), followed by a widespread transcriptional misregulation (Csoka et al., 2004). Fibroblasts from HGPS patients also exhibit accumulation of DNA damage and chromosomal instability with cell passaging, as well as faster telomere shortening (Gonzalo and Kreienkamp, 2015).

Molecular Mechanisms of WRN-Related Progeroid Syndromes WS is one of several progeroid syndromes caused by mutations in a DNA repair gene (Coppedè and Migliore, 2010). Particularly, WRN is a nuclear protein that possesses DNA dependent ATPase, 3′ → 5′ helicase, 3′ → 5′ exonuclease, and DNA strand annealing activities. WRN is therefore regarded as a “caretaker of the genome” allowing the maintenance of genome stability during DNA replication, recombination, repair, and transcription; furthermore, WRN is required for telomere maintenance and apoptosis (Muftuoglu et al., 2008). Most of the mutations detected in WS patients are null mutations, resulting in the absence of a functional WRN protein in nuclei (Friedrich et al., 2010; Oshima and Hisama, 2014), so that

26  SECTION | I  Aging in Humans

cells from WS patients display impaired DNA replication, chromosomal instability, telomere shortening, and premature senescence with cell passing in culture (Melcher et al., 2000).

MANAGEMENT AND THERAPEUTIC APPROACHES No cure is currently available to halt the development of progeria symptoms in HGPS children. Drugs, dietary regimens, hydrotherapy, and exercise are recommended to the patients to counteract atherosclerosis, body fat reduction, and muscular atrophy. Low-dose aspirin and an adequate oral hydration are recommended for the prevention of cardiovascular and cerebrovascular complications. Sunscreens, eye care, shoe pads, and hearing aids are also frequently used, and the extraction of primary teeth may help to avoid crowding (Gordon et al., 2015). Three agents are under investigation in clinical trials for HGPS: lonafarnib, pravastatin, and zoledronate, all acting as inhibitors of the posttranslational farnesylation of the progerin protein, and lonafarnib treatment has improved the rate of weight gain, vascular distensibility, bone structure, neurosensory hearing, and life span in human clinical trials (Gordon et al., 2015). For what is concerning WS, available treatments are only symptomatic and include cholesterol-lowering drugs in case of abnormal lipid profile, treatment of skin ulcers, surgical treatment of cataracts, control of type 2 diabetes mellitus, and standard treatment of tumors. Smoking avoidance and dietary/physical regimens are recommended to reduce atherosclerosis risk (Coppedè, 2013).

CELL CULTURE MODELS Fibroblasts and Lymphoblasts From Hutchinson–Gilford Progeria Syndrome Individuals Fibroblasts and lymphoblasts from HGPS individuals, including immortalized fibroblasts, are commercially available for research purposes from the aging repository of the Coriell Cell Repository (CCR) (Camden, NJ) and from the Progeria Research Foundation (PRF) cell and tissue bank (http://www.progeriaresearch.org). For example, the PRF cell and tissue bank was established in 2002, and actually collects over 100 cell lines, including those from individuals with either the classic LMNA mutation or with nonclassic LMNA or ZMPSTE24 mutations, coupled to cell lines of some of their first-degree relatives. Each sample is accompanied by a detailed characterization of the patient and these cells constitute an invaluable material for researchers that has been largely used to unravel the molecular mechanisms leading to classic and atypical progeria (Table 2.2). Some of their main applications are listed below. Primary dermal fibroblasts and lymphoblastoid cell lines from HGPS individuals and their first-degree relatives, obtained from the aging repository of the CCR and the PRF cell and tissue bank, were used in 2003 to find the gene causing HGPS (Eriksson et al., 2003). The paper revealed the common LMNA c.1824C>T point mutation in classic HGPS cases, as well as structural nuclear membrane abnormalities in HGPS primary fibroblasts (Eriksson et al., 2003). Subsequent studies in HGPS fibroblasts from individuals carrying the LMNA c.1824C>T mutation, obtained from CCR and PRF, revealed that the disease was associated with significant changes in nuclear shape that worsened as cells aged in culture, including lobulation of the nuclear envelope, thickening of the nuclear lamina, loss of peripheral heterochromatin, and clustering of nuclear pores (Goldman et al., 2004). It was also observed that the severity of these structural defects correlates with an apparent increase in progerin protein (Goldman et al., 2004). In 2005, Glynn and Glover observed that progerin remains farnesylated and that the exposure to a farnesyltransferase inhibitor (FTI) caused a significant improvement in the nuclear morphology in HGPS fibroblasts, suggesting that FTIs may represent a therapeutic option for patients with HGPS (Glynn and Glover, 2005). Similarly, using HGPS fibroblasts carrying the LMNA c.1824C>T mutation obtained from the PRF cell bank, Columbaro et al. (2005) observed that the combined treatment with the FTI mevinolin and the histone deacetylase inhibitor trichostatin A rescued heterochromatin organization and transcript levels in HGPS fibroblasts, and lowered progerin levels (Columbaro et al., 2005). Studies in HGPS fibroblasts revealed that progerin production impairs DNA damage response and repair (Liu et al., 2005) and interferes with mitosis and cell cycle progression (Cao et al., 2007; Dechat et al., 2007). In addition, studies in skin biopsies of healthy individuals revealed that progerin expression is a biomarker of normal cellular aging and may potentially be linked to terminal differentiation and senescence in elderly individuals (McClintock et al., 2006); however, the progerin transcript expression is more than 160-fold lower in fibroblasts of unaffected controls than in HGPS fibroblasts (Rodriguez et al., 2009). Several other investigations have been performed to reveal proteins and complexes interacting with progerin and/or impaired as a consequence of progerin overexpression in HGPS dermal fibroblasts (Lemire et al., 2006; Pegoraro et al., 2009; Harten et al., 2011; Viteri et al., 2010), a synergistic relationship between telomere dysfunction and progerin production during the induction of cell senescence was observed (Cao et al., 2011a), and mitochondrial dysfunction and accumulation of oxidized proteins was detected in

Premature Aging Syndrome Chapter | 2  27

TABLE 2.2  Examples of Cell and Mouse Models of Premature Aging Syndromes Disease

Model

Description

References

HGPS

Fibroblasts

Primary dermal fibroblasts from HGPS individuals

Eriksson et al. (2003) and Goldman et al. (2004)

WS

Fibroblasts

Primary dermal fibroblasts from WS individuals

Thompson and Holliday (1983), Weirich et al. (1996), Morita et al. (1997), Crabbe et al. (2004) and Gray et al. (1997)

HGPS

iPSCs

Induced pluripotent stem cells from HGPS dermal fibroblasts

Zhang et al. (2011), Liu et al. (2011), Nissan et al. (2012), Xiong et al. (2013), Lo et al. (2014) and Zhang et al. (2014)

WS

iPSCs

Induced pluripotent stem cells from WS dermal fibroblasts

Cheung et al. (2014) and Shimamoto et al. (2014)

HGPS

LmnaHG mice

Lmna mutant mice that exclusively produce progerin

Yang et al. (2005) and Yang et al. (2006)

HGPS

LMNAG608G BAC mice

Mice that carry the human HGPS LMNAG608G mutation on a BAC

Varga et al. (2006)

HGPS

Zmpste24−/− mice

Zmpste24 deficient mice

Bergo et al. (2002) and Pendas et al. (2002)

HGPS

nHG mice

Lmna mutant mice that produce a nonfarnesylated version of progerin

Yang et al. (2008)

HGPS

Tetop-LAG608G mice

Mice with inducible expression of progerin under the control of a tet-operon

Sagelius et al. (2008), Sagelius et al. (2008) and Rosengardten et al. (2011)

HGPS

keratin 14-progerin mice

Mice expressing progerin under the control of the keratin 14 promoter

Wang et al. (2008)

HGPS

LmnaG609G mice

Mice that carry a progerin-producing Lmna allele

Osorio et al. (2011)

WS

WrnΔhel/Δhel mice

Mice with a deletion in the helicase domain of the murine Wrn gene

Lebel and Leder (1998)

WS

Wrn−/− mice

Null mutant Wrn mice

Lombard et al. (2000)

WS

Wrn−/−

Mice null with respect to both Wrn and Terc

Chang et al. (2004)

WS

hMW mice

Transgenic mice over-expressing the human mutant WRN gene

Yamamoto et al. (2008)

Terc−/−

mice

BAC, bacterial artificial chromosome; HGPS, Hutchinson–Gilford progeria syndrome; iPSCs, induced pluripotent stem cells; WS, Werner syndrome.

HGPS fibroblasts (Viteri et al., 2010; Rivera-Torres et al., 2013). Furthermore, the accumulation of progerin in the nuclear lamina altered histone methylation in heterochromatin, disrupting heterochromatin-lamina interactions (McCord et al., 2013). These changes were linked to transcriptional misregulation likely contributing to global loss of spatial chromatin compartmentalization in late passage HGPS fibroblasts (McCord et al., 2013). Collectively, studies performed using these cell lines revealed the nuclear processes impaired in HGPS that likely contribute to the premature aging phenotype and provided novel insights on the function of normal and mutant lamin A (Goldman et al., 2004; Arancio et al., 2014; Csoka et al., 2004; Gonzalo and Kreienkamp, 2015). In addition, several attempts to rescue the normal cellular phenotype with FTIs and other compounds have been performed in HGPS fibroblasts (Glynn and Glover, 2005; Columbaro et al., 2005), including recent evidence that rapamycin decreased the formation of insoluble progerin aggregates and induced progerin clearance through autophagy, abolished nuclear blebbing, and delayed the onset of cellular senescence (Cao et al., 2011b). Similarly, it was shown that sulforaphane, an antioxidant derived from cruciferous vegetables enhances progerin clearance by autophagy and reverses the phenotypic changes caused by progerin accumulation in HGPS fibroblasts (Gabriel et al., 2015). More recently, it was shown that the antioxidant methylene blue alleviates nuclear and mitochondrial abnormalities in HGPS fibroblasts (Xiong et al., 2016).

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Fibroblasts and Lymphoblasts From Werner Syndrome Individuals Fibroblasts and lymphoblasts from WS individuals, including immortalized fibroblasts, are also available for research purposes from the aging repository of CCR and from other tissue banks, including the Goto Collection of RIKEN Bioresource Center (https://www.brc.riken.jp/lab/cell/english/index_gmc.shtml), and those cells have largely been used to investigate the molecular mechanisms of the disease (Thompson and Holliday, 1983; Weirich et al., 1996; Morita et al., 1997; Crabbe et al., 2004; Gray et al., 1997). Early studies in the field revealed a shorter life span in culture of cells from patients with WS (Thompson and Holliday, 1983). Chromosomal analyses in fibroblasts and lymphocytes from WS patients revealed that chromosomal aberrations occur frequently and randomly (Weirich et al., 1996), so that it was clear that cells from WS patients have a reduced replicative life span in culture and that genomic instability occurs cytogenetically in the form of chromosome breaks and translocations and molecularly by multiple large deletions (Morita et al., 1997). Chromosomal aberrations are frequently caused by telomere dysfunctions in WS fibroblasts (Crabbe et al., 2004) and studies in cells from WS individuals provided important information on the function of normal and mutant WRN protein (Morita et al., 1997; Gray et al., 1997). Unfortunately, cultured fibroblasts and lymphocytes from WS patients are largely heterogeneous in terms of the carried WRN mutations, show premature senescence in culture, and give rise to multiple pseudodiploid clones, mostly marked by random balanced reciprocal translocations involving a different spectrum of chromosomes in different individuals (Melcher et al., 2000; Hoehn et al., 1975; Scappaticci et al., 1990). To overcome these and other limits recent induced pluripotent stem cells (iPSCs) of premature aging disorders have been produced and their applications will be discussed in the next section.

Induced Pluripotent Stem Cells as Models of Premature Aging Disorders Pluripotent stem cells have self-renewal capacity and can differentiate into any cell type, so that they overcame the limits posed by the fact that except for skin fibroblasts or lymphoblastoid cells from HGPS or WS patients, other living tissue samples, such as for example neurons, are inaccessible for investigation (Liu et al., 2012). In 2007, a method was described to reprogram differentiated human fibroblasts into a pluripotent state by overexpression of four transcription factors, namely Oct3/4, Sox2, Klf4, and c-Myc (Lo Cicero and Nissan, 2015), and soon-after iPSC have been generated to model several human disorders, including premature aging (Lo Cicero and Nissan, 2015). In 2011, Zhang et al. derived iPSC from HGPS dermal fibroblasts. These cells were then differentiated into neural progenitors, endothelial cells, fibroblasts, vascular smooth muscle cells, and mesenchymal stem cells. Lamin A and progerin were absent from undifferentiated iPSC, but progerin production and its aging-associated phenotypic consequences were restored on differentiation. Particularly, progerin levels were highest in mesenchymal stem cells, followed by vascular smooth muscle cells and fibroblasts, and these cells showed increased DNA damage and nuclear abnormalities (Zhang et al., 2011). In the same year another group derived iPSCs from fibroblasts obtained from patients with HGPS, observing absence of progerin and associated nuclear damages and epigenetic changes in iPSCs that were restored on differentiation (Liu et al., 2011). In 2012, Nissan et al. generated iPSCs from HGPS and control fibroblasts, confirming that A-type lamins were absent from undifferentiated iPSCs, but expressed in a variety of iPSC-derived cells including keratinocytes, melanocytes, retinal pigment epithelial cells, and mesenchymal stem cells. Differentiation of iPSC along the neural lineage was obtained for both control and HGPS cell lines, and the authors observed that a microRNA, miR-9, negatively controls lamin A, and progerin expression in neural cells, suggesting that the restricted expression of miR-9 to that cell lineage could protect neural cells from progerin accumulation in HGPS (Nissan et al., 2012). Other investigators used iPSCs reprogrammed from HGPS fibroblasts to induce adipocyte formation, revealing a severe lipid storage defect in HGPS cells at late differentiation stage, suggesting that progerin impairs the terminal differentiation stage of adipogenesis (Xiong et al., 2013). Lo et al. (2014) observed an upregulation in the expression of vanilloid transient potential channels 2 (TRPV2) in endothelial cells derived from HGPS iPSCs, that enhanced a cytosolic Ca2⁺ elevation under hypotonicity, resulting in apoptotic cell death. Zhang et al. (2014) showed that smooth muscle cells differentiated from HGPS iPSCs die from a caspase-independent cell death, resulting from down-regulation of poly(ADP-ribose) polymerase 1 (PARP1) leading to prolonged mitosis and death. In summary, researchers are currently using cells differentiated from HGPS iPSCs to unravel the molecular mechanisms of the premature aging-related phenotype in various tissues (Lo Cicero and Nissan, 2015). In addition, iPSCs are increasingly applied to evaluate the effects of drugs currently administered or proposed to HGPS children in these cellular models (Blondel et al., 2014), as well as to investigate the effects of novel compounds that could counteract disease progression (Lo Cicero et al., 2016). It was recently demonstrated that the WRN protein associates with the de novo methyltransferase DNMT3B in the chromatin of differentiating pluripotent cells and is therefore involved in de novo DNA methylation, a key step of stem cell differentiation (Smith et al., 2010). iPSCs from a patient with atypical WS, caused by an LMNA mutation, displayed normal nuclear membrane morphology compared to donor fibroblasts, but their differentiated progeny reproduced the disease phenotype as in the case of HGPS iPSCs (Ho et al., 2011). Mesenchymal stem cells and neural stem/progenitor cells were

Premature Aging Syndrome Chapter | 2  29

differentiated from iPSCs obtained from WS fibroblasts (Cheung et al., 2014), and the authors observed recurrence of premature senescence associated with accelerated telomere attrition and defective synthesis of the lagging strand telomeres in mesenchymal stem cells, but not in neural stem cells (Cheung et al., 2014). Shimamoto et al. (2014) described the effects of long-term culture on WS iPSCs, observing that they acquired and maintained infinite proliferative potential for self-renewal over 2 years, showed no premature upregulation of senescence-associated genes, and maintained chromosomal stability over long-term culture. Collectively, those studies have shown that WS iPSCs maintain their telomeres with reactivation of endogenous telomerase, maintain stable chromosomal profiles, and are stable in culture for several passages without morphological changes and loss of growth capacity (Cheung et al., 2014; Shimamoto et al., 2014), thus representing a very promising model for the investigation of the pathological changes associated with differentiation. Furthermore, WS iPSCs might provide opportunities for drug screening as well as for clinical applications (Shimamoto et al., 2015).

MOUSE MODELS The discovery of LMNA, ZMPSTE24, and WRN genes as causative of either typical or atypical forms of premature aging syndromes has led to the creation of several mouse models of premature aging (Zhang et al., 2013), some of which are listed in Table 2.2 and will be briefly discussed thereafter.

Mouse Models of Hutchinson–Gilford Progeria Syndrome The first mouse model of HGPS was generated by a deletion mutation in Lmna creating an allele, LmnaHG, which exclusively produces the progerin protein (HG indicates HGPS) (Yang et al., 2005). Homozygous and heterozygous HG mice accumulated progerin and showed phenotypes resembling HGPS in children, including loss of subcutaneous fat, alopecia, osteoporosis leading to rib fractures, micrognathia, and premature death, but no sign of cardiovascular damage could be observed in these animals (Yang et al., 2005, 2006). Subsequently, a mouse model that carries the human HGPS LMNAG608G mutation on a bacterial artificial chromosome (BAC) was generated (Varga et al., 2006). These mice did not show external characteristics of HGPS, but a progressive loss of vascular smooth muscle cells was observed (Varga et al., 2006). Both models were used to demonstrate the potential of FTIs in alleviating progeroid phenotypes (Yang et al., 2005, 2006; Capell et al., 2008) reinforcing similar observations obtained in HGPS fibroblasts and suggesting that farnesylated A-type lamins partially contribute to the HGPS phenotype (Glynn and Glover, 2005; Columbaro et al., 2005). Following these observations, Zmpste24 deficient mice (Zmpste24−/−) were generated as models of premature aging disorders (Bergo et al., 2002; Pendas et al., 2002), and, as expected, those animals accumulated a farnesylated prelamin A and showed features of progeria (Bergo et al., 2002; Pendas et al., 2002). Another model was generated expressing a nonfarnesylated version of progerin, to evaluate if a nonfarnesylated progerin protein could elicit some of the HGPS features (Yang et al., 2008). These animals were identical to HG mice (carriers of LmnaHG alleles) except for the CAAX motif that was mutated to inhibit farnesylation, were called nHG mice, showed less severe phenotypes than those observed in HG mice and no response to FTIs (Yang et al., 2008). Inducible mice expressing progerin under the control of a tet-operon were generated, and called tetop-LAG608G (Sagelius et al., 2008a,b). A targeted expression of this gene on epidermal cells resulted in dermal fibrosis, incomplete development of sebaceous glands, and loss of subcutaneous fat (Sagelius et al., 2008a,b). Impaired wound-healing ability and decreased stem cell population in the epidermal tissues were later observed in these animals (Rosengardten et al., 2011). Another model that selectively expresses human progerin in the skin and in cells of the hair follicle was generated, in which progerin expression was driven by the keratin 14 promoter, known as keratin 14-progerin mice (Wang et al., 2008). However, keratin 14-progerin mice showed only an abnormal nuclear morphology but had normal hair growth and wound healing ability as well as normal growth rates and life span (Wang et al., 2008). More recently, Osorio et al. (2011) generated a mouse model that carries a Lmna allele (LmnaG609G) with a mutation (G609G), which is equivalent to the LMNA G608G mutation found in human HGPS patients. With respect to previous progeria mouse models that only phenocopy some of the features seen in HGPS patients, LmnaG609G mice are so far the best mouse model of HGPS, as they phenocopy most of the features of the disease, including cardiovascular abnormalities (Osorio et al., 2011). All these animal models of progeria have been largely used to investigate the functions of individual A-type lamins during tissue development and provided valid in vivo systems for treatment testing (Swahari and Nakamura, 2016; Zhang et al., 2013).

Mouse Models of Werner Syndrome In 1998, Lebel and Leder created a murine model of WS by a deletion of a portion of the helicase domain of the murine homolog of the human WRN gene. These mutant mice are referred to as WrnΔhel/Δhel animals and showed embryonic or

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perinatal survival disadvantage, but those that survived had no signs of premature aging or increased rates of tumor formation (Lebel and Leder, 1998). Furthermore, mouse embryo fibroblasts derived from homozygous WrnΔhel/Δhel embryos showed premature loss of proliferative capacity (Lebel and Leder, 1998). However, most homozygous mice were observed for fewer than 13 months, so that firm conclusions regarding their longevity and further age-related morbidity could not be drawn at that time (Lebel and Leder, 1998). Subsequently, Lombard et al. (2000) generated Wrn−/− mice bearing a mutation that mimics the null mutations found in clinically ascertained WS patients. These mice were viable, fertile, did not show signs of premature aging and survived until at least 2 years of age. However, fibroblasts from these animals showed premature senesce in culture (Lombard et al., 2000). Interestingly, Wrn−/−/p53−/− mice showed an increased mortality rate relative to Wrn−/+/p53−/− animals, and the authors suggested a synergy between p53 and WRN mutations for the determination of life span (Lombard et al., 2000). To further address this issue, WrnΔhel/Δhel mice were mated with p21 or p53 null mice to generate double mutants (Lebel et al., 2001), and p53 null/WrnΔhel homozygous mutant mice showed an acceleration of tumor formation as well as a change in the tumor spectrum compared to p53 null mice (Lebel et al., 2001). In contrast, the p21 null/WrnΔhel homozygous mutant mice did not show an acceleration of tumorigenesis (Lebel et al., 2001). Further studies in WrnΔhel/Δhel mice revealed increased reactive oxygen species in serum and cardiac tissue, followed by cardiac fibrosis (Massip et al., 2006). Also PARP-1 null/WrnΔhel/Δhel mice have been generated and showed developmental defects in embryos and a progressive increase in oxidative stress with age (Deschênes et al., 2005). However, mouse cells from WrnΔhel/Δhel and Wrn−/− mice have discordant phenotypes, WrnΔhel/Δhel mice have a prooxidant status and a shorter mean life span than wild type ones, but mouse model neither displays premature aging or an elevated risk of tumorigenesis in the absence of additional perturbations (Lebel and Leder, 1998; Lombard et al., 2000). A comparison of the cellular phenotypes of fibroblasts from Wrn−/− mice with WRN-mutant or WRN-depleted human fibroblasts showed that WRN loss confers a strong cellular phenotype in early passage human fibroblasts, but not in mouse fibroblasts (Dhillon et al., 2010), partially explaining why Wrn-mutant mice fail to develop an organismal phenotype that resembles human WS (Dhillon et al., 2010). The lack of a premature aging phenotype resembling human WS in the above described Wrn-deficient mice led Chang and coworkers to generate mice null with respect to both Wrn and Terc (encoding the telomerase RNA component), called Wrn−/− Terc−/− mice (Chang et al., 2004). The authors observed that late-generation Wrn−/− Terc−/− mice showed telomere dysfunction and a classical Werner-like premature aging syndrome, characterized by premature death, graying of hairs, alopecia, osteoporosis, type II diabetes, and cataracts. Cultured cells from this mouse model also showed accelerated replicative senescence, accumulation of DNA-damage, and increased chromosomal instability, resembling features observed in cells from WS patients (Chang et al., 2004). Further investigations in Wrn−/− Terc−/−–mutant mice revealed that age-related osteoporosis is the result of impaired osteoblast differentiation in the context of intact osteoclast differentiation (Pignolo et al., 2008), and additional studies revealed that Wrn−/− Terc−/−–mutant mice recapitulate the human bone aging phenotype and are useful models for studying age-related osteoporosis (Brennan et al., 2014). Transgenic mice overexpressing a human mutant WRN gene were generated and called hMW mice (Yamamoto et al., 2008). These animals were used to investigate the role of WRN protein in homologous recombination events and in conjunction with different DNA damaging agents have provided insight into mechanisms of genomic instability and DNA repair (Yamamoto et al., 2008). In addition, to those studies, several investigators have used mouse models of WS to evaluate the potential effects of therapeutic compounds, for example vitamin C supplementation restored healthy aging in WrnΔhel/Δhel mice and reversed several age-related abnormalities in adipose tissues and liver endothelial defenestration, genomic integrity, and inflammatory status (Massip et al., 2010). Furthermore, vitamin C supplementation reduced oxidative stress in liver and heart tissues, reversed hypertriglyceridemia, hyperglycemia, and insulin resistance and reduced fat weight in WrnΔhel/Δhel mice (Lebel et al., 2010), and reversed the concentrations of markers of cardiovascular disease and inflammation to wild type values in those animals (Aumailley et al., 2015). Similarly, resveratrol supplementation improved the hyperglycemia and the insulin resistance phenotype in these Wrn-mutant mice and reversed liver steatosis and lipid peroxidation (Labbé et al., 2011). Resveratrol, however, did not improve the hypertriglyceridemia, inflammatory stress, nor extended the mean life span of these mutant mice, and resveratrol-treated mutant mice exhibited an increase in the frequency of lymphomas and of several solid tumors (Labbé et al., 2011).

CONCLUSIONS The identification of the genetic basis of premature aging syndromes has led to a better characterization of either typical or atypical forms of HGPS and WS (Coppedè, 2013), as well as to the availability of primary dermal fibroblasts from patients carrying common or less frequent LMNA and WRN mutations. In parallel, several animal models have been generated bearing mutations in premature aging-related genes (Zhang et al., 2013). Both cell cultures and animal models have provided an invaluable resource for the investigation of the molecular mechanisms leading to the premature aging phenotypes, as well as for testing drugs and compounds supposed to be able to counteract their development, and some of those compounds are

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already in human clinical trials (Gordon et al., 2015). More recently, a method was described to reprogram differentiated human fibroblasts into a pluripotent state, and iPSCs were generated from dermal fibroblasts of individuals with premature aging syndromes (Zhang et al., 2011; Cheung et al., 2014; Shimamoto et al., 2014). These cells are then differentiated into different cell lineages, allowing a better understanding of the consequences of the expression of mutant proteins in various human tissues. In addition, iPSC technology could also pave the way for stem cell therapy in progeria, although this approach requires considerable validation and extensive research.

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Defective prelamin A processing and muscular and adipocyte alterations in Zmpste24 metalloproteinase-deficient mice. Nat Genet 31, 94–99. Pignolo, R.J., Suda, R.K., McMillan, E.A., Shen, J., Lee, S.H., Choi, Y., Wright, A.C., Johnson, F.B., 2008. Defects in telomere maintenance molecules impair osteoblast differentiation and promote osteoporosis. Aging Cell 7, 23–31. Rivera-Torres, J., Acín-Perez, R., Cabezas-Sánchez, P., Osorio, F.G., Gonzalez-Gómez, C., Megias, D., Cámara, C., López-Otín, C., Enríquez, J.A., Luque-García, J.L., Andrés, V., 2013. Identification of mitochondrial dysfunction in Hutchinson-Gilford progeria syndrome through use of stable isotope labeling with amino acids in cell culture. J Proteom 91, 466–477. Rodriguez, S., Coppedè, F., Sagelius, H., Eriksson, M., 2009. Increased expression of the Hutchinson-Gilford progeria syndrome truncated lamin A transcript during cell aging. Eur J Hum Genet 17, 928–937. Rosengardten, Y., McKenna, T., Grochova, D., Eriksson, M., 2011. Stem cell depletion in Hutchinson-Gilford progeria syndrome. Aging Cell 10, 1011–1020. Sagelius, H., Rosengardten, Y., Hanif, M., Erdos, M.R., Rozell, B., Collins, F.S., Eriksson, M., 2008a. Targeted transgenic expression of the mutation causing Hutchinson-Gilford progeria syndrome leads to proliferative and degenerative epidermal disease. J Cell Sci 121, 969–978. Sagelius, H., Rosengardten, Y., Schmidt, E., Sonnabend, C., Rozell, B., Eriksson, M., 2008b. Reversible phenotype in a mouse model of HutchinsonGilford progeria syndrome. J Med Genet 45, 794–801. Scappaticci, S., Forabosco, A., Borroni, G., Orecchia, G., Fraccaro, M., 1990. Clonal structural chromosomal rearrangements in lymphocytes of four patients with Werner’s syndrome. Ann Genet 33, 5–8. Shimamoto, A., Kagawa, H., Zensho, K., Sera, Y., Kazuki, Y., Osaki, M., Oshimura, M., Ishigaki, Y., Hamasaki, K., Kodama, Y., Yuasa, S., Fukuda, K., Hirashima, K., Seimiya, H., Koyama, H., Shimizu, T., Takemoto, M., Yokote, K., Goto, M., Tahara, H., 2014. Reprogramming suppresses premature senescence phenotypes of Werner syndrome cells and maintains chromosomal stability over long-term culture. PLoS One 9, e112900. Shimamoto, A., Yokote, K., Tahara, H., 2015. Werner syndrome-specific induced pluripotent stem cells: recovery of telomere function by reprogramming. Front Genet 6, 10. Smith, J.A., Ndoye, A.M., Geary, K., Lisanti, M.P., Igoucheva, O., Daniel, R., 2010. A role for the Werner syndrome protein in epigenetic inactivation of the pluripotency factor Oct4. Aging Cell 9, 580–591. Swahari, V., Nakamura, A., 2016. Speeding up the clock: the past, present and future of progeria. Dev Growth Differ 58, 116–130. Thompson, K.V., Holliday, R., 1983. Genetic effects on the longevity of cultured human fibroblasts. I. Werner’s syndrome. Gerontology 29, 73–82. Ullrich, N.J., Gordon, L.B., 2015. Hutchinson-Gilford progeria syndrome. Handb Clin Neurol 132, 249–264.

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Varga, R., Eriksson, M., Erdos, M.R., Olive, M., Harten, I., Kolodgie, F., Capell, B.C., Cheng, J., Faddah, D., Perkins, S., Avallone, H., San, H., Qu, X., Ganesh, S., Gordon, L.B., Virmani, R., Wight, T.N., Nabel, E.G., Collins, F.S., 2006. Progressive vascular smooth muscle cell defects in a mouse model of Hutchinson-Gilford progeria syndrome. Proc Natl Acad Sci USA 103, 3250–3255. Viteri, G., Chung, Y.W., Stadtman, E.R., 2010. Effect of progerin on the accumulation of oxidized proteins in fibroblasts from Hutchinson Gilford progeria patients. Mech Ageing Dev 131, 2–8. Wang, Y., Panteleyev, A.A., Owens, D.M., Djabali, K., Stewart, C.L., Worman, H.J., 2008. Epidermal expression of the truncated prelamin A causing Hutchinson-Gilford progeria syndrome: effects on keratinocytes, hair and skin. Hum Mol Genet 17, 2357–2369. Weirich, H.G., Weirich-Schwaiger, H., Kofler, H., Sidoroff, A., Fritsch, P., Schachtschabel, D.O., Schweiger, M., Hirsch-Kauffmann, M., 1996. Werner syndrome: studies in an affected family reveal a cellular phenotype of unaffected siblings. Mech Ageing Dev 88, 1–15. Werner, O., 1904. On Cataract in Conjunction with Scleroderma (dissertation). Schmidt and Klaunig, Kiel, Germany. Xiong, Z.M., LaDana, C., Wu, D., Cao, K., 2013. An inhibitory role of progerin in the gene induction network of adipocyte differentiation from iPS cells. Aging (Albany NY) 5, 288–303. Xiong, Z.M., Choi, J.Y., Wang, K., Zhang, H., Tariq, Z., Wu, D., Ko, E., LaDana, C., Sesaki, H., Cao, K., 2016. Methylene blue alleviates nuclear and mitochondrial abnormalities in progeria. Aging Cell 15, 279–290. Yamamoto, M.L., Reliene, R., Oshima, J., Schiestl, R.H., 2008. Effects of human Werner helicase on intrachromosomal homologous recombination mediated DNA deletions in mice. Mutat Res 644, 11–16. Yang, S.H., Bergo, M.O., Toth, J.I., Qiao, X., Hu, Y., Sandoval, S., Meta, M., Bendale, P., Gelb, M.H., Young, S.G., Fong, L.G., 2005. Blocking protein farnesyltransferase improves nuclear blebbing in mouse fibroblasts with a targeted Hutchinson-Gilford progeria syndrome mutation. Proc Natl Acad Sci USA 102, 10291–10296. Yang, S.H., Meta, M., Qiao, X., Frost, D., Bauch, J., Coffinier, C., Majumdar, S., Bergo, M.O., Young, S.G., Fong, L.G., 2006. A farnesyltransferase inhibitor improves disease phenotypes in mice with a Hutchinson-Gilford progeria syndrome mutation. J Clin Investig 116, 2115–2121. Yang, S.H., Andres, D.A., Spielmann, H.P., Young, S.G., Fong, L.G., 2008. Progerin elicits disease phenotypes of progeria in mice whether or not it is farnesylated. J Clin Investig 118, 3291–3300. Yu, C.E., Oshima, J., Fu, Y.H., Wijsman, E.M., Hisama, F., Alisch, R., Matthews, S., Nakura, J., Miki, T., Ouais, S., Martin, G.M., Mulligan, J., Schellenberg, G.D., 1996. Positional cloning of the Werner’s syndrome gene. Science 272, 258–262. Zhang, J., Lian, Q., Zhu, G., Zhou, F., Sui, L., Tan, C., Mutalif, R.A., Navasankari, R., Zhang, Y., Tse, H.F., Stewart, C.L., Colman, A., 2011. A human iPSC model of Hutchinson Gilford progeria reveals vascular smooth muscle and mesenchymal stem cell defects. Cell Stem Cell 8, 31–45. Zhang, H., Kieckhaefer, J.E., Cao, K., 2013. Mouse models of laminopathies. Aging Cell 12, 2–10. Zhang, H., Xiong, Z.M., Cao, K., 2014. Mechanisms controlling the smooth muscle cell death in progeria via down-regulation of poly(ADP-ribose) polymerase 1. Proc Natl Acad Sci USA 111, E2261–E2270.

FURTHER READING Takahashi, K., Tanabe, K., Ohnuki, M., Narita, M., Ichisaka, T., Tomoda, K., Yamanaka, S., 2007. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872.

Chapter 3

Models, Definitions, and Criteria for Frailty David B. Hogan University of Calgary, Calgary, AB, Canada

INTRODUCTION The observation that aging is associated with declining resilience (ability to resist or recover from a stressor) (Whitson et al., 2016; Ukraintseva et al., 2016) arose as soon as members of our species survived into old age. Why this occurred was speculated about. For example in the Hippocratic tradition it was held aging was caused by a progressive loss of the body’s store of heat, resulting in a depletion of vitality and increased vulnerability (Thane, 1993). Various terms were used to refer to this state. Benjamin Rush, a signer of the American Declaration of Independence, held that the loss of vitality led to a predisposing debility with the older individual unable to remain in balance with his world (Haber, 1986). I.L. Nascher wrote in 1914 about senile cachexia or debility, which he felt represented the “vitiated condition of the senile organism” arising from “some change in the character of the blood” due to aging (Hogan, 2006). During most of the last century adjectives such as infirm and/or disabled were generally used to describe persons with heightened late-life vulnerability (Hogan et al., 2003). In the early 1970s the Federal Council on Aging in the United States began using frail elderly to describe a particular segment of the older population (Hogan et al., 2003), while later that decade James W. Vaupel, Kenneth G. Manton, and Eric Stallard introduced frailty as a way to recognize individual differences in mortality rates at given ages that could be used to create more accurate life tables (Vaupel et al., 1979). By 1991 frail elderly was added to the National Library of Medicine’s MeSH database. It was defined as, “Older adults or aged individuals who are lacking in general strength and are unusually susceptible to disease or to other infirmity.” (MeSH) Over the intervening quarter of a century there has been a rapid increase in the number of biomedical papers published on the topic. Current thinking holds the core feature of frailty is a state of increased vulnerability to stressors arising from impairments in multiple, interrelated systems that lead to a decline in homeostatic reserve, robustness, and resilience (Bergman et al., 2007). It is seen as a useful way to appreciate the heterogeneity of the health of older persons and predicting outcomes such as death and the need for institutionalization. Frailty is present in an estimated 15%–25% of community-dwelling, 20%–50% hospitalized, and an even higher proportion of institutionalized older adults (those aged 65 and over) (BandeenRoche et al., 2015; Song et al., 2010; Hogan et al., 2017). In this short overview I will deal first with the two most utilized models for frailty. I will then elaborate on the working definition for frailty I have provided followed by a more comprehensive examination of available criteria for the identification of frail older persons.

MODELS AND MECHANISMS The two favored conceptualizations of frailty currently are the frailty phenotype and frailty index (Cesari et al., 2014; Walston and Bandeen-Roche, 2015). With the former, frailty is considered to represent a specific physiological state manifested by a cycle of sarcopenia, chronic undernutrition, and declining energy expenditure potentially triggered by a variety of insults (Walston, 2015). It is deemed present if the person has three or more of five pre-defined criteria: unintentional weight loss, complaints of fatigue/exhaustion, slow gait speed (compared to peers), weak handgrip strength (compared to peers), and low level of physical activity (compared to peers) (Fried et al., 2001). In contrast, the accumulation of potential health deficits is used to calculate a frailty index (Mitnitski et al., 2017). The index is the ratio of health deficits (which can be symptoms, signs, diseases, disabilities, and/or laboratory ­abnormalities) present in a given individual to the total of health deficits considered. For example, if 10 of 40 possible deficits were pres­ ent, the person’s frailty index would be 10/40 or 0.25. When calculating an index 30 or more health deficits should be considered. Recommendations on how to create a frailty index including direction on the selection and scoring of health deficits have been published (Searle et al., 2008). A network model based on the assumption that the health of an individual can be represented by a complex network of interconnected nodes in either a damaged or undamaged state provided Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00003-8 Copyright © 2018 Elsevier Inc. All rights reserved.

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theoretical support for this approach to frailty. Computational simulation led to results that aligned well with observational data from population studies (Mitnitski et al., 2017). These approaches have been used to produce highly predictive models for certain outcomes associated with frailty such as mortality but have not necessarily furthered our understanding of it. Clearly there is much more to learn. An on-going question is whether frailty and aging (or more accurately senescence) are essentially the same. Senescence refers to the time-dependent changes seen in living organisms that have cumulative and deleterious effects (Yates, 1996). A senescent organism lacks resiliency. As previously noted increased vulnerability is a hallmark of frailty. At some very advanced but currently unspecified age a degree of frailty will likely become universal. Few if any frail individuals, though, develop the state solely because of senescence. Other factors (e.g., behaviors, life styles, diseases) typically play a role. While very advanced age is arguably a sufficient cause of frailty, it is not a necessary one. The genetics of aging, age-associated diseases, and age-associated functional decline will likely become increasingly relevant to our understanding of frailty (Hamet and Tremblay, 2003; Dato et al., 2012; Martin, 2005; Viña et al., 2016). While genes without doubt play a role, environmental influences are equally if not more important. An interesting study using data from two Danish cohorts suggested a strong genetic influence on the development of frailty especially in men (Dato et al., 2012). Genetic influences would operate partially if not primarily through the mechanism of susceptibility to diseases. A few studies about the potential relationship between genetic polymorphism and frailty have been published. The Apolipoprotein E (ApoE) gene is referred to as a frailty gene (Gerdes et al., 2000). Carriers of the epsilon 2 allele are more likely to survive to extreme old age, while epsilon 4 carriers are less likely. The differential mortality rates for the various ApoE genotypes extend into extreme old age (Corder et al., 2000). No consistent picture has yet emerged in work on the genetic influences on immune and muscle function. Genetic variation in interleukin-6 (IL-6) was not associated with frailty in older women (Walston et al., 2005), but a significant genetic association with the IL-10 promoter gene has been found (van den Biggelaar et al., 2004). In the latter study those genetically predisposed to lower cytokine production seemed predisposed to frailty, which goes against the prevailing thinking about frailty. Polymorphism in the insulin-like growth factor-2 (IGF2) was modestly associated (only 1% of the variance was attributable to IGF2 genotype) with grip strength in men aged 64–74 (Sayer et al., 2002). Those with the AA genotype were marginally stronger than those with the GG genotype. Angiotensin-converting enzyme (ACE) gene polymorphism is associated with response to physical training (Montgomery et al., 1999). Low ACE activity was associated with a greater response to intensive physical training in young white male British Army recruits. Other studies have shown that the use of ACE-inhibitors for hypertension is associated with maintained muscle mass, muscle strength, and walking speed in older individuals (Onder et al., 2002; Di Bari et al., 2004). Agedependent regulation of gene expression requires study. Transcriptional profiling of human frontal cortex from individuals aged 26 to a 106 showed both reduced expression of certain genes (e.g., involved with synaptic transmission and vesicular transport) and the induction of genes (e.g., involved with stress response and DNA repair) after the age of 40 (Lu et al., 2004). The greatest diversity in gene expression was seen between 40 and 70 when a “switch-over” in genetic expression seems to occur. Middle and early old age may represent a critical period for the development of frailty in our later years. The concepts of allostasis and hormesis deal with the relationship between an organism and its environment. They may have particular relevance to frailty and could explain how early life events could have late-life consequences. Allostasis (literally meaning “achieving stability through change”) is a dynamic regulatory process that allows the organism to adapt to the challenges of their environment. It is an extension of the concept of homeostasis (the ability or tendency of an organism or a cell to maintain internal equilibrium by adjusting its physiological processes). Many physiologic parameters do not remain constant but vary significantly in response to perceived stress. The neuroendocrine, autonomic nervous, and immune system responses, while beneficial in the short run can be damaging long-term if not shut down when no longer needed. Allostatic load refers to the price of the chronic overactivation of these regulatory systems (McEwen, 2003, 2004). While there are no studies showing a direct association with frailty, a summary measure for allostatic load was found to be an independent predictor of functional decline in the MacArthur studies of successful aging (Karlamangla et al., 2002). Hormesis refers to the beneficial effects of low doses of potentially harmful substances. It is becoming apparent that environment stress does more than “cull the herd” by eliminating the weakest individuals (Semenchenko et al., 2004). A number of mild stresses (e.g., cold, heat, irradiation, caloric restriction) have been found to be associated with increased longevity in various animal models. The proposed mechanism is that the stress induces stimulation of maintenance and repair pathways (Tattan, 2004; Radek et al., 2005). Frailty may be the long-term outcome of a complex interaction between the type, severity, and timing of environmental stresses and the nature (and consequences) of the response to these stresses. Dysfunction of key organ systems might explain the phenomena of frailty. This would include musculoskeletal abnormalities (e.g., sarcopenia Marcell, 2003; Doherty, 2003; van ltallie TB, 2003), endocrine deficiency states (e.g., gonadal hormones, DHEA, growth hormone/IGF-1 Morley et al., 2005), or immune dysfunction (e.g., high levels of inflammatory markers like TNF-α and IL-6 Cohen, 2000, T-cell phenotypes Johnstone et al., 2017). It is not felt, though, that frailty arises from single system abnormalities (e.g., frailty ≠ sarcopenia). There is widespread agreement that a core feature of frailty is the dysfunction of multiple physiologic systems. Different investigators have proposed various combinations of the following abnormalities

Frailty Chapter | 3  37

as underlying frailty-diminished aerobic capacity; abnormalities in the neurological system (e.g., cognition, balance, and gait); musculoskeletal problems; precarious or deficient nutritional states; endocrine-metabolic dysfunction; stimulation of the immune system; and/or cardiovascular concerns (Buchner and Wagner, 1992; Lipsitz and Goldberger, 1992; Lipsitz, 2002; Bortz, 2002; Campbell and Buchner, 1997). Endocrine-immune dysregulation is a particularly favored partnering (Walston, 2004; Joseph et al., 2005; Ferrucci and Guralnik, 2003). The development of biomarkers (Viña et al., 2016) and animal models (Kane et al., 2016) for frailty may accelerate research on the basic underlying mechanisms for this condition. Life course epidemiology studies the long-term effects on later health or disease risk of physical or social exposures during gestation, childhood, adolescence, young adulthood, and later adult life (Kuh et al., 2003). It allows the integration of biological, behavioral, clinical, psychological, and social processes that interact across a person’s life (Ben-Shlomo and Kuh, 2002). This is not a new concept in aging research. Over 60 years ago Nathan Shock wrote, “In the broadest sense, problems of growth, development, and maturation are as much a part of gerontology as are those of atrophy, degeneration, and decline.” (Baker and Achenbaum, 1992) He emphasized the need to examine aging over the entire life span. The likelihood of developing certain chronic conditions (e.g., type 2 diabetes, ischemic heart disease) appears to depend in part on the early environment (Gluckman and Hanson, 2004). A noxious stimulus at a critical, sensitive period of early life may lead to permanent structural, physiologic, and/or metabolic changes (Godfrey and Barker, 2001). These changes in the young organism might confer immediate survival advantages but at a subsequent cost—especially if there is a mismatch between the developmental and adult environments (e.g., constrained fetal growth followed to exposure to high-calorie foods after birth). This is not to deny the importance of adult risk factors—life course epidemiology allows the joint study of both early life and adult factors. Understanding the biological, clinical, psychological, life style, and social factors that influence physical performance in midlife may provide clues to the origin of frailty in old age. Whether frailty risk accumulates or there is a required chain of events is unknown (Kuh et al., 2003). It is also unknown whether there are critical or sensitive periods for the development of frailty. Middle age might be a critical period (Lu et al., 2004). Confounding, mediating, and modifying factors will require consideration. The very complexity of the life course approach in the study of frailty would be an attraction—it would allow the consideration of a mix of both early and late risk factors as well as biomedical and psychosocial influences. The Alameda County Study looked at risk factors for frailty over the previous three decades in an older cohort (Strawbridge et al., 1998). It was found that (listed alphabetically) depression, fair or poor self-rated health, heavy drinking, physical inactivity, prevalence of chronic conditions, prevalence of chronic symptoms, and smoking were adult risk factors that predicted the occurrence of what they defined as frailty (i.e., problems in two or more of the following domains— physical functioning, nutritional status, cognition, sensation). While we have no direct evidence linking early-life factors and frailty, suggestive data are available. Muscle weakness is typically included as one of its characteristics. Grip strength is often used in epidemiological studies to measure this attribute. The Medical Research Council National Survey of Health and Development found a positive relationship between birth weight and grip strength at the age of 53 (Kuh et al., 2002). The Hertfordshire cohort study found that grip strength in the mid sixties was significantly associated with birth weight (Sayer et al., 2004). A suggested mechanism for the association is that birth weight is related to the number of muscle fibers at birth. Because of the loss of muscle fibers with aging, a deficit in the number at birth could predispose an individual to sarcopenia later in life. Other early-life factors that may be associated with the development of late-life frailty include exposure to infections and chronic stress. The induction of chronic inflammation might explain the relationship between early-life infections and late-life morbidity and mortality. The reduction in lifetime exposure to infectious diseases and other sources of inflammation may to contributing to the decline we have seen throughout the developed world in late-life mortality (Finch and Crimmins, 2004). As noted previously activation of the immune system may play a role in the etiology of frailty. An interesting and potentially testable hypothesis is whether lifetime exposure to infectious diseases influences the incidence of frailty. A study found that chronic cytomegalovirus infection was associated with the presence of frailty (Schmaltz et al., 2005). High IL-6 levels increased the strength of the association. Challenging social conditions (e.g., having a relatively lower social standing) over the life course can be associated with chronic stress and increased susceptibility to various disease states (Brunner, 1997). When survivors of the Whitehall Study were resurveyed after a 29-year gap, men in clerical or manual jobs at middle age were four times more likely to report poor physical performance than senior administrators (Breeze et al., 2001). It was estimated that at most 20% of this could be explained by baseline differences in cardiorespiratory disease and risk factors. Chronic stress has been shown to be associated with a variety of biological effects including increases in IL-6 levels (Kiecolt-Glaser et al., 2003) and accelerated telomere shortening (Epel et al., 2004). Fig. 3.1 presents a descriptive model based on a life course approach for the development of frailty. Genotype and prenatal environment will determine the birth phenotype. The specific nature of the prenatal environmental could lead to development plasticity, predictive adaptive responses, and developmental disruption (Gluckman and Hanson, 2004). When

38  SECTION | I  Aging in Humans

Adult Environment

Birth Phenotype

Frailty Risk

Adult Phenotype

Adaptive Response

Genotype

Prenatal Environment

Aging

Disease(s)

Lifestyle Behaviors Attitudes

Postnatal Environment ? Mismatch

FIGURE 3.1  Life course model for frailty.

there is a mismatch between the prenatal and the predicted postnatal environment (e.g., predicted postnatal food restriction vs. abundant postnatal food availability), the risk of problems down the road might be increased. Senescence, the physical and social environment, coping strategies, affect, lifestyle/behavioral choices, and the presence of disease(s) can contribute to the onset of frailty in the aged individual. In this model disability and healthcare utilization would be a consequence of frailty and modified by the relative assets and deficits of the individual.

DEFINITION As noted previously, there is agreement that frailty is a syndrome encountered in older individuals that is marked by increased vulnerability to a number of adverse outcomes (Bergman et al., 2007). Other hallmarks are the presence of multisystem impairment and the concept of a gradient. While vulnerability is present to a degree in all of us, frailty is marked by a greater than “normal” susceptibility. Frailty will likely continue to be treated as either a dichotomous value (frail/not frail) or categorized on a short ordinal frailty scale (e.g., not present, mild, moderate, and severe frailty) to make the data easier to manage and understand. A frailty index though inherently treats it as a continuous variable (Cesari et al., 2014). There is likely a diverse mix of predisposing, precipitating, enabling, and reinforcing factors (see Table 3.1). It is hoped that a characteristic clustering of characteristics will allow for its accurate identification (see Table 3.2). The consequences of frailty in a given individual will depend on their particular balance of assets and deficits (see Table 3.3). The physical and social environment of the older person coupled with how they cope with their limitations will be modifiers of outcomes. Even though a consensus has not been achieved on a specific definition for frailty, a number of subtypes of frailty have been proposed (see Table 3.4). Darwin wrote, “Those who make many species are the ‘splitters,’ and those who make few are the ‘lumpers.’” (Gallagher, 2002) At this stage in its uncertain evolution, caution should be exercised in “splitting” frailty into a number of “species” before we reach consensus on its defining features. Frailty was initially used as another term for disability (Hogan et al., 2003). While frailty has been in the main disentangled from limitations in basic activities of daily living (ADL) like eating, bathing, dressing, and toileting (Fried et al., 2004), there is considerable overlap with disabilities in both instrumental ADL (IADL) like managing finances and mobility (Fried et al., 2001). In the Cardiovascular Heart Study (CHS) 27.4% of those categorized as frail had an ADL disability, while 59.7% and 71.7%, respectively, had an IADL or mobility one (Fried et al., 2001). To further complicate the relationship disability can be an outcome of frailty. If frailty leads to disability in an individual (Albert et al., 2002), it does not disappear with the onset of the disability. Diseases alone (e.g., cardiovascular disease Newman et al., 2001, chronic kidney disease Shlipak et al., 2004, HIV Levett et al., 2016) or in combination (i.e., multiple morbidity) are associated with frailty. In the CHS study only 7.3% of frail subjects had no chronic disease compared to 23.2% in those who were not frail (Fried et al., 2001). Sixty-eight percent of frail individuals had two or more chronic conditions compared to 40% in the not frail group (Fried et al., 2001). It should be noted, though, that it was a small proportion of those with comorbidities (10.5%) who were also frail (Fried et al., 2001). Nearly all frail individuals have at least one chronic condition and most will have two or more. The presence of comorbidities, though, is not specific for frailty.

Frailty Chapter | 3  39

TABLE 3.1  Select Candidate Predisposing, Precipitating, Enabling, and Reinforcing Factors Sociodemographic

Diseases

 Age

  Presence of comorbidity (two or more conditions)

 Sex

  Specific conditions

  Socioeconomic status

  Cardiovascular disease

  Social standing

  Cerebrovascular disease

 Education

   Chronic renal failure

  Social engagement/social network

   Dementia (e.g., Alzheimer’s disease)

  Social support

  Depression

Attitudes

  Diabetes mellitus

 Affect

   Fractures (especially hip)/osteoporosis

  Perceived degree of autonomy/control

  HIV

  Self-rated health/life satisfaction

  Osteoarthritis

Behaviors

  Parkinson’s disease

 Smoking

  Renal failure

  Alcohol intake   Activity level/exercise

TABLE 3.2  Select Candidate Manifestations or Markers of Frailty Symptoms/Presentations

Laboratory Features

  Chronic symptoms

 Albumin

  Depressive symptoms

 Cholesterol

 Anxiety

 Hemoglobin/hematocrit

 Stamina/energy/fatigue

  C-reactive protein

  Atypical presentations

  Factor VIII

 Delirium

 D-dimer

 Falls

 Interleukin-6

Physical Findings/Performance Measures

  Insulin-like growth factor I or IGF-I

  Body composition/nutritional status

  Dehydroepiandrosterone sulfate or DHEA-S

  Blood pressure

 Testosterone

  Presence of postural hypotension

  Luteinizing hormone

  Muscle strength

  Cortisol/DHEA-S ratio

 Balance

  Response to dexamethasone suppression

  Gait speed

  Plasma osmolality

  Lower extremity function   Upper extremity function   Motor processing (coordination, movement planning, and speed)   Neurocognitive processing (alertness, attention, multitasking)   Cognition measures  Vision/hearing

40  SECTION | I  Aging in Humans

TABLE 3.3  Select Consequences of Frailty Mortality

Increased Health Care Utilization

Deterioration in Activities of Daily Living (ADL)/Leisure Activities

 Institutionalization

  Basic ADL   Instrumental ADL   Advanced ADL   Recreational/leisure activities

  Medication use   Adverse drug effects  Hospitalization   Community-based services   Physician visits   Use of medical devices  Cost

TABLE 3.4  Subtypes of Frailty (Listed Alphabetically) Cognitive: The presence of demonstrable cognitive decline or being at risk for significant cognitive deterioration; it is the opposite of “cognitive vitality” (Fillit et al., 2002). Dynamic and Static: Dynamic frailty would be a significant worsening over time on one or more preselected marker(s) of frailty; static frailty occurs when subjects are found to be in the lowest quintile, at a point in time, on preselected frailty markers; dynamic frailty requires longitudinal data collection while static frailty is based on cross-sectional data (Puts et al., 2005). Global and Intrinsic: Global frailty includes “intrinsic frailty (see following) and consequences” (i.e., changes in functional independence, social roles, psychosocial factors, and healthcare utilization) (Studenski et al., 2004). Intrinsic is defined as “physiologically based organ system impairments and physical performance limitations, such as losses of strength, endurance, balance, body weight, and mobility.” (Studenski et al., 2004) Physical: Defined as impairments in the physical abilities needed to live independently (Hogan et al., 2003); described as “a final common pathway, in which the effect of disease, disuse, and aging across organ systems contribute to further decline and adverse events or states” (Studenski et al., 2004); operational criteria used to identify subjects with physical frailty range from looking at a single attribute (e.g., slow gait speed Hardy and Gill, 2004) to utilizing a battery (e.g., manifesting 2+ of low peak aerobic power, self-reported difficulty or need for assistance with two instrumental activities of daily living or one basic activity of daily living, and modified Physical Performance Test score of 18–22) (Villareal et al., 2001). Physiologic: Incorporates the concepts of age-associated declines in physiological systems/reduced physiologic reserve and vulnerability (Katz et al., 1994; Hawkes et al., 1998; Farquharson et al., 2001). Primary and Secondary: Primary causes of frailty would be age-related mechanisms (e.g., changes in gene expression, oxidative DNA damage, telomere shortening) while secondary causes would be diseases (e.g., congestive heart failure, depression, hypothyroidism, malignancy) (Walston, 2004; Fried et al., 2004). Psychosocial: Equated to the presence of depressive symptoms; de Jonge wrote that, “Subjects experiencing depressive symptoms before the onset of a somatic event may have fewer resources to deal with a stressful event… Depressive symptoms reported before by elderly people may thus signal psychosocial frailty” (de Jonge et al., 2004); Katz elaborated on this and stated that “… (late-life) depression is a state associated with cognitive dysfunctions that interfere with coping, adaptation, and resilience.” (Katz, 2004)

CRITERIA Many criteria have been proposed for identifying frail older individuals (Bergman et al., 2007). Two systematic reviews on the detection of frailty examined 22 and 27 instruments respectively with no preferred measure recommended (Sternberg et al., 2011; Bouillon et al., 2013). Frailty measures can be categorized as judgment-based (including “foot-of-the-bed” assessments), physical performance tests (such as gait speed, grip strength, and chair stands), physical frailty determinations (the frailty phenotype is a good example of this approach), multidimensional instruments (which extend physical frailty determinations by including other dimensions such as cognition and psychological state), and frailty indices (Hogan et al., 2017). The most appropriate one to use would depend on the specific purpose(s) of the assessor, population being studied, setting (e.g., community, hospital, and nursing home), timing of the evaluation, available data, and the experience and training of the assessor. As noted previously the frailty phenotype and index are the approaches most commonly used and will be discussed in further detail. While the frailty phenotype was an important step forward, there are persisting questions about these criteria. First, were the components selected essential to the recognition of frailty? They are not specific for frailty. A number of chronic diseases can have similar manifestations. Chronic obstructive pulmonary disease, for example, is associated with diminished

Frailty Chapter | 3  41

grip strength (Rantanen et al., 1998), complaints of fatigue (Walke et al., 2004), limited physical activity (Garcia-Aymerich et al., 2004), slow gait speed (Butcher et al., 2004), and weight loss (Schols, 2000). Fried and her colleagues recognized the nonspecific nature of their criteria. In their validation study excluded individuals with significant cognitive impairment/ Alzheimer’s disease, depression, and Parkinson’s disease as “these conditions could potentially present with frailty characteristics as a consequence of a single disease.” (Fried et al., 2001) In their subsequent writings they have suggested secondary frailty due to diseases as a subtype of frailty (see Table 3.4). Primary frailty, from their perspective, would arise from aging-associated processes such as changes in gene expression, oxidative DNA damage, and telomere shortening (Walston, 2004). They theorized a substantial interaction between primary and secondary mechanisms in the development of frailty. Of the five characteristics, weight loss seems the most problematic. Frailty is not necessarily a wasting condition. Obese seniors appear to have a high prevalence of frailty (Villareal et al., 2004). The combination of sarcopenia and obesity (the sedentary obese) delineate a group at particularly high risk for functional decline (Pierson, 2003). The second major question deals with what was not included as a component. It is debatable that all the essential clinical characteristics of frailty are captured. Cognition, depressive symptoms, psychological attributes (e.g., positive affect Ostir et al., 2004), and others are also candidate criterion (see Table 3.2). The origin of the factors included by Fried was a survey of like-minded individuals—academic geriatricians (Fried et al., 2004). A more diverse group would have added other perspectives (Surowiecki, 2004). For example, older patients and their caregivers, when considering frailty, prioritized emotional and social factors higher than clinicians (Studenski et al., 2004). Excluding cognitive and psychological attributes in the definition frailty has been justified by stating the inquiry is limited to “physical” frailty (see Table 3.4) (Hogan et al., 2003; Studenski et al., 2004; Hardy and Gill, 2004; Villareal et al., 2001; Gill et al., 2002; Hadley et al., 1993). The wisdom of proposing a Cartesian mind–body dualism is debatable. For example, predisposing or enabling factors underpinning physical frailty may also be associated with impaired cognition. Chronic inflammation is felt to play a central role in the pathogenesis of frailty and increased inflammatory proteins have been found to be associated with an increased risk of developing dementia (Englehart et al., 2004). A correlation between cognitive processing speed and physical performance measures has been shown (Binder et al., 1999). There are data-linking depressive features and subsequent physical functioning. Depressive symptoms at baseline predicted a decline in physical performance measures (standing balance, walking speed, and chair rises) in 4 years time (Pennix et al., 1998). Compared to nondepressed individuals and those with transient symptoms, older subjects with persistent depressive symptoms were at an increased risk for a worsening of their functional abilities (Lenze et al., 2005). Multidimensional instruments proposed for the identification of frailty, such as the Groningen Frailty Indicator (Schuurmans et al., 2004), include cognition and affect as core characteristics. The frailty index approach has been criticized for the large number of factors requiring consideration and its mathematical nature (Hubbard et al., 2009). As well others feel it is nonspecific and will not lead to insights on mechanisms, etiology, or treatment (Walston and Bandeen-Roche, 2015). Do the various approaches to detecting frailty describe the same group of individuals? The answer is probably not. Comparative studies are now being conducted to determine how they compare in predicting the outcomes such as mortality (Theou et al., 2013; Campitelli et al., 2016). As well, their respective reliability, sensitivity to change, and practicality have been and should be examined and contrasted. The challenges in establishing criteria for frailty are analogous to those faced in establishing the diagnostic criteria for the metabolic syndrome (also known as syndrome X, the dysmetabolic syndrome, the insulin resistance syndrome, and the deadly quartet). This is a cluster of abnormalities (glucose intolerance, obesity, hypertension, dyslipidemia) that are well-documented risk factors for cardiovascular disease in themselves (Eckel et al., 2005). The grouping of certain characteristics known to independently predict adverse outcomes can only be justified if they are synergistic in their effect and/or have a common etiology (Domanski and Proschan, 2004). This appears to be the case in the metabolic syndrome. Whether a similar synergistic effect on outcomes will be seen with the components of frailty requires confirmation.

