Handbook of Cardiovascular Behavioral Medicine [1 ed.] 9780387859590, 9780387859606, 0387859594

Citation preview

Shari R. Waldstein Willem J. Kop Edward C. Suarez William R. Lovallo Leslie I. Katzel Editors

Handbook of Cardiovascular Behavioral Medicine

Handbook of Cardiovascular Behavioral Medicine

Shari R. Waldstein • Willem J. Kop • Edward C. Suarez • William R. Lovallo • Leslie I. Katzel Editors

Handbook of Cardiovascular Behavioral Medicine With 42 Figures and 17 Tables

Editors Shari R. Waldstein Department of Psychology University of Maryland, Baltimore County Baltimore, MD, USA

Willem J. Kop Department of Medical and Clinical Psychology Tilburg University Tilburg, The Netherlands

Division of Gerontology Geriatrics and Palliative Medicine Department of Medicine University of Maryland School of Medicine Baltimore, MD, USA Geriatric Research Education and Clinical Center Baltimore Veterans Affairs Medical Center Baltimore, MD, USA Edward C. Suarez Department of Psychiatry and Behavioral Sciences Duke University Medical Center Durham, NC, USA

William R. Lovallo Department of Psychiatry and Behavioral Sciences University of Oklahoma Health Sciences Center Oklahoma City, OK, USA Behavioral Sciences Laboratories Department of Veterans Affairs Medical Center Oklahoma City, OK, USA

Leslie I. Katzel Division of Gerontology Geriatrics and Palliative Medicine Department of Medicine University of Maryland School of Medicine Baltimore, MD, USA Geriatric Research Education and Clinical Center Baltimore Veterans Affairs Medical Center Baltimore, MD, USA ISBN 978-0-387-85959-0 ISBN 978-0-387-85960-6 (eBook) https://doi.org/10.1007/978-0-387-85960-6 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my brilliant mentor, Steve Manuck, a constant source of inspiration. I am forever grateful for the role you’ve played in my life. S.R.W.

Preface

Cardiovascular disease is the leading cause of morbidity and mortality in the United States and globally. It is well recognized that traditional risk factors for cardiovascular disease have limited predictive utility in the identification of new cardiovascular disease cases and outcomes. Investigators have long argued that application of a biopsychosocial research paradigm in this field may be of particular utility in understanding cardiovascular disease pathogenesis. Accordingly, a subdiscipline within the field of behavioral medicine – cardiovascular behavioral medicine – examines interrelations among biological, behavioral, psychological, social, and environmental factors in cardiovascular health and disease. In 1989, Schneiderman and colleagues published a seminal work entitled Research Methods in Cardiovascular Behavioral Medicine. Since that time, there has been an exponential increase in the amount and scope of work in this topic area, but no similar edited volume has been undertaken. Here we present a compendium of work in the field of cardiovascular behavioral medicine, the purposes of which are to summarize research advances in this area, promote transdisciplinary research and clinical practice, and encourage researchers and clinicians to consider all relevant facets of the disease process in their evaluation and study of cardiovascular disease pathogenesis and outcomes. We are honored that Dr. Schneiderman has provided the lead chapter for this volume. The volume contains four sections. Section I provides perspectives on the past, present, and future of cardiovascular behavioral medicine, an overview of basic cardiovascular anatomy and physiology, cardiovascular disease classification, and application of the biopsychosocial model to the study of cardiovascular disease. Section II covers multiple sociodemographic, behavioral, psychosocial, biomedical, psychophysiological, and environmental risk factors for cardiovascular disease. These chapters offer a discussion of construct definitions, measurement issues, and epidemiological evidence for relations to cardiovascular disease. Section III offers a review of multilevel influences in specific cardiovascular disease entities, the evidence base for relevant biopsychosocial interventions, evaluation of the impact of

vii

viii

Preface

cardiovascular diseases on behavior, and consideration of common comorbidities. Section IV covers select statistical and bioethical topics relevant to the field of cardiovascular behavioral medicine. Baltimore, USA Tilburg, The Netherlands Durham, USA Oklahoma City, USA Baltimore, USA October 2022

Shari R. Waldstein Willem J. Kop Edward C. Suarez William R. Lovallo Leslie I. Katzel Editors

References Schneiderman, N., Weiss, S. M., & Kaufmann, P. G. (Eds.). (1989). Handbook of research methods in cardiovascular behavioral medicine. Springer.

Acknowledgments

We thank our authors for their wonderful contributions and patience during the compilation of this volume. We are also grateful to many individuals for their technical assistance during the course of this project: Allyssa Allen, Eddie Alsina, Lauren Faulkner, Jasmine Gulati, Kiranpreet Kaur, Daniel Leibel, Peter MacIver, Ogechukwu Okeke, Noel Quinn, Danielle Shaked, Ruichen Sun, and Megan Williams. Long live “Bookfests!” Lastly, a very special thanks to Janice Stern for her wise counsel and support.

ix

Contents

Volume 1 Section I Cardiovascular Disease: Background and Biopsychosocial Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2

3

4

1

Cardiovascular Behavioral Medicine: Past, Present, and Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neil Schneiderman

3

Introduction to Cardiac Anatomy, Physiology, and Pathophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Shah, Shabnam Seydafkan, and David Sheps

23

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaston K. Kapuku and Willem J. Kop

45

The Biopsychosocial Perspective on Cardiovascular Disease . . . . . Andrew Steptoe and Roberto La Marca

Section II Relations of Cardiovascular Risk Factors to Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Sociodemographic Risk Factors 5 Childhood Factors in Adult Risk for Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kristen Salomon, Danielle L. Beatty Moody, Kristi E. White, and Taylor M. Darden

81

99

101

6

Aging Changes in Cardiovascular Structure and Function . . . . . . Jerome L. Fleg and Daniel E. Forman

127

7

Risk Factors for Ischemic Heart Disease in Women . . . . . . . . . . . . Biing-Jiun Shen, Uta Maeda, Stacy Eisenberg, and C. Noel Bairey Merz

163

xi

xii

8

9

Contents

Stress and Heart Disease in Women: The Stockholm Women’s Intervention Trial in Coronary Heart Disease Study . . . Kristina Orth-Gomér, May Blom, and Christina Walldin

193

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and Measures . . . . . . . . . . . . . . . . . . . . . . . . . . Camara Jules P. Harrell, Tanisha I. Burford, and Renee Davis

207

10

Socioeconomic Status and Cardiovascular Disease . . . . . . . . . . . . Linda C. Gallo, Steven D. Barger, Addie L. Fortmann, and Smriti Shivpuri

231

11

Health Disparities and Cardiovascular Diseases . . . . . . . . . . . . . . . Kimberly M. Fordham, Michael Golden, Kolawole S. Okuyemi, and Susan A. Everson-Rose

265

b 12

13

14

Behavioral Risk Factors Nicotine Dependence and Cardiovascular Diseases: Biobehavioral and Psychosocial Correlates . . . . . . . . . . . . . . . . . . Mustafa al’Absi, Motohiro Nakajima, Paige Green, Karen Petersen, and Lorentz Wittmers Alcohol and the Cardiovascular System: Implications for Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . William R. Lovallo Impact of Specific Diets and Nutritional Supplements on Cardiovascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surya M. Artham, Dharmendrakumar Patel, Carl J. Lavie, Richard V. Milani, and James H. O’Keefe

287

313

331

15

Understanding Obesity and Cardiometabolic Risk Robyn Osborn Pashby and Tracy Sbrocco

............

357

16

Physical Activity/Exercise and Cardiovascular Disease . . . . . . . . . Charles F. Emery, Erin A. K. Truong, and Kendea N. Oliver

379

17

Sleep as a Bio-behavioral Risk Factor for Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martica H. Hall, Jeffrey M. Greeson, and Elizabeth J. Pantesco

18

Methodological Challenges Associated with the Measurement of Medication Adherence in Patients with Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antoinette Schoenthaler and Sheba Sethi

411

441

Contents

xiii

c Psychosocial and Environmental Risk Factors 19 Personality Factors in Cardiovascular Disease: The Big Five and Type D Personality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Henneke Versteeg, Angélique A. Schiffer, and Susanne S. Pedersen

471

20

Hostility and Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . John C. Barefoot and Redford B. Williams

503

21

Negative Emotions: Depression, Exhaustion, and Anxiety . . . . . . . Lawson R. Wulsin

525

22

Positive Psychological Well-Being and Cardiovascular Disease . . . Julia K. Boehm and Laura D. Kubzansky

541

23

Stress and the Development of Atherosclerotic Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bryce Hruska and Brooks B. Gump

571

24

Work and Cardiovascular Diseases Johannes Siegrist

........................

593

25

Social Support and Cardiovascular Disease . . . . . . . . . . . . . . . . . . Susan M. Czajkowski, S. Sonia Arteaga, and Matthew M. Burg

605

26

Racism, Ethnic Discrimination, and Cardiovascular Health: Conceptual and Measurement Issues . . . . . . . . . . . . . . . . . . . . . . . Elizabeth Brondolo, Danielle L. Beatty Moody, Luis M. Rivera, and Angela Monge

27

Religion, Spirituality, and Cardiovascular Disease . . . . . . . . . . . . . Kevin S. Masters

28

Aggregation of Psychosocial Risk Factors: Models and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timothy W. Smith, Jenny M. Cundiff, and Brian R. Baucom

29

Contexts and Cardiovascular Health . . . . . . . . . . . . . . . . . . . . . . . Jorge Luna and Gina Lovasi

30

Environmental Toxicants and Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brooks B. Gump

631

655

675 701

737

Volume 2 d 31

Biomedical Risk Factors Genetics in Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . Jeanne M. McCaffery

755

xiv

Contents

32

Hypertension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daichi Shimbo, Keith M. Diaz, Matthew M. Burg, and Joseph E. Schwartz

33

The Measurement of Lipids and Lipoproteins in Behavioral Medicine Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Catherine M. Stoney

34

Insulin, Glucose, and the Metabolic Syndrome in Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katri Räikkönen, Eero Kajantie, Kadri Haljas, Ola Hansson, and Johan G. Eriksson

771

787

809

35

Inflammation, Atherosclerosis, and Psychological Factors . . . . . . . Edward C. Suarez

833

36

Hemostasis and Endothelial Function . . . . . . . . . . . . . . . . . . . . . . . Roland von Känel and Simon L. Bacon

861

e 37

38

Psychophysiological Risk Factors Catecholamines and Catecholamine Receptors in Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . Christine Tara Peterson, Michael G. Ziegler, and Paul J. Mills

891

The Assessment of Autonomic Influences on the Heart Using Impedance Cardiography and Heart Rate Variability . . . . . Julian F. Thayer, Anita L. Hansen, and Bjorn Helge Johnsen

911

......................

941

39

Hypothalamic-Pituitary-Adrenal Axis Petra H. Wirtz

40

Ambulatory Monitoring and Ecological Momentary Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thomas W. Kamarck, Mustafa al’Absi, David Epstein, Emre Ertin, Stephen Intille, Gregory Kirk, Santosh Kumar, Kenzie L. Preston, Mark Rea, Vivek Shetty, Saul Shiffman, Dan Siewiorek, Asim Smailagic, Clem Stone, and Manju Venugopal

975

41

Cardiovascular Reactivity and Risk for Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005 William Gerin

42

Neuroimaging and the Study of Cardiovascular Stress Reactivity Annie T. Ginty, John P. Ryan, and Peter J. Gianaros

43

Asymmetric Innervation of the Heart . . . . . . . . . . . . . . . . . . . . . . . 1049 Richard D. Lane, Hugo Critchley, and Peter Taggart

1033

Contents

xv

Section III Biopsychosocial Factors in Cardiovascular Disease, Its Treatments, and Comorbidities . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1069

a Cardiovascular Disease 44 Biopsychosocial Factors in Coronary Artery Disease . . . . . . . . . . . 1071 Daniel A. Nation, Neil Schneiderman, and Philip M. McCabe 45

Chest Pain: Cardiac and Non-Cardiac . . . . . . . . . . . . . . . . . . . . . . 1093 Mark W. Ketterer, Mark A. Lumley, John Schairer, Amjad Farha, and Walter Knysz

46

Mental Stress-Induced Myocardial Ischemia: Prevalence, Clinical Significance, and Treatment Implications . . . . . . . . . . . . . 1107 Wei Jiang, James A. Blumenthal, Jenny T. Wang, and Andrew Sherwood

47

Acute Behavioral and Psychosocial Triggers of Myocardial Infarction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131 Andrew J. Wawrzyniak, Nadine S. Bekkouche, and David S. Krantz

48

Cardiac Arrhythmias and Sudden Cardiac Death . . . . . . . . . . . . . 1149 William Whang and Matthew M. Burg

49

Behavioral Medicine Treatments for Heart Failure . . . . . . . . . . . . 1171 Laura S. Redwine, Barry H. Greenberg, and Paul J. Mills

50

Stroke and Carotid Artery Disease . . . . . . . . . . . . . . . . . . . . . . . . . 1207 Susan A. Everson-Rose and Kimberly M. Fordham

51

Congenital Heart Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1227 Adrienne H. Kovacs and Graham J. Reid

b Cardiovascular Disease: Interventions, Impact, and Comorbidities 52 Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1247 Tanya M. Spruill, Emily M. Contrada Anderson, and Richard J. Contrada 53

Heart Transplantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1271 Sara S. Nash and Peter A. Shapiro

54

Measuring Behavioral Outcomes in Cardiac Rehabilitation . . . . . 1293 David E. Verrill

55

The Psychological Treatment of Cardiac Patients Wolfgang Linden and Alena Talbot Ellis

. . . . . . . . . . . . . 1317

xvi

Contents

56

Quality of Life and Subjective Health: Strengthening the Subjective Perspective in Cardiology . . . . . . . . . . . . . . . . . . . . . . . 1341 Christoph Herrmann-Lingen

57

Cardiovascular Disease and Cognitive Function . . . . . . . . . . . . . . 1463 Shari R. Waldstein, Carrington R. Wendell, Danielle Shaked, Megan M. Hosey, Stephen L. Seliger, and Leslie I. Katzel

58

Diabetes and Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . 1393 Felicia Hill-Briggs, Mohammad Naqibuddin, and Sherita Hill Golden

59

HIV-1 Spectrum Disease, Psychological Distress, and Cardiometabolic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415 Barry E. Hurwitz, Roger C. McIntosh, and Jeffrey M. Greeson

Section IV Statistical Modeling and Ethics in Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1443

60

Measuring Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1445 Alan B. Zonderman, Gregory Dore, and Nicolle A. Mode

61

Confounding, Mediation, Moderation, and General Considerations in Regression Modeling . . . . . . . . . . . . . . . . . . . . . 1467 Michael A. Babyak and Laust Hvas Mortenson

62

Systematic Reviews and Meta-analysis in Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1493 Seth M. Noar and Noel T. Brewer

63

Ethical Issues in Cardiovascular Behavioral Medicine Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1511 Leslie I. Katzel and Adil Shamoo

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1529

About the Editors

Shari R. Waldstein, Ph.D., is Professor of Psychology, Behavioral Medicine Track Director, and Affiliate Professor of Gerontology at the University of Maryland, Baltimore County; Professor of Medicine in the Division of Gerontology, Geriatrics and Palliative Medicine at the University of Maryland School of Medicine; and Research Scientist for the Geriatric Research Education and Clinical Center at the Baltimore Veterans Affairs Medical Center. She completed her A.B.. in Psychology at Duke University; received her M.S. and Ph.D. in Clinical Psychology, with specialty training in cardiovascular behavioral medicine, from the University of Pittsburgh; and completed clinical internship training in neuropsychology at Brown University. Dr. Waldstein’s interdisciplinary research examines (a) relations of cardiometabolic risk factors and diseases to neurocognitive function and subclinical brain pathology assessed by neuroimaging; (b) biopsychosocial correlates of cardiometabolic risk profiles; and (c) variation and disparities in these associations as a function of age, race, socioeconomic status, and sex/gender. She has been supported by multiple grants from the National Institutes of Health and other organizations for over 25 years. Dr. Waldstein currently serves on the Executive Council of the Academy of Behavioral Medicine Research (ABMR) and has previously served as President of the American Psychosomatic Society (APS), as a member of the APS Executive Council, and as Chair of the APS Professional Education Committee; as Member-at-Large for Division 38 (Society for Health Psychology) of the American Psychological Association (APA); and as Chair of the Education and Training Council for the Society of Behavioral Medicine (SBM). She is presently an Editorial Board member for xvii

xviii

About the Editors

the journal Psychosomatic Medicine and previously served as an Associate Editor for Health Psychology and a regular member of the Mechanisms of Emotion and Stress in Health (MESH) study section for the National Institutes of Health. Dr. Waldstein is a Fellow of the ABMR, SBM, and Division 38 of the APA. She was the recipient of an Early Career Award from the APS, an Outstanding Scientific Contributions to Health Psychology Award from Division 38 of the APA, and an Outstanding Contributions to Science Award from the Maryland Psychological Association. Dr. Waldstein was the 2015–2016 Lipitz Professor of the Arts, Humanities, and Social Sciences at UMBC. Willem J. Kop, Ph.D., is Professor of Medical and Clinical Psychology at Tilburg University, the Netherlands. Dr. Kop received his Ph.D. from Maastricht University, the Netherlands, in 1994, after which he took a position at the Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA, until 2006. He then took a position at the University of Maryland in the Division of Cardiology where he directed the Behavioral Cardiology Program. Dr. Kop investigates cardiovascular diseases and other disorders where fatigue, depression, and mental stress interact with the autonomic nervous and immune systems. These studies involve psychological evaluations using Structured Clinical Interviews, ambulatory monitoring techniques, as well as controlled procedures to induce mental stress. The general scope of his research program is to identify the biological mechanisms by which psychological factors affect disease outcomes and to what extent these relationships are mediated by health behaviors, such as physical activity. His current research integrates novel technological developments in ambulatory remote patient monitoring and e-Health with biobehavioral processes relevant to health and disease. Dr. Kop has been Principal Investigator on several studies funded by the National Institutes of Health. He is actively involved in the fields of Psychosomatic and Behavioral Medicine and Health Psychology. He is the current Editor-in-Chief of one of the leading journals in his field: Psychosomatic Medicine. Dr. Kop has also served on several editorial boards, including Health Psychology and Brain, Behavior and Immunity,

About the Editors

xix

and was a member of various NIH review panels and other international scientific review boards. Dr. Kop is the recipient of the 1998 Early Career Award of the American Psychosomatic Society and the 2002 Outstanding Contributions to Health Psychology Award from the American Psychological Association. Edward C. Suarez, Ph.D., M.A., is Professor in the Department of Psychiatry and Behavioral Sciences at Duke University Medical Center and in the Department of Psychology and Neuroscience at Duke University in Durham, North Carolina. He received his A.B.. in Mathematics and Psychology and his Ph.D. in Psychology from the University of Miami, and a M.A. in Bioethics from Wake Forest University. The focus of his research has been exploring the relation of psychosocial factors, such as hostility, depression, and anger, to established and emerging early risk markers of atherosclerotic cardiovascular disease and Type 2 diabetes, and the moderating influences of gender and race as well as alcohol consumption, family history, socioeconomic status, and physical activity. The focus of his most current projects has been: (1) dysregulation of the feedback loop between the hypothalamic-pituitary-adrenal axis and the inflammatory response system, and how dysregulation relates to symptoms of depression and emotional stress responses in men and women, and (2) personalized health planning and mindfulness meditation as interventions for reducing the risk of Type 2 diabetes in prediabetic adults. His interest in metabolism has led him to expand his research to the relation of micronutrients to risk of depression and early markers of Type 2 diabetes and how race influences these associations. His work has been supported by the National Heart, Lung and Blood Institute from 1988 to 2014. He was the Guest Editor of a Special issue of Brain, Behavior and Immunity that focused on the influences of gender, race, and ethnicity on neuroimmune parameters. He has been on the editorial boards of numerous journals, a member of various advisory boards for the National Institutes of Health (NIH), and is a Fellow of the Society of Behavioral Medicine, which awarded him their New Investigator Award in 1989.

xx

About the Editors

William R. Lovallo, Ph.D., is Regents Professor Emeritus of Psychiatry and Behavioral Sciences at the University of Oklahoma Health Sciences Center and a Senior Research Career Scientist and Director of the Behavioral Sciences Laboratories at the VA Medical Center in Oklahoma City. He obtained his A.B.. in Psychology from UCLA; his M.A. in Experimental Psychology from the University of Colorado, Boulder; and his Ph.D. in Biological Psychology from the University of Oklahoma. His research concerns cardiovascular and neuroendocrine responses to psychological stress, and his most current project is devoted to understanding stress reactivity and emotion regulation in young adults with a family history of alcoholism. His work has been supported by the Department of Veterans Affairs, the National Institutes of Health, and the John D. and Catherine T. MacArthur Foundation. He has served as the Associate Director of the MacArthur Research Network on Mind-Body Interactions and as a Council Member and President of the American Psychosomatic Society. He has been on the editorial boards of the International Journal of Psychophysiology, Psychosomatic Medicine, the International Journal of Behavioral Medicine, and the Annals of Behavioral Medicine. He has served on numerous NIH advisory groups and has been a Fellow of the American Psychological Association and the Society of Behavioral Medicine. His book, Stress & Health: Biological and Psychological Interactions is widely used in psychology, medicine, anthropology, and nursing programs. Leslie I. Katzel, M.D., Ph.D., is an Associate Professor of Medicine in the Division of Gerontology, Geriatrics and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine; Director of the Baltimore Veterans Affairs Medical Center Geriatrics Research, Education, and Clinical Center (GRECC); and co-PI along with Drs. Jay Magaziner and Alice Ryan of the University of Maryland Claude D. Pepper Older Americans Independence Center (UM-OAIC). He obtained his B.S. in Physics from The Cooper Union for the Advancement of Science and Art, his Ph.D. in Biophysics from the Johns Hopkins University, and his M.D. from the University of Maryland School of

About the Editors

xxi

Medicine. He completed an internal medicine residency at the University of Maryland and fellowship training in geriatrics at Johns Hopkins with additional training as a medical staff fellow at the National Institute of Aging/ Gerontology Research Center. Dr. Katzel is a boardcertified internist and geriatrician. For the past 30 years he has been principal investigator or co-investigator on grants funded by the NIH and Department of Veteran Affairs that focus on the performance of exercise and lifestyle interventions in older adults with numerous medical comorbidities, including metabolic syndrome, chronic kidney disease, Parkinson’s disease, peripheral arterial disease, HIV, and stroke. The overall hypothesis of his work is that adverse changes in functional performance, cognition, and metabolic profiles that occur with aging, obesity, sedentary lifestyle, and disease can in part be reversed with weight loss and exercise interventions. He has also collaborated on several research studies with Dr. Waldstein and colleagues that examined the impact of aging, hypertension, chronic kidney disease, race, and socioeconomic status on neurocognitive function and neuroimaging outcomes. He has published more than 140 journal articles and book chapters. Dr. Katzel also has a longstanding interest in research ethics. He is former chair and vice chair of the University of Maryland Institutional Review Board (IRB) and served as a site visitor and council member for the Association for the Accreditation of Human Research Protection Programs (AAHRPP). Dr. Katzel is chair of the VA Maryland Health Care System (VAMHCS) Research and Development Committee and chair of the University of Maryland Embryonic Stem Cell Research Oversight Committee (ESCRO).

Contributors

Mustafa al’Absi Department of Family Medicine and Biobehavioral Health, University of Minnesota Medical School, Duluth, MN, USA S. Sonia Arteaga Division of Cardiovascular Sciences, Clinical Applications and Prevention Branch National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA Surya M. Artham Ochsner Heart and Vascular Institute, Ochsner Medical Center, New Orleans, LA, USA Michael A. Babyak Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Simon L. Bacon Department of Exercise Science, Concordia University, Montréal, QC, Canada Montreal Behavioural Medicine Centre, Hôpital du Sacré-Cœur de Montréal, Montréal, QC, Canada C. Noel Bairey Merz Cedars-Sinai Heart Institute, Los Angeles, CA, USA John C. Barefoot Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Steven D. Barger Department of Psychological Sciences, Northern Arizona University, Flagstaff, AZ, USA Brian R. Baucom Department of Psychology, University of Utah, Salt Lake City, UT, USA Danielle L. Beatty Moody Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA Nadine S. Bekkouche Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA May Blom Division of Evidence-Based Medicine, Stockholm County Council, Stockholm, Sweden xxiii

xxiv

Contributors

James A. Blumenthal Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Julia K. Boehm Department of Psychology, Chapman University, Orange, CA, USA Noel T. Brewer Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Elizabeth Brondolo Department of Psychology, St. John’s University, Jamaica, NY, USA Tanisha I. Burford Department of Psychology, Hampton University, Hampton, VA, USA Matthew M. Burg Section of Cardiovascular Medicine, Departments of Internal Medicine and Anesthesiology, Yale School of Medicine, West Haven, CT, USA Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, USA Richard J. Contrada Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA Emily M. Contrada Anderson Department of Continuing Nursing Education, Lippincott Williams & Wilkins, Clearwater, FL, USA Hugo Critchley Department of Medical Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, East Sussex, UK Jenny M. Cundiff Department of Psychology, University of Alabama, Tuscaloosa, AL, USA Susan M. Czajkowski Health Behaviors Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA Taylor M. Darden Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA Renee Davis Department of Psychology, Howard University, Washington, DC, USA Keith M. Diaz Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, USA Gregory Dore Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD, USA Stacy Eisenberg Department of Psychology, University of Southern California, Los Angeles, CA, USA Gregory Dore: deceased.