CONCLUSION There is potential harm in labeling an older person as frail. While it is a term frequently used about seniors, when discussing their status it is rarely used by older persons. Labeling a person can adversely change how they view themselves and how others view them. Studies have shown that priming older individuals with stereotypes can influence decision-making about life-prolonging interventions (Levy et al., 1999–2000), performance on memory testing (Levy, 1996), and gait speed (Hausdorff et al., 1999). An examination of the under investigation and undertreatment of cancer in old age concluded that it could not be wholly explained by appropriate adjustments for the condition of individual patients (Turner et al., 1999). While the adjective “frail” was often used to describe older patients, it was not clearly defined. Older patients must not be inappropriately denied (or prescribed) interventions on the basis of a specious diagnostic label. Frailty is one of the more exciting research areas in clinical gerontology. What leads to it, how best to recognize frailty, and ultimately what we can do to prevent or treat it remains uncertain.

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RECOMMENDED RESOURCES Canadian Frailty Network - http://www.cfn-nce.ca/.

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The burden of symptoms among community-dwelling older persons with advanced chronic disease. Arch Intern Med 164, 2321–2324. Walston, J., Bandeen-Roche, K., 2015. Frailty: a tale of two concepts. BMC Med 13, 185. Walston, J., Arking, D.E., Fallin, D., Li, T., Beamer, B., et al., 2005. IL-6 gene variation is not associated with increased serum levels of IL-6, muscle, weakness, or frailty in older women. Exp Gerontol 40, 344–352. Walston, J., 2015. Connecting age-related biological decline to frailty and late-life vulnerability. Nestle Nutr Inst Workshop Ser 83, 1–10. Walston, J., January 28, 2004. Frailty – the search for underlying causes. Sci Aging Knowl Environ 4, e4. Whitson, H.E., Duan-Porter, W., Schmader, K.E., Morley, M.C., Cohen, H.J., Colón-Emeric, C.S., 2016. Physical resilience in older adults: systematic review and development of an emerging construct. J Gerontol A Biol Sci Med Sci 71 (4), 489–495. Yates, F.E., 1996. Theories of aging: biological. In: Birren, J.E. (Ed.), Encyclopedia of Gerontology – Age, Aging and the Aged, vol. II. Academic Press, San Diego, pp. 545–555.

Chapter 4

Immunological Methods and the Concept of Inflammaging in the Study of Human Aging Tamas Fülöp1, Graham Pawelec2, Jacek M. Witkowski3, Alan A. Cohen1, Carl Fortin1, Aurelie Le Page1, Hugo Garneau1, Gilles Dupuis1, Anis Larbi4 1University 4Agency

of Sherbrooke, Sherbrooke, QC, Canada; 2University of Tübingen, Tübingen, Germany; 3Medical University of Gdansk, Gdansk, Poland; for Science Technology and Research, Singapore, Singapore

INTRODUCTION Aging is a very complex physiological process involving all body systems. One of the most important is the immune system as it is essential for the defense of the organism against all challenges either from outside or inside the body (Fulop and Robert, 2014). It also interacts intimately with all other bodily systems, including the endocrine and the nervous systems, by virtue of its distribution throughout all tissues. Studying immunity and aging is of great importance for eventually designing interventions to modulate degenerative diseases, the prevalence of which increases with age. This is a challenge because the immune system is considered one of the most integrative and complex bodily systems (Fulop et al., 2014a). Understanding this complexity is a major endeavor required for comprehending mutual interactions contributing to aging within the body; this is a rapidly advancing field (Martelli et al., 2016; Grignolio et al., 2014; Pereira and Akbar, 2016; Müller and Pawelec, 2015). The immune system can be studied at different levels, including phenotypic and molecular dimensions, which are to a great extent molded by the evolutionary niche of the organism and the challenges to the immune system that it faces throughout life. Focusing on humans will result in a better understanding of the complex process of aging and of age-related diseases some of which will be specific to that species (Fülöp et al., 2016). The unraveling of the changes in the immune system with human aging is still in its infancy and necessitates much more study as most of our knowledge comes from animal models especially mice (Marandu et al., 2015; Youm et al., 2016). However, we know that animal models cannot capture the complexity found in humans, especially those related to species-specific longevity. In this chapter, we will summarize our present knowledge and its implications for the understanding of the aging process itself, illustrated by the different experimental approaches applied.

PHENOTYPIC ANALYSES The immune system is composed of an innate and an adaptive arm, each comprising many different cell types. The first data to be obtained, which are necessary to characterize them, are the phenotypic features of these cells (Larbi and Fulop, 2014; Effros, 2016; Di Benedetto et al., 2015). With the increasingly widespread use of multicolor flow cytometry, currently reaching up to 30 channels using fluorochrome-labeled antibodies (i.e., allowing at least 28 different molecules to be identified simultaneously at the single cell level), it is possible to use deep phenotyping to individually characterize the complexity of the immune system (Schmidt et al., 2016; Schlickeiser et al., 2016). We increasingly understand how cell types first considered as homogeneous populations are actually extremely diverse (Stervbo et al., 2015b). Presently, other methods are being developed to characterize the phenotype of these cells even more deeply, such as flow cytometry using metal isotopes to label antibodies instead of optical fluorochromes and mass spectrometry as the readout (cytometry time-of-flight, or “CyTOF”) (Wistuba-Hamprecht et al., 2017; Weber and Robinson, 2016). This is essential to determine the phenotype of these immune cells as finely as technically possible to obtain the maximum amount of information on the whole immune history of the individual organism (including the lasting “memory” effects of challenges by microbes or intrinsic antigens over the lifetime). Phenotyping studies also allow the imputation of the functions of the specific cell types in the sample. Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00004-X Copyright © 2018 Elsevier Inc. All rights reserved.

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Innate Immune System Aging greatly affects the innate immune system; however, the study of its properties in relation to aging was neglected for a long time. It is composed of several very distinct cells such as natural killer cells (NK cells), neutrophils, monocyte/macrophages, and dendritic cells (DC) (Lopez-Sejas et al., 2016; Bandaranayake and Shaw, 2016; Montgomery and Shaw, 2015). The use of sophisticated techniques led to the assessment of the changes occurring with aging in the innate immune system. Thus, for instance, different NK cell subpopulations reflecting the developmental state of the whole NK cell population can be distinguished (Tarazona et al., 2015; Pera et al., 2015). Of the known surface receptors determining the subpopulations of NK cells one of the most important is CD56, reflecting the state of maturation and functionality of the cell. With aging the proportion of cells in the CD56bright population decreases, while the CD56dim population increases with the eventual acquisition of CD57 as a marker of senescence or of activation. Importantly, NK cells also express the CD16, KIR, and CD94 surface markers in great quantity (Isitman et al., 2016). The exact significance of these changes (but note: they are mostly measured as differences between young and old populations and assumed to be changes) is still debated. These studies exemplify the notion that the deep surface phenotyping of NK cells will lead to a better understanding of the immune history and the diversity of these cells, without directly assessing their functional capabilities. Monocytes were also initially considered to be a homogeneous population, but recent studies indicate that the surface receptors CD14 and CD16 identify different subpopulations, which are present at varying proportions in the blood (Dalton et al., 2014). It is evident that these markers may indicate differences in some functional characteristics such as cytokine production, phagocytic capacity, or free radical productions, but they certainly do not directly determine them. The most abundant are the “classical” (80%, CD14+CD16−), with lower proportions of “nonclassical” (15%, CD14lowCD16high), and “intermediate” (5%, CD14highCD16high). These proportions are indicative and we know that they can change during aging and in many age-related diseases (Puchta et al., 2016; Pararasa et al., 2016; Baëhl et al., 2016; de Pablo-Bernal et al., 2016). However, until now we have few data in humans concerning the age-associated phenotypic changes in monocytes. Monocytes differentiate into macrophages, which play an important role in fighting infections and cancers (Gutknecht and Bouton, 2016; Haniffa et al., 2015). There are many surface molecules, which determine their phenotype and most importantly, their functional class, the two most important of which are the M1 (inflammatory) and M2 (antiinflammatory/ restorative) classes. The M2 macrophages can themselves be subdivided into at least three subclasses (Montenegro-Burke et al., 2016). There are many surface markers and intracellular molecules defining these macrophages; the most important for M1 are CD80 and CD86, and for M2, CD163, arginase, and IL-10. Data on the distribution of macrophage subsets in the elderly are very scarce and we really do not know their physiological partitioning and relevance, and even less about how they behave in age-related diseases (Wang et al., 2015; Verschoor et al., 2014). There is also plasticity between states and the cells may be reprogrammed from one state to the other in either direction. Their study in humans is of practical importance as their modulation and reprogramming, such as, for example, during cancer, may be a valuable target for treatments. The use of multicolor flow cytometry and CyTOF will help to understand these cells better. Neutrophils are the first cells to arrive at the site of an infection or other inflammatory challenge. These short-lived cells are instrumental in defense against infections, cancer, and the priming of the adaptive immune response (Uribe-Querol and Rosales, 2015; Odobasic et al., 2016). For a long time, neutrophils were thought to be homogeneous, but again, probably this is not the case. Surface markers useful for identifying neutrophil subpopulations are CD177 or CD66 b (Sagiv et al., 2015). Functionally, the former neutrophil population is better characterized by its dual role, i.e., maturation and chemotaxis, while the latter is characterized by a higher propensity to adhere to endothelial cells. There are almost no data on these important subpopulations related to human aging. Finally, DCs are professional antigen-presenting cells, which express different surface molecules depending on their origin and their maturation state. DCs have been shown to have decreased chemotaxis, endocytosis, toll-like receptor (TLR) surface expression, and antigen presentation capacity in older individuals. As for other innate cells, DCs also produce more proinflammatory cytokines such as tumor necrosis factor in the elderly, but less interferon as a result of decreased costimulatory receptor expression such as TLR7. The intracellular signaling is also altered in DCs with aging in humans (Magrone and Jirillo, 2014; Qian et al., 2011; Linton and Thoman, 2014).

Adaptive Immune System The adaptive arm of the immune system consists of T cells and B cells. Surface markers can be used to determine their immune history and their functional capacities and are becoming increasingly sophisticated for subdividing existing populations. The multicolor assessment either by fluorescence flow cytometry or more recently by CyTOF mass spectrometry has largely contributed to this knowledge. The use of different specific antibodies is the key for these studies.

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The universal marker of T lymphocytes is the expression of the T cell receptor for antigen (TCR) and CD3 chains. There are two major subsets of CD3+ T cells carrying either the CD8 receptor for MHC class I molecules (mostly cytotoxic T cells) or the CD4 receptor for MHC class II molecules (mostly helper and regulatory T cells). These are further subdivided using other cell surface markers such as CD28, CD27, CCR7, and CD45 into differentiation stages representing naïve, effector, and memory T cells (Onyema et al., 2015; Sallusto et al., 2014; Romero et al., 2007). These various subpopulations determine the ability of the immune system to react to different challenges and more specifically their numbers and proportions reflect the immune history of an individual (Larbi and Fulop, 2014; Sansoni et al., 2014). As such, in aging there is an increase in memory T cells as an obvious consequence of previous acute and chronic antigenic stimulation (Larbi and Fulop, 2014; Ouyang et al., 2003; Shin and Kang, 2015). It is often assumed that these T cell subpopulations also represent a homogeneous functional class, but this not the case. Deep phenotyping helps to further subdivide them into many different subpopulations. Surface marker phenotyping may also help to determine whether these cells are exhausted or senescent (Wherry and Kurachi, 2015). This is a fundamental distinction, which should be made in aging research because the determination of these specific states may direct therapeutic interventions (Yosef et al., 2016; Nishijima et al., 2016). Thus, exhausted T cells represent a population, which maintains some functionality but because of certain characteristics such as the expression of negative receptors such as PD1 or CTLA4 cannot function as they should. Senescent T cells may also be present; some may express surface KLRG1 and CD57, but there are no unequivocal markers for senescent cells (Onyema et al., 2012; Effros, 2004). These cells can retain cytotoxic function but not proliferative capacity and may acquire the senescence-associated secretory phenotype (SASP) (Loaiza and Demaria, 2016; Tchkonia et al., 2013). A higher proportion of senescent cells exhibiting the SASP phenotype in older individuals has already been documented for several different tissue types (including lymphocytes). Extrinsic modulation of the exhausted cells may be able to restore their functionality, while probably for the senescent cells the only efficient intervention is their physical elimination (which has been proven effective in mouse models) (Hellmann et al., 2016; Chang et al., 2016). This may or may not be desirable in humans. However, by the techniques used we can follow the interventions and their effects on age-related diseases (Kaeberlein et al., 2015; Seals et al., 2016). The gamma/delta T cells, characterized by different forms of their TCR receptors (containing the gamma and delta chains rather than alpha and beta chains typical for the major peripheral T cell population, CD4+ and CD8+ T cells) are also a good example of the diversification of the subpopulations. There are two main subpopulations, delta-1 and -2, which possess different specificities and functions. Changes in the frequencies of these subpopulations of T cells have been reported in elderly individuals (Wistuba-Hamprecht et al., 2013; Tan et al., 2016). Surface marker determination resulted in the finding the delta-2 subpopulations change much less than the delta-1 in older individuals. This is an entirely new finding, showing that among an (assumed) homogeneous T lymphocyte population there may be variability leading to functional maintenance with aging (Tan et al., 2016). The other major cell type of the adaptive arm is antibody-producing B lymphocytes. These cells produce the necessary antibodies at levels sufficient to provide protective immunity against pathogens. These cells are also subdivided into different subpopulations (naive, memory, preswitched, and postswitched B cells), which are marked by specific surface receptors (Leandro, 2013). The physiological switch from the early IgM producers to the cells making IgG and other immunoglobulins is a defining factor for the different subpopulations. With aging antibody specificity and affinity decreases, this may reflect changes in the proportions of B cell subpopulations, as well as intrinsic changes within the subsets. For example, this might reflect the existence of various B cell subpopulations present only in elderly individuals (Boyd et al., 2013; Frasca et al., 2016).

FUNCTIONAL ANALYSES It is clearly not sufficient in any biological setting to study the constitutive cellular immune components solely phenotypically. It is essential to gain insight into their functional properties as well, and even more important to assess the functional reserves of the cells, especially for immune cells in the environment of an aging organism (Fulop et al., 2016). Many functions of the immune cells, evolved to protect against internal and external challenges, are redundant but distinct from one cell type to another. There are also large differences between innate and adaptive immune system functionality with age. The most commonly applied methods to study immune cell functionality are related to cell biology such as those used for the assessment of phagocytosis, free radical production, chemotaxis and proliferation, as well as cytotoxicity, and cytokine secretion.

Innate Immune System The innate immune system defends the organism with limited specificity primarily for molecules shared between microorganisms but lacking in vertebrates and has marginal memory function. It is phylogenetically older than the adaptive immune system and is present in all multicellular species is the first line of defense to be triggered by a challenge and may

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react within seconds (Thaiss et al., 2016). Phagocytic cells, predominantly neutrophils and monocytes/macrophages, are particularly well equipped to carry out these functions. Thus the process of defense starts with the recognition of the challenge via different cell surface receptors, including Fcγ receptors (CD16, CD32), the C3 and TLR (Kaufmann and Dorhoi, 2016; Gordon, 2016); indeed, one of the most important discoveries in this area in recent years was the demonstration of the existence of types of receptor, which can distinguish between different structures of invading organisms, such as DNA (ssDNA, dsDNA), RNA, or LPS (Roers et al., 2016; Köberlin et al., 2016). The presence of these receptors confers the ability to respond to challenges arising from evolutionarily conserved molecules such as the pathogen-associated molecular patterns recognized by pathogen recognition receptors expressed on the surface on innate cells, including not only the TLR but also NOD-like receptors and Rig-like receptors (Rivera et al., 2016). The study of these receptors is crucial for the understanding of the functioning of innate immune cells, as well as for the role that they may play in aging, either by directly combating pathogens or supporting subsequent adaptive immune responses or both (Fortin et al., 2007; Weinberger et al., 2016; Fulop et al., 2004; Goldberg and Dixit, 2015). It is not only sufficient to study their expression but also the functions elicited by them as well as the receptor signaling leading to these functions. In addition, to the response to pathogens, these cells also contribute to the dysregulation represented by a low-grade inflammation, termed “inflammaging” by Claudio Franceschi and suggested to be a major mechanism of tissue damage and organismal deterioration in aging (Franceschi et al., 2000, 2017; Salvioli et al., 2013; Cannizzo et al., 2011) (Fig. 4.1). So, what are the crucial changes in functions to be studied to understand their roles in aging? Innate immune cells are able to specifically migrate by following a chemotactic gradient to the site of infection and are already primed on arrival, as can be determined by the most commonly used technique (the Boyden chamber) (Mikami et al., 2015). The start of their action will normally result in a reduction of the amount of microorganisms present by

FIGURE 4.1  Deep phenotyping by multicolor flow cytometry of the innate and adaptive immune system associated with multiplex, omics, and western blot techniques to assess the complexity of the whole system leading to the state of inflammaging as a master driver of aging and age-related diseases.

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engulfing them by the process of phagocytosis. This can be studied by using preprepared kits for flow cytometry such as the Phagotest or by microscopy using radiolabeled or fluorescent products (Baëhl et al., 2015; Drevets et al., 2015). The final stage is to kill the pathogens either intracellularly or extracellularly, which also results in increased levels of proinflammatory molecules. The major effector mechanisms of innate immune cells rely on the release of toxic factors from lysosomes, such as free radicals (reactive oxygen species (ROS) or nitrosylated molecules) as well as other effects such as enzymes and antimicrobial peptides (Carneiro et al., 2016). These events can be studied using kits or by flow cytometry (Pavelescu, 2015; Xing et al., 2016). The factors released in this process must be strictly regulated to limit damage to the host itself. This is achieved by triggering apoptosis of the effector cells to limit the reaction and/or neutralized by antioxidants and antiinflammatory molecules. All these events contribute to acute inflammation, which is essential to control infections. The main effector cells in these reactions, neutrophils, have a short lifetime and are rapidly eliminated from the inflammatory site, whereas monocytes and tissue-resident macrophages are relatively long-lived. This balance between these two cell types and the question of resolution of acute inflammation (beneficial) versus long-duration chronic inflammation (damaging) may be crucial in aging. To perform their functions, a cascade of signaling pathways must be initiated by the relevant cell surface receptors, leading to gene transcription to mediate effector functions. The most important receptors common to these cells are, as already mentioned, TLR, Fcγ, and chemokine receptors. Changes in their numbers with age are still controversial especially in humans; however, with some exceptions the numbers of these receptors per cell are not thought to change with age (Fortin et al., 2008). Studies have shown that signaling pathways are generally altered either in their proximal events including MyD88, PI3K, and Lyn or at the distal events including NFκB (Fulop et al., 2004, 2014b). As the three most important pathways are the JAK, Erk and PI3K pathways and they have many interconnections, all three were found to be altered in the elderly to some extent under various stimulating conditions, leading to changed functions such as chemotaxis, free radical production, killing, and antigen presentation (Fulop et al., 2004). One finding remains constant: the increased activation/activity of most of the signaling molecules such as PI3K already at the basal state, indicating not only reactivity to the microenvironment but also the need for a readiness to respond very quickly (Robert and Fulop, 2014). However, the corollary of this “readiness” state is the decreased specific reaction once it is needed by the system. The reason for this trade-off between readiness and the decreased reactivity is not known exactly, but it could be very important in an evolutionary perspective. It can be put forward that maintaining a state of readiness is energetically more conservative than the energy required for the reactivation of the functions. This could also mean some form of memory for the innate immune response. However, the level of these functions, if we consider the clinical setting in healthy elderly, should be still enough to combat infections. It was suspected for many years, but is now mostly accepted, that the innate immune system does indeed possess a form of memory (Netea et al., 2016). This type of immune memory is very different from that found in the adaptive immune arm and has been called “trained innate memory” to distinguish it from the latter. This is an important phenomenon throughout the different biological kingdoms from plants to animals. This seems to indicate that the proinflammatory phenotype induced by a specific or nonspecific stimulation can be detected up to 3 months after the original stimulation but perhaps even longer; however, no data are presently available concerning its possible duration (Kleinnijenhuis et al., 2012). The molecular basis of trained memory, at least in monocytes/macrophages, may be epigenetic regulation of genetic programs involved in cellular metabolism by the action of H3K4me3 enhancer on the promoter regions (Quintin et al., 2012; Oppermann, 2013; Kyburz et al., 2014; Ostuni et al., 2013). Thus, enhancer activation persisted and mediated a faster and stronger response on restimulation, rather than returning to the latent state when the original stimulus ceased. This was designated a “latent enhancer” effect (Ostuni et al., 2013) and suggested to contribute to a sustained proinflammatory functional phenotype. Accordingly, a metabolic basis for trained immunity has been documented (Cheng et al., 2014). Thus, the various stimuli for trained immunity including microbes, nutrients, and other stimulating agents are able to induce a metabolic shift from oxidative phosphorylation to aerobic glycolysis (the Warburg effect) via the activation of mTOR and the effector—hypoxia-inducible factor 1α resulting in an epigenetic reprogramming explaining this trained innate immunity phenotype of macrophages (Saeed et al., 2014). These studies clearly underline the necessity to use new techniques at the level of genetics and epigenetics/metabolism to assess immune complexity for better understanding the interactions and their effects on innate cell functions. The question arises whether the chronic low-level inflammation commonly observed in the elderly might not also be an adaptive mechanism with unfortunate side effects; the trained innate memory as a result of the epigenetic shift toward aerobic glycolysis may represent a state aiding the maintenance of necessary immune reactivity, but contributing to inflammaging (Baëhl et al., 2015). This age-related low-grade inflammation creates a vicious circle of self-sustained maintenance which, on the one hand, helps to combat the challenge, but on the other hand causes a decreased immune response to acute or chronic challenge demonstrated in these cells during aging. However, not all patients suffer from infections, so there

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still remain protective effector reactivities of the innate immune system with aging. The importance of these phenomena is underlined by the demonstration that they are present in posttrauma situations, such as hip fracture, in the atherosclerotic process and in Alzheimer’s disease (Baëhl et al., 2015; Le Page et al., 2015). Thus, we suggest that trained innate immune cells contribute to the maintenance of immunosurveillance at the price of a chronic proinflammatory state increasing with age and also contributing to age-related pathologies (Perna et al., 2016; Horvath et al., 2015; Horvath and Ritz, 2015). Directly along this line of reasoning, recently the group led by Janet Lord demonstrated that inhibition of PI3K in resting human neutrophils significantly improves their functions (Sapey et al., 2014). The causes of these alterations are numerous, including the extrinsic ones such as the hostile inflammatory milieu leading to an already increased activation level at basal state and the intrinsic ones such as membrane alterations or the disequilibrium between feed forward pathways and the inhibitory feedback loops (Fortin et al., 2006). Because the relevant receptors are assembled in the plasma membrane, age-associated changes in its composition resulting in altered physical properties and fluidity could be the first locus for dysregulated function in the elderly. It has been known for many years that the lipid composition and the fluidity of the plasma membrane do change with age also in cells of the immune system (Fulop et al., 2014b). Several studies from the 1970s, including our own, showed that membrane cholesterol content is decreased in neutrophils from elderly people. This destabilizes the structure and the fluidity of the membrane. While the role/importance of membrane substructures on immune functions has been questioned, there is an increasing interest to understand this aspect of membrane biology and its effect on the signaling cascade as our microscopic and chemicophysical modeling systems are progressing at a sophistication level not known before. Lately, several new data concerning modulation of the membrane state have emerged, which should be very quickly applied to studies of aging (Dillard et al., 2016; Yeo et al., 2016; Xu et al., 2015). Furthermore, the age-related proinflammatory milieu, including proinflammatory cytokines, free radicals, and glycosylated proteins (Maillard reaction) all contribute to the cell membrane alterations and to the consequent receptor-altered signaling with aging. To better understand these processes we would need more deep molecular interactome studies in these cells, which would lead to better possibilities for interventions. Human studies are still scarce concerning the innate/myeloid compartment (especially the monocyte/macrophages). However, most of the studies that have been performed convey the idea that the abovementioned functions of the innate immune system are all decreased with aging, with two exceptions: adhesion, which does not change, and proinflammatory cytokine secretion, which increases with age. We should again stress that most of these data are cross-sectional, comparing older to younger subjects, and not really in their physiological context of how they should protect the elderly organism itself. We need a change in the paradigm to better understand the immune alterations with aging to better integrate the appreciation of its consequences.

The Adaptive Immune System Following the activation of the innate system, the adaptive arm may also be triggered. Adaptive immunity has been extensively studied in the frame of aging, in humans as well as animals such as mice. The paradigm states that there are profound alterations with aging, which explain the increase of infections, cancer, and autoimmune diseases with aging (Fülöp et al., 2016; Macaulay et al., 2013; Pawelec, 2014; Fulop et al., 2013). The methods available are numerous and sophisticated to make real breakthroughs in this field (Fulop et al., 2014a). Lifelong activation resulting from exposures to pathogens, together with thymic involution at puberty, results in decreased levels of naive T cells in the periphery, more pronounced in the CD8+ than in the CD4+ T cell compartment (Palmer, 2013). There may be a concomitant increase in memory and late-stage differentiated T cells, but this is only seen in subjects infected with cytomegalovirus (Pawelec et al., 2005, 2012; Solana et al., 2012; van Baarle et al., 2005). These CMV-specific CD8+ T cells probably help to prevent CMV reactivation by killing virus-infected cells, thereby controlling virus replication even at the expense of immune exhaustion (Appay et al., 2011). Thus, this memory T cell “inflation” may be caused by chronic antigenic stimulation, and this, coupled with insufficient renewal of naïve cells, represents the main hallmark of adaptive immunosenescence (Larbi and Fulop, 2014; Qi et al., 2014; Appay and Sauce, 2014). The decrease in naïve T cells may not be as dramatic as was previously believed because T cells of thymic origin, specifically identified by high numbers of T cell receptor excision circles (TREC), may be replaced by other types of naïve T cells originating from homeostatic proliferation in the periphery (Appay and Sauce, 2014). Their exact role and mainly whether they may compensate for thymic involution is not known. Memory T cells can be identified by their shorter telomeres, fewer TRECs, lower, or absent expression of CD27 and CD28, and in the latest stage of differentiation, reexpression of CD45RA and acquisition of CD57. Functionally, T cells can be divided into subtypes such as Th1, Th2, Th17, and regulatory T regulatory cell (Treg) subtypes, which are also differently distributed in younger and older individuals (Stervbo et al., 2015a). Many findings reinforce the notion of a role for the adaptive immune system in the determination of longevity of elderly people. Not only may the accumulated memory cells fill the “immune space,” but also their specific functions, including

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clonal expansion, IL-2 production and their abilities to help other T cells and B cells with specific effector functions, are altered. This is the basic paradigm for the establishment of the immune risk phenotype (IRP), which was based on findings in very elderly people (85 years of age) and is associated with 2, 4, and 6 year survival at follow-up (Pawelec et al., 2009). This cluster of parameters consisted of high numbers of CD28-negative CD8+ T cells resulting in an inverted CD4:CD8 ratio and poor proliferative responses to mitogens, together with low numbers of B cells and cytomegalovirus (CMV)seropositivity. The IRP was weakly associated with survival, but when combined with the independent risk factors cognitive impairment and high IL 6 levels became much more so. These studies derived from a southern Swedish population of birth cohorts from around 1900. However, despite the application of the IRP in several other populations, it has never been demonstrated that it is in fact predictive of mortality in other contexts. In fact, immune risk factors for mortality are extremely context-sensitive as most dramatically illustrated by recent studies on the Belgian population [BELFRAIL]. Three-year mortality in this population, not only from later birth cohorts but also around 83 years of age, was predicted by totally different immune risk factors than in the Swedish (Adriaensen et al., 2017). These studies greatly advance our knowledge in the field of biomarker discovery and inclusion of dynamic parameters taking into account the reserve of the organism as a surrogate measure for the adaptive immune system but emphasizing the very different status of different human cohorts. Functional changes associated with T cell aging are related to alterations in signaling pathways, including TCR, CD28, and cytokine receptor signaling (Fulop et al., 2014b; Varin et al., 2008; Larbi et al., 2011; Goronzy et al., 2012). The very early step in TCR signaling is the activation of the protein tyrosine kinase (PTK) Lck (Yeo et al., 2016; Le Saux et al., 2012). Its activity is controlled by a multicomponent module comprising the protein tyrosine phosphatase (PTPase) CD45 and the PTK Csk bound to the scaffold protein PAG (CBP) (Le Page et al., 2014). It was shown that changes occurring in the very proximal signaling events in T cell activation may reduce the efficiency of the immune response (Le Page et al., 2014). We have recently shown that dysregulation of Csk/PAG/CD45 loop in aged T cells favors the inactive form of Lck, providing a molecular explanation to altered T cell responses in aging. Moreover, it was found that negative feedback inhibitory events were also compromised in these elderly subjects. SHP-1 activity was higher in healthy elderly subjects than in young individuals, an observation consistent with the decreased T cell response (Le Page et al., 2014). It is of note, that the pharmacological inhibition of SHP-1 resulted in recovery of TCR/CD28-dependent lymphocyte proliferation and IL-2 production of healthy elderly to levels similar to those of young adults, suggesting the possibility of improving T cell response in healthy elderly. Identical mechanisms were also uncovered in case of altered Erk activation in regard to the DUSP4 by the group of Goronzy (Yu et al., 2012; Li et al., 2012). Intrinsic alterations have been also demonstrated at the level of the T cell membrane as the cholesterol content was found to be increased, interfering with the coalescence of the lipid rafts as necessary for adequate signaling (Larbi et al., 2006). Increased membrane cholesterol results in the alteration of the formation of the signaling microclusters recently demonstrated by membrane reconstitution with physicochemical techniques (Dillard et al., 2016; Yeo et al., 2016; Xu et al., 2015). These studies emphasized again the importance of the membrane structure and physicochemical properties in the initiation of signaling by Lck-ZAP70 and Slp76. Other important modifiers of T cell signaling are ROS, increased in the process of aging and hypothesized to be the main mediators of age-associated tissue damage (Harman, 2006). During CD28 activation, levels of reduced glutathione are decreasing and those of cytosolic ROS are increased (Ray et al., 2012; Larbi et al., 2007). It was shown that in T cells of aged individuals there is no modulation of ROS, which remains high (Griffiths et al., 2011). The above described conditions inhibit TCR signaling through lowered expression of TCR/CD3, diminished phosphorylation of ZAP70 and altered Ca2+ mobilization (Goronzy et al., 2012; Cope et al., 1997). This could probably involve also the production of proinflammatory cytokines, by modified activities of the calpain–calpastatin system (Mikosik et al., 2013, 2016). Furthermore, this redox status may also selectively affect T cell subsets, according to the specific priming conditions and their state of differentiation (Goronzy et al., 2012). Therefore, redox status in T cells has important consequences for their activation and consequent differentiation, especially in healthy elderly subjects. The persistence of low amounts of proinflammatory cytokines, concomitant with increased production of ROS, which are both features of inflammaging, converge to diminish T cell function in older persons, IL-2 production, and clonal expansion. As outlined above, alterations in T cell activation in the healthy elderly may result from accumulation of memory T cells due to repetitive antigenic stimulation over the life span. Recently, progress has been made in linking the development of the memory phenotype and signaling and the concomitant cellular metabolism orchestrated by the mTOR pathways (Chisolm and Weinmann, 2015; Pearce, 2013). It is now accepted that the memory phenotype emerges because of the persistent activation of MAPK p38. The fundamental metabolic requirements of senescent primary human CD8+ T cells were elucidated and it was shown that p38 MAPK blockade reversed senescence via an mTOR-independent autophagy pathway (Henson et al., 2015). These results challenge our belief that mTOR governs the senescence of T cells. In the meantime, recent results have suggested that inhibition of mTOR may increase the humoral immune response to influenza vaccination in the elderly (Mannick et al., 2014).

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Although there is a great deal of information on immune cell signaling alterations leading to altered phenotypes, T cell subsets and functions with aging, there is a need to pursue these studies to find nodes and hubs, which are crucial for the integration of the signaling pathways, to facilitate effective interventions. In this respect high-throughput analysis of the very early signaling events and metabolism considered from a systems biology perspective would be the most rewarding (Cevenini et al., 2010; Poland et al., 2014). Another potentially important difference between younger and older people concerns the frequency and activity of Tregs, which may lead to increased dampening of immune reactivity in older adults (Holcar et al., 2015; Jagger et al., 2014). These alterations will need to be entered into the equation, together with the changes in the innate and adaptive immune response to fully understand immunosenescence. The role of CMV in this context also remains to be fully elucidated. There is compelling evidence that CMV is the driving force for the accumulation of late-stage differentiated CD8+ T cells with aging, this is clearly not an effect of age per se, because CMV-seronegative elderly do not show similar memory inflation. Consistent with this, the effects of in younger people is similar. The reason why so many CMV-specific CD8+ TEMRA T cells accumulated in the elderly may be related to their low-affinity receptors and possible response to exogenous cytokines in an antigen–nonspecific manner (Griffiths et al., 2013). The origin of these cytokines may be the SASP acquired by many cell types in the elderly, as alluded to above (Herranz et al., 2015; Byun et al., 2015; Lasry and Ben-Neriah, 2015). T cells themselves may also contribute to this in that they also show a Hayflick limit (Effros and Pawelec, 1997). Thus, while the role of CMV in memory expansion as a key trigger for skewing the T cell repertoire is unequivocal, the inflammatory milieu widely existing with aging may also be another independent key factor (Fulop et al., 2015).

INFLAMMAGING Inflammaging as alluded to above is a state associated with increased concentrations of proinflammatory mediators in extracellular fluids developing over time, at least partly because of constant antigenic challenges in daily life. This antigenic stimulation can be provided either by pathogens such as CMV, herpes simplex virus-1, or by cellular and molecular debris arising from transformations caused by ROS, by the Maillard reaction (e.g., advanced glycation end products), by nitrosylation and by cancer cells. These constantly generated antigens stimulate both innate and adaptive immunity resulting in the lowgrade inflammation. Nonimmune processes also contribute to this phenomenon, such as increased cell death, oxidative stress, altered nutritional status, intestinal leakage, gut microbiome dysbiosis, hormone dysregulation, and other factors. However, as discussed above, the low-grade inflammatory state may represent a hormetic response that maintains potentially readiness to respond to stronger challenges, such as a higher pathogen load, duration, or extent. The downside of this is a lesser additional effector response (conceptualized as “immune paralysis”) at the same time as this heightened baseline inflammatory state (Baëhl et al., 2015, 2016; Fulop et al., 2016). This then contributes to chronic inflammatory disease because the readiness of the innate system causes excessive inflammation and tissue damage. Nonetheless, the adaptive system may still react to a new challenge, albeit at a lower level. This level may be adequate for the elderly, but far from that necessary in young subjects. In line with these considerations, recent findings in centenarians and semisuper centenarians indicate that their immune system is relatively “young,” suggesting that immune “fitness” is important in longevity (Arai et al., 2015). Many published data are consistent with this, for example, regarding phagocytic cell functions and ROS production. Nonetheless, levels of proinflammatory other cytokines, especially IL 6, are raised in centenarians (Pinti et al., 2014). This may illustrate a general principle that resistance to the negative effects of agents that can have both protective and damaging effects may provide the crucial impetus for longevity. Thus it can be postulated that a low-grade inflammation may be compatible or even necessary to become centenarian. This reinforces the hormetic conception of the low-grade inflammation with aging and militates in favor of the adaptive character of the immune changes with aging. Certainly it underlines that centenarians do not need an immune system resembling that of young individuals. Furthermore, many centenarians suffer from chronic inflammatory diseases and still may do quite well.

A DYSREGULATORY APPROACH INTEGRATING THE IMMUNE SYSTEM AND OTHER SYSTEMS Clearly the immune system does not exist in isolation but is influenced by and in turn influences many other systems such as the central and the peripheral nervous system, the endocrine system, and others (Mate et al., 2014). This fits perfectly to the new approach of the study of aging, which states that aging is the sum of results of the dysregulation of different system(s) from a normal regulatory level (i.e., a homeostatic state), which is not necessarily identical between young and old and may well reflect adaptations to intrinsic and extrinsic changes related to aging. Thus dysregulations are neither obligatory detrimental nor beneficial but indicate a state of dyshomeostasis. This led to the important insight that the proinflammatory state cannot be considered separately form the antiinflammatory state, a finding that will likely be expanded on and nuanced by inclusion of other systems

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and subsystems. This is a completely new approach, which can be studied by various statistical analyses considering smaller or larger data bases obtained in elderly. This can also lead to the discovery of biomarkers and their use in clinics. This permits the integration of the omics, single cell assessment, and systems biology. Different statistical approaches may be warranted in cases when the dysregulation(s) follow a single canalized pathway (as appears to be the case for inflammaging and likely metabolic syndrome) (Morrisette-Thomas et al., 2014; Cohen et al., 2015; Dusseault-Belanger et al., 2013), and for cases where dysregulation can produce a wide array of phenotypes sharing little beyond their departure from a homeostatic state (Li et al., 2015; Milot et al., 2014; Arbeev et al., 2016). An appealing hypothesis is that canalized dysregulations occur as a result of adaptations to the aging process and thus reflect an optimized response to an imperfect situation, whereas noncanalized dysregulations reflect a true loss of homeostatic control and may themselves be the imperfect situation causing the canalized responses. An additional application of these kinds of integrative statistical approaches could be for the identification of cell types in both the innate and adaptive immune systems, and their changes with aging. Immunology has generally considered individual cells to belong to discrete types that can be distinguished based on their surface markers. Certainly this is a valid paradigm for many of the major classes (T cells vs. B cells, CD4+ vs. CD8+), but many examples are starting to emerge of less distinct classes: classes based on the levels of surface receptors rather than just their presence or absence, or classes confounded by some subpopulations that are present for two markers that were supposed to distinguish populations (e.g., CD45RA+ vs. CD45RO+). It would thus appear that variation in cell surface receptors can happen in different ways. Discrete variation produces populations of cells with distinct functions and properties (“classes” or “types”), whereas continuous variation produces cells with functions and properties that vary along a gradient. This distinction is important because if we wrongly consider cells varying along a gradient as being from discrete classes, we are likely to (1) misidentify many cells with intermediate phenotypes and (2) fail to understand the true biological processes driving cell diversity and thus to misunderstand the functional consequences of this diversity. For example, suppose that a population of cells varies along a gradient that determines their affinity for two types of pathogens (A and B), such that cells with high affinity for A have low affinity for B and vice versa. If there is a true gradient, a reasonable strategy would be to have a large population of cells with an intermediate affinity for both, maximizing the flexibility of a response. This might be a particularly good strategy during aging when the total population declines and the ability to maintain large numbers of cells at both extremes of the gradient is compromised. If we falsely divide the cells into A-affinitive and B-affinitive types, we might obtain very strange or confusing results depending on where along the gradient we happened to put the threshold, and it would be impossible to understand any strategies that involve utilization of intermediate values along the gradient. To be clear, there are not yet clear data showing to what extent gradients versus classes are important in structuring the variation in immune cell types; nonetheless, there is no reason beyond precedent to suppose that classes dominate gradients among the more specialized cells. Analyses such as cluster analysis and principal components analysis should be able to provide a great deal of clarity on this subject if applied judiciously. We hypothesize that both gradients and classes play important roles that can be teased apart with such methods, and that these distinctions will be particularly important for understanding changes in the immune system during aging. Many of the changes in immune cells with age may reflect adaptive changes along gradients, and we are likely to miss the adaptive nature of these changes if we separate the cells into arbitrary classes. This way to approach the immune changes with aging can be easily applied to other systems such as the neuronal or endocrine systems.

CONCLUSION Clearly, organismal aging is a very complex process, and no single physiological system can be made responsible for it, certainly not the immune system. If that would be the case, its modulation could stop or at least slow down the aging process. However, its study in the scope of a systems biology approach is essential. New studies indicate that the uniformly deleterious view of the changes in the immune system with aging is evolving toward a more dynamic adaptive view, which is essential if we would uncover the real role of this system in the aging process and in diseases. Given the multifactorial nature of aging, immune system dysregulation can certainly be said to contribute to aging. It is likely that only longitudinal studies in humans will be able to unequivocally document the role of the immune system in the aging process. Such knowledge will allow the design of rational interventions to reconstitute appropriate immunity, and increase quality of life and the functional health span of elderly people.

ACKNOWLEDGMENTS This work was partly supported by grants from the Canadian Institutes of Health Research (No. 106634 and No. 106701), the Université de Sherbrooke, and the Research Center on Aging; the Deutsche Forschungsgemeinschaft (DFG PA 361-22) and a grant from the Croeni Foundation (GP); Polish Ministry of Science and Higher Education statutory grant 02–0058/07/262 to JMW; Agency for Science Technology and Research (A*STAR) to AL. AAC is supported by a New Investigator Salary Award from the Canadian Institutes of Health Research and is a member of the Fonds de recherche du Quebec-Santé-supported Centre de recherche sur le vieillissement and Centre de recherche du CHUS.

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Immunological Methods and the Concept of Inflammaging in the Study of Human Aging Chapter | 4  57

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Chapter 5

Vulnerability and Experiential Health in Old Age—A Qualitative Perspective Anneli Sarvimäki The Age Institute, Helsinki, Finland

INTRODUCTION Models and descriptions of aging are dependent on what kind of methods have been used to construct the model or ­description in question. Models and descriptions based on qualitative methods focus on the aging persons’ own experiences, memories, and understandings. The purpose of this paper is to illuminate the contribution of qualitative methods to our knowledge of aging and to present two research topics based on qualitative methods; vulnerability and experiential health in old age.

On the Relevance of Studying Older Persons’ Own Experiences and Understandings Research has shown that there is sometimes a discrepancy between how older people rate and experience their health and functional capacity and the results of measurements and ratings conducted by professionals or researchers (Cress et al., 1995; Reuben et al., 1995). Jylhä (1994) pointed out that in rating their health older persons offered a variation of descriptions and interpretations of health. Their understanding of health was not necessarily the same as the researchers. According to Jylhä, self-rated health constituted a crossroad between the psychosocial and the biological world. She presented a model of individual health evaluation as a subjective and cognitive process involving cultural and historical conceptions of health, comparisons to reference groups, and cultural conventions in expressing opinions (Jylhä, 2009). Bowling et al. (2003), who studied quality of life in old age, argued along the same line and claimed that valid models of older persons’ quality of life need to take into account the persons’ own perceptions to be sensitive to the values of different social groups. Hendry and McVittie (2004) as well as Borglin et al. (2005) pointed out that in studying older persons’ quality of life, neglecting the older persons’ own understandings tended to restrict the concept and overlook important aspects. They advocated an experiential and qualitative approach to the study of older persons’ quality of life. People’s subjective experiences and understandings seem to influence the process of aging. For instance, both self-rated health (Jylhä, 2009) and feelings of loneliness (Tilvis et al., 2011) have been shown to predict mortality indicating that persons who rate their health as weak and those who feel lonely die younger than those who feel healthy and not lonely. To deepen our understanding of older persons’ lives and the process of aging, it is thus important to create models and descriptions of aging based on the older persons’ own experiences and understandings, on how they themselves interpret and give meaning to their lives and aging. Although there is no necessary connection between philosophy and research methodology, the articulation of the world in terms of experiences, understandings, and meaning as akin to the characterization of the human world in the philosophic tradition of existentialism and phenomenology. This tradition forms the frame of reference for the studies presented in this paper.

AGING IN THE WORLD OF MEANINGS The basic existentialist question is “What does it mean to live as a human being?” Different existentialists may offer slightly different answers, but a common answer would be that to live as a human being is to be inseparably connected with the world; human beings live in relationships with the world. Freedom of choice, responsibility, and awareness of one’s mortality are conditions of human life. Human beings have a choice as to how they live and by making choices they define themselves as persons. Thus, by making choices they become responsible for who they are and since their choices also affect other human beings they become responsible for them as well (Heidegger, 1962; Sartre, 1966). Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00005-1 Copyright © 2018 Elsevier Inc. All rights reserved.

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In choosing how to live, human beings give meaning to their lives. According to Frankl (1969) the will to meaning is the most basic driving force in human life. By living toward the future human beings realize their existential meaning; they define goals, make choices and act. Looking back on their lives they give meaning to the past by interpreting their lived experiences (Reker and Chamberlain, 2000). Human beings can thus be said to live in a world of meanings. They try to make sense of things and constantly interpret the world around them. Based on these meanings they experience the world, act and make choices. During the process of aging the perspective of the future shrinks, while the perspective of the past grows. Declining strength and the closeness to death makes existential meaning more and more uncertain: What can I still achieve? What can I hope for? Will time run out? Am I still needed? By reflecting on and interpreting the past the aging persons give meaning to their lived experiences. From an existentialist point of view, aging can be characterized as searching for, creating and maintaining purpose and meaning under the conditions of vulnerability in the last stages of life (Sarvimäki, 2015). Because of its central aspect in human life, meaning has also been viewed as a dimension of quality of life. Sarvimäki and Stenbock-Hult (2000) have introduced a model of quality of life in older persons defining quality of life as a sense of well-being, meaning, and value.

QUALITATIVE METHODS AS THE STUDY OF MEANINGS There are similarities as well as differences between the qualitative methods. Qualitative methods used in aging research commonly deal with talk that is transcribed to text or with written material such as letters and diaries. In talk and texts older persons express how they experience life and aging, how they conceive e.g., health and how they understand their lived experience. How the research process goes on depends on which qualitative method is adopted. Content analysis, interpretive description, interpretive phenomenology, hermeneutics, and narrative method have been used in the studies presented in this paper. Most qualitative methods involve a process of content analysis of transcribed interviews or other texts, where words, sentences, or passages are extracted and coded. The codes are then organized into categories or themes. By further reading, reflecting and revising the categories or themes are organized into more overarching categories or themes on a higher level of abstraction. The results are generally presented in a figure, a table, or as themes and quotes in the text (Elo and Kyngäs, 2008; Elo et al., 2014). The result of the content analysis can be presented on a descriptive level, but most of the times some interpretation of latent meaning is involved. The method can then be characterized as interpretive description (Thorne et al., 2004) or interpretive phenomenology (Benner, 1994) depending on the background philosophy. Benner’s (1994) interpretive phenomenology has its roots in, among others, Heidegger’s (1962) philosophy. Heidegger is also an important background philosopher for the qualitative tradition of hermeneutics. In hermeneutics interpretation is both a philosophy and a method. As a philosophy it refers to understanding as constitutive for our existence in the world. Interpretation and understanding reveal the world and its possibilities to us. As a method hermeneutics stands for a process described as the hermeneutic circle or spiral, where we go from a vague holistic conception of a phenomenon to analyzing its parts and then continue to a new and deeper understanding of the phenomenon (Radnitzky, 1970). The deeper understanding is the result of an act of interpretation that gives the phenomenon a new meaning (Lindseth and Norberg, 2004). The narrative method approaches the text as a story or as stories. First the participants interpret their experiences by telling a story in an interview or by writing down their memories. The story can be about their whole life, a life story, or a specific situation. The researcher analyzes the stories, looking for a plot, and makes a new interpretation. The interpretation gives the story a new meaning. When the story is analyzed by interpreting meaning, it is analyzed from the viewpoint of content. Another approach is to analyze form, which focuses on aspects such as complexity, choice of words, and feelings evoked by the story (Riessman, 1993; Lieblich et al., 1998).

THE MEANING AND EXPERIENCE OF VULNERABILITY IN OLD AGE The rationale behind studying the meaning of vulnerability in old age lies in the everyday experience and vast amount of research showing how health and functional capacity decline with the coming of age. Old age is associated with frailty (Schröder-Butterfill and Marianti, 2006; Andrew et al., 2008). The purpose of the studies presented here was to illuminate older persons’ own meanings and experiences related to vulnerability. The studies were conducted within a philosophic and conceptual framework.

The Philosophic and Conceptual Framework According to the existentialist philosophy that forms the framework for this research, vulnerability is one of the conditions of human life along with awareness of mortality, dependency, freedom, responsibility, the frailty of human relationships, and

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existential loneliness. In this sense all human beings are vulnerable. A person who faces the conditions of life, who ­realizes what freedom means and takes responsibility for his or her choices, is living what the existentialists call the authentic life. The realization of one’s freedom, responsibility, loneliness, and mortality tends to evoke anguish and fear. The authentic life thus requires openness and courage to encounter the conditions of life (Heidegger, 1962; Sartre, 1966; Tillich, 1962). A person may, however, try to avoid the anguish and fear that comes with the authentic life and choose the inauthentic life. This means denying one’s freedom and living as if one was not responsible, vulnerable, and mortal. The person who chooses the inauthentic life may hide behind a role (Sartre, 1966), hide into the crowd, and become just anyone, das Mann (Heidegger, 1962), or treat others as if they were objects (Buber, 1958). Persons may protect themselves from vulnerability, pain, and suffering by choosing the inauthentic life, but at the same time they give up freedom and responsibility. The lesson learned from the existentialist philosophy was that vulnerability, although it is associated with anguish and pain, is also a positive state because it is part of the authentic life. As Hoffmaster (2006) puts it: the recognition of our vulnerability affirms our humanity. The conceptual analysis revealed the same double aspects as the philosophic inquiry. Vulnerability was conceptualized both as frailty and weakness and as openness and sensitivity. As a result of the conceptual analysis the following definition was proposed. Vulnerability means that a person is easy to hurt or harm because he or she is 1. frail and weak 2. unprotected against physical or verbal violation, at risk 3. open, sensitive and susceptible to stimuli (Sarvimäki and Stenbock-Hult, 2004; Sarvimäki et al., 2010). Two empirical studies focusing on older persons were conducted, one of the meaning of vulnerability to older persons (Sarvimäki and Stenbock-Hult, 2016) and one of the experiences of vulnerability among older family caregivers (Sarvimäki et al., 2016). While the existentialist and conceptual studies resulted in an overview of what Rogers (Rogers, 2014) referred to as universal vulnerability, the empirical studies led us to what she called contextual vulnerability, a more specific vulnerability that was caused by contextual factors. In this case the contextual factors were related to the process of aging and being an older family caregiver in a Nordic country.

The Meaning of Vulnerability to Older Persons For the study of the meaning of vulnerability to older persons, 14 persons aged 70–96 years were interviewed (12 women and 2 men). The method used was interpretive description. The results showed that vulnerability was inextricably intertwined with becoming and being old. The core meaning was that a deeper sense of vulnerability was associated with aging. In addition to this, vulnerability meant Being easily harmed, Becoming an old person, Being an old person in society, Reactions when being violated and hurt, Protection, and Vulnerability as strength. (Sarvimäki and Stenbock-Hult, 2016). The results of the study are synthetized with the philosophic and conceptual framework into a model of vulnerability in old age (Fig. 5.1). According to the model of vulnerability in old age, older persons are vulnerable in the same universal sense as all other human beings. They face the same conditions of human life. The contextual factors of old age, however, give vulnerability specific meanings as people grow old. One contextual factor is the closeness to death. Although all people are mortal and death can strike at any age, you are generally closer to death when you are old than when you are young. Awareness of one’s mortality becomes more relevant as one grows old. In a previous study using interpretive phenomenology and focusing on falls and the meaning of falls in older persons, falling was perceived as the first step toward death (Mahler and Sarvimäki, 2010). Older persons are more exposed and vulnerable in a deeper sense (Sarvimäki and Stenbock-Hult, 2016). Another set of contextual factors has to do with declining strength and changes in the social conditions as one grows old. In that perspective vulnerability means frailty in terms of losing functional capacities and close relationships. Mental losses include losing interest in what is going on and social losses can mean losing a close friend to death or Alzheimer’s disease (Sarvimäki and Stenbock-Hult, 2016). The loss of physical strength means “living the vulnerable body,” which is associated with situations that can be experienced as humiliating and undignified, like not being able to get up after falling, being incontinent, and dirtying one’s clothes (Mahler and Sarvimäki, 2012). As a consequence, life tends to become more limited and uncertain. Vulnerability as frailty also includes changes in societal status. Older persons describe how they are no longer encountered as individuals but depersonalized into a gray mass, disregarded, excluded, and treated as incompetent (Sarvimäki and Stenbock-Hult, 2016). In existentialist terms older persons are treated in an inauthentic way, as das Mann or as a thing, although they themselves feel very much that they are competent individuals.

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VULNERABILITY A condition for being human The authentic life: being open and facing one’s freedom, responsibility and mortality despite anguish, pain and fear

VULNERABILITY IN OLD AGE A deeper sense of vulnerability, being more exposed BEING UNPROTECTED

BEING SENSITIVE–A STRENGTH

BEING FRAIL Becoming old: losses, limitations, uncertainty

Having the capacity to feel Developing and maturing as a person

Being an old person in society: being depersonalized and excluded Being easily harmed: hurt, violated Reacting when being harmed harmed: anger, sadness, disappointment, dwelling

NEEDING PROTECTION

Help from others Self-protection

FIGURE 5.1  The meaning of vulnerability to older persons.

Being vulnerable in terms of frailty associated with becoming old and being old in society also means being easily harmed. The combination of being both unprotected and frail makes older persons exposed not only to physical harm but also to mental and ethical harm. Mental harm means feeling hurt and violated when insulted and misunderstood. It means being diminished and devaluated as a person. Being vulnerable in terms of being easy to harm ethically, means a person’s human dignity is at risk. Mental and ethical harm evoke feelings of anger, sadness, and disappointment. Sometimes it is difficult to get past an insult or having been diminished as a person. People can get stuck and dwell on harmful situations (Sarvimäki and Stenbock-Hult, 2016). As unprotected and frail, older persons need help from other people or they need protection from security systems. If they do not get help in time or if the security system fails, they are even more vulnerable. Help and protection do not only include physical aspects but also mental. Older persons need help to deal with difficult feelings when they have been violated. They also use self-protection against mental harm. This can mean not to care too much about what other people say and to rationalize why someone behaves in the way he does. Protecting oneself requires mental ability, “being in your right senses” (Sarvimäki and Stenbock-Hult, 2016). Persons suffering from dementia, for instance, are in a weak position when it comes to protecting oneself. In that sense they are more vulnerable than others and in need of protection.

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Sensitivity is a double-edged sword. As people grow old they may become more sensitive and susceptible to other persons’ opinions, especially if they feel violated and excluded. So, in that way too, they become more vulnerable. But on the other hand, older persons also refer to sensitivity as a resource. Being able to feel—even after having been harmed—means that the person is still an active and living human being. This can be turned into strength. Working through difficult feelings and talking to someone becomes a learning process that helps the older person become more empathetic and not wanting to hurt others (Sarvimäki and Stenbock-Hult, 2016). The meanings that older persons themselves associate with vulnerability are compatible with the existentialist framework and the result of the conceptual analysis. They speak of vulnerability in old age as being more exposed, unprotected, which is one aspect of the authentic life. Pain and anguish are other aspects of the authentic life. Older persons give words to this aspect when speaking expressively and clearly of vulnerability as being frail and sensitive and thus easy to harm physically and mentally. But at the same time they are aware of the double character of sensitivity. They can see that being sensitive and having difficult feelings is part of their humanity and a resource for personal development. The individual and societal contextual factors of aging are reflected in the situations and examples they refer to (Sarvimäki and Stenbock-Hult, 2016).

The Vulnerability of Older Family Caregivers Another model of vulnerability in old age was constructed on the basis of a study of older family caregivers’ experiences. A focus group of four family caregivers (two women and two men aged 66–82 years) was interviewed once a month during a period of 10 months. The analysis was conducted using deductive content analysis using an unconstrained matrix based on a previous study of nurses’ vulnerability (Stenbock-Hult and Sarvimäki, 2011). The older family caregivers’ experiences of vulnerability were constructed into one core theme Being human—having conscience and responsibility, sometimes being angry and disappointed, and six themes; Having feelings, Experiencing moral agony, Being harmed, Having courage, Protecting oneself, and Maturing and developing (Sarvimäki et al., 2016). Fig. 5.2 presents a model of older family caregivers’ vulnerability based on the results of the study integrated with the philosophic and conceptual framework. Family caregivers experience their role as caregivers as being human. Having consciousness, being responsible, and caring for another person is seen as an expression of human dignity (Sarvimäki et al., 2016). This is compatible with the existentialist framework, which emphasizes responsibility both for oneself and for others as part of the conditions of human life. Acknowledging these conditions and facing up to the responsibilities despite painful feelings such as anger and disappointment means human dignity. In existentialist terms, it means choosing the authentic life: not only accepting responsibility but also the pain and agony that comes with it. The contextual factors of family caregiving are reflected in the experiences of vulnerability. Their situation as older family caregivers, make them frail and susceptible to harm. They suffer when witnessing the family member’s decline and they feel under pressure both from the person cared for and society. The caregivers tend to feel insecure and constantly worried about what may happen to their family member. At the same time, since they are old too, they are often exhausted and feel they may die before the person they care for. Feelings of loneliness, because they can no longer communicate with the family member, mean they are mentally harmed. They are also at risk of becoming socially lonely since they become more and more isolated from former friends and relatives. The harm they suffer from can be characterized as ethical: they have a constantly bad conscience and feel guilty because they are not always able to help their family member and sometimes show they are angry (Sarvimäki et al., 2016). The duty to care, although a salient trait of being human, can be experienced as a burden. Moral anger is directed toward society, which does not necessarily provide older family caregivers with enough support. The harm they experience sometimes makes them choose inauthentic alternatives to protect themselves. They may conceal themselves and not show their true feelings or they can dissociate themselves by becoming like robots just focusing on the task at hand (Sarvimäki et al., 2016). Choosing the inauthentic way out of a burdening situation is understandable and does not mean that the person has chosen inauthenticity as a way of life. As Buber (1958) says, being authentic all the time would be too exhausting. We must sometimes choose the inauthentic stance to protect ourselves. The sensitivity that makes the older family caregivers susceptible to harm also opens up for positive feelings. A long and close relationship between the family members makes it possible to share experiences and warm feelings. In addition to irritation and disappointment, the caregivers can feel compassion and closeness. By opening up about their feelings to other people they themselves can receive understanding and compassion. One way to deal with the vulnerabilities is to face the situation and admit one’s limitations. This can be seen as an alternative to the inauthentic way of protecting oneself. As an authentic alternative, admitting one’s limitations requires courage. Speaking up against harmful treatment from the service system also requires courage. The situations the family caregivers have to deal with and the feelings and harm they

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VULNERABILITY A condition for being human The authentic life: being open and facing one’s freedom, responsibility and mortality despite anguish, pain and fear

OLDER FAMILY CAREGIVERS’ VULNERABILITY Being human: Having consciousness and responsibility, sometimes being angry and disappointed

BEING FRAIL AND EASY TO HARM

BEING SENSITIVE AND OPEN

Being harmed: feeling pain and being under pressure, feeling insecure and worried, being violated, lonely

Feeling: Having both positive and negative feelings for the person cared for

Moral agony: bad conscience, guilt, anger and shame, the burden of duty

Openness: to the person cared for and other people

HAVING COURAGE PROTECTING ONESELF– CHOOSING TO BE INAUTHENTIC

Admitting one’s limitations Speaking up

Concealing oneself Dissociating oneself

MATURING AND DEVELOPING Being aware of one’s vulnerability Self-confidence

FIGURE 5.2  Older family caregivers’ vulnerability.

experience can make them reflect on themselves as persons and human beings. This in turn can lead to self-acceptance and forgiveness. They may also learn they can manage a stressful situation, which can lead to growing self-confidence. This enables the older family caregivers develop as persons and turn their vulnerabilities into strengths (Sarvimäki et al., 2016).

The Contribution of the Models to Our View of Aging The models of the meaning and experiences of vulnerability in old age contribute to our knowledge of aging in that they shed light on aging in the world of meanings, in this case the meanings of vulnerability. In that sense they complement models of biophysical aging. The models also add to our knowledge of vulnerability. The philosophical and conceptual frameworks as well as the empirical studies show that vulnerability does not only make human beings weak and susceptible to harm. The openness and sensitivity that is associated with vulnerability is also a resource for personal development and the authentic life. Furthermore, the models point to the relationship between universal and contextual vulnerability (Rogers, 2014). The meanings of vulnerability to older persons and the older family caregivers’ experiences of vulnerability reflect

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universal vulnerability and at the same time point out the contextual factors. An additional picture of contextual vulnerability was drawn in a study on the meaning of vulnerability to nurses caring for older persons (Stenbock-Hult and Sarvimäki, 2011). Since vulnerability is contextual in addition to universal, the contents of the models may vary from one culture and society to another.

EXPERIENTIAL HEALTH IN OLD AGE Being vulnerable in terms of frailty, i.e., feeling worn out, ill and closer to death, does not rule out experiencing health. The meaning of health in old age can be described as feeling well, going on with one’s normal life, and remaining the same person as before despite illness and ailments of old age. This characterization explains why older persons can rate their health as good although professionals and researchers may rate their health as bad. They just have different conceptions of health. The characterization above can be illuminated by a number of studies of the experiences of health in older persons. Feeling well seems to be an integral part of the experience of health. Older persons report they experience health when they feel they have strength, feel well, and experience peace of mind. Experiencing health is also associated with feeling well in comparison with others of the same age who seem to be worse off. Furthermore, health is a source of the life the person is used to. Health means being able to do the normal daily things that one is used to do, to go on living as before. This includes being able to move around, getting dressed, cleaning the house, taking care of the garden, and maybe get involved in a new relationship. Health in this sense is a source of gratitude since in old age being able to do all this cannot be taken for granted. Life becomes insecure and uncertain (Berg et al., 2006; Kulla et al., 2010). Health as the continuation of normality also includes being the same person as one used to be. This means still being included in society as an active and living member and a feeling of belongingness and value. Being able to be of use to one’s family, to give and receive support and to engage in meaningful interactions with others strengthen the older person’s identity and are central to the experience of health (Berg et al., 2006). A further aspect of normality is the home. Older persons seem to associate health with still being able to live in one’s own home. Many of the activities that represent normality are carried out in the home or close to home: cleaning, shopping, cooking, and washing up. Living at home represents life history, identity, and meaningful activities (Berg et al., 2006). The home is a place for freedom and independence, a meeting place for social relationships and full of specific meanings associated with memories and daily routines (Mahler et al., 2014). Well-being can be understood as being at home in the world and familiar with nature, tools and other human beings (Sarvimäki, 2006). For many older persons their home is their world (Mahler and Sarvimäki, 2010; Mahler et al., 2014). Living at home and being familiar with everything in and around the home thus represents well-being for an older person. Severe illnesses, falls or radically declined functional capacity, however, may change the home into a hostile and unfamiliar place. Older persons’ experiences of health are reflected in their experiences of what promotes health. Being enabled to feel well and keep up continuity and normality is at the core of health promoting experiences. This involves receiving confirmation and respect for the person one is and receiving information, hope, and motivation. Coming back home after being in hospital, for instance, can give hope and motivation (Berg et al., 2006). Further experiences of health promotion include having an inner dialog with oneself, having social contacts, having contact with and a passion for the environment, including nature (Björklund et al., 2008). Contacts with care professionals are experienced as health promoting when the professionals believe the older persons’ story, when they share a working relationship together and the professionals offer individualized care (Björklund et al., 2009). Zest for life and confidence in the future are experienced as important health resources (Kulla et al., 2006). Experiential health in older people as expressed in these studies does not exclude disease and illness. It is compatible with a holistic and existentialist view of health as the meanings human beings give to their being in the world from the viewpoint of personal judgment and values, and how well they cope with health challenges in daily life (Berg and Sarvimäki, 2003). In the studies referred to above, the older persons viewed their health from the perspective of the life they were used to live and the person they were used to be. They experienced health as feeling well and having strength to continue doing things they wanted to do. As such, experiential health in old age represents a positive and holistic concept of health. It comes close to Nordenfelt’s (1995) definition of health as having the ability, in standard circumstances, to achieve vital goals, and to the World Health Organization’s definition of health as a positive concept and a resource for daily life (WHO, 1986).

RESEARCHING SUBJECTIVITY—COMMENTS ON RESEARCH ETHICS The models of vulnerability in old age and the descriptions of experiential health in old age are based on older persons’ own understandings and experiences in their lifeworld. They are thus constructed by the use of subjective elements. This brings to light certain value aspects of research.

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All human activity is guided by four value systems: the ethical, the esthetic, the scientific, and the economic (Sarvimäki and Sandelin Benkö, 2001). Which value system dominates depends on the activity in question. In research, the scientific value system constitutes the basic value system guiding the process toward valid knowledge and new insights. Ethical, esthetic, and economic values all influence the process in their own way. This also goes for the research on vulnerability and experiential health in old age presented in this paper. A central value in all scientific activity is objectivity. In doing qualitative research, however, esthetic values such as empathy, fantasy, and using oneself as a tool in the knowledge creating process play an important role. So it seems that qualitative research is subjective in two ways: the area of research consists of subjective elements and the researcher’s subjectivity, preunderstanding, is an important research tool (Radnitzky, 1970). In research, however, objectivity is not absolute, but rather to be interpreted as intersubjectivity. To be accepted and valid, a model based on qualitative research must be the result of a transparent research process and the codes and interpretations must be checked by a fellow researcher. In qualitative research objectivity is dealt with in terms of credibility, dependability, and confirmability (Lincoln and Guba, 1985). The researcher’s subjectivity can be described as controlled subjectivity in the service of knowledge. Handling research participants’ feelings, experiences, and understandings is a delicate business requiring ethical sensitivity. In an interview situation, participants often open up and talk about difficult experiences. The interviewer needs to be sensitive and empathetic but at the same time stick to his/her role as a researcher. The analysis and interpretation of interview data also requires sensitivity and respect for the participants’ dignity. Denzin (1989) reminds us that our ethical obligations, in the end, are always related to the persons who have trusted us with their lives and their stories, not to our project or discipline. In carrying out the studies that form the basis for the models and descriptions of vulnerability and experiential health in old age presented here, efforts were made to control subjectivity and be ethically sensitive. In addition to their scientific contribution, the vulnerability models have been shown to have practical value as they have been useful in the education of students and professionals in health and social care.

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Lincoln, Y., Guba, E., 1985. Naturalistic inquiry. SAGE Publications, Newbury Park (CA). Lindseth, A., Norberg, A., 2004. A phenomenological hermeneutic method for researching lived experience. Scand J Caring Sci 18, 145–153. Mahler, M., Sarvimäki, A., 2010. Indispensable chairs and comforting cushions – falls and the meaning of falls in six older persons’ lives. J Aging Stud 24, 88–95. Mahler, M., Sarvimäki, A., 2012. Fear of falling from a daily perspective; narratives from daily life. Scand J Caring Sci 26, 38–44. Mahler, M., Sarvimäki, A., Clancy, A., Stenbock-Hult, B., Simonsen-Rehn, N., Liveng, A., Zidén, L., Johannessen, A., Hörder, H., 2014. Home as a health promotion setting for older people. Scand J Public Health 42 (Suppl. 15), 36–40. Nordenfelt, L., 1995. On the nature of health: an action theoretic approach, 2nd enlarged and revised edition. Reidel Publishing Company, Dordrecht. Radnitzky, G., 1970. Contemporary schools of metascience. Scandinavian University Books, Göteborg (Sweden). Reker, G., Chamberlain, K., 2000. Introduction. In: Reker, G., Chamberlain, K. (Eds.), Exploring existential meaning. Optimizing human development across the life span. Sage, Thousand Oaks, pp. 1–4. Reuben, D.B., Valle, L.A., Hayes, R.D., et al., 1995. Measuring physical function in community-dwelling older persons: a comparison of self-administered, interviewer-administered, and performance-based measures. JAGS 43, 17–23. Riessman, C.K., 1993. Narrative analysis. SAGE Publications, Newbury Park. Rogers, W., 2014. Vulnerability and bioethics. In: Mackenzie, C., Rogers, W., Dodds, S. (Eds.), Vulnerability. New essays in ethics and feminist philosophy. Oxford University Press, Oxford, pp. 60–87. Sartre, J.-P., 1966. Being and nothingness. Washington Square Press, New York. Sarvimäki, A., Sandelin Benkö, S., 2001. Values and evaluation in health care. J Nurs Manag 9, 129–137. Sarvimäki, A., Stenbock-Hult, B., 2000. Quality of life in old age described as a sense of well-being, meaning, and value. J Adv Nurs 32, 1025–1033. Sarvimäki, A., Stenbock-Hult, B., 2004. Sårbarhet som utgångspunkt för etiken i äldrevården (Vulnerability as the point of departure for ethics in elder care, in Swedish). Gerontologia 18, 153–158. Sarvimäki, A., Stenbock-Hult, B., 2016. The meaning of vulnerability to older persons. Nurs Ethics 23, 372–383. Sarvimäki, A., Stenbock-Hult, B., Heimonen, S., 2010. Ikääntyminen ja mielen haavoittuvuus – haavoittuvuus riskinä ja voimavarana (Ageing and vulnerability of the mind – vulnerability as risk and resource, in Finnish). Gerontologia 24, 169–178. Sarvimäki, A., Stenbock-Hult, B., Sundell, E., Oesch-Börman, C., 2016. The vulnerability of family caregivers in relation to vulnerability as understood by nurses. Scand J Caring Sci. http://dx.doi.org/10.1111/scs.12325. Sarvimäki, A., 2006. Well-being as being well – a Heideggerian look at well-being. Int J Qual Res Health Well Being 2006 (1), 4–10. Sarvimäki, A., 2015. Elämän tarkoituksellisuus, merkitys ja mielekkyys vanhuudessa (The purpose and meaning of life in old age, in Finnish). In: Heimonen, S., Fried, S. (Eds.), Vanhuuden mieli. (“The sense of old age”, in Finnish). The Age Institute, Helsinki (Finland), pp. 9–20. Schröder-Butterfill, E., Marianti, R., 2006. A framework for understanding old-age vulnerabilities. Ageing Soc 26, 9–35. Stenbock-Hult, B., Sarvimäki, A., 2011. The meaning of vulnerability to nurses caring for older people. Nurs Ethics 18, 31–41. Thorne, S., Reimer Kirkham, S., O’Flynn-Magee, K., 2004. The analytic challenge in interpretive description. Int J Qual Method 3, 1–11. Tillich, P., 1962. The courage to be. The Fontana Library, Glasgow. Tilvis, R., Laitala, V., Routasalo, P., Pitkälä, K., February 22, 2011. Suffering from loneliness indicates significant mortality risk of older people. J Aging Res;2011:534781. doi:4061/2011/534781. WHO, 1986. The Ottawa Charter for health promotion. The World Health Organization, Geneva.

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Chapter 6

Body Composition in Older Adults M.A. Camina Martín, B. de Mateo Silleras, M.P. Redondo del Río University of Valladolid, Valladolid, Spain

INTRODUCTION The aging population has been increasing significantly in recent decades, and this pattern is expected to continue over the next few decades. As the proportion of the world’s elderly population continues to increase, the importance of diagnosing and treating alterations in nutritional status will also rise. Human aging entails multiple social, psychological, and biological changes that make the elderly more susceptible to alterations in nutritional status that can, in turn, impact their health negatively. The importance of measuring body composition in older adults lies in the need of establishing energy stores and protein mass, determining skeletal mineral status, hydration status, and energy expenditure (Heymsfield et al., 1989) among other aspects. According to Bedogni et al. (1999), nutritional status can be regarded operationally as resulting from the interaction of body composition, energy balance, and body functionality. Body composition is the best long-term indicator of nutritional status, and it is of interest because of its association with body functionality (Bedogni et al., 1999). Consequently, body composition analysis becomes crucial for understanding the effects of nutritional status changes on health. This chapter describes age-related body composition changes and provides the reader with a brief overview of the body composition measurement methods that can be used for routine assessment and monitoring in clinical practice.

OVERVIEW AND UNDERSTANDING OF BODY COMPOSITION AND COMPARTMENTS The human body consists of more than 40 components that can be organized into different levels, making up the various body compartments (Wang et al., 1992). A number of body composition models of varying complexity have been described (Wang et al., 1992; Woodrow, 2007). In the 1960s Siri (1961) and Brozěk et al. (1963) popularized the two-compartment model, the simplest model for body composition analysis. In this model, the human body is divided up into a fat body compartment (fat mass, FM) and a fat-free body compartment (fat-free mass, FFM). From this model, new concepts emerged in the field of body composition that laid the foundations for the development of the current multicompartment models. Currently, thanks to the development of new body composition analysis methods, the major human body components can be organized and studied at five different, independent levels (Wang et al., 1992): atomic, molecular, cellular, tissue system, and whole body (Fig. 6.1). Each level and its components are distinct, but biochemical and physiological connections make the five levels consistent and function as an entity. The atomic level includes the 11 major elements that make up body mass: oxygen, hydrogen, nitrogen, carbon, sodium, potassium, chlorine, phosphorus, calcium, magnesium, and sulfur. From these 11 components, the body compartments defined on the basis of the molecular or the cellular models can be estimated very accurately (Ellis, 2000, 2005). However, element quantification requires complex techniques, such as dilution methods, neutron activation analysis, or whole-body potassium-40 counting (Ellis, 2000, 2005), which are not applicable in clinical practice. Body composition analysis at the molecular level is performed according to biochemical criteria, so similar molecules are grouped in body compartments. At this level, which is the most studied, the human body can be divided according to several compartment models that vary in complexity. The simplest one is the previously mentioned two-compartment model, in which the human body is divided into FM and FFM; these can be estimated by several body composition analysis methods, such as hydrodensitometry, anthropometry, and bioimpedance analysis (Wang et al., 1992; Ellis, 2000, 2005) among others. The FFM can be divided into water content and solids, giving rise to a three-compartment model. Finally, the FFM solids can be divided into proteins and minerals, thus forming the four-compartment model, in which the human body would be constituted by FM, body water, proteins, and minerals (Wang et al., 1992). Although the three- and fourcompartment models overcome the shortcomings of the two-compartment model, additional techniques would be needed Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00006-3 Copyright © 2018 Elsevier Inc. All rights reserved.

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FIGURE 6.1  Five-level multicompartment model of body composition. (Adapted from Wang, Z.M., Pierson, R.N., Heymsfield, S.B., 1992. The five level method: a new approach to organizing body-composition research. Am J Clin Nutr 56, 19–28.)

to measure the body water and solids in the FFM compartment. These techniques, such as neutron activation analysis and dual-energy X-ray absorptiometry (DXA), are not normally applicable for routine body composition assessment and monitoring in clinical practice. At the cellular level, the human body can also be divided into three or four compartments. In the three-compartment model, body mass is composed of cells, extracellular fluids, and extracellular solids. However, the four-compartment model decomposes the cell compartment into fat and body cell mass (BCM), which is the metabolically active cellular component. The study of these compartments requires the use of several techniques of body composition analysis, including dilution techniques, DXA, and whole-body potassium-40 counting (Wang et al., 1992; Ellis, 2000, 2005). Nevertheless, as will be explained below, bioelectrical impedance vector analysis (BIVA) also allows semiquantitative assessment of the individual’s hydration status and BCM. This model is important in physiological evaluations because cells constitute the functional biological element of human body (Salmi, 2003). The tissue level, as suggested by the name, divides the human body into body tissues: adipose, skeletal muscle, bone, visceral, and other tissues. The main interest of the study of body composition at this level lies in determining the regional distribution of body adiposity because it allows dividing adipose tissue into subcutaneous and visceral tissue. As is well known, the determination of regional body fat distribution is of great interest due to its relation to metabolic diseases. On the other hand, studying bone and muscle mass in the elderly is undoubtedly important. Musculoskeletal system alterations caused by involution or aging are very important factors in the problems that the elderly suffer, given that such alterations can trigger physical frailty, functional dependence, and disability in the elderly (Edwards et al., 2015; Milte and Crotty, 2014). Among the different methods that allow the determination of these tissues are computerized axial tomography (Seidell et al., 1990) and nuclear magnetic resonance (Kvist et al., 1988; Van der Kooy and Seidell, 1993). Finally, the whole-body level concerns body size, shape, and physical characteristics, dividing the human body into regions such as appendages, trunk, and head. These areas are usually described by anthropometric measures, including stature, body weight, volume, circumferences, and diameters, as well as segmental lengths. Models that include more compartments measured separately will estimate human body composition more accurately. However, this involves the use of various techniques and equipment that are not viable for any purpose other than scientific research. As mentioned above, methods based on biochemical criteria are currently the most studied and used in both clinical practice and epidemiology. Among the various models based on these criteria, the two-compartment model has been the most popular because of its relative simplicity. However, this model does not allow assessing the main aging-related changes that occur at the cellular and tissue levels, and considering those changes is of great value because of their relationship to metabolic diseases and geriatric syndromes. For this reason, in this chapter we focus on aging-related changes in FM and FFM, but differentiating the changes in the main FFM components defined on the basis of the cellular and tissue levels of body composition analysis.

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AGING-RELATED CHANGES IN BODY COMPOSITION Aging influences morphological and physiological changes in all cells, tissues and organs of the body, affecting the functioning of all body systems. The main body composition changes consist of increased FM and decreased FFM, the latter mainly at the expense of reduced muscle and bone mass.

Aging-Related Changes in Fat Mass There is general agreement that both body mass and FM gradually increase as a person gets older and then tend to decrease (Guo et al., 1999). The factors involved in FM increase include reduced physical activity, as well as lower growth and sex hormone secretion and a reduction in resting metabolic rate. In contrast, the causes of the subsequent decline in FM are not clearly understood. Some researchers have suggested that this could be produced, at least in part, by the anorexia of aging and by undiagnosed diseases affecting body composition (Mott et al., 1999). The growth pattern of FM is similar to that of body mass, with an average annual gain of 0.37 and 0.41 kg in men and women, respectively (Guo et al., 1999). However, the age at which body mass and FM begin to decrease varies among studies. With regard to body weight, a prospective population-based study with an 11-year follow-up conducted on 21,565 and 24,337 Norwegian men and women aged 20 to >80 years evidenced that body weight gradually rose until the age of 70 (Drøyvold et al., 2006). However, the increase in body weight appears to reduce gradually for each subsequent age-group. Specifically, in this study the mean increase in body weight was 7.9 and 7.3 kg for men and women aged 20–29 years; 5.9 and 7.0 kg for men and women aged 30–39 years; 4.3 and 6.0 kg for men and women aged 40–49 years; 2.4 and 3.4 kg for men and women aged 50–59 years; and 1 and 0.7 kg for men and women aged 60–69 years. After the age of 70, body weight decreased 1.3 and 2.4 kg in men and women aged 70–79 years, and 2.4 and 5.6 kg in men and women aged ≥80 years (Drøyvold et al., 2006). However, the individual trajectories of body weight and BMI are very heterogeneous in older adults, and weight cycling or weight instability is common in people aged 65 or older (Arnold et al., 2010). As for total body fat, the age at which it begins to decrease varies among studies. Silver et al. (1993) reported that FM percentage increased slightly up to 84 years, while a lower FM percentage was observed in both males and females from the age of 85 years. Ding et al. (2007) also observed that FM percentage initially increased from age 70–80 years and then leveled off from age 81–84 years. In contrast, Mott et al. (1999) evidenced that FM began to decrease early. Those authors measured FM in a sample of 1324 volunteers of four ethnicities aged 20–94 years by using a four-component body composition model (a reference method for measuring FM) and they found a curvilinear relation between age and FM in all but one of the groups studied (Puerto Rican volunteers), indicating a peak FM percentage from 55 to 71 years: this was followed by a decline in the older age-groups. Provisional age-, sex-, and ethnic-specific healthy body fat percentage ranges that correspond to the BMI thresholds for underweight (12 months of regular unprotected sex—reported in married women >35 years of age is more than double relative to younger cohorts (Chandra et al., 2013) (Fig. 9.2). In fact, based on data from Assisted Reproductive Technology (ART) cycles, the percentage of embryo transfers that result in live births following cycles using fresh embryos derived from a female’s own oocytes decreases steadily with advanced reproductive age (Society for Assisted Reproductive Technology, 2010, 2013) (Fig. 9.3). The likelihood of taking home a live offspring drops close to 0% when women reach their mid-40s (Society for Assisted Reproductive Technology, 2010, 2013) (Fig. 9.3). This decrease in fertility with advanced reproductive age is referred to as the “maternal age effect” and is largely rooted in defects at the level of the egg because if women use donor oocytes from reproductively young, healthy women to conceive, the maternal age effect is effectively abrogated (Check et al., 2011) (Fig. 9.3). Thus, the biological age of the oocyte contributes significantly to reproductive outcomes. In addition to infertility, women of advanced reproductive age have a higher risk of miscarriages, twinning, and pregnancy complications. Spontaneous abortions occur in up to 10% of clinically recognized pregnancies, and one of the largest risk factors is maternal age (de la Rochebrochard and Thonneau, 2002). In women over 40 years of age, the incidence of spontaneous abortion can increase to >30%. Interestingly, this risk appears to be compounded with paternal age if the woman is ≥35 years old and the man is ≥40 years old (de la Rochebrochard and Thonneau, 2002). Reproductive aging is Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00009-9 Copyright © 2018 Elsevier Inc. All rights reserved.

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FIGURE 9.1  Hallmarks of female reproductive aging. (A) The female reproductive system is unique as it shows overt signs of functional decline prior to other organ systems in the human body. (B) Female reproductive aging is characterized by a decline in gamete quantity and an increase in poor quality oocytes. (Adapted from http://www.benbest.com/lifeext/aging.html and Broekmans, F.J., Soules, M.R., Fauser, B.C., 2009. Ovarian aging: mechanisms and clinical consequences. Endocr Rev 30 (5), 465–493.)

FIGURE 9.2  Infertility increases with advanced reproductive age. Infertility among nulliparous married women by age in the United States from 2006 to 2010. (Adapted from Chandra, A., Copen, C.E., Stephen, E.E., 2013. Fertility and Impaired Fecundity in the United States, 1982–2010: Data from the National Survey of Family Growth. National Health Statistics Reports, p. 67.)

FIGURE 9.3  The age-associated decline in fertility is largely due to defects at the level of the egg. The percentage of embryo transfers that resulted in live births for assisted reproduction technology (ART) cycles using fresh embryos from a woman’s own eggs (dashed line) versus ART cycles using fresh embryos from donor eggs (solid line). The likelihood of a fertilized egg implanting and giving rise to a live offspring is related to the age of the female who produced the egg (i.e., the biological age of the egg). (Adapted from Society for Assisted Reproductive Technology, 2013. 2011 Assisted Reproductive Technology National Summary Report. Centers for Disease Control and Prevention, American Society for Reproductive Medicine, Society for Assisted Reproductive Technology, US Dept of Health and Human Services; Society for Assisted Reproductive Technology, 2010. 2008 Assisted Reproductive Technology Success Rates: National Summary and Fertility Clinic Reports. Centers for Disease Control and Prevention, American Society for Reproductive Medicine, Society for Assisted Reproductive Technology, US Dept of Health and Human Services.)

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FIGURE 9.4  The birth rate in women of advanced reproductive age is increasing. (A) The distribution of all births by age of the mother in the United States in 1980 and 2009 shows that there is a shift in the overall age distribution of women giving births to older individuals. (B) Birth rates per 1000 women plotted according to age range of the mother in the United States from 1990 to 2013. ((A) Adapted from Martin, J.A., Hamilton, B.E., Osterman, M.J., 2012. Three decades of twin births in the United States, 1980–2009. NCHS Data Brief (80), 1–8. (B) Adapted from Martin, J.A., Hamilton, B.E., Osterman, M.J., Curtin, S.C., Matthews, T.J., 2015. Births: final data for 2013. Natl Vital Stat Rep 64 (1), 1–65.)

also associated with increased conception of spontaneous dizygotic twins that is independent of ART use, and multiple gestations have inherent pregnancy risks to both the mother and offspring (Hoekstra et al., 2008; Martin et al., 2012). Women ≥35 years old also have significant pregnancy risks relative to younger women (18–34 years old), including gestational diabetes, placenta previa, Ceasarian section, low birth weight, preterm birth, and stillbirth (Jolly et al., 2000). Advanced reproductive age is a major risk factor for birth defects, which occur in ∼3%–5% of pregnancies (Hollier et al., 2000). Maternal age–related birth defects are chromosomal and nonchromosomal in origin. For example, Trisomies 13, 18, and 21 increase with maternal age, and their prevalence has risen over time coincident with delayed childbearing (Mai et al., 2013). Nonchromosomal birth defects associated with advanced reproductive age include heart defects, hypospadias, other male genital defects, craniosynostosis, club foot, and diaphragmatic hernia (Hollier et al., 2000; Ooki, 2013; Reefhuis and Honein, 2004). The fertility consequences of reproductive aging are becoming more widespread as women worldwide are delaying childbearing for reasons ranging from effective contraception, increased presence in the work force, changes in values and family structures, and financial and economic concerns (Mills et al., 2011; Johnson et al., 2012). In the United States, the percentage of all births to mothers of advanced reproductive age is increasing (Martin et al., 2012, 2015; Mathews and Hamilton, 2009, 2016) (Fig. 9.4). Moreover, the mean maternal age at birth of the first child is rising. Between 2000 and 2014, the mean age of first-time mothers increased from 24.9 to 26.3 years, with the most pronounced increase observed in the last 5 years (Mathews and Hamilton, 2009, 2016). These general observations are not limited to the United States as similar trends have been reported globally—even in developing countries (Barclay and Myrskyla, 2016). As a result of delayed childbearing, combined with the biological maternal age effect, there has been increased reliance on ART for conception (Gleicher et al., 2014).

Health Consequences of Female Reproductive Aging Ovarian hormones produced by growing follicles (e.g., estrogen) regulate downstream organ systems and are important for cardiovascular, bone, immune, cognitive, and sexual health (Traub and Santoro, 2010). Therefore, the declining ovarian function and lack of endocrine function that occurs during reproductive aging can have much broader general health implications beyond fertility. In fact, a recent study in humans demonstrated that menopause accelerates aging (Levine et al., 2016). Furthermore, transplantation of ovaries from reproductively young mice into ovariectomized mice of advanced reproductive age increased life span suggesting that sustained ovarian function contributes to overall health (Mason et al., 2009). Although menopause is a physiologic process, the gap between menopause and life span is widening. While the average age of menopause (48–52 years old) has stayed constant for centuries, life expectancy has steadily increased with women now living until almost 80 years old (Fig. 9.5). Thus, women will be living longer with an endocrine milieu that is significantly different from that experienced during their reproductive years. The events and consequences of perimenopause and menopause will affect every woman despite her race, ethnicity, and geography. In this book chapter, we review the models and mechanisms of physiologic reproductive aging, consider factors that influence reproductive aging, discuss the clinical assessment and management of reproductive aging, and highlight emerging technologies and therapeutic strategies to counteract female reproductive aging.

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FIGURE 9.5  The gap between fertile-span and life span is widening. Whereas the average age at menopause has remained constant for centuries, the average life span has increased due to advanced medical technologies and interventions. Thus, more women are living longer following the menopausal transition. (Adapted from Gynaecology by Ten Teachers, 19th Edition; Chapter 18: The Menopause.)

PHYSIOLOGIC AGE-RELATED DECLINE IN FERTILITY AND REPRODUCTIVE FUNCTION Ovarian Reserve and Endocrinology Females are born with a fixed ovarian reserve or number of primordial ovarian follicles that are in the resting phase and contain immature oocytes. At puberty and throughout a woman’s life, a subset of these follicles undergo maturation under the influence of hormones, including follicle stimulating hormone (FSH), via the hypothalamic–pituitary–ovarian axis. Development occurs through the primary, secondary (preantral), tertiary (antral), and preovulatory (Graafian) follicle stages, leading to the ovulation of a mature oocyte. The activated follicles that are not ovulated are eliminated by either apoptosis or atresia. When depletion of the primordial follicle pool reaches a threshold, ovarian failure and a decline in fertility occurs (Fig. 9.1B). Menstrual cycle length is controlled by both the rate and quality of follicular growth and development. It is normal for cycle length to vary in any individual woman (Treloar et al., 1967). When women are in their late 30s, cycle lengths tend to be shorter. This is because of increases in FSH as well as decreases in inhibin levels, which are seen clinically as accelerated follicular growth (Hofmann et al., 1998; Lenton et al., 1984). Anywhere from 2 to 8 years prior to menopause, cycles tend to lengthen again (Treloar et al., 1967). There is also an acceleration of follicular loss in the last 10–15 years prior to menopause, which begins when follicle numbers reach about 25,000. This number is generally reached at ages 37–38 (Faddy et al., 1992; Gougeon et al., 1994). When the number of follicles is depleted, menopause occurs. Generally, cycle length variations reflect follicular phase differences. Within a few years after menarche, the luteal phase becomes extremely consistent, lasting 13–15 days, and remains this consistent until the perimenopausal period (Treloar et al., 1967). This means that women who have a 26-day cycle generally ovulate on day 11–13. Interestingly, only about 15% of cycles in reproductive-aged women are 28 days in length. Most women have cycles lasting from 24 to 35 days, and approximately 20% of women have irregular cycles (Belsey and Pinol, 1997).

FOLLICLE QUALITY: THE GAMETE PERSPECTIVE The ovarian follicle is the functional unit of the ovary and consists of a germ cell (oocyte) surrounded by somatic cells (granulosa and theca cells) that support the growth and development of the gamete. Evidence suggests that both the oocyte and somatic compartments of the follicle exhibit changes with advanced reproductive age. A prominent deterioration in oocyte quality begins when women reach advanced reproductive age (mid to late thirties), and this is attributed to multiple factors. Here we highlight the most prominent and well-characterized age-associated changes in oocyte quality with a focus on aberrant chromosome structure and segregation as well as organelle function.

Meiotic Determinants of Gamete Quality One of the most central determinants of oocyte quality is the ability of the cell to faithfully segregate its chromosomes during meiosis to become haploid. This is essential to ensure that a viable diploid zygote is formed following fertilization. Meiosis involves two rounds of cell division without an intervening round of DNA replication. In humans, meiosis in females is initiated during fetal development when oocytes enter meiosis and undergo events of prophase of Meiosis I, including homologous chromosome pairing, synapsis, and recombination (Hassold et al., 2007; Hassold and Hunt, 2001;

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FIGURE 9.6  Egg aneuploidy is unequivocally associated with advanced reproductive age. The incidence of trisomies in clinically recognized pregnancies increases ­dramatically when women reach advanced reproductive age. This contributes to infertility, miscarriages, and birth defects. (Adapted from Hassold, T., Hunt, P., 2001. To err (meiotically) is human: the genesis of human aneuploidy. Nat Rev Genet 2 (4), 280–291.)

Hunt and Hassold, 2008; Nagaoka et al., 2012). The oocytes enter a dictyate arrest that lasts until ovulation, which can occur decades later between puberty and menopause (Hassold et al., 2007; Hassold and Hunt, 2001; Hunt and Hassold, 2008; Nagaoka et al., 2012). In response to ovulatory cues, oocytes resume meiosis and complete Meiosis I with separation of homologous chromosomes and extrusion of the first polar body. The cell then proceeds through Meiosis II (MII) and becomes arrested at metaphase of MII at which point the cell is capable of being fertilized. MII is only completed on fertilization when sister chromatids are separated with the extrusion of the second polar body (Hassold et al., 2007; Hassold and Hunt, 2001; Hunt and Hassold, 2008; Nagaoka et al., 2012). Despite the critical importance of meiosis, this process is highly error prone in females. For example, the incidence of aneuploidy in sperm is ∼1%–2%, whereas it is ∼20% in eggs of reproductively young females (Hassold and Hunt, 2001). Aging is unequivocally associated with increased egg aneuploidy, which can reach incidences upward of 30%–60% (Jones and Lane, 2013) (Fig. 9.6). The majority of the maternal chromosome segregation errors originate during Meiosis I, perhaps because this cell division involves the unique segregation of homologous chromosomes and is so protracted relative to MII (Hassold and Hunt, 2001; Hunt and Hassold, 2008). Maternal age–related aneuploidy contributes to infertility, miscarriages, and birth defects (Nagaoka et al., 2012). One of the most frequently occurring chromosomal aneuploidies observed in oocytes from women of advanced reproductive age is trisomy 21, which results in offspring with Down’s syndrome (Nagaoka et al., 2012). Understanding the molecular mechanisms underlying the reproductive age-associated increase in aneuploidy is a major research focus. The mouse has proved to be an important research model as several aspects of reproductive aging in mice parallel what is observed in humans, including a reduced ovarian reserve, abnormal endocrine function, increased egg aneuploidy, and subfertility (Pan et al., 2008; Chiang et al., 2010; Duncan et al., 2009; Hirshfeld-Cytron et al., 2011). As described in more detail below, there are multiple proposed etiologies for the meiotic origins of age-associated aneuploidy, ranging from alterations in chromosome architecture and associated proteins to defects in the cell cycle machinery and spindle apparatus. Importantly, the mechanisms underlying aneuploidy are likely multifactorial with several factors simultaneously contributing to reduced gamete quality.

Chromosome Architecture Chromosome architecture plays an important role in mediating proper chromosome segregation and clearly impacts age-associated increases in aneuploidy. For example, during prophase of Meiosis I, recombination occurs and involves the crossing over and large exchange of DNA between homologous chromosomes to increase genetic diversity. The physical manifestations of crossover sites, known as chiasmata, also serve as linkages that tether homologous chromosomes together until the appropriate time during the metaphase-to-anaphase transition during Meiosis I (Nagaoka et al., 2012). The absence (achiasmate) or suboptimal positioning of crossover sites that are either too close (proximal) or too far (distal) from the chromosome centromere

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increases the susceptibility of homologous chromosomes to missegregate on the resumption of meiosis (Nagaoka et al., 2012). There is a clear association between altered recombination in clinically recognized trisomies that show a maternal-age dependence. For example, trisomies 16, 18, and 21 all increase with reproductive age, albeit with chromosome-specific variations in slopes (Nagaoka et al., 2012). Trisomy 21 is associated with achiasmate, distal, and proximal crossover sites, whereas trisomies 16 and 18 are only associated with distal and achiastmate configurations, respectively (Nagaoka et al., 2012). Telomeres are other aspects of chromosome structure that may contribute to age-associated aneuploidy in the female gamete. Telomeres are regions at the end of chromosomes that are composed of the repeated DNA sequence TTAGGG(n), mediated by the enzyme telomerase, along with associated proteins that protect the blunt ends of DNA and prevent the cell from triggering a DNA damage response (Kalmbach et al., 2015; Keefe and Liu, 2009). In mitotically active cells, telomere ends shorten with every cell cycle, and this telomere erosion ultimately leads to replicative senescence, which is a hallmark of many aging cells (Kalmbach et al., 2015; Keefe and Liu, 2009). Although oocytes within the ovary are arrested in prophase of Meiosis I and are not dividing, their telomeres are subject to erosion over time by increased exposure to oxidative damage with age (Keefe, 2016; Yamada-Fukunaga et al., 2013). In mice, experimental reduction of telomeres in oocytes results in several reproductive aging phenotypes, including aberrant recombination, abnormal meiotic spindles, and poor embryo outcomes (Keefe, 2016). Moreover, in human eggs, the length of telomeres in polar bodies is correlated with embryo fragmentation (Keefe, 2016). Interestingly, treatments that extended fertility in the mouse, such as a 12-month resveratrol diet, had a protective effect on telomere length (Liu et al., 2013). In addition to telomeres, the overall structure of meiotic chromosomes is sensitive to age. Chromosome stretching experiments using glass micropipettes demonstrate that the micromechanical properties of egg chromosomes isolated from reproductively old mice are altered relative to young counterparts (Hornick et al., 2015). Specifically, it requires more force to stretch a chromosome a given distance when isolated from eggs from an aging mouse (Hornick et al., 2015). This increased meiotic chromosome stiffness may reflect differences in chromosome-associated proteins, since proteins such as condensins and cohesins influence chromosome structure and micromechanical properties.

Chromosome-Associated Proteins Quite dramatic age-associated changes occur in chromosome-associated cohesin proteins, and deterioration of chromosome cohesion has been proposed as a primary mechanism underlying reproductive age-associated aneuploidy (Chiang et al., 2012). Cohesin proteins form multi-subunit ringlike structures that serve as the molecular glue holding chromosomes together until the appropriate time during meiosis (McNicoll et al., 2013; Remeseiro et al., 2013). Cohesin is found along chromosome arms and at the centromeres, and it is removed in a regulated, stepwise fashion during meiosis (Jones and Lane, 2012, 2013). Cohesin is first removed from the chromosome arms but maintained at the centromere at the metaphase-to-anaphase transition during Meiosis I to allow resolution and separation of homologous chromosomes while keeping sister chromatids together (Jones and Lane, 2012, 2013). At the same transition during MII, centromeric cohesion is removed allowing the sister chromatids to separate (Jones and Lane, 2012, 2013). Alterations in chromosome cohesion can result in aneuploidy (Xu et al., 2005; Hodges et al., 2005). Cohesin is a likely protein target of female reproductive aging because it is loaded only once onto chromosomes during S phase, which occurs in the oocyte during fetal development and is not thought to turnover or be replenished (Revenkova et al., 2010; Jessberger, 2010). Thus, the cohesin that was established during fetal development must remain functional until ovulation and meiotic resumption, which can be up to months in mice and decades in human. Genetic disruption of oocyte cohesin proteins in mice results in increased aneuploidy and accelerated reproductive aging (Xu et al., 2005; Hodges et al., 2005). In fact, mutations in a gene encoding a meiosis-specific subunit of the cohesin ring are associated with premature ovarian failure in humans (Caburet et al., 2014). Moreover, a deterioration in chromosome cohesion has been documented with increased physiologic age in both mouse and human eggs (Chiang et al., 2010; Duncan et al., 2012; Garcia-Cruz et al., 2010; Merriman et al., 2012; Zielinska et al., 2015). This cohesion deterioration is evident as a reduction in chromosomeassociated cohesin levels, larger interkinetochore distances between sister chromatids which reflects a decrease in cohesin strength and increased aneuploidy (Chiang et al., 2010; Duncan et al., 2012; Garcia-Cruz et al., 2010; Merriman et al., 2012; Zielinska et al., 2015). Interestingly, the majority of reproductive age-related aneuploidies in mice and humans are due to premature separation of sister chromatids, which is consistent with a loss of chromosome cohesion (Chiang et al., 2010; Duncan et al., 2012; Zielinska et al., 2015). Taken together, it is clear that age-associated changes in chromosome structure and chromosome-associated proteins can impact gamete quality. Evidence is also emerging that direct damage to the DNA as well as epigenetic alterations also contribute to reproductive aging. A discussion of these factors is beyond the scope of this chapter, but the reader is referred to existing literature on these important topics (Keefe, 2016; Titus et al., 2013; Ge et al., 2015; Shao et al., 2015; Yue et al., 2012).

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Spindle Assembly and Cell Cycle Regulation Not only do changes at the chromosomal level impact the age-associated increase in aneuploidy but so do alterations in spindle–chromosome interactions and cell cycle signaling. For example, for chromosomes to segregate faithfully during meiosis, they must stably attach to spindle microtubules and orient correctly, so that they will move to the appropriate pole (Chmatal et al., 2015; Davydenko et al., 2013). Chromosome attachment to the spindle is mediated by the kinetochore, a proteinaceous structure that assembles on centromeric DNA. Erroneous kinetochore–microtubule interactions that are not corrected will result in aneuploidy (Chmatal et al., 2015; Davydenko et al., 2013). Eggs from reproductively old mice exhibit increased misaligned chromosomes on the metaphase plate as well as fewer stable and correct kinetochore–microtubule attachments at Meiosis I relative to young controls (Shomper et al., 2014). This problem is compounded by the observation that the spindle assembly checkpoint (SAC), a quality control checkpoint that prevents cell cycle progression until chromosomes are properly attached to the spindle, appears defective in eggs from mice of advanced reproductive age relative to young controls (Yun et al., 2014). The SAC is also involved in the egg’s response to DNA damage (Collins et al., 2015; Marangos et al., 2015). Because the SAC in eggs is compromised with advanced reproductive age, many eggs isolated from reproductively old mice are able to progress through meiosis in the presence of DNA damage (Collins et al., 2015; Marangos et al., 2015). Thus, DNA damage induced genomic instability along with aneuploidy may be characteristic features of reproductive aging in the mammalian egg.

Cytoplasmic Determinants of Gamete Quality Results from nuclear transfer experiments suggest that although the nuclear compartment of the egg is the primary contributor to meiotic defects observed with reproductive aging, cytoplasmic components play a key role as well (Liu and Keefe, 2004, 2007). In fact, transfer of cytoplasm from reproductively young eggs can partially rescue the meiotic defects observed in eggs from an accelerated aging mouse model (Liu and Keefe, 2004). The oocyte is one of the largest cells in the body, and its cytoplasm contains many maternally derived factors that accumulate during oogenesis. These maternal factors comprise the cytoplasmic determinants of gamete quality and include proteins, mRNA, organelles, and signaling molecules whose proper functioning are essential for early embryogenesis prior to zygotic genome activation (Gosden and Lee, 2010). Compelling evidence suggests that the cytoplasmic quality of the gamete changes with advanced reproductive age, and here—as examples— we consider alterations in mitochondria, nucleolus, and ribosome function, calcium signaling, and gene expression.

Mitochondria Mitochondria—the energy producing organelles of the cell—are likely the most well-studied organelles with respect to reproductive aging in the mammalian oocyte. Mitochondria increase in number during oogenesis from ∼6000 in oocytes within primordial follicles to ∼400,000 in the mature egg (Meldrum et al., 2016). Mitochondrial production then ceases in the preimplantation embryo and is not reinitiated until the blastocyst stage, so the number of mitochondria per cell is continuously diluted in the early embryo. One of the main functions of mitochondria is adenosine triphosphate (ATP) production by oxidative phosphorylation, and it is well-accepted that the functional status of mitochondria contributes to oocyte quality. Eggs from women of advanced reproductive age have altered mitochondrial morphology, including swollen structures with disrupted cristae. These mitochondria have decreased activity and produce less ATP (Meldrum et al., 2016). This reduced energy production contributes to spindle abnormalities, chromosomal segregation errors, and compromised embryo development (Meldrum et al., 2016). Mitochondria have their own genome or mitochondrial DNA (mtDNA) that encodes for proteins involved in ATP production along with genes encoding transfer RNA and ribosomal RNA. The mtDNA is unstable and has a mutation rate 25 times greater than nuclear DNA (May-Panloup et al., 2016). In addition to defects in mitochondrial function, eggs from women of advanced reproductive age also have increased mtDNA mutations, which can decrease gamete quality and increase the risk of passing on mitochondrial mutations to offspring (May-Panloup et al., 2016). The relationship between mtDNA levels, oocyte quality, and reproductive age is further considered in the Clinical Screening Methods for Reproductive Aging section.

Nucleolus and Ribosomes The oocyte nucleolus and ribosomes are emerging as other organelles that may be sensitive to reproductive-age–associated changes. The growing oocyte is one of the most translationally active cells in the body as it accumulates large amounts of maternal products during oogenesis (Eichenlaub-Ritter and Peschke, 2002). To meet such high demands for protein synthesis, ribosome biogenesis is essential. The nucleolus is the key site of ribosome biogenesis and is an organelle in which its structure is intimately related to its function. A comprehensive and comparative analysis of nucleolar markers in oocytes from reproductively young and old mice revealed that advanced reproductive age is associated with changes indicative of altered nucleolar function. More ribosomes were found in the cytoplasm of old oocytes accompanied by increased

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expression of the ribosomal protein RPS2 (Jasti et al., Aging Cell, in revision). RNA-Seq data demonstrated that protein quality control mechanisms are downregulated in follicles from reproductively old mice (Jasti et al., Aging Cell, in revision). Thus, the functional capacity of aging ribosomes with regard to translational fidelity and protein production may be compromised with age, which would have a direct impact on the cytoplasmic quality of the egg.

Calcium Signaling The ability of eggs to activate following fertilization decreases with advanced reproductive age, and it is known that calcium oscillations are essential for mediating key events of egg activation (i.e., cell cycle resumption, cortical granule exocytosis, selective recruitment and degradation of maternal mRNAs, and pronuclear formation) (Wakai and Fissore, 2013). These results suggest that there may be a potential age-associated defect in calcium signaling within the egg. Studies in a physiologic aging mouse model demonstrated that while advanced reproductive age leads to a reduced ability of eggs to replenish calcium stores from the extracellular environment, physiologic calcium oscillations occur normally, indicating that safeguards exist for maintaining appropriate calcium homeostasis (Haverfield et al., 2016). Nevertheless, significant differences in calcium oscillation profiles were observed following parthenogenetic activation of eggs from reproductively old mice compared to young counterparts (Haverfield et al., 2016).

Gene Expression Several studies have interrogated age-associated gene expression differences in both mouse and human oocytes. Although, it is beyond the scope of this book chapter to review these findings in detail, these studies have provided important insights into molecular pathways that are dysregulated with age and likely impact gamete quality, including chromatin structure, genome stability, methylation, mitochondrial function, cell cycle regulation, chromosome segregation, oxidative stress, and protein quality control (Pan et al., 2008; Grondahl et al., 2010; Hamatani et al., 2004) (Jasti et al., Aging Cell, in revision).

FOLLICLE QUALITY—THE SOMATIC CELL PERSPECTIVE Granulosa Cells Oocytes rely on bidirectional communication with their surrounding granulosa cells (Kidder and Vanderhyden, 2010). Specifically, granulosa cells provide oocytes with nutrients, metabolic precursors, growth factors, and hormones, thereby regulating oocyte growth and development, transcriptional activity, and progression through meiosis. Primordial follicles are surrounded by a layer of squamous somatic granulosa cells that do not proliferate until they are activated to grow. Thus, the squamous granulosa cells in a primordial follicle from a female of advanced reproductive age have existed for decades and may therefore accumulate damage due to aging. In fact, several age-associated phenotypes have been observed in granulosa cells, including increased oxidative damage to lipids, proteins, and DNA, decreased scavenging of reactive oxygen species, and accumulation of advanced glycation end-products (Tatone et al., 2008, 2010; Tatone and Amicarelli, 2013). Together, these factors likely negatively impact the somatic cell compartment’s ability to support the development of high-quality oocytes.

Ovarian Stroma and Microenvironment The ovarian microenvironment in which follicles develop can also impact oocyte quality. Recent studies in mouse models of physiologic reproductive aging demonstrate that fibrosis, or the excess accumulation of extracellular matrix that replaces parenchymal tissue, increases in the ovarian stroma with advanced reproductive age (Briley et al., 2016). Fibrosis is associated with inflammation, and interestingly, ovarian fibrosis is accompanied by infiltration of multinucleated macrophage giant cells that are often observed in settings of chronic inflammation (Briley et al., 2016). Consistent with this, ovaries from mice of advanced reproductive age produce and secrete significantly higher levels of inflammatory cytokines, and follicles isolated from these mice have an inflammatory gene expression signature (Briley et al., 2016) (Jasti et al., Aging Cell, in revision). We have observed similar patterns of age-associated fibrosis in human ovaries and inflammatory cytokine production in human follicular fluid, suggesting that this phenomenon is broadly applicable to ovarian aging (unpublished data). Fibrosis and chronic inflammation can lead to aberrant tissue architecture and function. For example, fibrosis of the vasculature may constrict blood flow to the ovary and result in a hypoxic environment that is known to negatively impact follicle development and quality (Makanji et al., 2014). The aging ovarian microenvironment has a long-lasting impact on follicle development. For example, when preantral follicles are isolated from animals of advanced reproductive age and grown in culture, they exhibit altered hormone production and reduced gamete quality compared to those from reproductively young cohorts (Hirshfeld-Cytron et al., 2011). Thus, removing ovarian follicles from the aging microenvironment is not sufficient to reverse the adverse reproductive outcomes.

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ACCELERATORS OF REPRODUCTIVE AGING—GENETICS, LIFESTYLE, AND THE ENVIRONMENT In addition to physiologic aging, many exogenous factors and exposures can accelerate the process of reproductive aging (Meldrum et al., 2016; Hart, 2016). Primary ovarian insufficiency (POI; i.e., premature ovarian failure or premature menopause) is characterized by amenorrhea, hypoestrogenism, and elevated gonadotropins in women under the age of 40 (Chapman et al., 2015). POI is relatively common, occurring in 1% of all women (Chapman et al., 2015). In this section, we provide a brief overview of major factors that can cause POI, including genetics, obesity, stress, substance abuse, and environmental and occupational exposures.

Genetics POI is heterogeneous, but there is clearly a genetic component as 10%–30% of idiopathic cases of POI are in women who also have a first-degree relative who is also affected (Chapman et al., 2015). Chromosomal abnormalities ranging from numerical defects to X-deletions, X-autosome translocations, and X-isochromosomes can cause POI (Qin et al., 2015). In addition, candidate gene approaches have demonstrated that mutations or variants in several genes are causative, including BMP15, PGRMC1, FMR1, GDF9, FIGLA, FSHR, NOBOX, NR5A1, NANOS3, STAG3, SYCE1, MCM8/9, and HFM1 (Qin et al., 2015). However, the genetic basis of POI is much more complex as these genes only account for a small fraction of POI cases. The advent of large genome wide association studies combined with technologies such as next-generation sequencing will uncover novel genetic factors that underlie POI. The reader is referred to a comprehensive review on this topic (Qin et al., 2015).

Obesity Many studies have documented decreased fertility and reproductive function in obese women, including anovulation and irregular menses (Zain and Norman, 2008; Jungheim et al., 2013). A variety of reproductive organs and cells are negatively impacted by obesity. For example, obesity-associated suppression of GnRH in the hypothalamus influences oligo/anovulation (Tortoriello et al., 2004; Jain et al., 2007); increased ovarian rigidity and granulosa cell apoptosis impairs oocyte recruitment and quality and results in anovulation (Hirshfeld-Cytron et al., 2011; Jungheim et al., 2010; Woodruff and Shea, 2011); and suboptimal decidualization decreases uterine receptivity in the uterine endometrium (Bellver et al., 2007). In addition, compared to nonobese women, anti-Müllerian hormone (AMH) levels in obese women are lower in later reproductive years (Freeman et al., 2007). AMH is a protein that is expressed in ovarian granulosa cells of growing follicles, and it inhibits the initiation of primordial follicle growth into the primary stage and decreases FSH sensitivity in small antral follicles (Dewailly et al., 2014; La Marca et al., 2009; Visser et al., 2006; Weenen et al., 2004; Durlinger et al., 2002; Gruijters et al., 2003; Josso et al., 2001; Lee and Donahoe, 1993). AMH is an indirect measure of the ovarian reserve, with low levels being indicative of reduced reproductive potential (see Clinical Assessment of Reproductive Aging section). The most common reproductive ailment associated with obesity is polycystic ovary syndrome (PCOS), which influences hyperinsulinemia and hyperandrogenism (Wilkes and Murdoch, 2009; Franks, 2006; Pasquali and Casimirri, 1993). While mitigating the negative effects of PCOS is challenging, weight reduction has been associated with improvement of abnormal menses and fertility rates while reducing hyperandrogenism and hyperinsulinemia (Pasquali et al., 1997, 2003; Kiddy et al., 1992).

Stress Not much is known about the effects of daily stressors on female fertility. A recent study demonstrated for the first time a prospective association between salivary stress biomarkers and time to pregnancy (Lynch et al., 2014). Additionally, prenatal maternal stress as a result of bereavement may influence nonchemical endocrine disruption (Barrett and Swan, 2015). Stress during pregnancy is associated with longer anogenital distance (AGD) in female offspring, an indication of higher exposure to prenatal androgen, and a weak association with shorter AGD in male offspring (Barrett et al., 2013). This manifests in masculinized play behavior in female offspring during childhood and decreased fecundity later in life (Barrett et al., 2013, 2014; Plana-Ripoll et al., 2016; Hines et al., 2002).