Contributors

xxv

Alena Talbot Ellis Department of Psychology, University of British Columbia, Vancouver, BC, Canada Charles F. Emery Departments of Psychology and Internal Medicine, and Institute for Behavioral Medicine Research, Ohio State University, Columbus, OH, USA David Epstein Real-World Assessment, Prediction, and Treatment Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD, USA Johan G. Eriksson Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland Emre Ertin Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA Susan A. Everson-Rose Department of Family Medicine and Community Health and Program in Health Disparities Research, University of Minnesota Medical School, Minneapolis, MN, USA Amjad Farha Heart and Vascular Institute, Henry Ford Hospital, Wayne State University, Detroit, MI, USA Jerome L. Fleg Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA Kimberly M. Fordham Department of Family Medicine and Community Health and Program in Health Disparities Research, University of Minnesota Medical School, Minneapolis, MN, USA Daniel E. Forman Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA VA Boston Healthcare System, Boston, MA, USA Addie L. Fortmann Scripps Whittier Diabetes Institute, Scripps Health, San Diego, CA, USA Linda C. Gallo Department of Psychology, San Diego State University, San Diego, CA, USA William Gerin Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Peter J. Gianaros Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA Annie T. Ginty Department of Psychology, Baylor University, Waco, TX, USA Michael Golden Department of Family Medicine and Community Health and Program in Health Disparities Research, University of Minnesota Medical School, Minneapolis, MN, USA

xxvi

Contributors

Sherita Hill Golden Department of Medicine, Division of Endocrinology and Metabolism, and Department of Epidemiology, and the Welch Center for Prevention, Epidemiology, and Clinical Research, The Johns Hopkins Medical Institutions, Baltimore, MD, USA Paige Green Basic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA Barry H. Greenberg Advanced Heart Failure Program, Department of Medicine, University of California, San Diego, La Jolla, CA, USA Jeffrey M. Greeson Department of Psychology, Rowan University, Glassboro, NJ, USA Brooks B. Gump Department of Public Health, Food Studies, and Nutrition, Syracuse University, Syracuse, NY, USA Kadri Haljas Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland Martica H. Hall Departments of Psychiatry, Psychology, and Clinical and Translational Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA Anita L. Hansen Department of Psychosocial Science, University of Bergen, Bergen, Norway Ola Hansson Department of Clinical Sciences, Malmö University Hospital, Malmö, Sweden Camara Jules P. Harrell Department of Psychology, Howard University, Washington, DC, USA Christoph Herrmann-Lingen Department of Psychosomatic Medicine and Psychotherapy, University of Göttingen Medical Center, Göttingen, Germany Felicia Hill-Briggs Department of Medicine, Division of General Internal Medicine, and Department of Health, Behavior, and Society, and Welch Center for Prevention, Epidemiology, and Clinical Research, The Johns Hopkins Medical Institutions, Baltimore, MD, USA Megan M. Hosey Division of Rehabilitation Psychology and Neuropsychology, Department of Physical Medicine and Rehabilitation, Johns Hopkins School of Medicine, Baltimore, MD, USA Bryce Hruska Department of Public Health, Food Studies, and Nutrition, Syracuse University, Syracuse, NY, USA

Contributors

xxvii

Barry E. Hurwitz Departments of Psychology and Biomedical Engineering, University of Miami, Coral Gables, FL, USA Behavioral Medicine Research Center and Division of Endocrinology and Metabolism, School of Medicine, University of Miami, Miami, FL, USA Stephen Intille Khoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA Wei Jiang Departments of Psychiatry and Behavioral Sciences and Medicine, Duke University Medical Center, Durham, NC, USA Bjorn Helge Johnsen Department of Psychosocial Science, University of Bergen, Bergen, Norway Roland von Känel Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, and University of Zurich, Zurich, Switzerland Eero Kajantie Children’s Hospital, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland Thomas W. Kamarck Departments of Psychology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA Gaston K. Kapuku Department of Pediatrics, Medicine, and Population Health Sciences, Augusta University, Medical College of Georgia, Georgia Prevention Institute, Augusta, GA, USA Leslie I. Katzel Division of Gerontology, Geriatrics and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA Geriatric Research Education and Clinical Center, Baltimore Veterans Affairs Medical Center, Baltimore, MD, USA Mark W. Ketterer Heart and Vascular Institute, Henry Ford Medical Group, Detroit, MI, USA Gregory Kirk Departments of Epidemiology and Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA Walter Knysz Consultation-Liaison Psychiatry, Henry Ford Medical Group, Detroit, MI, USA Willem J. Kop Department of Medical and Clinical Psychology, Tilburg University, Tilburg, The Netherlands Adrienne H. Kovacs Peter Munk Cardiac Centre, University Health Network, The University of Toronto, Toronto, ON, Canada

xxviii

Contributors

David S. Krantz Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA Laura D. Kubzansky Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA Santosh Kumar Department of Computer Science, University of Memphis, Memphis, TN, USA Roberto La Marca Department of Clinical Psychology and Psychotherapy, University of Zurich, Zurich, Switzerland Richard D. Lane Department of Psychiatry, University of Arizona, Tucson, AZ, USA Carl J. Lavie Ochsner Heart and Vascular Institute, Ochsner Medical Center, New Orleans, LA, USA Wolfgang Linden Department of Psychology, University of British Columbia, Vancouver, BC, Canada William R. Lovallo Department of Psychiatry and Behavioral Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Behavioral Sciences Laboratories, Department of Veterans Affairs Medical Center, Oklahoma City, OK, USA William R. Lovallo Department of Psychiatry and Behavioral Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA Behavioral Sciences Laboratories, Department of Veterans Affairs Medical Center, Oklahoma City, OK, USA Gina Lovasi Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA Mark A. Lumley Department of Psychology, Wayne State University, Detroit, MI, USA Jorge Luna Value Institute, New York – Presbyterian Hospital, New York, NY, USA Uta Maeda Department of Psychology, University of Southern California, Los Angeles, CA, USA Kevin S. Masters Department of Psychology, University of Colorado Denver, Denver, CO, USA Philip M. McCabe Departments of Psychology and Biomedical Engineering, University of Miami, Miami, FL, USA Jeanne M. McCaffery Department of Allied Health Sciences, University of Connecticut, Storrs, CT, USA

Contributors

xxix

Roger C. McIntosh Department of Psychology, University of Miami, Coral Gables, FL, USA Richard V. Milani Ochsner Heart and Vascular Institute, Ochsner Medical Center, New Orleans, LA, USA Paul J. Mills Department of Family Medicine and Public Health, Center of Excellence for Research and Training in Integrative Health, University of California San Diego, La Jolla, CA, USA Nicolle A. Mode Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD, USA Angela Monge Department of Psychology, St. John’s University, Jamaica, NY, USA Laust Hvas Mortenson Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark Motohiro Nakajima Department of Family Medicine and Biobehavioral Health, University of Minnesota Medical School, Duluth, MN, USA Mohammad Naqibuddin Department of Medicine, Division of Endocrinology and Metabolism and Welch Center for Prevention, Epidemiology, and Clinical Research, The Johns Hopkins Medical Institutions, Baltimore, MD, USA Sara S. Nash Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA Daniel A. Nation Department of Psychological Science and Institute for Memory Disorders and Neurological Impairments, University of California Irvine, Irvine, CA, USA Seth M. Noar School of Media and Journalism, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA James H. O’Keefe Mid-America Heart Institute, Kansas City, MO, USA Kolawole S. Okuyemi Department of Family Medicine and Community Health and Program in Health Disparities Research, and Center for Health Equity, University of Minnesota Medical Center, Minneapolis, MN, USA Kendea N. Oliver Department of Psychiatry and Behavioral Medicine, Lahey Hospital and Medical Center, Burlington, MA, USA Kristina Orth-Gomér Karolinska Institutet, Stockholm, Sweden Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany Elizabeth J. Pantesco Department of Psychological and Brain Sciences, Villanova University, Villanova, PA, USA Kristina Orth-Gomér: deceased.

xxx

Contributors

Robyn Osborn Pashby Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA Dharmendrakumar Patel Ochsner Heart and Vascular Institute, Ochsner Medical Center, New Orleans, LA, USA Susanne S. Pedersen Department of Medical Psychology, University of Southern Denmark, Odense, Denmark Department of Cardiology, Odense University Hospital, Odense, Denmark Karen Petersen Department of Psychology, The College of St. Scholastica, Duluth, MN, USA Christine Tara Peterson Department of Family Medicine and Public Health, Center of Excellence for Research and Training in Integrative Health, University of California San Diego, La Jolla, CA, USA Kenzie L. Preston Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA Katri Räikkönen Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland Mark Rea Lighting Research Center, Rensselaer Polytechnic Institute, Troy, NY, USA Laura S. Redwine Department of Family Medicine and Community Health, Miller School of Medicine, University of Miami, Miami, USA Graham J. Reid Department of Psychology, and Departments of Family Medicine, and Paediatrics, Children’s Health Research Institute, Western University, London, ON, Canada Luis M. Rivera Department of Psychology, Rutgers University, Newark, Newark, NJ, USA John P. Ryan Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA Kristen Salomon Department of Psychology, University of South Florida, Tampa, FL, USA Tracy Sbrocco Department of Medical and Clinical Psychology, Uniformed Services University, Bethesda, MD, USA John Schairer Heart and Vascular Institute, Henry Ford Hospital, Wayne State University, Detroit, MI, USA Angélique A. Schiffer Department of Psychiatry and Medical Psychology, Catharina Hospital, Eindhoven, Eindhoven, The Netherlands

Contributors

xxxi

Neil Schneiderman Departments of Psychology, Medicine, Public Health Sciences, Psychiatry and Behavioral Sciences, and Biomedical Engineering, and Behavioral Medicine Research Center, University of Miami, Coral Gables, FL, USA Antoinette Schoenthaler Departments of Population Health and Medicine, New York University Grossman School of Medicine, New York, NY, USA Joseph E. Schwartz Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, USA Stephen L. Seliger Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA Sheba Sethi Packard Health, Ann Arbor, MI, USA Shabnam Seydafkan Division of Cardiology, Emory University, Atlanta, GA, USA Amit Shah Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA Danielle Shaked Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA Adil Shamoo Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA Peter A. Shapiro Department of Psychiatry, Columbia University Medical Center, New York, NY, USA Biing-Jiun Shen Department of Psychology, School of Social Sciences, Nanyang Technological University, Nanyang, Singapore David Sheps Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA Andrew Sherwood Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Vivek Shetty Departments of Oral and Maxillofacial Surgery and Biomedical Engineering, University of California, Los Angeles, Los Angeles, CA, USA Saul Shiffman Departments of Psychology, Psychiatry, Pharmaceutical Sciences, and Clinical Translational Research, University of Pittsburgh, Pittsburgh, PA, USA Daichi Shimbo Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, USA Smriti Shivpuri Department of Psychology, San Diego State University, San Diego, CA, USA Johannes Siegrist Center for Health and Society, Faculty of Medicine, HeinrichHeine-University Duesseldorf, Duesseldorf, Germany

xxxii

Contributors

Dan Siewiorek Departments of Electrical and Computer Engineering and Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Asim Smailagic Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA Timothy W. Smith Department of Psychology, University of Utah, Salt Lake City, UT, USA Tanya M. Spruill Department of Population Health, New York University School of Medicine, New York City, NY, USA Andrew Steptoe Department of Epidemiology and Public Health, University College London, London, UK Clem Stone Department of Psychology in Education, University of Pittsburgh, Pittsburgh, PA, USA Catherine M. Stoney Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA Edward C. Suarez Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Peter Taggart Institute of Cardiovascular Science, University College London, London, UK Julian F. Thayer Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Erin A. K. Truong Department of Psychology, The Ohio State University, Columbus, OH, USA Manju Venugopal Innovative Diagnostics Technologies, LLC, Atlanta, GA, USA David E. Verrill Department of Kinesiology, University of North Carolina at Charlotte, Charlotte, NC, USA Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, and University of Zurich, Zurich, Switzerland Henneke Versteeg Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands Shari R. Waldstein Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA Division of Gerontology, Geriatrics and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA Geriatric Research Education and Clinical Center, Baltimore Veterans Affairs Medical Center, Baltimore, MD, USA

Contributors

xxxiii

Christina Walldin Division of Evidence-Based Medicine, Stockholm County Council, Stockholm, Sweden Jenny T. Wang Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Andrew J. Wawrzyniak Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Carrington R. Wendell Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA William Whang Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York, NY, USA Kristi E. White Hennepin County Medical Center & University of Minnesota Center for Spirituality & Healing, Minneapolis, MN, USA Redford B. Williams Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Petra H. Wirtz Department of Psychology, University of Konstanz, Constance, Germany Lorentz Wittmers Department of Physiology and Pharmacology, University of Minnesota Medical School, Duluth, MN, USA Lawson R. Wulsin Departments of Psychiatry and Family Medicine, University of Cincinnati, Cincinnati, OH, USA Michael G. Ziegler Department of Medicine, University of California, San Diego Medical Center, San Diego, CA, USA Alan B. Zonderman Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD, USA

Section I Cardiovascular Disease: Background and Biopsychosocial Model

1

Cardiovascular Behavioral Medicine: Past, Present, and Future Neil Schneiderman

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathophysiology of Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biobehavioral Interventions in Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Present Context and Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 5 7 10 13 14 18

Abstract

Associations between psychosocial factors and disease have been noted for more than two millennia. By the middle of the twentieth century, scientists identified multiple risk factors for cardiovascular disease (CVD). A handful of modifiable risk factors, including psychosocial variables, seem responsible for nearly all CVD morbidity and mortality although the effects of genes on those risk factors are considerable. The interactive role of an atherogenic diet and psychosocial stress on the progression of atherogenesis in nonhuman animals has been demonstrated experimentally as has the decrease in atherosclerosis progression due to psychosocial factors. At present large-scale, longitudinal, population-based observational studies are examining the biobehavioral, psychosocial, and sociocultural factors that influence CVD risk and health outcomes. Increasingly such studies are using sophisticated genomic, biomarker, vascular, and neuroimaging techniques to examine disease processes. Lifestyle and psychosocial interventions decrease CVD vulnerability in high-risk populations and prevent recurrence N. Schneiderman (*) Departments of Psychology, Medicine, Public Health Sciences, Psychiatry and Behavioral Sciences, and Biomedical Engineering, and Behavioral Medicine Research Center, University of Miami, Coral Gables, FL, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_1

3

4

N. Schneiderman

of myocardial infarction and death. These results need replication in rigorous randomized controlled trials. Cardiovascular behavioral medicine research has undergone substantial growth during the past three decades and has a bright future. Keywords

History · Modifiable risk factors · Pathophysiology · Psychosocial-behavioral interventions · Population-based studies

Introduction Cardiovascular behavioral medicine was founded on the premise that behavioral, psychosocial, and sociocultural processes can influence cardiovascular regulation and disease. More specifically, the area is an integral part of the interdisciplinary field of behavioral medicine research and practice and is concerned with the development and integration of sociocultural, psychosocial, and biobehavioral knowledge relevant to health and illness and the application of this knowledge to cardiovascular disease prevention, health promotion, etiology, pathogenesis, diagnosis, treatment, and rehabilitation. Because our understanding of the issues involved in this interdisciplinary field has evolved over more than two millennia, it is important to consider the underpinnings of the field of cardiovascular behavioral medicine. Hippocrates, a Greek physician born in 460 BC and often considered the father of medicine, believed that disturbances in temperament can lead to disease [2, 59]. The advent of physical medicine during the Renaissance brought about the development of techniques permitting the direct investigation of physical phenomena. Andreas Vesalius [75], a Flemish physician, published his anatomical text De Humani Corporis Fabrica (On the Make-Up of the Human Body) based upon his dissections of the human body. Then, William Harvey [30] described how blood is propelled by the heart and circulation throughout the body. By the middle of the nineteenth century, Claude Bernard [6] was able to point out that the maintenance of life is dependent upon keeping an organism’s internal environment constant in the face of a changing external environment. Subsequently, Walter Cannon [10] coined the term homeostasis to describe the process of maintaining stability in the face of environmental change. Cannon [9, 11] provided evidence that homeostasis can be threatened by psychologically meaningful as well as physical challenges. Hans Selye [70] used the term stress to describe the effects of any agent that seriously threatens homeostasis. Selye recognized that stress responses are designed to be adaptive. He also realized that severe, prolonged stress responses can cause tissue damage and disease. The findings that psychologically meaningful challenges could influence homeostasis and disease processes conflicted with the distinction promulgated in the seventeenth century by René Descartes [18] between psyche and soma. Based upon the distinction made by Descartes (who really was distinguishing between body and soul), Western medicine became guided by the belief that the onset and

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

5

progression of physical illness could be reduced to a purely biological explanation without consideration of mental processes (i.e., thoughts and emotions). The proposition that the causes and course of disease can be explained solely by physical mechanisms is referred to as biological reductionism, and in its narrowest interpretation, the onset and progression of any specific disease should be attributable to a single cause. By the middle of the twentieth century, however, the high mortality rate due to infectious diseases was eclipsed by the rates for chronic disorders including cardiovascular diseases (CVD) and cancer. Because these diseases appeared to involve multiple risk factors, scientific investigations necessarily changed from the search for a single causative agent to the interactions occurring among etiologic agents, the host, and its environment. Furthermore, it appeared that thoughts and emotions could influence disease processes. This position was argued effectively by George Engel [21], who claimed that a biopsychosocial model was needed to replace the narrow biological reductionism prevalent in Western medicine. By the time that the original Handbook of Research Methods in Cardiovascular Behavioral Medicine [66] (a volume that I coedited with Peter Kaufmann and Stephen Weiss) was published in 1989, the roles of behavioral and psychosocial processes in the pathogenesis of disease were beginning to be accepted in parts of the biomedical community. During the ensuing years the role of sociocultural, psychosocial, and biobehavioral factors in the pathogenesis of disease has become ever more widely accepted. The chapters written for the present volume illustrate the extent to which these biopsychosocial approaches have contributed to our understanding of the pathophysiology and management of CVD through discoveries in basic science, clinical investigation, and public health.

Risk Factors By the middle of the twentieth century, it became apparent that some diseases such as CVD could not be attributed to a single cause. Consequently, investigations such as the Framingham Heart Study were initiated to examine the possible multiple risk factors for CVD [15]. The Framingham Study was a prospective, longitudinal investigation that collected behavioral, biological, clinical, and demographic information on 6000 participants between 30 and 50 years of age. Within 10 years the investigators prospectively identified three major risk factors for CVD as cigarette smoking, elevated cholesterol, and high blood pressure [36]. Since then, it has been widely thought that four modifiable traditional risk factors (i.e., smoking, hypertension, hypercholesterolemia, type 2 diabetes mellitus) account for about 50% of the risk for CVD [8, 31]. Powerful evidence supporting the thesis that there are multifactorial risk factors for myocardial infarction (MI) has been provided by INTERHEART [82], which was a standardized case-control study of acute MI carried out in 52 countries representing every inhabited continent. The 15,152 cases and 14,820 control participants were compared in terms of self-reported smoking, history of hypertension, history of diabetes, consumption of alcohol, physical activity, dietary patterns, and psychosocial factors, tape measurements for

6

N. Schneiderman

adiposity, and blood measurements for apolipoproteins (Apo). The study age distribution was determined by the inclusion of MI patients, with a median age in years for men in the 50s and for women in the 60s. INTERHEART [82] found that nine easily assessed and potentially modifiable risk factors accounted for more than 90% of the population attributable risk for an initial acute MI. Population attributable risk refers to the incidence rate of a condition (in this case MI) associated with or attributable to exposure to a specific risk factor. This should not be confused with the proportion of explained variance, which is conceptually different. In any event, the effect of the risk factors contributing to population attributable risk was consistent in men and women, across geographic regions and by racial/ethnic groups. Thus the study findings appear to be widely generalizable. Worldwide, the two most important risk factors are smoking and abnormal lipids, which together account for about two-thirds of the population attributable risk of MI. Other important risk factors having odds ratios of two or greater in univariate analyses include psychosocial factors, hypertension, diabetes, and abdominal obesity. Conversely, daily consumption of fruits or vegetables, moderate or strenuous exercise, and moderate consumption of alcohol were reported to be protective. Although the INTERHEART [82] study represents a major scientific achievement, it should be recognized that in order to carry out such a monumental undertaking, it was necessary for the investigators to make a number of scientific compromises. Thus, in order to maximize the generalizability of their findings, the investigators needed to sacrifice precise characterization and examination of the mechanisms and processes underlying risk factors. Rather than using fasting blood, for example, to evaluate separately triglycerides, high-density lipoprotein (HDL)-, and low-density lipoprotein (LDL)-cholesterol, the INTERHEART investigators used the ratio of ApoB/Apo A1 from non-fasting blood as a global index of abnormal lipids. Also, neither blood pressure, plasma insulin, nor blood glucose was assessed directly. In similar fashion, psychosocial stress was examined by four simple questions about financial stress, major life events in the past year, and stress at home and at work [62]. Depression was evaluated by a modified version of the short form of the Composite International Diagnostic Interview questionnaire [58]. Each of the psychosocial variables was significantly associated with increased risk of MI. Examination of the extensive content of the present volume indicates that a study such as INTERHEART could only scratch the surface of our understanding of precise relationships among cardiovascular risk factors, disease processes, and major adverse CVD outcomes. Thus, the measurement deficiencies in INTERHEART, strategically necessary as they may have been to accomplish study objectives, quite surely led to variations in estimated risk that could be improved by more precise and sensitive measurements (e.g., direct assessment of blood pressure, fasting lipid components, and impaired glucose tolerance as well as the more comprehensive assessment of psychosocial distress). Similarly, the INTERHEART study was not able to elucidate the putative variables mediating the association between traditional risk factors and cardiovascular mortality including inflammation, insulin resistance, oxidative stress, and platelet coagulation.

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

7

Although INTERHEART provided tantalizing clues relating psychosocial factors to MI, it provided little information about how such variables might interact with one another or with other risk factors in order to moderate CVD processes. In contrast, INTERHEART provided a broad outline of the risk factors that should be addressed for the primary and secondary prevention of CVD. This included the importance of attending to psychosocial risk. Although the population attributable risk in INTERHEART for severe global stress was less than for smoking, it was comparable to the risk posed by abnormal lipids, hypertension, diabetes, or abdominal obesity. The finding in INTERHEART that psychosocial factors accounted for 32.5% of the population attributable risk for MI is consistent with the emphasis placed in the present volume on relationships between psychosocial factors and CVD. Although a wide range of epidemiological studies have called attention to potentially modifiable risk factors, it should be recognized that the outcomes of CVD are the result of the joint effects of risk genes, the environment, and behavior upon these risk factors. Heritability estimates based upon the Netherlands Twin Registry, for example, suggest that at least in this one country such estimates are 75% for nicotine dependence [76], 34–67% for blood pressure (deGeus et al., [17], 61–83% for lipid levels [4], 64–81% for body mass index [69], and 36% for major depression [49]. Thus, one can expect that on the basis of genomic analyses, future studies shall begin to identify the extent to which specific individuals are particularly vulnerable to CVD events and may be candidates for targeted interventions.

Pathophysiology of Cardiovascular Disease Cardiovascular disease comprises a wide range of clinical and subclinical conditions, of which MI and stroke are among the most common life-threatening conditions. The underlying cause of CVD is atherosclerosis in the coronary and other arteries. Elevated lipid levels, particularly cholesterol, have been tied to the development of coronary atherosclerosis and subsequent MI following plaque rupture and/or intracoronary clot formation. By narrowing or occluding the lumen of the coronary arteries, atherosclerosis disrupts the metabolic balance of the myocardium. Thus, disruption of blood flow to heart muscle has long been known to be the most frequent lethal complication of coronary artery disease [41]. Early lesions in the coronary arteries are characterized by the deposition of cholesterol-filled macrophages within the vessel wall, whereas more advanced lesions involve a necrotic core surrounded by cholesterol-laden macrophages and a more superficial layer of smooth muscle cells with a dense fibrous cap. The more acute complications of coronary artery disease stem from the disruption of atherosclerotic lesions such as plaque hemorrhage or rupture. Highly thrombogenic properties of the plaque contents then lead to the clotting of surrounding blood associated with plaque disruption. This can then result in the formation of a tethered mass of coagulated blood called a mural thrombus, or the embolization of thrombogenic plaque components, which can occlude downstream vessels and lead to MI.

8

N. Schneiderman

Plasma lipid levels are important, but they do not account for all of the variance in lesion formation [74]. Lesions often appear in regions surrounding vessel bends suggesting that turbulence and sheer stress play a role in lesion initiation. The hemodynamic forces are normally attenuated by functional characteristics of the endothelium, including the maintenance of a smooth, nonadhesive, and selectively permeable surface, as well as the ability to alter vessel diameter in response to activation of various receptors [16]. In the healthy endothelium this is accomplished by the release of nitrous oxide, in turn causing flow-mediated vasodilation in the vascular smooth muscle. Considerable research now indicates that endothelial dysfunction, characterized by increased endothelial surface adhesiveness and permeability, and a reduction in the ability of vessels to dilate is an early development of atherosclerotic lesions. Although the notion that arterial injury might precipitate atherosclerotic lesions can be traced as far back as a paper by Virchow in 1856 [77], formal statement of the hypothesis that injury to the endothelium might be the precipitating event was advanced nearly 120 years later by Ross and Glomset [64]. During the next two decades important progress was made in the study of endothelial dysfunction [63], but at the time the Handbook of Research Methods in Cardiovascular Behavioral Medicine [67] was published, the cardiological community still fixed most of its attention upon a relatively simple hydraulic model of cardiovascular regulation and dysregulation [65]. Since then a great deal has been learned about the critical role played by the endothelium. In the context of hyperlipidemia, endothelial dysfunction may begin with the sequestration of plasma lipids within the endothelial extracellular matrix [83]. Enzymes such as nicotinamide adenine dinucleotide phosphate (NADPH)-oxidase, which is present within the cell membrane, can oxidize LDL (OX-LDL), which in turn can damage the endothelial cell membrane leading to activation of the endothelial cell layer [53]. The activated endothelium increases its adhesiveness through elevated expression of cell adhesion molecules (e.g., ICAM-1, VCAM-1, L-selectin) causing circulating monocytes to adhere to the vessel wall where they become activated by OX-LDL and differentiate into macrophages [34]. These macrophages then consume OX-LDL. The macrophages also secrete pro-inflammatory cytokines (i.e., interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), IL-1β), which promote further activation of the endothelium and the circulation of adhesion molecules perpetuating the inflammatory cascade. Within the past quarter century, investigators have begun studying endothelium-dependent vasodilation in humans by addressing the changes in diameter of the brachial artery during reactive hyperemia [43]. Briefly, flow to the forearm is occluded using a cuff. Following cuff deflation, flow-mediated hyperemia occurs as brachial artery blood flow increases to accommodate the dilated resistance vessels. Images of the brachial artery and the development of plaque are obtained using high-resolution ultrasound recording. Assays have also become widely available for the assessment of adhesion molecules, pro-inflammatory cytokines, and OX-LDL. Thus, advances in commercially

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

9

available research methods have facilitated the study of the pathophysiology of CVD in both animals and humans. Prospective, population-based studies have indicated that inflammation is central to the development of CVD [61]. The development of acute coronary syndrome (i.e., MI or unstable angina) is caused by decreased blood flow due to occlusive thrombus formation over a ruptured atherosclerotic plaque [3]. Platelet aggregation depends upon the interaction of membrane glycoproteins, which are receptors for adhesive proteins and the major determinant of thrombus formation [13]. Not all plaques appear equally prone to rupture. Those that are, known as vulnerable plaques, have large lipid cores, high macrophage content, and a thin fibrous cap. Most acute coronary syndrome events are associated with vulnerable plaques rather than the development of totally obstructive lesions. One explanation concerning the basis of the vulnerable plaque is that some arterial vessels undergo positive remodeling, in which the external elastic membrane adjacent to the plaque becomes outwardly displaced [68]. Thus the volume in these plaques is greater than occurs in plaques that undergo negative remodeling. In these latter less vulnerable negatively remodeled plaques, the elastic membrane becomes smaller at the lesion site. Thus, positive remodeling, while maintaining relatively normal lumen size, may be associated with an increased risk of an acute coronary syndrome event. Although the mechanisms underlying positive remodeling are unknown, inflammation and positive remodeling appear to be related [57]. The relationships among biobehavioral variables, positive remodeling, vulnerable plaque, and acute coronary events are presently unknown but worthy of study. Although important studies relating psychosocial factors to arteriosclerosis and atherosclerosis date back at least as far as Lang [42], Ratcliffe and Snyder [60], Henry et al. [32], and Nerem et al. [51], an important series of studies by Kaplan, Manuck, Clarkson, and their collaborators clearly demonstrated the interactive role of an atherogenic diet and psychosocial stress in the development of atherosclerosis in nonhuman primates. Briefly, Kaplan et al. [37] placed male cynomolgus monkeys on a moderately atherogenic diet for 20 months. Half of the monkeys were housed in a socially stable situation, whereas social instability was induced in the other half by frequent reorganization of group membership. Social status in terms of dominance and subordination was assessed by animals’ wins and losses during fighting. The major finding of the study was that dominant males, when placed in an unstable social situation, developed more coronary atherosclerosis than the other animals. Subsequently, Kaplan et al. [39] found that the excessive atherosclerosis observed among dominant male monkeys living in disrupted social groups could be inhibited by the beta-adrenergic antagonist propranolol, which inhibited the stress-induced increase in pulse rate and blood pressure typically associated with sympathetic nervous system arousal. In contrast to the findings observed in male cynomolgus macaque monkeys, studies conducted in female crab eating macaque monkeys exposed to an atherogenic diet have reported that monkeys that were subordinate in their social group experienced moderate ovarian impairment and were at elevated risk for developing

10

N. Schneiderman

atherosclerosis [38]. Interestingly, ovariectomy eliminated the “protection” from diet-induced atherosclerosis typically observed in dominant premenopausal female monkeys [1], thereby implicating estrogen as the protective factor. Thus, the monkey studies demonstrated the manner by which social environment, diet, temperament (dominance/submission), and sex can interact to influence the course of atherogenesis. Whereas the studies just described have convincingly demonstrated the impact of psychosocial variables upon the development of atherosclerosis, more recent animal research has suggested that behavioral contexts may also inhibit atherogenesis and offer cardioprotection. This research used the Watanabe heritable hyperlipidemic rabbit, which is an inbred strain of rabbit that exhibits hypercholesterolemia, elevated plasma LDL levels, and premature severe atherosclerosis due to a single-gene mutation. In a study by McCabe et al. [47], instituting a stable socially supportive environment resulted in the decreased progression of atherosclerosis. Watanabe rabbits were assigned either to an unstable condition in which unfamiliar rabbits were paired daily, with the pairing switched each week; a stable condition, in which littermates were paired daily for the entire study; or an individually caged condition. The investigators found that the animals in the stable social condition exhibited more affiliative social behavior and less agonistic behavior than those in the unstable social condition and significantly less aortic atherosclerosis than those in the other two conditions. These differences among conditions could not be explained by variations in serum lipids, plasma corticoids, or gonadal steroids. However, in the Watanabe rabbit model, an unstable social environment compared to a stable social environment was shown to be related to elevated plasma catecholamine level, which is an index of sympathetic nervous system activation [56]. It would thus appear that useful animal models have been developed that can help elucidate important relationships between psychosocial factors and atherogenic processes.