Substance Abuse Although genetic and environmental factors are difficult to mitigate, substance abuse (e.g., caffeine, alcohol, nicotine, cannabinoid) is unique due to its voluntary ingestion. Despite a detailed list of guidelines to optimize fertility treatments, a significant portion of women continue to consume alcohol and smoke in the months leading to pregnancy (Vo et al., 2016). Consumption of caffeine and alcohol, while potentially harmful to a pregnancy, do not appear to affect reproductive aging

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(Kinney et al., 2007). Female caffeine consumption in relation to fertility has been studied extensively; however, results appear inconsistent (Wesselink et al., 2016). While caffeine consumption may lower estrogen and progesterone levels during the luteal phase (Schliep et al., 2012), it may play a role in stimulating ovulation (Kapidaki et al., 1995). With regard to alcohol, a meta-analysis of 41,339 women indicates that low and moderate intake may be associated with a later onset of menopause (Taneri et al., 2016). However, further studies are required to determine exactly what level of alcohol consumption begins to delay menopause. Smoking, in particular, is the most documented and targeted form of substance abuse. Between 2009 and 2013, 17.3% of working women of reproductive age were current and former smokers according to a National Health Interview Survey (Mazurek and England, 2016). Smoking adversely impacts fertility by delaying conception, depleting follicle count in the ovaries, inducing gene damage during gametogenesis, increasing the chances of miscarriage, and decreasing the success rate of in vitro fertilization (IVF) (Wallach et al., 1996; Augood et al., 1998; Weisberg, 1985; Hull et al., 2000; Mattison et al., 1989; El-Nemr et al., 1998; Künzle et al., 2003; Richthoff et al., 2008; Zenzes, 2000; Gormack et al., 2015). It has been suggested that the primary mechanism by which smoking adversely affects fertility is the depletion of ovarian reserve (Sharara et al., 1994). Additionally, smoking also disrupts endocrine function by increasing FSH levels during cycle transition and lowering luteal-phase progesterone levels (Windham et al., 2005). Although human studies are less prevalent and mechanisms are not as detailed, negative effects of marijuana use have been documented. A reduction in oocytes harvested in IVF cycles has been observed in marijuana users, but more cohort studies are required to establish a causal relationship between marijuana use and reproductive aging (Wang et al., 2006; Alvarez, 2015).

Harmful Occupational and Environmental Exposures There are a variety of potentially harmful chemicals that may negatively affect female fertility that individuals may come in contact with on a daily basis (Sharara et al., 1998; Gray and Ostby, 1998; DeMatteo et al., 2013). Heavy metals such as lead, bisphenol A (BPA) in plastics used for consumer products, perchlorethylene in dry cleaning, toluene in printing and cosmetology, ethylene oxide (EtO) in industrial chemicals, perfluorinated chemicals in consumer products, and mixed solvents have all been associated with impaired fecundity (La Rocca et al., 2014; Lyngsø et al., 2014; Pak et al., 2013; Louis et al., 2013; Dominguez et al., 2016; Srivastava et al., 2015; Mendola et al., 2008; Domínguez et al., 2016). Interestingly, BPA exposure has been shown to influence the meiotic spindle and chromosome segregation in both rodent and human eggs (Machtinger et al., 2013; Susiarjo and Hunt, 2008). Herbicides and fungicides can also cause impaired endocrine function and fertility (Greenlee et al., 2003; Orton et al., 2009; Tinneberg and Gasbarrini, 2013). Exposure to pesticides and similar chlorinated hydrocarbons increases the risk of miscarriage (Mendola et al., 2008; Greenlee et al., 2003; Tinneberg and Gasbarrini, 2013; Bretveld et al., 2006). Adverse pregnancy outcomes have also been thoroughly documented in healthcare workers that are routinely exposed to antineoplastic drugs (Connor et al., 2014).

INDUCERS OF REPRODUCTIVE AGING—IATROGENIC INSULTS Medical interventions for treatment of various conditions can have the unintended consequence of threatening off-target tissue function. One of the most well-recognized iatrogenic drivers of reproductive aging and infertility is cancer treatment, which may involve a combination of surgery, chemotherapy, radiation, and biologics (Pavone et al., 2011, 2013, 2014, 2016; Waimey et al., 2015; Lawson et al., 2012, 2015; Sonmezer and Oktay, 2004; Ethics Committee of the American Society for Reproductive Medicine, 2005; Quintero et al., 2005; Jeruss and Woodruff, 2009; Quinn et al., 2010). To have a successful natural pregnancy, a female must have an adequate ovarian reserve, a functioning hypothalamic–pituitary–ovarian (HPO) axis, and a uterus that can support a growing fetus. Alterations to any of these areas due to cancer treatment could compromise the ability of a female cancer survivor to conceive or carry biologic children. Combination cancer therapy is now the norm, and these treatments can have both endocrinologic and anatomic effects on the HPO axis and female reproductive tract. These effects can lead to pubertal derangements, hormonal dysregulation, infertility, pregnancy complications, and sexual dysfunction. It is difficult to know the actual risk for patients from any given therapy because risk depends on age, gender, and dose/combination of therapy, as well as the patient’s initial ovarian reserve (Metzger et al., 2013; Armuand et al., 2014). However, even in young children who have a large ovarian reserve, cancer treatments can significantly impact reproductive function. For example, the Childhood Cancer Survivor Survey (CCSS) reported that 6.3% of childhood survivors experience acute ovarian failure immediately after treatment, and 8% experience premature ovarian failure at some point after the end of treatment (Metzger et al., 2013).

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Chemotherapy Many chemotherapeutic agents negatively impact fertility (Pavone et al., 2016; Waimey et al., 2015; Maltaris et al., 2007; Minton and Munster, 2002; Mattle et al., 2005; Reichman and Green, 1993; Bedoschi et al., 2016). Chemotherapy diminishes ovarian function by depleting the pool of primordial follicles, and it also targets cells that are actively dividing, including growing ovarian follicles (Fig. 9.7). Histologic sections of ovaries from women exposed to chemotherapy show changes that include decreased numbers or complete absence of follicles as well as stromal fibrosis (Bedoschi et al., 2016; Rosendahl et al., 2010; Blumenfeld et al., 2008; Morgan et al., 2012). Age is strongly associated with the gonadotoxicity of chemotherapy in that the gonadotoxic effects are more pronounced in postpubertal compared to prepubertal females (Mahajan, 2015; Meirow and Wallace, 2009; Blumenfeld et al., 1999). This is explained by the presence of fewer primordial oocytes in the ovaries of postpubertal females at baseline, hence less ovarian reserve to offset the cytotoxicity of the cancer treatment (Bedoschi et al., 2016). Thus, as a female ages, her ovarian reserve decreases, increasing her risk of impaired fertility after cancer treatment (Meirow et al., 2010). The actual gonadotoxic risk varies based on type of chemotherapy, and the effects are often dose dependent and cumulative. Combinations of chemotherapies and other modalities such as radiation can have additive effects. Alkylating agents are thought to be the most gonadotoxic (Blumenfeld et al., 2000; Bedaiwy et al., 2011). Traditional myoablative conditioning used prior to stem cell transplantation also has a high likelihood of iatrogenic POI in 95%–100% of cases. As newer chemotherapy and targeted regimens are developed, their gonadotoxicity will have to be carefully assessed (Lee et al., 2006).

Radiation In addition to chemotherapy, radiation is another commonly used cancer treatment (Rodriguez-Wallberg and Oktay, 2014; Miller et al., 1981; Trotti et al., 2000; Moeller et al., 2007; Siegel et al., 2012). Radiation induces cell damage by causing double-stranded DNA breaks (Rodriguez-Wallberg and Oktay, 2014; Wolff et al., 1988; Ward, 1990). Radiation affects multiple systems in females, including the ovaries, hypothalamic-pituitary-gonadal (HPG) axis, and the uterus (RodriguezWallberg and Oktay, 2014; Wallace et al., 2003). Pelvic radiation to the ovary targets both developing follicles and resting primordial follicles, in addition to inducing apoptosis (Wallace et al., 2003; Ross et al., 2014) (Fig. 9.7). Cranial radiation can directly damage the hypothalamus and pituitary (Constine et al., 1993; Chrousos et al., 1982; Lam et al., 1991; Shalet, 1993). The resulting endocrinopathies affect sexual development, which leads to altered puberty and infertility (Rappaport et al., 1982; Sklar and Constine, 1995). Hypogonadotropic hypogonadism may also ensue, leading to other endocrinopathies that can also affect fertility (Hayes et al., 2013; Boehm et al., 2015; Brignardello et al., 2013). Total body radiation restricts uterine blood flow and can cause impaired uterine growth leading to increased risk of spontaneous miscarriage, premature labor, and low birth weight in future offspring (Critchley et al., 2002; Critchley and Wallace, 2005; Norwitz et al., 2001; Nakayama et al., 2008). The median dose of radiation needed to decrease the ovarian follicular pool by 50% is reported to be as low as 2 Gy (Nakayama et al., 2008; Rodriguez-Wallberg and Oktay, 2010). Similar to chemotherapy, there is an agerelated effect based on ovarian reserve: the ovaries of older patients with relatively diminished ovarian reserve tolerate less radiation than younger patients (Wallace et al., 2003, 2005; Critchley et al., 2002; Chung et al., 2013). In addition, a single high dose of radiation is more toxic to the ovaries than multiple fractionated doses of radiation. Radiation doses commonly used to treat pelvic tumors routinely exceed these thresholds, placing women with these diagnoses at considerable reproductive risk. The effect of radiation is compounded when given in conjunction with an alkylating chemotherapy (Nakayama et al., 2008; Rodriguez-Wallberg and Oktay, 2010). FIGURE 9.7  Exposure to cancer therapies can accelerate reproductive aging. Exposure to medical interventions such as chemotherapy and radiation can cause medically induced or iatrogenic reproductive aging. Chemotherapy and radiation can target primordial follicles, accelerate the loss of the ovarian reserve, and lead to premature menopause. (Adapted from the Oncofertility Consortium.)

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Surgery Gynecologic malignancies account for ∼1 million new cancer cases worldwide, and about 10% of these cases are in patients 18IU/L had a 100% specificity for failure to achieve a live birth (Scott et al., 2008). Basal estradiol concentration by itself has little value as an ovarian reserve test but can provide additional information when done with basal FSH. The HPO axis works via negative feedback system. Therefore, early elevation in serum estradiol reflects advanced follicular development and an early selection of a dominant follicle, which is commonly seen with advanced reproductive aging. Elevated estradiol will also suppress FSH concentrations and possibly mask a high FSH. When both estradiol and FSH are elevated, ovarian response to stimulation is likely to be very poor (Evers et al., 1998).

CLINICAL SCREENING METHODS FOR REPRODUCTIVE AGING Despite the infertility and birth defect risks associated with reproductive aging, it is well-established that the age at which both men and women are first starting to build their families is increasing. Individuals of advanced reproductive age rely more heavily on ART procedures to conceive, and successful outcomes are particularly challenging in this population due

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to the limited egg quantity and quality. Moreover, the clinical markers of reproductive potential described above are not predictive of offspring outcomes. Thus, there is a heightened clinical need to develop and use screening tools to select the healthiest embryo with the best developmental potential for transfer. The use of preimplantation genetic screening (PGS), assessment of mtDNA, and live imaging of early cleavage divisions are important clinical screening tools and considered in more detail below.

Preimplantation Genetic Screening PGS involves biopsy of a preimplantation stage embryo (e.g., blastomere or trophectoderm biopsy) followed by genetic testing using various approaches (Geraedts and Sermon, 2016). In contrast to preimplantation genetic diagnosis, which is used for patients with a high risk of transmitting genetic or chromosomal abnormalities to their children, PGS is used to increase IVF pregnancy rates in patients with infertility or recurrent pregnancy loss (Thornhill et al., 2005). Specific indications for PGS include advanced maternal age, repeated IVF failure, and repeated miscarriages in couples. Using this technology, embryos are screened for numerical chromosome abnormalities, or aneuploidy, which is the leading cause of implantation failure and miscarriage during the first trimester (Hassold et al., 2007; Brezina et al., 2012; Hodes-Wertz et al., 2012; Rubio et al., 2003; Voullaire et al., 2002). In fact, comprehensive chromosome screening of trophectoderm biopsies can facilitate single embryo transfer in the setting of advanced reproductive age (Schoolcraft and Katz-Jaffe, 2013). Although these techniques may improve IVF outcomes by identifying healthy embryos, there are additional ethical concerns to consider in their use of identifying an embryo with the most “desired” set of traits (Hens et al., 2012).

mtDNA Assessment Because mitochondria are so critical for energy production and because the early preimplantation embryo relies on mitochondria generated during oogenesis, the number of mitochondria an oocyte is endowed with may be predictive of fertilization and embryo development potential (Meldrum et al., 2016; May-Panloup et al., 2016). mtDNA content is reflective of mitochondrial number and can be detected in single cells, so mtDNA copy number threshold may serve as an important clinical marker (Meldrum et al., 2016; May-Panloup et al., 2016). In fact, growing evidence suggests that mtDNA levels are lower in oocytes from women of advanced reproductive age or with diminished ovarian reserve compared to young cohorts or those with normal ovarian reserves (May-Panloup et al., 2016). Interestingly, however, blastocysts from women of advanced reproductive age or aneuploid blastocysts have high mtDNA levels, perhaps indicative of a compensatory mechanism in the early embryo for mitochondrial insufficiency (Fragouli et al., 2015). Future research is needed to clearly define how mtDNA levels correlate with reproductive outcomes and to define clinically meaningful thresholds.

Time-Lapse Microscopy Noninvasive time-lapse microscopy (TLM) demonstrated that parameters related to the first mitotic embryonic divisions prior to embryonic genome activation are predictive of development to the blastocyst stage and ploidy status (Chavez et al., 2012; Wong et al., 2010). TLM has since been applied clinically to monitor human embryo morphokinetics (the precise timing of specific morphologic events) with the goal of defining a profile to identify the best embryo for single embryo transfer (Kovacs, 2014). Approximately 20 morphokinetic patterns have been identified in human embryos, but the predictive value of many of these has not been adequately tested (Kovacs, 2014). Early study results, however, are promising. An extensive retrospective analysis indicated that the use of TLM instead of standard incubation improved pregnancy rates by 20% (Meseguer et al., 2012). Another study indicated further augmentation of overall IVF pregnancy rates for cleavage-stage transfer using the Eeva (Early Embryo Viability Assessment) tracking software (Conaghan et al., 2013). Although TLM is emerging as a useful embryo selection tool, further research is necessary to outline morphometric standards in the setting of various infertility diagnoses and treatment methods (intracytoplasmic sperm injection (ICSI)/IVF). Moreover, whether TLM confers a specific advantage for women of advanced reproductive age has not been systematically investigated.

COUNTERACTING REPRODUCTIVE AGING In addition to establishing markers of reproductive aging and developing screening tools to improve reproductive outcomes in women of advanced reproductive age, efforts are also underway to actively counteract the effects of reproductive aging. For example, women of advanced reproductive age may elect to conceive using donor eggs and embryos, or women may cryopreserve their eggs at a young age, affording themselves the opportunity to delay reproduction by freezing their biological clock in time. While these approaches bypass the effects of reproductive aging, therapeutic strategies are also being

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engineered to ameliorate outcomes. For example, age-associated defects in mitochondrial quantity and function contribute to the decline in oocyte quality (see Cytoplasmic Determinants of Gamete Quality section). Thus, mitochondria are important targets for therapeutic interventions. These strategies are discussed in more detail below.

Oocyte or Embryo Donation Oocyte or embryo donation from young individuals is an established standard and effective treatment for age-related infertility because the pregnancy rates with such methods are significantly higher than those obtained with controlled ovarian hyperstimulation or IVF in women of advanced reproductive age (Check et al., 2011). In fact, live birth rates per embryo transfer are >50% in women undergoing egg and embryo donation (Check et al., 2011). However, due to adverse obstetrical outcomes associated with advanced reproductive age, elective single embryo transfer is the preferred method in this population (Jolly et al., 2000; Petropanagos et al., 2015). Moreover, although pregnancies and live births have been reported in women in their late 50s and early 60s via egg and embryo donation, American Society for Reproductive Medicine (ASRM) ethical committee guidelines suggest 55 years old as an upper age-limit for this procedure due to the high risk nature of the pregnancy and the limited longevity of the individual (Ethics Committee of the American Society for Reproductive Medicine, 2013).

Social Egg Freezing Although oocyte and embryo donation is successful, it does not afford the opportunity for women to have their own biological children. Social egg freezing on the other hand does and is now possible since ASRM removed the experimental label of oocyte cryopreservation in 2013 (Practice Committees of American Society for Reproductive Medicine, 2013). The procedure of oocyte cryopreservation involves hormonal stimulation, transvaginal retrieval of oocytes from the ovaries, oocyte freezing using vitrification or slow freezing methods, and storage. When ready to conceive, cryopreserved oocytes are thawed and inseminated using standard ART procedures. Oocyte cryopreservation is relatively successful with a live birth rate of 2%–12% for women under 38 years of age (Argyle et al., 2016). Thus, in a process now referred to as “social egg freezing” women can elect to cryopreserve their oocytes at a young age, and delay childbearing. This procedure maintains reproductive autonomy, since a sperm donor is not required. Many women view this option as “fertility insurance” and find it attractive due to personal, professional, financial, and psychological factors (Argyle et al., 2016). However, this application of oocyte cryopreservation has been contraindicated by several professional societies because of the limited data on the safety, efficacy, cost-effectiveness, and emotional risks of oocyte cryopreservation in young, healthy women (Petropanagos et al., 2015; Ethics Committee of the American Society for Reproductive Medicine, 2013). Moreover, social egg freezing requires additional ART procedures that have their own risks, involves pregnancy at older ages that has complications for both the mother and offspring, and importantly, does not guarantee a pregnancy or child later in life. Nevertheless, there has been an increase in social egg freezing in efforts to combat reproductive aging consequences.

Emerging Therapeutic Strategies Oocyte mitochondrial dysfunction is a hallmark of reproductive aging, and thus therapeutic strategies to ameliorate energy production are actively being developed. One such strategy that is already in clinical use is autologous germline mitochondrial transfer (AUGMENT) (Woods and Tilly, 2015). In this technique, ovarian tissue is harvested from a patient, and mitochondria are isolated from putative oogonial stem cells (OSCs) within the tissue. These purified germline mitochondria are then injected into the cytoplasm of the patient’s own oocytes to improve gamete quality and developmental potential. Because the mitochondria and oocytes are patient-matched, there is no concern about mitochondrial heteroplasmy. AUGMENT has been tested in clinical studies, and women of advanced reproductive age with a history of failed ART cycles have conceived and had children following this procedure (Oktay et al., 2015). However, these findings must be interpreted with caution because there is no way to actually prove that the reported pregnancies were solely a result of the AUGMENT procedure versus chance alone. The AUGMENT procedure is highly controversial as it relies on the existence of OSCs that support neo-oogenesis and, thereby, challenge the dogma of the fixed ovarian reserve. The physiologic relevance of OSCs has been debatable as their identity and presence in vivo has been elusive, and de novo oogenesis is not required to maintain reproductive function (Lei and Spradling, 2013; Zarate-Garcia et al., 2016). Moreover, women still go through menopause, so if OSCs do exist, their function and regenerative capacity also decline with age. This begs the question of how OSCs in women of advanced reproductive age still contain healthy mitochondria suitable for the AUGMENT procedure. Additional strategies to improve reproductive outcomes are based on increasing mitochondrial function and rely on supplementation with “mitochondrial nutrients” or molecules that protect mitochondria from oxidative damage, including

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resveratrol, Coenzyme Q10 (CoQ10), and other antioxidants (Liu et al., 2013; Ben-Meir et al., 2015; Silva et al., 2015). In a mouse model of physiologic reproductive aging, long-term oral administration of resveratrol protected against reproductive decline relative to controls (Liu et al., 2013). Similarly, dietary supplementation of CoQ10 in a mouse model of reproductive aging was also able to reverse the age-related decline in oocyte quality and quantity (Ben-Meir et al., 2015). CoQ10 supplementation as a potential method to treat age-associated infertility has been translated from mouse to human (Bentov et al., 2014). In a double blind, placebo-controlled, randomized clinical trial, women of advanced reproductive age (35–43 years of age) were administered CoQ10 2 months prior to and during IVF-ICSI cycle with the intention of improving oocyte energy production and, thereby, reducing oocyte aneuploidy. Although not statistically significant, the incidence of aneuploidy in the CoQ10 group was lower compared to controls and the clinical pregnancy rate was higher (Bentov et al., 2014). These results, although still preliminary, suggest that targeting mitochondrial function to improve reproductive aging outcomes holds promise.

SUMMARY AND FUTURE DIRECTIONS As physiologic aging animals, such as the mouse, are gaining traction as important experimental model systems for reproductive aging research, we are likely to uncover new biology and mechanisms. Such understanding of this physiologic aging continuum in the female gamete is critical, since much of our current knowledge is gleaned from “postovulatory aging” models in which the period between completion of meiotic maturation and fertilization is experimentally delayed (Lord and Aitken, 2013). Postovulatory aging is unique, and the mechanisms underlying this phenomenon may be quite different from those contributing to physiologic reproductive aging. Indeed, as described in this chapter, female reproductive aging is multifactorial—involving multiple cell types, organelles, and cellular pathways. Thus, therapeutic strategies to ameliorate reproductive aging will have to integrate such complexity, and there is unlikely to be a single, catch-all approach. Nevertheless, there is a need to continually identify the consequences, mechanisms, markers, and treatments of reproductive aging—a process that affects 50% of the population, impacts all women regardless of race, ethnicity, or geography, and is a global issue as women worldwide are delaying childbearing.

ACKNOWLEDGMENTS The authors thank Dr. Timothy Duncan for assistance with figures. This work was supported by the Center for Reproductive Health After Disease (P50 HD076188) from the National Centers for Translational Research in Reproduction and Infertility (NCTRI).

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Chapter 10

Adrenopause Peter J. Hornsby University of Texas Health Science Center, San Antonio, TX, United States

Adrenopause is the age-related decline in the production of dehydroepiandrosterone (DHEA) and its sulfate (DHEAS) by the human adrenal cortex. The name echoes the more well-known and well-studied processes of menopause and andropause (Hughes and Chatterjee, 2016; Lois et al., 2014), but its significance for human health is much less clear than those two agerelated endocrine changes. Here I will refer to DHEA and its sulfate as DHEA(S), where both steroids are being referred to together. The function of DHEA(S) is still uncertain, despite much research, speculation, and clinical trials in human patients. These controversies are not covered here, but they have been discussed extensively in recent excellent reviews (Hughes and Chatterjee, 2016; Lois et al., 2014). Instead, we may view the phenomenon of adrenopause as a biological phenomenon in search of a biological explanation. Understanding it and modeling it experimentally may offer insights into the nature of the declines in function of other organs in aging.

ANIMAL MODELS FOR ADRENOPAUSE: THE CHALLENGES The main challenge researchers face in this area is the lack of any current animal model that can be validly used to study and understand the process of adrenopause. I will review the reasons for this statement in this chapter, but I begin by giving an overview of the problems, which can be summarized as follows: 1. DHEA(S) synthesis by the adrenal gland occurs only in those mammalian species that have an adrenal cortex with a zone (zona reticularis, ZR) that is specialized for this function. Moreover, even in those species that have a DHEA(S)secreting ZR the life history of the ZR may not resemble that observed in humans, as described below. In short, an ideal animal model that reproduces the life history of human DHEA(S) synthesis is simply not available. 2. Many rodents, and in particular laboratory rats and mice, do not synthesize DHEA(S) from their adrenal glands. Although some rodents have a zone of the cortex that morphologically resembles the primate ZR, this zone does not typically synthesize DHEA(S). Given the widespread use of the mouse in biomedical research, the question then arises whether it is possible to “humanize” the mouse in terms of adrenal DHEA(S) synthesis. In theory, there can be two types of approach to this kind of humanization: a. Genetic modification of the mouse to make the adrenal cortex synthesize DHEA(S). The steps to accomplish this are simply unknown—we do not know enough about the molecular and cellular basis of the development of a DHEA(S)synthesizing ZR to be able to formulate any hypothesis that could be tested by genetic modification of mice to accomplish this. The aim is definitely not impossible, but no scheme to accomplish this can be currently proposed. Moreover, the number of genes that may need to be modified to accomplish this could be very large. b. Transplantation of human cells of various types to generate DHEA(S)-secreting organoids in immunodeficient mouse models. In view of the foregoing discussion, this becomes the most reasonable approach to an animal model, but by no means a straightforward one. The challenges will be discussed below. I point out that the proof of principle of this approach has been demonstrated, but most of the work needed has yet to be done.

THE LIFE HISTORY OF DHEA(S) PRODUCTION BY THE HUMAN ADRENAL CORTEX The synthesis of DHEA(S) by the adrenal cortex correlates over the human life span with the development of the ZR (Fig. 10.1). The ZR is absent or very thin before age 6–7, at which age the ZR starts to increase in thickness and DHEA(S) begins to be detectable in the circulation. The appearance of the ZR and the accompanying synthesis of DHEA(S) is termed adrenarche. The ZR reaches its maximal development by age 20–30 and thereafter begins a long slow involution. DHEA(S) levels peak at young adult age and then decline throughout adulthood into old age. The term “adrenopause” might be Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00010-5 Copyright © 2018 Elsevier Inc. All rights reserved.

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FIGURE 10.1  Phases of plasma DHEAS concentrations over the life span in humans. Before birth (A), the fetal zone of the adrenal cortex secretes large amounts of DHEA(S); following birth (B), the fetal zone rapidly involutes (C). DHEAS levels remain very low until 6–7 years of age (D, adrenarche), when plasma DHEAS concentrations begin to rise coincident with the development of the zona reticularis in the adrenal cortex. The achievement of the peak concentration of plasma DHEAS in young adulthood (E) is followed by a progressive decline in adrenal secretion of DHEA(S) (F). The ordinate represents age in years; the scale is expanded before 10 years of age. (Reproduced with permission from Hornsby, P.J., 2004. Aging of the human adrenal cortex. Sci Aging Knowl Environ 2004, RE6.)

misleading, suggesting a specific age of transition by analogy to menopause, but the established term “andropause” also refers to a gradual decline in a hormone level. Before birth the adrenal cortex also secretes a large amount of DHEA(S) due to the presence of a different specialized zone, the fetal zone, which involutes after birth. The natural history of the increase and decrease in DHEA(S) synthesis has been the topic of reviews that have covered these phenomena in detail, and I refer the reader to these reviews for more detailed information (Hornsby, 2004, 2005, 2012).

FACTORS INVOLVED IN THE DEVELOPMENT AND DECLINE OF THE DHEA(S)-SECRETING ZONE, THE ZONA RETICULARIS To understand the decline in the ZR in old age it is first necessary to understand the factors that determine the development of zonation of the human adrenal cortex generally (Bird, 2012). In Fig. 10.2 the anatomy of the human adrenal gland is shown, together with the key differences in gene expression that result in DHEA(S) synthesis by the ZR. Steroid 17α-hydroxylase (CYP17A1) is expressed throughout the zona fasciculata (ZF) and the ZR. In those two zones the expression of 3β-hydroxysteroid dehydrogenase (HSB3B2) is confined to the ZF, as shown by immunohistochemistry and by microdissection of the zone (Endoh et al., 1996). The expression of HSD3B2 in the ZF ensures that any 17α-hydroxypregnenolone produced by the enzymatic action of CYP17A1 on pregnenolone is converted to 17α-hydroxyprogesterone (and then to deoxycortisol and cortisol); thereby this expression prevents the synthesis of DHEA(S). On the other hand, the expression of cytochrome b5 in the ZR increases the C17,20-lyase activity of CYP17A1, thus increasing the production of DHEA from pregnenolone versus 17α-hydroxypregnenolone (Rege and Rainey, 2012; Rege et al., 2016; Bhatt et al., 2016). The expression of sulfotransferase type 2A results in the sulfation of much of the DHEA and the secretion of DHEAS as a final product of the ZR (Hui et al., 2009; Mueller et al., 2015). There are many other differences in gene expression that are unique to the human ZR (Rege et al., 2014). Any model of adrenopause or adrenarche must address the factors that determine these specific molecular characteristics of ZR cells. Echoing the thought expressed by Pihlajoki et al.—“Never underestimate the complexity of remodeling”—I can summarize the situation as the following: while many factors involved in the setting up and maintenance of zonation have been described, we are not very close to a full understanding of the process (Pihlajoki et al., 2013, 2015; Xing et al., 2015; Pignatti et al., 2017; Vinson, 2016). Moreover, the great majority of the progress that has been made enables us to understand more about the process of differentiation of the ZF and zona glomerulosa (ZG) in the mouse gland, but almost nothing about the differentiation of the ZR in the human or primate adrenal gland. It has long been suspected that ZF cells differentiate from ZG cells. In older experiments, we showed that bovine ZG cells placed in culture lost their ZG properties while acquiring ZF properties (Crivello et al., 1982). More recently lineage tracing has shown that ZF cells differentiate from ZG cells in the mouse cortex in vivo (Pignatti et al., 2017; Chang et al., 2011; Freedman et al., 2013). While it is strongly suspected that the only possibility for the origin of ZR cells is that they differentiate from ZF cells, this has not been directly investigated in vivo. Moreover, when we performed a few preliminary experiments involving the transplantation of separated human ZR cells into immunodeficient mice, we did not find evidence of maintenance of ZR function, but instead there may have been a reversion of the cells to a ZF-like state (Thomas et al., 2002). In Fig. 10.2 a hypothetical morphogen gradient is indicated—a substance originating in cells within or close to the capsule or ZG and then being carried by the centripetal blood flow through the zones toward the ZR. Some time ago I proposed that adrenocortical steroids synthesized by one or more zones could form such a gradient (inverse from that shown

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FIGURE 10.2  Adrenal gland anatomy: zonation of the adrenal cortex and the synthesis of specific steroids. (A) The zones of the human adrenal cortex and the steroids they secrete. (B) The structure of the three zones (C, capsule; ZG, zona glomerulosa; ZF, zona fasciculata; ZR, zona reticularis). (C) The blood vessels of the adrenal cortex; the direction of blood flow is indicated (AP, medullary artery; C, capsule; Cap, capillaries; CP, capsule plexus; F, zona fasciculata; G, zona glomerulosa; IC, smooth muscle in the wall of the adrenal vein; M, medulla; MV, medullary veins; R, zona reticularis; SA, adrenal artery; SV, adrenal vein; V, veins). On the left of panel (B) the zonal patterns of expression of key genes involved in determining DHEA(S) synthesis are indicated. CYB5A, cytochrome b5; CYP17A1, steroid 17α-hydroxylase; HSD3B2, 3β-hydroxysteroid dehydrogenase; SULT2A1, sulfotransferase type 2A. (Reproduced with permission from Hornsby, P.J., 2004. Aging of the human adrenal cortex. Sci Aging Knowl Environ 2004, RE6.)

in the figure with the highest concentrations in the inner part of the cortex) (Hornsby, 1987). This possibility still needs to be considered, and in fact has been proposed again more recently as a factor in ZR differentiation (Williams et al., 2012; Voutilainen and Jaaskelainen, 2015). However, in the intervening years a very large number of potential morphogens in the Wnt, Shh, and other families of molecules have been shown to have roles in the differentiation and maintenance of the adrenal cortex (Xing et al., 2015; Drelon et al., 2015). These morphogens are controlled by, and regulate, an equally large number of key transcription factors, such as SF1, DAX1, and WT1 (Bandiera et al., 2013). Which, if any, of these factors is responsible for determining the differentiation of ZR cells from ZF cells at the venous end of the capillary bed is as yet unknown. Thus the factors involved in the development of the ZR in midchildhood (adrenarche) remain undetermined (Bird, 2012; Voutilainen and Jaaskelainen, 2015; Belgorosky et al., 2008).

POSSIBLE CAUSES OF THE DECLINE OF THE ZONA RETICULARIS The decline of the ZR could be related to structural changes in the adrenal cortex as a whole (Hornsby, 2004). For example, if there were problems maintaining the overall thickness of the cortex due to changes in the vasculature, the ZR may suffer most of the damage through its position at the venous end of the capillary bed. While the mechanisms of the age-related

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decline of the ZR are unknown, one major clue that should not be overlooked is the expression of MHC class II molecules on the surface of ZR cells (Hornsby, 2004). This was first shown by Khoury et al. (1987). Although the significance of this expression has not been directly investigated, the observation supports the hypothesis that the ZR is a “temporary” tissue structure like the ovarian corpus luteum (Hornsby, 2004).

NATURAL ANIMAL MODELS OF ADRENOPAUSE Given the fact that the adrenal cortex in most commonly used laboratory rodents does not make DHEA(S), such species do not form models for adrenopause. Among those species that have a specialized ZR that does synthesize DHEA(S), it is potentially possible that such species may give insights into adrenopause (Conley et al., 2012). However, the major problems are (1) those species that would be the most informative are difficult if not impossible to work with as experimental species; and (2) in those species that are more commonly used in biomedical research, age-related changes in DHEA(S) synthesis are not necessarily caused by a senescent decline of some type, versus a developmental process. The rhesus macaque (Macaca mulatta) has a DHEA(S)-synthesizing ZR and so possibly could form a model for the senescent decline in human DHEA(S) synthesis (Nguyen et al., 2008, 2009; Conley et al., 2011, 2013; Sorwell et al., 2014; Lasley et al., 2016). The chimpanzee has a human-like ZR (Parker et al., 2014), but it is unlikely that this species will be considered as an experimental animal in biomedical research. The common marmoset (Callithrix jacchus) also secretes DHEA(S) from the ZR and is a more attractive animal model, as it is smaller than other commonly used nonhuman primates, easier to house and has a relatively short life span, enabling more meaningful interventional studies in aging. It does provide several advantages for the study of ZR function and for studying adrenopause (Pattison et al., 2009), but it is not clear that there is a true adrenarche in this species, and thereby any age-related decline may not resemble that in humans. Nevertheless, the ability to perform antiaging strategies in this species (Ross et al., 2015) does open the possibility that if factors are discovered that have generalized antiaging activity, and also slow down the decline in DHEA(S), they may prove useful to test clinically or in the humanized models described below.

SETTING UP HUMANIZED MOUSE MODELS OF ADRENOPAUSE Potential Genetic Modification in Mice to Generate a Humanized Model A theoretical possibility—one that is likely not close to being possible in practice—is that the mouse could be genetically modified sufficiently to generate an animal that models human DHEA(S) production. As discussed above, this would require much more knowledge about the mechanisms of zonation in the human adrenal cortex, or specifically the molecular mechanisms of ZR differentiation. At the moment the genetic modifications needed to achieve this are unknown. Moreover, this may need the simultaneous modification of many genes, thus increasing the difficulty of creating such a model. Additionally, because a model of adrenopause is needed, it may require even more modifications to ensure that there is an age-dependent decline in the function of the ZR that could be further studied.

Setting Up an Adrenocortical Cell Organoid Model in Mice to Study Adrenopause An alternative approach is to humanize the mouse by using actual human cells rather than by genetic modification. In previous reviews I have detailed experiments carried out over the past 20 years, which show that isolated adrenocortical cells can be transplanted into animal models, specifically immunodeficient mice, and will function in their new hosts (Hornsby, 2001, 2012; Huang et al., 2007; Ruiz-Babot et al., 2015). In these experiments differentiated human and bovine adrenocortical cells were transplanted, and they retained the functionality expected of ZF cells in their hosts. In most cases the cells were originally derived from ZF cells, but in a small number of preliminary experiments ZR cells were transplanted. In those cases we did not detect evidence of continued ZR function of the transplanted cells, perhaps suggesting that the cells had adopted a ZF phenotype in the host animal. However, these experiments mostly indicate the scope of the problems involved in generating a transplant model that recapitulates appropriate function of zonally derived cells; generating a truly zoned structure has not yet been reported. In this lab we have transplanted adrenocortical cells into the mouse adrenal gland (orthotopic cell transplantation, Cardoso et al., 2010) and it is possible that this approach could be promising for setting up a humanized adrenal gland in the mouse—one where the component mouse cells have been replaced with human cells. However, most of the work that could generate a human zoned, functional adrenocortical organoid in the mouse has yet to be done. The presently available evidence is in the nature of a proof of concept—the cells do survive long term, become

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vascularized by the host cardiovascular system, and show appropriate ZF function—but an unknown amount of work need to be done to establish a true zoned structure that would form an animal model for adrenopause. In the work to date on generating adrenal organoids in the mouse, fully differentiated cells were transplanted. It is likely that the transplantation of authentic adrenocortical stem cells may generate an organoid that forms a superior model, particularly if the organoid becomes organized into the normal functional zones. The identity of adrenocortical stem cells is a topic of active investigation in many labs, with several distinct populations having been proposed as candidates (Bandiera et al., 2013; Wood et al., 2013; Walczak and Hammer, 2015; Vidal et al., 2016). In many organ systems (e.g., the hematopoietic system) stem cells are assayed by transplantation into an ablated host, and the ability of the cells to restore normal function of the organ system is used as a measure of the true “stemness” of the transplanted cell population. The adrenocortical stem cell field has not yet reached this stage. This development will be needed for further progress to be made; in particular, characterizing the stem cell population from the mouse ought to facilitate isolating the corresponding population from the human adrenal gland. Another promising approach, now carried out in several organ systems, is the generation of functional organoids from pluripotent stem cells, either embryonic stem cells or induced pluripotent stem cells (Lancaster and Knoblich, 2014). For example, functional gastrointestinal tract organoids can be generated from pluripotent stem cells and can be transplanted into host animals (Gehart and Clevers, 2015). Work along these lines has not yet been reported for the adrenal cortex, although some promising preliminary evidence is available (Sonoyama et al., 2012; Yazawa et al., 2016). One other possible approach is the forced transdifferentiation of a suitable cell type into adrenocortical cells. In a prior review I detailed many of these efforts (Hornsby, 2012). Given the present state of development of the generation of differentiated human cells from pluripotent cell types, it seems unlikely that forced transdifferentiation would produce cells that are superior in properties from those derived by differentiation from pluripotent cells, but the pathway remains as an alternative to be considered (Yazawa et al., 2011). Another recent approach for the generation of tissue-specific stem cells has been the forced “dedifferentiation” of differentiated cells back to an earlier differentiated state by the overexpression of YAP/TAZ (Panciera et al., 2016). Because this expression needs only to be temporary, as in the generation of induced pluripotent stem cells from differentiated cell types, this and related strategies could prove to be very useful for generation of stem cells independent of being able to isolate the stem cells directly from the native tissues.

SUMMARY AND CONCLUSIONS As documented here, the task of setting up an appropriate animal model for adrenopause is a daunting one. In this author’s opinion, the most promising approach is the continued study of the various pathways of human pluripotent stem cell differentiation, which should yield authentic human adrenocortical organoids in vitro. Such organoids may be able to be transplanted into adrenalectomized immunodeficient mice, where they may become vascularized and replace the function of the host animal’s adrenal glands. If this successfully generates a humanized mouse model for the adrenal cortex, it then becomes possible that adrenopause can be simulated in that model, either via natural aging or by applying stresses to the organoids hypothesized to resemble the age-related changes in the human adrenal gland that lead to adrenopause. These could include changes in the vascular supply, for example. Overall, these aims are distant from what is feasible at the present time, but given the rapid progress in pluripotent stem cell differentiation in other organ systems, exciting developments in modeling human adrenal function and adrenopause can be anticipated.

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Conley, A.J., Stanczyk, F.Z., Morrison, J.H., Borowicz, P., Benirschke, K., Gee, N.A., Lasley, B.L., 2013. Modulation of higher-primate adrenal androgen secretion with estrogen-alone or estrogen-plus-progesterone intervention. Menopause 20, 322–328. Crivello, J.F., Hornsby, P.J., Gill, G.N., 1982. Metyrapone and antioxidants are required to maintain aldosterone synthesis by cultured bovine adrenocortical cells. Endocrinology 111, 469–479. Drelon, C., Berthon, A., Mathieu, M., Martinez, A., Val, P., 2015. Adrenal cortex tissue homeostasis and zonation: a WNT perspective. Mol Cell Endocrinol 408, 156–164. Endoh, A., Kristiansen, S.B., Casson, P.R., Buster, J.E., Hornsby, P.J., 1996. The zona reticularis is the site of biosynthesis of dehydroepiandrosterone and dehydroepiandrosterone sulfate in the adult human adrenal cortex resulting from its low expression of 3β-hydroxysteroid dehydrogenase. J Clin Endocrinol Metab 81, 3558–3565. 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Huang, Q., Chen, M., Liang, S., Acha, V., Liu, D., Yuan, F., Hawks, C.L., Hornsby, P.J., 2007. Improving cell therapy – experiments using a cell transplantation model in immunodeficient mice. Mech Ageing Dev 128, 25–30. Hughes, I.A., Chatterjee, V.K., 2016. Adrenarche and adrenopause. In: Jameson, J.L., De Groot, L.J. (Eds.), Endocrinology: Adult and Pediatric, seventh ed. Elsevier/Saunders, Philadelphia, pp. 1833–1840. Hui, X.G., Akahira, J., Suzuki, T., Nio, M., Nakamura, Y., Suzuki, H., Rainey, W.E., Sasano, H., 2009. Development of the human adrenal zona reticularis: morphometric and immunohistochemical studies from birth to adolescence. J Endocrinol 203, 241–252. Khoury, E.L., Greenspan, J.S., Greenspan, F.S., 1987. Adrenocortical cells of the zona reticularis normally express HLA-DR antigenic determinants. Am J Pathol 127, 580–591. Lancaster, M.A., Knoblich, J.A., 2014. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125-1–1247125-9. Lasley, B., Conley, A., Morrison, J., Rao, C.V., 2016. Identification of immunoreactive luteinizing hormone receptors in the adrenal cortex of the female rhesus macaque. Reprod Sci 23, 524–530. Lois, K., Kassi, E., Prokopiou, M., Chrousos, G.P., 2014. Adrenal androgens and ageing. In: Hershman, J.M. (Ed.), Endotext: Endocrinology of Aging. MDText.com, Inc., South Dartmouth, MA. Mueller, J.W., Gilligan, L.C., Idkowiak, J., Arlt, W., Foster, P.A., 2015. The regulation of steroid action by sulfation and desulfation. Endocr Rev 36, 526–563. Nguyen, A.D., Mapes, S.M., Corbin, C.J., Conley, A.J., 2008. Morphological adrenarche in rhesus macaques: development of the zona reticularis is concurrent with fetal zone regression in the early neonatal period. J Endocrinol 199, 367–378. Nguyen, A.D., Corbin, C.J., Pattison, J.C., Bird, I.M., Conley, A.J., 2009. The developmental increase in adrenocortical 17,20-lyase activity (biochemical adrenarche) is driven primarily by increasing cytochrome b5 in neonatal rhesus macaques. Endocrinology 150, 1748–1756. Panciera, T., Azzolin, L., Fujimura, A., Di Biagio, D., Frasson, C., Bresolin, S., Soligo, S., Basso, G., Bicciato, S., Rosato, A., Cordenonsi, M., Piccolo, S., 2016. Induction of expandable tissue-specific stem/progenitor cells through transient expression of YAP/TAZ. Cell Stem Cell 19, 725–737. Parker Jr., C.R., Grizzle, W.E., Blevins, J.K., Hawkes, K., 2014. Development of adrenal cortical zonation and expression of key elements of adrenal androgen production in the chimpanzee (Pan troglodytes) from birth to adulthood. Mol Cell Endocrinol 387, 35–43. Pattison, J.C., Abbott, D.H., Saltzman, W., Conley, A.J., Bird, I.M., 2009. Plasticity of the zona reticularis in the adult marmoset adrenal cortex: voyages of discovery in the New World. J Endocrinol 203, 313–326. Pignatti, E., Leng, S., Carlone, D.L., Breault, D.T., 2017. Regulation of zonation and homeostasis in the adrenal cortex. Mol Cell Endocrinol 441, 146–155. Pihlajoki, M., Heikinheimo, M., Wilson, D.B., 2013. Never underestimate the complexity of remodeling. Endocrinology 154, 4446–4449. Pihlajoki, M., Dorner, J., Cochran, R.S., Heikinheimo, M., Wilson, D.B., 2015. Adrenocortical zonation, renewal, and remodeling. Front Endocrinol (Lausanne) 6, 1–14 article 27. Rege, J., Rainey, W.E., 2012. The steroid metabolome of adrenarche. J Endocrinol 214, 133–143. Rege, J., Nakamura, Y., Wang, T., Merchen, T.D., Sasano, H., Rainey, W.E., 2014. Transcriptome profiling reveals differentially expressed transcripts between the human adrenal zona fasciculata and zona reticularis. J Clin Endocrinol Metab 99, E518–E527. Rege, J., Karashima, S., Lerario, A.M., Smith, J.M., Auchus, R.J., Kasa-Vubu, J.Z., Sasano, H., Nakamura, Y., White, P.C., Rainey, W.E., 2016. Agedependent increases in adrenal cytochrome b5 and serum 5-androstenediol-3-sulfate. J Clin Endocrinol Metab 101, 4585–4593. Ross, C., Salmon, A., Strong, R., Fernandez, E., Javors, M., Richardson, A., Tardif, S., 2015. Metabolic consequences of long-term rapamycin exposure on common marmoset monkeys (Callithrix jacchus). Aging (Albany NY) 7, 964–973. Ruiz-Babot, G., Hadjidemetriou, I., King, P.J., Guasti, L., 2015. New directions for the treatment of adrenal insufficiency. Front Endocrinol (Lausanne) 6, 70. Sonoyama, T., Sone, M., Honda, K., Taura, D., Kojima, K., Inuzuka, M., Kanamoto, N., Tamura, N., Nakao, K., 2012. Differentiation of human embryonic stem cells and human induced pluripotent stem cells into steroid-producing cells. Endocrinology 153, 4336–4345.

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Sorwell, K.G., Kohama, S.G., Urbanski, H.F., 2014. Testosterone increases circulating dehydroepiandrosterone sulfate levels in the male rhesus macaque. Front Endocrinol (Lausanne) 5, 101. Thomas, M., Wang, X., Hornsby, P.J., 2002. Human adrenocortical cell xenotransplantation: model of cotransplantation of human adrenocortical cells and 3T3 cells in scid mice to form vascularized functional tissue and prevent adrenal insufficiency. Xenotransplantation 9, 58–67. Vidal, V., Sacco, S., Rocha, A.S., da Silva, F., Panzolini, C., Dumontet, T., Doan, T.M., Shan, J., Rak-Raszewska, A., Bird, T., Vainio, S., Martinez, A., Schedl, A., 2016. The adrenal capsule is a signaling center controlling cell renewal and zonation through Rspo3. Genes Dev 30, 1389–1394. Vinson, G.P., 2016. Functional zonation of the adult mammalian adrenal cortex. Front Neurosci 10, 238. Voutilainen, R., Jaaskelainen, J., 2015. Premature adrenarche: etiology, clinical findings, and consequences. J Steroid Biochem Mol Biol 145, 226–236. Walczak, E.M., Hammer, G.D., 2015. Regulation of the adrenocortical stem cell niche: implications for disease. Nat Rev Endocrinol 11, 14–28. Williams, R.M., Ward, C.E., Hughes, I.A., 2012. Premature adrenarche. Arch Dis Child 97, 250–254. Wood, M.A., Acharya, A., Finco, I., Swonger, J.M., Elston, M.J., Tallquist, M.D., Hammer, G.D., 2013. Fetal adrenal capsular cells serve as progenitor cells for steroidogenic and stromal adrenocortical cell lineages in M. musculus. Development 140, 4522–4532. Xing, Y., Lerario, A.M., Rainey, W., Hammer, G.D., 2015. Development of adrenal cortex zonation. Endocrinol Metab Clin N Am 44, 243–274. Yazawa, T., Kawabe, S., Inaoka, Y., Okada, R., Mizutani, T., Imamichi, Y., Ju, Y., Yamazaki, Y., Usami, Y., Kuribayashi, M., Umezawa, A., Miyamoto, K., 2011. Differentiation of mesenchymal stem cells and embryonic stem cells into steroidogenic cells using steroidogenic factor-1 and liver receptor homolog-1. Mol Cell Endocrinol 336, 127–132. Yazawa, T., Imamichi, Y., Miyamoto, K., Khan, M.R., Uwada, J., Umezawa, A., Taniguchi, T., 2016. Induction of steroidogenic cells from adult stem cells and pluripotent stem cells. Endocr J Sep. 29 Epub ahead of print.

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Chapter 11

What Sets Iceland Apart in Understanding Human Aging Adalsteinn Gudmundsson1,2, Pálmi V. Jónsson1,2 1Landspitali

University Hospital, Reykjavík, Iceland; 2University of Iceland, Reykjavik, Iceland

INTRODUCTION Facing the global phenomenon of population aging many countries are sharing their interest in collaborative research projects relating to aging. Although most countries are seeing decline in mortality rates, western societies have in addition experienced secular declines in fertility rates, which continues to sustain a steady increase in the ratio of older to younger generation (Kinsella and Phillips, 2005). New approaches and frameworks of longitudinal databases are needed, which take into account both differences and similarities in aging-related processes across ethnic and national boundaries (The National Academy of Sciences, 2001). Fortunately, this has evolved at a time when travel and use of long-distance communication through the Internet make longdistance collaboration and cross-national research easier and more sophisticated than ever before. In the context of cross-national research, Iceland has unique potential for contribution. The purpose by this chapter is to give the reader some background information on the aging population and research environment in Iceland. How and why the rugged terrain of this island of 103,000 km2 (43,000 square miles) located in the North Atlantic in a strategic location between North America and mainland Europe has become a fertile ground for both current and future research on aging will be described.

HISTORY Iceland was first settled between 870 and 930 AC by Norwegian Vikings. Recent research has shown that 20%–25% of the founding males had Gaelic ancestry. The majority of the females are thought to have come from the British Isles during the time of settlement. At the end of the initial settlement period, the population is estimated to have numbered approximately 30,000 (Halldorsson, 2003). A period of favorable climate conditions sustained a local population growth through the 12th century when the estimated population reached a plateau at 80,000. This era was followed by centuries of colder climate and several periods of substantial population reduction occurred. Two epidemics, of plague in the 15th century and several smallpox epidemics in the 16th through to early 18th century, reduced the population of the entire island to as low as 30,000 on more than one occasion. The fallout from a volcanic eruption in 1875 devastated the Icelandic economy and caused widespread famine. During the last quarter of the 19th century, approximately 20% of Iceland’s population emigrated, mostly to Canada and the United States (Jonsson, 1998). This explains why the population of Iceland who has remained in relative genetic isolation through 1100 years is genetically quite homogeneous and many share common ancestors. Screening for individual mutations in the population is facilitated by the Icelanders’ fascination with genealogy. Details of births, marriages, and deaths have been kept in church records for more than three centuries. Extensive computerized genealogy databases have been created, which allow the pedigree of most Icelanders to be traced back to the 17th century and for many further back to the settlement of the island. Islendingabok is the only genealogy database in the world, which covers a whole nation. It includes information on more than 95% of all Icelanders from 1703 and onward. It includes information on about 700,000 Icelanders, which is about half of all Icelanders who have lived on the island from its settlement (Islendingabok, 2003). These facts of history lead to a strong founder effect, and the genealogy database facilitates greatly genetic research. An important contribution to the Icelandic genealogy database was established by the consensus in 1703, which was ordered by the King of Denmark. This was a milestone in the history of population studies as it was the first extant nominal census in the world that includes every member of a country’s population, with name, age, and social status. This census of 50,366 inhabitants of Iceland in 1703 was in 2013 recommended for inclusion in the Memory of the World, a UNESCO list of registered world heritage (UNESCO, 2013). Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00011-7 Copyright © 2018 Elsevier Inc. All rights reserved.

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Iceland’s founder population provides advantages for researches studying complex disorders such as many of the ageassociated diseases. This has been repeatedly demonstrated in past two decades with the discovery of genes and biomarkers associated with, cardiovascular disease, central nervous system diseases, osteoporosis, obesity, and cancer (Gudbjartsson et al., 2015). Although Iceland has only recently (1944) become a sovereign state after many centuries of colonialism, it has since World War-II, transformed from being one of Europe’s poorest into being among the richest countries in the world by per capita measures. Its culture and social structure resembles other Scandinavian countries and strong ties remain to the European Union. Iceland’s relation to the European Union is mainly based on the Agreement on the European Economic Area, which came into effect in 1994. Still, in more than just a geographic sense Iceland is closer to North America than most of Europe. It has grown into and benefited from being an “in between” country with cultural and strong economic and trading ties to the United States over the last 50 years. Cooperation in health sciences has been steadily growing between Iceland and the United States. A first milestone in this regard was the establishment of the laboratory at Keldur lead by Dr. Bjorn Sigurdsson in 1946 through the Rockefeller Foundation. Dr. Sigurdsson’s work resulted in the original concept of slow viral infections (Palsson, 1994). More recently, deCODE Genetics company, which is headquartered in Reykjavik, has become a global leader in analyzing and understanding the human genome. Using their unique expertise and Icelandic population resources, deCODE has discovered key genetic risk factors for dozens of common diseases ranging from cardiovascular disease to cancer and Alzheimer’s disease (deCODE Genetics, 2017). The Age, Gene/Environment Susceptibility (AGES) Study, which is a phase of the cohort study of the Icelandic Heart Association (IHA) that has been in progress since 1967. The study has been arranged in cooperation with the National Institute on Aging of the United States Department of Health with support from the National Institutes of Health in the United States and the Icelandic government (Harris et al., 2007). In 2008 the population of Iceland went through a major financial crisis, which involved the default of all three of the country’s major privately owned commercial banks. Relative to the size of its economy, Iceland’s systemic banking collapse was the largest experienced by any country in economic history. In spite of a severe economic depression in 2008–2010 and significant political unrest the effects on measurable adverse health outcomes were almost imperceptible (Karanikolos et al., 2013).

FIGURES AND FACTS Iceland is one of the least densely populated countries in the world with a total population of 340,110 people in 2017, averaging 3.3 inhabitants per square kilometer (Statice, 2017). About two-thirds of the population lives in and around the capital of Reykjavik, which is located on the southwest corner of the island. Mainly due to effects of the Gulf Stream, Icelanders enjoy a warmer climate than its subarctic location would indicate. The average July temperatures have passed 13°C in recent years from being 10.5°C in previous decades (Statice, 2015). This has by many been viewed as one of the strongest indicators of global warming. The average temperature in January remains around the freezing point. Both by ethnical and socioeconomical measures, the population are homogeneous. Still, its society and people offer a prototype of a western society with an advanced but small infrastructure. The past two decades with economic growth and low unemployment figures has stimulated immigration of workers from a variety of countries. In 2016 the number of immigrants had reached 31,812 or 9.6% of the population (Statice, 2017). Hence, the historic advantage of a homogeneous population is about to disappear due to globalization. Instead we are gaining valuable prospective information on how a western society adapts to influx of immigrants of all ages and its effect on care and care giving of older people. In the year 2014 the costs of health care, including long-term care, were 8.8% of the gross domestic product. The health care system is nationalized with 81.8% of total cost paid for through taxes (Euro, 2014). By conventional measures and indicators through health statistics, Icelanders enjoy good health status. By health care access and quality index based on mortality Iceland scored number 2 of 195 countries by an analysis in 2015 (GBD 2015 Healthcare Access and Quality Collaborators, 2017). Strong economy, favorable social situations, and good access to health care have brought Iceland into a leading position as having one of the world’s highest life expectancies at birth. Life expectancy at birth has increased from 61.0 years for women in 1921–1930 to 83.7 years in 2016 (Statice, 2017). During the same period, the life expectancy for men has increased from 56.2 to 80.7 years, the highest in the world along with Switzerland (WHO, 2016). This gender difference in life expectancy of less than 3 years in Iceland is smaller than in other developed countries, where it is usually 4–6 years. Although the group of older citizens in Iceland has increased remarkably by absolute numbers in recent years the proportion of people older than 65 years remains less than 14% mostly due to relatively high fertility rate with 1.81 births/woman and immigration. Ninety-one percent of Icelanders over age 65 live independently in the community (Statice, 2017).

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A major disadvantage of the health care system in Iceland is its waiting lists. This is particularly true in Reykjavik where long lists remain for nursing homes and where there is congestion of elderly awaiting discharges from acute hospitals. Iceland has become a very popular tourist destination and in 2017 the number of international visitors arriving via air flights or cruise ships is anticipated to exceed two million (Ferdamalastofa, 2017). This is sevenfold the number of inhabitants and has put a substantial strain on Iceland’s health care system (Heimisdottir, 2015). Worldwide, the number of older travelers is increasing rapidly. Health care systems at popular tourist destinations such as Iceland are increasingly confronted with situations relating to traveling of older people with multiple chronic morbidities and taking multiple medications. The risk of travel-related diseases is more than twofold in people with underlying medical conditions in comparison to healthy travelers. The importance of careful and thorough preparations before travel and tailoring available emergency services to older travelers with multimorbidity and vulnerability requires increased recognition (Gudmundsson et al., 2016).