Biobehavioral Interventions in Cardiovascular Disease As noted above, INTERHEART found that nine potentially modifiable risk factors, including some psychosocial variables, accounted for more than 90% of the population attributable risk for an initial acute MI [62, 82]. To the extent that behavioral medicine interventions are based upon teaching coping strategies and behavior change techniques, it should be expected that those intervention trials that are aimed at preventing initial or recurrent MI would be directed at decreasing the influence of CVD risk factors. Several meta-analyses have examined randomized psychosocial and biobehavioral interventions in patients with CVD [12, 20, 44, 45]. Most studies compared the behavioral intervention with usual care. Dusseldorp and colleagues studied the effects of health education and stress management in 37 studies. They reported a 34% reduction in cardiovascular mortality, a 29% reduction in MI recurrence, and significant positive effects for blood pressure, cholesterol, body weight, smoking, physical exercise, and eating habits. Those CVD rehabilitation programs that

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

11

successfully improved risk factor profiles also seemed more effective in decreasing cardiovascular mortality and MI recurrences than were those that were not successful in risk factor reduction. Two major clinical trials have found that lifestyle interventions targeting weight loss and an increase in physical activity can reduce the incidence of diabetes in persons who are at high risk [40, 72]. Diabetes is a major risk factor for CVD. The Finnish Diabetes Prevention Study Group randomly assigned 522 middle-aged, overweight men and women at high risk for diabetes because of impaired glucose tolerance to either an intervention or a control group. Each participant in the intervention group received individualized counseling aimed at reducing weight, total intake of fat, and intake of saturated fat, while increasing intake of fiber and physical activity. Participants in the intervention group had seven sessions with a nutritionist during the first year of the study and one session every 3 months thereafter. The participants also received individual guidance on increasing physical activity, improving endurance, and participating in resistance training sessions. There was a significant difference in the cumulative incidence of diabetes after 4 years between those in the intervention (11%) and those in the control (23%) group. Thus, the risk of diabetes was reduced significantly by 58% in the intervention group, and the reduced incidence of diabetes was directly related to changes in lifestyle. In a similar trial the Diabetes Prevention Program Research Group [40] randomly assigned 3234 nondiabetic persons with elevated fasting glucose and post-glucose load plasma concentrations to either placebo, the blood glucose-reducing drug metformin, or a lifestyle modification program aiming at a 7% reduction in weight and at least 150 min of physical activity per week. After an average of 2.8 years the average incidence of diabetes was 11.0%, 7.8%, and 4.8% in the placebo, metformin, and lifestyle groups, respectively. Thus, the lifestyle intervention reduced the incidence of diabetes by 58% and metformin by 31% compared with the placebo condition. Based upon the success of the Diabetes Prevention Research Program and the Finnish Diabetes Prevention Trial, the Look AHEAD (Action for Health in Diabetes) randomized clinical trial compared the effects of an intensive lifestyle intervention versus a diabetes support and education control group on the major CVD events in 5145 overweight or obese participants with type 2 diabetes [80]. At 4 years, intervention participants showed greater improvements than control participants in terms of weight loss, fitness, hemoglobin A1c, systolic blood pressure, and HDL - cholesterol. Reduction in LDL - cholesterol, however, was greater in the control participants owing to greater use of lipid-lowering medications. Although the changes in weight loss and CVD risk factors were largely sustained in subsequent years, the trial was stopped after a median follow-up of 9.6 years on the basis of a futility analysis indicating that the intervention did not reduce the rate of CVD events [81]. The exact reasons for the trial obtaining a null result are unknown. However, several possible explanations for the lack of significant difference in the rates of CVD events between groups seem apparent. One of these is that there may have been an intensification of medical management in the control group

12

N. Schneiderman

that influenced the result. Thus, for example, all participants had to have an established relationship with a primary care provider, and patients could be using any type of glucose-lowering medication although the percentage of those using insulin in the trial was restricted to less than 30%. Patients and their healthcare providers in both groups received annual reports on the patients’ updated CVD risk factor profiles as well as the goals recommended by the American Diabetes Association. A second factor to consider is that the patients who enrolled in the study may not have been typical people with diabetes since in spite of being overweight and having type 2 diabetes, they had relatively normal or low risk factor profiles for smoking, blood pressure, triglycerides, and cholesterol and were able to successfully complete a maximal-fitness test at baseline. A third factor to consider is that the amount of weight loss was relatively small and took place in patients who generally seemed to otherwise be in generally good health. In this regard it might be noted that the relationship between obesity and cardiometabolic diseases is complex. Genomewide association studies, for example, have identified variants in the insulin-resistant substrate 1 gene for which an adiposity-increasing allele has been associated with favorable cardiometabolic outcomes [73]. Whereas some meta-analytic reviews have indicated that multicomponent psychosocial- biobehavioral interventions are able to reduce mortality and secondary event rates in patients with established CVD [20, 44], these findings appear to be somewhat less robust when considering the results of studies that have emphasized psychosocial treatment [45]. This may not be too surprising when one considers the multifactorial nature of CVD risk. Few of the psychosocial-behavioral randomized clinical trials conducted on postMI patients meet the reporting criteria of the Consolidated Standards of Reporting Trials (CONSORT) statement [50]. Those trials that approximated these standards have yielded both positive and null results. Because of the heterogeneity of the procedures employed, the exact reasons for discrepancies in results remain speculative. Nevertheless there appear to be impressive similarities among the trials that obtained positive results; these studies also seemed to differ in important ways from those that reported null results. The three major studies that have reported positive results are the Recurrent Coronary Prevention Project (RCPP) conducted by Friedman et al. [25], the Stockholm Women’s Intervention Trial for Coronary Heart Disease (SWITCHD) conducted by Orth-Gomér et al. [55], and the Secondary Prevention in Uppsala Primary Health Care Project (SUPRIM) led by Gulliksson [29]. Large-scale trials that obtained null results include a study by Jones and West [35], the Montreal Heart Attack Readjustment Trial (M-HART) conducted by Frasure-Smith et al. [23], and the Enhancing Recovery in Coronary Heart Disease (ENRICHD) trial conducted by Berkman et al. [5]. The RCPP, SUPRIM, and SWITCHD began treatment at least several months after the CVD event; conducted same-sex groups (men in RCPP; women in SWITCHD; separate men’s and women’s groups in SUPRIM); used cognitive behavior therapy, relaxation training, and attention to lifestyle problems during treatment; employed a large number of sessions in the intervention group

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

13

(an average of 38 sessions over 4.5 years in RCPP; up to 20 sessions over the course of a full year in SUPRIM and SWITCHD); and followed up patients for at least 4.5 years in RCPP, a mean of 7.1 years in SWITCHD, and 7.8 years in SUPRIM. It is worth mentioning that both the intervention and control participants received extensive traditional risk factor counseling (diet, exercise, medication adherence) in the RCPP and that knowledge of the heart, medication adherence, healthier lifestyle, and training skills as well as psychosocial issues were important intervention topics covered in SWITCHD and SUPRIM. The therapists in RCPP, SUPRIM, and SWITCHD were specifically trained to use behavior change techniques in order to conduct behavioral interventions with cardiac patients as was also true in ENRICHD. In contrast to the RCPP, SUPRIM, and SWITCHD, which reported positive outcomes with regard to recurrence (RCPP and SUPRIM) and mortality rate (SWITCHD), the trial by Jones and West [35] enrolled patients within 28 days after MI and provided seven weekly psychological counseling and therapy relaxation and stress management sessions (some in a group format). Other components of rehabilitation dealing with smoking, diet, weight control, or exercise were not included in the program. Data on the age, sex distribution, or race/ethnicity of participants were not described in the published article. The intervention in M-HART [23] began a week after discharge with a phone call, which was repeated monthly for a year. If the participant reported distress, a cardiology nurse made a home visit and provided reassurance, education, practical advice, and, if warranted, referral to a healthcare provider. About three quarters of intervention condition participants received 5–6 1 h nursing visits. Nurses were not given specific training (e.g., cognitive or behavioral stress management) for implementing the protocol beyond their cardiology nursing training. The ENRICHD trial randomized post-MI patients who were clinically depressed and/or had low social support [5]. Intervention was initiated at a median of 17 days after MI for a median of 11 sessions throughout 6 months. The focus of these sessions was on treating depression and reducing perceived isolation. About 30% of participants also received group-based cognitive behavior therapy and relaxation in mixed groups of women and men. The behaviorally based randomized clinical trials that have been conducted thus far on post-MI patients suggest that treatments involving trained behavior interventionists, group-based cognitive behavior therapy, relaxation training, and attention to lifestyle issues including medication adherence, diet, exercise, and domestic and work stressors can reduce CVD morbidity [25, 29] and mortality [55].

Comment Since the publication of the Handbook of Research Methods in Cardiovascular Behavioral Medicine [66], our understanding of the bases of cardiovascular medicine has undergone important changes as is evident from the chapters in this volume. Conceptions about the pathogenesis of CVD still rely heavily on the importance of

14

N. Schneiderman

multiple risk factors, but there has also been increased awareness that most of the variance accounting for CVD events is actually provided by modifiable risk factors [82] and that psychosocial factors are important contributors [62]. There is also increased awareness that the modifiable risk factors themselves are influenced by genetic variables [4, 17, 76]. These genetic variables may make some individuals and groups particularly susceptible to the effects of specific identifiable risk factors. In addition to our increased knowledge concerning specific risk factors, we are leaning about the role played by these risk factors in relation to such biological processes as inflammation, oxidative stress, fibrinolysis, and coagulation. This has led to a better understanding of the complexities of the coronary vasculature, particularly with regard to the structure and function of the coronary endothelium as well as vascular remodeling. The past quarter century has also seen an increased focus in cardiovascular behavioral medicine on attending to lifestyle variables as well as psychosocial factors in terms of primary and secondary CVD prevention. It is worth noting, however, that the pioneer randomized clinical trial in behavioral CVD intervention was the RCPP [25] and that this trial emphasized both lifestyle and psychosocial changes as components of the intervention. Thus, intervention participants were trained using cognitive and behavioral techniques to focus upon making lifestyle as well as psychosocial changes part of their treatment. In terms of behavior change techniques, both the Diabetes Prevention Program [40] and the Look AHEAD trial [80] developed detailed manuals describing specific procedures for promoting behavior change. In addition to also using treatment manuals, the RCPP [25], SUPRIM [29], and SWITCHD [55] emphasized the need to influence psychosocial and lifestyle adjustments that may be required to optimize secondary CVD prevention. Although the RCPP, SUPRIM, and SWITCHD each produced impressive positive results, there is still a major need to replicate and amplify the results of these studies in rigorous, large-scale, multicenter, randomized controlled trials that can partial out some of the demographic (e.g., racial/ethnic background, sex, socioeconomic status), psychosocial (e.g., temperament and personality, marital and work stressors, social support), and lifestyle (e.g., medication adherence, diet, physical activity, and smoking) variables that influence specific behavioral and biological determinants of risk.

Present Context and Future Prospects Contemporary cardiovascular behavioral medicine research has been built upon a foundation consisting of basic behavioral and biological sciences, population-based studies, and randomized clinical trials. The edifice that ultimately emerges will depend upon the degree of success that occurs in integrating the ingredients that were used to lay the foundation. Further development of this cardiovascular behavioral medicine structure will also depend upon the success of researchers in this field exploiting a rapidly expanding knowledge base, employing emerging technologies including the Internet to address important scientific questions, and raising a cadre of

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

15

capable new scientists possessing the skills to capitalize on emerging scientific opportunities and having the sagacity to recognize important healthcare needs in our society. The necessity of using a combination of basic science, population science, and randomized clinical trials to resolve health problems is exemplified by contemporary attempts to decrease morbidity and mortality in depressed patients with CVD. Previous epidemiological research has established that depression is associated with an increased risk of mortality after MI [22]. The null results obtained in the ENRICHD randomized clinical trial suggest that we may not yet know how to decrease mortality in depressed patients after MI [5]. Adequate resolution of the problem, including a rationale understanding of treatment, will likely require us to better clarify depressive symptoms, use biological assays and neuroimaging to provide more adequate differential diagnosis between depressive symptoms related to vascular inflammation and those related to other causes, and employ advanced behavioral and possibly biological (including genomic) technology to optimize treatment. Although behavioral scientists have long been involved in epidemiological studies, this trend is sure to continue and will inevitably lead to an increase in the number of cardiovascular behavioral medicine researchers who will engage in large-scale, population-based, interdisciplinary, multicenter observational studies. These investigations will focus on how biobehavioral, psychosocial, and sociocultural factors influence health status, health behaviors, CVD risk, and outcomes and will provide important insights concerning the biological processes intervening between psychosocial/lifestyle variables and health outcomes. The National Institute of Health/National Heart Lung and Blood Institute (NIH/NHLBI) has sponsored several large, multicenter studies examining relationships between psychosocial/lifestyle factors and disease outcomes including the Coronary Risk Development in Young Adults (CARDIA) Study [26], Multi-Ethnic Study of Atherosclerosis (MESA) [7], and the Hispanic Community Health Study/ Study of Latinos (HCHS/SOL) [71]. Such studies are beginning to set the stage for well-powered investigations that will allow cardiovascular behavioral medicine scientists to examine the links between sociocultural, psychosocial, and biobehavioral variables on the one hand and health outcomes on the other using vascular and brain imaging as well as genomic analysis. At present consortia involving multiple multicenter studies are beginning to collaborate on genomic investigations involving many thousands of participants. Therefore opportunities are certain to present themselves for behavioral scientists to relate genomics to cardiovascular risk factors and exposures in the determination of health outcomes. Several strategies have been used to attempt to relate genes to cardiovascular risk factors. One of these, the candidate gene approach, relies upon contemporary understanding of the biology and pathophysiology of a disease risk to select appropriate genes for study. Thus, for example, some candidate gene studies have attempted to examine obesity. A major problem that has plagued the candidate gene study of obesity is that obesity clearly has many determinants so that it is hardly surprising that many genetic variants contribute to it. In order to detect the expected small effects of these many genetic variants, candidate gene studies need to

16

N. Schneiderman

be well-powered and thus rely on meta-analyses or the pooling of data from largescale studies. In spite of these limitations, at least five variants in candidate genes have been found to be robustly associated with obesity-related traits [46]. These genes include MC4R (melanocortin 4 receptor), ADRB3 (beta 3 adrenergic receptor), PCSK1 (prohormone convertase 1), BDNF (brain-derived neurotrophic factor), and CNR1 (endocannabinoid receptor 1). Another approach that is being increasingly employed involves genome-wide association studies. This approach examines the entire genome without making prior assumptions in order to identify previously unsuspected genetic loci associated with a known risk factor or disease. The approach was made possible early in this century when the Human Genome Project and Celera Genomics in parallel completed the human genome sequencing of the four chemical base pairs of DNA (A with T; C with G). The projects identified and mapped the approximately 30,000 genes of the human genome. Subsequently, the HapMap consortium [24] characterized millions of common DNA sequence variations of a single base, termed single-nucleotide polymorphisms (SNPs), across the genome in various human populations. Genomewide association studies typically have two stages. The first is a discovery stage, which is then followed by one or more replications. During the past decade projects such as HapMap have led to the development of high-throughput genome-wide genotyping chips (e.g., Affymetrix; Illumina) containing as many as a million SNPs. Thus, genome-wide association studies allow interrogation of the entire genome with a high degree of resolution with the aim of identifying common genetic variations underlying disease. Such studies have led to the identification of loci associated with obesity, diabetes, blood pressure, and cigarette smoking [52]. Since the completion of the Human Genome Project in 2003, some research in the behavioral medicine community has begun to focus on gene-environment interactions. One way that gene-environment interactions occur is when genetic factors affect measured phenotypes differently as a function of different environmental exposures. Thus, for example, men carrying the e4 allele of the apolipoprotein E gene (APOE) have an increased smoking-related risk for CVD events [33]. Documentation of such gene-environment interactions, involving specific gene polymorphisms, has stimulated interest among behavioral medicine researchers interested in cardiovascular disorders. Job strain, decision latitude, and alpha-2 beta adrenergic receptor polymorphisms, for example, have been reported to interact and associate with elevated blood pressure in men [54]. Studies have also begun to examine the heritability of cardiovascular reactivity phenotypes in acute laboratory experiments [48, 79]. Although most of these behavioral studies have been carried out on relatively small samples, it can be expected that the number of high-quality, wellpowered gene-environment studies relevant to cardiovascular behavioral medicine will increase rapidly during the coming years. Besides the structural genomic studies just described, functional genomic studies focus on the basics of protein synthesis and how genes are “switched on” to provide messenger (m)RNA. Crick [14] originally thought that each gene codes for one specific mRNA molecule that in turn codes for a specific protein. However, it subsequently became evident that after being transcribed, most mRNA molecules

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

17

undergo an editing process with some segments being spliced out. This allows a gene to lead to more than one type of mRNA molecule and consequently more than one type of protein. In this way many stimuli, including hormones and cytokines, are able to activate transcription factors. This, of course, will be the focus of much interest in future cardiovascular behavioral medicine research. An important technique for measuring gene expression is the reverse transcriptase polymerase chain reaction (PCR). Extensive libraries of PCR assays have been developed that can measure mRNA from nearly any human gene of interest. Although originally used to study candidate genes, microassay techniques have become available that allow the activities of tens of thousands of genes to be examined simultaneously. It would appear that recent advances in structural and functional genomics will lead to many future discoveries in cardiovascular behavioral medicine. Capitalizing upon the advances that have been made in measuring gene expression is already critical for progress in basic research that is important for cardiovascular behavioral medicine. Equally important will be the involvement in genomic analyses by behavioral scientists involved in observational studies and randomized clinical trials. Besides genomic analyses, cardiovascular imaging has become increasingly important in cardiovascular behavioral medicine research. The assessments of carotid artery intimal-medial thickness (IMT) and plaque and the study of aortic and coronary artery calcification (CAC) as subclinical markers of disease, for example, have been associated with biobehavioral, psychosocial, and sociocultural factors in both cross-sectional and longitudinal studies. Among middle aged women, for example, higher levels of perceived hopelessness have been associated with carotid IMT assessed increases in subclinical atherosclerosis [78]. Similarly, women in unsatisfying marriages have shown more rapid carotid IMT progression of atherosclerosis and more aortic calcification longitudinally than women in more satisfactory marriages [27]. In addition, large multicenter prospective studies such as MESA [7] have been specifically examining the phenotypic concomitants of subclinical CVD with the intent of predicting progression to clinically overt cardiovascular disease in diverse samples of men and women. Because the trajectory of atherosclerotic CVD typically occurs over multiple decades before manifesting itself in terms of diagnosable adverse clinical events, it is important to relate biobehavioral, psychosocial, and sociocultural variables to subclinical markers of disease as early as possible. The adverse childhood experience (ACE) study, for example, has shown that the number of severe adverse experiences during childhood is associated with the adult prevalence of heart disease and other chronic illnesses [19]. Thus, there will be an increasing number of opportunities for scientists, interested in cardiovascular behavioral medicine, to study biopsychosocial-disease process relationships throughout the life span with the intent of preventing as well as treating disease. A final topic that I would like to mention is the promise provided by neuroimaging for the study of cardiovascular behavioral medicine. Having spent more than 50 years researching the central nervous system regulation of the circulation in animal models, I feel that an understanding of cardiovascular neurobiology is central

18

N. Schneiderman

to our solving important problems in cardiovascular behavioral medicine. For one thing it has provided behavioral scientists with important links to biochemistry, cellular physiology, immunology, molecular biology, and pharmacology. For another, it has helped scientists in our field to understand pathophysiology and disease processes. With these thoughts in mind, I am happy to see behavioral scientists working along the frontiers of human structural and functional neuroimaging. Thus, for example, I have been pleased to see human research examining the direct covariation between carotid IMT and amygdala reactivity and functional connectivity assessed using functional magnetic resonance imaging during the presentation of angry and fearful facial expressions [28]. Future studies will certainly relate biopsychosocial variables to disease processes using both functional and structural neuroimaging.

References 1. Adams MR, Kaplan JR, Clarkson TB, Koritnik DR (1985) Ovariectomy, social status, and atherosclerosis in cynomolgus monkeys. Arteriosclerosis 5(2):192–200. PMID 3977777 2. Asimov I (1982) Asimov’s biographical encyclopedia of science and technology, 2nd rev edn. Doubleday, Garden City 3. Badimon JJ, Zaman A, Helft G, Fayad Z, Fuster V (1999) Acute coronary syndromes: pathophysiology and preventive priorities. Thromb Haemost 82:997–1004. PMID 10605815 4. Beekman M, Heijmans BT, Martin NG, Pederson NL, Whitfield JB et al (2002) Heritabilities of apolipoprotein and lipid level in three countries. Twin Res Hum Genet 5:87–97. PMID 11931686 5. Berkman LF, Blumenthal J, Burg M, Carney RM, Catellier D et al (2003) Effects of treating depression and low perceived social support on clinical events after myocardial infarction: the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) randomized trial. JAMA 289:3106–3116. PMID 12813116 6. Bernard C (1865/1961) An introduction to the study of experimental medicine (trans: Greene HC). Collier 7. Bild DE, Bluemke DA, Burke GL, Detrano R, Roux AVD et al (2002) Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol 156:871–881. PMID 12397006 8. Braunwald E (1997) Shattuck lecture – cardiovascular medicine at the turn of the millennium: triumphs, concerns and opportunities. N Engl J Med 337:1360–1369. PMID 9358131 9. Cannon WB (1928) The mechanism of emotional disturbance of bodily functions. N Engl J Med 198:165–172 10. Cannon WB (1929) Bodily changes in pain, hunger, fear and rage, 2nd edn. Appleton, New York 11. Cannon WB (1935) Stresses and strains of homeostasis (Mary Scott Newbold Lecture). Am J Med Sci 189:1–14 12. Clark AM, Hartling L, Vandermeer B, McAlister FA (2005) Secondary prevention program for patients with coronary artery disease: a meta-analysis of randomized control trials. Ann Intern Med 143:659–672. PMID 16263889 13. Coller BS (1997) Platelet GP IIb/IIIa antagonists: the first anti-integrin receptor therapeutics. J Clin Investig 99:1467–1471. PMID 9119988 14. Crick F (1970) Central dogma of molecular biology. Nature 227:561–563. PMID 4913914 15. Dawber TR, Meadors CF, Moore FE (1951) Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health 41:279–290. PMID 14819398 16. Deanfield JE, Halcox JP, Rabelink TJ (2007) Endothelial function and dysfunction: testing and clinical relevance. Circulation 115(10):1285–1295. PMID 17353456

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

19

17. deGeus EJC, Kupper N, Boomsma DI, Snider H (2007) Bivariate genetic modeling of cardiovascular stress reactivity: does stress uncover genetic variance? Psychosom Med 69:356–364. PMID 17510291 18. Descartes R (1637/1956) Discourse on method (trans: LaFleur LJ). Sammes 19. Dong M, Giles WH, Felitti VJ, Dube SR, Williams JE et al (2004) Insights into causal pathways for ischemic heart disease: adverse childhood experiences study. Circulation 110:1761–1766. Get PMID # 20. Dusseldorp E, van Elderen T, Maes S, Meulman J, Kraaij V (1999) A meta-analysis of psychoeducational programs for coronary heart disease patients. Health Psychol 18:506–519. PMID 10519467 21. Engel GL (1977) The need for a new medical model: a challenge for biomedicine. Science 196 (4286):129–136. PMID 847460 22. Frasure-Smith N, Lespérance F, Talajic M (1993) Depression following myocardial infarction. Impact on 6-month survival. JAMA 270:1819–1825. PMID 8411525 23. Frasure-Smith N, Lesperance F, Prince R, Verrier P, Garber RA et al (1997) Randomized trial of home-based psychosocial nursing intervention for patients recovering from myocardial infarction. Lancet 350:473–479. PMID 9274583 24. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL et al (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861. PMID 17943122 25. Friedman M, Thoresen CE, Gill JJ, Ulmer D, Powell LH, Price VA, Brown B, Thompson L, Rabin DD, Breall WS et al (1986) Alteration of Type A behavior and its effect on cardiac recurrences in post myocardial infarction patients: summary results of the Recurrent Coronary Prevention Project. Am Heart J 112:653–665. PMID 3766365 26. Friedman GD, Cutter GR, Donahue RP, Hughes GH, Hulley SB et al (1988) CARDIA: study design, recruitment and some characteristics of the examined subjects. J Clin Epidemiol 41:1105–1116. PMID 3204420 27. Gallo LC, Troxel WM, Kuller LH, Sutton-Tyrrell K, Edmundowicz D et al (2003) Marital status, marital quality, and atherosclerotic burden in postmenopausal women. Psychosom Med 65:952–962. PMID 14645772 28. Gianaros PJ, Hariri AR, Sheu LK, Muldoon MF, Sutton-Tyrrell K et al (2009) Preclinical atherosclerosis covaries with individual differences in reactivity and functional connectivity of the amygdala. Biol Psychiatry 65:943–950. PMID 19013557 29. Gulliksson M, Burell G, Vessby B, Lundin L, Toss H, Svärdsudd K (2011) Randomized controlled trial of cognitive behavioral therapy vs standard treatment to prevent recurrent cardiovascular events in patients with coronary heart disease: secondary prevention in Uppsala primary health care project (SUPRIM). Arch Intern Med 171:134–140. PMID 21263103 30. Harvey W (1628) Exercitatio anatomica de motu cordis et sanguinis in animalibus (Anatomical exercise on the motion of the heart and blood in animals) 31. Hennekens CH (1998) Increasing burden of cardiovascular disease: current knowledge and future directions for research on risk factors. Circulation 97:1095–1102. PMID 9531257 32. Henry JP, Ely DL, Stephens PM, Ratcliffe HL, Santisteban GA, Shapiro AP (1971) The role of psychosocial factors in the development of arteriosclerosis in CBA mice. Observations on the heart, kidney and aorta. Atherosclerosis 14(2):203–218. PMID: 5165781 33. Humphries SE, Talmud PJ, Hawe E, Bolla M, Day INM et al (2001) Apolipoprotein E4 and coronary heart disease in middle-aged men who smoke: a prospective study. Lancet 358:115– 119. PMID 11463413 34. Jialai I (1993) Evolving lipoprotein risk factors: lipoprotein and oxidized low-density lipoprotein. Clin Chem 44:1927–1832 35. Jones DA, West RR (1996) Psychological rehabilitation after myocardial infarction: multicentre randomized controlled trial. BMJ 313:1517–1521. PMID 8978226 36. Kannel WB, Dawber TR, Kagan A, Revotskie N, Stokes J III (1961) Factors of risk in the development of coronary heart disease– follow-up experience. The Framingham Study. Ann Intern Med 55:33–50. PMID 13751193 37. Kaplan JR, Manuck SB, Clarkson TB, Lusso FM, Taub DM (1982) Social status, environment, and atherosclerosis in cynomolgus monkeys. Arteriosclerosis 2(5):359–368. PMID 6889852

20

N. Schneiderman

38. Kaplan JR, Adams MR, Clarkson TB, Koritnik DR (1984) Psychosocial influences on female ‘protection’ among cynomolgus macaques. Atherosclerosis 53(3):283–295. PMID 6543317 39. Kaplan JR, Manuck SB, Adams MR, Weingand KW, Clarkson TB (1987) Inhibition of coronary atherosclerosis by propranolol in behaviorally predisposed monkeys fed an atherogenic diet. Circulation 76:1364–1372. PMID 3677359 40. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM (2002) Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346(6):393–403. PMID 11832527 41. Kumar V, Mitchell R, Fausto N (2007) Robbins basic pathology, 8th edn. Elsevier, Amsterdam 42. Lang CM (1967) Effects of psychic stress on atherosclerosis in the squirrel monkey (Saimiri sciureus). Proc Soc Exp Biol Med 126:30–34 43. Lieberman EH, Gerhard MD, Uehata A, Walsh BW, Selwyn AP, Ganz P, Yeung AC, Creager MA (1994) Estrogen improves endothelium-dependent, flow-mediated vasodilation in postmenopausal women. Ann Intern Med 121:936–941. PMID 7978718 44. Linden W, Stossel C, Maurice J (1996) Psychosocial interventions for patients with coronary artery disease. Arch Intern Med 156:745–752. PMID 8615707 45. Linden W, Phillips MJ, Leclerc J (2007) Psychological treatment of cardiac patients: a metaanalysis. Eur Heart J 24:2972–2984. PMID 17984133 46. Loos RJ (2009) Recent progress in the genetics of common obesity. Br J Clin Pharmacol 68:811–829. PMID 20002076 47. McCabe PM, Gonzales JA, Zaias J, Szeto A, Kumar M, Herron AL, Schneiderman N (2002) Social environment influences the progression of atherosclerosis in the Watanabe heritable hyperlipidemic rabbit. Circulation 105:354–359. PMID 11804992 48. McCaffery JM, Pogue-Geile MF, Ferrell RE, Petro N, Manuck SB (2002) Variability within alpha- and beta-adrenoreceptor genes as a predictor of cardiovascular function at rest and in response to mental challenge. J Hypertens 20:1105–1114. PMID 12023679 49. Middledorp CM, Birley AJ, Cath DC, Gillespic NA, Willemsen G et al (2005) Familial clustering of major depression and anxiety disorders in Australian and Dutch twins and siblings. Twin Res Hum Genet 8:609–615. PMID 16354503 50. Moher D, Schulz KF, Altman D (2001) The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. JAMA 285:1987–1991. PMID 11308435 51. Nerem RM, Levesque MJ, Cornhill JF (1980) Social environment as a factor in diet-induced atherosclerosis. Science 208(4451):1475–1476. PMID 7384790 52. Nolte IM, McCaffery JM, Snieder H (2010) Candidate gene and genome-wide association studies in behavioral medicine. In: Steptoe A (ed) Handbook of behavioral medicine: methods and applications. Springer, New York 53. Ohara Y, Peterson TE, Harrison DG (1993) Hypercholesterolemia increases endothelial superoxide anion production. J Clin Invest 91:2546–2551. PMID: 8390482 54. Ohlin B, Berglund G, Nilsson P, Melander O (2007) Job strain, decision latitude and alpha 2βadrenergic receptor polymorphism significantly interact, and associate with higher blood pressures in men. J Hypertens 25:1613–1619. PMID 17620957 55. Orth-Gomér K, Schneiderman N, Wang H, Walldin C, Bloom M, Jernberg T (2009) Stress reduction prolongs life in women with coronary disease: the Stockholm Women’s Intervention Trial for Coronary Heart Disease (SWITCHD). Circ Cardiovasc Qual Outcomes 2:25–32. PMID 20031809 56. Paredes J, Szeto A, Levine JE, Zaias J, Gonzales JA, Mendez AJ, Llabre MM, Schneiderman N, McCabe PM (2006) Social experience influences hypothalamic oxytocin in the WHHL rabbit. Psychoneuroendocrinology 31:1062–1075. PMID 16963189 57. Pasterkamp G, Shoneveld AH, van der Wal AC, Haudenschild CC, Clarijs RJ, Becker AE et al (1998) Relation of arterial geometry to luminal narrowing and histologic markers for plaque vulnerability: the remodeling paradox. J Am Coll Cardiol 32:655–662. PMID 9741507

1

Cardiovascular Behavioral Medicine: Past, Present, and Future

21

58. Patten SB (1997) Performance of the Composite International Diagnostic Interview Short Form for major depression in community and clinical samples. Chronic Dis Can 18:109–112. PMID 9375257 59. Porter R (1994) The biographical dictionary of scientists, 2nd edn. Oxford University Press, New York 60. Ratcliffe HL, Snyder RL (1967) Arteriosclerotic stenosis of the intramural coronary arteries of chickens: further evidence of a relation to social factors. Br J Exp Pathol 48(3):357–365. PMID 6026973 61. Ridker PM, Hennekens CH, Buring JE, Nader R (2000) C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 342:836–844. PMID 10733371 62. Rosengren A, Hawken S, Ounpuu S, Silwa K, Zubaid M et al (2004) Association of psychosocial risk factors with risk of acute myocardial infarction in 11119 cases and 13648 controls from 52 countries (the INTERHEART study): case-control study. Lancet 364:953–962. PMID 15364186 63. Ross R (1993) The pathogenesis of atherosclerosis: a perspective for the 1990s. Nature 362:801–809. PMID 8479518 64. Ross R, Glomset JA (1973) Atherosclerosis and the arterial smooth muscle cell: proliferation of smooth muscle is a key event in the genesis of the lesions of atherosclerosis. Science 180 (93):1332–1339. PMID 4350926 65. Rushmer RF (1989) Structure and function of the cardiovascular system. In: Schneiderman N, Kaufmann P, Weiss SM (eds) Handbook of research methods in cardiovascular behavioral medicine. Plenum, New York 66. Schneiderman N, Kaufmann P, Weiss SM (eds) (1989a) Handbook of research methods in cardiovascular behavioral medicine. Plenum, New York 67. Schneiderman N, Chesney MA, Krantz DS (1989b) Biobehavioral aspects of cardiovascular disease: progress and prospects. Health Psychol 8:649–676. PMID 2700341 68. Schoenhagen P, McErlean ES, Nissen SE (2000) The vulnerable coronary plaque. J Cardiovasc Nurs 15:1–12. PMID: 11061217 69. Schousboe K, Willemsen G, Kyvik KO, Mortensen J, Boomsma DI, Cornes BK et al (2003) Sex differences in heritability of BMI: a comparative study of results from twin studies in eight countries. Twin Res Hum Genet 6:409–421. PMID 14624725 70. Seyle H (1956) The stress of life. McGraw-Hill, New York 71. Sorlie PD, Avilés-Santa L, Wassertheil-Smoller S, Kaplan RC, Daviglus ML et al (2010) Design and implementation of the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol 20:629–641. PMID 20609343 72. Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, KeinanenKiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M (2001) Finnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 344(18):1343–1350. PMID 11333990 73. van Vliet-Ostaptchouk JV, den Hoed M, Luan J, Zhao KK et al (2013) Pleiotropic effects of obesity-susceptibility loci on metabolic traits: a meta-analysis of up to 37,874 individuals. Diabetologia 56:2134–2146. PMID: 23827965 74. VanderLaan PA, Reardon CA, Getz GS (2004) Site specificity of atherosclerosis: site-selective responses to atherosclerotic. Arterioscler Thromb Vasc Biol 24:12–22. PMID 14604830 75. Versalius A (1543) De humani corporis fabrica (On the make up of the human body) 76. Vink JM, Willemsen G, Boomsma DI (2005) Heritability of smoking initiation and nicotine dependence. Behav Genet 35:397–406. PMID 15971021 77. Virchow RLK (1856) Thrombose und Embolie. Gefässentzündung und septische Infektion. In: Gesammelte Abhandlungen zur wissenschaftlichen Medicin. Von Meidinger & Sohn, Frankfurt am Main, pp 219–732. Translation in Matzdorff AC, Bell WR (1998) Thrombosis and embolie (1846–1856). Canton, Massachusetts: Science History Publications. ISBN 0-88135-113-X

22

N. Schneiderman

78. Whipple MO, Lewis TT, Sutton-Tyrrell K, Matthews KA, Barinas-Mitchell E et al (2009) Hopelessness, depressive symptoms, and carotid atherosclerosis in women the study of women’s health across the national (SWAN) heart study. Stroke 40:3166–3172. PMID 19713542 79. Williams RB, Marchuk DA, Siegler IC, Barefoot JC, Helms MJ et al (2008) Childhood socioeconomic status and serotonin transporter gene polymorphism enhance cardiovascular reactivity to mental stress. Psychosom Med 70:32–39. PMID 18158371 80. Wing RR, Bahnson JL, Bray GA, Clark JM et al (2010) Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus. Arch Intern Med 170:1566–1575. Look AHEAD trial. PMID 20876408 81. Wing RR, Bolin P, Brancati FL, Bray G et al (2013) Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med 39:145–154. PMCID: 23796131 PMC3791615 82. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L, INTERHEART Study Investigators (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 364:937–952. PMID 15364185 83. Zeiher AM, Drexler H, Wollschlager H, Just H (1991) Endothelial dysfunction of the coronary microvasculature is associated with coronary blood flow regulation in patients with early atherosclerosis. Circulation 84:1984–1992. 1934373

2

Introduction to Cardiac Anatomy, Physiology, and Pathophysiology Amit Shah, Shabnam Seydafkan, and David Sheps

Contents Cardiovascular Anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiac Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atherosclerotic Cardiovascular Disease Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiac Pathophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical Syndromes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stable and Unstable Angina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acute Coronary Syndromes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagnostic Tools in Clinical Cardiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrocardiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Holter Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exercise-ECG Stress Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Echocardiogram Stress Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nuclear Stress Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mental Stress Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calcium Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CT Angiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coronary Catheterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24 28 30 31 33 33 34 35 35 35 35 36 36 37 37 37 38 38

A. Shah Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA S. Seydafkan Division of Cardiology, Emory University, Atlanta, GA, USA D. Sheps (*) Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_2

23

24

A. Shah et al.

Congestive Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Types of Cardiomyopathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40 41 43 43

Abstract

The cardiovascular system is one of the most critical and disease-prone organ systems in the body. The heart and surrounding blood vessels are responsible for delivering oxygen-rich blood from the lungs throughout the body and returning deoxygenated blood from the body toward the lungs. Cessation or decreased function of this system may occur through many mechanisms involving the nerves supplying the heart, the muscle of the heart itself, and the blood vessels feeding the heart and surrounding organs. In this chapter, we discuss the anatomy of the cardiovascular system, pathology that may affect its function, and tests that may be done to detect and prevent such disease. This chapter includes discussion of coronary artery disease, congestive heart failure, and cardiac arrhythmias. These diseases will be described from both the basic and clinical perspectives to lay the foundation for a more detailed discussion of such topics in later chapters of this handbook. Keywords

Coronary artery disease · Congestive heart failure · Cardiac arrythmias · Cardiovascular physiology · Cardiovascular pathophysiology Cardiovascular disease is the leading cause of death in the world, accounting for nearly 40% of deaths in high-income countries and close to 30% of deaths elsewhere [10]. Death related to heart disease can occur from many different etiologies, including atherosclerotic disease, rheumatic heart disease, congestive heart failure, and arrhythmias. In this chapter, we will discuss the basics of heart function, survey the various causes of heart disease and death, and also discuss modern diagnostic technologies in screening and diagnosis of heart disease.

Cardiovascular Anatomy The heart serves to move deoxygenated blood from the body into the lungs, as well as oxygenated blood from the lungs to the rest of the body (see Fig. 1) [9]. It is a cone-shaped structure that is primarily divided into four muscular chambers, and the direction of blood flow is regulated by four valves. Deoxygenated (venous) blood first enters the heart through the right atrium (RA). During the phase of relaxation called diastole, the deoxygenated blood passes through the tricuspid valve from the right atrium into the right ventricle (RV). Then, during the contraction phase of systole, the blood flows from the right ventricle, through the pulmonary valve, and into the pulmonary artery. Blood then enters the lungs for oxygenation and returns into the heart via the pulmonary veins into the left atrium (LA). During diastole,

2

Introduction to Cardiac Anatomy, Physiology, and Pathophysiology

25

Fig. 1 Anatomy of the heart and major vessels (modified drawing from Malmivuo J, Plansey R. Bioelectromagnetism. Oxford University Press, New York, 1995)

oxygenated blood passes from the mitral valve into the left ventricle (LV), which causes the left ventricle to be stretched and become filled with blood. During systole, the mitral valve closes, the left ventricle contracts, and oxygenated blood flows through the aortic valve into the aorta to be distributed throughout the body. The heart contains many layers of tissue. It is surrounded by the pericardium, a fibroserous sac. Because the pericardium is separated from the heart by a thin layer of fluid, the heart is able to move within the body with minimal friction. Furthermore, because the pericardium has many connections to the surrounding tissue, it helps to keep the heart in place. Within the pericardium is the heart muscle (myocardium), which contains the muscular tissue that generates the contractions and is the thickest layer of the heart. Within the heart, the cardiac skeleton is a fibrous structure that serves as a point of attachment for the valves. The endocardium lines

26

A. Shah et al.

Fig. 2 Anatomy of the heart and major vessels (modified drawing from Encyclopedia Britannica online, http://www.britannica.com/EBchecked/topic/720793/cardiovascular-disease)

the inside of the heart. Thus, the LV is the main heart chamber involved in propelling oxygenated blood through the body’s circulation. Contraction of the myocardium is regulated by the conduction system (see Fig. 2). The impulse to contract originates in the sinoatrial (SA) node, which is a small mass of pacemaker cells in the wall of the right atrium. The pacemaker cells are named as such because they fire spontaneously, exhibiting what is called automaticity. After initial depolarization, which is caused by the influx of calcium and sodium ions, the electrical signal propagates from the sinoatrial node during diastole (like a domino effect) throughout the right, and then left, atrium, causing contraction of the atria, which pushes blood into the relaxed ventricles. The propagating signal then stimulates the pacemaker cells at the atrioventricular (AV) node, which lies inferior and posterior to the interatrial septum. The signal then continues to propagate distally to the bundle of His, which splits (bifurcates) into two conducting nerves, the left bundle branch and the right bundle branch, which stimulate the left and right ventricles to contract. Nervous impulses then travel to Purkinje fibers, which depolarize the myocardium from the endocardium to the myocardium. Of note, the left bundle branch divides into the left anterior fascicle and left posterior fascicle. Details regarding abnormalities of the cardiac rhythm are presented below. Substantial individual differences exist in the duration of the cardiac cycle (i.e., heart rate), but typical values range from 60 beats per minute

2

Introduction to Cardiac Anatomy, Physiology, and Pathophysiology

27

Fig. 3 Example of normal electrocardiogram

(bpm) at rest up to 140 bpm with substantial physical exertion. A typical normal ECG with the duration of the various intervals between P-wave and QRS complexes is presented in Fig. 3. Although the SA node has a pacemaker capacity, and can independently control the heart rate, the body modulates this rate through the autonomic nervous system. Both sympathetic (excitatory) and parasympathetic (inhibitory) nerves connect to the SA node and can modulate both the heart rate as well as the strength of myocardial contraction. Normally, the heart is under tonic inhibitory control via the vagus nerve, which conducts parasympathetic signals. Both sympathetic and parasympathetic nerves receive input from multiple structures in the brain, including the prefrontal cortex, anterior cingulate cortex, the amygdala, and various subcortical structures, including the dorsal motor nucleus, the cervical sympathetic ganglia, and rostral ventrolateral medulla. This connection of cortical and subcortical structures of the brain and the heart via the autonomic nervous system may partially underlie cardiovascular physiologic effects of affective states such as depression and anxiety. The heart muscle and nervous system of the heart receive oxygen and nutrients from the coronary arteries. The coronary arteries arise from the aortic root, are typically 1–1.5 mm wide, and course anteriorly around the pulmonary artery (Fig. 4). Unlike the other arteries in the body, coronary arteries receive blood from the aorta during diastole, when the aortic valve is closed. High pressured blood that has just ejected into the aorta also enters the coronary arteries, which are located immediately above the aortic valve. The largest coronary artery is the left main artery, which primarily supplies the left ventricle. It branches into the left anterior descending (LAD) artery and the left circumflex artery (LCX). The LAD travels down the anterior surface of the heart and gives off septal branches that supply blood to the interventricular septum (the wall between left and right ventricles), as well as diagonal branches that supply the anterior surface of the LV. The left circumflex artery (LCX) travels along the atrioventricular groove (border between atrium and ventricle) and reaches to the posterior surface of the heart. It also supplies a large obtuse marginal branch, which supplies the lateral and posterior walls of the LV. The right coronary artery (RCA) travels along the right AV groove as it moves blood posteriorly. It supplies the RV via acute marginal branches, as well as the AV node via the AV nodal artery. In most people (85%), the RCA becomes the posterior

28

A. Shah et al.

Fig. 4 Layout of coronary arteries (modified from http://houstonheartcenter.com)

descending artery (PDA) and supplies the inferior portion of the heart; otherwise, the PDA is supplied from the LCX (8%) or a combination of LCX and PDA. The coronary arteries have numerous branches that penetrate inside the heart; additionally, blood flows through tiny vascular channels directly connected to the ventricles, called the thebesian veins. Tiny collateral connections also exist between coronary arteries. Because they are so small (140/90 mmHg, documented at three separate clinic readings (see below for additional details). Blood pressure is typically measured using an inflatable cuff placed on the upper arm (i.e., assessing blood pressure in the brachial artery). Traditionally, blood pressure was noninvasively measured using a mercury manometer, and this “mercury sphygmomanometer“method is still considered the gold standard. Consequently, blood pressure is expressed in millimeters of mercury (mmHg) above the surrounding atmospheric pressure (set at zero to facilitate interpretation). The actual unit for pressure is Pascal, and 120 mmHg is equivalent to 16 kPa and 80 mmHg to 11 kPa. It is important to keep in mind that an occasional blood pressure reading in the medical office has poor reproducibility. Therefore, caution should be exerted to use single clinic readings as predictors of CVD risk. More recently, semiautomated methods have become common and are used in most clinical settings. Other ways of evaluating blood pressure have been developed including home blood pressure monitors, blood pressure assessments from the finger for beat-to-beat tracking, and 24-h ambulatory blood pressure monitoring [42, 83].

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

51

Mechanisms and Risk Factors Despite tremendous research efforts on the etiology of hypertension, the specific causes remain unknown in 90% of the cases (referred to as “essential hypertension” or “primary hypertension”; whereas the term “secondary hypertension” is used when the etiology is known). Genetic factors, physiological dysregulation, and biobehavioral factors have been shown to interfere with blood pressure homeostasis. Blood pressure is also influenced by several physiological factors, including cardiac output, total peripheral resistance, and arterial stiffness. Blood pressure increases with emotional arousal, physical activity, and a wide range of disease states. Short-term changes in blood pressure are regulated by baroreceptors that project to the brain to regulate blood pressure via the autonomic nervous system and endocrine systems. This section focuses on hypertension, but it should be noted that some disease states cause hypotension (e.g., sepsis, severe blood loss, anorexia nervosa), resulting in dizziness, fainting, and weakness. Lifestyle factors associated with hypertension are excessive salt intake, smoking, high alcohol consumption, and obesity. Blood pressure elevation is intertwined with weight gain. Findings from the Framingham study reveal that compared to normal weight (BMI 30 Kg/m2), overweight women, and obese women have elevated risks of developing new onset of hypertension (risk estimates1.5, 2.2, 1.7, and 2.6, respectively). There is large variation in the prevalence of hypertension depending on geographic location, rural or urban settings, and educational level. Low socioeconomic status is associated with an elevated risk of developing hypertension in industrialized countries. These associations indicate that behavioral and socioenvironmental factors play an important role as risk indicators for hypertension.

Treatment The question of how best to control blood pressure is far from being settled despite the development of a multitude of pharmacological and behavioral interventions. Treatment recommendations are formulated and regularly updated by hypertension experts, but additional efforts are needed to integrate behavioral and pharmacological interventions. Health-promoting lifestyle modifications to prevent the progressive rise in blood pressure and CVD in prehypertensive and hypertensive individuals have been shown to be effective. Such modifications include losing excess body weight, quitting smoking, dietary modifications (particularly salt reduction), positive psychological changes (e.g., minimization of stress, relaxation, reinforcement of positive affect), and increasing physical activities to levels recommended by Centers for Disease Control and Prevention (CDC) (e.g., for adults: 150 min moderate-intensity aerobic activity per week; for children more than 60 min of moderate-intensity aerobic activity per day).

52

G. K. Kapuku and W. J. Kop

Pharmacological “antihypertensive” medications are typically initiated when lifestyle interventions are insufficient to reduce blood pressure below target levels. Pharmacotherapy options include, among others, diuretics (thiazide type) which enhance diuresis (i.e., bodily water excretion by increased urine production) and other vascular effects resulting in blood pressure reduction; calcium channel blockers (calcium antagonists), which induce vasodilation by reducing systemic vascular resistance and lowering vascular tone (calcium antagonists promote diuresis to lesser extent); and angiotensin-converting enzyme inhibitors (ACE inhibitors) and angiotensin receptor blockers (ARB), also known as angiotensin II receptor antagonists, as well as rennin inhibitors. All lower blood pressure by inhibiting the renin-angiotensin-aldosterone system with subsequent blood volume reduction and vasodilation. Beta-adrenergic blocking agents (“beta-blockers”) and antialdosterone therapy (spironolactone, triamterene, and eplerenone) are also used in hypertension but are not the first-choice medications. Other antihypertensive medications include vasodilators (hydralazine, minoxidil) and centrally acting agents (clonidine, methyldopa, guanfacine). The treatment is tailored to age, race, and stage of hypertension. For example, calcium channel blockers and diuretics are effective for Blacks and diuretic, ACE inhibitor, ARB, or calcium channel blockers for Whites. If patients have diabetes mellitus or chronic kidney disease, antihypertensive medications have been recommended at earlier stages of high blood pressure (e.g., JNC-7 and JNC-8). Antihypertensive drugs can be prescribed as monotherapy, but often multiple antihypertensive drugs are used (combination therapy). Compared with placebo, only low-dose thiazide diuretics and ACE inhibitors have been shown to reduce all-cause mortality in hypertensive patients. It is evident that hypertension control remains a thorny issue despite tremendous effort invested in controlling blood pressure. Recent CDC reports show that blood pressure is not adequately controlled in many hypertensive individuals in the USA; approximatively 40% of males aged 65–74 have uncontrolled hypertension. This number reaches 70% in males aged 20–40 [73]. In rare cases, invasive interventions are used to treat hypertension. Renal artery angioplasty has progressively replaced aortorenal bypass surgery. Ongoing research explores the effects of percutaneous renal denervation therapy. Secondary hypertension resulting from hyperaldosteronism can be treated pharmacologically or resection of the aldosterone-producing adenoma (APA). Secondary hypertension related to pheochromocytoma (a condition associated with high amounts of catecholamines) and hyperthyroidism can be controlled by (partial) resection of adrenal and thyroid glands, respectively.

Ischemic Heart Disease In describing diseases related to myocardial blood supply, it is important to differentiate between three interrelated conditions: coronary artery disease, coronary heart disease, and ischemic heart disease (see Fig. 1). Coronary artery disease (CAD) is the underlying disease process of coronary heart disease

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

53

(CHD). CAD is present when atherosclerotic plaques result in narrowing of the (large epicardial) coronary arteries. These plaques can become gradually more obstructive, resulting in reduced coronary supply. Ischemic heart disease involves a broader spectrum of diseases than CHD and is characterized by inducibility of myocardial ischemia resulting from two primary etiologies (see below under “mechanisms” for more details): (1) it develops in individuals with CAD who have a more than 50% narrowing of the lumen of the coronary arteries that supply the heart muscles with oxygenated blood (in this case ischemic heart disease is the same as CHD), and/or (2) it develops as a consequence of dysfunction of the small vessels in the myocardium, referred to as coronary microvascular disease (in this case ischemic heart disease does not fall under the heading CHD because the larger epicardial coronary arteries are not involved). The latter situation explains the inducibility of myocardial ischemia in individuals who do not have obstructive CAD (see previous chapter in this volume for details related to the pathophysiology of ischemic heart disease). CAD is typically a gradually progressive disease process. The main symptomatic clinical manifestation of chronic ischemic heart disease is angina pectoris (often just called “angina”). Angina is chest pain or discomfort resulting from myocardial ischemia (i.e., poor perfusion of the heart muscle by oxygenated blood resulting from an imbalance of coronary supply and cardiac demand). Myocardial ischemia, and hence anginal complaints, can occur in the absence of obstructive CAD if coronary microvascular disease is present. Not all patients with ischemic heart disease experience the typical chest pain with myocardial ischemia and instead experience “angina equivalents,” including fatigue, dyspnea, palpitations, diaphoresis, or pain occurring in a region other than the chest. These symptoms are referred to as angina equivalents because they may result from transient myocardial ischemia (they are sometimes also referred to as “atypical cardiac complaints” because exertional chest pain is “typical” for CAD-related myocardial ischemia). The term “atypical” is probably better avoided, because typical chest pain is primarily “typical” for men between 50 and 70 years old and also because the term “atypical” potentially creates confusion between “angina equivalents” and “noncardiac chest pain.” Noncardiac chest pain (sometimes referred to as “atypical chest pain”) is a common clinical condition and includes chest pain with low probability of cardiac origin. The differential diagnosis can be complicated as patients with CAD also experience chest pain in the absence of ischemia. The acute clinical manifestation of CAD (and hence CHD) is the acute coronary syndrome. Acute coronary syndromes include myocardial infarction and unstable angina pectoris (see Fig. 1) with chest pain occurring at rest or minimal exertion as a consequence of myocardial ischemia following the abrupt interruption of coronary blood supply to the heart. This condition is what most people mean if they mention having had a “heart attack,” but patient self-reports are not very accurate and also may include noncardiac chest pain and other heart symptoms that do not meet criteria for an acute coronary syndrome. Stable angina pectoris typically occurs in patients with ischemic heart disease with physical exertion. The New York Heart Association and also the Canadian

54

G. K. Kapuku and W. J. Kop

Society of Cardiovascular disease have provided guidelines to grade the functional severity of heart conditions [4]. This grading system is mainly based on patients’ subjective symptoms and functionality. Stable angina is the typical manifestation of ischemic heart disease in the nonacute phase and can occur in patients with CAD as well as those with microvascular disease. Unstable angina is defined as (recent onset) anginal with severe chest pain or discomfort at rest or minimal exertion suggesting severe myocardial ischemia. Unstable angina is a critical phase of CHD with widely variable signs, symptoms, and long-term outcomes. Ischemic ECG changes are seen in about 25% of patients with unstable angina. However, the myocardial ischemia explaining unstable angina is transient and does not result in permanent damage of the heart muscle and is therefore not accompanied by a marked increase in biomarkers of tissue damage (e.g., troponin). Although ECG markers of ischemia, such as ST and/or T wave changes, can occur in unstable angina, they do not persist. Initially unstable angina and (non-ST elevation) myocardial infarction may therefore be difficult to untangle since elevations of biological markers of cardiac tissue damage (e.g., troponins) may not be detectable as late as 12 h after the first symptoms. Chest pain that occurs at rest and/or minimal exertion is typical for unstable angina and represents severe coronary artery disease or unstable atherosclerotic plaques. In cases where patients only have angina at rest but no angina with minimal exertion, CAD is typically not the causing factor and unstable angina therefore not the fitting diagnosis. If patients have coronary microvascular disease only and no CAD, then unstable angina is not very likely to occur.

Myocardial Infarction Myocardial infarction is permanent damage to the heart muscle caused by sustained myocardial ischemia. Because myocardial infarction is defined as an event in which there is evidence of myocardial injury or necrosis (i.e., death of cardiac muscle), biomarkers of tissue damage (troponins) are currently the main diagnostic tool for its diagnosis. A diagnosis of myocardial infarction is therefore made if the following criteria are met [34]: (1) increases in cardiac enzymes and/or positive increase in troponin levels, combined with (a) symptoms of unstable angina, (b) positive acute ECG changes (transient ST segment elevations of
1 mm in 2 or more contiguous leads, ST segment depressions of
1 mm, new T-wave inversions of
1 mm, and/or new left bundle branch block), or (c) if cardiac imaging reveals areas of loss of contractile heart muscle, then this will also be consistent with the diagnosis of myocardial infarction. Myocardial infarction is categorized into ST segment elevation myocardial infarction (STEMI) and non-ST segment elevation myocardial infarction (NSTEMI). Identification of Q wave at presenting ECG in STEMI reflects myocardial necrosis and predicts a twofold increase in 1 year mortality [63]. Other ECG abnormalities occur in about half of the patients with NSTEMI (e.g., ST segment depression or T-wave inversion). Based on the increased use of high-

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

55

sensitivity troponins (i.e., higher sensitivity to detect minimal heart muscle damage), it is expected that the relative number of NSTEMI will increase and unstable angina decrease. The symptoms of myocardial infarction are the same as those of unstable angina. However, it is important to note that approximately 50% of incident myocardial infarctions as detected on ECG have occurred without a definite or probable hospitalization for this condition [6]. This condition has been referred to as “silent myocardial infarction,” but it is possible that these individuals did experience chest pain or angina equivalents (i.e., the event may not have been “silent”) but that they did not seek medical care. Immediate complications of myocardial infarction include acute heart failure, life-threatening arrhythmias, and sudden cardiac death. In addition, myocardial infarction increases the risk of developing chronic heart failure with reduced ejection fraction. Coronary vasospasm: Short-term spasm of the coronary arteries can cause transient ischemia and result in acute angina complaints, referred to as Prinzmetal’s angina or variant angina. Many patients with this condition do not have detectable CAD on angiography. Coronary microvascular disease: In addition to angina and acute coronary syndromes, cardiovascular behavioral medicine also is involved in patients where the underlying coronary disease pathology is less well understood. Coronary microvascular disease, sometimes referred to as cardiac syndrome X, involves chest pain (or angina equivalents) occurring in the absence of significant narrowing of the coronary arteries. Some evidence suggests that this syndrome is more prevalent among postmenopausal women, but further research in this area is needed. The symptoms are thought to be caused by myocardial ischemia resulting from microvascular disease since the large coronary arteries are usually minimally narrowed. Coronary microvascular disease can have a major impact on quality of life as the condition is chronic, and diagnostic tools and treatment options are still in development.

Epidemiology Every 42 s a myocardial infarction occurs in the USA. The lifetime risk of developing CHD is 49% for men and 32% for women. After the age of 70 years, these percentages are 35% and 24%, respectively. Approximately 15.5 million persons older than 20 years have CHD in the USA [72]. The incidence of CHD has declined over time in industrialized countries, while it is rising in other parts of the world. Over 4 million people undergo diagnostic testing for cardiac disease using exercise testing in the USA alone, often combined with imaging [48]. These data indicate that ischemic heart disease continues to be a major public health problem worldwide, associated with substantial burden on the individual patient and the immediate social environment as well as major socioeconomic costs at the national and international level.

56

G. K. Kapuku and W. J. Kop

Diagnosis The diagnosis of ischemic heart disease is based on symptoms and signs combined with medical diagnostic tests that range from noninvasive ECGs to invasive heart catheterization. Symptoms of ischemic heart disease are typically classified based on physician or nurse evaluation of a patient’s functional capacity. Similar to the classic New Your Heart Association (NYHA) four-category classification that is often used for determining the severity of heart failure symptoms, the Canadian Society of Cardiovascular disease has developed a clinical tool to evaluate patients suffering from angina, distinguishing the following classes [16]: Class 0, asymptomatic, patients may have mild myocardial ischemia; Class I, anginal symptoms occur with strenuous, rapid, or prolonged exertion at work or recreation, and ordinary physical activity can be performed without causing symptom (e.g., walking several blocks, climbing stairs); Class II, symptoms occur with moderate exertion (e.g., walking up a hill or more than two blocks or climbing one flight of stairs) resulting in slight limitations of ordinary activities (the symptoms can also occur with ordinary activities when they are performed rapidly, after meals, in cold, in wind, under emotional stress, and/or during the first few hours after waking up. The patient is comfortable at rest); Class III: symptoms occur with mild physical activities (e.g., walking 1–2 blocks or climbing stairs), but the patient is comfortable at rest; and Class IV: any physical activity causes limiting symptoms; patients may also experience symptoms at rest. Patients with Class IV angina often also have unstable angina. As mentioned above, not all patients experience typical chest pain and may experience dyspnea (shortness of breath), fatigue, or other angina equivalents that can be classified using this functional classification. Several questionnaires have also been developed to assess the presence of angina pectoris, including the Rose Questionnaire and the Seattle Angina Questionnaire.