LIFESTYLE The nutritional value of diet in Iceland improved significantly during the last century. On a negative side, the country’s consumption of fish per capita has diminished and was in 2002 only slightly above many other European countries (Halldorsson, 2003). Dietary changes in Iceland since 2002 have mostly been toward recommended intake set by the Icelandic Nutrition Council. A national nutrition survey in 2012 showed that consumption of bread, biscuits, cakes, margarine, highly processed meat products, chips, sugared soft drinks, and whole milk was lower than in 2002, while consumption of wholegrain bread, oat meal, fruits, vegetables, meat, and cod liver oil was higher. Fish intake was unchanged. There is a clear social gradient, with those who have better education or higher incomes eating more vegetables. The prevalence of obesity has been increasing in Iceland. In 1990 a national survey showed that 8% of people age 18–80 years were obese or with BMI >30. In a new survey in 2011, this ratio had increased to 21% (Steingrimsdottir et al., 2014). One study of adult Icelanders concluded that one of four Icelandic men and one of five women do not participate in regular physical activity. Sedentary lifestyle has become even more common among Icelanders than in the neighboring countries (Gudmundsdottir et al., 2004). Follow-up surveys on the number of daily smokers in Iceland have shown favorable results in recent decades. In 1985, approximately 43% of men and 37% of women smoked on a daily basis, but in 2011 the figures were 14% for both men and women (Landlaeknir, 2014). Although consumption of alcohol in liters per capita is lower than in most of Europe, the consumption has been steadily increasing and reached 6.3 L per person in 2012, which was an increase by 35% since 1992 (OECD-iLibrary, 2016).

GERIATRIC CARE The importance of geriatric care has been recognized and geriatric evaluation and management units and interdisciplinary teamwork are well established in Iceland (Jonsson, 1998). Interest among physicians in formal geriatric training has increased rapidly during the past 25 years and in 2017, 21 fellowship-trained geriatricians had appointments at academic hospitals. Like most other Icelandic specialist trained doctors, Icelandic geriatricians receive their specialist training abroad, either in Europe or the United States. The number of Icelandic geriatricians is by international comparison a very favorable per capita ratio and does, more than, match the estimated proportional lack of geriatricians described in many developed nations. On itself, the geriatric services in Iceland could provide a useful learning experience for many other countries in their attempts to equip their health care systems with more geriatricians. Iceland was one of the first countries to adopt the comprehensive Resident Assessment Instrument (RAI) in nursing homes from the United States by a mandate in 1996. InterRAI is a collaborative not-for-profit network of researchers in over 30 countries committed to improving care for persons who are disabled or medically complex through the development, evaluation, and implementation of instruments for comprehensive assessment (The InterRAI Organization, 2017). Although each interRAI instrument was developed for a particular population, they are all designed to form an integrated health information system. InterRAI instruments share a common language, and thus address clinical concepts consistently across instruments and different health and social service settings. Using common measures they enable clinicians and providers in different care settings to improve care continuity and thus integrate care for individuals. Each instrument consists of items, outcome measures, assessment protocols, case-mix algorithms, and quality indicators, which are the product of rigorous research and testing to establish their reliability and validity. A crucial consequence of the international adoption of the RAI is that comparison of long-term care between countries has become feasible (Jonsson and Palsson, 2003). Subsequently, more interRAI assessment tools have been adopted in Iceland such as home care, postacute care, mental

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health, and the hospital suite of instruments, all paving the way toward a seamless information system in care of old people and formation of longitudinal databases. Data can inform research in one sector, across sectors (e.g., home care and long-term care), or for geographic comparisons, both within nations and internationally. A unique attribute of interRAI systems is that it allows comparisons of outcomes of similar persons in different health and social situations internationally, and thus the identification and sharing of best practices. Multiple examples of such comparisons have come from the ADHOC study, which was funded by the European Union (Sørbye et al., 2009). The systematic and complete collection of clinical information in a setting, community, or nation provides unique and unexpected opportunities. A case in point is a recent application of the national Icelandic nursing home data, where the RAI MDS 2.0 has been completed on all nursing homes residents in Iceland three times a year for the last 15 years. This database provided information on individuals with intact cognitive function over multiple time points and between the ages of 80 and 100 years. They thus can act as “supercontrols” for genetic studies in persons with Alzheimer’s disease. Using this approach, and using complete human genome sequencing, a mutation in amyloid precursor protein was identified that protects against Alzheimer’s disease and age-related cognitive decline (Jonsson et al., 2012). Similarly, a variant of the triggering receptor expressed on myeloid cells 2 (TREM2) gene was shown to be associated with an increased risk of Alzheimer’s disease (Jonsson et al., 2013). Both discoveries were dependent on access to high-quality data on cognitive function of the control group, as assessed with the Cognitive Performance Scale score of the Icelandic interRAI nursing home database. Icelandic law mandates use of the Nursing Home Preadmission Assessment (NHPA); no one can enter a nursing home without undergoing this certified need assessment. A multidisciplinary team performs the NHPA; the team consists of a physician, nurse, and a social worker. The assessment is in standard form and content. It remains valid for up to 12 months and is expected to be revised if the applicant’s condition or situation changes during this time (Johannesdottir and Jonsson, 1995). The structure of care for the elderly is similar in the five Nordic countries (Sweden, Finland, Denmark, Norway, and Iceland). A close collaboration existed between the Nordic Professors in geriatric medicine from 1985 for 20 years. The collaboration produced strategic documents relating to geriatric assessment and geriatric rehabilitation (Sletvold et al., 1996). Globalization has heralded more extensive cooperation at the European level from the beginning of the 21st century, somewhat overshadowing the Nordic cooperation. Arising out of the European Commission FP7 grant call, “Investigator-driven clinical trials for optimisation of management of elderly patients with multiple diseases” in 2011, the SENATOR consortium (involving partners in nine European countries) was funded in 2012. This clinical trial, entitled “SENATOR: Development and clinical trials of a new Software ENgine for the Assessment & optimisation of drug and non-drug Therapy in Older peRsons” investigates whether a software assisted standardized and multidimensional intervention advising on medication appropriateness will reduce incident adverse drug reactions (ADR) in older patients with multimorbidity and acute illnesses that are treated in hospital by specialists other than geriatricians. The University Hospital of Iceland in Reykjavik is one of the six clinical sites of the SENATOR trial. SENATOR is the first multicentre international clinical trial of its kind involving a software-driven prescribing optimization intervention specifically for older, multimorbid patients aimed at reducing ADRs as part of the global patient safety agenda. SENATOR project results will be available by mid-2018 (Roy et al., 2017). In 1999 an Institute on Gerontological Research (IGR) was established by the University of Iceland and Landspitali University Hospital. As an umbrella-like organization its main goals are to coordinate and facilitate broad research and collaboration in gerontology at University of Iceland and the University Hospital. Researchers from the IGR are involved with, to mention but few, research at deCODE company (Alzheimer’s and longevity studies), the AGES study, and in European and Nordic collaboration (Jonsson et al., 2006, 2008; Fialová et al., 2005). In 2017 there are 11 PhD studies in progress within the IGR focusing on gerontological topic areas.

RESEARCH ENVIRONMENT During the past two decades, there has been an increase in expenditure on research and development work. Icelanders spent 2.2% of their gross domestic product on research and development undertakings in 2014, compared to 1.1% in 1990 but still remains slightly below the average of OECD countries (OECD-iLibrary, 2016). The benefits of this increased investment may be measured, for instance, by the favorable outcome of Icelandic scientists in international cooperation and innovation, which has led to growth in employment and in the exports of goods and services based mainly on knowledge. In 2016, Iceland ranked number 2 in the world by measuring the degree of preparation of a nation or community to participate in and benefit from information and communication technologies (ITU, 2017).

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TABLE 11.1  Iceland: Advantages for Health-Related Research in the 21st Century A small prototypic western society with advanced infrastructure Official health registries l Universal access to health care l Advanced training abroad common l Founder population and extensive genealogy databases l Environmental uniformity l The general public supports and participates in research l Strong infrastructure in information technology l High-tech biomedical research companies l l

Because of Iceland’s small size and scale of resources, the majority of Icelandic physicians and many other disciplines seek their postgraduate training abroad, either in Europe or North America. This has benefited health care and research in Iceland as the majority repatriates after 5–10 years, bringing back a diversity of skills, experience, ambitions, and initiative. This, in addition to useful and lasting contacts of Icelandic doctors with their colleagues at leading hospitals abroad paves the way to achieve success on an international scale. Although the smallness of the country limits resources, it is balanced by advantages of short routes of communication among individuals, institutions, and businesses. Through health registries, which may date back a century or more, Iceland offers rich resources for health-related research. These registries cover the entire population, including cancer prevalence, cardiovascular health, and mortality. Linking the Icelandic health registries to the extensive genealogy database is beginning to underscore the importance of genetic predisposition for an entire population (Gudbjartsson et al., 2015). A national cancer registry was founded by the Icelandic Cancer Society (ICS) in 1954. It is a high-quality, populationbased registry that contains information on around 49,000 individual cancer cases diagnosed from the outset (Sigurdardottir et al., 2012). Currently more than 1500 cases of cancer are diagnosed annually and the most commonly affected organs are the prostate for men and the breast for women, closely followed by lung and colorectum. Research in cancer epidemiology has been one of the main functions of the ICS since the cancer registry was founded. One important research focus concerns the consequences of carrying a mutation in the BRCA2 gene, both for prostate cancer patients and for breast cancer patients (Tryggvadottir et al., 2007; Jonasson et al., 2016). The public is generally supportive of medical research, and the high public participation rate is a distinctive advantage of locating such research in Iceland (see Table 11.1). Furthermore, the population has lived through and been exposed to relatively similar environmental conditions (climate, altitude, diet, pollutants, and infectious agents) in comparison to larger nations (Gudmundsson, 2004). It is interesting to note that the total population of individuals older than 65 is only 44,100 (20,800 men and 23,300 women) (Statice, 2017). This is a “manageable” size on its own and harbors unique opportunities for research on health and disease pertinent in an entire population.

BIOMEDICAL RESEARCH IN ICELAND AND DISCOVERY OF DRUG TARGETS Favorable situations have paved the way for a local establishment of biotech companies in the past two decades. One of them is deCODE Genetics, which is one of the leading companies in the world in discovery of genetic risk factors for common diseases (deCODE Genetics, 2017). Its gene discovery engine is driven by deCODE’s unique approach and resources, including detailed genetic and medical information on some 500,000 individuals from around the globe taking part in the companies discovery work and proprietary statistical algorithms and informatics tools for gathering, analyzing, visualizing, and storing large amounts of data. Since the founding of the company in 1996, deCODE Genetics has been focused on meeting this challenge by using the latest technology for analyzing DNA to assemble as much data as possible across a large and well-defined group of people within a population and to mine it for correlations. The deCODE track record in gene discovery is a testament to the robustness of this approach, from the era of linkage studies using microsatellite markers to the advent of whole-genome sequencing. In the gene discovery work in Iceland, deCODE has gathered genotypic and medical data from more than 160,000 volunteer participants, comprising well over half of the adult population. Using Iceland’s uniquely comprehensive genealogical records, deCODE has also put together a genealogy database covering the entire present day population and stretching back to the founding of the country more than 1000 years ago. The combination of size of the population, the generous participation of so many people in deCODEs discovery work, the genealogies, and high-quality universal health care make very large-scale studies of

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virtually any common disease possible. At the same time, deCODE’s work minimizes the selection bias that confronts research in larger, more stratified populations, and enables the company to impute or predict genotypes using the genealogies, multiplying manyfold the amount of data that can be derived from genotyping and sequencing (deCODE Genetics, 2017). The International Myeloma Foundation (IMF) has teamed up with a research group at the University of Iceland in the first nationwide study to screen for monoclonal gammopathy of undetermined significance (MGUS). MGUS is a benign precursor for developing multiple myeloma. Myeloma is a cancer of the blood plasma cells that is estimated to affect more than 200,000 people worldwide. As MGUS is a benign precursor of myeloma it is easily undiagnosed until it begins to seriously affect patients’ health. Given the low rates of MGUS diagnosis, researchers have struggled to study this transition. With its tiny population plus the most detailed national medical and genetic databases of any country in the world the IMF identified Iceland as the unique experimental setting. In 2016 the IMF announced its funding for the first comprehensive screening study that aims to prevent myeloma before it develops. The funded study, titled “iStopMM (Iceland Screens Treats or Prevents Multiple Myeloma),” will examine blood samples from 140,000 people in Iceland over the age of 40, seeking the earliest signs of the blood cancer. With more than 75,000 people registered and nearly 20,000 blood samples as of mid-April 2017, the project is already the largest myeloma study ever conducted (The Scientist, 2017).

THE AGES–REYKJAVIK STUDY One of the largest and longest-lasting epidemiological studies available involving a representative unselected population is the Reykjavik study, which the IHA launched in 1967 around an initiative to battle against increases in cardiocvascular diseases in Iceland. The study invited 25,000 men and women born between 1907 and 1934. The response rate was high (76%) for clinic visits and participants were followed from once to six times for up to 30 years by thorough questionnaires, medical examinations, and biochemical measurements, which accumulated vast resources of information (Hardarson et al., 2001). The quality and depth of the data collected in the Reykjavik study has been widely recognized and has lead to over 300 publications. The IHA has participated in the international WHO coordinated project on heart disease (MONICA) and maintains a detailed record of heart attacks and coronary artery interventions for the entire Icelandic population (Hjarta, 2017). In 2001, the National Institute of Health and the IHA announced their collaboration. In anticipation of the sequencing of the human genome and description of the human proteome, the Age, Gene/Environment Susceptibility–Reykjavik Study (AGES–Reykjavik) was initiated in 2002. AGES–Reykjavik was designed to examine risk factors, including genetic susceptibility and gene/environment interaction, in relation to disease and disability in old age (Harris et al., 2007). The study is multidisciplinary, providing detailed phenotypes related to the cardiovascular, neurocognitive (including sensory), and musculoskeletal systems and to body composition and metabolic regulation. Relevant quantitative traits, subclinical indicators of disease, and medical diagnoses are identified by using biomarkers, imaging, and other physiologic indicators. The AGES–Reykjavik sample is drawn from the established population-based cohort, the Reykjavik Study. The AGES– Reykjavik cohort, with cardiovascular risk factor assessments earlier in life and detailed late-life phenotypes of quantitative traits, has created a comprehensive study of aging nested in the relatively genetically homogeneous Icelandic older population. This approach has already produced hundreds of important research papers.

CONCLUSIONS Through private and public funding, Iceland is being established as a model for opportunities in research on aging. Iceland entered the 20th century as an isolated founder population of poor sheep farmers and small-scale fishermen but at the turn of the 21st century Iceland had already transformed into knowledge-based high-tech society whose still interrelated and aging population will deliver important lessons on the interactions between age-associated diseases, the environment, and genetic components. This cutting-edge aging research in Iceland is already contributing to our understanding of how to maintain a good health, independence, and active participation in late life.

REFERENCES deCODE Genetics, 2017. www.decode.com/research/. Euro, 2014. Iceland Health Systems Review: WHO. www.euro.who.int. Ferdamalastofa, 2017. Icelandic Tourist Board. www.ferdamalastofa.is/en. Fialová, D., Topinková, E., Gambassi, G., Finne-Soveri, H., Jonsson, P.V., et al., 2005. Potentially inappropriate medication use among home care elderly patients in Europe. JAMA 293, 1348–1358. GBD 2015 Healthcare Access and Quality Collaborators, July 15, 2017. Healthcare access and quality index based on mortality from causes amenable to personal healthcare in 195 countries and territories, 1990–2015: a novel analysis from the global burden of disease 2015 study. Lancet 390 (10091), 231–266.

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Gudbjartsson, F.G., Helgason, H., Gudjonsson, S.A., Zink, F., Oddson, A., et al., 2015. Large scale whole genome sequencing of the Icelandic population. Nat Genet 27, 435–444. Gudmundsdottir, S.L., Oskarsdottir, D., Franzson, L., Indridason, O.S., Sigurdsson, G., 2004. The relationship between physical activity, body mass index, body composition and grip strength in an Icelandic population. Icel Med J 90, 479–486. Gudmundsson, A., Stevenson, J.M., Petrovic, M., Somers, A., Onder, G., et al., 2016. Challenges and risks for older travelers with multimorbidity: focus on pharmacotherpy. Eur Geriatr Med 7, 407–410. Gudmundsson, A., 2004. Research on aging in Iceland: future potentials. Mech Ageing Dev 125, 133–135. Halldorsson, M., 2003. Health Care Systems in Transition: Iceland. WHO Regional Office for Europe on Behalf of the European Observatory on Health Systems and Policies, Copenhagen. Hardarson, T., Gardarsdottir, M., Gudmundsson, K.T., Thorgeirsson, G., Sigvaldason, H., et al., 2001. The relationship between educational level and mortality. The Reykjavik Study. J Intern Med 249, 495–502. Harris, T.B., Launer, L.J., Eiriksdottir, G., Kjartansson, O., Jonsson, P.V., et al., 2007. Age, gene/environment susceptibility-Reykjavik study: multidisciplinary applied phenomics. Am J Epidemiol 165, 1076–1087. Heimisdottir, M., 2015. Increasing tourism. A challenge for health care. Editorial. Icel Med J 101, 307. Hjarta, 2017. Icelandic Heart Association. www.hjarta.is. Islendingabok, 2003. A Complete Database of All Available Icelandic Genealogy Information. www.islendingabok.is. ITU, 2017. Measuring the Information Society Report 2016. www.itu.int/en. Johannesdottir, G.B., Jonsson, P.V., 1995. The preadmission nursing home assessment (PNHA) in Reykjavik in 1992. Icel Med J 81, 233–241. Jonasson, J.G., Stefansson, O.A., Johannsson, O.T., Sigurdsson, H., Agnarsson, B.A., et al., 2016. Oestrogen receptor status, treatment and breast cancer prognosis in Icelandic BRCA2 mutation carriers. Br J Cancer 115, 776–783. Jonsson, P.V., 1998. Letter from Reykjavik. Ann Intern Med 128, 941–945. Jonsson, P.V., Palsson, H., 2003. Toward Informed and Evidence Based Elderly Care: the RAI Experience in Iceland. Mibank report www.milbank.org. Jonsson, P.V., Finne-Soveri, H., Jensdottir, A.B., Ljunggren, G., Bucht, G., et al., 2006. Co-morbidity and functional limitation in older patients underreported in medical records in Nordic Acute Care Hospitals when compared with the MDS-AC instrument. Age Ageing 35, 434–445. Jonsson, P.V., Noro, A., Finne-Soveri, H., Jensdottir, A.B., Ljunggren, G., et al., 2008. Admission status to acute medical care is predictive for one-year outcomes in older patients: a prospective study in five Nordic countries. Running heading: predictors of outcome of acute care. A clinical research study. Aging Clin Exp Res 20, 533–539. Jonsson, T., Atwal, J.K., Steinberg, S., Snaedal, J., Jonsson, P.V., et al., 2012. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature 488, 96–99. Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P.V., et al., 2013. Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med 368, 107–116. Karanikolos, M., Mladovsky, P., Cylus, J., Thomson, S., Basu, S., et al., 2013. Financial crisis, austerity, and health in Europe. Lancet 381, 1323–1331. Kinsella, K., Phillips, D.R., 2005. Global aging: the challenge of success. Popul Bull 60, 1. Landlaeknir, 2014. The Nordic Monitoring System 2012–2014. www.landlaeknir.is. OECD-iLibrary, 2016. Health at a Glance – OECD Indicators. www.oecd-ilibrary.org. Palsson, P.A., 1994. Dr. Bjorn Sigurdsson (1913–1959). A memorial tribute. Ann NY Acad Sci 724, 1–5. Roy, L., Selvarani, S., Cherubini, A., Cruz-Jentoft, A.J., Petrovic, M., et al., 2017. The SENATOR project: developing and trialling a novel software engine to optimize medications and nonpharmacological therapy in older people with multimorbidity and polypharmacy. Ther Adv Drug Saf 8, 81–85. Sigurdardottir, L.G., Jonasson, J.G., Stefansdottir, S., Jonsdottir, A., Olafsdottir, G.H., et al., 2012. Data quality at the Icelandic Cancer Registry: comparability, validity, timeliness and completeness. Acta Oncol 51, 880–889. Sletvold, O., Tilvis, R., Jonsson, A., Schroll, M., Snædal, J., Engedal, K., et al., 1996. Geriatric work-up in the Nordic countries. The Nordic approach to comprehensive geriatric assessment. Dan Med Bull 43, 350–359. Sørbye, L.W., Garms-Homolová, V., Henrard, J.C., Jonsson, P.V., et al., 2009. Shaping home care in Europe: the contribution of the aged in home care project. Maturitas 62, 235–242. Statice, 2015. Statistical Yearbook of Iceland. www.statice.is. Statice, 2017. Statistics Iceland. www.statice.is. Steingrimsdottir, L., Valgeirsdottir, H., Halldorsson, T.I., Gunnarsdottir, I., Gisladottir, E., et al., 2014. National nutrition surveys and dietary changes in Iceland. Icel Med J 100, 659–664. The InterRAI Organization, 2017. www.interrai.org/. The National Academy of Sciences, 2001. Preparing for an Aging World: The Case for Cross National Research. www:books.nap.edu/catalog/10120.html. The Scientist, 2017. Learning from Icelands Model for Genetic Research. www.the-scientist.com/. Tryggvadottir, L., Vidarsdottir, L., Thorgeirsson, T., Jonasson, J.G., Olafsdottir, E.J., et al., 2007. Prostate cancer progression and survival in BRCA2 mutation carriers. J Natl Cancer Inst 99, 929–935. UNESCO, 2013. The 1703 Census of Iceland. UNESCO Memory of the World. www:unesco.org. WHO, 2016. World Health Statistics. www.who.int/gho/publications/world_health_statistics/en/.

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Section II

Animal Models: Vertebrates

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Chapter 12

Reproductive Tract Lesions in Aged Nonhuman Primates Beth K. Chaffee, Elizabeth R. Magden The University of Texas M. D. Anderson Cancer Center, Bastrop, TX, United States

INTRODUCTION Geriatric nonhuman primates succumb to many of the same disease processes as humans. The reproductive tract is not spared, and we see an increased incidence in many of the same lesions that are observed in geriatric humans. Some of the similarities in lesions include ovarian atrophy and neoplasia, uterine leiomyomas and endometriosis, and benign prostatic hyperplasia (BPH). However, we also note differences in the frequency of some lesions—nonhuman primates appear to rarely develop mammary neoplasia or neoplasia of the male reproductive tract. In this chapter, we explore the reproductive tract lesions observed in aged nonhuman primates and discuss how these changes may serve as models for human reproductive disease or as potential investigative platforms for further analysis on why nonhuman primates fail to develop certain aging lesions.

FEMALE REPRODUCTIVE SYSTEM Ovary The ovaries of nonhuman primates typically have the same basic structure. The ovary is connected to the uterus via the oviduct (fallopian tube). The ovary is composed of a vascular medulla, a cortex that contains the follicles in various stages of development, and supporting stroma (Diogo et al., 2015). The primordial, or undeveloped, follicles are composed of a primary oocyte surrounded by a single layer of follicular epithelium. As the follicle is stimulated it increases in size, the number of follicular cell layers increases and becomes the granulosa layer, and the zona pellucida develops. The surrounding ovarian stroma forms the thecal cell layer around the follicle. With increased maturity, the developing follicle’s granulosa cell layers will split, forming a fluidfilled antrum. At the time of ovulation the follicle will rupture, releasing the oocyte that is surrounded by the zona pellucida and a layer of granulosa cells that form the corona radiata. Next the ruptured follicle collapses and undergoes luteinization, becoming the corpora luteum. The granulosa cells release progesterone, which prepares the uterus for pregnancy. Different species maintain the corpus luteum for different lengths of time, but as the luteinizing hormone levels drop and the corpus luteum is no longer being stimulated to produce progesterone, the corpus luteum involutes, forming a corpus albicans. During each reproductive cycle in the female, multiple follicles will begin undergoing maturation, but only a limited number (depending on the species) will undergo ovulation. Follicles that are not bound for ovulation will undergo atresia. In these atretic follicles the oocyte degenerates and the granulosa cells break apart. Eventually the atretic follicle will be replaced with fibrous connective tissue, termed a corpus fibrosum.

Cycles The reproductive cycles of nonhuman primates are often split into those with menstrual cycles versus those with estrus cycles. Species defined as having a menstrual cycle will have externally visible evidence of the shedding of the endometrial lining of the uterus at the end of the cycle, if a fertilized egg has not implanted. The degree of menstrual bleeding, however, will vary amongst the species. Species categorized as having an estrus cycle display increased sexual receptivity near the time of ovulation when the chances of fertilization occurring are the highest. The length and seasonality of these cycles varies with the species (Wolfe-Coote, 2005). Menopause is characterized by the termination of ovulation and/or menstrual bleeding that accompanies reproductive senescence. Some researchers have proposed this reproductive senescence is unique to humans, while others describe a reproductive decline in many species of nonhuman primates (Walker and Herndon, 2008). Female chimpanzees exhibit Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00012-9 Copyright © 2018 Elsevier Inc. All rights reserved.

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signs of reproductive senescence between the ages of 35–50, baboons show signs around age 26, and macaques stop menstruating at approximately 25 years of age (Walker and Herndon, 2008). Chimpanzee menopause is similar to the human process as it is accompanied by an age-related depletion of ovarian follicles and an increase in fetal loss with advancing age (Jones et al., 2007). Baboons show evidence of perimenopause around 19 years of age with an increase in variations to the menstrual cycle, a phenomenon observed in women aged mid-30s to early 50s. Rhesus macaques (Macaca mulatta) show elevations in gonadotropin concentrations and decreased levels of basal progesterone (P4) and estradiol (E2), a trend that mirrors human menopause (Walker and Herndon, 2008). While the literature supports the occurrence of menopause in nonhuman primates, this event occurs relatively later in their life span in comparison to humans. Menopause occurs in several species of nonhuman primates, although it can be difficult to observe given that it coincides with the near-end life expectancy of many nonhuman primates. The similarities in menopause between humans and nonhuman primates could be further explored for potential as a model for this human life event.

Atrophy In all species, as the female ages, the primordial follicles in the cortex become depleted or atretic and are replaced by connective tissue and vascular structures. The number of developing follicles decreases with age, with more atretic follicles and corpora albicans present. The amount of fibrous tissue in the ovary generally increases with aging in most species as the follicles are depleted, undergo atresia, and are replaced with corpora fibrosa and corpora albicans. Mineralization of the ovary can also occur with aging and is one of the most common changes seen in aged macaque and baboon ovaries (MarrBelvin et al., 2010; Ishmael, 1975). Inflammatory cell infiltrates can be identified in aging ovaries and is typically composed of mononuclear inflammatory cells such as macrophages and lymphocytes. In aged squirrel monkeys, both ovaries will normally contain multiple clusters of granulosa cells, which resemble the granulosa cell tumors seen in other species and rarely in humans (Walker et al., 2009).

Neoplasia Ovarian neoplasia in nonhuman primates appears to be relatively uncommon. Granulosa cell tumors and teratomas are reported to be the most common neoplasms of the rhesus macaque, in contrast to women, in which epithelial neoplasms predominate (Kumar et al., 2015). Other ovarian neoplasms reported in NHPs include cystadenomas/cystadenocarcinomas, choriocarcinomas (Farman et al., 2005), thecomas (Lapin, 1982), arrhenoblastomas (Lowenstine, 1986), luteomas, dysgerminomas, sarcomas, and teratomas. Compared to the other types of neoplasms, teratomas tend to occur in younger animals (Moore et al., 2003).

Cysts Ovarian cysts are a common incidental finding in the ovaries of aged NHPs. Cysts can originate from follicles, corpora lutea, rete ovarii, or from mesonephric duct remnants. Immunohistochemistry of the lining cells can help distinguish the different origins (Marr-Belvin et al., 2010).

Uterus The shape of the uterus varies depending on the species. A bicornuate uterus has two prominent horns and is more common in species that give birth to multiple offspring. A simplex uterus with a relatively large body and lack of prominent horns is seen in the majority of nonhuman primates that give birth to a single offspring (Ankel-Simons, 2007). Thickening of the vessel wall within the uterus is associated with multiparity and is termed “obliterative vascular fibrosis” (Cline et al., 2008).

Neoplasia Uterine leiomyomas, also known as “fibroids,” are benign tumors of smooth muscles and are one of the most commonly encountered neoplasms of the female reproductive tract in many NHP species, including macaques and chimpanzees. It is not uncommon to have multiple leiomyomas in a single animal (Chaffee et al., 2016). Women also commonly develop multiple leiomyomas, many of which are asymptomatic. However, leiomyomas can lead to pelvic pain, irregular bleeding and infertility. In the United States, the most common cause for hysterectomy is the presence of leiomyomas (Sparic et al., 2016).

Endometrial Polyps and Carcinomas Endometrial polyps occur in NHPs and can lead to irregular bleeding. There is conflicting reports of whether these polyps tend to occur in older or younger populations (Cline et al., 2008; Kaspareit et al., 2007). Polyps are composed of a fibrovascular stromal core covered by a layer of endometrial epithelium and can contain hyperplastic or malignant cells. In contrast to polyps,

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endometrial carcinoma is rare, with only rare reports in the literature (Strozier et al., 1972). This is in contrast to women, in which endometrial carcinoma is the leading cancer of the female reproductive tract in the United States (Society AC, 2016). Myometrial hemangiomas are rare but have been reported in cynomolgus macaques (Cline et al., 2008).

Endometriosis/Adenomyosis Endometriosis is the presence of ectopic endometrial glands and stroma outside the uterus, while adenomyosis is defined as endometrial tissue within the myometrium. This condition is common in women and certain species of nonhuman primates that menstruate. The incidence of endometriosis increases with age and most commonly affects the uterus and ovaries. However, endometriosis can be present throughout the abdomen and is also reported in the thoracic cavity. Previous caesarian section, hysterectomy, radiation therapy, and exposure to estrogens are several of the risk factors for development of endometriosis in both humans and other primates (Simmons, 2016; Fanton et al., 1986; Cooper and Gabrielson, 2007).

Cystic Endometrial Hyperplasia Estrogen can induce endometrial hyperplasia in some species of nonhuman primates, similar to what is seen in women. “Cystic endometrial hyperplasia” that is characterized by enlarged cystic glands, without marked proliferation of the epithelium, is a common aging change in most species.

Vagina/Cervix Normal Anatomy The female external genitalia vary among the species. For example, the clitoris in some new world monkeys is easily mistaken for a penis due to being large and pendulous (Wolfe-Coote, 2005).

Neoplasia Cervical and vaginal neoplasia is relatively rare in NHPs, although the presence of intraepithelial neoplasia might be more that what has been appreciated historically (Wood et al., 2004). Leiomyomas similar to those seen in the uterus can also occur in the cervix in humans and nonhuman primates, although they are less common in this location (Varras et al., 2003).

Vaginitis Chronic mononuclear inflammatory cell infiltrates in the vagina and cervix are often seen as an incidental background finding and the incidence may increase with age. The vaginal mucosa thickness can also vary as the animal ages, becoming either thicker or thinner (Cline et al., 2008; Appt and Ethun, 2010).

Sex Skin The majority of old world monkeys and chimpanzees possess sex skin in the perineal region that becomes reddened, enlarged, and thickened during periods of sexual receptivity and ovulation. In chimpanzees this tissue is confined to the perineal region and undergoes a cyclical tumescence with ovulation. In macaques species, the reddened sex skin can also be present on the tail, upper legs, nipples, and facial region (Wolfe-Coote, 2005; Magden et al., 2015).

Neoplasia Squamous cell carcinoma and adenocarcinoma have been reported in the sex skin of a chimpanzee, but neoplasia of this tissue is considered rare (Beck et al., 2016).

MALE REPRODUCTIVE SYSTEM Testicle Normal Anatomy One of the biggest differences in testicle anatomy is the large variation in testis-to-body-weight ratios between species of nonhuman primates. Rhesus and cynomolgus macaques have similar testis-to-body-weight ratios, which are significantly higher than that observed in baboons (Papio spp.) (Mubiru et al., 2008). Sperm production also varies significantly between the species and is likely indicative of reproduction strategy. Chimpanzees produce 223 times more sperm than gorillas and 14 times more sperm than orangutans (Fujii-Hanamoto et al., 2011).

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Neoplasia While testicular neoplasia is relatively rare in nonhuman primates, several case reports highlight the occasional occurrence. A cotton-top tamarin (Saguinus oedipus) presented with a biphasic malignant sex cord–stromal tumor of the testicle with metastases to the abdominal cavity, paravertebral musculature, and lymph nodes (Yearley et al., 2008). In the rare human case of testicular granulosa cell tumor, a high rate of metastasis is also observed (Ditonno et al., 2007; Hisano et al., 2006; Suppiah et al., 2005). Other testicular neoplasias observed in nonhuman primates include testicular seminoma in an owl monkey (Aotus spp.) (Gozalo et al., 1992), rhesus macaque (M. mulatta) (McClure, 1973, 1980), a black howler (Alouatta caraya) (Maruffo and Malinow, 1966), and a common marmoset (Callithrix jacchus) (Murphy, 1984). A sertoli cell tumor was documented in an owl monkey (Aotus trivirgatus) (Fiske et al., 1973), and interstitial cell tumors have been reported in a capuchin monkey (Cebus albifrons) (Young, 1980), and a western lowland gorilla (Gorilla gorilla) (Jones et al., 1980).

Degeneration In a retrospective study with 32 aged (>35 years old) chimpanzees, 20 of the animals were identified as having various stages of degenerative changes in their testicular tissue. These changes ranged from loss of spermatids and seminiferous tubular epithelium to thickening of the tubular basement membrane and fibrosis (Chaffee et al., 2016).

Granuloma Similar to humans, nonhuman primates that have undergone a vasectomy have an increased risk of developing sperm granulomas. In one study following five vasectomized rhesus macaques (M. mulatta), three of the five vasectomized monkeys developed sperm granulomas following their vasectomies (Peng et al., 2002).

Penis Normal Anatomy There is a large variation in normal penis anatomy across the vast number of nonhuman primate species. Most do possess a baculum or penis bone, but the size is often quite small. 1. Neoplasia   Penile neoplasia is very rare in nonhuman primates. One case was reported in 1983 of a squamous cell carcinoma on the prepuce and penis of a rhesus macaque (M. mulatta) (Hubbard et al., 1983). 2. Erectile dysfunction   Erectile dysfunction has been observed in nonhuman primates with atherosclerosis. While this disorder is rarely reported in nonhuman primates, deficits in both erectile and ejaculatory function were observed in atherosclerotic adult cynomolgus macaques (Macaca fascicularis). The urogenital vasculature of these animals showed atherosclerotic obstruction of the internal iliac, internal pudendal, and penile arteries, which led to the observed erectile dysfunction (Adams et al., 1984). These observations suggest atherosclerotic macaques may provide a model for erectile dysfunction in human males. 3. Viral lesions   Virus-associated venereal lesions have been reported in nonhuman primates. In one report, a colobus monkey (Colobus guereza) had a papilloma on his penile shaft that was attributed to a papillomavirus (O’Banion et al., 1987).

Prostate Gland Physiological studies have shown that there is a high degree of similarity between the prostates of nonhuman primates and human males. The prostate contains cranial and caudal lobes, with the cranial lobe being lobulated and the caudal lobe having a globular and smooth surface (Lewis et al., 1981). There is a large size variation of the cranial and caudal lobes between the different species of nonhuman primate (Mubiru et al., 2008). On a microscopic level, the acini located in the cranial lobe are larger and more irregular in comparison to the caudal lobe acini, and the epithelial cells of the cranial lobe are tall columnar compared to the low columnar to cuboidal epithelial cells of the caudal lobe (Lewis et al., 1981). Despite these anatomic similarities, nonhuman primates do not develop prostate disease with the frequency observed in man (Lewis et al., 1981). Prostate diseases are rare in nonhuman primates, with just a few reported cases of neoplasia and BPH.

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Neoplasia Prostate neoplasia is a rare finding in nonhuman primates. A prostatic carcinoma has been reported in a rhesus macaque (M. mulatta) (Hubbard and Wood, 1985). Prostate adenocarcinomas have been reported in a squirrel monkey (Saimiri sciureus) (Lewis et al., 1981), a rhesus macaque (M. mulatta) (Stout and Engle, 1940), and a slow loris (Nycticebus coucang) (Wadsworth et al., 1985).

Benign Prostatic Hyperplasia BPH is an uncommon occurrence in nonhuman primates. In the few reported cases it has been diagnosed in a middle-aged squirrel monkey (S. sciureus), middle-aged to geriatric macaques (Macaca spp.) (Adams and Bond, 1979; Wakui et al., 1989; McEntee et al., 1996), and an adult chimpanzee (Pan troglodytes) (Steiner et al., 1999). In the squirrel monkey, the BPH was characterized microscopically with both cystic glandular and fibromuscular nodular hyperplasia (Adams and Bond, 1979). In the macaque cases the BPH primarily originated in the basal cell compartment of the cranial lobe and closely mimicked human prostatic basal cell lesions (Wakui et al., 1989; McEntee et al., 1996). In one case of a 17-year-old cynomolgus macaque, the hyperplasia involved both glandular and basal cells and was accompanied by a rise in levels of prostate-specific antigen, which is used as an indicator of prostate disease in humans (Mubiru et al., 2008). A study evaluating 16 aged chimpanzees found three animals (18%) with hyperplastic glandular epithelium (Chaffee et al., 2016).

CONCLUSION While there are many similarities in the reproductive systems of nonhuman primates and people, and a few key differences, the frequency of many disease processes varies among the human and nonhuman primate species. Many reproductive disorders observed in humans are rarely observed in NHP species. For example, ovarian neoplasia appears to be an infrequent lesion in nonhuman primates, and when it does occur, the predominant tumor type is a granulosa cell tumor versus the epithelial neoplasia that occurs most frequently in humans. Interestingly, prostate and breast cancer, which are exceedingly common in humans, seem to be rare in all species on nonhuman primates. We have also described reproductive lesions that occur with some frequency in NHPs. In female NHPs, it is not uncommon to observe ovarian atrophy and/or cysts, uterine leiomyomas, and endometriosis. These lesions can also occur with frequency in human females. Some aging changes of the reproductive tract are seen across the species including ovarian and testicular degeneration and atrophy. These similarities and differences in the observed reproductive tract lesions present an opportunity. For those nonhuman primates that develop similar disease to that observed in people, we can study the occurrence, learn more about the disease, and develop therapeutics and new treatment options. For those lesions that do not occur frequently (or have never been observed) in nonhuman primates, we can ask the question, why do NHPs fail to develop these lesions? Each instance is an opportunity to study a reproductive disease process and advance our knowledge of reproductive medicine.

REFERENCES Adams, M.R., Bond, M.G., 1979. Benign prostatic hyperplasia in a squirrel monkey (Saimiri sciureus). Lab Anim Sci 29 (5), 674–676. Adams, M.R., et al., 1984. Erectile failure in cynomolgus monkeys with atherosclerosis of the arteries supplying the penis. J Urol 131 (3), 571–573. Ankel-Simons, F., 2007. Chapter 11-reproductive organs, reproduction, and growth. In: Primate Anatomy, third ed. Academic Press, Burlington, pp. 521–531. Appt, S.E., Ethun, K.F., 2010. Reproductive aging and risk for chronic disease: insights from studies of nonhuman primates. Maturitas 67 (1), 7–14. Beck, A.P., et al., 2016. Malignant neoplasia of the sex skin in 2 chimpanzees (Pan troglodytes). Comp Med 66 (2), 154–161. Chaffee, B.K., et al., 2016. Spontaneous reproductive tract lesions in aged captive chimpanzees. Vet Pathol 53 (2), 425–435. Cline, J.M., et al., 2008. Selected background findings and interpretation of common lesions in the female reproductive system in macaques. Toxicol Pathol 36 (7), 142s–163s. Cooper, T.K., Gabrielson, K.L., 2007. Spontaneous lesions in the reproductive tract and mammary gland of female non-human primates. Birth Defects Res B Dev Reprod Toxicol 80 (2), 149–170. Diogo, R., Muchlinski, M.N., Hartstone-Rose, A., 2015. Chapter 4-comparative anatomy of primates A2-Muehlenbein. In: Michael, P. (Ed.), Basics in Human Evolution. Academic Press, Boston, pp. 43–55. Ditonno, P., et al., 2007. Testicular granulosa cell tumor of adult type: a new case and a review of the literature. Urol Oncol 25 (4), 322–325. Fanton, J.W., Hubbard, G.B., Wood, D.H., 1986. Endometriosis: clinical and pathologic findings in 70 rhesus monkeys. Am J Vet Res 47 (7), 1537–1541. Farman, C.A., et al., 2005. Ovarian choriocarcinoma in a rhesus monkey associated with elevated serum chorionic gonadotropin levels. Vet Pathol 42 (2), 226–229. Fiske, R.A., Woodward, J., Moreland, A.F., 1973. Sertoli cell tumor in an owl monkey. J Am Vet Med Assoc 163, 1206–1207. Fujii-Hanamoto, H., et al., 2011. A comparative study on testicular microstructure and relative sperm production in gorillas, chimpanzees, and orangutans. Am J Primatol 73 (6), 570–577.

154  SECTION | II  Animal Models: Vertebrates

Gozalo, A., Nolan, T., Montoya, E., 1992. Spontaneous seminoma in an owl monkey in captivity. J Med Primatol 21 (1), 39–41. Hisano, M., et al., 2006. Granulosa cell tumor of the adult testis: report of a case and review of the literature. Clinics (Sao Paulo) 61 (1), 77–78. Hubbard, G.B., Wood, D.H., 1985. Prostatic carcinoma in a rhesus macaque (Macaca mulatta). Vet Pathol 22, 88–90. Hubbard, G.B., Wood, D.H., Fanton, J.W., 1983. Squamous cell carcinoma with metastasis in a rhesus monkey (Macaca mulatta). Lab Anim Sci 33 (5), 469–472. Ishmael, J., 1975. Occurrence of calcium deposits in the ovaries of the baboon. Lab Anim 9 (4), 383–386. Jones, D.M., Dixson, A.F., Wadsworth, P.F., 1980. Interstitial cell tumour of the testis in a western lowland gorilla (Gorilla gorilla gorilla). J Med Primatol 9 (5), 319–322. Jones, K.P., et al., 2007. Depletion of ovarian follicles with age in chimpanzees: similarities to humans. Biol Reprod 77 (2), 247–251. Kaspareit, J., et al., 2007. Spontaneous neoplasms observed in cynomolgus monkeys (Macaca fascicularis) during a 15-year period. Exp Toxicol Pathol 59 (3–4), 163–169. Kumar, V., Abbas, A.K., Aster, J.C., 2015. Robbins and Cotran Pathologic Basis of Disease, ninth ed. Elsevier/Saunders, Philadelphia (PA). xvi, 1391 pp. Lapin, B.A., 1982. Use of nonhuman primates in cancer research. J Med Primatol 11 (6), 327–341. Lewis, R.W., et al., 1981. The prostate of the nonhuman primate: normal anatomy and pathology. Prostate 2 (1), 51–70. Lowenstine, L.J., 1986. Neoplasms and proliferative disorders in nonhuman primates. In: Benirschke, K. (Ed.), Primates: The Road to Self-sustaining Populations. Springer New York, New York, NY, pp. 781–814. Magden, E.R., Simmons, J.H., Abee, C.R., 2015. Nonhuman primates. In: Fox, E.A. (Ed.), Laboratory Animal Medicine (Chapter 17). Marr-Belvin, A.K., et al., 2010. Ovarian pathology in rhesus macaques: a 12-year retrospective. J Med Primatol 39 (3), 170–176. Maruffo, C.A., Malinow, M.R., 1966. Seminoma in a howler monkey (Alouatta caraya). J Pathol Bacteriol 91 (1), 280–282. McClure, H.M., 1973. Tumors in nonhuman primates: observations during a six-year period in the Yerkes primate center colony. Am J Phys Anthropol 38 (2), 425–429. McClure, H., 1980. Neoplastic diseases in nonhuman primates: literature review and observations in an autopsy series of 2176 animals. In: Montali, R., Migaki, G. (Eds.), The Comparative Pathology of Zoo Animals. Smithsonian Institution Press, Washington, pp. 549–565. McEntee, M.F., et al., 1996. Characterization of prostatic basal cell hyperplasia and neoplasia in aged macaques: comparative pathology in human and nonhuman primates. Prostate 29 (1), 51–59. Moore, C.M., et al., 2003. Spontaneous ovarian tumors in twelve baboons: a review of ovarian neoplasms in non-human primates. J Med Primatol 32 (1), 48–56. Mubiru, J.N., et al., 2008. Nonhuman primates as models for studies of prostate specific antigen and prostatic diseases. Prostate 68 (14), 1546–1554. Murphy, A., 1984. Testicular tumour in a marmoset (Callithrix jacchus). In: Tucker, M.J., Wadsworth, P.F. (Eds.), Symposium on Marmoset Pathology. ICI Pharmaceuticals, Macclesfield (Cheshire). O’Banion, M.K., et al., 1987. Venereal papilloma and papillomavirus in a colobus monkey (Colobus guereza). Intervirology 28 (4), 232–237. Peng, B., et al., 2002. Quantitative (stereological) study of the effects of vasectomy on spermatogenesis in rhesus monkeys (Macaca mulatta). Reproduction 124 (6), 847–856. Simmons, H.A., 2016. Age-associated pathology in rhesus macaques (Macaca mulatta). Vet Pathol 53 (2), 399–416. Society AC, 2016. Cancer Facts & Figures 2016. American Cancer Society, Atlanta. Sparic, R., Mirkovic, L., Malvasi, A., 2016. Epidemiology of uterine myomas: a review. 9 (4), 424–435. Steiner, M.S., et al., 1999. The chimpanzee as a model of human benign prostatic hyperplasia. J Urol 162 (4), 1454–1461. Stout, A.P., Engle, E.T., 1940. Spontaneous primary carcinoma of prostate in monkey (Macaca mulatta). Am J Cancer 39, 334–337. Strozier, L.M., et al., 1972. Endometrial adenocarcinoma, endometriosis, and pyometra in a rhesus monkey. J Am Vet Med Assoc 161 (6), 704–706. Suppiah, A., et al., 2005. Adult granulosa cell tumour of the testis and bony metastasis. A report of the first case of granulosa cell tumour of the testicle metastasising to bone. Urol Int 75 (1), 91–93. Varras, M., et al., 2003. Clinical considerations and sonographic findings of a large nonpedunculated primary cervical leiomyoma complicated by heavy vaginal haemorrhage: a case report and review of the literature. Clin Exp Obstet Gynecol 30 (2–3), 144–146. Wadsworth, P.F., Jones, D., Pugsley, S.L., 1985. A survey of mammalian and avian neoplasms at the Zoological Society of London. J Zoo Anim Med 16, 73–80. Wakui, S., et al., 1989. Prostatic basal cell hyperplasia in a cynomolgus monkey (Macaca fascicularis). Vet Pathol 26 (5), 447–448. Walker, M.L., Herndon, J.G., 2008. Menopause in nonhuman primates? Biol Reprod 79 (3), 398–406. Walker, M.L., et al., 2009. Ovarian aging in squirrel monkeys (Saimiri sciureus). Reproduction 138 (5), 793–799. Wolfe-Coote, S., 2005. The Laboratory Primate. Elsevier, San Diego. Wood, C.E., et al., 2004. Cervical and vaginal epithelial neoplasms in cynomolgus monkeys. Vet Pathol 41 (2), 108–115. Yearley, J.H., et al., 2008. Biphasic malignant testicular sex cord-stromal tumor in a cotton-top tamarin (Saguinus oedipus) with review of the literature. Vet Pathol 45 (6), 922–927. Young, W.A., 1980. Gynecomastia in a pet monkey. Vet Med Small Anim Clin 75 (8), 1229–1230.

Chapter 13

Age-Related Changes to the Bony Structure and Musculature of the Shoulder in a Nonhuman Primate Model Anthony C. Santago II1, Johannes F. Plate2, Katherine R. Saul3 1The

MITRE Corporation, McLean, VA, United States; 2Department of Orthopaedic Surgery Wake Forest Baptist Health, Winston–Salem, NC, United States; 3Department of Mechanical and Aerospace Engineering North Carolina State University, Raleigh, NC, United States

INTRODUCTION Age-related changes to the musculoskeletal system of the upper limb are thought to be associated with progression to disability in humans. The successful completion of activities of daily living (ADLs) is an important marker of the ability to live independently; performance of many ADLs requires strength and coordination in the upper limb (Lundgren-Lindquist and Sperling, 1983), and the reduction in the capability of the upper limb to perform ADLs has been associated with worse outcomes following hospitalization in the elderly (Abizanda et al., 2007). Age-related pathology of the shoulder including osteoarthritis and rotator cuff tears may lead to marked disability and pain affecting the performance of functional tasks. Approximately 13%–26% of individuals older than 70 years are affected by shoulder pain (Luime et al., 2004). Osteoarthritis in the shoulder has been shown to increase with age (Kobayashi et al., 2014; Mantila Rossa et al., 2012) leading to approximately 30,000 total shoulder arthroplasties performed in the United States in 2006, mainly for the treatment of symptomatic shoulder osteoarthritis (American Academy of Orthopaedic Surgeons, 2008). The shoulder experiences exaggerated declines in strength with aging compared to the elbow and wrist (Vidt et al., 2012) and shoulder strength is a limiting factor in both reaching and pulling tasks (Daly et al., 2013). Rotator cuff tears are a common degenerative shoulder injury resulting in decreased strength and function (Vidt et al., 2016), predominantly affecting older adults (Yamamoto et al., 2010). With increased life expectancy and higher activity levels of the aging population, the prevalence of shoulder pathology including rotator cuff tears and degenerative shoulder disease is expected to increase (Oh et al., 2009), warranting further research of the underlying pathophysiologic mechanisms of aging in the shoulder. Understanding longitudinal changes to structure, function, and the interplay between the two would lend insight into early predictive factors for future disability that are difficult to assess in a cross-sectional study design. However high cost, long life span, and the need for invasive measurements to fully characterize skeletal muscle make longitudinal studies of the musculoskeletal system difficult to perform in humans. An animal model of upper extremity aging would more easily allow for longitudinal studies by limiting many of the logistical concerns associated with human subjects. Unfortunately, data regarding musculoskeletal degeneration in animals resulting strictly from normal aging are limited (Choi et al., 2013; Hagen et al., 2004; McKiernan et al., 2011; Plate et al., 2014). Small animals (mouse, rat, and rabbit) have been widely used for shoulder research. The rat has similar anatomic features to humans (Derwin et al., 2007; Edelstein et al., 2011; Soslowsky et al., 1996) and has become an indispensable model for studying shoulder degeneration (Mannava et al., 2011; Soslowsky et al., 2000), including surgically induced rotator cuff tears (Edelstein et al., 2011; Soslowsky et al., 1996; Mannava et al., 2011), tendon to bone healing (Bedi et al., 2010; Plate et al., 2014), muscular changes following injury (Soslowsky et al., 2000; Barton et al., 2005), and biologically enhanced repair strategies (Derwin et al., 2010). However, rat studies limit translation to humans because rats are quadrupedal with weight bearing forelimbs, they have limited overhead and multidirectional shoulder movement, and they are small (Derwin et al., 2007; Gerber et al., 1999; Mannava et al., 2013). Larger animal models, including the rabbit (Derwin et al., 2010; Grumet et al., 2009; Gupta and Lee, 2007; Rowshan et al., 2010; Rubino et al., 2008), dog (Derwin et al., 2007), and sheep (Gerber et al., 2004; Turner, 2007), share pathophysiological features of shoulder degeneration; however, the acromion and the coracoid process are small or not present, which differs from human shoulder anatomy (Derwin et al., 2010). A nonhuman primate model offers a solution that mitigates many of these problems (Table 13.1). Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00013-0 Copyright © 2018 Elsevier Inc. All rights reserved.

155

TABLE 13.1  Comparison of Various Animal Models Utilized for Shoulder Research

Advantages

Rat/Mouse

Rabbit

Dog

Sheep/Goat

Nonhuman Primates

Comparable rotator cuff anatomy with supraspinatus tendon translating underneath an enclosed arch l Widely available and inexpensive l Lowest demand (care, facilities) l Large sample size

l

Fibrofatty infiltration following injury l Relatively inexpensive l Low demand (care, facilities)

l

Assessment of tendon-tobone healing l Close to human size l Comparable biomechanical loads of the rotator cuff

l

Use of standard human repair techniques l Assessment of tendon-to-bone healing l Close to human size

l

Limited multidirectional movement of the shoulder l Small scale compared to human l Significant fatty infiltration following surgical rotator cuff injury only in combination with suprascapular nerve transection l Quadrupedal, weight-bearing l No retears following rotator cuff repair

l

Limited comparability of anatomy l Use of subscapularis tendon l Quadrupedal, weight-bearing

l

Limited multidirectional movement l Different anatomy of acromion and coracoid l Quadrupedal, weight-bearing l Moderate demand (care, facilities) l Expensive

l

Limited multidirectional movement l Different anatomy l Use of infraspinatus tendon l Quadrupedal, weight-bearing l High demand (care, facilities) l Expensive

l

Chronic rotator cuff tear partially persists, but spontaneous healing with scar-tissue forming a “pseudo-tendon”

Spontaneous healing with scar tissue

l

l

Similar anatomy Similar insertional rotator cuff tendon anatomy l Similar age-related degenerative changes of the shoulder l Use of standard human repair techniques l Multidirectional shoulder movement l Assessment of tendon-to-bone healing l

Disadvantages

l

Semiterrestrial, weight-bearing forelimbs l Highest demand (care, facilities) l Highly expensive for longitudinal studies l Ethical concerns

Chronic rotator cuff tear condition

l

l

Chronic condition for muscular changes l Spontaneous healing

l

Spontaneous healing with scar tissue

l

Outcome measures

l

CT, MRI Gait analysis l Histological analysis l Functional in vivo assessment

l

CT, MRI, ultrasound Gait analysis l Histological analysis

l l

CT, MRI, ultrasound Histological analysis

l

l

CT, MRI, ultrasound Gait analysis l Histological analysis

l

l

l

l

Areas of research

l

Pathomechanism of age-related degeneration, intrinsic and extrinsic rotator cuff injury (impingement, overuse) l In vivo functional biomechanic studies l Molecular pathways l Rehabilitation

l

Pathomechanism of muscular changes l Biomechanical studies l Tendon-to-bone healing with/without scaffold augmentation

l

Tendon-to-bone healing with/without scaffold augmentation l Biomechanical studies l Mechanical strength of repair techniques

l

In vivo biomechanical assessment of chronic rotator cuff tears l Mechanical strength of repair techniques

l

Cost per animal (US Dollars)a

CD-1 mouse: $8, Lewis rat: $40

New Zealand white rabbit: $100–$200

Hound: $1000

Sheep/Goat: $1000

$2500–$500024

Approximate per diem rates per animal (US Dollars)b

$1

$4

$14

$17

$10

Healing response to chronic injury has not been assessed

CT, MRI, ultrasound Histological analysis l Assessment of physical activity, walking speed, and functional use of upper extremity and overhead activity Pathomechanism of age-related degeneration l In vivo tendon-to-bone healing l Biomechanical studies l Molecular pathways l Mechanical strength of repair techniques

CT, computed tomography; MRI, magnetic resonance imaging. aMean approximate cost for illustration. Prices may vary by vendor, institution, and type of animal (species, age, strain). bMean per diem rates according to the Animal Resource Program at two U.S. academic institutions as of 10/2012. Reprinted from Plate, J.F., et al., 2013. Age-related degenerative functional, radiographic, and histological changes of the shoulder in nonhuman primates. J Shoulder Elbow Surg 22, 1019–1029, with permission from Elsevier.

Age-Related Changes to the Bony Structure and Musculature of the Shoulder Chapter | 13  157

Reports suggest that the African vervet monkey (Chlorocebus pygerythrus) may be a promising species to use as a human surrogate to study age-related musculoskeletal changes. Reductions in muscle fiber force in the vastus lateralis (Choi et al., 2013) and reductions in muscle mass of the hind limb (Kavanagh et al., 2016) similar to human aging have been reported. In addition, measures of physical performance that incorporate the upper limb were diminished in older vervets (Choi et al., 2013; Shively et al., 2012). Recent work has demonstrated that the vervet undergoes age-related structural changes to shoulder similar to what is experienced in humans such as a reduction of glenoid version angle and diminished physiological cross-sectional area (PCSA) of shoulder muscles, which is proportional to strength (Plate et al., 2013; Santago et al., 2015). This chapter outlines the evidence supporting the use of the vervet model as a surrogate for naturally occurring bony and muscular age-related changes to human shoulder.

Vervet Background and Demographics Two recent studies quantified age-related musculoskeletal degeneration of the shoulder in a cohort of female vervets (Plate et al., 2013; Santago et al., 2015). The adult female vervet monkeys (C. pygerythrus) (Table 13.2) were obtained from the Wake Forest Primate Center, a research colony established on St. Kitts Island in the 1970s (Fairbanks and McGuire, 1986; McGuire, 1974). All procedures were conducted in compliance with state and federal laws, standards of the US Department of Health and Human Services, and regulations and guidelines established by the Institutional Animal Care and Use Committee. The institution is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care. Animals were housed among social groups of approximately 15–40 animals, allowed to roam freely in large, inside/outside pens (30 m2), and to feed ad libitum. Euthanasia was performed as part of a larger experiment exploring immunologic and physiologic parameters and their relationship to aging. Prior to euthanasia, full-body computed tomography (CT) scans were performed on sedated animals (Ketamine) using a 32-slice CT scanner (Toshiba Aquilion; Toshiba America Medical Systems, Tustin, CA, USA) with 350 μm isotropic resolution and 0.5 mm slices just prior to the end of the study. Both the left and right upper limb of five middle-aged and six older adult female vervet monkeys were obtained. Vervets aged older than 20 years were categorized as older adults; the 26.4-year-old monkey is the oldest known female vervet in captivity, originating from the original colony (Plate et al., 2013).

Osseous Characteristics Osteoarthritis in the shoulder has been shown to increase with age (Kobayashi et al., 2014; Mantila Rossa et al., 2012). In a study of five middle-aged and six older vervet shoulders, older vervets demonstrated similar degenerative changes of bony shoulder structures compared with humans based on CT evaluation (Plate et al., 2013). Specifically, prior to euthanasia, TABLE 13.2  Characteristics of Vervet Specimens Age (years)

Age Group (MA/OA)

Body Mass (kg)

Arm Length (Right) (mm)

Forearm Length (Right) (mm)

11.76

MA

6.57

119.55

108.80

9.43

MA

4.68

112.15

100.69

11.53

MA

5.87

114.01

105.99

11.63

MA

4.92

108.18

100.17

11.53

MA

7.08

113.99

109.99

21.48

OA

6.14

119.42

109.21

25.75

OA

4.00

107.67

103.71

26.41

OA

4.93

127.48

114.22

23.73

OA

5.51

111.17

108.51

19.86

OA

4.63

113.80

107.99

21.55

OA

5.12

111.46

106.15

MA, middle aged; OA, older adult. Reprinted from Santago, A.C., et al., 2015. Age-related structural changes in upper extremity muscle tissue in a nonhuman primate model. J Shoulder Elbow Surg 24, 1660–1668, with permission from Elsevier.

158  SECTION | II  Animal Models: Vertebrates

degenerative changes of the glenohumeral joint were evaluated on two-dimensional (2D) coronal CT images and were graded according to the Kellgren and Lawrence (KL) classification system (Kellgren and Lawrence, 1957). Three-dimensional (3D) CT-image reconstructions (AquariusNET; TeraRecon, Foster City, CA, USA) were used to identify glenoid deformity and osteophytes. The joint space was measured on axial 2D CT scans as the shortest distance between the humeral head and the glenoid fossa. The glenoid version angle was assessed from 3D reconstructed images that were resliced to create an axial section. The version angle was measured with a line drawn connecting the anterior and posterior glenoid rim relative to a line tangent to the body of the scapula; negative angles indicating retroversion and positive angles indicating anteversion. Retroversion of the glenoid on CT scans has been found to correspond to degenerative wear of the posterior articular surface or glenoid dysplasia (Walch et al., 1998). The presence of partial and/or full thickness rotator cuff tears as discontinuity of the supraspinatus and/or infraspinatus tendon was assessed on coronal CT images. The acromiohumeral distance (AHD) was measured to assess superior translation of the humeral head using 2D CT image reconstructions, which were standardized by setting the axes parallel to the short and long axes of the scapular body (Lochmuller et al., 1997). Sixty-seven percent of the older vervets had significant degenerative changes in their shoulders compared to middleaged animals based on the evaluation of CT images for signs of osteoarthritis. Shoulders in four of six older vervets (eight shoulders) had degenerative changes of the glenoid and humeral head with osteophyte formation on the posterior/inferior glenoid, while no gross degenerative changes were observed in any of the middle-aged animals (P = .005) (Fig. 13.1). Two of the eight shoulders were classified as KL Grade 1, three shoulders as Grade 2, one shoulder as Grade 3, and two shoulders in one older vervet as Grade 4 changes with massive osteophyte formation, humeral head deformity, and joint space narrowing. Glenoid version angle significantly correlated with age, with an increase in glenoid retroversion with increased age. The glenoid of the older vervets was significantly retroverted (mean ± SEM; −2.2 degrees ± 0.5 degrees) compared to middle-aged vervets (mean ± SEM; 2.6 degrees ± 0.5 degrees, P 60% of the lung parenchyma is affected

Involves >three organs/body systems and associated with hemorrhage/ thrombosis

Arteritis

Grade 3 lesions considered possible contributing cause of death in cases with more severe disease or cause of death in the absence of other lesions; grade 4 lesions considered cause of death in the absence of a more severe neoplastic lesion, in which case considered contributing cause of death.

lesions identified (similar to the scoring system described above) and calculating a total score for an individual animal (Brayton et al., 2012; Treuting et al., 2008; Snyder et al., 2016; Ladiges et al., 2013; Ikeno et al., 2006; Bokov et al., 2011). Disease or lesion burden based on pathological findings may serve as an indication of health span (Ladiges et al., 2013; Zhang et al., 2014; Neff et al., 2013; Wilkinson et al., 2012), although challenges exist with assessing health span in mice and health span does not necessarily correlate with life span (Fischer et al., 2016). Members of a cohort can have disease burden determined and then the average disease burden can be calculated for one treatment group versus another (Treuting et al., 2008; Snyder et al., 2016).

Cause of Death and Contributing Cause of Death Cause of death has been defined as the “disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of accident or violence which produced the fatal injury” (World Health Organization, 2010; Long, 2004) and as the “overall cause that leads to the proximate cause [of death]” (Long, 2004). Guidelines to determine cause of death have been published, although many of these focus on toxicity and carcinogenesis studies (Ettlin et al., 1994; Long, 2004; Kodell et al., 1995). Authors determining cause of death in rodent studies have divided pathological lesions into categories such as “incidental,” “probably incidental,” “fatal,” and “probably fatal,” with lesions that are “fatal” or “probably fatal” considered causes of death (Ettlin et al., 1994). In one report describing cause-of-death analysis, severe lesions were considered the likely or “probable” cause of death, with other, less severe lesions representing “contributory” causes (Kodell et al., 1995). In this scheme, more than one contributory cause could be assigned, and “equivocal” causes of death were also possible (Kodell et al., 1995). “Unknown” causes of death are those for which a probable, contributory, or equivocal cause cannot be identified (Kodell et al., 1995). As mentioned previously, the specific criteria used in a study

200  SECTION | II  Animal Models: Vertebrates

(A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

FIGURE 16.1  (A). Ventral view of a mouse presenting for necropsy. Bar indicates the region of initial incision through the skin. The skin incision should then be continued longitudinally in a cranial and caudal direction as indicated by the arrows. (B). Internal organs of a normal rat at necropsy. (C). Internal organs of a mouse at necropsy. The liver and spleen are moderately and diffusely enlarged, most likely due to a neoplastic process. (D). Appearance of the liver from the mouse in Fig. 16.1A. Histiocytic sarcoma was diagnosed histologically. (E). Appearance of the kidney, with a roughened, “cobblestone” appearance (white arrow) and an enlarged adrenal gland (black arrows) in an aged rat with chronic progressive nephropathy and pheochromocytoma. (F). Pituitary adenoma (black arrow) in a rat. (G). Enlarged and hemorrhagic bladder identified at necropsy in a mouse with obstructive uropathy. (H). Lungs that fail to collapse at necropsy in an immunocompromised mouse with Pneumocystis murina infection. B, bladder; Ce, cecum; H, heart; K, kidney; L/Li, liver; Lu, lung; GI, gastrointestinal tract; Si, small intestine; Sp, spleen; S/St, stomach; T, testes.

Determining Cause of Death and Contributing Causes of Death in Rodent Aging Studies Chapter | 16  201

TABLE 16.4  Example Grading Scheme for Neoplasms Identified on Gross or Histological Evaluation at End of Life Neoplasia—Benign

Neoplasia—Malignant

Grade 0

No tumor

No tumor

Grade 1

Small neoplasm (5 mm) associated with mild to moderate hemorrhage, compression, and/or necrosis of surrounding tissue or multiple benign tumors within an organ l Multifocal pulmonary adenoma l Large and ulcerated cutaneous mass

Neoplasm confined to one organ, with minimal to mild effects on the surrounding tissue, and without metastasis. l Focal pulmonary adenocarcinoma l Hepatocellular carcinoma

Grade 3

Larger neoplasm of potentially vital organ that causes moderate to severe compression of adjacent normal tissue such that may contribute to moribund state l Pituitary adenoma >5 mm diameter

Neoplasms with metastasis to one organ or with moderate to severe observable effects on the surrounding tissue such as compression, hemorrhage, thrombosis, infarction, or necrosis; or infiltration of most of the organ by neoplastic cells l Hemangiosarcoma with infarction l Splenic lymphoma with involvement of mesenteric lymph node

Grade 4

n/a

Neoplasm with involvement of three or more organs l Histiocytic sarcoma with involvement of liver, uterus, mesenteric lymph node, and lung

to classify a lesion as “fatal” should be clearly elucidated in an effort to make cause-of-death determination as objective as possible. In addition to distinguishing between fatal and incidental neoplasms, determining the rate at which the tumor is thought to contribute to death or a moribund state (rapidly fatal vs. not rapidly fatal) may also facilitate cause-of-death analysis for an individual animal (Peto Analysis, 2001). Assigning a single cause of death in rodent aging studies is attractive from a data analysis and descriptive standpoint; however, as animals age, more than one substantial or severe disease process is likely to be identified on histopathological evaluation (Treuting et al., 2008). In this scenario and based on the work of others (Ettlin et al., 1994; Long, 2004; Kodell et al., 1995), the most severe or widespread disease process is assigned as the primary, or most likely, “cause of death.” Other diseases identified that are considered likely to impair the animal’s well-being and hasten clinical deterioration are assigned as “contributory causes of death.” Associated conditions may be observed as a direct result of lesions responsible for death or morbidity, such as hemorrhage, infarction, or necrosis with large or vascular neoplasms or fibrous osteodystrophy with renal failure (Snyder et al., 2016). Studies presenting cause of death in aging rodents have been published for many common mouse and rat strains, and examples are provided in Table 16.5. It is important to realize, however, that cause of death within a rodent population not only varies by species and strain, but that even within the same rodent strain cause of death can be influenced by substrain and housing conditions such as diet, bedding, excluded pathogens, and other factors (Brayton et al., 2001, 2012; Haseman et al., 1994; Lipman et al., 1993; Nohynek et al., 1993). Obviously, genetically engineered mice may have distinct causes of death that result both from their genetic manipulation as well as the influence of background strain (Brayton et al., 2012). Examples include eosinophilic crystalline pneumonia as a common cause of death in 129S4/SvJae mice (Hoenerhoff et al., 2006) and an increased incidence of rhabdomyosarcoma in aged C57BL/10ScSn-Dmdmdx/J (dystrophin deficient “Mdx” mice) (Chamberlain et al., 2007). Immunodeficient strains also are more likely to die from opportunistic bacterial and fungal infections (Villano et al., 2014; Percy and Barta, 1993). For example, increased mortality secondary to renal inflammatory lesions cause by Klebsiella oxytoca and Enterococcus was reported in NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG) mice (Foreman et al., 2011). Pneumocystis pneumonia as a cause of death has also been reported in severely combined immunodeficient and other mice (Roths et al., 1990; Walzer et al., 1989) (Fig. 16.1H). Immunosuppressed mouse strains also may have an increased tendency toward certain fatal neoplastic diseases, such as thymic lymphoma in Prkdcscid and NOD.CB17-Prkdcscid (NOD-scid) mice (Custer et al., 1985; Shultz et al., 1995, 2005).

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TABLE 16.5  Common Causes of Death or Contributing Causes of Death in Selected Studies Evaluating Cause of Death in Aged Rodents References

Mean or Median Life Span

Common Causes of Deatha,b

C57BL/6

Blackwell et al. (1995)

Median: 836 days (male) 817 days (female)

Hematopoietic neoplasia Hepatocellular neoplasia Nephropathy Inflammation Hemangioma/hemangiosarcoma Pituitary adenoma

CD1

Maita et al. (1988)

66% mortality at 109 weeks of age (male) 63% mortality at 109 weeks of age (female)

Amyloidosis (frequency decreased over time) Urinary obstruction (males) Nephropathy Hematopoietic neoplasia Pulmonary adenocarcinoma Hepatocellular carcinoma (males) Mammary adenocarcinoma (females)

129S4/SvJae

Ward et al. (2000)

Necropsy at 27 months; 6/88 mice with nonlife-threatening disease at terminal sacrifice were excluded

Uterine neoplasia (hemangiosarcoma/ hemangioma/other) Uterine hematoma/thrombosis Pulmonary carcinoma Megaesophagus Eosinophilic crystalline pneumonia Polyarteritis (males) Abscess secondary reproductive glands

129S6/SvEvTac

Radaelli et al. (2016)

Median: 770 days (female) 778 days (male)

Hematopoietic neoplasia Polyarteritis Eosinophilic crystalline pneumonia Sepsis Abscess/suppurative metritis/pyometra

B6;129 (Mixed C57BL/6 and 129Sv/E background)

Conover et al. (2010)

Median: 96 weeks (female) 97 weeks (male)

Hematopoietic neoplasia Bronchoalveolar carcinoma Hemangioma/hemangiosarcoma Hepatocellular carcinoma Atrial thrombosis Nephropathy Per Haines et al. (2001), abscess/infection/pneumonia common cause of death in addition to hematopoietic neoplasia

Four-way cross mice Cross between AKR/J ×  DBA/2J F1 females and C57BL/6J ×  SJLJ F1 males

Chrisp et al. (1996)

Median: 23 months (female) 30 months (male)

Hematopoietic neoplasia (lymphoma) Pituitary adenoma Glomerular amyloidosis

UM-HET3

Miller et al. (2011)

Median: 843–891 days (female) 780–851 days (male)

Hematopoietic neoplasia Hemangiosarcoma Pulmonary carcinoma Mammary carcinoma Fibrosarcoma

BALB/c* Female only b/c males removed due to fighting

Madison et al. (1968)

29% mortality prior to 18 months of age

Pneumonia Pulmonary neoplasia Hematopoietic neoplasia Szymanska et al. (2014) also identified a high incidence of mammary and hepatocellular neoplasms in addition to lung and hematopoietic neoplasms

Mouse

Determining Cause of Death and Contributing Causes of Death in Rodent Aging Studies Chapter | 16  203

TABLE 16.5  Common Causes of Death or Contributing Causes of Death in Selected Studies Evaluating Cause of Death in Aged Rodents—cont’d References

Mean or Median Life Span

Common Causes of Deatha,b

FVB/N Female only

Panchenko et al. (2016)

Mean: 600 ± 12.2 days

Hematopoietic neoplasia (lymphoma) Pulmonary adenocarcinoma

C3Hf/He

Holland et al. (1978)

Median: Approximately 800 days (female) and 900 days (male)

Fibrosarcoma Hepatocellular carcinoma Hematopoietic neoplasia Mammary adenocarcinoma Nephropathy Ovarian adenocarcinoma

Nude (Fox1nu) Germ free on a mixed BALB/c and C3H background

Holland et al. (1978)

Median: Approximately 700 days (female and male)

Hematopoietic neoplasia Cecal volvulus

Rat Sprague Dawley

Keenan et al. (1994)

Pituitary neoplasia Nephropathy

Harlan RCHan:WIST

Blankenship et al. (2016)

62% mortality (male) and 59% mortality (female) at 104 weeks

Pituitary neoplasia Mammary neoplasia Per Ettlin et al. (1994) also skin tumors and chronic nephropathy in KfM-Wistar rats

Fischer F-344

Kodell et al. (1995)

Not available

Mononuclear cell leukemia Pituitary adenoma Thurman et al. (1994) also identified a high rate of these neoplasms in dead and moribund F-344 rats with a median age of survival 115 weeks (female) and 102 weeks (male)

aIncluded bCause

if accounted for >5% of animals in either or both male and female cohort (or more than 1 animal in cohorts of less than 20 animals). of death may vary within a strain over time and by housing conditions, substrain, and other factors.

For neoplastic diseases, histopathological evaluation of hematoxylin and eosin stained sections is likely to lead to a diagnosis of tissue type of origin (e.g., round cell, mesenchymal, and epithelial); however, specifying the cell type of origin may require advanced diagnostics such as immunohistochemistry (Fig. 16.2A–H), or in some cases, advanced molecular diagnostics. For inflammatory diseases, additional diagnostics such as bacterial culture and sensitivity, polymerase chain reaction or special stains (e.g., tissue Gram stain for bacteria, Gomori methenamine silver, or Periodic acid–Schiff stain for fungal organisms) may be required to reach an etiologic diagnosis.

CASE EXAMPLE: CAUSE-OF-DEATH ANALYSIS IN A COHORT OF AGED MALE C57BL/6 MICE TREATED WITH VEHICLE OR SYSTEMIC RAPAMYCIN Cause of death was determined in a small cohort of aged male 19–20-month-old C57BL6/JNia mice obtained from the NIA treated with intraperitoneal rapamycin at 8 mg/kg daily for 90 days (n = 20 male mice) or vehicle (n = 20 male mice). The histology data have been previously published, although limited immunohistochemistry data obtained subsequently are unpublished (Bitto et al., 2016). Before the end of treatment, five mice (three vehicles, two rapamycin) died of non– age-related causes and were excluded from further analysis. Of the remaining 35 mice, 29 were available for end-of-life pathological analysis. Histopathology was performed on the suggested standard minimum database of tissues as previously described (Snyder et al., 2016) for 14 vehicle-treated and 15 rapamycin-treated mice. All neoplasms identified were characterized as benign or malignant and graded based on size; effects on the surrounding tissue (necrosis, hemorrhage, and thrombosis); and number of organs affected as previously described (Table 16.4). Nephropathy, cardiomyopathy,

204  SECTION | II  Animal Models: Vertebrates

(A)

(E)

(I)

(B)

(F)

(J)

(C)

(G)

(K)

(D)

(H)

(L)

FIGURE 16.2  Immunohistochemistry and hematoxylin and eosin (HE) histology from three mice with hematopoietic neoplasia. (A–D) Mouse 1 with B-cell lymphoma; (E–H) Mouse 2 with histiocytic sarcoma; (I–L) Mouse 3 with histiocytic sarcoma and neoplasia in the liver (I), hyaline droplets in the renal tubular cells (J), and intravascular neoplasia in the lung (K and L). (A, E, I–K) HE. (B, F, and L) MAC2 immunohistochemistry (brown) with hematoxylin counterstain (blue). Positive staining for histiocytes. (C and G) CD3 immunohistochemistry (brown) with hematoxylin counterstain (blue). Positive staining for T lymphocytes. (D and H) CD45 immunohistochemistry (brown) with hematoxylin counterstain (blue). Positive staining for B lymphocytes.

acidophilic macrophage pneumonia, and arteritis, if present, were also graded as previously described (Table 16.3). Foreign body–associated periodontitis and gingivitis (Sundberg et al., 2011b; Duarte-Vogel and Lawson, 2011) were noted in many animals and were recorded if severe. Other conditions identified that were considered possible to contribute to a moribund state (gastrointestinal ulceration and hemorrhage; suppurative otitis media) were recorded when present. Major histological findings and cause of death from this cohort are presented in Table 16.6. The most severe lesion in each mouse, among neoplastic lesions scoring a 3 or 4 or nonneoplastic lesions scoring a 4, was defined as the cause of death. If no lesion scoring a 3 or a 4 was identified, of if a lesion of 3 was considered unlikely to be fatal, then the cause of death was undetermined. Neoplasms involving multiple tissues (grade 4) were considered the

Determining Cause of Death and Contributing Causes of Death in Rodent Aging Studies Chapter | 16  205

TABLE 16.6  Cause-of-Death Analysis in a Population of Aged Male C57BL6/JNia Vehicle (n = 14)

Rapamycin Treated (n = 15)

Hematopoietic neoplasia l Lymphoma (presumptive) l Histiocytic sarcoma (presumptive)

1 12

1 8

Hemangiosarcoma (presumptive)

1 (based on absence of MAC2 positive ­staining of neoplastic cells)

1

Hepatocellular carcinoma

0

1

Undetermined

0

4

Associated lesions

Hepatic infarction and necrosis

Splenic thrombosis

Neoplasia: l Pulmonary neoplasia l Gastrointestinal neoplasia l Thyroid neoplasia

7 5 1 1

9 6 2 1

Nonneoplastic: l Gastric ulceration and hemorrhage l Suppurative otitis media l Nephropathy (score > 3) l Acidophilic macrophage pneumonia (>3)

1 1 1 0

2 6 5 1

Cause of Death

Contributing Cause of Death

most severe lesion, followed by large malignant neoplasms (hemangiosarcoma, hepatocellular carcinoma) of a single organ accompanied by necrosis, hemorrhage, or vascular thrombosis (grade 3). In all of the vehicle-treated mice, neoplasia involving the liver and various other organs was identified. In most cases, there was an intravascular component to the neoplasia with involvement of the hepatic centrilobular veins and sinusoids (Fig. 16.2I). Small pulmonary vessels were expanded by neoplastic cells in many cases and in some cases there were eosinophilic cytoplasmic droplets within the proximal renal tubules consistent with excess lysozyme production by histiocytic sarcoma (Fig. 16.2J). Immunohistochemistry for MAC2 was performed in three cases, which showed positive staining of infiltrating neoplastic histiocytes in the liver and lung in two cases and no positive staining in the third case (Fig. 16.2K and L). Cause of death in 13/14 of the vehicle-treated mice was considered to be hematopoietic neoplasia. One case was presumptively diagnosed as hemangiosarcoma based on absence of MAC2 staining, although further immunohistochemistry (e.g., Factor VIII) was not pursued. Among the rapamycin-treated mice, hematopoietic neoplasia involving the liver with an intravascular component was also common (9/15 mice). Of the remaining six mice, one mouse had hepatocellular carcinoma presumed to be the cause of death, with hepatic degeneration, fibrosis, and telangiectasia; one mouse had splenic hemangiosarcoma; and the other four mice did not have an identifiable malignant tumor or end-stage cardiac or renal disease and the cause of death was undetermined. Of the four mice with undetermined causes of death, three were in an autolyzed postmortem state with only small, benign neoplasms noted histologically and one had a nephropathy score of 3, gastrointestinal ulceration and hemorrhage, otitis, and a small pulmonary adenoma, all of which may have contributed to death. In two of cases with undetermined cause of death, changes in the liver were suspicious for less severe intravascular neoplasia, although autolysis prevented definitive assessment. The high incidence of presumptive histiocytic sarcoma in both the vehicle control and experimental cohorts in this case example underscores the importance of using contemporary controls (ideally littermates of the experimental mice) raised and housed in the same conditions as the experimental cohort. Also, this case example illustrates the limitations of histopathological assessment of hematoxylin and eosin stained slides alone. Based on cell morphology, distribution of lesions, and secondary changes such as the presence of eosinophilic droplets within the cytoplasm of the proximal renal tubular epithelial cells, many of the hematopoietic neoplasms could be separated into “probable lymphoma” or “probable histiocytic sarcoma.” However, immunohistochemistry was required for definitive diagnosis of the cell type of origin and ruled out a presumptive diagnosis of histiocytic sarcoma in one case.

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CONCLUSION Cause-of-death analysis in rodent aging studies offers a succinct way to summarize the pathological data and compare major histological lesions in mice of different genotypes or treatment groups, which is more informative and can be complementary to life span determination alone. Cause-of-death determination also can provide insight into the pathogenesis or mechanism of action of longevity increasing interventions. Performing cause-of-death analysis requires a plan for careful monitoring of mice with euthanasia at endpoint criteria for optimal tissue preservation, consistent necropsy and data recording techniques, and assessment of a standard set of major organs for potentially fatal lesions. Lesion grading based on predetermined and defined criteria facilitates cause-of-death assessment. In cross-sectional and other studies in which cause-of-death analysis is not appropriate, pathological assessment of the standard set of tissues may enable lesion, or disease, burden determination.

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Rudmann, D., Cardiff, R., Chouinard, L., Goodman, D., Kuttler, K., Marxfeld, H., et al., 2012. Proliferative and nonproliferative lesions of the rat and mouse mammary, zymbal’s, preputial, and clitoral glands. Toxicol Pathol 40 (6 Suppl.), 7S–39S. Ruehl-Fehlert, C., Kittel, B., Morawietz, G., Deslex, P., Keenan, C., Mahrt, C.R., et al., 2003. Revised guides for organ sampling and trimming in rats and mice–part 1. Exp Toxicol Pathol 55 (2–3), 91–106. Scudamore, C.L., Busk, N., Vowell, K., 2014. A simplified necropsy technique for mice: making the most of unscheduled deaths. Lab Anim 48 (4), 342–344. Shultz, L.D., Schweitzer, P.A., Christianson, S.W., Gott, B., Schweitzer, I.B., Tennent, B., et al., 1995. Multiple defects in innate and adaptive immunologic function in NOD/LtSz-scid mice. J Immunol 154 (1), 180–191. Shultz, L.D., Lyons, B.L., Burzenski, L.M., Gott, B., Chen, X., Chaleff, S., et al., 2005. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2R gamma null mice engrafted with mobilized human hemopoietic stem cells. J Immunol 174 (10), 6477–6489. Smith, R.S., Xavier, M., Sundberg, J.M., 1996. Ulcerative blepharitis in aging inbred mice. In: Mohr, U., Dungworth, D.L., Capen, C.C., Carlton, W.W., Sundberg, J.P., Ward, J.M. (Eds.), Pathobiology of the Aging Mouse, vol. 2. ILSI Press, Washington, DC. Snyder, J.M., Ward, J.M., Treuting, P.M., 2016. Cause-of-death analysis in rodent aging studies. Vet Pathol 53 (2), 233–243. Sundberg, J.P., Berndt, A., Sundberg, B.A., Silva, K.A., Kennedy, V., Bronson, R., et al., 2011a. The mouse as a model for understanding chronic diseases of aging: the histopathologic basis of aging in inbred mice. Pathobiol Aging Age Relat Dis 1. Sundberg, J.P., Rozell, B., Everts, H., 2011b. Association between hair-induced oronasal inflammation and ulcerative dermatitis in C57BL/6 mice. Comp Med 61 (3), 204–205 author reply 5. Sundberg, J.P., Taylor, D., Lorch, G., Miller, J., Silva, K.A., Sundberg, B.A., et al., 2011c. Primary follicular dystrophy with scarring dermatitis in C57BL/6 mouse substrains resembles central centrifugal cicatricial alopecia in humans. Vet Pathol 48 (2), 513–524. Szymanska, H., Lechowska-Piskorowska, J., Krysiak, E., Strzalkowska, A., Unrug-Bielawska, K., Grygalewicz, B., et al., 2014. Neoplastic and nonneoplastic lesions in aging mice of unique and common inbred strains contribution to modeling of human neoplastic diseases. Vet Pathol 51 (3), 663–679. Tatar, M., 2009. Can we develop genetically tractable models to assess healthspan (rather than life span) in animal models? J Gerontol Ser A Biol Sci Med Sci 64 (2), 161–163. Thoolen, B., Maronpot, R.R., Harada, T., Nyska, A., Rousseaux, C., Nolte, T., et al., 2010. Proliferative and nonproliferative lesions of the rat and mouse hepatobiliary system. Toxicol Pathol 38 (7 Suppl.), 5S–81S. Thurman, J.D., Bucci, T.J., Hart, R.W., Turturro, A., 1994. Survival, body weight, and spontaneous neoplasms in ad Libitum-fed and food-restricted Fischer-344 rats. Toxicol Pathol 22 (1), 1–9. Treuting, P.M., Snyder, J.M., 2015. Mouse necropsy. Curr Protoc Mouse Biol 5 (3), 223–233. Treuting, P.M., Linford, N.J., Knoblaugh, S.E., Emond, M.J., Morton, J.F., Martin, G.M., et al., 2008. Reduction of age-associated pathology in old mice by overexpression of catalase in mitochondria. J Gerontol Ser A Biol Sci Med Sci 63 (8), 813–822. Treuting, P.M., Snyder, J.M., Ikeno, Y., Schofield, P.N., Ward, J.M., Sundberg, J.P., 2016. The vital role of pathology in improving reproducibility and translational relevance of aging studies in rodents. Vet Pathol 53 (2), 244–249. Ullman-Cullere, M.H., Foltz, C.J., 1999. Body condition scoring: a rapid and accurate method for assessing health status in mice. Lab Anim Sci 49 (3), 319–323. Vanhooren, V., Libert, C., 2013. The mouse as a model organism in aging research: usefulness, pitfalls and possibilities. Ageing Res Rev 12 (1), 8–21. Villano, J.S., Rong, F., Cooper, T.K., 2014. Bacterial infections in Myd88-deficient mice. Comp Med 64 (2), 110–114. Walzer, P.D., Kim, C.K., Linke, M.J., Pogue, C.L., Huerkamp, M.J., Chrisp, C.E., et al., 1989. Outbreaks of Pneumocystis carinii pneumonia in colonies of immunodeficient mice. Infect Immun 57 (1), 62–70. Ward, J.M., Thoolen, B., 2011. Grading of lesions. Toxicol Pathol 39 (4), 745–746. Ward, J.M., Anver, M.R., Mahler, J.F., Devor-Henneman, D.E., 2000. Pathology of mice commonly used in genetic engineering (C57BL/6; 129; B6,129; and FVB/N). In: Ward, J.M., Mahler, J.F., Maronpot, R.R., Sundberg, J.P. (Eds.), The Pathology of Genetically Engineered Mice. Iowa State University Press, Ames, Iowa. Wilkinson, J.E., Burmeister, L., Brooks, S.V., Chan, C.C., Friedline, S., Harrison, D.E., et al., 2012. Rapamycin slows aging in mice. Aging Cell 11 (4), 675–682. World Health Organization, 2010. International Statistical Classification of Diseases and Related Health Problems, fourth ed. World Health Organization, Malta. 195 p. Yu, B.P., Masoro, E.J., Murata, I., Bertrand, H.A., Lynd, F.T., 1982. Life span study of SPF Fischer 344 male rats fed ad libitum or restricted diets: longevity, growth, lean body mass and disease. J Gerontol 37 (2), 130–141. Zhang, Y., Bokov, A., Gelfond, J., Soto, V., Ikeno, Y., Hubbard, G., et al., 2014. Rapamycin extends life and health in C57BL/6 mice. J Gerontol Ser A Biol Sci Med Sci 69 (2), 119–130.

FURTHER READING Pathology of Aging Mice Review Articles: Brayton, C.F., Treuting, P.M., Ward, J.M., 2012. Pathobiology of aging mice and GEM: background strains and experimental design. Vet Pathol 49 (1), 85–105. Pettan-Brewer, C., Treuting, P.M., 2011. Practical pathology of aging mice. Pathobiol Aging Age Relat Dis 1.

Mouse Necropsy Technique: Scudamore, C.L., Busk, N., Vowell, K., 2014. A simplified necropsy technique for mice: making the most of unscheduled deaths. Lab Anim 48 (4), 342–344. Treuting, P.M., Snyder, J.M., 2015. Mouse necropsy. Curr Protoc Mouse Biol 5 (3), 223–233.

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International Harmonization of Nomenclature and Diagnostic Criteria (INHAND) References: Berridge, B.R., Mowat, V., Nagai, H., Nyska, A., Okazaki, Y., Clements, P.J., et al., 2016. Non-proliferative and proliferative lesions of the cardiovascular system of the rat and mouse. J Toxicol Pathol 29 (3 Suppl.), 1S–47S. Creasy, D., Bube, A., de Rijk, E., Kandori, H., Kuwahara, M., Masson, R., et al., 2012. Proliferative and nonproliferative lesions of the rat and mouse male reproductive system. Toxicol Pathol 40 (6 Suppl.), 40S–121S. Dixon, D., Alison, R., Bach, U., Colman, K., Foley, G.L., Harleman, J.H., et al., 2014. Nonproliferative and proliferative lesions of the rat and mouse female reproductive system. J Toxicol Pathol 27 (3–4 Suppl.), 1S–107S. Frazier, K.S., Seely, J.C., Hard, G.C., Betton, G., Burnett, R., Nakatsuji, S., et al., 2012. Proliferative and nonproliferative lesions of the rat and mouse urinary system. Toxicol Pathol 40 (4 Suppl.), 14S–86S. Greaves, P., Chouinard, L., Ernst, H., Mecklenburg, L., Pruimboom-Brees, I.M., Rinke, M., et al., 2013. Proliferative and non-proliferative lesions of the rat and mouse soft tissue, skeletal muscle and mesothelium. J Toxicol Pathol 26 (3 Suppl.), 1S–26S. Kaufmann, W., Bolon, B., Bradley, A., Butt, M., Czasch, S., Garman, R.H., et al., 2012. Proliferative and nonproliferative lesions of the rat and mouse central and peripheral nervous systems. Toxicol Pathol 40 (4 Suppl.), 87S–157S. Mecklenburg, L., Kusewitt, D., Kolly, C., Treumann, S., Adams, E.T., Diegel, K., et al., 2013. Proliferative and non-proliferative lesions of the rat and mouse integument. J Toxicol Pathol 26 (3 Suppl.), 27S–57S. Nolte, T., Brander-Weber, P., Dangler, C., Deschl, U., Elwell, M.R., Greaves, P., et al., 2016. Nonproliferative and proliferative lesions of the gastrointestinal tract, pancreas and salivary glands of the rat and mouse. J Toxicol Pathol 29 (1 Suppl.), 1S–125S. Renne, R., Brix, A., Harkema, J., Herbert, R., Kittel, B., Lewis, D., et al., 2009. Proliferative and nonproliferative lesions of the rat and mouse respiratory tract. Toxicol Pathol 37 (7 Suppl.), 5S–73S. Rudmann, D., Cardiff, R., Chouinard, L., Goodman, D., Kuttler, K., Marxfeld, H., et al., 2012. Proliferative and nonproliferative lesions of the rat and mouse mammary, zymbal’s, preputial, and clitoral glands. Toxicol Pathol 40 (6 Suppl.), 7S–39S. Thoolen, B., Maronpot, R.R., Harada, T., Nyska, A., Rousseaux, C., Nolte, T., et al., 2010. Proliferative and nonproliferative lesions of the rat and mouse hepatobiliary system. Toxicol Pathol 38 (7 Suppl.), 5S–81S.

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Chapter 17

Rat Models of Cognitive Aging Joseph A. McQuail, Sarah A. Johnson, Sara N. Burke, Jennifer L. Bizon University of Florida, Gainesville, FL, United States

INTRODUCTION Improved standards of living and medical advances have reduced mortality and increased life expectancy. In the United States alone, the number of people over the age of 65 years is projected to double from 40 million at the time of the 2010 census to more than 80 million by the year 2050, or slightly greater than 20% of the US population (West et al., 2014). However, our growing senior population remains highly susceptible to age-related cognitive decline and age-associated neurological disorders, such as Alzheimer’s disease (AD). Indeed, prevalence of dementia escalates from 5% in 71–79 year olds to 37% in those over the age of 90 (Plassman et al., 2007). However, the full extent of cognitive decline that manifests with age is not entirely understood and is likely underreported (Institute of Medicine, 2015). All age-related cognitive disabilities contribute a significant burden with regard to reduced individual quality of life as well as increased financial strain on the health-care system and society, and this burden continues to accelerate. Characterizing the underlying neurobiological processes that contribute to cognitive decline in later life remains a fundamental strategy to combat this health problem. Animal models of aging complement human studies and provide the opportunity to assess directly the neurobiological changes that subserve age-related cognitive decline. Ultimately, the use of animal models will contribute to improved diagnosis and treatment options for humans with cognitive impairment.

Using Rats to Model Cognitive Aging Rats provide a translationally appropriate and reproducible model in which to investigate age-related changes to cognition and neural systems. There are sufficient similarities between rodents and humans in the anatomy of brain systems, including those that are particularly vulnerable to aging (e.g., the medial temporal lobe system and prefrontal cortex (PFC)), to examine age-related changes in brain and behavior in a parallel manner. A rodent model is further favored as these animals are widely available and suitable for invasive procedures necessary for neurobiological research. Rats afford a particular advantage because researchers can design well-powered and tightly controlled experiments that draw on a deep literature of behavioral neuroscience research using this specific animal. In contrast to transgenic or knockout mouse models of cognitive impairment and neurological disease, many strains of rat provide naturalistic models of age-related impairment in which some rats demonstrate cognitive decline, whereas others have cognitive abilities on par with young adult rats. Leveraging naturally occurring rat models circumvents some of the challenges associated with transgenic mouse studies, including developmental abnormalities or accelerated onset of pathology that is not reflective of human disease presentation, thereby providing a rich model for uncovering the neurobiological factors responsible for age-related cognitive loss.

Selection of Rat Strain to Model Cognitive Aging Though many different strains of rat are used for behavioral and neurobiological research, special care should be taken when choosing a rat strain in which to conduct cognitive aging research. Popular strains used for aging research include Sprague Dawley (SD; Drapeau et al., 2003; Martínez-Serrano et al., 1996), Long–Evans (LE; Gallagher et al., 1993; Barense et al., 2002), Fisher 344 (F344; Bizon et al., 2009; Beas et al., 2013), Brown Norway (BN; Markowska and Savonenko, 2002), and F344 × Brown Norway F1 (F344 × BN-F1; Nieves-Martinez et al., 2012; McQuail and Nicolle, 2015) rats. Some critical factors to consider when selecting a rat strain include variability in health and life span inherent to the strain, strain-specific, and sex-specific differences in cognition, and supplier availability. Significantly, these strains differ greatly with respect to genetic characteristics. Both the SD and LE strains are outbred, imparting greater genetic and phenotypic diversity relative to the F344 and BN strains, which are inbred. The F344 × BN-F1 rat is produced by crossbreeding the latter two strains Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00017-8 Copyright © 2018 Elsevier Inc. All rights reserved.

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to create hybrid offspring with limited interindividual variation but greater genetic variance due to reduced homozygosity. The desire to investigate age-related changes to behavior using a rat model with greater or lesser genetic/phenotypic diversity relative to another may reflect specific needs of the study (e.g., Markowska and Savonenko, 2002; van der Staay and Blokland, 1996). Additionally, certain strains may be more prone to tumors of the pituitary gland, a factor that can cause variability in circulating hormone levels and blindness through compression of the optic nerve. Such pathology can confound cognitive and neurobiological data if behavioral tests are not carefully designed and aged rats are not properly screened for such tumors at sacrifice. Another consideration is the sex of rats chosen; the endocrine system certainly can influence memory and brain function, and it is well established that the endocrine system in rats, as in humans, is modified with aging. When using aged female rats one must therefore take into consideration circulating hormone levels in young and aged rats, as the estrous cycle in females is cyclic in young rats, becomes erratic at middle ages, and ceases at later ages (Savonenko and Markowska, 2003). While much research to date frequently examines aging in male and female rats as separate lines of study, recent mandates from the National Institutes of Health (NIH) to consider sex as a key biological variable will certainly give rise to a better understanding of age-related differences to brain aging in males and females. Finally, availability must be a consideration for those wishing to conduct cognitive aging research. Indeed, three rat strains (F344, BN and F344 × BN-F1) are currently available from the Aging Rodent Colony that supports funded research from the National Institute on Aging, a constituent institute of the NIH (NIA, 2011). In recent years, our laboratories have jointly made considerable progress in characterizing age-related cognitive and neurobiological changes in male rats of the F344 and F344 × BN-F1 strains in an effort to grow the utility of this valuable research resource. For this reason, this chapter will largely focus on recent behavioral and neurobiological findings from aging rats of these two strains.

AGING AND THE MEDIAL TEMPORAL LOBE The medial temporal lobe is particularly vulnerable to age-related decline across species (Rosenzweig and Barnes, 2003; Buckner, 2004) and these structures are homologous across rodents and humans. Fig. 17.1 compares the anatomy of the medial temporal lobe in the human to the rat brain. Over 60 years of research (Scoville and Milner, 1957) has unequivocally shown that the medial temporal lobe is critical for memory processing. This medial temporal lobe memory system includes the hippocampal formation and surrounding cortical regions comprising the lateral and medial entorhinal cortices, perirhinal cortex, and postrhinal cortex, which is referred to as parahippocampal cortex in humans (Eichenbaum, 2000). Hippocampal subregions are shown in the lower diagram (Fig. 17.1), which highlights circuitry in a coronal section of the rat hippocampus. Neurons residing in layer II of the entorhinal cortex project to dentate gyrus granule cells and CA3 pyramidal cells directly via the perforant path. Granule cells in turn project to pyramidal neurons of CA3 via the mossy fiber pathway, and CA3 sends projections to CA1 pyramidal neurons via Schaffer collaterals, which are also recurrent and form an auto-associative loop within CA3 (Amaral and Witter, 1989, 1995). Function of this circuit has been linked to the processing of explicit or declarative memory in humans, including conscious memories of events, people, and places (Eichenbaum, 2004; Squire, 2004). Compromised function of the medial temporal lobe circuit has been extensively studied across species and contributes to specific memory deficits in normal aging, as well as in neurodegenerative conditions such as AD (Balota et al., 2000; Dickerson and Eichenbaum, 2010; Gallagher and Koh, 2011; Leal and Yassa, 2013). Individual differences in age-related decline of the medial temporal lobe circuit are noted in humans as well as rodents. Across species, some aged subjects maintain mnemonic abilities well into advanced age, whereas others experience decline of cognitive capacities relative to younger individuals (Morrison and Baxter, 2012; Cansino, 2009). The basis for these age-related individual differences in cognition is a compelling focus of neurobiological research. Indeed, focusing on such variability among aged populations allows the investigator to compare neurobiological changes in the brain not only between young and aged subjects but also among aged rats that differ with respect to cognitive ability. This in turn may permit the identification of neurobiological mechanisms that promote adaptive plasticity or compensation in the aging brain, which allow certain individuals to maintain intact cognition well into the later stages of adulthood (Ash and Rapp, 2014; Gray and Barnes, 2015).

Spatial Learning and Memory The Morris water maze is a well-established apparatus to evaluate hippocampal-dependent spatial learning and memory in rats (Morris et al., 1982; Morris, 1981; Moser et al., 1993; Steffenach et al., 2005), thought to tap processes that parallel human explicit or episodic-like memory (Robitsek et al., 2008; Tomás Pereira and Burwell, 2015). Consistent with this role of the hippocampus in forming long-term memories, our laboratories employ a multiday place-learning version of the water maze wherein rats must use visual cues surrounding a pool of water to learn the fixed location of an escape platform submerged just below the water’s surface. Specifically, rats receive three trials per day for eight consecutive days. In each

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FIGURE 17.1  Schematic of the medial temporal lobe system, including the hippocampus and associated structures in human and rat brain. The top left diagram shows an overview of the human brain. The general area of the medial temporal lobe is identified; the hippocampal formation is shaded green, the entorhinal cortex is shaded orange, and the parahippocampal gyrus comprising the perirhinal and postrhinal cortices are shaded blue and yellow, respectively. The bottom left panel shows a circuit diagram and demonstrates the flow of information within this system. Structures that are part of the medial temporal lobe in this system are colored as above. Generally, the neocortex sends afferent projections to the hippocampus from the entorhinal cortex via parahippocampal structures. The top right diagram shows the rat brain, with structures color-coded using the same scheme as shown in the human brain. An enlarged coronal section through hippocampus illustrates the basic cellular circuitry within this region. Projections from entorhinal cortex (perforant path) carry information to the granule cell layer (DG; red). The hilus (Hi; orange) is the region encompassed on either side by the granule cell layer of the dentate gyrus. General information flow in the hippocampus entails granule cell projections to the pyramidal neurons of hippocampus proper (i.e., CA3 and CA1, in turn), before exiting the structure via the subiculum and sending information back to the neocortex.

training trial, rats are released from various starting points at the maze’s perimeter and permitted to swim until they locate the platform or for up to 90 s, at which time they are placed on the platform. This is followed by a 60-s intertrial interval that comprises 30 s spent on the escape platform followed by 30 s in a holding cage outside of the maze. Every sixth trial is a probe trial where the platform is lowered and unavailable for escape during the first 30 s of the swim. Following the 30-s probe trial, the platform is covertly raised to permit escape and maintain the usual response–reinforcement contingency of the task. At the conclusion of place-learning, all rats are trained on a cued version of the task, where the platform is visible above the water’s surface, to identify and exclude rats with visual or physical impairments that could confound interpretation of behavioral data. Spatial learning is primarily assessed by calculating the average proximity of the rat to the escape platform location during the probe trials (Maei et al., 2009). The use of probe trials and a sensitive measure of performance is essential to accurate characterization of spatial learning as even rats with hippocampal lesions can effectively escape onto the training platform using nonspatial (i.e., circling) strategies (Devan et al., 1996; Devan and White, 1999). While training schedules vary from lab to lab, distributed training enhances retention relative to massed training (Spreng et al., 2002) and interpolating probes trials throughout training improves detection of differences in spatial learning compared to a single probe conducted at the conclusion of testing (Markowska et al., 1993). With regard to the latter, performance across multiple probe trials can be integrated into a composite measure of performance, termed the “spatial learning index.” This index is the sum

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FIGURE 17.2  Typical performance in the Morris water maze of F344 (top) and F344×BN-F1 rats (bottom). Young rats were tested at 6 months of age and aged were tested at 22 (F344) or 24 (F344 × BN-F1) months of age. Note that both young (blue circles) and aged rats (red circles) performed comparably on the very first trial demonstrating similar sensorimotor and motivational abilities. As shown in the large panels on the left, both age-groups learn over the course of time, but the aged rats of either strain were significantly impaired in this task in comparison to young adult rats. However, the graphs on the right show that despite an overall impairment in the aged group compared to young, there was substantial individual variability among aged rats such that some aged rats performed on par with young rats and others performed outside the range of young, demonstrating impairment on this task. Note that a higher Learning Index Score indicates poorer performance. About half of the aged rats performed outside the range of the young rats and were considered aged-impaired, and about half of the aged rats performed as well as the young rats and were considered aged-unimpaired.

of probe measures that are weighted accorded to the learning curve of young adult rats. As this measure is derived from the proximity of the rat to the escape platform, better spatial learning produces a lower index score and worse spatial learning, a higher index score. Using this measure, and comparing to strain-matched young adults at 6 months of age, robust behavioral impairment is detected within F344 rats at 22 months of age (Bizon et al., 2009) and in F344 × BN-F1 rats at 24 months of age (McQuail and Nicolle, 2015; Fig. 17.2). As mentioned earlier, performance of individual aged rats follows a broad distribution such that some exhibit performance that is within the normative range of young adults, while others show varying degrees of impaired learning. As such, the spatial learning index, and the range of individual differences one observes when using this measure, lends the aging F344 and F344 × BN-F1 rat models to sensitive correlative analyses that test for associations between hippocampal-dependent cognition and various neurobiological factors or behaviorally dissociable forms of cognition that change with aging.

Perceptual Discrimination In addition to encoding spatial locations and routes traveled in day-to-day activities, explicit memory serves the critical function of differentiating similar recurrent episodes, which may share many overlapping features. For example, on the shorter timescale, remembering whether you locked the front door as you leave the house on a given day. Or, on the longer timescale, remembering who attended a holiday party from one year to the next. This aspect of explicit memory can

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be thought of as perceptual stimulus discrimination and, such as the spatial abilities described in the previous section, is associated with hippocampal and medial temporal lobe function (Yassa and Stark, 2011; Kesner and Rolls, 2015). It is therefore not surprising that perceptual discrimination abilities are particularly vulnerable in aging. In humans, this aspect of explicit memory function can be assessed with what has previously been referred to as a “behavioral pattern separation” task (Kirwan and Stark, 2007; Stark et al., 2013; Yassa et al., 2011b) or is now known as the “mnemonic similarity task” (Stark et al., 2015; Reagh and Yassa, 2014; Huffman and Stark, 2017). In these tasks, subjects must distinguish between previously seen target images and novel lure images, which range in similarity to the known targets. Several groups have consistently demonstrated that older adults are impaired in accurately identifying lure images as novel when they are similar to targets, and that these impairments are associated with hippocampal dysfunction (Stark et al., 2013, 2015; Yassa et al., 2011b; Huffman and Stark, 2017; Toner et al., 2009; Holden et al., 2013; Reagh et al., 2016; Trelle et al., 2017). Intriguingly, in subjects who have been screened on neuropsychological test batteries such as the Hopkins Verbal Learning Test or Rey Auditory Verbal Learning Test, perceptual discrimination abilities correlate with delayed recall performance, which is among the subset of test items attributed to hippocampal explicit memory function (Stark et al., 2013). It is also worth noting that perceptual discrimination deficits can be detected in older individuals who do not show impaired delayed recall relative to younger adults, suggesting the mnemonic similarity task is particularly sensitive to changes in cognitive function that emerge with aging (Stark et al., 2013; Yassa et al., 2011b; Reagh et al., 2016). In devising tests for perceptual discrimination abilities in young and aged rodents, there are several factors to consider. First, is the sensory modality under consideration ethologically significant to rodents? Second, is detection or early processing of stimuli within the sensory modality prone to age-related decline in rodents? And finally, can the degree of feature overlap or distinctiveness of stimulus pairs be varied parametrically to test the contribution of target-lure similarity in a controlled manner? Over the past 10 years, we have validated several protocols that address these criteria, which offer sensitivity to reliably detect age-associated impairments in abilities of aged F344 and F344 × BN-F1 hybrid rats to discriminate between similar stimuli.

Olfactory Discrimination Perhaps more than any other sensory modality, rats rely on olfactory information to support ongoing behavior, for example, in foraging, navigation, social recognition, and avoiding predators (Whishaw and Kolb, 2004). This is reflected by the pronounced size of their olfactory bulbs relative to the cerebral hemispheres, and dense reciprocal connections formed by olfactory sensory areas and piriform cortex with the medial temporal lobe, particularly the perirhinal and lateral entorhinal cortices (Gottfried, 2010; Stettler and Axel, 2009; Lin et al., 2006). For these reasons, many tasks used to assess learning and memory in rodents employ olfactory stimuli. For example, as described in the previous edition, olfactory discrimination tasks can be used to assess PFC-dependent cognitive flexibility and set-shifting abilities in young and aged rats (Barense et al., 2002). Similar approaches can also be used to test discrimination abilities, in which rats are presented with two pots filled with cage bedding material, each with a distinct odorant cue swiped across the wall of the pot (e.g., peppermint, hazelnut, and citrus) (LaSarge et al., 2007). Briefly, rats learn across trials to discriminate a target odorant (S+), signaling the baited pot with buried food reward, from a lure odorant (S−), signaling the nonbaited pot. For each trial, rats can choose to dig in one of the pots. Olfactory discrimination abilities are assessed as the number of incorrect responses (i.e., choice of the lure odorant, nonbaited pot) required to achieve criterion performance of six consecutive correct trials (LaSarge et al., 2007). Using this task, it has been shown that aged, relative to young adult F344 rats, were impaired in learning olfactory discriminations. Control experiments testing aged rats’ olfactory detection thresholds confirmed the discrimination deficits were not attributable to decline of olfactory sensory processing. In this set of experiments, all rats were initially characterized on our place-learning version of the water maze task, described above, prior to proceeding in olfactory discrimination and threshold testing. Intriguingly, individual aged rats classified as spatially impaired relative to young adults showed more pronounced olfactory discrimination deficits than aged rats deemed spatially unimpaired, and correlational analyses revealed that rats with better olfactory discrimination abilities also showed better spatial memory abilities. Of particular note, aged, spatially impaired F344 rats did not improve in learning olfactory discriminations across multiple discrimination problems. Rather, they continued to require an equivalent number of trials to reach criterion, whereas young and spatially unimpaired aged rats learned faster on their second and third problems. Together, these data suggest that in cross-characterizing aged F344 rats on the spatial water maze task and olfactory discrimination task, a subpopulation of aged animals was identified that was more prone to cognitive decline and, perhaps, showing more rapid progression of hippocampal and medial temporal lobe dysfunction (LaSarge et al., 2007). To build on this finding, and address the need for parametric variation in distinctiveness of odorant stimuli, more recent olfactory perceptual discrimination tasks have employed aliphatic compounds as stimuli. With a greater difference

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FIGURE 17.3  Overview of perceptual discrimination tasks and performance of young and aged rats. Rats were tested for their ability to identify a reinforced target stimulus relative to nonreinforced “lures” of progressively similar features in olfactory (left), visual (center), and spatial (right) perceptual domains. Importantly, the physical attributes of the lure stimuli can be varied in a graded manner by the experimenter to create problems of increasing perceptual similarity by increase feature overlap (carbon chain length, object similarity, spatial proximity, etc.). When perceptual discriminations are different (i.e., target and lure are unrelated with little feature overlap), young and aged rats exhibit similar performance and high accuracy (lower panels). As feature overlap between target and lures increases, correct choices decrease in both age-groups, but this decline is greater in aged rats relative to young. Importantly, olfactory discrimination is significantly related to spatial learning ability (lower left panel). When aged rats were characterized as unimpaired or impaired on the Morris water maze before testing on olfactory discrimination problem, it is only impaired rats that show impairment in discriminating olfactory problems of moderate difficult, whereas age-matched unimpaired rats show discrimination abilities on par with young adults (compare red and purple lines to blue). Though aged rats are also impaired on object and spatial discrimination (lower center and lower right panels), these impairments are not reliably related to individual differences in spatial learning.

in carbon chain length between target and lure compounds, the odors of the two stimuli are more distinct, whereas a smaller difference in carbon chain length renders the odors more similar (Fig. 17.3). To test olfactory perceptual discrimination abilities in young and aged F344 rats, we adapted a go/no-go task for automated, liquid-dilution olfactometer operant chambers previously used in mice (Yoder et al., 2014; Gamble and Smith, 2009; Smith et al., 2008). The olfactometers comprise standard rodent-sized operant chambers with Plexiglas walls. Each chamber is fitted with its own ventilation system that maintains positive air pressure and restricts odorant stimulus delivery to a “sniffing port.” Delivery of odorant stimuli and

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behavioral protocols is controlled remotely via custom software. Rats are first shaped to insert their head in the sniffing port and keep their noses within the port to sample stimulus air for between 200 ms and 1 s, a duration that provides adequate sampling of the odorant stimuli. Rats are then trained to identify a target odorant stimulus (S+), signaling delivery of liquid reward via a metal licking tube inserted in the sniffing port, relative to a lure odorant stimulus (S−), which does not signal reward. In the go/no-go task design, four response outcomes are possible: (1) the rat holds its head in the sniffing port and licks in response to the target odorant/S+ (correct detection); (2) the rat withdraws its head and fails to lick for the target/ S+ (incorrect, “miss”); (3) the rat holds its head in the port and licks for the lure odorant/S− (incorrect, “false alarm”); and (4) the rat withdraws its head and does not lick for the lure odorant/S− (correct rejection). Olfactory discrimination performance is assessed as the proportion of correct responses (correct detections and rejections) across the 200 trials completed in each test session. With this approach, we have found discrimination accuracy in young adult F344 rats depends on the structural similarity of aliphatic compounds used in pairwise target-lure discriminations. Specifically, young rats consistently make >90% correct responses to the target odorant when lures differ by 4–5 carbon atoms but make significantly fewer correct responses (70%–80%) to the target when lures differ by only 1 carbon atom (Yoder et al., 2014). We more recently have replicated our prior finding that aged F344 rats are impaired in olfactory discrimination learning relative to their young adult counterparts (LaSarge et al., 2007; Yoder et al., 2017). Specifically, aged rats required significantly more training to reach criterion performance of ≥85% correct responses out of 200 trials in a session than young rats. However, this impairment in aged was only observed for similar target-lure pairs, which differed in length by 3 or 1 carbon atoms, and not for distinct target-lure pairs that differed in length by 5 carbon atoms (Yoder et al., 2017). This finding aligns with results from human studies that show age-related perceptual discrimination impairments are observed only when target and lure stimuli share significant feature overlap (Yassa and Stark, 2011; Stark et al., 2013, 2015; Yassa et al., 2011a,b; Huffman and Stark, 2017; Toner et al., 2009; Holden et al., 2013; Reagh et al., 2014, 2016). In rats cross-characterized on the spatial water maze task, we also replicated our prior finding that aged spatially impaired rats were most profoundly impaired in acquiring olfactory discriminations of similar odorants, while aged spatially unimpaired rats learned these discriminations within the same number of training blocks as young rats. Once again, correlational analyses revealed F344 rats with better olfactory discrimination abilities also showed better spatial memory abilities (Yoder et al., 2017). These results suggest the olfactory task offers sensitivity to age-related discrimination impairments in F344 rats that could reflect broader decline of hippocampal- and medial temporal lobe-dependent memory functions. This is supported by one other study in which lesion of the ventral dentate gyrus of young adult rats impaired olfactory discrimination of aliphatic compounds differing by 2 or 1 carbon atoms but not 4 or 3 carbon atoms (Weeden et al., 2014). Although at present no other studies have explicitly tested the role of the hippocampus in olfactory perceptual discrimination as it relates to memory, ongoing experiments suggest accurate discrimination of similar odorants coincides with activation of broad neuronal ensembles in both the hippocampus and perirhinal cortex (Johnson et al., 2016b). Furthermore, discrimination of odorant mixtures that contain overlapping components requires neural activity in the lateral entorhinal cortex and coordinated function of the olfactory bulb with piriform and entorhinal areas (Chapuis and Wilson, 2013; Chapuis et al., 2013; Wilson, 2009; Wilson et al., 2013), again implicating the medial temporal lobe in this function. Finally, a substantive literature has begun to link olfactory impairments to memory loss in humans with mild cognitive impairment, who are at greater risk for progression to AD (Devanand et al., 2010; Doty and Kamath, 2014; Graves et al., 1999; Mobley et al., 2014; Wilson et al., 2007). Given this convergent evidence, olfactory discrimination warrants further use as a tool for investigating neurobiological mechanisms of cognitive decline in aged rodents.