Diagnostic Tests Tests for the diagnosis of ischemic heart disease are described in the previous chapter in this volume and are not described in detail here. In brief, the ECG at rest, in the context of ischemic heart disease, is primarily used for the detection of acute coronary syndromes (ST elevation, ST depression, T wave inversion, and Q waves) and arrhythmias. The presence of inducible ischemia can be detected by exercise stress testing with ECG monitoring for ST segment depression and other indicators of ischemia, echocardiography (wall motion abnormalities), or nuclear imaging with single-photon emission computed tomography (SPECT) or, more rarely, positron emission tomography (PET) for the detection of exercise or pharmacologically induced perfusion defects. Ambulatory ECG assessments over 24–48 h (Holter monitoring) can also be used to determine the presence of inducible ischemia, but this technique is currently rarely used for that purpose (Holter monitoring remains a common tool for the diagnosis of arrhythmias; see below). To

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

57

measure abnormalities in the coronary arteries (i.e., independent of the inducibility of ischemia), electron beam computed tomography (EBCT) can be used to obtain coronary calcium scores (an index of stable progressive coronary artery disease). Coronary computed tomography angiography (coronary CTA) is increasingly used to visualize the coronary anatomy and patency of epicardial (large) coronary blood vessels. All these tests are noninvasive and have minimal risk (except for exposure to low-grade radiation with the nuclear and CT tests). Finally, angiography based on coronary catheterization is the gold standard for the detection of coronary artery disease. This procedure is invasive with more than minimal risks. The coronary angiogram reveals the severity of the luminal narrowing of the large epicardial coronary arteries, and a narrowing (also called lesion) > 50% is generally flow-limiting and considered “diagnostic” for clinically significant coronary artery disease. Coronary measurements are preferentially obtained with an automated computerized edge detection system. CAD is typically described as 1-, 2-, or 3-vessel disease, referring to involvement of the three main coronary arteries. At present, there is no established diagnostic tool for coronary microvascular disease, and the diagnosis is made based on the combination of inducible ischemia in the absence of detectable CAD based on coronary catheterization.

Mechanisms and Risk Factors These diagnostic tests build on the mechanisms of ischemic heart disease, with the main goal of detecting and treating CHD. The underlying disease process of CHD is the gradual narrowing of the major coronary arteries as a consequence of atherosclerosis. Arteriosclerosis is characterized by thickening and stiffening of the arterial wall due to deposition of lipids and other blood-based particles resulting in plaques that are covered by a fibrous cap. The coronary arteries lie on top of the heart muscle and are therefore called epicardial arteries; they transport blood to the heart muscle itself (i.e., they are not part of the main circulatory system through the body; see the previous chapter in this volume for more details and figures). The atherosclerotic disease process involves accumulation of lipids as well as immune system particles such as macrophages and T cells in the coronary vessel wall. These atherosclerotic plaques can gradually calcify which results in stiffening of the arteries and is the basis for EBCT calcium score tests. Coronary atherosclerosis is typically a gradual disease process, ultimately resulting in a flow-limiting narrowing of the coronary artery (detectable by coronary CTA and coronary catheterization). Anginal chest pain and angina equivalents reflect myocardial ischemia. Myocardial ischemia occurs when there is an imbalance between coronary supply of oxygenated blood to the heart muscle (which is often impaired in patients with >50% narrowing of one or more coronary arteries) and cardiac demand (determined by heart rate, blood pressure, and contractility of the heart muscle). These cardiac demand-inducing physiological factors increase in response to exercise, and that is why exercise testing is used in the diagnosis of ischemic heart disease (with ECG, echocardiography, or nuclear imaging). In patients with stable CAD, the coronary supply is

58

G. K. Kapuku and W. J. Kop

typically reduced as a consequence of the narrowing in the coronary artery but still sufficient in resting conditions; when the demand is increased (e.g., during exercise testing), then the supply becomes insufficient, and patients may develop myocardial ischemia and chest pain. There is, however, not a 1 to 1 relationship between exercise-induced ischemia and chest pain, and chest pain in the absence of myocardial ischemia is often referred to as noncardiac chest pain. In cases of rupture of the atherosclerotic plaque, when the fibrous cap breaks, the coronary vessel may suddenly be occluded, and plaque contents may be exposed to blood which causes acute thrombus formation (a blood clot in the coronary artery). These processes result in acute severe and sustained myocardial ischemia, which – if untreated – leads to injury or death of heart muscle in the region of the heart that is supplied by that coronary artery (i.e., myocardial infarction).

Treatment Tremendous progress has been made in the care of stable CAD and acute coronary syndromes. The overall goal of the treatment is to restore coronary blood supply and reduce ischemic burden. Treatment of chronic ischemic heart disease involves medical therapy and/or revascularization. In addition to these interventions directly targeting myocardial ischemia, lifestyle and risk factor modification and cardiac rehabilitation are also used for optimal treatment. In case of acute coronary syndromes, the immediate goal is to recover artery patency via antifibrinolytic, anticoagulation therapies, and/or reopening the occluded coronary artery using interventional cardiology techniques. Patient safety and long-term outcomes are at the center of the decision-making process between pharmacological treatment, PCI, and CABG. In this section, we will first review revascularization and medical therapies for chronic ischemic heart disease and then briefly review interventions for acute coronary syndromes. Revascularization can be achieved via percutaneous coronary interventions (PCI) (also known as percutaneous transluminal coronary angioplasty; PTCA) or coronary artery bypass graft (CABG) surgery. The risks and short-term recovery trajectory of PCI are much more favorable than CABG, which entails major surgery. The choice between PCI and CABG is guided by feasibility and risk assessment taking into account: age, sex, lifestyle, risk factors, severity and anatomical characteristics of CAD, cardiac history (e.g., prior myocardial infarctions, PCI, or CABG), left ventricular function, comorbid diseases, and other medical treatment. PCI is an invasive but nonsurgical procedure that is increasingly used because of its good short-term results. PCI involves placing and inflating a balloon across a coronary narrowing and is often performed immediately after the diagnostic coronary catheterization angiogram. During PCI, the balloon is inflated such that the artery is opened (i.e., revascularization of the heart muscle distal of the lesion). The opened artery segment is typically supported with a stent that can be coated with pharmacological agents that prevent re-narrowing of the dilated segment. Various types of stents including metal, drug-eluting, and bioresorbable scaffold are used to

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

59

optimize PCI results and prevent restenosis. As a consequence of the balloon inflation, the coronary artery is temporarily completely blocked for a few seconds, but ischemic ECG changes or severe angina are rare during PCI (about 10%) because of the short duration of occlusion. Transradial coronary stenting (i.e., via the arm instead of the groin) is widely used to reduce discomfort related to PCI. During PCI, heparin is usually used to maintain the activated clotting time below 300 s. Elective PCI is performed with antiplatelet drug (e.g., aspirin, dipyridamole, ticlopidine, dextran, and GpIIb/IIIa inhibitors). Other medications administered during PCI may include nitrates, calcium channel blockers, and beta-blockers. The incidence of adverse events during 4 years follow-up has decreased significantly from 29.9% after PCI in the early 1990s to 16.9% after the introduction of stenting, mostly because of the reduction in the rate of repeat PCIs from 22.3% to 10.7% [61]. Recent developments in drug-eluting stents and mechanical stent properties have further reduced the recurrent event rate. In the setting of acute coronary syndromes, emergency (primary) PCI can be performed, in addition to pharmacological thrombolysis (see below). Acute PCI is associated with poorer outcomes than elective PCI, partly because it is done without the abovementioned medications and partly because of the clinical characteristics of patients requiring emergency PCI. CABG involves revascularization of the heart muscle by creating a “bypass” over the narrowing in a coronary artery. The bypass starts at a well-functioning artery, and the other end is attached after the obstruction to restore blood flow. This surgery is usually performed while the heart is temporarily stopped, which necessitates use of a cardiopulmonary bypass (i.e., “on-pump-stopped”). It is also possible to perform CABG on a beating heart without using a cardiopulmonary bypass (“off-pump”) or using partial assistance of a cardiopulmonary bypass (“on-pump beating”). Successful CABG is associated with a 59% actual 5-year survival rate and significantly improved functional class. In addition to PCA and CABG, pharmacological therapies (in this setting called “medical therapies”) can be very effective in reducing ischemia. The primary pharmacological medications to reduce myocardial ischemia and angina include nitrates, beta-adrenergic blocking agents, and calcium channel blockers. Because blood pressure is a primary factor in myocardial demand and vascular regulation is a key factor in coronary supply, there is substantial overlap in medications that are effective in reducing myocardial ischemia and those that are used to treat hypertension (see above). In addition to “anti-ischemic” medications, patients with CHD may also use lipid-lowering medications and/or ACE inhibitors and other medications to preserve left ventricular function when ejection fraction is 49.2 g/ m2.7 for men and >46.7 g/m2.7 for women). In addition to height 2.7, other LVM thresholds have also been determined (e.g., using body surface area and non-exponentiated height) [26, 101].

Mechanisms and Risk Factors The pathophysiological mechanisms involved can vary widely based on the nature of the cardiomyopathy, as described above. At the cellular level, several mutant proteins can disturb cardiac structure and function, resulting in impaired contractility and conduction defects that increase the likelihood of life-threatening arrhythmias. In addition, LVH can develop as a secondary response to hypertension and other CVDs. Finally, LVH can develop as a normal physiological response to repeated aerobic exercise, obesity, and other conditions associated with high cardiac demand.

Treatment Treatment of cardiomyopathies depends on the severity of symptoms and the type of cardiomyopathy and includes lifestyle changes, medications, or surgery. Patients with an increased risk of arrhythmias may also receive an implantable cardioverter defibrillator. Left ventricular hypertrophy can be reduced with the aforementioned blood pressure-lowering therapies including pharmacological treatment. Evidence suggests that beta-adrenergic blocking agents result in less LVM reduction than other antihypertensive medications. Weight reduction has a greater effect on LV cavity size than it does on wall thickness. Aerobic intervention and weight reduction can have additive beneficial effects on blood pressure and cardiac mass in hypertensive patients.

Heart Failure Heart failure (HF) is a major public health problem bearing high morbidity and causing death in half of HF patients within 5 years of diagnosis [1]. Heart failure is a physiological state of insufficient cardiac output to meet the needs of the body and lungs. The term congestive heart failure is often used, with “congestion” referring to the buildup of fluid in tissues and veins (water retention and edema (swelling) in the limbs and feet or pulmonary edema). HF is classified in left, right, and biventricular

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

65

failure based on the anatomical site of disturbance translating into differing clinical presentations. The current literature makes an important distinction between HF based on the left ventricular ejection fraction (LVEF) [47]. LVEF is the proportion of blood pumped out of the heart during a single contraction (systole) and given as a percentage. HF resulting from reduced ejection fraction (HFrEF; also called systolic HF) occurs when the LVEF is less than 40%, whereas HF with preserved ejection fraction (HFpEF; also called diastolic HF) is not accompanied by poor LVEF. HFpEF occurs when the heart muscle contracts well but the ventricle does not adequately fill in the diastolic (relaxation) phase of the heart cycle. The primary signs and symptoms of all types of HF are dyspnea (shortness of breath), excessive tiredness, and leg swelling. In many HF patients, reduced heart function starts with deterioration in diastolic function progressing to systolic dysfunction and ending in the degradation of both. Evidence suggests that diastolic dysfunction plays a primary role in most patients with chronic HF, which is in contrast to the previously held view that HF primarily results from systolic dysfunction. However, once patients have experienced a myocardial infarction, their risk of HFrEF increases substantially.

Epidemiology A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee states that one out of five people after age 40 will develop HF during their lifetime [2]. In industrialized countries, the prevalence of HF is approximately 2% among adults, and this percentage increases dramatically with age, such that in individuals aged 65 and above the estimated prevalence ranges between 6% and 10%. Unlike other cardiovascular conditions, the incidence and prevalence of HF display a marked increase over the past decades which goes beyond the population trends in aging. More than 5.8 million individuals in the USA and more than 23 million worldwide suffer from heart failure [11]. Blacks suffer from HF at a rate twice that of Whites. In those over 65 years of age, HF is more prevalent in women than men. These health disparities are explained, in part, by a possible difference in pathophysiological mechanisms. Most studies have shown that, unlike Whites, Blacks have HF of predominantly nonischemic origin with salt-dependent hypertension playing a major role. This pattern suggests that diastolic abnormalities are the major precipitating factors for HF in Blacks.

Diagnosis The typical signs and symptoms of HF are dyspnea, tiredness, and swelling of the legs. Dyspnea is usually worse with exercise but can become worse while lying down and may wake the person at night. Extreme tiredness and reduced exercise tolerance are also a common feature of HF and reflect insufficient circulation of oxygenated blood throughout the body resulting from poor cardiac pump function.

66

G. K. Kapuku and W. J. Kop

In contrast, chest pain is not typical for HF. Congestion is common in HF and is often seen in the legs but also arms and feet as well as pulmonary edema and ascites (swollen abdomen). Diseases with symptoms that are similar to HF include anemia, obesity, kidney failure, liver problems, and thyroid dysfunction. A common sign of HF is rales (i.e., a crackling sound in the lungs). This chapter focuses on chronic HF, but chronic HF can suddenly progress into acute HF (decompensation), especially in the setting of pneumonia, myocardial infarction, and infection (see also Fig. 1). The functional severity can be classified using the New York Heart Association criteria (see above; class I limited symptoms to IV symptoms at rest or minimal exertion). These criteria are frequently used in clinical cardiology, but their reproducibility and validity are low. The American College of Cardiology and the American Heart Association have introduced a four-stage classification: Stage A, patients at high risk for developing HF in the future but currently have no functional or structural heart disorder; Stage B, presence of a structural heart disorder but no symptoms (similar to NYHA class I): Stage C, previous or current HF symptoms in the context of an underlying structural heart problem, but managed with medical treatment (similar to NYHA class II–III); and Stage D, advanced disease requiring hospital-based support, a heart transplant, or palliative care (similar to NYHA class IV). In addition to signs and symptoms of chronic HF, various diagnostic tests are used to determine the presence and etiology of HF, including echocardiography or cardiac MRI (to document cardiac structure and function), chest X-ray, ECG, and blood tests. Blood tests can be used to measure biomarkers of HF, of which natriuretic peptides are the most important. Release of atrial natriuretic peptide and B-type natriuretic peptide (BNP) occurs in response to excess pressure and/or volume loads in the heart. The original name for BNP was brain-derived natriuretic peptide because it was first identified in extracts of porcine brains. In humans, BNP is mainly produced in cardiac tissue, and excretion increases in response to atrial distension and increased sheer tension in the ventricles [20, 39, 49, 92]. The release of BNP is modulated by calcium ions. Natriuretic peptides cause natriuresis (i.e., the process of sodium (Na+) excretion in the urine) and vasodilatation through nitric oxide and/or prostaglandin release [105]. In addition, HF and natriuretic peptides levels are influenced by inhibition of the sympathetic nervous system, the renin-angiotensinaldosterone system, and endothelial regulation [36, 90]. Currently, BNP and related molecules are used to track cardiac function and risk for morbidity and mortality among patients with HF [65].

Mechanisms and Risk Factors Patients with HF have impaired myocardial contractility. In addition, heart rate is increased in HF because of compensatory prolonged sympathetic stimulation of the heart which downregulates β1 receptors (these constitute 70% of cardiac β receptors) [19]. The rennin-angiotensin-aldosterone system is also activated in HF, inducing peripheral vasoconstriction with subsequent increased myocardial demand and renal

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

67

hypoperfusion which, in turn, maintains increased release of angiotensin II and aldosterone. Angiotensin II is a powerful vasoconstrictor, and aldosterone is a promoter of fibrotic tissue buildup in the myocardium; both these processes worsen HF. Precipitating factors for chronic HF are myocardial ischemia, hypertension, atrial fibrillation and tachyarrhythmias, heart valve disease, and cardiomyopathy (see above and Fig. 1). Other factors associated with the development of HF are alcohol overconsumption, infection, smoking, poor treatment compliance, improper medical treatment, and psychological factors such as depression. In particular, poor treatment adherence is frequently encountered in patients with HF.

Treatment The standard pharmacological treatment of chronic HF typically consists of an ACE inhibitor or ARB combined with a beta-adrenergic blocking agent. Diuretics can be useful for reducing fluid retention. More severe HF is often also treated with aldosterone antagonists (e.g., spironolactone, eplerenone) or hydralazine with a nitrate (the latter has been reported to be relatively effective in Black patients with HF) [18]. These drugs are geared toward alleviating symptoms, particularly those that are exacerbated by fluid buildup. In addition, these treatments can lower end-diastolic filling pressure and/or improve cardiac pump function. Studies of medical treatment under everyday circumstances indicate that some aspects of the HF management and patient self-care can be improved with beneficial results. The medical treatment for acute HF consists of oxygen supplementation, diuretic therapy, vasodilatory therapy (nitroglycerin, nitroprusside, and nesiritide), and inotropic agents (dobutamine, dopamine and milrinone), in addition to ACE inhibitors, ARB, or angiotensin receptor-neprilysin inhibitors. In addition to treatment of HF itself, interventions may also address the adverse secondary consequences of chronic HF, particularly life-threatening arrhythmias and critical pump failure. To prevent life-threatening arrhythmias and sudden death, implantable cardioverter defibrillators are used. Cardiac resynchronization therapy (e.g., biventricular pacing) allows to control abnormal conduction. In severe heart failure, left ventricle assisted devices (battery-operated mechanical pumps) can be surgically implanted to help bridge to heart transplantation. Specific details can be found in the 2017 report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America [103].

Cardiac Arrhythmias and Sudden Cardiac Death Arrhythmia refers to abnormal rhythm of the heart. Normal rhythm originates from the sinus node and is a regular heart beat in the range of 50–100 beats per minute at rest [97]. Abnormal rhythms are classified by their frequency and origin. Frequency-

68

G. K. Kapuku and W. J. Kop

related classification distinguishes between bradycardia (slow frequency) and tachycardia (fast heart rate). Arrhythmias can originate from atria or ventricles and are therefore called supraventricular and ventricular arrhythmias, respectively (see Fig. 1 and previous chapter in this volume for details). The most common supraventricular arrhythmias are atrial fibrillation, paradoxical supraventricular tachycardia (PSVT), atrial flutter, and atrial tachycardia. The fast atrial rhythm does not transmit to the ventricle because of the filter exerted by the A-V node. The conductivity of supraventricular arrhythmia to the ventricle can be irregular as in atrial fibrillation or regular as in PSVT, flutter, and atrial tachycardia. Atrial fibrillation (AF) is associated with a fast resting heart rate (about 150 beats per minute) and often asymptomatic, although some patients have substantial problems feeling heart palpitations, lightheadedness/fainting, chest pain, or dyspnea. AF may occur in the absence of comorbidity (previously referred to as “lone AF”). Another type of AF is postoperative AF which occurs in about 40% of cardiac surgery patients. Usually supraventricular tachyarrhythmias are well tolerated but can become hemodynamically taxing and/or impose an increased risk of stroke (due to clot formation) or HF in which case conversion to sinus rhythm is needed. Ventricular tachyarrhythmias include relatively benign and life-threatening conditions. Premature ventricular beats (PVB; also called premature ventricular complexes, PVC) and non-sustained ventricular tachycardia are in most cases not lifethreatening. Life-threatening ventricular arrhythmias are ventricular tachycardia and especially ventricular fibrillation. If untreated, ventricular fibrillation is fatal because it results in cardiac arrest with major hemodynamic toll. Sudden cardiac death is typically defined as mortality occurring within an hour of cardiac symptom onset with no evidence for another cause. As patients are often not alive when assessments are made, the diagnosis of sudden death is often complicated. People who are successfully reanimated are often described as having “survived sudden cardiac death.” The purported cause of sudden cardiac death is ventricular arrhythmia, such as ventricular tachycardia or ventricular fibrillation.

Epidemiology AF is the most common clinical heart arrhythmia. The estimated prevalence of AF is approximately 3–6 million in the USA, which is expected to double by 2050 [37]. The prevalence of AF increases with age and in the presence of comorbidity such as hypertension, diabetes, thyrotoxicosis, and mitral valve disorder. The prevalence of PVB is 69–96% in asymptomatic elderly evaluated with ambulatory ECG. PVB can be exacerbated by exercise. In the elderly, the presence of PVB in combination with ischemic heart disease can reflect poor prognosis. Sudden cardiac death continues to be a common cause of mortality. In the USA, sudden death accounts for approximately 16% of all deaths, although estimates are imprecise because of the aforementioned validity problems. Autopsy studies could document a cardiac origin for sudden death in approximately 60% cases [31, 71].

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

69

Underlying CVDs include coronary artery disease or abnormalities, HCM, dilated cardiomyopathy, right ventricular dysplasia, and myocarditis.

Diagnosis Many chronic arrhythmias are asymptomatic. The first step in dealing with arrhythmia is a precise diagnosis because each specific arrhythmia requires a unique treatment approach. Standard ECG or 24-h ambulatory ECG (Holter) recordings are key to the diagnosis of abnormal rhythms. Electrophysiological studies are invasive procedures that allow the recording of intracardiac electric activity, and the abnormal rhythm may be induced in the laboratory which can help diagnose the underlying mechanisms [43, 66, 94]. These diagnostic procedures help to distinguish atrioventricular nodal reentry tachycardia (AVNRT) from AV-reciprocating tachycardia (AVRT). They also help differentiate whether the tachycardia is atrial tachycardia, sinoatrial reentry, or ventricular tachycardia. Some arrhythmias may be of unknown origin or subsequent to congenital/ acquired cardiac conduction problems (e.g., QT prolongation) or electrolyte imbalance (e.g., hypokalemia) [66, 104]. Arrhythmia can exist singly or in combination with other heart problems such as CAD, cardiomyopathies, hypertension, congenital heart disease, or HF.

Mechanisms and Risk Factors Normal aging is associated with changes in the cardiac conduction system. These changes include sinus node dysfunction (e.g., sick sinus syndrome), slowing of AV nodal conduction, left axis deviation, bundle branch blocks, and an increased prevalence of both supraventricular and ventricular premature beats and arrhythmias. The AV node is under control of the parasympathetic nervous system via the vagal nerve. Activation of the vagal nerve leads to slowing electric pulse propagation by prolonging the time that it takes for the impulse to travel between the atrium and ventricle. When examining supraventricular arrhythmias with reentry as primary mechanism, AVNRT is the most common type (60%). The substrate for AVNRT is the presence of dual AV nodal pathways (i.e., anterior and posterior pathways) constituting the reentry circuit. The slow path is usually anterior, while the fast path is commonly posterior. Tachycardia is initiated when an atrial premature complex is blocked in the fast pathway but can conduct via the slow pathway. This type of arrhythmia occurs more often in otherwise healthy women. In contrast, AVRT is more common in men and associated with a faster heart rate than AVNRT. Under normal circumstances the rate of the sinus node is under inhibition of the parasympathetic nervous system, and conduction between the atria and ventricles goes through the AV node. However, in the pre-exciting syndrome including the

70

G. K. Kapuku and W. J. Kop

Wolff-Parkinson-White syndrome (i.e., Kent bundle atria to ventricle), LownGanong-Levine syndrome (James bundle atria to bundle of His), and Mahaim type (Mahaim fibers), there are accessory conduction pathways (i.e., an additional electrical conduction pathway between two parts of the heart) that result in the emergence of a reentrant tachycardia circuit, in association with supraventricular tachycardia. In these disorders, ventricular excitation occurs earlier than in normal conduction via AV node. Ventricular tachycardia (sustained heart rate of >120 beats per minute with a wide QRS on the ECG) occurs most commonly as a result of myocardial scarring (damage of the heart muscle) following a prior myocardial infarction. Dead myocardium cannot conduct electric activity, potentially causing a circuit around the scar tissue. This is why acute myocardial infarctions can lead to life-threatening arrhythmias. Abnormalities of ventricular muscle repolarization can also cause (polymorphic) ventricular tachycardia. Ventricular tachycardias are critically important in the care of CVD as they can progress to ventricular fibrillation and sudden cardiac death.

Treatment There is a wide range of treatments for the broad spectrum of arrhythmias [106]. For AF, rate control with medications (e.g., beta-adrenergic blocking agents, calcium channel blockers) can be effective or rhythm control with electrical cardioversion to restore normal sinus rhythm. PSVT may be terminated with adenosine. Other more invasive surgical procedures, such as catheter ablation, are also used to restore sinus rhythm in patients with AF and Wolff-Parkinson-White (WPW) syndrome. Most patients with AF also receive anticoagulant medications to reduce the AF-related risk of stroke. Life-threatening ventricular arrhythmias in the acute phase are typically treated with cardiac defibrillation. In addition, pharmacological agents (e.g., procainamide, sotalol, or amiodarone) are used to treat ventricular tachycardia. These antiarrhythmic drugs require careful monitoring due to their toxicity. In addition, medical devices (pacemaker, implantable cardioverter defibrillator) have been effective in controlling heart rhythm and preventing lethal arrhythmias. Expert electrophysiologists have formulated guidelines for arrhythmia management [77, 82]. Behavioral interventions such as stress management techniques may also be used to reduce stress and slow heart rate, but more research is needed to establish their effects in patients with cardiac arrhythmias.

Stroke and Cerebrovascular Disease Strokes, or cerebrovascular accidents, are the second leading cause of dementia and the fourth leading cause of death in the USA. Stroke is a loss of brain function as a result of poor perfusion of the brain due to blood vessel narrowing with arteriosclerosis process or clot formation (e.g., ischemic stroke), or vessel rupture (i.e., hemorrhagic stroke). The resulting cognitive impairments and/or neurological

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

71

deficits depend on the location and extent of the lesions; these also have prognostic implications. Stroke has major adverse psychological consequences to patients, families, and healthcare providers. Strokes are associated with permanent brain damage, whereas a transient ischemic attack (TIA) is a temporary (less than 2 h) episode of neurologic dysfunction caused by focal brain ischemia without infarction of brain tissue (the spinal cord or retina can also have TIAs). Hemorrhagic stroke can also occur secondary to aneurysm (i.e., spontaneous subarachnoid hemorrhage), and this condition clusters within families indicating a genetic predisposition.

Epidemiology In the USA, the prevalence of stroke is roughly 3% of the adult population. Globally, there were 6.5 million stroke-related deaths in 2013, making stroke the second leading cause of death behind ischemic heart disease [6]. In adults aged 35–44 years, the incidence of stroke is 30–120 in 100,000 per year, and for those aged 65–74 years, the incidence is 670–970 in 100,000 per year. Although overall population trends indicate decreases in stroke incidence over the past decades, the incidence of strokes secondary to aneurysm is the only type of stroke that has not declined. In childhood strokes are rare and predominantly related to sickle cell anemia [86].

Diagnosis In the acute phase, stroke (and often TIA) requires emergency care admission. Imaging techniques such as CT scans or MRI are combined with a physical exam and symptom assessments. Symptoms typically start suddenly (seconds to minutes) and do not progress further in most patients. The symptoms depend on the area of the brain affected but often involve sudden-onset face weakness, abnormal speech, and arm “drift” (when asked to raise both arms, an individual with stroke may involuntarily let one arm drift downward). Headaches are common in subarachnoid hemorrhage and cerebral venous thrombosis, whereas other strokes are often not accompanied by headache. It is remarkable that many strokes are “silent” (i.e., asymptomatic) with an estimated incidence of five times the rate of symptomatic strokes. In addition, approximately 30–40% of all ischemic strokes do not have an obvious explanation (curiously termed “cryptogenic” stroke). For ischemic strokes, the bifurcation of the common carotid artery (the bulb region) into the internal and external carotid arteries is a critical area. The easy access to this region by noninvasive ultrasound evaluations allows regular screening and early identification of external lesions (intima-media thickness, plaque formation). Transcranial Doppler, a technique that evaluates intracranial arteries, is even more helpful in African Americans, Japanese, and Chinese because of their high propensity to develop intracranial disease. Imaging is more sensitive and specific for

72

G. K. Kapuku and W. J. Kop

hemorrhagic than for ischemic strokes, with MRI giving better diagnostic information than CT scans for most patients.