Object Discrimination As mentioned above, the majority of studies documenting perceptual discrimination deficits in older adult human subjects have employed images of everyday objects or equivalent complex visual stimuli (Stark et al., 2013, 2015; Yassa et al., 2011a,b; Huffman and Stark, 2017; Toner et al., 2009; Holden et al., 2013; Reagh et al., 2016; Trelle et al., 2017). Based on these findings, a rodent object discrimination task that more closely parallels the human mnemonic similarity task and its variants has recently been developed. The most common approach used to test visual recognition and discrimination in rats is through presentation of three-dimensional objects (Warburton and Brown, 2015; Burke and Barnes, 2015), as this exploits their natural tendency to explore novel items in their environment (Whishaw and Kolb, 2004). To parametrically vary the distinctiveness of objects, stimuli can be constructed from LEGO blocks, which has previously been used to manipulate feature overlap in tests of spontaneous object recognition in rats (Bartko et al., 2007a,b; Burke et al., 2011) and continuous object discrimination in monkeys (Burke et al., 2011). In this way, the shared three-dimensional volume and visible front-facing features can be increased based on the number of “pips” or “bits” that a target object and lure object have in common. A previous study using stimuli that shared between 12.5% and 92% feature overlap confirmed that in

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both young and aged macaque monkeys, the amount of training required to reach criterion performance of ≥90% correct responses was linearly related to shared feature overlap between target and lure objects (Burke et al., 2011). However, aged monkeys required a significantly greater number of training trials than young to reach criterion on the pairs with greatest feature overlap (Burke et al., 2011), which directly parallels prior findings from human subjects showing selective impairments with high target-lure similarity (Stark et al., 2013). Therefore, in a series of more recent experiments, a similar task design was used in young and aged F344 × BN-F1 hybrid rats. The rodent similarity-dependent object discrimination task is carried out on a single-armed maze, bounded by a start area at one end and a choice platform at the opposite end (Johnson et al., 2017a). The start area is an enclosed arm continuous with the main arm of the maze, while the choice platform is an open area bounded by walls of the same height, with two food wells recessed in the floor centered relative to the rat’s point of entry. Rats are first shaped to alternate between the start area and choice platform by providing food reward (a small piece of cereal) at both locations. Rats then learn procedural aspects of the continuous forced-choice discrimination task through training to discriminate a pair of standard, “junk” objects that differ in size, texture, and shape. For this pair of objects, one is always the target (S+), signaling the presence of a food reward in the well it conceals, while the other is always the lure (S−). Objects are placed over the food wells on the choice platform. The position of the target object, concealing the left versus right food well, is pseudorandomized across training trials within each session to avoid perpetuation of a side bias. Rats are trained to a criterion performance of ≥81% correct responses within a session of 32 trials, over the course of two consecutive days. After reaching criterion performance on one or two standard “junk” object pairs, rats are trained to criterion with a set of two relatively distinct LEGO objects (see Fig. 17.3). As for initial training, one object serves as the target (S+), and the other as a lure (S−). Rats are then tested for similarity-dependent discrimination abilities with the same target object, but a series of novel lure objects that range in their shared feature overlap with the target from 50% (Lure 1), to 71% (Lure 2), to 90% (Lure 3) (see Fig. 17.3). Trials with presentation of a distinct lure object (“Frog”) and an identical copy of the target object are also included as controls. These trials with identical copies of the target are particularly critical for testing that rats are not making their object choice based on their ability to smell the concealed food reward or based on scent markings they leave behind on the target object. Multiple copies (3–8) of each object are cycled in and out across trials in each test session, and all objects are thoroughly wiped down with 70% ethanol between test sessions for this reason. In each test, rats complete a total of 50 trials, with 10 trials of each type (Frog, Lure 1, Lure 2, Lure 3, identical) pseudorandomly interleaved across the session. Therefore, discrimination abilities are assessed as the proportion correct responses for each trial type in the test session. Both young and aged F344 × BN-F1 rats show a linear relationship between target-lure object feature overlap and test performance, such that discrimination accuracy decreases as feature overlap increases (Fig. 17.3). In addition, aged rats are selectively impaired in accurately distinguishing the target from Lure 2 and Lure 3 objects, which share 71% and 90% feature overlap with the target, respectively, relative to young adult rats (Johnson et al., 2017a) (Fig. 17.3). These deficits in discriminating similar objects are maintained across four consecutive, identical test sessions. However, unlike olfactory discrimination abilities in aged F344 rats, object discrimination abilities in aged F344 × BN-F1 rats did not correlate with spatial memory abilities assessed on the spatial water maze task (Johnson et al., 2017a). This is most likely because cohorts of aged rats that completed these experiments were not impaired in spatial learning or memory relative to their young adult counterparts. Nevertheless, analyses comparing the individual rats with relatively poor spatial memory with those showing relatively poor object discrimination confirmed these populations did not overlap. It is somewhat surprising that object discrimination deficits in aged rats did not correlate with the spatial memory, as it is considered an index of hippocampal-dependent function in young and aged rats (Gallagher et al., 1993; Tomás Pereira and Burwell, 2015; Redish and Touretzky, 1998), and mnemonic similarity task performance in human subjects has most consistently been linked to hippocampal activity (Yassa and Stark, 2011; Yassa et al., 2011a,b; Reagh and Yassa, 2014; Doxey and Kirwan, 2015). One possible explanation for the lack of association is that the hybrid rat strain has a longer life span than the F344 strain, thus aged F344 rats tested at 24 months of age may show advanced progression of medial temporal lobe dysfunction and memory impairments relative to aged F344 × BN-F1 hybrid rats tested at the same point in their life span. This underscores the particularly sensitivity of the similarity-dependent object discrimination task to age-related cognitive decline, as all aged hybrid rats were significantly impaired relative to young adults, despite showing no change in their spatial learning and memory abilities. Cross-characterization of the hybrid rats on other hippocampal-dependent tasks will be an important step forward in clarifying the neurobiological factors underlying object discrimination deficits. The ability to discriminate between perceptually similar objects has most often been associated with the perirhinal cortex, based on its link to object recognition across species (Burke and Barnes, 2015; Burke et al., 2012). However, as discussed above, functional neuroimaging experiments in young and aged humans have consistently identified the hippocampus, and in particular the CA3 and dentate gyrus subfields, to accurate object discrimination performance (Yassa and Stark, 2011; Kirwan and Stark, 2007; Yassa et al., 2011a,b; Reagh and Yassa, 2014; Doxey and Kirwan, 2015; Bakker et al.,

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2008; Kirwan et al., 2012). More recent studies have begun to explicitly test the requirement of hippocampal subfields in similarity-dependent object discrimination in rodent models, and results suggest these structures are necessary for accurate discrimination when stimulus pairs are similar (McTighe et al., 2009; Talpos et al., 2010, 2012), and particularly when they are novel (Johnson et al., 2017b). In standard tests of object recognition and continuous object discrimination paradigms, rats with hippocampal lesions can remain unimpaired (Forwood et al., 2005; Good et al., 2007; Lehmann et al., 2007; Mumby, 2001; Mumby et al., 1999, 2002; O’Brien et al., 2006; Winters et al., 2004; Jackson-Smith et al., 1993). In contrast, perirhinal lesions in rats impair performance on tests of object recognition (Abe et al., 2009; Ennaceur and Aggleton, 1997; Ennaceur et al., 1996; Mumby and Pinel, 1994; Winters and Bussey, 2005; McTighe et al., 2010), distinct object discrimination (Kesner et al., 2001; Winters et al., 2010), and modified spontaneous recognition in which similar objects are presented that share feature overlap (Bartko et al., 2007a,b). Indeed, many have proposed that the perirhinal cortex plays a critical role in discriminating similar complex visual stimuli (Baxter, 2009; Bussey et al., 2005; Cowell et al., 2006; Murray and Bussey, 1999; Murray et al., 2007; Suzuki and Baxter, 2009; Barense et al., 2005, 2011). While the specific neural mechanisms underlying hippocampal and perirhinal cortical contributions to distinguishing similar objects have not yet been explored, one study has associated deficits with decreased perirhinal function in aged humans (Ryan et al., 2012), and another has shown discriminating increasingly similar image stimuli correlates with neural firing patterns of perirhinal neurons in young adult rats (Ahn and Lee, 2017).

Spatial Discrimination A final domain in which the perceptual discrimination of complex stimuli has been tested in both humans and rodent models is that of spatial locations. As in experiments assessing object discrimination abilities, aged humans are also impaired relative to young in distinguishing between similar, but not distinct, locations on a two-dimensional grid (Reagh et al., 2014; Stark et al., 2010; Holden et al., 2012). This ability may relate more generally to spatial navigation abilities, as resolving similar environmental cues and landmarks would contribute to this function. However, it has recently been found older adult subjects cross-characterized on similarity-dependent object discriminations and two-dimensional spatial discriminations are consistently more impaired with object stimuli than with spatial stimuli, and object discrimination deficits are more reliably associated with delayed recall performance on neuropsychological test batteries (Reagh et al., 2016). Nonetheless, given the association of spatial abilities with hippocampal function, we sought to determine whether aged rats are impaired in distinguishing similar spatial locations, paralleling impairments in discriminating similar olfactory or three-dimensional object stimuli. One approach to testing spatial discrimination is to train rats in a modified radial-arm maze protocol, such that they learn to identify a single target arm that is adjacent to (similar) or distant from (distinct) a single distractor arm. In this paradigm, aged rats are impaired in discriminating between similar, adjacent arms, but are unimpaired in distinguishing between distinct arms on opposite sides of the apparatus (Gracian et al., 2013). An alternate approach that can be implemented is a delayed match-to-place protocol (Gilbert et al., 1998; Gilbert et al., 2001). On each trial in this type of task, rats first learn to traverse an open platform maze to retrieve a food reward at a target location, signaled by one copy of a neutral column object (sample phase). Rats then are replaced on the maze and must choose between returning to the same target location and a new lure location, both signaled by identical copies of the neutral column object (choice phase). The similarity of the target and lure locations can be parametrically varied by decreasing the distance by which they are separated (see Fig. 17.3). Rats are first trained on procedural aspects of the task by learning to accurately discriminate the target location from distant lure locations (96 or 80 cm), to a criterion of ≥81% correct responses out of 16 trials/session on two consecutive days. Spatial discrimination abilities are then assessed over five daily test sessions in which target and lure locations are separated by 88, 48, or 15 cm. Trials with each level of similarity are pseudorandomly interleaved across each session, with 6 trials at each separation to a total of 18 trials/session (Johnson et al., 2016a). Performance on the task is assessed as the proportion of correct responses for each level of similarity. As for both olfactory and object discrimination paradigms, performance of young and aged rats decreases as a function of target-lure location similarity (Fig. 17.3). Furthermore, aged rats were selectively impaired in distinguishing the target location from a lure location separated by 48 cm (Johnson et al., 2016a). As in our studies of object discrimination abilities, spatial discrimination impairments in aged F344 × BN-F1 rats did not correlate with spatial learning or memory abilities assessed on the water maze task (Johnson et al., 2016a, 2017a). However, it is intriguing to note that, as in human subjects, object discrimination impairments observed in aged rats were more pronounced than spatial discrimination impairments (Johnson et al., 2016a, 2017a) (see Fig. 17.3). In line with age-related disruptions in hippocampal neural circuitry, studies in young rats have shown the dentate gyrus is necessary to distinguishing similar or overlapping spatial locations. For example, in the same task employed by Gracian et al. (2013), young adult rats with dentate gyrus lesions were impaired in discriminating adjacent arms,

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but not arms located on opposite sides of the radial maze (Morris et al., 2012). Similarly, knockdown of dentate gyrus NMDA receptor function increased generalization of conditioned fear in young rats across multiple spatial contexts (McHugh et al., 2007). On a delayed match-to-sample task designed to parametrically assess spatial discrimination abilities, total hippocampal lesions impaired discrimination of locations separated by distances of 82.5 cm or less, but not 105 cm (Gilbert et al., 1998), and dentate gyrus-specific lesions impaired discrimination when locations were separated by distances of 60 cm or less (Gilbert et al., 2001). In contrast, CA3 lesions impaired discrimination of locations across all spatial separations (15–105 cm), suggesting rats were simply unable to remember the target location (Gilbert and Kesner, 2006), while lesions specific to CA1 were without effect (Gilbert et al., 2001). Collectively, these findings support the requirement of hippocampus for discriminating similar spatial locations and point to hippocampal dysfunction as a potential basis for these deficits observed in aged rats, though the specific neural mechanisms contributing to this dysfunction have not yet been explicitly tested.

AGING AND THE PREFRONTAL CORTEX Though not as extensively studied as the medial temporal lobe, the PFC is also susceptible to decline with normal aging. The PFC is essential for a variety of behaviors categorically defined as “executive functions.” This term encompasses processes that support goal-directed behavior, inhibitory control, adaptive modification, and decision-making. While anatomical parallels between the human and rodent PFC remain an area of continuing debate, behavioral evidence consistently demonstrates that the rat medial PFC (mPFC) is functionally homologous to the primate dorsolateral PFC (dlPFC). The mPFC is comprised of the anterior cingulate (AC), prelimbic (PL) and infralimbic (IL) cortices (Fig. 17.4). These subregions receive afferents from diverse cortical and subcortical regions; dorsal mPFC (mainly the AC) predominantly receives input from sensory and motor cortices whereas the ventral mPFC (PL and IL) receive thalamic and limbic inputs (Hoover and Vertes, 2007). In turn, these brain regions project to motor, limbic and autonomic centers (Gabbott et al., 2005) with the PL subserving more limbic-cognitive functions and the IL controlling visceral and autonomic output (Vertes, 2004). Our laboratories have developed a battery of food-motivated behavioral tasks for the rat that evaluate discrete components of PFC-dependent cognitive processes, including working memory, cognitive flexibility, and decision-making. Each of these tasks is conducted in standardized, computer-controlled operant testing chambers equipped with cue lights, response levers, and food pellet dispensers. This arrangement greatly reduces interexperimenter variability and eliminates or minimizes confounding influences from hippocampus and medial temporal lobe as in other tasks that evaluate behavior in spatial settings (e.g., radial-arm maze or Y-maze). Significantly, work from our group and others has verified that permanent lesion or pharmacological inactivation of the mPFC impairs behavioral performance across tasks, further supporting that this brain region is a critical neural substrate for executive functions in the rat (Sloan et al., 2006; Floresco et al., 2008; Churchwell et al., 2009).

FIGURE 17.4  Schematic showing the dorsolateral prefrontal cortex in human (top left) brain and the medial prefrontal cortex of rat brain (top right). In both instances, these regions are shades in purple. The bottom panel shows a coronal section through rat brain in which medial prefrontal cortex is subdivided into constituent subregions; anterior cingulate cortex (AC), prelimbic cortex (PL), and infralimbic cortex (IL).

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Working Memory Using F344 rats, we model working memory, or the ability to briefly maintain information to guide ongoing, goal-directed behavior (Baddeley, 1986), using the delayed response task (Dunnett, 1985; alternatively termed “delayed match-to-­sample” task, see Fig. 17.5). In this task, each trial is comprised of three phases. In the sample phase, a single lever (either left or right, pseudorandomly varied between trials) is inserted into the chamber for the rat to press. The lever is then retracted, initiating the variable duration delay phase that ranges between 0 and 24 s (delay durations are pseudorandomly varied within each block of trials). Throughout delay, rats must nose-poke at the central food trough. This requirement eliminates the possibility that rats use nonmnemonic mediating strategies (i.e., positioning before the sample lever) to guide performance following the delay. The first nose-poke after the expiration of the delay timer triggers insertion of both the left and right levers for the choice phase of the trial. In this phase, the correct response is to press the same lever that was presented in the sample phase (i.e., “match to sample”) and results in delivery of a food pellet. Pressing the other lever is an incorrect choice and is not reinforced. The task is self-paced, i.e., rats cannot omit or skip trials, and rats typically complete greater than 100 trials per 40-min test session offering ample opportunity to repeatedly sample performance across a range of delay intervals. When delays are absent or brief (i.e., 0–4 s), “choice accuracy,” or the percentage of correct choices, is high, generally greater than 90% in both young adults and older rats. These data indicate rats of either age can effectively follow procedural requirements of the task. However, increasing the duration of delay between the sample and choice phases significantly erodes performance, even in young adult rats. This delay-dependent decrement is evidence that this task is taxing to accurate maintenance of information in working memory stores. Significantly, aged rats show a greater delay-dependent

FIGURE 17.5  Age-related impairment of working memory in the F344 rat. Working memory is evaluated using a delayed response task (top left panel, see associated text for details). Choice accuracy is reduce with increased delay before the choice phase, but this decrement in greater in aged rats compared to young (top center panel). Despite the pronounced group differences, it is important to note that the range of performance of individual rats spans a broad range including overlapping populations of young and aged rats with similar levels of working memory accuracy (top right). These individual differences are leveraged to determine that better working memory is associated with greater expression of NR2A in the aged PFC (bottom left panel) but lower expression of GABA(B)R subunits (bottom center and right panels).

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impairment of performance relative to young adults, consistent with the notion that aging reduces the capacity to retain information in working memory stores (Beas et al., 2013; Bañuelos et al., 2014; McQuail et al., 2015, 2016; Fig. 17.5). Working memory is theorized to depend on persistent firing within recurrent networks of glutamatergic pyramidal neurons; the ability to sustain activity in the absence of bottom-up sensory-driven input is thought to encode information in working memory (Goldman-Rakic, 1995). Coordinated actions of local GABAergic interneurons refine stimulus tuning of PFC microcircuits and minimize influence from distracting stimuli (Wang et al., 2004). Aging increases the relative level of inhibitory activity on mPFC pyramidal neurons. Specifically, there is an increase in the frequency of inhibitory postsynaptic currents on aged pyramidal neurons in the mPFC as well as enhanced GABAergic tone mediated by GABA(B) receptors (GABA(B)R; Bañuelos et al., 2014; Bories et al., 2013). Concurrently, there is a significant age-associated reduction in expression of NMDA receptor (NMDAR) subunits in the mPFC (McQuail et al., 2016) Loss of NMDARs may be especially problematic as their slow channel kinetics are more favorable to support sustained neural activity relative to other glutamate receptor subtypes (i.e., AMPA receptors; Wang et al., 2013). Collectively, this evidence suggests that the aged mPFC is less excitable with age but diverse changes to NMDARs and GABA(B)Rs could differentiate successful from unsuccessful cognitive aging. This molecular evidence, in turn, informs new strategies to normalize PFC pyramidal neuron activity and restore working memory in aging rats. Though NR1-, NR2A-, and NR2B-NMDAR subunits are highly expressed in the mPFC, it is specifically loss of the NR2A subunit that correlates with severity of age-related working memory impairment (McQuail et al., 2016; Fig. 17.5). Restoring the function of this receptor by potentiating residual NR2A-NMDARs to enhance working memory poses a significant challenge as few ligands can selectively target NR2A without also interacting with the NR2B subunit. In the absence of truly selective ligands, other approaches that can differentiate NR2A-containing NMDARs from those that contain an NR2B subunit are needed. NR2A is predominantly anchored within the postsynaptic density and directly associated with the synaptic scaffold PSD-95 (McQuail et al., 2016; Groc et al., 2006). Conversely, NR2B is a relatively mobile subunit that trafficks to extrasynaptic sites (Groc et al., 2006). Regardless of their location, NMDARs require binding of a coagonist, either serine or glycine, in addition to glutamate for receptor activation. Astrocytic transporters effectively exclude glycine from the synaptic cleft, leaving serine to function as the sole coagonist of synaptic NMDARs (Papouin et al., 2012). These findings suggest that inhibiting degradation of serine and prolonging its actions in the synaptic cleft could provide a means to target synaptically enriched NR2A-NMDARs. Exposing aged mPFC pyramidal neurons to an inhibitor of d-amino acid oxidase, the enzyme that degrades serine, increases charge transfer through NR2A-NMDARs (McQuail et al., 2016). Further, directly infusing this inhibitor into the mPFC of aged rats performing the delayed response task produces a significant improvement of working memory accuracy (McQuail et al., 2016). Collectively, these findings are consistent with an indispensable role for NMDARs in working memory and further specify that enhancing activity of NR2A-NMDARs is a promising route to rescue impaired working memory. Whereas enhancing NMDAR signaling presents one strategy to rescue diminished pyramidal neuron excitability and impaired working memory in aging, blocking excessive GABA signaling transduced by GABA(B)Rs also shows promise in ameliorating age-related cognitive decline. Indeed, despite an overall, age-related reduction in expression of GABA(B)R subunits, individual aged rats that show the lowest expression of GABA(B)R1b and GABA(B)R2, the GABA(B)R subunits that localize to dendrites of pyramidal neurons, show the most accurate working memory performance (Bañuelos et al., 2014; Fig. 17.5). This suggests that downregulation of GABA(B)Rs is an adaptive phenotype that possibly reduces the deleterious consequences of age-related increases in extracellular GABA on pyramidal neuron excitability. In support of this hypothesis, either systemic administration or intra-mPFC infusion of a GABA(B)R antagonist reverses working memory in aged rats (Bañuelos et al., 2014). This effect appears to relate specifically to changes in mPFC excitability with age as infusing the same drug into the young adult mPFC is without effect on working memory performance. In all, coordinated contributions from NMDARs and GABA(B)Rs contribute to normal working memory function and aging is associated with a net reduction of mPFC pyramidal neuron excitability that impairs working memory. Therapies that potentiate residual NMDARs or block excess GABA tone transduced via GABA(B)Rs can correct this imbalance to promote normal neural excitation and reverse age-related impairment of working memory.

Cognitive Flexibility Cognitive flexibility can be evaluated by an automated version of the set-shift task that presents multidimensional stimuli and evaluates the capacity of the rat to flexibly modify its response strategy following an unsignaled shift in the response-reinforcement contingency (Floresco et al., 2008; Fig. 17.6). In the first phase of the task, rats are trained to attend to a cue light that is illuminated above either the left or right lever (pseudorandomly varied between trials). The correct response is a press on the lever beneath the illuminated cue light. Rule learning is measured as the number of

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FIGURE 17.6  Impaired cognitive flexibility in the aged F344 rat. Cognitive flexibility is evaluated using a set-shifting task (top panel, see associated text for details). Though young and aged rats can acquire the initial discrimination rule in a similar number of trials, aged rats are impaired at shifting their response strategy to conform to the new rule, but as with other tasks, a broad range of individual differences in performance in evident (bottom left panel). Better set-shifting abilities are associated with greater expression of GABA(B)R1b subunits in the aged PFC (bottom right panel; note that the x-axis is plotted in reverse as fewer trials to criterion is evidence of better performance).

trials needed to reach a criterion of 10 consecutive correct trials or “trials to criterion.” On reaching criterion, the rule is covertly shifted to reinforce lever presses on a single lever (left or right, counterbalanced between rats) without respect to the location of the illuminated cue light. In other words, the rat must learn to shift, or modify, its response strategy to respond to the location of the lever, rather than the illuminated cue light. Once again, the measure of interest is the number trials required to a reach criterion of 10 consecutive correct trials. When young adult and aged rats are trained on this task, both age-groups require a similar number of trials to learn the initial rule but aged rats need significantly more trials than young adult rats to adapt their response pattern following the rule switch (Beas et al., 2013, 2017; Fig. 17.6). An analysis of errors made in the course of learning the new rule reveals that aged rats are more likely to persist in using the previously learned rule, consistent with the explanation that age-related deficits in cognitive flexibility are due to perseveration. Cognitive flexibility is highly sensitive to changes in prefrontal GABAergic signaling. Indeed, experimental perturbation of interneuron development produces cognitive rigidity in rodents (Jacobson et al., 2006; Bissonette et al., 2012, 2014; Cho et al., 2015) and human neuropsychiatric disorders that present with deficits in cognitive flexibility cooccur with prefrontal interneuron dysfunction (Everett et al., 2001; Hashimoto et al., 2007; Maldonado-Avilés et al., 2009; GonzalezBurgos et al., 2011; Lewis et al., 2012). Consistent with an essential role for GABA to support cognitive flexibility, agerelated loss of GABA(B)R in the mPFC is strongly correlated with severity of set-shifting impairment (Fig. 17.6) and age-dependent set-shift impairment is reversed by either systemic administration or intra-mPFC infusion of a GABA(B)R agonist (Beas et al., 2017). Importantly, GABA(B)R agonists specifically facilitate the ability to switch from using the initial rule to the new rule as control studies in young rats reveal that this drug does not alter discrimination abilities or impair retention of the initial rule (Beas et al., 2016). It is also interesting to note that, whereas there is a negative relationship between GABA(B)R expression and working memory in aging rats, there is a positive relationship between GABA(B)R expression and set-shifting. Further, a GABA(B)R antagonist improves working memory in aging, whereas a GABA(B)R

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FIGURE 17.7  Attenuated delay discounting in aged F344 rats. Decision-making is measured using the delay-discounting task (left panel, see associated text for details). As the delay to receive the large reward is increased, young rats robustly shift their choice strategy to favor smaller immediate rewards. However, aged rats do not robustly discount the value of the larger reward as delay increases, revealing that aging attenuates impulsive choice (right panel).

agonist improves set-shifting. Collectively, these findings reveal that aging produces diverse changes in cortical inhibition that have distinct consequences for particular subtypes of PFC-dependent cognition.

Decision-Making Operant decision-making tasks present rats the opportunity to choose between rewards of varied value (e.g., number of food pellets) that are associated with different costs (e.g., delay to receive rewards, risk of reward omission or risk of footshock). In the delay-discounting task (or intertemporal choice task), rats are presented with two levers (Fig. 17.7). The one lever always delivers a single pellet immediately after the lever press. Pressing the other lever will always deliver four pellets, but the delay to receive these pellets is systematically increased from 0 to 60 s over the course of the testing session. Rats reliably choose the lever associated with the larger reward nearly 100% of the time when no delay is imposed between lever choice and reward delivery. However, as the delay to receive the larger reward increases, young adult rats shift their preference toward choosing the lever that delivers the smaller, immediate reward (Simon et al., 2010). In other words, young adults progressively discount the value of the larger reward relative to the increasing delay. When aged rats are tested on this task, the discounting slope is significantly attenuated compared to young adults; aged rats continue to prefer the larger reward despite the increasing delay duration (Simon et al., 2010; Fig. 17.7). Importantly, control measures verify that aged rats can discriminate reward magnitudes and perceive delays as effectively as young adults. Further, when the order of delays is reversed, aged rats shift their choice behavior to favor larger reward as delay decreases, strongly suggesting that attenuated discounting of the large reward does not reflect a perseverative pattern of responding established in the first block of trials. At this time, there is sparse understanding of how aging modulates the neural circuitry involved in decision-making. Until recently, it was assumed that reduced impulsive choice observed in older humans comes about as a function of greater life experience (i.e., “wisdom”; Green et al., 1996, 1999). However, the fact that aged rats reared in closely controlled environments with no relevant prior experiences also show attenuated impulsive choice leads us to conclude that the aging process itself modifies the neural circuits that mediate decision-making. Future studies that explore the neurobiological mechanisms that contribute to this phenotype will be foundational and informational to our understanding of a variety of conditions associated with changes impulsivity and decision-making.

CONCLUDING REMARKS Although behavioral assessment in rodent models often investigates the underlying contributions of individual brain regions, and the overview provided in this chapter delineates tasks as such, all higher cognitive functions require orchestrated interactions across the medial temporal lobes and prefrontal cortices. In support, recent studies have emphasized the importance of interactions among the hippocampus, perirhinal, and prefrontal cortices for encoding and retrieving object-place associations in rodents (Jo and Lee, 2010; Hernandez et al., 2017; Barker and Warburton, 2015). It is therefore noteworthy that

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aged F344 × BN-F1 rats are impaired in acquiring object-place associations, relative to young adult rats (Hernandez et al., 2015). While structures in the medial temporal lobes and prefrontal cortices are among the brain regions most vulnerable to advanced age, the neurobiological manifestations of aging differ across these areas and future research will be needed to determine how systems-level interactions among these circuits change over the life span. In fact, no consensus has been reached regarding the impact of aging on system-level network interactions. One theory contends that variations in cognitive performance among aged individuals reflect differences in neural maintenance (Nyberg et al., 2012). In this view, successfully aging animals have intact neural systems, resembling those of young, with no dysfunction in any of the structures that comprise the network. Neural maintenance predicts that animals without interregion-dependent task impairments have normal function across the brain. A second theory proposes that the range of performance across aged individuals is due to differences in the ability to compensate on the network level when one brain area is compromised (Cabeza et al., 2002; Davis et al., 2008). This neural compensation view would predict that dysfunction in one brain region could initiate altered network connectivity in associated structures to promote better behavioral outcomes. As new technology becomes available for manipulating large-scale neural circuits, future research will be able to define how age-related alterations within one brain region impacts system-level interactions to aggregate impairments across networks or provoke compensation. The rodent behavioral models discussed herein will undoubtedly prove useful in advancing this goal and will facilitate the design of novel therapeutic strategies for enhancing cognitive function in advanced age.

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Papouin, T., Ladépêche, L., Ruel, J., Sacchi, S., Labasque, M., Hanini, M., et al., August 3, 2012. Synaptic and extrasynaptic NMDA receptors are gated by different endogenous coagonists. Cell 150 (3), 633–646. Plassman, B.L., Langa, K.M., Fisher, G.G., Heeringa, S.G., Weir, D.R., Ofstedal, M.B., et al., 2007. Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology 29 (1–2), 125–132. Reagh, Z.M., Yassa, M.A., October 7, 2014. Object and spatial mnemonic interference differentially engage lateral and medial entorhinal cortex in humans. Proc Natl Acad Sci USA 111 (40), E4264–E4273. Reagh, Z.M., Roberts, J.M., Ly, M., DiProspero, N., Murray, E., Yassa, M.A., March 2014. Spatial discrimination deficits as a function of mnemonic interference in aged adults with and without memory impairment. Hippocampus 24 (3), 303–314. Reagh, Z.M., Ho, H.D., Leal, S.L., Noche, J.A., Chun, A., Murray, E.A., et al., April 2016. 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West, L.A., Cole, S., Goodkind, D., He, W., 2014. 65+ in the United States: 2010. US Census Bur Spec Stud (Internet). Available from: https://www.census.gov/content/dam/Census/library/publications/2014/demo/p23-212.pdf. Whishaw, I.Q., Kolb, B., 2004. The Behavior of the Laboratory Rat. Oxford University Press, New York. 520 p. (Internet). Available from: http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780195162851.001.0001/acprof-9780195162851. Wilson, R.S., Schneider, J.A., Arnold, S.E., Tang, Y., Boyle, P.A., Bennett, D.A., July 2007. Olfactory identification and incidence of mild cognitive impairment in older age. Arch Gen Psychiatry 64 (7), 802–808. Wilson, D.I.G., Watanabe, S., Milner, H., Ainge, J.A., December 2013. Lateral entorhinal cortex is necessary for associative but not nonassociative recognition memory. Hippocampus 23 (12), 1280–1290. Wilson, D.A., July 2009. Pattern separation and completion in olfaction. Ann NY Acad Sci 1170 (1), 306–312. Winters, B.D., Bussey, T.J., January 5, 2005. Transient inactivation of perirhinal cortex disrupts encoding, retrieval, and consolidation of object recognition memory. J Neurosci 25 (1), 52–61. Winters, B.D., Forwood, S.E., Cowell, R.A., Saksida, L.M., Bussey, T.J., June 30, 2004. Double dissociation between the effects of peri-postrhinal cortex and hippocampal lesions on tests of object recognition and spatial memory: heterogeneity of function within the temporal lobe. J Neurosci 24 (26), 5901–5908. Winters, B.D., Bartko, S.J., Saksida, L.M., Bussey, T.J., February 2010. Muscimol, AP5, or scopolamine infused into perirhinal cortex impairs two-choice visual discrimination learning in rats. Neurobiol Learn Mem 93 (2), 221–228. Yassa, M.A., Stark, C.E.L., October 2011. Pattern separation in the hippocampus. Trends Neurosci 34 (10), 515–525. Yassa, M.A., Mattfeld, A.T., Stark, S.M., Stark, C.E.L., May 24, 2011a. Age-related memory deficits linked to circuit-specific disruptions in the hippocampus. Proc Natl Acad Sci USA 108 (21), 8873–8878. Yassa, M.A., Lacy, J.W., Stark, S.M., Albert, M.S., Gallagher, M., Stark, C.E.L., September 2011b. Pattern separation deficits associated with increased hippocampal CA3 and dentate gyrus activity in nondemented older adults. Hippocampus 21 (9), 968–979. Yoder, W.M., Setlow, B., Bizon, J.L., Smith, D.W., May 1, 2014. Characterizing olfactory perceptual similarity using carbon chain discrimination in Fischer 344 rats. Chem Senses 39 (4), 323–331. Yoder, W.M., Gaynor, L.S., Burke, S.N., Setlow, B., Smith, D.W., Bizon, J.L., February 7, 2017. Interaction between age and perceptual similarity in olfactory discrimination learning in F344 rats: relationships with spatial learning. Neurobiol Aging 53, 122–137.

Chapter 18

Life Extension in Dwarf Mice Andrzej Bartke, Justin Darcy, Rong Yuan Southern Illinois University School of Medicine, Springfield, IL, United States

INTRODUCTION The realization that growth hormone (GH), insulin-like growth factor 1 (IGF1), and insulin play important roles in the control of mammalian aging is a relatively recent development. In 1996, it was reported that hypopituitary Ames dwarf mice live significantly longer than their normal siblings (Brown-Borg et al., 1996). These animals have primary deficiency of GH, prolactin (PRL), and thyroid-stimulating hormone (TSH) with secondary suppression of circulating levels of IGF1, insulin, and thyroid hormones (details and references in the next section of this chapter). Soon afterward, Kimura et al. (1997) demonstrated that mammalian IGF1 receptor (IGF1R) and insulin receptor (IR) genes exhibit significant homology to the daf-2 gene in a round worm, Caenorhabditis elegans, a key gene in the signaling pathway that controls aging and longevity in this species. Research results reported during the next few years provided evidence for prolonged longevity in another mouse mutant with deficiencies in GH, PRL, and TSH, and in mice with various spontaneous or induced mutations affecting only the somatotropic (GH/IGF1) axis. Moreover, several genes involved in the control of aging in yeast, C. elegans and the fruit fly, Drosophila melanogaster, were shown to be homologous to genes related to IGF1 and insulin signaling in mammals (reviews in Guarente and Kenyon, 2000; Tatar et al., 2003). The picture that emerges from these studies is that an ancient pathway, widely conserved during evolution, is involved in the control of aging in organisms from distant taxonomic groups, and that in mammals this pathway includes IGF1 and insulin, their receptors, and proteins that become activated after these receptors bind the corresponding ligands. GH, which is produced in vertebrates and apparently has no counterpart in insects, stimulates IGF1 biosynthesis, promotes insulin resistance, and has a variety of other actions potentially related to aging. In this chapter, we will list key characteristics of mice in which mutations of single genes related to GH synthesis or action are associated with significant extension of longevity. We will also review mechanisms, which are suspected of mediating the effects of altered somatotropic signaling on aging and longevity, and briefly discuss controversies surrounding the relationship of GH to aging. Finally, we will provide some information on the husbandry and breeding of long-lived mutant dwarf mice.

ORIGIN AND CHARACTERISTICS OF SNELL DWARF AND AMES DWARF MICE In laboratory stocks of house mice (Mus musculus), several spontaneous mutations cause hereditary dwarfism. The first mutation with a major impact on growth and adult body size was described by George Snell over 80 years ago (Snell, 1929). This mutation was originally named dwarf, genetic symbol dw, but subsequently was referred to by various names including Snell–Bagg dwarf mice and Snell–Smith mice and is now known as Snell dwarf, Snell dwarf mouse, or Snell dwarf mutant of the pituitary factor1 (Pit1) gene, Pit1dw. Snell dwarfism is due to a recessive autosomal mutation of the Pit1 gene located on chromosome 16 that results in a Trp to Cys conversion in the POU-homeodomain (Li et al., 1990). Animals homozygous (Pit1dw/dw) for this mutation appear to be normal at birth, but their growth soon begins to lag behind their normal (Pit1+/+) and heterozygous (Pit1dw/+) siblings and they reach approximately 1/3 of the adult body weight of normal mice. Early studies of Snell dwarf mice demonstrated that these animals lack GH- and TSH-producing cells in the pituitary, are GH deficient and hypothyroid, and readily grow in response to exogenous GH (Bartke, 1979a, reviewed in Grüneberg, 1952). Reciprocal transplants of anterior pituitaries between dwarf and normal animals provided clear evidence that hormonal deficits and reduced growth in these animals are due to inherited anomalies in the pituitary rather than altered hypothalamic control of pituitary function (Carsner and Rennels, 1960). Subsequent studies resulted in the demonstration that in addition to deficiency of GH and TSH, Snell dwarf mice also lack PRL (Bartke, 1965, reviewed in Bartke, 1979b). Homozygous expression of Pit1dw/J (mutant allele carried in Jackson dwarf mouse), a mutation that independently arose at the same (Pit1) locus, produces the same phenotype as Snell dwarfism (Eicher and Beamer, 1980). Compound heterozygotes Pit1dw/Pit1dw/J are dwarf, do not seem to differ from homozygous Pit1dw mice, and have been used in research on aging (Flurkey et al., 2001). Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00018-X Copyright © 2018 Elsevier Inc. All rights reserved.

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Another autosomal recessive mutation producing dwarfism in the mouse was described in 1961 by Schaible and Gowen. It was shown to be genetically unrelated to Snell dwarfism and located on a different chromosome and was named Ames dwarf, genetic symbol df. After cloning of the affected gene on chromosome 11 and observing the mutation was a Ser to Pro conversion in the α1 helix of the PROP1-homeodomain, with a df defect evident at embryonic day 13, Ames dwarf also became known as a mutant of the prophet of Pit1 (Prop1), Prop1df (Sornson et al., 1996). Mice homozygous for the Ames dwarf mutation are also GH, TSH, and PRL deficient (Bartke, 1979a, 1979b). Although the primary hormonal deficits and resulting phenotype of Snell and Ames dwarf mice appear to be identical, detailed comparisons of the effects of these mutations expressed on the same genetic background have not been made, and subtle differences may exist. For example, the time course of postnatal changes in the number of hypothalamic (tuberoinfundibular) dopaminergic neurons was shown to differ between Snell and Ames dwarf mice (Phelps, 2004).

LONGEVITY OF DWARF MICE In the past 20 years, accumulating evidence has shown that Ames and Snell dwarf mice have extended longevity. However, one of the first studies to address this issue concluded that Snell dwarf mice are extremely short-lived because they are immunodeficient and succumb to infectious diseases at a very early age (Fabris et al., 1972). These findings must have been unique to this particular population or to the husbandry conditions because shortly afterward other investigators working with Snell dwarf mice reported that they did not encounter early mortality of the dwarfs (Shire, 1973; Schneider, 1976). However, these reports did not provide any information on the average or maximal longevity of the dwarf mice. In contrast to the study of Fabris et al. (1972), in a paper published in the same year, Silberberg (1972) referred to Snell dwarf mice as having an “unusually long life span” and reported data from dwarfs that were killed at ages ranging up to 41 months. Although no data on longevity or survival plots were provided or referenced, this was the first mention of extended longevity of these mutants. Moreover, this report included important evidence for delayed aging of joint cartilage in Snell dwarf as compared to normal mice, as well as absence of osteoarthrosis in the dwarf (Silberberg, 1972). In 1996, Brown-Borg et al. reported that Ames dwarf mice live significantly longer than their normal siblings with the increase in average life span being over 45% in males and over 60% in females. The survival plot included in this report suggests that the maximal life span of Ames dwarf mice was similarly increased and that there was a delayed onset of agerelated mortality in the dwarfs with little change in the slope of the survival curve. Extended longevity of Ames dwarf, compared to normal mice, was replicated in subsequent studies and in animals fed different diets (Bartke et al., 2001b, 2004). Flurkey et al. (2001) reported significantly extended longevity of Snell dwarf mice with an increase of average life span of 42%. The same report contained important evidence for delayed aging of the immune system and collagen in Snell dwarf as compared to normal mice (Flurkey et al., 2001). The conclusion that the biological process of aging is delayed and/or slower in both Snell and Ames dwarf mice compared to normal animals received further support from the studies of cognitive function (Kinney et al., 2001) and incidence and severity of neoplastic and nonneoplastic disease (Ikeno et al., 2003; Vegara, 2004) in these animals. Extension of both the average and the maximal life span in these mutants is also consistent with this conclusion. Thus, prolonged longevity of Snell dwarf and Ames dwarf is associated with a major extension of “health span” and multiple symptoms of delayed aging (Arum et al., 2014a, 2014b). One disadvantage to working with Ames and Snell dwarf mice is that their mutations result in disruptions of several hormonal axes. Because of this, the hypothesis that lack of GH is the key mediator of longevity in these mutants has been questioned, despite the overwhelming evidence from other GH/IGF1 deficient mouse strains, which are discussed later in this chapter. To further support GH as the main mediator of longevity in these mice, Panici et al. (2010) treated Ames dwarf mice with GH for 6 weeks during their early postnatal period and observed a significant decrease in longevity. Studies in Snell and Ames dwarf mice provided clear evidence that, similar to what was discovered earlier in yeast, worms, and flies (Guarente and Kenyon, 2000; Tatar et al., 2003), mutations of single genes can delay aging and significantly increase life expectancy in mammals. Similar extension of average and maximal longevity in two types of dwarfism, which are due to mutations of different genes located on different chromosomes but result in essentially identical defects in endocrine function, indicates that prolonged longevity is due to these endocrine deficits rather than to some other unknown effects of Prop1 and/or Pit1 mutations. Results obtained in mice with mutations or targeted disruptions of genes related specifically to somatotropic signaling suggest that deficiency of GH is an important and probably the main cause of prolonged longevity of Ames and Snell dwarf mice. These results are discussed below.

GROWTH HORMONE RECEPTOR KNOCKOUT MICE The first animal model of GH resistance was produced by Zhou et al. (1997) by targeted disruption of the GH receptor (GHR)/GH-binding protein (GHBP) gene in the mouse. Disruption of the GHR/GHBP gene was accomplished by homologous recombination, replacing a region of 500 base pairs containing the 3′ portion of exon 4, and 5′ portion of intron 4/5

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with a neo cassette. GHR/GHBP knockout mice (hereafter referred to as GHRKO) lack functional GHR, thus exhibiting GH resistance, which is manifested by a dramatic decrease in circulating IGF1 levels, reduced growth, and an approximately 60% reduction in adult body weight. GHRKO mice are of nearly normal size at birth, exhibit profound growth deficits starting before weaning, but thrive under standard laboratory conditions and almost all of them are fertile. Plasma GH levels are elevated presumably due to lack of negative GH feedback. In addition to reduced IGF1 and diminutive size, GHRKO mice exhibit greatly reduced insulin levels, small reduction in circulating levels of thyroxine and 3′,3,5-­triiodothyronine, hyperprolactinemia, reduced levels of follicle stimulating hormone, delayed puberty, and various quantitative deficits in reproductive function (Zhou et al., 1997; Hauck et al., 2001; Chandrashekar et al., 2004). In 2000, Coschigano et al. reported that the GHRKO (−/−) mice live significantly longer than both normal (+/+) and heterozygous (+/−) siblings from the same population. The increase in average life span ranged from 38% to 55%, depending on the gender and genotype of the control animals (+/+ or +/−) (Coschigano et al., 2000). The observation of prolonged longevity of the GHRKO as compared to normal mice was replicated in subsequent studies of the same author (Coschigano et al., 2003) as well as in another laboratory in animals with different genetic background (Bartke et al., 2002). Importantly, Coschigano et al. (2003) reported significantly extended life span of GHRKO mice backcrossed to the long-living C57BL/6 strain. Studies of cognitive function (Kinney et al., 2001; Kinney-Forshee et al., 2004) and end-of-life pathology in GHRKO mice (Ikeno et al., 2009) suggested that these animals experience delayed aging, similar to Snell and Ames dwarf mice. Suppressed tumorigenesis has been suggested as a major reason for the extended longevity of Ames dwarf mice, Snell dwarf mice, and GHRKO mice. Because the genetic backgrounds of these mice are different, tumor incidences cannot be compared directly; however, comparison to wild-type (WT) littermates provides clues that may be useful to identify mechanisms related to the antitumorigenesis. The tumor incidence in Ames dwarf mice and their WT littermates is above 90%, with a similar number of tumors per mouse (Ikeno et al., 2003). However, when compared to WT mice, fatal tumors occur at an older age in Ames dwarf mice. Interestingly, GHRKO mice show a 22% reduction in tumor incidence and 47.5% less of a tumor burden, along with significantly delayed fatal tumors, when compared to WT littermates (Ikeno et al., 2009). These two studies were conducted by the same group of researchers, who concluded that the pathological profile of GHRKO mice is similar to the effects of calorie restriction on age-related pathology (Ikeno et al., 2009). Therefore, the reduced action of the GH/IGF1 axis, and the subsequent pathophysiological changes, including reduced occurrence and delayed onset of tumors, could be a major underlying mechanism of the extended longevity observed in GH-deficient and GH-resistant mice. It should also be mentioned that Snell dwarf mice have a significantly reduced tumor incidence compared to their WT littermates (18% vs. 82%) (Vegara, 2004). However, the sample size used in this study (11 Snell dwarf mice) was very small.

OTHER MUTATIONS AFFECTING THE SOMATOTROPIC AXIS Evidence for the prolonged longevity observed in mice with reduced activity of the somatotropic axis is not limited to results obtained in GHRKO, Snell dwarf and Ames dwarf mice. Sun et al. (2013) reported that mice with knockout of growth hormone releasing hormone (GHRH) live 46% longer than their WT littermates. GHRH knockout (GHRHKO) mice have significantly reduced body weight, increased fat percentage, decreased circulating IGF1 levels, and elevated adiponectin. Interestingly, microarray analysis of the gene expression in liver revealed altered expression of genes related to xenobiotic detoxification, stress resistance, and insulin signaling (Sun et al., 2013). Flurkey et al. (2001) reported that “little” mice, with a mutation of the GHRH receptor (GHRHR), genetic symbol lit, and the resulting GH deficiency live approximately 25% longer than normal mice if fed a low-fat diet. “Little” (Ghrhrlit) mice exhibit profound, although not complete, suppression of circulating GH levels and a reduced rate of postnatal growth, with the body weight of young adults being reduced to about 50% of normal. Afterward, this difference in body weight gradually diminishes due to a combination of slow growth and progressive development of obesity in little mice. If this mutation is maintained on a C57BL/6 background, and the animals are fed a standard diet that contains 7% fat, the obesity of “little” mice is very pronounced, and their life span tends to be comparable to that of normal mice. Reducing fat content of the diet to 4% prevents extreme obesity of these mutants and uncovers their propensity to outlive their normal siblings (Flurkey et al., 2001). Targeted disruption of IGF1 or IGF1R genes produces animals with profoundly suppressed fetal growth, low birth weight, and high perinatal mortality. However, animals heterozygous for the “knockout” of the IGF1R gene (IGF1R+/−) exhibit a fairly small reduction in postnatal growth, do not suffer health deficits as a result of the knockout, are fertile, and have increased resistance to the lethal oxidative stress-related effects of paraquat. Female IGF1R+/− mice live longer than normal females from the same stock (Holzenberger et al., 2003; Bokov et al., 2009), but males do not live longer. A 50% reduction in the levels of IGF1R in various organs of IGF1R+/− mice suggests that these animals are partially resistant to IGF1. Importantly, these findings suggest IGF1 signaling is involved in the control of mammalian longevity, and demonstrate that a modest reduction of somatotropic signaling that does not lead to overt dwarfism or compromised fertility can be sufficient to increase stress resistance, slow down aging, and increase female life span. Although the role of IGF1 in the control of aging

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was strongly suggested by the results obtained in GHRKO, GHRHKO, “little,” and hypopituitary dwarf mice, the potential role of GH actions not mediated by IGF1 could not be evaluated in these animals. Thus, work with IGF1R+/− mice stands out as the first report that specifically associates reduced IGF1 signaling with extended longevity in a mammal. This association is supported by the observation in 31 mouse-inbred strains that plasma IGF1 levels negatively correlated with longevity (P 140 animals per group). Thus, it currently appears that metformin might have a small but significant effect on life span that might vary depending on the mouse model studied.

Resveratrol Resveratrol is polyphenolic flavonoid found in food sources such as grapes, berries, peanuts, and red wine. It is widely used as a dietary supplement, and it is reported to activate the deacetylase activity of sirtuins (Howitz et al., 2003). Resveratrol has been reported to increase the life span of C. elegans and D. melanogaster (Wood et al., 2004; Wang et al., 2013); however, Bass et al. (2007) were unable to show any effect of resveratrol on the life span of D. melanogaster and observed only a small increase in the life span of C. elegans. In 2006, Baur et al. reported that feeding resveratrol (0.04%) w/w to C57BL/6 mice fed a high fat diet (60% calories from fat) starting at 12 months of age improved the survival of the mice and reduced the risk of death of mice by 31%; however, the study was not complete at the time of this publication. When Pearson et al. (2008) completed the survival study, they found that resveratrol significantly increased the life span of the mice fed the high fat diet to that of the mice fed a standard diet. However, resveratrol had no effect on the life span of the mice fed a standard diet. Resveratroltreated mice did show a marked reduction in the age-related deterioration such as decreased inflammation, reduced cataract formation, increased aortic elasticity, and maintained bone mineral density (Pearson et al., 2008). The ITP tested the effect of feeding resveratrol to UM-HET3 mice and found no effect of resveratrol on the life span of either male or female mice when resveratrol (300 and 1200 ppm) was initiated at 12 months of age (Miller et al., 2011). To address the speculation that the lack of effect of resveratrol arose because the mice were started on resveratrol at 12 months of age, Strong et al. (2013) started feeding resveratrol at 4 months of age. Again, no statistically significant effect of resveratrol on the life span of male and female UM-HET3 mice was observed. Thus, resveratrol appears to have no effect on the life span of mice; however, there are data suggesting that resveratrol might improve various physiological processes that decline with age (Markus and Morris, 2008).

Rapamycin Rapamycin is a macrolide produced by the bacteria Streptomyces hygroscopicus. In 1999, the FDA approved the use of rapamycin in combination with other immunosuppressive agents to prevent the rejection of organs in transplant patients (Camardo, 2003). Because of its antiproliferative properties, rapamycin was approved by the FDA to prevent restenosis in coronary arteries, and many clinical trials are underway evaluating the use of rapamycin analogs to prevent the recurrence of various cancers. The specific binding of rapamycin to FKBP12 inhibits the activity of mTORC1 leading to a decrease in protein synthesis, increased autophagy, and inhibition of cell growth (Stanfel et al., 2009). However, long-term rapamycin treatment also appears to inhibit mTORC2 activity (Sarbassov et al., 2006). Studies from Saccharomyces cerevisiae (Kaeberlein et al., 2005), C. elegans (Jia et al., 2004; Vellai et al., 2003), and D. melanogaster (Kapahi et al., 2004) have shown that inhibition of TOR pathway through genetic manipulations and mutations extended the life span of invertebrates. Furthermore, rapamycin treatment has been shown to increase the chronological life span of yeast (Powers et al., 2006) and the life span of D. melanogaster (Bjedov et al., 2010). In 2009, Harrison et al. showed for the first time that feeding rapamycin (14 ppm) to mice effectively inhibited mTOR activity and led to the extension of median and maximum life spans of both male and female UM-HET3 mice; the increase in median life span was ∼18% for both males and females, respectively. The most exciting aspect of this discovery was

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that the increase in life span was observed even when rapamycin treatment was started late in life (20 months of age). In a follow-up study, the ITP showed that rapamycin increased the median life span of male and female UM-HET3 mice by 10% and 18%, respectively, when initiated at 9 months of age (Miller et al., 2011). Interestingly, the extension in life span was similar whether initiated at 9 or 20 months of age. Currently there are eight reports showing that rapamycin increases the life span of various strains of mice (Table 19.2) (Miller et al., 2011; Harrison et al., 2009; Anisimov et al., 2011; Neff et al., 2013; Miller et al., 2014; Zhang et al., 2014; Fok et al., 2014; Bitto et al., 2016). For example, using the same does of rapamycin as used by Harrison et al. (2009) and Fok et al. (2014) found that rapamycin initiated at 4 months of age increased the medium life span of male and female C57BL/6 mice by 7% and 14%, respectively. Although rapamycin increases life span in both males and females, the data currently indicate that the effect is consistently greater in female mice. In 2014, the ITP tested the effect of different concentrations of rapamycin (4.7, 14 and 42 ppm) on the life span of UM-HET3 mice (Miller et al., 2014). Female mice showed a significant increase in their median life span at all three concentrations of rapamycin: 21% and 26% increase with 4.7, 14, and 42 ppm rapamycin, respectively (Miller et al., 2014). On the other hand, rapamycin significantly increased life span only at 14 ppm (13%) and 42 ppm (23%) in male mice (Miller et al., 2014). These data again indicate that rapamycin is less effective at increasing the life span of male mice. Of particular importance is the recent study by Bitto et al. (2016) where they showed that transient treatment (3 months) of old mice with a high dose (42 ppm) of rapamycin increased the median life span of male and female mice by 14% and 9%, respectively. This study suggests that at very high doses of rapamycin male mice might be as responsive, if not more, as female mice. Rapamycin has also been shown to increase the life span of many mouse models of diseases, especially cancer. p53 homozygous and heterozygous male mouse models showed significantly increased mean life span (30% and 10%–28%, respectively) and reduced development of tumors when treated with rapamycin starting at 2–5 months of age (Comas et al., 2012; Komarova et al., 2012). Rapamycin treatment has been shown to reduce incidence of colon tumors and significantly increase the life span of the Apc cancer mouse models (Apc Δ716 and Apc min/+) (Fujishita et al., 2008; Hasty et al., 2014). Rapamycin treatment also increased the survival and reduced neuroendocrine tumors in both male and female Rb1+/− mice (Livi et al., 2013). Furthermore, rapamycin was shown to decrease the tumor size and number of tumors and to increase the mean life span of HER-1/neu, a cancer-prone mouse model (Anisimov et al., 2010). In addition, Lmna−/− mice (which are deficient in A-type lamins that give rise to laminopathies) treated with rapamycin showed significantly increased median and maximum life spans in both male and female mice (Ramos et al., 2012). However, there are a few examples where rapamycin did not increase life span. Transgenic mouse models of amyotrophic lateral sclerosis show no increase in life span (Zhang et al., 2011), and we recently found that rapamycin decreased the life span of db/db obese mice (Sataranatarajan et al., 2016). As reported above, rapamycin effectively reduces the incidence and progression of tumors in various mouse models of cancer. Apart from cancer, rapamycin has also been shown to delay the incidence of many other age-related diseases such as cardiovascular and neurodegenerative diseases. Rapamycin has been shown to prevent the formation and progression of atherosclerotic plaques in LDL receptor knockout (KO) mice and ApoE KO mice respectively (Mueller et al., 2008; Pakala et al., 2005). Rapamycin has also been shown to improve memory and reduce amyloid plaque deposition in transgenic models of Alzheimer’s disease (Caccamo et al., 2010; Spilman et al., 2010; Majumder et al., 2011). Further, rapamycin improves the cardiac and skeletal muscle function in mice lacking A-type lamins (Ramos et al., 2012). In addition, rapamycin has also been shown to be neuroprotective in mouse models of Huntington’s disease (Ravikumar et al., 2004) and Parkinson’s disease (Malagelada et al., 2010). In addition, to delaying/reducing many age-related diseases, rapamycin has also been reported to reduce pathological lesions that are associated with aging (Miller et al., 2011; Zhang et al., 2014; Wilkinson et al., 2012) and improves many physiological functions (Neff et al., 2013; Johnson et al., 2013; Richardson, 2013). Thus, the current evidence indicates that the increased longevity seen in mice arises because rapamycin retards aging. Rapamycin is the most successfully studied pharmacological intervention to date. It is unique among all the other compounds tested by the ITP because of the following: (1) it significantly increases mean and maximum life span of both male and female mice, (2) it results an extension of life span as great if not greater than any other compound tested, (3) it is effective over a wide range of concentrations (1.5–42 ppm), (4) it is effective in wide range (ten) of mouse strains and mouse models of disease, (5) it has been shown to positively impact life span when initiated late in life, (6) it has minimum toxicity in mice (Richardson, 2013) and in a nonhuman primate (Ross et al., 2015; Tardif et al., 2015), (7) overall healthspan appears to be improved (Ravikumar et al., 2004; Malagelada et al., 2010), and (8) the FDA has approved rapamycin for various uses in humans and the toxicity profiles of rapamycin and its rapalogues are well characterized in humans (Soefje et al., 2011).

GENETIC MANIPULATIONS THAT INCREASE LIFE SPAN As noted above, the first demonstration that a genetic mutation could increase the life span of mice occurred in 1996 (Brown-Borg et al., 1996). Table 19.3 lists all of the studies published since 1996 that have reported an increase in life span of mice by a genetic manipulation (Coschigano et al., 2000; Bartke et al., 2001; Flurkey et al., 2001, 2002;

Mouse Model

Median Survival, Days, of WT Mice (# Mice) Strain

Median Survival, Days, of Mutant Mice (# Mice)

Increase in Maximum Life Span

Increase in Median Life Span

Male

Female

Male

Female

Male

Female

Male

Female

References

Growth Hormone Signaling Ames dwarf mice

Mixed Ames

723 (13)a

718 (15)a

1076 (17)a

1206 (17)a

49%a

68%a

Yesb

Yesb

Brown-Borg et al. (1996)

GHR/BP−/−

Mixed 129OLA/ BalbC

629 (7)a

749 (13)a

975 (7)a

1031 (11)a

55%a

38%a

NDc

NDc

Coschigano et al. (2000)

Ames dwarf miced

Mixed Ames

Combined sexes 725 (26)

Combined sexes 995 (23)

37%

Ghrhrlit/lit

C57BL/6

886 (≈15)e

857 (≈15)e

1093 (≈17)e

1070 (≈17)e

23%

Snell dwarf mice

Mixed DW/C3H

827 (23)

814 (48)

1068 (20)

1231 (23)

GHR/BP−/−

C57BL/6

866 (22)

850 (17)

941 (14)

GHR/BP−/−d

BALB/c

893 (≈20)

921 (≈20)

GHRH-KOd

C57BL/6 × 129SV

614 (56)

666 (52)

Yesb

Bartke et al. (2001)

25%

15%f

Flurkey et al. (2001)

29%

51%

27%

51%

Flurkey et al. (2002)

1023 (19)

9%

20%

Yesb

Yesb

Coschigano et al. (2003)

1165 (≈20)

1198 (≈20)

30%

30%

20%

17%

Bonkowski et al. (2006)

928 (39)

756 (58)

51%

43%

18%

33%

Sun et al. (2013)

IGF-1/Insulin Signaling Yesg

FIRKO

Mixed (U)

Combined sexes 909 (34)

Combined sexes 1005 (32)

11%

Igf1r +/−

129/Svh

586 (16)a

679 (12)a

756 (19)a

16%a,i

PappA−/−

Mixed C57BL/6 × 129

Combined sexes 698 (20)

Combined sexes 960 (20)

38%a,j

34%a,j

Conover and Bale (2007)

Irs2+/−k

C57BL/6

Combined sexes 781 (29)

Combined sexes 825 (31)

17%

17%

Taguchi et al. (2007)

Irs2+/− brain

C57BL/6

Combined sexes 791 (93)

Combined sexes 936 (65)

18%

18%

Taguchi et al. (2007)

Irs1−/−

C57BL/6

791 (35)

738 (21)

855 (10)

971 (14)

8%i

32%

No

21%l

Selman et al. (2008)

IGF1RKO+/−, brain

C57BL/6

853a (U)

966a (U)

821a (U)

888a (U)

13%a

8%a

No

No

Kappeler et al. (2008)

Igf1r+/−

568 (17)a

33%a

No

Bluher et al. (2003) Yesb

Holzenberger et al. (2003)

(Combined sexes, n = 42)

(Combined sexes, n = 27)

C57BL/6

1004 (55)

948 (68)

920 (52)

1013 (47)

No

7%i

No

7%i

Bokov et al. (2011)

IRKO+/−

C57BL/6J

810 (11)a

930 (8)a

835 (19)a

936 (27)a

3%i

No

14%

No

Nelson et al. (2012)

p110αD933A/WT

C57BL/6

799 (26)

840 (36)

818 (42)

858 (33)

2%

2%i

Yesb

No

Foukas et al. (2013)

254  SECTION | II  Animal Models: Vertebrates

TABLE 19.3  Knockout and Transgenic Mouse Models of Increased Longevity

Akt+/−

C57BL/6

794 (101)a

857 (103)a

721 (79)a

827 (80)a

8%

15%

10%l

8%l

Nojima et al. (2013)

Igf1r+/−

C57BL/6

863 (36)

803 (38)

836 (23)

889 (34)

No

11%a

No

10%

Xu et al. (2014)

IGF-1 Tg heart

FVB

724 (39)

ND

890 (38)

ND

19%

ND

No

ND

Li and Ren (2007)

Pten Tg

C57BL6/CBA

714 (42)a

783 (52)a

843 (21)a

870 (19)a

10%

3%

7%

5%

Ortega-Molina et al. (2012)

S6K−/−

C57BL/6

862 (26)

829 (23)

867 (19)

982 (29)

No

18%

No

10%

Selman et al. (2009)

mtor+/−

C57BL6/S129

770 (40)

786 (30)

770 (14)

888 (17)

No

13%

No

18%

Lamming et al. (2012)

129S1 × C57BL/6Ncr

687 (10)

795 (24)

840 (17)

945 (26)

22%

19%

Yesb,l

Yesb,l

Wu et al. (2013)

129 Sv/J

ND

U (12)

ND

U (10)

ND

Yesb

ND

15%

Liu et al. (2005)

129 Sv/J × Balb/c

Combined sexes U (5)

Combined sexes U (9)

Yesb

31%

C57BL/6

Combined sexes U (5)

Combined sexes U (8)

10%

Um

Surf1−/−

Mixed C57BL/6 × DBA/2

605 (21)

680 (22)

750 (23)

810 (25)

24%

19%

U

Yesb

Dell’agnello et al. (2007)

mGsta4−/−

C57BL/6

ND

735 (50)

ND

836 (50)

ND

14%

U

5%i

Singh et al. (2010)

TRX-Tgn

C57BL/6

Sex unknown U (53)

mTOR signaling

mlst8+/− mTORΔ/Δ Mitochondria Mclk1+/−

35%

22% Yesb

Mitsui et al. (2002) Schriner et al. (2005)

Yesb

Conti et al. (2006)

MCAT Tg

Mixed C57BL/6 × C3H

779 (52)

779 (50)

990 (26)

900 (36)

27%

16%

Yesb

UCP2 Tg brain

C57BL/6

695 (36)

554 (31)

787 (53)

660 (26)

12%

20%

No

SOD2 Tgo

C57BL/6 × C3H

Sex unknown 828 (30)

Sex unknown 864 (24)

4%p

Yesb

Hu et al. (2007)

UCP1-Tg skeletal muscle

C57BL/6

Combined sexes U (53)

Combined sexes U (51)

11%

No

Gates et al. (2007)

clk-1 Tg

C57BL/6NCr

607 (47)a

675 (46)a

744 (50)a

770 (50)a

23%a

14%a

20%

12%

Takahashi et al. (2014)

Sirt6 Tg (MOSES)

CB6F1

797 (U)

863 (U)

896 (U)

858 (U)

15%

No

16%

No

Kanfi et al. (2012)

Sirt1-Tg Brain (BRASTO)

C57BL/6

930 (U)

9%

16%

4%

6%

Satoh et al. (2013)

Sirtuins

Total number of males = 119; females = 126 849 (U)

799 (U)

926 (U)

Continued

Extension of Life Span in Laboratory Mice Chapter | 19  255

Sex unknown U (53)

Mouse Model

Median Survival, Days, of WT Mice (# Mice) Female

Median Survival, Days, of Mutant Mice (# Mice) Male

Female

Increase in Maximum Life Span

Increase in Median Life Span

Strain

Male

Male

Female

Male

Female

BubR1 Tg

C57BL/6-SV129

Combined sexes U (60)

Combined sexes U (57)

Yesb

INK-ATTAC Tg

C57BL/6-129Sv-FVB

623 (31)q

647(26)q

769 (22)r

838 (37)r

23%

30%

No

No

C57BL/6

626 (31)q

713 (27)q

843 (25)r

875 (26)r

14%

23%

No

No

References

Cell Senescence Yesb

Baker et al. (2013) Baker et al. (2016)

Inflammation and Fat Metabolism β/β (knock-in)

U

U

MIF KO

C57BL/6 × 129/SvJae

ND

774 (24)

ND

895 (39)

ND

16%

ND

Yesb

Harper et al. (2010)

Dgat1−/−

C57BL/6J

ND

746 (30)

ND

942 (30)

ND

26%

ND

18%

Streeper et al. (2012)

N/Iκbκbl/l, Brain

C57BL/6

U (20)

ND

U (25)

ND

23%

ND

20%

ND

Zhang et al. (2013)

FAT10-KO

C57BL/6

818 (52)

851 (60)

959 (114)

1023 (126)

17%

20%

Yesb

Yesb

Canaan et al. (2014)

AC5−/−

Mixed C57BL/6 × 129

Combined sexes 750 (25)

Combined sexes 990 (13)

32%

RIIβ−/−

C57BL/6

884 (20)

U (20)

1005 (20)

U (20)

13%

No

14%l

No

Enns et al. (2009)

Klotho Tg

C3H

715 (29)a

697 (25)a

936 (22)a

830 (29)a

31%a

19%a

Yesb

Yesb

Kurosu et al. (2005)

FGF21 Tg liver

C57BL/6

837 (32)

864 (35)

1086 (37)

1230 (40)

30%

42%

17%

16%

Zhang et al. (2012)

FVB

Sex unknown U (33)

Yesb

U

22%

Chiu et al. (2004)

cAMP pathway 23%

Yan et al. (2007)

Hormones

Others αMUPA Tg p66shc−/−t MT Tg heart AgRP−/−u

129 SvEv FVB Mixed C57BL/6 × 129sv

Sex unknown 761 (14) 795

(55)a

ND

Combined sexes U (16)

Sex unknown U (33) Sex unknown 973 (15) 904

(55)a

ND

Combined sexes U (21)

20%s

U

Miskin and Masos (1997)

28%

Yesb

Migliaccio et al. (1999)

14%a

Yesb

9.8%

ND

Yesb

ND

Yang et al. (2006) Redmann and Argyropoulos (2006)

256  SECTION | II  Animal Models: Vertebrates

TABLE 19.3  Knockout and Transgenic Mouse Models of Increased Longevity—cont’d

PEPCK Tg Muscle

U

U

U

U

Uv

Hakimi et al. (2007)

Sp53/Sp16/ SArf/TgTert

C57BL/6 × DBA/2

Sex unknown U (68)

Sex unknown U (27)

40%

Yesb

(Tomas-Loba et al., 2008)

Agtra−/−

Mixed C57BL/6 × 129sv

Combined sexes 644 (16)

Combined sexes 707 (21)

10%

Yesb

Benigni et al. (2009)

ETAKO, heart

C57BL/6 J

Sex unknown 756 (U)

Sex unknown 909 (U)

20%

Yesb

U (U)

∼17%

Atg5-Tg hMTH1-Tg Myc

±

C57BL/6 C57BL/6 C57BL/6

U (U)

U (U)

Sex unknown U (42) 881 (42)

810 (37)

U (U)

Ceylan-Isik et al. (2013) Yesb

Pyo et al. (2013)

16%

Yesb

979 (39)

11%

21%

Yesb

Yesb

Hofmann et al. (2015)

ND

U

ND

Moritoh et al. (2016)

14%

No

No

Nobrega-Pereira et al. (2016)

Sex unknown U (34) 975 (42)

∼17%

Yesb

Ip6K3−/−

B6.Cg

U (30)

ND

U (30)

ND

Yesb

G6PD-Tg

C57BL/6

U (28)

U (28)

U (28)

U (32)

No

De Luca et al. (2013)

Extension of Life Span in Laboratory Mice Chapter | 19  257

BRASTO, brain-specific Sirt1-overexpressing; MCAT, mitochondrial targeted catalase; ND, no study was performed to determine the effect on life span; U, unknown (value not available in the publication). aMean life span. bLife span curve shows increase in maximum life span. However, data not available or cannot be determined in the publication. cNo information is available on life span curve or maximum life span. dEffect of CR on life span was also tested. eMales and females were combined. Totals were 31 for wild-type and 35 for Ghrhrlit/lit. fPooled data from males and females. gMaximum life span was extended by 5 months. hAuthors state in the text that they observed similar changes in longevity in 129/B6 mice. iStatistically not significant. jSeparation by sex showed significant increases in life span for both males (33%) and females (41%). kSelman et al. (2008) have reported no significant increase in life span of Irs2+/− mice. lMaximum life span was calculated as the mean age of the oldest 20% of mice from each genotype. mLife span study not finished to assess maximum life span. nPerez et al. (2011) reported that male Tg(TRX1)+/0 mice significantly extended the earlier part of life span, compared with wild-type littermates, but no significant life span extension was observed in females. oJang et al. (2009) have reported no significant increase in life span of SOD2 Tg mice. pNo statistical analysis is available in the publication. qINK-ATTAC transgenic mice with vehicle administration. rINK-ATTAC transgenic mice with AP20187 administration starting at 12 months of age. sTransgenic mice appear to live longer, e.g., female transgenic mice lived 20% longer than WT mice. tRamsey et al. (2014) reported no significant increase in life span of p66shc−/− mice on three different backgrounds: 129Sv, C57BL/6J and hybrid F1 (C57BL/6J × 129Sv). uMice consumed alternate diets as follows: at 3 weeks of age chow; at 16 weeks HF diet; at 21 weeks LF diet; at 41 weeks chow; at week 42 HF diet until death. As such, this study was not originally designed to be a longevity study per se. vTransgenic mice appear to live longer, e.g., female transgenic mouse at 30 months of age gave birth to a litter of pups.

258  SECTION | II  Animal Models: Vertebrates

FIGURE 19.3  Survival curves for wild-type (WT) and p66Shc−/− mice. (A) Survival curves for WT and p66Shc−/− 129SvEv mice. (B) Survival curves for WT and p66Shc−/− 129 Sv mice (males and females combined). ((A) Graph taken from Migliaccio, E., et al., 1999. The p66shc adaptor protein controls oxidative stress response and life span in mammals. Nature 402 (6759), 309–313 with permission. (B) Graph taken from Ramsey, J.J., et al., 2014. The influence of Shc proteins on life span in mice. J Gerontol A Biol Sci Med Sci 69 (10), 1177–1185 with permission.)

Coschigano et al., 2003; Bonkowski et al., 2006; Sun et al., 2013; Bluher et al., 2003; Holzenberger et al., 2003; Conover and Bale, 2007; Taguchi et al., 2007; Selman et al., 2008; Kappeler et al., 2008; Bokov et al., 2011; Nelson et al., 2012; Foukas et al., 2013; Nojima et al., 2013; Xu et al., 2014; Li and Ren, 2007; Ortega-Molina et al., 2012; Selman et al., 2009; Lamming et al., 2012; Wu et al., 2013; Liu et al., 2005; Dell’agnello et al., 2007; Singh et al., 2010; Mitsui et al., 2002; Schriner et al., 2005; Conti et al., 2006; Hu et al., 2007; Gates et al., 2007; Takahashi et al., 2014; Kanfi et al., 2012; Satoh et al., 2013; Baker et al., 2013, 2016; Chiu et al., 2004; Harper et al., 2010; Streeper et al., 2012; Zhang et al., 2013; Canaan et al., 2014; Yan et al., 2007; Enns et al., 2009; Kurosu et al., 2005; Zhang et al., 2012; Miskin and Masos, 1997; Migliaccio et al., 1999; Yang et al., 2006; Redmann and Argyropoulos, 2006; Hakimi et al., 2007; Tomas-Loba et al., 2008; Benigni et al., 2009; Ceylan-Isik et al., 2013; Pyo et al., 2013; De Luca et al., 2013; Hofmann et al., 2015; Moritoh et al., 2016; Nobrega-Pereira et al., 2016). When evaluating these studies, it is important to note that most of the studies have not been replicated either by the same or a different laboratory. This is very important as shown in Fig. 19.3 for the p66sch KO mouse. In 1999, a great deal of excitement was generated when Migliaccio et al. (1999) reported that knocking out the p66sch gene increased life span of mice by ∼30%. For the following 13 years, these mice were used as a model to identify what pathways were potentially involved in the life span extension of the p66sch KO mice, suggesting that increased resistance to oxidative stress and mitochondrial function were critical pathways in aging. The life span data published by Migliaccio et al. (1999) were based on only 14 to 15 mice per group. In 2014, Ramsey et al. conducted a detailed study in which they measured the life span of p66sch KO mice in either the C57BL/6 or 129 background. Using over 80 mice/group in 129 background and over 65 mice/group in C57BL/6 background, they showed that knocking out p66sch had no effect on life span in either background. Another example of why it is important to replicate life span is shown in Fig. 19.4 for mice genetically engineered to have reduced expression of the IGF-1 receptor (Igf1r+/−). Holzenberger et al. (2003) initially reported that female 129 Sv Igf1r+/− mice lived 33% longer than the wild-type (WT) mice (Fig. 19.4A). However, when our group measured life span in female C57BL/6 Igf1r+/− and WT mice, we observed only a modest but significant increase (6%–7%) in the life span (Bokov et al., 2011) (Fig. 19.4B). Subsequently, Holzenerger’s laboratory reported that female C57BL/6 Igf1r+/− mice lived 10% longer than WT mice (Xu et al., 2014) (Fig. 19.4C). To determine if the genetic background of the mice was responsible for the difference in life span extension between the first study and the subsequent studies, we measured the life span of Igf1r+/− mice in the 129 × 57BL/6 F1 background (Fig. 19.4D). While the Igf1r+/− mice still lived longer than the WT mice in the F1 background, the increase in mean life span was only 6%, and there appeared to be no increase in maximum survival. Thus, while IGF-1 receptor deficiency increases the life span of mice, the increase is much less than the 33% increase initially reported by Holzenberger et al. (2003). Part of this difference is probably due partially to the smaller number of mice used in the first study (see Table 19.3 for a comparison). However, another major factor that can affect how much an intervention increases life span is the survival of the control mice, which is an indication of the quality of the housing conditions. As shown in Table 19.3, the mean life span of the control, WT mice in the study by Holtzenberger et al. (2003) were only 22.6 months. In contrast the mean life spans of the control mice were 30.6, 27.9, and 35.6 months for the life spans of the C57BL/6 mice reported by Bokov et al. (2011), Xu et al. (2014), and the 29 × 57BL/6 (unpublished data), respectively (Holzenberger et al., 2003). Thus, optimizing the survival of the mice resulted in a reduced difference in the life spans of the Igf1r+/− and WT mice, the longer the control mice lived, the less of an extension of life span by the reduced expression of IGF-1 receptor. Thus, when one is evaluating the effect of a manipulation on aging, it is very important that studies be conducted under conditions that optimize the life span of the mice.

Extension of Life Span in Laboratory Mice Chapter | 19  259

(B)

(A)

Igf1r+/-

Igf1r+/+

(C)

Igf1r+/+

(D)

Igf1r +/-

Igf1r +/+

Igf1r+/-

100

50

0

Igf1r+/+

0

250

500

Igf1r+/-

750 1000 1250 1500

Days Igf1r+/−

FIGURE 19.4  Survival curves for wild-type (WT) and female mice. (A) Survival curves for WT (Igf1r+/+) and Igf1r+/−129 Sv mice. (B) Survival +/− curves for WT and Igf1r C57BL/6 mice. (C) Survival curves for WT and Igf1r+/− C57BL/6 mice. (D) Survival curves for WT (n = 61) and Igf1r+/− (n = 24) 129 × C57BL/6 F1 mice. The life span of the Igf1r+/− mice was significantly longer (P = .01) (unpublished data). ((A) Graph taken from Holzenberger, M., et al., 2003. IGF-1 receptor regulates life span and resistance to oxidative stress in mice. Nature 421 (6919), 182–187 with permission. (B) Graph taken from Bokov, A.F., et al., 2011. Does reduced IGF-1R signaling in Igf1r+/− mice alter aging? PLoS One 6 (11), e26891 with permission. (C) Graph taken from Xu, J., et al., 2014. Longevity effect of IGF-1R(+/−) mutation depends on genetic background-specific receptor activation. Aging Cell 13 (1), 19–28 with permission.)

Growth Hormone Signaling Pathway Reduction in GH signaling through mutations or silencing of genes essential for GH biosynthesis or action has consistently shown a significant effect on life span extension in mice. GH-deficient mouse models such as Ames dwarf mice, Snell dwarf mice, the “little” mice (Ghrhrlit/lit), GH releasing hormone KO mice (GHRH−/−), and GH receptor/binding protein KO mice (GHR/BP−/−) all have extended life span as shown in Table 19.3. While findings from Ames dwarf mice and Snell dwarf mice support the hypothesis that inhibition of GH signaling pathway is a key contributor to the life span extension in mice, the accompanying lack of thyroid stimulating hormone and prolactin in these two mouse models made it difficult to draw conclusions on the selective effect of GH signaling on life span extension (Brown-Borg et al., 1996; Bartke et al., 2001; Flurkey et al., 2002). Manipulations that specifically target the GH signaling pathway, such as Ghrhrlit/lit mice (a missense mutation in the growth hormone releasing hormone receptor (Ghrhrlit/lit)), GHR/BP−/− mice and GHRH−/− mice all showed a significant effect on life span extension in mice (Coschigano et al., 2000; Flurkey et al., 2001; Sun et al., 2013). Importantly, life span extension due to lack of GH or GHR is seen in both males and females, and these findings are reproducible in different laboratories. Life span extension in GHR/BP−/− mice was also reproducible in different genetic backgrounds, emphasizing the importance of GH signaling in life span (Coschigano et al., 2000, 2003; Bonkowski et al., 2006). Therefore, a large number of studies with different animal models show that reduced GH signaling leads to increased life span in mice. However, Sonntag et al. (2005) found no increase in life span in a dwarf rat model of GH and IGF-1 deficiency. Currently, it appears that global GHR-deficiency is essential for life span extension because life span was not altered or only slightly increased in mice in which GHR is conditionally knocked down in either liver, fat, or muscle (Sun and Bartke, 2014; List et al., 2013, 2014, 2015). It appears that the impact of GH-deficiency is due to early effects because Ames dwarf

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mice, which were injected with GH for 6-weeks starting at 2 weeks of age, significantly reduced the life span of Ames dwarf mice (Panici et al., 2010). In general, mouse models with reduced GH signaling have increased fat mass, improved insulin sensitivity, reduced tumor incidence, and elevated stress resistance. While multiple factors could contribute to extended life span in GH-deficient mice, it is possible that insulin sensitivity, reduced tumor incidence, and increased stress resistance are playing a major role in life span extension in GH-deficient mice.

IGF-1/Insulin Signaling (IIS) Pathway IIS is an evolutionarily conserved pathway that responds to changes in nutrients and environmental conditions to coordinate growth, differentiation, and metabolism. Studies in invertebrates strongly support the role of IIS pathway in the regulation of longevity. In C. elegans, more than a twofold increase in life span is achieved by a single mutation that reduces the IIS pathway (Friedman and Johnson, 1988; Kenyon et al., 1993; Morris et al., 1996; Kimura et al., 1997). Similarly, in D. melanogaster, reduction in IIS pathway resulted in a substantial increase in life span (Clancy et al., 2001; Tatar et al., 2001). Similar to the findings in invertebrates, studies in mouse models also suggest that reduced insulin signaling is associated with enhanced life span. The fat-specific insulin receptor knockout (FIRKO) mouse was the first mouse model to demonstrate that reduced insulin signaling can extend life span in mice (Bluher et al., 2003). Subsequent studies in various mutant mice models with reduced insulin signaling also showed a significant extension in life span: insulin receptor substrate 2 KO mice (Irs2+/− mice) (Taguchi et al., 2007); brain-specific Irs2+/− mice (bIrs2+/− mice) (Taguchi et al., 2007); pregnancy-associated plasma protein-A KO mice (PappA−/− mice) (Conover and Bale, 2007); Irs1−/− mice (Selman et al., 2008); mice overexpressing phosphatase and tensin homologue (Pten Tg mice) (Ortega-Molina et al., 2012); insulin receptor knockout mice (IRKO+/− mice) (Nelson et al., 2012); hypomorphic PI3K mice (p110aD933A/WT mice) (Foukas et al., 2013), and Akt+/− mice (Nojima et al., 2013)]. Pten Tg mice and Akt+/− mice exhibited a longevity phenotype in both males and females. In contrast, Irs1−/− mice showed an increase in life span only in female mice, and in p110aD933A/WT mice and IRKO+/− mice only males had increased life span. Studies using FIRKO, Irs2+/−, and bIrs2+/− mice were performed in combined sexes. Reduction in IGF-1 signaling also appears to increase life span in mice. The four studies with Igf1r+/− mice shown in Fig. 19.4 indicate that reduced IGF-1 signaling results in a modest increase in life span only in females. Brain-specific Igf1r+/− mice have only an extension in medium life span, and this is seen in both sexes (Kappeler et al., 2008). The PappA gene codes for pappalysin-1 that cleaves IGF-1–bound IGF-binding protein 4 increase the availability of IGF-1. The deletion of the PappA gene reduces the availability of IGF-1, and similar to other mouse models with reduced IGF1 signaling, PappA−/− mice showed an increase in life span in a study where sexes were combined (Conover and Bale, 2007). It is often argued that improved insulin sensitivity is associated with enhanced healthspan and life span, e.g., DR and dwarf mice have improved insulin sensitivity (McCarter et al., 2007; Enns et al., 2009; Davidson et al., 2002; Al-Regaiey et al., 2007; Argentino et al., 2005) and insulin resistance is associated with diabetes and several markers of accelerated aging (Kadowaki, 2000; Fink et al., 1983; Park et al., 2014). Therefore, it was of interest in determining how genetic alterations in the IGF-1/insulin signaling pathway affected the insulin sensitivity of the genetically modified mice. FIRKO (Bluher et al., 2003), p110αD933A/WT (Nojima et al., 2013), Irs2+/− (Taguchi et al., 2007), and Pten Tg mice (Ortega-Molina et al., 2012) all show improved insulin sensitivity. However, insulin sensitivity in PappA−/− mice was not significantly different from control mice (Conover and Bale, 2007), and Irs1−/− mice (Selman et al., 2008), Igf1r+/− mice (Bokov et al., 2011), and IRKO+/− (Nelson et al., 2012) have reduced insulin sensitivity in comparison to control mice. These findings suggest that improved insulin sensitivity is not an absolute requirement for increased life span in IIS mutants. In fact, studies by Nelson et al. (2012) have shown that a mouse model (IRKO+/− mice) with reduced insulin sensitivity had increased life span, and mice models with improved insulin sensitivity (protein tyrosine phosphatase 1B KO mice, PTP-1B−/− mice, and mice overexpressing peroxisome proliferator activated receptor-α coactivator, (PGC-1α-Tg) have reduced life span, suggesting that insulin sensitivity in these mouse models is inversely related to longevity. Even though it is well established that impaired GH/IGF-1 signaling results in an increase in life span in mice, studies supporting the importance of sufficient levels of circulating GH and IGF-1 for healthy aging, including humans, makes the role of GH signaling in aging highly controversial. Thus, two contrasting concepts are present in the current literature: (1) normal levels of GH and IGF-1 accelerate aging, whereas their deficiency exert antiaging effects and (2) the age-related reduction in GH and IGF-1 contribute to the decline of physiological function and the aging phenotype can be delayed or reversed by replacing these hormones. For example, Sonntag et al. (2005) found that GH treatment of GH-deficient rats (which showed no decrease in life span and resembled adult-onset GH-deficiency) from 4 to 14 weeks of age increased median and maximum life spans by 14% and 12%, respectively. It has been proposed that in GH-deficient mice models, developmental programming of pathways that regulate cellular stress resistance, not GH-deficiency itself, contributes to

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life span extension (Sonntag et al., 2012). This view is supported by the finding that development of a cellular multistress resistance phenotype occurred when Ames dwarf mice were injected with GH (Panici et al., 2010).

mTOR Signaling Pathway As described above, genetic manipulations that reduce TOR signaling in invertebrates increase life span (Kaeberlein et al., 2005; Jia et al., 2004; Vellai et al., 2003; Kapahi et al., 2004). Therefore, investigators have also studied the effect of genetically manipulating mTOR signaling in mice. In mammals, mTOR is found in two major complexes: mTORC1 (which consists of mTOR, Raptor, mLST8, FKBP38, PRAS40, and Deptor) and mTORC2 (which consists of mTOR, Rictor, mLST8, Protor1/2, Sin1, and Deptor). mTORC1 regulates growth and differentiation and mTORC2 regulates insulin signaling (Johnson et al., 2013). Two genetic interventions that reduce mTOR signaling have been shown to increase life span. Lamming et al. (2012) reported that mice heterozygous for both mTOR and mLST8 (mtor+/−mlst8+/−) show specific inhibition of mTORC1 activity and a significant increase (13%–18%) in both mean and maximum life spans of female but not male mice. Wu et al. (2013) studied mice with hypomorphic alleles for mTOR (mTORΔ/Δ). The mTORΔ/Δ mice have a 75% decrease in mTOR levels and have reduced mTORC1 and mTORC2 activities. The life span of mTORΔ/Δ mice was significantly increased by 19% and 22% in male and female mice, respectively. Selman et al. (2009) studied ribosomal S6 protein kinase 1 KO mice (S6K1−/−). mTORC1 phosphorylates and activates S6K1, which in turn activates ribosomal protein S6 (RPS6), a component of the 40S translational machinery. In addition to RPS6, multiple S6K1substrates were identified and the mTORC1-S6K1 pathway regulates fundamental cellular processes, including transcription, translation, protein and lipid synthesis, cell growth/size, and cell metabolism (Magnuson et al., 2012). S6K1−/− mice have reduced body size, improved insulin sensitivity, and an 18% and 10% increase in medium and maximum life spans in female mice. The data from these three mouse models combined with the data on rapamycin demonstrate that the mTOR signaling pathway is important in longevity and aging. In addition, the S6K1−/− mice suggest that this arm of the mTOR signaling pathway is an important component of lifespan extension in mice.

Mitochondria Mitochondria play a central role in the aging process based on the casual link between mitochondrial dysfunction and age-associated phenotypes. Mitochondria are the site for ATP production as well as the major source of reactive oxygen species (ROS) production. Denham Harman in 1956 proposed the oxidative stress theory of aging that suggests that oxygen free radicals produced endogenously from normal metabolic processes play a role in the aging process. However, most of the mouse models that overexpress or are deficient in antioxidant enzymes did not alter life span in mice, questioning the oxidative stress theory of aging (Perez et al., 2009a,b). However, data from a few mouse models that overexpress antioxidant enzymes showed an increase in life span. Thioredoxin transgenic (TRX-Tg) mice that overexpress human thioredoxin (Mitsui et al., 2002) and MCAT (mitochondrial-targeted catalase) mice that overexpress catalase in mitochondria (Schriner et al., 2005) are reported to have a significant increase in life span. In addition, Hu et al. (2007) reported that mice that overexpress the mitochondrial Mn-superoxide dismutase (Sod2-Tg) have an increase in life span (Conti et al., 2006); however, they did not statistically analyze their data. Subsequent studies, in which more animals were studied and the data rigorously analyzed, were unable to show an increase in life span of TRX-Tg mice (Perez et al., 2011) or in mice overexpressing Sod2-Tg (Jang et al., 2009). Surprisingly, mouse models with impaired detoxification show an increase in life span. Mice heterozygous for glutathione peroxidase 4 (Gpx4), which is found in the mitochondria and catalyzes the detoxification of lipid hydroperoxides, showed an increase in median, but not maximum, life span (Ran et al., 2007). Similarly, mGsta4−/− mice in which 4-HNE detoxification is impaired have been reported to have a significant increase in life span. In mGsta4−/− mice, this unexpected effect was attributed to the activation of Nrf2 that leads to induction of antioxidants and antielectrophilic defenses (Singh et al., 2010). Unexpectedly, disruption of mitochondrial electron transport chain functionality has been shown to extend life span in C. elegans and D. melanogaster (Dillin et al., 2002; Lee et al., 2003; Rea et al., 2007; Copeland et al., 2009). A similar effect has also been reported in mice. Mice heterozygous for Clk1 (mClk1+/−), which encodes an enzyme necessary for coenzyme Q biosynthesis, have reduced levels of ROS and DNA damage and increased life span (Liu et al., 2005; Lapointe and Hekimi, 2008). Yet another mouse model with mild mitochondrial dysfunction that showed increased life span is Surf1−/− mice (Dell’agnello et al., 2007). Surf1−/− mice have nearly 50%–70% reduction in complex IV activity with no change in ATP production and elevated H2O2 production in brain (Pulliam et al., 2014; Lin et al., 2013). Uncoupling proteins (UCP) uncouple oxidative phosphorylation from respiration, thereby dissipating the proton gradient energy in the form of heat (Ricquier and Bouillaud, 2000). Tissue-specific overexpression of UCPs in mice has been

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shown to increase in life span. Mice that overexpress UCP2 exclusively in hypocretin (Hcrt) neurons (Hcrt-UCP2 mice) showed significant extension of median life span in males and females, which is associated with a reduction of body core temperature (Conti et al., 2006). Mice that overexpress UCP1 in skeletal muscle are yet another mouse model with increased medium life span (Gates et al., 2007). In contrast to Hcrt-UCP2 mice, UCP1 Tg mice have increased temperature and metabolic rate.

Sirtuins Silent information regulator 2 (Sir2) in yeast encodes a nicotinamide adenine dinucleotide–dependent histone deacetylase that has homologues in prokaryotes and eukaryotes (Imai et al., 2000; Landry et al., 2000). Deletion of Sir2 in yeast resulted in reduced life span (Kaeberlein et al., 1999) and an extra copy of Sir2 gene in yeast or its orthologues in C. elegans and D. melanogaster extended life span (Kaeberlein et al., 1999; Tissenbaum and Guarente, 2001; Rogina and Helfand, 2004). Mammals have seven homologues of yeast Sir2 protein: Sirt1–7 (Frye, 1999, 2000). Sirt1, the most studied sirtuin, is a nuclear-localized deacetylase that is involved in stress response pathways and metabolism (Guarente, 2013). Similar to the observation in invertebrates, mice lacking Sirt1 also have reduced life span (Boily et al., 2008; Li et al., 2008; Mercken et al., 2014). However, mice overexpressing Sirt1 did not show an increase in life span (Herranz et al., 2010; Bordone et al., 2007). On the other hand, transgenic mice that overexpress Sirt1 in brain (brain-specific Sirt1-overexpressing (BRASTO)) have an increase in medium and maximum life spans in both sexes (Satoh et al., 2013). Sirt6 is yet another sirtuin that has an impact on the life span of mice. Sirt6 is histone 3 deacetylase that specifically deacetylates H3K9 and H3K56 and plays key roles in DNA repair, telomerase function, and cell senescence (Mostoslavsky et al., 2006; Michishita et al., 2008; Tennen and Chua, 2011). Sirt6−/− mice have reduced life span and displayed phenotypes similar to premature aging (Mostoslavsky et al., 2006). Overexpression of Sirt6 extended life span of male mice; however, the life span of female mice was not significantly increased (Kanfi et al., 2012). Sirt2 has been shown to increase the life span of male but not female BubR1H/H mice, which expresses the hypomorphic BubR1 and showed accelerated aging and short life span (North et al., 2014).

Cell Senescence The phenomenon of cell senescence was discovered in 1965, when Hayflick’s group showed that normal cells in culture ceased dividing. Subsequently, it was found that senescent cells accumulate with age and disrupt tissue structure and function because of a distinctive secretory phenotype (Campisi, 2005; Coppe et al., 2008; Rodier et al., 2011). The recent development of genetically engineered mice made it possible for investigators to specifically kill senescent cells that express p16Ink4a (Baker et al., 2011, 2013). Two studies have shown that removal of senescent cells with this transgenic mouse can improve healthspan and life span in mice. Elimination of senescent cells that express the tumor suppressor p16Ink4a in the BubR1H/H mice delayed the onset of age-associated disorders; however, it did not significantly affect the life span of BubR1H/H mice (Baker et al., 2011). In a subsequent study with BubR1 transgenic mice that overexpressed BubR1 protein, eliminating senescent cells protected against aneuploidy and cancer and significantly increased life span (Baker et al., 2013). More recently, Baker et al. (2016) reported that WT mice in which senescent p16Ink4a-expressing cells were removed had a significant increase in life span in both male and female mice on two different genetic backgrounds. However, the life span of the control mice in this study was short. Because removal of senescent cells by genetically engineered mice leads to increased life span, there has been interest in the potential of using senolytic agents, which are small molecules that kill senescent cells, to increase life span and improve physiological functions that decline with age (Tchkonia et al., 2013). Dasatinib and quercetin are two such senolytics; and short‐term treatment with these senolytic drugs in chronologically aged or progeroid mice has been shown to reduce several aging‐related phenotypes (Kirkland, 2013; Kirkland and Tchkonia, 2015; Zhu et al., 2015). Chronic senolytic treatment has been shown to improve established vasomotor dysfunction in aged or atherosclerotic mice (Roos et al., 2016). Thus, it may be possible for using the clearance of senescent cells as an effective complementary therapy for cardiovascular diseases and other age-related diseases (Tchkonia et al., 2013; Kirkland and Tchkonia, 2015).

CONCLUSIONS Data generated over the past two decades demonstrate that life span/aging is much more malleable than the research community initially thought. Currently, more than 67 manipulations have been reported to increase the life span of mice (Tables 19.2 and 19.3). However, one must be cautious in assuming that all the manipulations listed in Tables 19.2 and 19.3 actually increase life span because most of the studies have not been replicated. The importance of replicating life span studies is

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shown by the study of p66sch KO mice (Fig. 19.3) where it was 13 years after the first publication reporting that p66sch KO mice lived ∼30% longer than control mice before it was demonstrated that deleting the p66sch gene did not increase life span. Even for Igf1r+/− mice, in which four studies showed a significant increase in life span (Fig. 19.4A), the increase in life span was much less (5%–11%) than the 33% initially reported (Fig. 19.4B–D). However, it is clear from the current data that the effect of the various manipulations on life span is most often sex dependent, i.e., life span is either increased in one sex but not the other or is much greater in one sex. These data point to the importance of measuring life span in both sexes and not combining sexes. In addition, it suggests that there might be major differences in the pathways that can be used to extend life span, and therefore retard aging, in males and females. Currently, there are three manipulations that consistently show an increased life span in both male and female mice and have been replicated by different laboratories in different genetic backgrounds. These manipulations are DR, rapamycin, and reduced GH-signaling. DR has been the most extensively studied manipulation with more than seven decades of research showing that DR increases the life span of a variety of animal models ranging from yeast to nonhuman primates (Fig. 19.1). Since the initial report by Brown-Borg et al. in 1996, eight studies of five different genetic manipulations that impact GH levels/signaling have been shown to increase life span of mice. The pharmaceutical rapamycin was shown to increase the life span of mice in 2009, and there are now 14 studies showing rapamycin increases the life span of mice. It is generally assumed that if a manipulation increases the mean and maximum life spans of an organism, aging is retarded by the manipulation. Implicit in this assumption is that the age-dependent changes in physiological functions (healthspan) have been delayed and/or improved. Except for DR and rapamycin (and to a more limited extent animal models with GH manipulations) very little, if any, data are available about how the manipulations shown in Tables 19.1–19.3 affect physiological functions that change with age. While DR and rapamycin appear to improve healthspan of mice (Richardson, 2013; Richardson et al., 2016b), not all physiological functions that change with age are improved by these two interventions. Therefore, it is important that investigators not only measure the effect of a manipulation on life span but to also determine the effect of the manipulation of a battery (not just one or two) of physiological functions that change with age (Richardson et al., 2016b). These data will be critical in considering which manipulations can potentially be translated to humans. Now that a large number of manipulations have been observed to increase the life span of mice, it will be of interest in the future to determine whether these manipulations increase life span through common or distinct mechanisms. Over the past two decades, investigators studying aging in invertebrates have used an epistasis-like approach to determine whether genetic or nutritional manipulations increase life span through common or independent pathways (Gems et al., 2002). This approach is the first step in identifying pathways that alter aging, and this knowledge is critical in developing future nutritional/pharmacological interventions that will improve healthspan. Currently, the only reports in which two manipulations that increase life span have been studied together are DR/GH-deficient models and rapamycin/metformin treatment. DR significantly increased the life span of the Ames dwarf mice (Bartke et al., 2001) and GHRH−/− mice (Sun et al., 2013), suggesting existence of different mechanisms in life span extension by GH-deficiency and DR. However, DR failed to extend overall, median, or mean life spans of GHR−/− mice, and increased maximum life span only in females (Bonkowski et al., 2006). More recently, Strong et al. (2016) reported that treating mice with both rapamycin and metformin increased the life span of mice more than either compound alone. These data point to the potential of combining treatments to extend life span that might be useful in humans. For the first time in human history, we are in a position to begin testing potential therapies that might retard age-related diseases and aging in humans. Therefore, it is important that the research community should not only pursue the discovery of other manipulations that increase aging but also develop strategies of translating the most promising interventions discovered to humans. First, it will be important to focus on manipulations that have minimal toxicity profile in mice and potentially humans, e.g., manipulations such as rapamycin and metformin, which are FDA approved for use in humans, should be at the top of the list to pursue. Second, it will be important to identify manipulations that do not require life-long application to be effective. One would ideally focus on manipulations that increase life span and healthspan in mice when implemented late in life or periodically. An intervention that would require humans to take it over most of their life span is not practical. Currently, rapamycin appears to meet these criteria because Harrison et al. (2009) showed that rapamycin significantly increased life span when implemented in the last third of life, and more recently, Kaeberline’s laboratory showed that treating old mice with rapamycin for 3 months increased life expectancy to 60% (Bitto et al., 2016). Third, it will be important to show that a manipulation increases life span of other species than mice to increase the probability that the manipulation will be effective in humans. Currently, there is an NIA-funded project studying the effect of rapamycin in a nonhuman primate, the common marmoset (Callithrix jacchus), and there are plans to study the effect of rapamycin on aging in dogs. However, studies using nonhuman primates and dogs are expensive and time-consuming; therefore, we suggest that the manipulation be tested in a rat model after it has been shown to increase life span in mice. Rats are not simply a “big” mouse, i.e., they might not respond the same as mice to an intervention. While DR seems to increase the life

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span of mice and rats similarly (Turturro et al., 1999), dwarf rats do not show an increase in life span (Sonntag et al., 2005) whereas dwarf mice show lifespan extension (Table 19.3). Rats and mice often show quite different sensitivities to drugs/ carconigens and exhibit different pathologies. For example, rats seem to be less affected by viral infections than mice; however, they more prone to bacterial infections (Percy and Barthold, 2007). Rats and mice also vary in their sensitivity to many carcinogens, e.g., mice are more susceptible to nitroureas than rats or humans (Gerson et al., 1986), rats are more sensitive to aflatoxin than mice (Degen and Neumann, 1981), and rats and mice show differences in mammary carcinogenesis (Nandi et al., 1995). In addition, the major pathologies observed with age vary in mice and rats. In mice, cancer is the primary cause of death with the type of cancer varying from strain to strain, e.g., mammary tumors, hemangiosarcomas, multicentric and thymic lymphomas, fibrosarcoma, and liver and lung carcinomas (Miller et al., 2011; Percy and Barthold, 2007). While cancer is also a major cause of death in rats (e.g., large granular lymphocytic leukemia, histiocytic sarcomas, and mammary, pituitary, and testicular tumors), chronic progressive nephropathy is often the major cause of death in rats and many other degenerative changes are seen in rats, e.g., hydronephrosis, myocardial degeneration, and fibrosis (Gems et al., 2002).

ACKNOWLEDGMENTS The efforts of authors were supported by NIH grant R01 AG045693 and a Senior Career Research Award from the Department of Veterans Affairs to Dr. Richardson.

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Chapter 20

Development and Validation of ECG Analysis Algorithm in Mice Mari Merentie1, Line Lottonen-Raikaslehto1, Seppo Ylä-Herttuala1,2 1University

of Eastern Finland, Kuopio, Finland; 2Heart Center, Kuopio University Hospital, Kuopio, Finland

INTRODUCTION Surface electrocardiography (ECG) is the primary tool in diagnosing patients with heart diseases. Mice have become increasingly important models for studying cardiovascular diseases, especially due to the ever-expanding number of genetically modified mouse strains (Scherrer-Crosbie and Thibault, 2008; Boukens et al., 2014). However, the mouse ECG is still a widely unexplored field and little is known about the relationship of mouse surface ECG to physiological and pathological changes in the heart. Therefore, the development of an accurate ECG analysis system for mouse ECG is of particular interest.

Cardiac Conduction System The heart has a specialized excitatory and conductive system that controls cardiac contractions. The electrical impulse is generated in the sinoatrial node, after which the impulse is conducted rapidly to the atrioventricular (A–V) node via the internodal pathways and via Bachmann’s bundle to the left atria. From the A–V node, the impulse is conducted to the ventricles via the bundle of His and the left and right bundle branches of Purkinje fibers (Guyton and Hall, 2006). There are no major differences in cardiac conduction system in mice compared to humans, although it is currently not clear whether mice have a true Bachmann’s bundle (VanderBrink et al., 1999; Kaese et al., 2013; Miquerol et al., 2004; Rentschler et al., 2001). In addition, unlike in the human heart, the His bundle in the mouse is in direct contact with the ventricular myocardium at the basal septum due to less fibrous insulation. As a result, the onset of ventricular activation occurs at the top of the septum and not on the left side of the septum, as in humans (Boukens et al., 2014). Myocardial electrical activity happens in the form of action potentials (APs), which are changes in the membrane potential associated with the depolarization of the cell membrane. Depolarization and repolarization are qualitatively similar in the human and mouse myocardium, but there are marked quantitative differences in electrical properties (Kaese and Verheule, 2012; Liu et al., 2004). The duration of the AP is approximately 50 ms in the mouse ventricle compared to the human value of 250 ms (Kaese and Verheule, 2012). The heart rate (HR) of an adult mouse at rest is 500–725 per min, which is about 10 times faster than in the normal adult human heart (Kaese and Verheule, 2012; Nerbonne, 2004). In both mice and humans, the ventricular APs consist of a fast depolarizing phase called the AP upstroke (Fig. 20.1). The repolarization differs between these two species (Kaese and Verheule, 2012; Nerbonne, 2004). In humans, after the AP upstroke, there is a plateau phase when the membrane potential remains at the same level before the repolarization phase (Guyton and Hall, 2006). Instead in mice, there is no distinctive plateau phase and no distinct time of repolarization, but the AP upstroke is followed by a rapid downstroke and a continuous phase of repolarization (Boukens et al., 2014; Kaese et al., 2013). This is because different ion currents are responsible for the AP shape in humans and mice (reviewed by Nerbonne and Kass, 2005).

Electrocardiogram Mouse ECG differs from human ECG in certain aspects. In human and mouse heart, the spread of depolarization through the atria causes the P wave, which precedes the contraction of atria (Guyton and Hall, 2006; Kaese and Verheule, 2012). The mouse P wave is sometimes followed by a small negative wave, which is proposed to either represent atrial repolarization (Speerschneider and Thomsen, 2013) or to occur when the atria are not positioned in the middle of the left arm lead and Conn’s Handbook of Models for Human Aging. https://doi.org/10.1016/B978-0-12-811353-0.00020-8 Copyright © 2018 Elsevier Inc. All rights reserved.

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FIGURE 20.1  The action potentials (APs) of human (left) and murine (right) ventricular myocytes in relation to the surface ECG. In the schematic representation of APs, the myocardium activated earliest is presented with continuous line and the myocardium activated latest is presented with dashed line. (Modified from Boukens, B.J., Rivaud, M.R., Rentschler, S., Coronel, R., November 1, 2014. Misinterpretation of the mouse ECG: ‘musing the waves of Mus musculus’. J Physiol 592 (Pt 21), 4613–4626.)

right arm lead (Boukens et al., 2014). The P wave is followed by PQ (or PR) interval, which reflects the conduction time from atria to the ventricles (Guyton and Hall, 2006; Kaese and Verheule, 2012). Ventricular contraction is initiated by the depolarization of ventricles, which is seen as the QRS complex in the surface ECG (Boukens et al., 2014; Guyton and Hall, 2006). The QRS duration varies across the different leads in mice. In the human heart, the first phase of repolarization of AP is transient and creates only a small change in the transmembrane potential (Fig. 20.1). However, in mice, the AP upstroke is followed immediately by a fast down stroke and consequently significant repolarization occurs already before the ventricular activation has been completed. Therefore, the mouse QRS complex is actually composed of ventricular activation and in addition, early repolarization (Boukens et al., 2013, 2014). If the ventricular conduction becomes slower in humans, the QRS duration is prolonged. Instead, when mouse ventricular conduction is slowed, the QRS complex ends before ventricular activation is finished. Thus, the QRS duration might underestimate the total activation time of the ventricles. Furthermore, the total activation time of the left ventricle (LV) is shorter than in the right ventricle and delayed left ventricular activation (e.g., in the case of left bundle branch block) does not necessarily lead to a linear increase in the QRS duration in the ECG (Boukens et al., 2013). Due to the different shape of ventricular AP in mice, the QRS complex is followed directly by a J wave (Fig. 20.2). J wave is situated temporally at the early repolarization (Liu et al., 2004; Speerschneider and Thomsen, 2013; Boukens et al., 2013). J waves may be also present in human ECG in early repolarization, hypothermia, and ion channel diseases, such as the Brugada syndrome (Antzelevitch, 2013). The human QRS complex and T wave are separated by an isoelectric ST segment, during which all ventricular cardiomyocytes are depolarized. This results from the plateau phase of the ventricular AP when no extracellular current flows and thus no voltage changes are seen in the surface ECG (Boukens et al., 2014; Speerschneider and Thomsen, 2013). The T wave represents the repolarization of the ventricles when the ventricular muscle fibers begin to relax (Guyton and Hall, 2006). Instead, in mice, no evident plateau phase exists and subsequently there is no clear time point of repolarization but instead a continuous repolarization phase after the upstroke phase (Kaese and Verheule, 2012). Consequently, the T wave is negative, has relatively small amplitude and it merges with the final part of the QRS complex (Boukens et al., 2014; Kaese and Verheule, 2012). The end of ventricular repolarization coincides with the end of the T wave on ECG (Boukens et al., 2013, 2014; Speerschneider and Thomsen, 2013). The end of the T wave is the point when the ECG deflection returns to the isoelectric line and it is most accurately determined with the tangent method (Speerschneider and Thomsen, 2013; Boukens et al., 2013; Lepeschkin and Surawicz, 1952). Since there is no isoelectric ST segment in mice, the segment between the J wave end and T wave end has been referred as JT segment (Boukens et al., 2014; Merentie et al., 2015; Fig. 20.2).

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FIGURE 20.2  Representative surface electrocardiography (ECG) curves of human and mouse. The P wave (atrial depolarization) and QRS complex (ventricular depolarization) is seen in human and mouse ECG. In human ECG, the positive T wave is visible at the end of repolarization. In mouse ECG, the QRS complex is followed by a J wave, which is temporally situated in the early repolarization. QRSp is the time interval from the beginning of the QRS complex to the end of the J wave. There is no clear ST segment or T wave as the T wave merges with the final part of the QRS complex. Instead, there is a JT segment between the J wave end and T wave end.​

In the majority of the mouse ECG studies, the HR-corrected QT time (QTc) has been calculated with Bazzet’s formula specifically modified for mice (Mitchell et al., 1998). However, it has been suggested that the absolute QT value should be preferred in anaesthetized mice since in anesthesia repolarization duration is independent of HR (Speerschneider and Thomsen, 2013).

The Aim Not much is known about the surface ECG findings associated with aging in mice. Previously it has been shown that in 24 months old Kunming mice the P wave duration is increased compared to 2-month-old controls (Luo et al., 2013) and the PR interval increases with aging in 129S1 female mice (Xing et al., 2009). ECG changes in common cardiac diseases, acute myocardial infarction (AMI) and left ventricular hypertrophy (LVH), have not been widely studied in mice. After AMI, ST elevation and the development of pathological Q waves was seen in FVB strain mice (Gehrmann et al., 2001), which resemble the changes seen in human AMI (Thygesen et al., 2012). Not much is known of the surface ECG changes associated with progressive LVH in the widely used transversal aortic constriction (TAC) disease model. In humans, LVH is associated with increased QRS complex amplitude in certain ECG leads, wider QRS complex, and secondary repolarization abnormalities (Thaler, 2010). Accurate, convenient and noninvasive measurement of ECG in mice is critical for characterizing disease phenotypes and translating these findings to human diseases. Therefore, we developed an accurate algorithm for analyzing mouse ECG and validated it by studying the ECG findings associated with aging, pharmacological manipulations (with atropine, metoprolol, and verapamil), AMI and progressive LVH models. The ECG was recorded simultaneously with the echocardiography allowing easy comparison to the echocardiographic findings. The analyses produced reliable information aiding the study of human cardiac diseases in mouse models.

MATERIALS AND METHODS Experimental Animals C57Bl/6J male mice (Harlan Laboratories, Indianapolis, IN) were used as the study animals. Mice were kept in standard housing conditions in the National Laboratory Animal Centre of University of Eastern Finland, Kuopio Campus. Diet and water were provided ad libitum. All of the animal procedures were approved by the National Animal Experiment Board of Finland and carried out in accordance with the guidelines of the Finnish Act on Animal Experimentation. The animal experiments conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH Publication No. 85-23, revised 1996).

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At the end of experiments, mice were euthanized with carbon dioxide and sacrificed. The hearts were collected and processed for histology as described previously (Merentie et al., 2015). Hematoxylin–eosin and Masson trichrome (Accustain trichrome stains; Sigma–Aldrich, St Louis, MO, USA) stainings were done from 5 μm thick paraffin-embedded sections and used for studying the general tissue morphology and the size of the infarction scar.

The Effects of Aging The changes caused by aging were studied with three C57Bl/6J mice groups of different age: the young (2–3 months old), the “middle aged” (12–14 months old), and the old (20–24 months old) mice.

Pharmacological Manipulations The effects of pharmacological manipulation of the cardiac conduction system were studied with 2 mg/kg atropine (Atropin 1 mg/ml, Takeda Oy, Helsinki, Finland), 2 mg/kg metoprolol (Seloken, AstraZeneca, Espoo, Finland) and 5 mg/kg verapamil (Verapamil, Orion, Espoo, Finland) in 5 months old C57BL/6J mice as described previously (Merentie et al., 2015). Drugs were given as intraperitoneal (ip) injections with saline (0.9% NaCl) injections being used as a control. The effects of drugs were monitored with transthoracic echocardiography (TTE) and ECG.

Myocardial Infarction Model and Model for Progressive Left Ventricular Hypertrophy A novel, less invasive surgical model of LAD ligation was used to induce AMI (Gao et al., 2010) in 3 months old C57Bl/6J mice. Each MI was confirmed by echocardiographic visualization and histological findings. TTE and ECG were registered prior to the operation (baseline), 1, 4, and 8 h and 1, 5, and 21 days postoperation. A modified TAC model (Rockman et al., 1991) was used for inducing progressive LVH in mice for 3–4 months old mice. TTE and ECG were recorded 2 and 4 weeks after the TAC and sham operations.

Electrocardiography Recording During Transthoracic Echocardiography Surface ECG signal (lead II via limb electrodes) was acquired during high-resolution TTE with Vevo 2100 Ultrasound System designed for small animals (Fujifilm VisualSonics Inc., Toronto, Ontario, Canada) under isoflurane anesthesia, as previously described (Merentie et al., 2015).

Electrocardiography Analysis The ECG data were exported as raw data format from Vevo software. The software used for analyzing mouse ECG was a custom-built software based on Kubios HRV (Tarvainen et al., 2014), which is written in the Matlab (MathWorks, Natick, MA, USA) and digital signal processing was performed with in house software written in the Matlab (MathWorks, Natick, MA, USA). The software was developed with cardiologists and experts in electrophysiology especially for analyzing mouse ECG, and it was designed to take into account the specific features of the mouse ECG. Readers interested in the software are directed to contact Kubios developers at [email protected]. A 60 s recording of ECG signal at a sampling rate of 8000 Hz was recorded and for the final anaysis a 30 s time interval having the smallest variation of the HR was chosen. The recorded ECG tracing was visually reviewed by the trained researcher for detecting possible arrhythmias or other aberrant ECG complexes. R-peak points were detected using automatic QRS-detection algorithm, after which all detections were visually verified. Defined time points included onset and offset of the P wave and Q wave, R and S wave peaks and offset of the QRS, QRSp and T wave. After the automated analysis, the wave positions were always verified and corrected by the trained specialist. Consequently, P wave duration, PQ interval, Q wave duration, QRS and QRSp width, and QT interval were calculated by the program. In addition, amplitudes of the P, Q, R, and S waves were defined using onset of the P wave as isoelectric line. For more detailed description please see Merentie et al., (2015). P wave duration was measured from the onset of the P wave to the point where the P wave returns to the baseline; the PQ interval from the beginning of the P wave to the beginning of the QRS complex; QRS complex from the beginning of the Q or R wave until the temporal halfway of S wave peak and J wave peak; J wave, from the end of the QRS complex to the end of the positive J wave, which is the point where J wave returns to isoelectric line in healthy mice; QRSp, time interval developed by us (Merentie et al., 2015), representing ventricular depolarization and early repolarization, the time from the beginning of the QRS complex to the end of the J wave; QT time is the time between the beginning of the QRS complex

Development and Validation of ECG Analysis Algorithm in Mice Chapter | 20  275

and the end of the T wave, when the curve returns to the isoelectric line or to the point where the first derivative of T wave end became nearly zero; QTc, QT time corrected for RR-interval with the formula specifically modified for mice from the Bazett’s formula used in human ECG analysis: QTc = mean QT/(RR/100)1/2 (the unit for QT and RR is ms) (Mitchell et al., 1998). Arrhythmias were detected from the ECG tracings as premature atrial or ventricular complexes, irregular RR-intervals equivalent to atrial fibrillation or wide complex tachycardia.

Transthoracic Echocardiography Measurements Along the ECG recordings, also TTE parameters were acquired and analyzed as previously described (Merentie et al., 2015). Shortly, the LV dimensions and ejection fraction (EF) were determined from the midpapillary parasternal short-axis M-mode images and the LV Mass was calculated by the Vevo software. The left atrium (LA) area was determined with the 2D area tool from parasternal long-axis B-Mode and M-Mode was used for calculating aortic root and LA diameter. For global LV function measurements (AMI experiment), the LV trace mode was used to analyze the parasternal long-axis view B-Mode images.

Statistical Analyses Statistical analyses were performed with Excel software with student’s paired t-test when comparing two groups and with GraphPadPrism 6.0 software (GraphPad Software, Inc., La Jolla, CA, USA) using one-way analysis of variance with Dunnet’s post hoc test when comparing three or more groups/time points. The used tests are indicated in the figure legends and the following symbols are used to designate the P-value: *P