Mechanisms and Risk Factors Most ischemic strokes are the consequence of occlusion in the internal carotid artery and its branches. These lesions are usually located in the neck at the bifurcation of the common carotid artery. Ischemic strokes are often caused by a thrombus or an embolus coming from elsewhere in the body (particularly in patients with AF). Angiographic studies have shown African Americans do have more vascular tortuosity and intracranial dilatation than Whites who have more proximal plaques [17, 38]. Moreover, African Americans have greater carotid intima-media thickening than European Americans [41]. These findings explain why African Americans more often experience strokes without premonitory TIAs than European Americans. The main risk factor for stroke is similar as those for other CVDs and includes high blood pressure (a very important risk factor) as well as smoking, obesity, dyslipidemia, diabetes mellitus, previous TIA, and atrial fibrillation.

Treatment Differential diagnosis of ischemic versus hemorrhagic stroke is critical in the acute medical assessment to evaluate the suitability of antithrombotic treatment because fibrinolysis and anticoagulation have adverse effects for patients with hemorrhagic strokes as they promote bleeding. In the acute phase of an ischemic stroke, removal of blood clots using tissue plasminogen activator (tPA) is effective in reducing adverse progression of stroke and should be administered within 3 h of symptom onset. This therapy can be successfully delivered even in rural area using telemedicine system linking a teaching hospital to remote rural hospitals. This program allows a valid National Institutes of Health Stroke Scale evaluation, review of CT scan images, confirmation of onset time, and directing a local emergency department to administer the tPA [44, 102]. Hemorrhagic strokes require a different treatment approach that includes stabilizing/supportive care, control of blood pressure and glucose, and in some cases neurosurgery to control the bleeding. In the prevention of stroke, the effort to control stroke risk factors including hypertension, diabetes, cardiac disease (particularly AF), sickle cell anemia, smoking, obesity, and oral contraceptive agents has translated into the decline of stroke. For example, it is estimated that strict hypertension control in African Americans may reduce strokes by over 50%. In Japan, stroke, which was the number one cause of morbidity four decades ago, has fallen to third place. This decline was achieved by increased blood pressure control and by lowering sodium intake [68]. For patients with documented vascular risk for stroke, endarterectomy which consists of removing the plaque in the carotid artery can also be used. Angioplasty and stenting are also used with good results.

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

73

Conclusions This chapter provides a general overview of the most common cardiovascular disorders relevant to behavioral medicine. These CVDs are all influenced by behavioral, psychological, and social factors. This handbook addresses the interrelationships between these CVDs with these multifactorial factors including depression, anxiety, perceived stress, personality traits, and socioenvironmental factors such as health disparities related to sex, socioeconomic, status and race/ethnicity. In our concluding remarks, we focus on a few remaining topics, relevant to the general aspects of CVD research, particularly (1) the use of composite measures; (2) emerging epidemiological patterns of CVD; and (3) the importance of the severity of underlying CVD in understanding associations between behavioral, psychological, and socioeconomic factors with CVD outcomes. One important issue concerns the consequences of multiple risk factors for the many expressions of CVD. This has led to the development of several risk scores, such as the Framingham Risk Score, among many others. These composite risk scores serve several useful purposes, including patient risk stratification and clinical management. However, as outlined above, these risk scores may obscure important specific mechanisms linking behavioral factors to CVD outcomes. It is therefore preferable that research efforts in cardiovascular behavioral medicine focus on the components of these risk factor scores rather than (or in addition to) these composite indices. Similarly, because CVD has many expressions, investigators often use composite endpoints such as major adverse cardiac events (MACE), which include CVD-related death, myocardial infarction and revascularization (PCI and/or CABG), and sometimes also new inducible myocardial ischemia, hospitalizations for unstable angina, incident HF, or all-cause mortality. MACE can also stand for major adverse cardiovascular events, which then may also include stroke and or peripheral artery disease. The main advantage of these composite endpoints is increased statistical power, but this comes at a loss of precision and, again, ambiguity regarding the underlying mechanism. It is therefore important to also report about the individual outcomes rather than only MACE. A second area relevant to cardiovascular behavioral medicine concerns population trends in CVD and its risk factors. In this chapter, we documented several trends in the epidemiology of CVD. The most important overarching factor is the aging population of industrialized countries, which comes with increased incidence of HF (particularly HFpEF) and AF. In addition, some CVD risk factors are increasing markedly in children and young adults, particularly physical inactivity, poor nutrition, and hypertension. These issues are discussed in the respective chapters in this handbook and provide important target areas for cardiovascular behavioral medicine. New digital technologies are becoming available that will provide novel opportunities for early detection and intervention of CVDs. For example, it will be critical to identify individuals at high risk of developing HF and its contributing CVDs. A particular focus on diastolic malfunction may be of particular importance. Mental stress (e.g., video game, mental arithmetic, social stressor) mimicking the daily life occurring stressful conditions may unmask left ventricular filling and

74

G. K. Kapuku and W. J. Kop

relaxation abnormalities concealed at rest [22, 60]. These diastolic function abnormalities are fingerprints of diastolic dysfunction, a well-recognized mechanism of HF. The third issue relates to integrating CVD pathophysiology with behavioral and psychological models. CVD is a gradually progression disease with subsequent acute clinical manifestations such as myocardial infarction, stroke, or sudden death. It is likely that long-term behavioral habits, psychological traits, and socioeconomic status affect the early disease progression. In previous publications, we have argued that transient “episodic” psychological risk factors are more important in the transition from stable CVD (e.g., single-vessel disease) to acute disease manifestation (e.g., plaque rupture and myocardial infarction) [67]. At progressed disease stages, acute psychological factors may act as trigger for adverse CVD events (e.g., anger-induced myocardial infarction). This categorization of psychological risk factors may help bring a systematic approach to the investigation of psychosocial risk factors for CVD. In this context, early life adversity may have similar effects on CVD as chronic psychological risk factors [10, 25, 32, 56, 57, 75, 76, 91]. The European Society for Cardiology has included psychological factors in their CVD screening tool [98]. This is an important development as psychological factors are closely related to health behaviors, and the decline in CVD-related mortality in industrialized countries is for a large part attributable to healthy lifestyle changes such as smoking cessation. In addition, behavioral medicine can provide helpful tools to healthcare providers to optimize patient-centered approaches to diagnosis and treatment of CVD. The chapters in this handbook further support the importance of psychosocial factors in cardiovascular disease and its clinical manifestations, which may lead to better patient care, improved quality of life, and longer survival.

References 1. Adams KF Jr (2001) New epidemiologic perspectives concerning mild-to-moderate heart failure. Am J Med 110(Suppl 7A):6S–13S 2. American Heart Association (2003) Heart disease and stroke statistics – 2003 update 3. Anderson KM, Wilson PW, Odell PM, Kannel WB (1991) An updated coronary risk profile. A statement for health professionals. Circulation 83:356–362 4. Association TCCotNYH (1994) Revisions to classification of functional capacity and objective assessment of patients with diseases of the heart. Little, Brown & Co, Boston 5. Baessler A, Kwitek AE, Fischer M, Koehler M, Reinhard W, Erdmann J, Riegger G, Doering A, Schunkert H, Hengstenberg C (2006) Association of the ghrelin receptor gene region with left ventricular hypertrophy in the general population: results of the Monica/Kora Augsburg echocardiographic substudy. Hypertension 47:920–927 6. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jimenez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS,

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

75

Muntner P (2017) Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation 135:e146–e603 7. Berenson GS, McMahan C, Voors AW, Webber L, Srinivasan SR, Frank GC, Foster TA, Blonde CV (1980) Cardiovascular risk factors in children: the early natural history of atherosclerosis and essential hypertension. Oxford University Press, New York 8. Berenson GS, Wattigney WA, Tracy RE, Newman WP, Srinivasan SR, Webber LS, Dalferes ER, Strong JP (1992) Artherosclerosis of the aorta and coronary arteries and cardiovascular risk factors in persons aged 6 to 30 years and studied at necropsy (the Bogalusa Heart Study). Am J Cardiol 70:851–858 9. Berger JS, Jordan CO, Lloyd-Jones D, Blumenthal RS (2010) Screening for cardiovascular risk in asymptomatic patients. J Am Coll Cardiol 55:1169–1177 10. Black PH, Garbutt LD (2002) Stress, inflammation and cardiovascular disease. J Psychosom Res 52:1–23 11. Braunwald E (1997) Shattuck lecture – cardiovascular medicine at the turn of the millennium: triumphs, concerns, and opportunities. N Engl J Med 337:1360–1369 12. Brown DW, Giles WH, Croft JB (2000) Left ventricular hypertrophy as a predictor of coronary heart disease mortality and the effect of hypertension. Am Heart J 140:848–856 13. Burke GL, Voors AW, Shear CL, Webber LS, Smoak CG, Cresanta JL, Berenson GS (1987) Cardiovascular risk factors from birth to 7 years of age: the Bogalusa Heart Study. Blood pressure. Pediatrics 80:784–788 14. Busjahn A, Knoblauch H, Knoblauch M, Bohlender J, Menz M, Faulhaber HD, Becker A, Schuster H, Luft FC (1997) Angiotensin-converting enzyme and angiotensinogen gene polymorphisms, plasma levels, cardiac dimensions. A twin study. Hypertension 29:165–170 15. Callow AD (2006) Cardiovascular disease 2005 – the global picture. Vasc Pharmacol 45:302–307 16. Campeau L (2002) The Canadian cardiovascular society grading of angina pectoris revisited 30 years later. Can J Cardiol 18:371–379 17. Caplan LR, Gorelick PB, Hier DB (1986) Race, sex and occlusive cerebrovascular disease: a review. Stroke 17:648–655 18. Carson P, Ziesche S, Johnson G, Cohn JN (1999) Racial differences in response to therapy for heart failure: analysis of the vasodilator-heart failure trials. Vasodilator-heart failure trial study group. J Card Fail 5:178–187 19. Cavallotti C, Bruzzone P, Mancone M (2002) Catecholaminergic nerve fibers and β-adrenergic receptors in the human heart and coronary vessels. Heart Vessel 17(1):30–35 20. Cheung BMY, Kumana CR (1998) Natriuretic peptides – relevance in cardiovascular disease. J Am Med Assoc 280:1983–1984. https://doi.org/10.1001/jama.280.23.1983 21. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, Roccella EJ, the National High Blood Pressure Education Program Coordinating C (2003) Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension 42:1206–1252. https://doi.org/10.1161/01.HYP.0000107251.49515.c2 22. Choksy P, Davis HC, Januzzi J, Thayer J, Harshfield G, Robinson VJ, Kapuku GK (2014) Brain natriuretic hormone predicts stress-induced alterations in diastolic function. Am J Med Sci 348:366–370 23. Collaboration TERF (2012) C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med 367:1310–1320 24. de Araújo GP, Ferreira J, Aguiar C, Seabra-Gomes R (2005) Timi, pursuit, and grace risk scores: sustained prognostic value and interaction with revascularization in NSTE-ACS. Eur Heart J 26:865–872 25. de Ruijter W, Westendorp RGJ, Assendelft WJJ, den Elzen WPJ, de Craen AJM, le Cessie S, Gussekloo J (2009) Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ. https://doi. org/10.1136/bmj.a3083.338

76

G. K. Kapuku and W. J. Kop

26. de Simone G, Daniels SR, Devereux RB, Meyer RA, Roman MJ, de Divitiis O, Alderman MH (1992) Left ventricular mass and body size in normotensive children and adults: assessment of allometric relations and impact of overweight. J Am Coll Cardiol 20:1251–1260 27. DeFilippis AP, Young R, Carrubba CJ, McEvoy JW, Budoff MJ, Blumenthal RS, Kronmal RA, McClelland RL, Nasir K, Blaha MJ (2015) An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med 162:266–275 28. Dekkers C, Treiber FA, Kapuku G, Van Den Oord EJ, Snieder H (2002) Growth of left ventricular mass in African American and European American youth. Hypertension 39:943–951 29. Dimsdale JE (2008) Psychological stress and cardiovascular disease. J Am Coll Cardiol 51:1237–1246 30. Dimsdale JE (2010) What does heart disease have to do with anxiety? J Am Coll Cardiol 56:47–48 31. Eckart RE, Scoville SL, Campbell CL, Shry EA, Stajduhar KC, Potter RN, Pearse LA, Virmani R (2004) Sudden death in young adults: a 25-year review of autopsies in military recruits. Ann Intern Med 141:829–834 32. Eisenmann JC (2004) Physical activity and cardiovascular disease risk factors in children and adolescents: an overview. Can J Cardiol 20:295–301 33. Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre MF, Allen C, Hansen G, Woodbrook R, Wolfe C (2015) The global burden of cancer 2013. JAMA Oncol 1:505–527 34. Fox KAA, Steg PG, Eagle KA, Goodman SG, Anderson FA Jr, Granger CB, Flather MD, Budaj A, Quill A, Gore JM, for the GI (2007) Decline in rates of death and heart failure in acute coronary syndromes, 1999–2006. JAMA 297:1892–1900. https://doi.org/10.1001/jama. 297.17.1892 35. Franklin SS, Khan SA, Wong ND, Larson MG, Levy D (1999) Is pulse pressure useful in predicting risk for coronary heart disease? Circulation 100:354–360 36. Geny B, Charloux A, Lampert E, Lonsdorfer J, Haberey P, Piquard F (1998) Enhanced brain natriuretic peptide response to peak exercise in heart transplant recipients. J Appl Physiol 85:2270–2276 37. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE (2001) Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the anticoagulation and risk factors in atrial fibrillation (atria) study. JAMA 285:2370–2375 38. Gorelick PB, Caplan LR, Hier DB, Parker SL, Patel D (1984) Racial differences in the distribution of anterior circulation occlusive disease. Neurology 34:54–59 39. Grandi AM, Laurita E, Selva E, Piantanida E, Imperiale D, Giovanella L, Guasti L, Venco A (2004) Natriuretic peptides as markers of preclinical cardiac disease in obesity. Eur J Clin Investig 34:342–348 40. Grundy SM, Balady GJ, Criqui MH, Fletcher G, Greenland P, Hiratzka LF, Houston-Miller N, Kris-Etherton P, Krumholz HM, LaRosa J, Ockene IS, Pearson TA, Reed J, Washington R, Smith SC Jr (1998) Primary prevention of coronary heart disease: guidance from Framingham: a statement for healthcare professionals from the Aha task force on risk reduction. Circulation 97:1876–1887 41. Hao G, Wang X, Treiber FA, Davis H, Leverett S, Su S, Kapuku G (2016) Growth of carotid intima-media thickness in black and white young adults. J Am Heart Assoc 5:e004147 42. Harshfield GA, Pickering TG, Blank S, Lindahl C, Stroud L, Laragh JH (1984) Ambulatory blood pressure monitoring: recorders, applications, and analysis. In: Weber M, Drayer J (eds) Ambulatory blood pressure monitoring. Steinhopff-Verlag, Darmstadt, pp 1–7 43. Hashiba K, Tanigawa M, Fukatani M, Shimizu A, Konoe A, Kadena M, Mori M (1989) Electrophysiologic properties of atrial muscle in paroxysmal atrial fibrillation. Am J Cardiol 64:20J–23J

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

77

44. Hess DC, Wang S, Gross H, Nichols FT, Hall CE, Adams RJ (2006) Telestroke: extending stroke expertise into underserved areas. Lancet Neurol 5:275–278 45. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P (2008) Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart 94:34–39 46. Hippisley-Cox J, Coupland C, Robson J, Brindle P (2010) Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: Cohort study using QResearch database. BMJ 341:c6624 47. Ho JE, Enserro D, Brouwers FP, Kizer JR, Shah SJ, Psaty BM, Bartz TM, Santhanakrishnan R, Lee DS, Chan C (2016) Predicting heart failure with preserved and reduced ejection fraction. Circ Heart Fail 9:e003116 48. Hoffmann U, Ferencik M, Udelson JE, Picard MH, Truong QA, Patel MR, Huang M, Pencina MJ, Mark DB, Heitner JF (2017) Prognostic value of noninvasive cardiovascular testing in patients with stable chest pain: insights from the promise trial. Circulation. https://doi.org/10. 1161/CIRCULATIONAHA.116.024360 49. Horl WH (2005) Natriuretic peptides in acute and chronic kidney disease and during renal replacement therapy. J Investig Med 53:366–370 50. Iliescu C, Yusuf S, Auerbach L, Tong A, Vooletich M, Cortes J, Divakaran V, Lotlikar S, Woods M, Talpaz M (2005) Impact of angiotensin converting enzyme inhibitors and carvedilol on recovery of cardiac function in imatinib associated cardiomyopathy. J Card Fail 11: S104–S104 51. Innes BA, McLaughlin MG, Kapuscinski MK, Jacob HJ, Harrap SB (1998) Independent genetic susceptibility to cardiac hypertrophy in inherited hypertension. Hypertension 31:741–746 52. James PA, Oparil S, Carter BL et al (2014) Evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the eighth joint national committee (JNC 8). JAMA 311:507–520 53. Jousilahti P, Vartiainen E, Tuomilehto J, Puska P (1999) Sex, age, cardiovascular risk factors, and coronary heart disease: a prospective follow-up study of 14 786 middle-aged men and women in Finland. Circulation 99:1165–1172 54. Julien J, Tranche C, Souchet T (2004) Left ventricular hypertrophy in hypertensive patients. Epidemiology and prognosis. Arch Mal Coeur Vaiss 97:221–227 55. Juo SH, Di Tullio MR, Lin HF, Rundek T, Boden-Albala B, Homma S, Sacco RL (2005) Heritability of left ventricular mass and other morphologic variables in Caribbean hispanic subjects: the Northern Manhattan Family Study. J Am Coll Cardiol 46:735–737 56. Kannel WB, Wilson P, Blair SN (1985) Epidemiological assessment of the role of physical activity and fitness in development of cardiovascular disease. Am Heart J 109:876–885 57. Kannel WB, Wolf PA, Castelli WP, D’Agostino RB (1987) Fibrinogen and risk of cardiovascular disease. The Framingham study. J Am Med Assoc 258:1183–1186 58. Kapuku GK, Treiber FA, Davis HC, Harshfield GA, Cook BB, Mensah GA (1999) Hemodynamic function at rest, during acute stress, and in the field: predictors of cardiac structure and function 2 years later in youth. Hypertension 34:1026–1031 59. Kapuku GK, Ge D, Vemulapalli S, Harshfield GA, Treiber FA, Snieder H (2008) Change of genetic determinants of left ventricular structure in adolescence: longitudinal evidence from the Georgia cardiovascular twin study. Am J Hypertens 21:799–805 60. Kapuku GK, Davis H, Murdison K, Robinson V, Harshfield G (2012) Stress reduces diastolic function in youth. Psychosom Med 74:588 61. Kastrati A, Hall D, Schömig A (2000) Long-term outcome after coronary stenting. Curr Control Trials Cardiovasc Med 1:48–54 62. Kawai S, Kitabatake A, Tomoike H, Group TCS (2007) Guidelines for diagnosis of Takotsubo (ampulla) cardiomyopathy. Circ J 71:990–992 63. Kholaif N, Zheng Y, Jagasia P, Himmelmann A, James SK, Steg PG, Storey RF, Westerhout CM, Armstrong PW (2015) Baseline q waves and time from symptom onset to st-segment

78

G. K. Kapuku and W. J. Kop

elevation myocardial infarction: insights from Plato on the influence of sex. Am J Med 128:914.e911–914.e919 64. Khot UN, Khot MB, Bajzer CT, Sapp SK, Ohman EM, Brener SJ, Ellis SG, Lincoff AM, Topol EJ (2003) Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 290:898–904. https://doi.org/10.1001/jama.290.7.898 65. Koglin J, Pehlivanli S, Schwaiblmair M, Vogeser M, Cremer P (2001) Role of brain natriuretic peptide in risk stratification of patients with congestive heart failure. J Am Coll Cardiol 38 (7):1934–1941 66. Konoe A, Fukatani M, Tanigawa M, Isomoto S, Kadena M, Sakamoto T, Mori M, Shimizu A, Hashiba K (1992) Electrophysiological abnormalities of the atrial muscle in patients with manifest Wolff-Parkinson-White syndrome associated with paroxysmal atrial fibrillation. Pacing Clin Electrophysiol 15:1040–1052 67. Kop WJ (1999) Chronic and acute psychological risk factors for clinical manifestations of coronary artery disease. Psychosom Med 61:476–487 68. Kubo M, Hata J, Doi Y, Tanizaki Y, Iida M, Kiyohara Y (2008) Secular trends in the incidence of and risk factors for ischemic stroke and its subtypes in Japanese population. Circulation 118:2672–2678. https://doi.org/10.1161/CIRCULATIONAHA.107.743211 69. Kupari M, Hautanen A, Lankinen L, Koskinen P, Virolainen J, Nikkila H, White PC (1998) Associations between human aldosterone synthase (cyp11b2) gene polymorphisms and left ventricular size, mass, and function. Circulation 97:569–575 70. Law M, Morris J, Wald N (2009) Use of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies. BMJ 338:b1665 71. Maron BJ, Doerer JJ, Haas TS, Tierney DM, Mueller FO (2009) Sudden deaths in young competitive athletes. Circulation 119:1085–1092 72. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Després J-P, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jiménez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB (2016) Executive summary: heart disease and stroke statistics – 2016 update. A Report From the American Heart Association. Circulation 133:447–454 73. National Center for Health S. Health, United States (2015) Health, United States, 2014: with special feature on adults aged 55–64. National Center for Health Statistics (US), Hyattsville 74. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and A (2004) The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 114:555–576. https://doi.org/10.1542/ peds.114.2.S2.555 75. Oparil S, Oberman A (1999) Nontraditional cardiovascular risk factors. Am J Med Sci 317:193–207 76. Paffenbarger RS Jr, Blair SN, Lee IM (2001) A history of physical activity, cardiovascular health and longevity: the scientific contributions of Jeremy N Morris, DSC, DPH, FRCP. Int J Epidemiol 30:1184–1192 77. Page RL, Joglar JA, Caldwell MA, Calkins H, Conti JB, Deal BJ, Estes NAM, Field ME, Goldberger ZD, Hammill SC, Indik JH, Lindsay BD, Olshansky B, Russo AM, Shen W-K, Tracy CM, Al-Khatib SM (2015) 2015 ACC/AHA/HRS guideline for the management of adult patients with supraventricular tachycardia. A report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines and the heart rhythm society 78. Perk J, De Backer G, Gohlke H, Graham I, Reiner Ž, Verschuren WMM, Albus C, Benlian P, Boysen G, Cifkova R, Deaton C, Ebrahim S, Fisher M, Germano G, Hobbs R, Hoes A, Karadeniz S, Mezzani A, Prescott E, Ryden L, Scherer M, Syvänne M, Op Reimer WJ, Vrints

3

Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and. . .

79

C, Wood D, Zamorano JL, Zannad F (2012) European guidelines on cardiovascular disease prevention in clinical practice (version 2012): the fifth joint task force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Eur Heart J 223(1):1–68 79. Pierce GL, Pajaniappan M, DiPietro A, Darracott-Woei-A-Sack K, Kapuku GK (2016) Abnormal central pulsatile hemodynamics in adolescents with obesity novelty and significance. Hypertension 68:1200–1207 80. Poch E, Gonzalez D, Gomez-Angelats E, Enjuto M, Pare JC, Rivera F, de La Sierra A (2000) G-protein beta(3) subunit gene variant and left ventricular hypertrophy in essential hypertension. Hypertension 35:214–218 81. Post WS, Larson MG, Myers RH, Galderisi M, Levy D (1997) Heritability of left ventricular mass the Framingham Heart Study. Hypertension 30:1025–1028 82. Priori SG, Blomström-Lundqvist C, Mazzanti A, Blom N, Borggrefe M, Camm J, Elliott PM, Fitzsimons D, Hatala R, Hindricks G, Kirchhof P, Kjeldsen K, Kuck K-H, Hernandez-Madrid A, Nikolaou N, Norekvål TM, Spaulding C, Van Veldhuisen DJ, Kolh P, Lip GYH, Agewall S, Barón-Esquivias G, Boriani G, Budts W, Bueno H, Capodanno D, Carerj S, Crespo-Leiro MG, Czerny M, Deaton C, Dobrev D, Erol Ç, Galderisi M, Gorenek B, Kriebel T, Lambiase P, Lancellotti P, Lane DA, Lang I, Manolis AJ, Morais J, Moreno J, Piepoli MF, Rutten FH, Sredniawa B, Zamorano JL, Zannad F (2015) 2015 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death the task force for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death of the European Society of Cardiology (ESC) endorsed by: Association for European Pediatric and Congenital Cardiology (AEPC). Eur Heart J 36:2793–2867 83. Rahimi K, Emdin CA, MacMahon S (2015) The epidemiology of blood pressure and its worldwide management. Circ Res 116:925–936 84. Ridker PM, Hennekens CH, Buring JE, Rifai N (2000) C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 342:836–843 85. Ridker PM, Buring JE, Rifai N, Cook NR (2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds risk score. JAMA 297:611–619 86. Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, Carnethon MR, Dai S, de Simone G, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Greenlund KJ, Hailpern SM, Heit JA, Ho PM, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, McDermott MM, Meigs JB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Rosamond WD, Sorlie PD, Stafford RS, Turan TN, Turner MB, Wong ND, Wylie-Rosett J (2011) Heart disease and stroke statistics – 2011 update: a report from the American heart association. Circulation 123: e18–e209 87. Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, Hailpern SM, Ho M, Howard V, Kissela B, Kittner S, Lloyd-Jones D, McDermott M, Meigs J, Moy C, Nichol G, O’Donnell C, Roger V, Sorlie P, Steinberger J, Thom T, Wilson M, Hong Y (2008) Heart disease and stroke statistics – 2008 update: a report from the American heart association statistics committee and stroke statistics subcommittee. Circulation 117:e25–e146 88. Roth GA, Huffman MD, Moran AE, Feigin V, Mensah GA, Naghavi M, Murray CJ (2015) Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation 132:1667–1678 89. Rutan GH, Kuller LH, Neaton JD, Wentworth DN, McDonald RH, Smith WM (1988) Mortality associated with diastolic hypertension and isolated systolic hypertension among men screened for the multiple risk factor intervention trial. Circulation 77:504–514 90. Sagnella GA (2001) Atrial natriuretic peptide mimetics and vasopeptidase inhibitors. Cardiovasc Res 51:416–428

80

G. K. Kapuku and W. J. Kop

91. Scarabin PY, Aillaud MF, Amouyel P, Evans A, Luc G, Ferrieres J, Arveiler D, Juhan-Vague I (1998) Associations of fibrinogen, factor vii and pai-1 with baseline findings among 10,500 male participants in a prospective study of myocardial infarction – the prime study. Prospective epidemiological study of myocardial infarction. Thromb Haemost 80:749–756 92. Schmitt M, Qasem A, McEniery C, Wilkinson IB, Tatarinoff V, Noble K, Klemes J, Payne N, Frenneaux MP, Cockcroft J, Avolio A (2004) Role of natriuretic peptides in regulation of conduit artery distensibility. Am J Physiol Heart Circ Physiol 287:H1167–H1171 93. Schunkert H, Hense HW, Holmer SR, Stender M, Perz S, Keil U, Lorell BH, Gunter GAJ (1994) Association between a deletion polymorphism of the angiotensin-converting-enzyme and left ventricular hypertrophy. N Engl J Med 330:1634–1638 94. Shimizu A, Fukatani M, Tanigawa M, Mori M, Hashiba K (1989) Intra-atrial conduction delay and fragmented atrial activity in patients with paroxysmal atrial fibrillation. Jpn Circ J 53:1023–1030 95. Shukla A, Yusuf SW, Daher I, Lenihan D, Durand J (2008) High mortality rates are associated with withdrawal of beta blockers and ace inhibitors in chemotherapy-induced heart failure 96. Sikri N, Bardia A (2007) A history of streptokinase use in acute myocardial infarction. Tex Heart Inst J 34:318–327 97. Spodick DH (1992) Normal sinus heart rate: sinus tachycardia and sinus bradycardia redefined. Am Heart J 124:1119–1121 98. van Montfort E, Denollet J, Vermunt JK, Widdershoven J, Kupper N (2017) The tense, the hostile and the distressed: multidimensional psychosocial risk profiles based on the esc interview in coronary artery disease patients – the thoresci study. Gen Hosp Psychiatry 47:103–111 99. Verhaaren HA, Schieken RM, Mosteller M, Hewitt JK, Eaves LJ, Nance WE (1991) Bivariate genetic analysis of left ventricular mass and weight in pubertal twins (the medical college of Virginia twin study). Am J Cardiol 68:661–668 100. Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A, Carter A, Casey DC, Charlson FJ, Chen AZ (2016) Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet 388:1545 101. Wachtell K, Bella JN, Liebson PR, Gerdts E, Dahlof B, Aalto T, Roman MJ, Papademetriou V, Ibsen H, Rokkedal J, Devereux RB (2000) Impact of different partition values on prevalences of left ventricular hypertrophy and concentric geometry in a large hypertensive population: the life study. Hypertension 35:6–12 102. Wang S, Gross H, Lee SB, Pardue C, Waller J, Nichols FT, Adams RJ, Hess DC (2004) Remote evaluation of acute ischemic stroke in rural community hospitals in Georgia. Stroke 35:1763–1768 103. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Colvin MM, Drazner MH, Filippatos GS, Fonarow GC, Givertz MM (2017) 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American college of cardiology/American Heart Association task force on clinical practice guidelines and the Heart Failure Society of America. J Am Coll Cardiol 70(6):776–803 104. Yano K, Hirata M, Matsumoto Y, Hano O, Mori M, Ahmed R, Mitsuoka T, Hashiba K (1989) Effects of chronic hypokalemia on ventricular vulnerability during acute myocardial ischemia in the dog. Jpn Heart J 30:205–217 105. Zellner C, Protter AA, Ko E, Pothireddy MR, DeMarco T, Hutchison SJ, Chou TM, Chatterjee K, Sudhir K (1999) Coronary vasodilator effects of BNP: mechanisms of action in coronary conductance and resistance arteries. Am J Physiol 276:H1049–H1057 106. Zimetbaum P (2012) Antiarrhythmic drug therapy for atrial fibrillation. Circulation 125:381–389

4

The Biopsychosocial Perspective on Cardiovascular Disease Andrew Steptoe and Roberto La Marca

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Origins and Challenges of the Biopsychosocial Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Animal Research in Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laboratory and Clinical Mental Stress Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ambulatory Monitoring in Cardiovascular Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Observational Epidemiological and Clinical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

82 82 84 85 89 91 94 94

Abstract

The biopsychosocial model originally formulated by George Engel provides an important theoretical framework for cardiovascular behavioral medicine. Yet despite extensive supporting evidence, the model is still not widely accepted within mainstream biomedical research. This chapter provides an overview of the biopsychosocial approach, extending this framework to contemporary behavioral medicine research. It is argued that a full appreciation of biobehavioral contributions to cardiovascular disease etiology and management requires the synthesis of evidence from four complementary research paradigms: animal studies, laboratory and clinical mental stress testing, ambulatory monitoring of cardiovascular and neuroendocrine function, and observational epidemiological and clinical studies. Each approach provides unique information that builds understanding

A. Steptoe (*) Department of Epidemiology and Public Health, University College London, London, UK e-mail: [email protected] R. La Marca Department of Clinical Psychology and Psychotherapy, University of Zurich, Zurich, Switzerland © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_4

81

82

A. Steptoe and R. La Marca

of the role of biobehavioral processes yet has both strengths and limitations. Integrating evidence from these diverse research areas constitutes a challenge but also an opportunity to realize the potential of the biopsychosocial perspective.

Keywords

Animal research · Laboratory stress testing · Ambulatory monitoring · Epidemiological studies · Stress reactivity

Introduction Cardiovascular behavioral medicine has its origins in the biopsychosocial perspective advocated by George Engel. This approach has now diversified into multiple research strategies with animal models, healthy individuals, and patients with cardiovascular disease, involving a large variety of scientific methods ranging from molecular genetics through controlled laboratory studies to population-level work. The purpose of this chapter is to provide an overview of the challenges arising out of the biopsychosocial approach, the methods used to investigate behavioral and psychosocial factors, and the types of evidence relevant to a biopsychosocial understanding of cardiovascular disorders. It will be argued that biopsychosocial thinking provides a paradigm through which the impact of psychological, social, and cultural factors is integrated with contemporary biomedical understanding of cardiovascular disease development and progression. This chapter expands on the biopsychosocial theoretical framework in which the relevance of psychosocial and biobehavioral research can be appreciated and recognized as enhancing conventional biomedical thinking.

Origins and Challenges of the Biopsychosocial Perspective It is sobering to peruse the contents of standard textbooks of cardiovascular disease and see the limited coverage given to behavioral medicine. For example, of the 89 chapters in the 8th Edition of Braunwald’s Heart Disease, just one focuses on “psychiatric and behavioral aspects of cardiovascular disease” [74]. Psychosocial factors fare a little better in Evidence-Based Cardiology [75], with 5 of the 81 chapters centered on behavioral medicine aspects of etiology and prevention. While phenomena like the placebo effect, which is largely psychosocially determined, have become well accepted in biomedical research, other psychosocial and biobehavioral contributions are still neglected. It would appear that despite 40 years of flourishing research, cardiovascular behavioral medicine does not play a leading role in mainstream biomedical thinking about cardiovascular disease. There are several reasons why the widespread acceptance of the biopsychosocial and behavioral medicine perspectives is still limited. Some are historical, and others

4

The Biopsychosocial Perspective on Cardiovascular Disease

83

are conceptual. Historically, some resistance stems from the way in which early psychosomatic theorists formulated models of the pathophysiology of medical conditions such as hypertension. These models placed intrapsychic conflicts and their physiological manifestations at the core of disease development, views that were so out of step with the evolving molecular and biological approach that there was no possibility of rapprochement. In effect, early psychosomatics postulated an alternative functional pathology that could not be integrated with biomedical understanding. A related factor that was recognized by Engel was the strong thread of dualism running through scientific medicine in the twentieth century. This is still reflected in the difference in emphasis placed by behavioral medicine and conventional biomedicine on the influence of the higher central nervous system on cardiovascular function. Of course, it is well recognized that the brainstem and other primitive regions of the central nervous system are involved in cardiovascular regulation and cardiac function and that the hypothalamus plays a key role in hormonal processes relevant to fluid volume and kidney function. But a key tenet of behavioral medicine is that the neocortex and limbic system, both involved in the central autonomic network [1], have a fundamental influence on “automatic” regulatory systems and translate environmental exposures and cognitive and emotional factors into modifications of hemodynamic, cardiac, and atherosclerotic processes. Biomedical scientists working in more conventional fields of cardiovascular research may acknowledge that such influences exist but that they have a less prominent impact than that identified in behavioral medicine. It is no accident that the first chapter of Guyton and Hall’s authoritative Textbook of Medical Physiology emphasizes the “automaticity” of the body’s primary regulatory systems [21]. One of George Engel’s fundamental achievements in formulating the biopsychosocial approach was to attempt to bring psychological and social factors into the mainstream of clinical science and practice [16]. The biopsychosocial approach explores how perceptions, beliefs, feelings, cognitions, personality traits, and the social context interact with biological processes in the development of ill health and the management of disability [72]. These ideas form one of the foundations of behavioral medicine, together with the application of experimental behavioral investigations to physical health problems. The biopsychosocial perspective has proved appealing in some specialties such as medical education and liaison psychiatry. Shorter [54] has argued that another reason for the failure of the biopsychosocial approach to be absorbed into broader biomedical thinking is that Engel’s formulation of this model coincided with spectacular developments in pharmacotherapy both in psychiatry and in fields such as cardiology, so that his integrated view was lost in the wake of apparent advances in symptom management. These advances were followed by other major developments in biomedicine such as the genetic revolution, the evolution of imaging techniques, and the rise of molecular medicine. Engel’s rejection of reductionism did not sit comfortably with these advances. What was not recognized in the early phases of the biopsychosocial movement was the diversity of research strategies that are relevant to this perspective. Engel was writing in an era in which psychophysiological methods were just becoming

84

A. Steptoe and R. La Marca

established, and psychosocial epidemiology was restricted to a few centers [24]. The technical advances in medical instrumentation and computation had not yet emerged that permit ambulatory monitoring, cardiac imaging, and other sophisticated methods described in later chapters of the Handbook. Psychological interventions designed to promote adjustment and improve the quality of life of cardiac patients had not evolved into systematic biobehavioral programs but were largely based on the individual psychotherapy model. However, this diversity of research strategies also brings with it important challenges, particularly related to the integration of knowledge acquired through different research paradigms. Cardiovascular behavioral medicine is faced with the dual tasks of synthesizing knowledge from different biobehavioral perspectives and then integrating this understanding into the broader biomedical framework. Some of the issues that need to be resolved are discussed in the following sections of this chapter. We highlight four broad research strategies in cardiovascular behavioral medicine (animal work, acute laboratory stress studies, ambulatory monitoring, and observational epidemiology) and point out some of the strengths and challenges associated with each approach.

Animal Research in Cardiovascular Behavioral Medicine Studies of animals are crucial to understanding the biology of stress and adaptation, the neurochemical systems underlying behaviorally driven physiological adjustments, and the factors contributing to behavioral influences on cardiovascular disease. They are fundamental to an integrated approach to stress, physiology, and pathogenesis, since experimental methods can be used to explore the complete sequence of events from initial reactions to environmental stimuli right through to clinical pathology. The causal effects of behavioral stimuli can therefore be established in a way rarely possible in research on humans, and physiological pathways can be identified that may be particularly suitable targets for intervention. There have been several important programs of animal research in cardiovascular behavioral medicine, including Henry’s work on social conflict and high blood pressure [25], Grippo and Johnson’s work on the biopsychological effects of chronic stress and social isolation in rodents [19], the research on social status and coronary atherosclerosis in cynomolgus macaques carried out by Kaplan, Manuck, Shively, and others [32, 53], and McCabe and Schneiderman’s work on social relationships and cardiovascular pathology in rabbits [38]. These studies share a number of characteristics, including the focus on social factors and social disruption as provokers of cardiovascular pathology (instead of arbitrary stimuli such as electric shock), the assessment of behaviors in addition to physiology, the exploration of mediating pathways, and the attention paid to individual differences as well as the impact of environmental conditions. The limitations to the interpretation of animal behavioral medicine research are well known. They include caution against the glib assumption that the environmental conditions giving rise to cardiovascular

4

The Biopsychosocial Perspective on Cardiovascular Disease

85

pathology have direct parallels in humans, and against the presumption that because a biological pathway is identified in an animal model, it will necessarily be relevant to clinical pathology in humans.

Laboratory and Clinical Mental Stress Testing The closest parallel to animal studies in human cardiovascular behavioral medicine is probably psychophysiological stress testing. Psychophysiological or mental stress testing involves the measurement of physiological responses to standardized stimuli such as problem-solving tasks or emotionally demanding and evaluative social interactions. A typical study begins with the instrumentation of the participant with measurement devices. This may be as simple as putting on a blood pressure cuff or electrocardiogram (ECG or EKG) electrodes, or much more complex if regional hemodynamics are being monitored, or cardiac function imaged using magnetic resonance imaging, radionuclide ventriculography, or another scan. This is followed by a period of rest so that baseline levels of physiological function can be established after calming and habituation to the laboratory setting. Data collection usually starts after the rest period and includes continuous monitoring together with repeated measurements of blood or saliva markers which are conducted only at time points of interest. Standardized tasks or stressors are then administered for periods that may last from a few minutes to several hours depending on the protocol. Physiological monitoring typically continues throughout the tasks and during the post-task recovery period for a variable length of type. Initially, research on psychophysiological stress responses focused almost exclusively on the task period, but there is growing interest in the recovery period for several reasons. First, important differences in responses may emerge during recovery that are not apparent during tasks themselves. For example, there is evidence that positive affect is more influential on the rate of post-stress recovery than on task responses themselves and is associated with more rapid recovery in blood pressure [4, 64]. Second, physiologic and psychological recovery from tasks also allows interesting insights into autonomic function and psychological phenomena such as post-stress cognitive perseveration [5, 7, 40]. Additionally, models of stress and adaptation such as the allostatic load concept posit that dysregulation of recovery processes is a major consequence of sustained exposure to environmental adversity [39]. Finally, several important physiological responses to stress are not immediate but only emerge after several minutes or hours. Thus, levels of cortisol in the saliva or blood can take up to 30 min after stress onset to peak, so may become evident several minutes after the stressor has terminated. Inflammatory cytokines such as interleukin (IL)-6 may continue to rise for up to 2 h following stress [63], while impairments of vascular endothelial function continue to develop for at least 90 min [18]. Mental stress testing is relevant not only to the biomedical and psychophysiological factors discussed in later chapters of the Handbook but also to the investigation of psychosocial, environmental, and behavioral risk factors assumed to be associated

86

A. Steptoe and R. La Marca

with physiological systems underlying the development of cardiovascular diseases. The method has several important advantages. First, stress testing involves the assessment of dynamic responses to behavioral and psychosocial stimuli, in contrast to clinical and epidemiological measures that are generally taken in the resting state. The method therefore directly explores an individual’s biological functionality in a systematic fashion. Second, physiological responses are measured under environmentally controlled conditions, reducing many of the sources of bias and individual difference that are present in other types of cardiovascular behavioral medicine research. Third, the study sample can be defined a priori by choosing appropriate inclusion and exclusion criteria. This allows direct comparisons of physiological stress responses in different groups, contrasting patients with and without illnesses such as hypertension or coronary heart disease, individuals at low and high risk of cardiovascular illness, or groups with and without characteristics such as depression or hostility that are relevant to cardiovascular risk. Fourth, stress testing allows experimental designs to be used in the study, with randomization to different conditions. This allows investigators to explore issues about stress-specific factors such as controllability [62], predictability [3], social support [29], and social evaluation [71] in a systematic fashion. Finally, the clinical or laboratory setting allows technically complex methods of physiological measurement to be used that are not possible in other situations. Many of the advances in understanding the biological pathways through which psychosocial factors and behaviors impact on cardiovascular function have emerged because research has moved beyond merely monitoring blood pressure, heart rate, and electrodermal activity and now includes measures of neuroendocrine and inflammatory activity, renal function, metabolic responses, realtime imaging of blood flow, and other parameters. The integration of psychophysiological testing with functional brain imaging is greatly enhancing understanding of the central nervous system processes underlying brain-body integration. Several types of challenges are used in mental stress testing, including problemsolving tasks, psychomotor tasks, simulated public speaking, and emotionally laden interviews. Tasks are not interchangeable but vary along dimensions that are relevant to cardiovascular responses. Issues such as ego-involvement, level of control, predictability, the types of coping response elicited, and the social dimensions of the situation are all relevant [14, 41, 43]. Some studies involve tasks that appear to be relevant to everyday life, while others involve stimuli that are designed to tap into psychological characteristics such as hostility or depression. The magnitude of physiological responses and their rate of recovery are also related to background stress levels in the participant’s life, as well as to psychological features [8]. A crucial element in all mental stress testing is that the conditions imposed are perceived as challenging and involving, or else responses are likely to be small [55]. This means that it is valuable always to assess affective state along with biological responses during mental stress testing, so that effects can be properly interpreted [61]. For example, when a group of participants in a study (such as people with hypertension) do not differ in physiological responses from controls, is this because there are

4

The Biopsychosocial Perspective on Cardiovascular Disease

87

genuinely no differences in responsivity, because the stimuli did not provoke sufficiently intense affective stress responses? Or did the stressor provoke stronger affective responses, but physiological hyposensitivity due to exhaustion and downregulation impeded any biological distinctions from emerging? These alternative explanations can only be distinguished when affective data are available. Mental stress testing in cardiovascular research has attracted a number of criticisms [45]. The primary reservations have concerned the poor reliability of laboratory cardiovascular responses, the intraindividual consistency between responses to different stress tasks which is sometimes only modest, the use of arbitrary stimuli that have limited ecological validity, and the lack of relevance of short-term acute responses (often tested only on one occasion) to everyday life and to clinical outcomes. On the reliability issue, most authorities now agree that when methods are applied carefully, psychophysiological stress testing does consistently elicit physiological responses with good test-retest reliability, not only in traditional measures such as blood pressure and heart rate but also in inflammatory and hemostatic variables [22, 28, 70]. In regard to intraindividual response consistency, it is important to recognize the different mechanisms underlying responses to various tasks, therefore to choose stimuli that are appropriate for the specific research question [35]. The question of ecological validity and relevance to everyday life has been extensively studied by assessing correlations between blood pressure and heart rate stress responses in the laboratory and values recorded in everyday life using ambulatory monitoring techniques. A substantial literature emerged in the 1980s and 1990s testing “laboratory/field” correlations, with evidence for moderate to high associations, particularly when stress testing is repeated to establish robust estimates, responses to different tasks are aggregated, longer periods of ambulatory monitoring are conducted, and more sophisticated statistical procedures are employed [30]. Research on associations between acute neuroendocrine responses and levels of function in everyday life is more limited, but here too robust relationships between individual differences in acute cortisol responses and diurnal profiles have been identified [33]. However, it could be argued that studies which evaluate laboratory/field correlations only address one aspect of the problem. First, such studies can only be carried out with a small set of cardiovascular parameters such as blood pressure, heart rate, and heart rate variability. Technology has not yet developed sufficiently to explore correlations for many of the other physiological parameters of interest such as inflammatory responses, endothelial function, and myocardial blood flow. Second, studies of laboratory/field correlations need to take account of the circumstances in everyday life in which measures are taken. It is simplistic to expect responses to externally generated acute stressors in the laboratory or clinic to correlate with values averaged over the day, since many of the latter will have been measured under resting or non-stressed conditions. In real life, individuals often have the possibility of choosing, avoiding, or at least anticipating stressful demands. Relatively few studies have yet taken the circumstances of the person’s everyday life into account

88

A. Steptoe and R. La Marca

[37, 58]. Ultimately, acute mental stress testing and ambulatory monitoring should not be seen as competing with one another but as complementary research paradigms addressing distinct but related issues. More important than the association between mental stress testing and ambulatory monitoring is the quantification of the clinical significance of acute cardiovascular responses. The magnitude of changes in cardiovascular, inflammatory, and hemostatic function during mental stress is not large. But if these responses are repeated multiple times or maintained over weeks and months, they may lead to larger or more persistent alterations in physiological function that have adverse clinical consequences. Since cardiovascular disorders take many years to evolve, longitudinal research linking acute responses prospectively with disease development is a slow business. Research to date has focused on the development of elevated blood pressure and hypertension and on the incidence of coronary heart disease [27]. A recent meta-analysis of prospective studies identified a small but significant association between both heightened cardiovascular reactivity and delayed recovery and progression of cardiovascular disease as indexed by blood pressure changes and markers of subclinical atherosclerosis [9]. However, effects were in general rather modest. A number of factors might be responsible. Almost all studies to date have measured blood pressure and heart rate reactivity. It is possible that these cardiovascular responses are not important in themselves but are markers of other physiological processes more directly involved in cardiovascular pathology. For instance, inflammatory pathways are central to atherogenesis, while hemostatic responses are critical in advanced coronary heart disease (CHD) and thrombus formation. It has been found that delayed cardiovascular recovery following stress is related to prolonged hemostatic and inflammatory responses [60], and greater greater IL-6 and fibrinogen stress responses predict increased ambulatory blood pressure prospectively [6]. Perhaps these biological responses are more important than blood pressure or heart rate. Large-scale prospective studies of inflammatory, neuroendocrine, and hemostatic responses to acute stress may in the future generate more consistent associations with progression of cardiovascular disease. Additionally, adding measures of stress exposure and coping style during the follow-up period may increase the strength of predictions of future cardiovascular health status, as would study of interactions with factors such as family history, adiposity, and physical fitness. Another issue in understanding the relationship with health outcomes is whether large responses are necessarily cardiotoxic. Apart from impaired post-stress recovery, another manifestation of chronic allostatic load may be blunted responses as the organism fails to mount an adequate response to external demands [39]. There is evidence that some negative health outcomes may be associated with blunted cardiovascular and neuroendocrine responses rather than hyperreactivity [13]. Mental stress testing is used extensively not only with healthy participants but in individuals with cardiovascular diseases such as CHD and hypertension. There is a significant literature concerning stress-induced transient myocardial ischemia, involving detailed cardiac imaging with radionuclide ventriculography, coronary

4

The Biopsychosocial Perspective on Cardiovascular Disease

89

angiography, and other techniques [66]. This research has established that vulnerable individuals respond to emotional challenges with alterations in myocardial blood flow or disturbances in cardiac rhythm that could theoretically contribute to the initiation of cardiac events such as acute myocardial infarction and other acute coronary syndromes (ACS). Studies of patients who have survived an acute cardiac event, and surveys of cardiac illness following natural disasters such as earthquakes, indicate that a proportion of ACS is preceded by severe emotional stress [44]. Such events typically occur against a background of advanced coronary atherosclerosis. The precise mechanisms of acute triggering by emotional stress are poorly understood, though there is limited evidence that patients susceptible to transient myocardial ischemia following standardized stress are vulnerable to triggering of real clinical events [52]. We therefore carried out a psychophysiological study comparing two groups of ACS survivors [67]. One group consisted of patients who had experienced acute emotional distress (stress, anger, or depression) in the 2 h before symptom onset, as identified by detailed interviewing in hospital after the cardiac event, while the second group had not. We found that the emotional trigger group showed impaired recovery of blood pressure following standardized stress, coupled with heightened platelet aggregation. These hemodynamic and hemostatic responses may characterize individuals who are at risk for the emotional triggering of acute cardiac events.

Ambulatory Monitoring in Cardiovascular Behavioral Medicine A second major strategy for investigating biopsychosocial integration in relation to cardiovascular disease in humans is to move outside the laboratory or clinic with ambulatory monitoring. Cardiovascular behavioral medicine has benefitted greatly from the developments in measurement technology that have led to noninvasive ambulatory blood pressure monitoring and EKG assessments with Holter monitors and other electronic instruments. Ambulatory cardiovascular monitoring is often coupled with other measures obtained under naturalistic conditions, such as repeated saliva collections. Ambulatory techniques provide information about people’s physiological function outside the clinic in everyday settings. This supplies incremental data on the relationship between the physiological system under investigation and adverse cardiac outcomes [11, 15]. EKG monitoring, for example, allows ischemic burden to be established, together with cardiac rhythm assessments that provide vital information in the diagnosis and management of clinical dysrhythmias [51]. Coupling these devices with measures of behavior and emotion has provided a window through which to evaluate biopsychosocial influences in cardiovascular function in everyday life [73]. This circumvents the problems of ecological validity present for mental stress testing, while still permitting examination of dynamic relationships between physiological function, emotion, and behavior. Additionally, the measures that are collected during ambulatory monitoring provide more robust estimates of

90

A. Steptoe and R. La Marca

clinical risk than are obtained using standard clinic or office assessments, since multiple observations in different situations can be aggregated. There have been two principal uses of ambulatory monitoring in cardiovascular behavioral medicine. The first is to evaluate associations between psychosocial factors and aggregate cardiovascular function at an interindividual level. For example, several studies have shown that work stress is more reliably related to elevated ambulatory blood pressure than it is to clinic blood pressure measures [56]. This makes sense because ambulatory measures are taken in the situation in which stress is elicited (namely, the workplace). Other studies have assessed associations between cardiovascular function in everyday life and the quality of marital relationships [68], hostility [48], and social support [26]. These studies typically employ a single measure of the psychosocial or social factor in question and involve betweenperson analyses of relationships with cardiovascular activity. The second approach to ambulatory data has been to study covariation between physiological function and emotion or behavior at a within-person level. This allows the investigator to test whether parameters such as blood pressure, heart rate, or heart rate variability are altered during episodes of emotional stress. For example, one study of school teachers involved recordings of ambulatory heart rate and heart rate variability over 4 days, during which participants completed hourly computerized diaries of worry and stressful events [47]. Both episodes of worry and stressful events were associated with elevated heart rate and reduced heart rate variability independently of biobehavioral variables. Another study of teachers showed that ambulatory blood pressure was greater on occasions over the day during which the participants reported low levels of control, compared with high control episodes [57]. Interestingly, experiences of low control over the day have also been linked with increases in carotid intima-medial thickness over time, suggestive of acceleration of atherosclerosis [31]. A further illustration of the value of ambulatory methods is provided by research on transient silent myocardial ischemia in everyday life. Frequent episodes of silent myocardial ischemia may be indicative of clinically threatening episodes of cardiac dysfunction and often occur without concomitant chest pain (hence silent). They can be accessed through detailed analysis of ST-segment deviations in ambulatory EKG records. Studies coupling this technology with collection of mood data using diaries have established that the risk of transient ischemia may be exacerbated in vulnerable individuals during periods of anger or emotional stress [17, 20]. Kop et al. [34] found that episodes of transient ischemia in everyday life that were linked with emotional stress were characterized by reduced heart rate variability in the minutes before onset. This is an example of how ambulatory methods provide insight into the linkage between emotional and physiological processes in a specific cardiovascular disorder. Ambulatory monitoring in everyday life provides rich data for cardiovascular behavioral medicine. However, the limitations of this technique need to be borne in mind. There are as yet relatively few biological variables that can be systematically studied in this way. Heart rate, heart rate variability, and blood pressure are the most

4

The Biopsychosocial Perspective on Cardiovascular Disease

91

common and nonintrusive, although repeated collections of bodily fluids such as saliva can be also used to explore neuroendocrine function relevant to cardiovascular disease [2]. New technologies may permit other parameters to be assessed in the future (e.g., continuous glucose monitoring and salivary expression of cytokines), although it is vital that measures are unobtrusive and do not interfere with ongoing activities. Another issue is that ambulatory data are complex, not only because of the high volume of repeated autocorrelated values but also because of the influence of factors such as concurrent physical activity, posture change, smoking, and eating. This necessitates the collection of contextual information and the use of more advanced statistical methods such as time series analysis, and multilevel modeling [65]. Ambulatory monitoring is very labor-intensive, and the peak effort from investigators comes only after data collection in the data editing and assimilation process. Additionally, the potential reactivity of measures (the fact that measuring may alter the phenomenon under investigation) needs to be taken into account. The purpose of ambulatory monitoring is to study physiological function in everyday life, but if people modify their behavior as a result of being monitored, this aim will be compromised. For example, there is some evidence that people alter their usual activities during periods of ambulatory blood pressure monitoring, possibly because they think the measurements will be inconvenient in certain situations, and are actually less physically active than on non-monitored days [12]. Unfortunately, evidence about these measurement effects is very limited and is difficult to obtain, so it is not known how widespread this pattern is and to what extent it affects the results of ambulatory studies.

Observational Epidemiological and Clinical Studies Mental stress testing and ambulatory monitoring provide crucial evidence about biobehavioral mechanisms in cardiovascular disease, but epidemiological and clinical studies are central to establishing the relevance of these mechanisms to clinical disease outcomes. Both strategies are used extensively in later chapters of the Handbook. The primary use of epidemiological studies is to document the impact of social, emotional, and behavioral factors in the development of cardiovascular diseases. Both case control and cohort studies are employed, with longitudinal observational investigations providing the most convincing data [59]. These studies involve the baseline assessment of a large population that is screened to ensure they do not already suffer from cardiovascular disease. Standard risk factors for cardiovascular disease are measured (such as smoking, blood pressure, family history, and cholesterol levels), along with exposure to the potential risk or protective factors being tested such as hostility, physical inactivity, chronic stress, or social support. The population is subsequently tracked over time to assess the development of CHD, hypertension, or cardiovascular mortality. Multivariate analyses are then carried out in order to test whether the hypothetical risk factor is associated with cardiovascular

92

A. Steptoe and R. La Marca

disease after covariates have been taken into account. This method is a mainstay of psychosocial epidemiological research identifying chronic stress, early life adversity, social isolation, depression, and other factors with CHD risk. By contrast, the purpose of clinical studies is to evaluate the psychosocial experience of patients who already have cardiovascular disorders and to examine the influence of these factors on prognosis, adaptation, and future quality of life. The major advantage of epidemiological studies to etiological understanding is that they are sufficiently large to document associations between psychosocial factors and future clinical cardiovascular outcomes, unlike laboratory or ambulatory monitoring studies. Only with this approach is it possible to draw definitive conclusions about whether a person’s life experience or psychological makeup predicts cardiovascular morbidity and mortality. But as with all research methods in cardiovascular behavioral medicine, there are limitations to the epidemiological method. It can be many years before sufficient cases accumulate for analysis, so it is difficult to answer scientific questions within an attractive time frame. Indeed, sometimes the interesting questions have changed before the study is complete, so there are many examples in the literature of studies being used to address subjects for which they were not designed. In addition, when a study lasts many years, it is difficult to be confident that exposure to the risk factor has remained stable, since the circumstances of participants may change; perhaps someone becomes less hostile, changes their job so that work stress is modified, or becomes more physically active. More challengingly, the meaning of psychosocial variables may not be the same in cohort studies started at different points in time. For instance, the proportion of individuals in the USA who graduated from high school increased from just over 40% in 1960 to more than 80% in 2000. This means that different people would be classified as high school graduates in studies started at different times, with implications for the use of education as an indicator of socioeconomic position. Another difficult issue is taking account of covariates and confounding. The problem is that the psychosocial or behavioral risk factor under investigation and the cardiovascular endpoint may both be related to a third underlying factor. Unless this is taken into account, misleading conclusions may be drawn. For example, some types of life stress are more common in people of lower socioeconomic status, and it is known that low socioeconomic status is an important determinant of CHD risk [10]; an association between life stress and future CHD may actually be due to confounding with socioeconomic position. But the issue of confounding is itself complicated, since there is sometimes no clear distinction between a confounding factor and a mediator. By way of illustration, a researcher may be interested in the impact of a factor such as hostility on CHD. Perhaps the study shows that hostility predicts future CHD but not when cigarette smoking is taken into account. Does this mean that the influence of hostility is confounded by smoking or mediated by smoking? If hostility increases the chances that someone smokes, then the association between hostility and CHD is not invalidated by taking smoking into account. Careful specification of hypothetical models is required to disentangle complications of this type. Reverse causality may also operate, when the exposure does not actually

4

The Biopsychosocial Perspective on Cardiovascular Disease

93

precede the onset of illness but is caused by it. For example, a person with early undiagnosed cardiac disease may feel fatigue and a diminution in his or her capacity to work effectively, resulting in reports of greater work stress; if this individual has a myocardial infarction at a later date, this might be interpreted as following work stress, whereas the reverse is actually the case. Ultimately, even the best designed longitudinal observational studies of psychosocial factors and cardiovascular disease cannot definitively establish causality. They do, however, provide compelling evidence that, taken in conjunction with findings from other types of investigation, make the causal argument the most parsimonious. Clinical studies are equally important in cardiovascular behavioral medicine and may help to establish the value of the biopsychosocial approach. They can involve many different research strategies including mental stress testing, clinical interviewing, and the administration of validated questionnaires. There are particular challenges to the standardization of procedures in clinical research because of variation in clinical practice in different centers over time. There are several important methodological issues in the interpretation of clinical studies. The first is that since clinical studies involve people with a cardiovascular disease, they are limited to survivors. A substantial number of cardiovascular deaths occur in people who have not previously been diagnosed with CHD [42], and the psychosocial and behavioral profiles of these individuals may not be known. Second, the impact of disease on the variables that are being assessed needs to be taken into account. When patients with CHD, hypertension, or other cardiovascular conditions are examined, their beliefs and cognitive models of illness come into play and may be influenced by their personal experience. For example, early research on the triggering of acute cardiac events involved interviewing patients about what they thought caused their heart attack, and it was found that stress was mentioned as the major cause in as substantial number of cases [69]. But this may have been due in part to retrospective bias, since subsequent work using more refined techniques indicates that acute stress is a probable trigger only in a relatively small proportion of cases [44]. Onset of cardiovascular illness may itself result in changes in relevant psychological characteristics. The issues of survivorship and changes with disease onset were documented more than 40 years ago using data from the Western Electric epidemiological study [36]. It was found that in comparison with survivors, people who subsequently died of CHD (without previous diagnoses) had different scores on a number of subscales of the Minnesota Multiphasic Personality Inventory (MMPI). Similarly, scores from surveys carried out before and after the onset of CHD indicated that ratings on the “neurotic triad” of scales increased, suggesting that the psychological responses of patients with known disease may not be representative of their premorbid state. A related issue is the impact of labeling with a cardiovascular diagnosis. Knowing that one has a cardiovascular condition may have an impact on emotional and behavioral processes. This has been studied most extensively with hypertension, since a substantial proportion of high blood pressure is undetected in the population. People are identified as having hypertension either through routine screening and

94

A. Steptoe and R. La Marca

checkups or because they go to their physicians complaining of nonspecific symptoms. Individuals identified in this way may not be typical. The label “hypertension” may cause also distress and affect quality of life [46]. Hamer et al. [23] assessed psychological distress in people who were found to have elevated blood pressure on medical screening, individuals who had a physician diagnosis of hypertension and normotensives. They found that distress scores were raised in aware hypertensives, but not in unaware hypertensives. These factors can influence biological responses as well. Studies of young men with raised blood pressure have shown that individuals who were told about their high blood pressure produced greater blood pressure and catecholamine responses to acute mental stress compared with those who were not informed of their hypertensive status [49, 50]. Findings such as these indicate that care must be taken in the interpretation and communication of psychological and physiological findings from people with diagnosed cardiovascular disorders.

Synthesis The purpose of this chapter has been to outline the diverse research strategies that can be employed in the development of a biopsychosocial approach to cardiovascular disease. A full understanding of the role of social, emotional, cognitive, and behavioral factors in cardiovascular disease etiology and management does not depend on any one of the research methods outlined here. All have both strengths and limitations. The challenge of the biopsychosocial perspective is to synthesize findings from different types of study. This is a genuinely interdisciplinary endeavor and requires sensitivity to different orientations to study design and interpretation. Only then will the great advances made in cardiovascular behavioral medicine be harnessed to more effective prevention and patient care. Acknowledgments Andrew Steptoe is supported by the British Heart Foundation.

References 1. Benarroch EE (1997) Central autonomic network: functional organization and clinical correlations. Futura, Armonk 2. Bhattacharyya MR, Molloy GJ, Steptoe A (2008) Depression is associated with flatter cortisol rhythms in patients with coronary artery disease. J Psychosom Res 65:107–113 3. Bohlin G, Eliasson K, Hjemdahl P, Klein K, Frankenhaeuser M (1986) Pace variation and control of work pace as related to cardiovascular, neuroendocrine and subjective responses. Biol Psychol 23:247–263 4. Bostock S, Hamer M, Wawrzyniak AJ, Mitchell ES, Steptoe A (2011) Positive emotional style and subjective, cardiovascular and cortisol responses to acute laboratory stress. Psychoneuroendocrinology 36:1175–1183 5. Brosschot JF, Pieper S, Thayer JF (2005) Expanding stress theory: prolonged activation and perseverative cognition. Psychoneuroendocrinology 30:1043–1049 6. Brydon L, Steptoe A (2005) Stress-induced increases in interleukin-6 and fibrinogen predict ambulatory blood pressure at 3-year follow-up. J Hypertens 23:1001–1007

4

The Biopsychosocial Perspective on Cardiovascular Disease

95

7. Chapleau MW, Sabharwal R (2011) Methods of assessing vagus nerve activity and reflexes. Heart Fail Rev 16:109–127 8. Chida Y, Hamer M (2008) Chronic psychosocial factors and acute physiological responses to laboratory-induced stress in healthy populations: a quantitative review of 30 years of investigations. Psychol Bull 134:829–885 9. Chida Y, Steptoe A (2010) Greater cardiovascular responses to laboratory mental stress are associated with poor subsequent cardiovascular risk status: a meta-analysis of prospective evidence. Hypertension 55:1026–1032 10. Clark AM, DesMeules M, Luo W, Duncan AS, Wielgosz A (2009) Socioeconomic status and cardiovascular disease: risks and implications for care. Nat Rev Cardiol 6:712–722 11. Conen D, Bamberg F (2008) Noninvasive 24-h ambulatory blood pressure and cardiovascular disease: a systematic review and meta-analysis. J Hypertens 26:1290–1299 12. Costa M, Cropley M, Griffith J, Steptoe A (1999) Ambulatory blood pressure monitoring is associated with reduced physical activity during everyday life. Psychosom Med 61:806–811 13. de Rooij SR (2013) Blunted cardiovascular and cortisol reactivity to acute psychological stress: a summary of results from the Dutch Famine Birth Cohort Study. Int J Psychophysiol 90:21–27 14. Dickerson SS, Kemeny ME (2004) Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. Psychol Bull 130:355–391 15. Eguchi K, Hoshide S, Ishikawa J, Pickering TG, Schwartz JE et al (2009) Nocturnal nondipping of heart rate predicts cardiovascular events in hypertensive patients. J Hypertens 27:2265–2270 16. Engel GL (1977) The need for a new medical model: a challenge for biomedicine. Science 196:129–136 17. Gabbay FH, Krantz DS, Kop WJ, Hedges SM, Klein J et al (1996) Triggers of myocardial ischemia during daily life in patients with coronary artery disease: physical and mental activities, anger and smoking. J Am Coll Cardiol 27:585–592 18. Ghiadoni L, Donald A, Cropley M, Mullen MJ, Oakley G et al (2000) Mental stress induces transient endothelial dysfunction in humans. Circulation 102:2473–2478 19. Grippo AJ (2009) Mechanisms underlying altered mood and cardiovascular dysfunction: the value of neurobiological and behavioral research with animal models. Neurosci Biobehav Rev 33:171–180 20. Gullette EC, Blumenthal JA, Babyak M, Jiang W, Waugh RA et al (1997) Effects of mental stress on myocardial ischemia during daily life. JAMA 277:1521–1526 21. Hall JE (2011) Guyton and Hall textbook of medical physiology, 12th edn. Saunders, Philadelphia 22. Hamer M, Gibson EL, Vuononvirta R, Williams E, Steptoe A (2006) Inflammatory and hemostatic responses to repeated mental stress: individual stability and habituation over time. Brain Behav Immun 20:456–459 23. Hamer M, Batty GD, Stamatakis E, Kivimaki M (2010) Hypertension awareness and psychological distress. Hypertension 56:547–550 24. Harburg E, Blakelock EH, Roeper EJ (1979) Resentful and reflective coping with arbitrary authority and blood pressure. Psychosom Med 41:189–202 25. Henry JP, Stephens PM (1977) Stress, health, and the social environment. Springer, New York 26. Horsten M, Ericson M, Perski A, Wamala SP, Schenck-Gustafsson K et al (1999) Psychosocial factors and heart rate variability in healthy women. Psychosom Med 61:49–57 27. Jennings JR, Kamarck TW, Everson-Rose SA, Kaplan GA, Manuck SB et al (2004) Exaggerated blood pressure responses during mental stress are prospectively related to enhanced carotid atherosclerosis in middle-aged Finnish men. Circulation 110:2198–2203 28. Kamarck TW, Lovallo WR (2003) Cardiovascular reactivity to psychological challenge: conceptual and measurement considerations. Psychosom Med 65:9–21 29. Kamarck TW, Manuck SB, Jennings JR (1990) Social support reduces cardiovascular reactivity to psychological challenge: a laboratory model. Psychosom Med 52:42–58 30. Kamarck TW, Schwartz JE, Janicki DL, Shiffman S, Raynor DA (2003) Correspondence between laboratory and ambulatory measures of cardiovascular reactivity: a multilevel modeling approach. Psychophysiology 40:675–683

96

A. Steptoe and R. La Marca

31. Kamarck TW, Muldoon MF, Shiffman SS, Sutton-Tyrrell K (2007) Experiences of demand and control during daily life are predictors of carotid atherosclerotic progression among healthy men. Health Psychol 26:324–332 32. Kaplan JR, Chen H, Manuck SB (2009) The relationship between social status and atherosclerosis in male and female monkeys as revealed by meta-analysis. Am J Primatol 71:732–741 33. Kidd T, Carvalho LA, Steptoe A (2014) The relationship between cortisol responses to laboratory stress and cortisol profiles in daily life. Biol Psychol 99:34–40 34. Kop WJ, Verdino RJ, Gottdiener JS, O’Leary ST, Bairey Merz CN et al (2001) Changes in heart rate and heart rate variability before ambulatory ischemic events. J Am Coll Cardiol 38:742–749 35. La Marca R, Waldvogel P, Thorn H, Tripod M, Wirtz PH et al (2011) Association between Cold Face Test-induced vagal inhibition and cortisol response to acute stress. Psychophysiology 48:420–429 36. Lebovits BZ, Shekelle RB, Ostfeld AM, Paul O (1967) Prospective and retrospective psychological studies of coronary heart disease. Psychosom Med 29:265–272 37. Light KC, Girdler SS, Sherwood A, Bragdon EE, Brownley KA et al (1999) High stress responsivity predicts later blood pressure only in combination with positive family history and high life stress. Hypertension 33:1458–1464 38. McCabe PM, Gonzales JA, Zaias J, Szeto A, Kumar M et al (2002) Social environment influences the progression of atherosclerosis in the watanabe heritable hyperlipidemic rabbit. Circulation 105:354–359 39. McEwen BS (2007) Physiology and neurobiology of stress and adaptation: central role of the brain. Physiol Rev 87:873–904 40. Mezzacappa ES, Kelsey RM, Katkin ES, Sloan RP (2001) Vagal rebound and recovery from psychological stress. Psychosom Med 63:650–657 41. Miller GE, Chen E, Zhou ES (2007) If it goes up, must it come down? Chronic stress and the hypothalamic-pituitary-adrenocortical axis in humans. Psychol Bull 133:25–45 42. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ et al (2015) Heart disease and stroke statistics-2015 update: a report from the American Heart Association. Circulation 131:e29–e322 43. Nater UM, La Marca R, Florin L, Moses A, Langhans W et al (2006) Stress-induced changes in human salivary alpha-amylase activity – associations with adrenergic activity. Psychoneuroendocrinology 31:49–58 44. Nawrot TS, Perez L, Kunzli N, Munters E, Nemery B (2011) Public health importance of triggers of myocardial infarction: a comparative risk assessment. Lancet 377:732–740 45. Parati G, Trazzi S, Ravogli A, Casadei R, Omboni S et al (1991) Methodological problems in evaluation of cardiovascular effects of stress in humans. Hypertension 17:III50-55 46. Pickering TG (2006) Now we are sick: labeling and hypertension. J Clin Hypertens (Greenwich) 8:57–60 47. Pieper S, Brosschot JF, van der Leeden R, Thayer JF (2007) Cardiac effects of momentary assessed worry episodes and stressful events. Psychosom Med 69:901–909 48. Raikkonen K, Matthews KA, Flory JD, Owens JF (1999) Effects of hostility on ambulatory blood pressure and mood during daily living in healthy adults. Health Psychol 18:44–53 49. Rostrup M, Kjeldsen SE, Eide IK (1990) Awareness of hypertension increases blood pressure and sympathetic responses to cold pressor test. Am J Hypertens 3:912–917 50. Rostrup M, Mundal HH, Westheim A, Eide I (1991) Awareness of high blood pressure increases arterial plasma catecholamines, platelet noradrenaline and adrenergic responses to mental stress. J Hypertens 9:159–166 51. Saksena S, Camm AJ (eds) (2011) Electrophysiological disorders of the heart, 2nd edn. Elsevier, Philadelphia 52. Sheps DS, McMahon RP, Becker L, Carney RM, Freedland KE et al (2002) Mental stressinduced ischemia and all-cause mortality in patients with coronary artery disease: results from the Psychophysiological Investigations of Myocardial Ischemia study. Circulation 105: 1780–1784 53. Shively CA, Musselman DL, Willard SL (2009) Stress, depression, and coronary artery disease: modeling comorbidity in female primates. Neurosci Biobehav Rev 33:133–144

4

The Biopsychosocial Perspective on Cardiovascular Disease

97

54. Shorter E (2005) The history of the biopsychosocial approach in medicine: before and after Engel. In: White P (ed) Biopsychosocial medicine: an integrated approach to understanding illness. Oxford University Press, Oxford 55. Singer MT (1974) Engagement – involvement: a central phenomenon in psychophysiological research. Psychosom Med 36:1–17 56. Steenland K, Fine L, Belkic K, Landsbergis P, Schnall P et al (2000) Research findings linking workplace factors to CVD outcomes. Occup Med 15:7–68 57. Steptoe A (2001) Perceptions of control and cardiovascular activity: an analysis of ambulatory measures collected over the working day. J Psychosom Res 50:57–63 58. Steptoe A, Cropley M (2000) Persistent high job demands and reactivity to mental stress predict future ambulatory blood pressure. J Hypertens 18:581–586 59. Steptoe A, Kivimaki M (2013) Stress and cardiovascular disease: an update on current knowledge. Annu Rev Public Health 34:337–354 60. Steptoe A, Marmot M (2006) Psychosocial, hemostatic, and inflammatory correlates of delayed poststress blood pressure recovery. Psychosom Med 68:531–537 61. Steptoe A, Vögele C (1991) The methodology of mental stress testing in cardiovascular research. Circulation 83:II14–II24 62. Steptoe A, Fieldman G, Evans O, Perry L (1993) Control over work pace, job strain and cardiovascular responses in middle-aged men. J Hypertens 11:751–759 63. Steptoe A, Willemsen G, Owen N, Flower L, Mohamed-Ali V (2001) Acute mental stress elicits delayed increases in circulating inflammatory cytokine levels. Clin Sci 101:185–192 64. Steptoe A, Gibson EL, Hamer M, Wardle J (2007) Neuroendocrine and cardiovascular correlates of positive affect measured by ecological momentary assessment and by questionnaire. Psychoneuroendocrinology 32:56–64 65. Stone AA, Shiffman S, Atienza AA, Nebeling L (eds) (2007) The science of real-time data capture. Oxford University Press, New York 66. Strike PC, Steptoe A (2003) Systematic review of mental stress-induced myocardial ischaemia. Eur Heart J 24:690–703 67. Strike PC, Magid K, Whitehead DL, Brydon L, Bhattacharyya MR et al (2006) Pathophysiological processes underlying emotional triggering of acute cardiac events. Proc Natl Acad Sci U S A 103:4322–4327 68. Tobe SW, Kiss A, Sainsbury S, Jesin M, Geerts R et al (2007) The impact of job strain and marital cohesion on ambulatory blood pressure during 1 year: the double exposure study. Am J Hypertens 20:148–153 69. Tofler GH, Stone PH, Maclure M, Edelman E, Davis VG et al (1990) Analysis of possible triggers of acute myocardial infarction (the MILIS study). Am J Cardiol 66:22–27 70. von Kanel R, Preckel D, Zgraggen L, Mischler K, Kudielka BM et al (2004) The effect of natural habituation on coagulation responses to acute mental stress and recovery in men. Thromb Haemost 92:1327–1335 71. Wadiwalla M, Andrews J, Lai B, Buss C, Lupien SJ et al (2010) Effects of manipulating the amount of social-evaluative threat on the cortisol stress response in young healthy women. Stress 13:214–220 72. White P (2005) Beyond the biomedical to the biopsychosocial: integrated medicine. In: White P (ed) Biopsychosocial medicine: an integrated approach to understanding illness. Oxford University Press, Oxford 73. Wilhelm FH, Grossman P (2010) Emotions beyond the laboratory: theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biol Psychol 84:552–569 74. Wulsin L, Barsky A (2015) Psychiatric and behavioral aspects of cardiovascular disease. In: Mann DL, Zipes DP, Libby P, Bonow RO (eds) Braunwald’s heart disease, 10th edn. Saunders, Philadelphia 75. Yusuf S, Cairns JA, Camm AJ, Fallen EJ, Gerch BJ (eds) (2010) Evidence-based cardiology. Wiley-Blackwell, Chichester

Section II Relations of Cardiovascular Risk Factors to Cardiovascular Disease

5

Childhood Factors in Adult Risk for Cardiovascular Disease Kristen Salomon, Danielle L. Beatty Moody, Kristi E. White, and Taylor M. Darden

Contents Early Emergence of “Adult” Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Race and Ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hostility and Anger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiovascular Stress Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk Factors Specific to Childhood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Childhood Adversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socioeconomic Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adverse Childhood Experiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moderators and Mediators of Childhood Adversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Low Birth Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 103 104 105 106 107 110 110 111 113 113 115 116 117

K. Salomon (*) Department of Psychology, University of South Florida, Tampa, FL, USA e-mail: [email protected] D. L. Beatty Moody · T. M. Darden Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA K. E. White Hennepin County Medical Center & University of Minnesota Center for Spirituality & Healing, Minneapolis, MN, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_5

101

102

K. Salomon et al.

Abstract

Cardiovascular disease (CVD) is a chronic, age-related disease, whose development begins in childhood. To fully understand the lifespan development of CVD, a focus on the childhood origins of the disease is necessary. This chapter examines the childhood origins of CVD from two vantage points. The first focuses on the early emergence of “adult” psychosocial CVD risk factors, including demographic factors, such as sex and ethnicity; environmental factors such as discrimination; and individual difference factors, such as hostility, anger, depression, and cardiovascular stress responses. The second vantage point focuses on identifying risk factors specific to childhood, such as childhood adversity and low birth weight. Childhood adversity is further distinguished by two primary foci: socioeconomic status and adverse childhood experiences. The chapter reviews each of these childhood-specific factors in turn. Commentary on the current state of the literature, its limitations, and recommendations for future research are offered throughout. Keywords

Early life adversity · Cardiovascular disease · Risk factors · Psychosocial factors · Age-related onset

Cardiovascular disease (CVD) is a chronic, age-related disease, whose development begins in childhood. Fatty streaks in the arteries can be found in children in their first decade of life [16], and extent of arteriosclerosis in children and adolescents is associated with blood pressure, cholesterol, triglycerides, insulin resistance, and obesity [9, 14, 16, 118, 128, 148, 177]. Blood pressure levels seen in childhood track into adulthood, predict incidence of adult hypertension, and are associated with indicators of left ventricular hypertrophy [10, 26, 34, 41, 152]. To fully understand the development of CVD, a focus on the disease’s origins in childhood is necessary. To this end, cardiovascular behavioral medicine researchers have examined childhood origins of CVD from two vantage points. The first focuses on how early in childhood “adult” psychosocial risk factors for CVD emerge and when in development they begin to predict individual differences in subclinical disease and prospective differences in risk. The second focuses on elucidating risk factors specific to childhood. Both vantage points have been critical in understanding the early origins of disease, and this chapter covers each in turn. First, we address the early emergence of “adult” psychosocial CVD risk factors, including sex, ethnicity, discrimination, hostility and anger, depression, and cardiovascular stress responses. Next, we review evidence identifying risk factors specific to childhood, such as childhood adversity and low birth weight. Childhood adversity is further distinguished by two primary foci: socioeconomic status and adverse childhood experiences (ACEs). While children can be diagnosed with cardiovascular disease, this is usually the result of acute injury or congenital defect and is not indicative of normative aging of the

5

Childhood Factors in Adult Risk for Cardiovascular Disease

103

cardiovascular system. Thus, this chapter will focus primarily on childhood factors that predict adult-onset CVD.

Early Emergence of “Adult” Risk Factors Sex Adult men and women show consistent differences in their risk for CVD, and these sex differences begin to appear in childhood. Boys exhibit higher levels than girls of many CVD risk factors and subclinical disease indices such as blood pressure, heart rate, left ventricular mass, carotid intima-medial thickness, and the extent of fatty streaks portending the higher risk of CVD incidence and mortality for men [16, 18, 79, 168, 172]. Many of these differences do not appear until after puberty and are therefore most readily explained by sex differences in estrogen. For example, prepubertal girls have stiffer large vessels than prepubertal boys, but this difference reverses after puberty when estrogen increases [2]. Additionally, differences in CVD risk disappear after age 75, suggesting that as women lose estrogen after menopause, their rates of CVD catch up to those of men. Thus, when examining sex differences in CVD risk among children, it is important to remember that differences may not emerge until after the pubertal transition.

Race and Ethnicity Racial and ethnic differences in CVD risk are well established and indicate that black Americans have a much higher prevalence of CVD than non-Hispanic white Americans. Similar to sex difference, racial differences in CVD risk emerge in childhood. Black-white differences in blood pressure levels are evident in children as young as 5 years and remain stable throughout adolescence, with evidence suggesting a greater rate of increase for black children than white [10, 46, 111, 173]. While growth, adiposity, and socioeconomic status explain some of these differences, just as in adults, racial differences remain after adjustment for these factors. Similarly, black children also have more unhealthy levels of biological risk factors including greater arterial stiffness, higher glycosylated hemoglobin, higher serum cholesterol, and lower HDL cholesterol [69, 84, 124, 176], although some studies indicate higher HDL cholesterol among black youth [15, 175]. Differences in cardiovascular reactivity have also been reported, with black youth exhibiting greater blood pressure reactivity to stress than white youth [163], although, again, other studies have failed to find racial differences in cardiovascular reactivity [5]. As with adults, health behaviors such as diet and physical activity, as well as differences in socioeconomic factors, may explain some, but not all, of these differences. For example, in a twin study of the heritability of blood pressure, shared environmental factors, such as diet and socioeconomic status (SES), explained little of the individual differences in blood pressure found between white and black youth [101]. Thus, while ethnic

104

K. Salomon et al.

differences in CVD risk appear in childhood, psychosocial factors may be at work to explain racial differences in childhood. Black Americans’ exposure to racial discrimination is the most commonly studied psychosocial factor proposed to explain racial disparities in disease risk.

Discrimination Racism and discrimination are often viewed as chronic stressors that may help to explain racial disparities in health across the lifespan. Racist attitudes and discriminatory behavior persist in the USA despite the strides made by the civil rights movement, desegregation, and equal opportunity laws. As a pervasive and chronic stressor, racism and discrimination limit the social, economic, and emotional lives of black Americans. As to physical health, studies with adult samples have demonstrated that racial discrimination is linked to elevated CVD risk factors [73, 134, 136], but far less work has examined this relationship in youth. Also, most work has focused primarily on older children and adolescents in cross-sectional designs. Many that have examined this link report no direct relationship between perceived racism and blood pressure in youth [37, 38, 115], but the relationship between racism and CVD risk in this age group may be moderated by coping responses [38], trait anger [37], and socioeconomic status [13]. Most of the studies noted above utilized measures of explicit racism exposure, i.e., measures that make it clear that mistreatment is due to race. The studies that have utilized more subtle or indirect measures suggest a positive relationship between discrimination exposure and CVD risk in youth. For example, black adolescents reporting greater unfair treatment on items that did not explicitly mention race or other causes exhibited higher night/day rations of ambulatory blood pressure, a risk factor for CVD [13]. Greater internalized racism (i.e., accepting and internalizing racist beliefs and attitudes) among 14–16-year-old girls was related to higher odds of insulin resistance, even after controlling for age, income, birth weight, hostility, physical activity, and family history of diabetes [32]. Racism-related vigilance, defined as the propensity to attend to environmental events that could be perceived as involving racism, was associated with greater cardiovascular reactivity and less large artery elasticity in black elementary and middle school boys, but not girls [39]. Finally, black children who were more likely to appraise ambiguous social scenarios as involving hostile intent exhibited greater cardiovascular reactivity [33]. Thus, in youth, explicit measures of racism may not be related to CVD risk cross-sectionally. However, ambiguous unfair treatment and how youth tend to interpret such treatment may drive relationships with CVD risk at earlier ages. The research on discrimination and CVD risk in youth is not only sparse but also limited in scope. These studies tend to use self-report measures that were developed for adult samples, which may be why measures of explicit racism do not show direct relationships to risk. Also, as with studies on adults, most of the research examining racism and health in youth focuses on black Americans. Little work examines this link among other racial or ethnic categories (e.g., Hispanic or Native American) or

5

Childhood Factors in Adult Risk for Cardiovascular Disease

105

among other socially stigmatized youth (e.g., sexual minorities). The few studies that do suggest the health effects of discrimination persist across other marginalized groups. In one study of adolescents of Mexican descent, perceived discrimination was related to poorer mental health and risky behaviors [47, 68]. In another study, Mexican American adolescents’ perceptions of discrimination were related to greater diurnal cortisol output [179]. While this research generally supports the role for discrimination in explaining disparities in health risk in youth, more research on minority populations is warranted. Also, no prospective works yet exist examining youth’s perceptions of discrimination and adult CVD risk. Given the large body of evidence that childhood adverse experiences predict adult CVD risk (see later in this chapter), and that exposure to interpersonal and institutional discrimination could be perceived as adverse experiences, prospective studies are needed. Finally, most of this work was conducted primarily with adolescent samples. While evidence suggests that children as young as 10 report experiencing racial discrimination, with 92% reporting some instance of discrimination by late adolescence [25], youth’s responses to discrimination and unfair treatment may be moderated by the importance of their ethnic identity to their daily experiences. Children do not begin to explore their ethnic identities until early adolescence [138], and an understanding of one’s own ethnic identity may be a critical precursor to interpreting events as attributable to racism. Also, as peer relations become a more important source of influence over social development in adolescence relative to childhood, adolescence may be a critical period of development for understanding race and interpreting mistreatment as discrimination. Thus, although little work examines younger children’s experience of discrimination, perceived discrimination may not play a role in explaining ethnic differences in CVD risk until adolescence. Also, as in adults, the influence of racism and discrimination on youth may also be transmitted through more structural features of their environment, such as familial and neighborhood SES, exposure to violence, and overall family stress. Finally, it is important to remember that, as with the effects of exposure to most environmental stressors, individual differences in how one responds to these stressors may attenuate or exaggerate their effects. Adolescence is a time of change and development; thus, factors such as ethnic identity, vigilance, hostility, and coping responses may moderate the direct relationship between adolescent perceptions of discrimination and health. Research with youth should take care to identify individual differences and other moderating factors to fully understand the effects of discrimination in youth.

Hostility and Anger Negative emotions such as anger and hostility are considered primary psychological risk factors for CVD. Hostility can also be conceived of as a trait, to include a cynical attribution style, hostile affect, and aggressive behavioral responding. Similar to research with adults, early work in the area of childhood hostility focused on identifying the Type A behavior pattern, a stable individual difference predicting CVD risk. Specifically, research showed that elements of the Type A behavior pattern, including

106

K. Salomon et al.

impatience, competitiveness, and aggression, could be reliably measured in children as young as kindergarten age [112], and individual differences in the Type A behavior pattern are moderate stability from adolescence to adulthood [166]. More importantly, Type A children were shown to be more physically aggressive than Type B children [112], suggesting that hostility emerges early as well. As with the adult literature, this research eventually became focused on the aspects of the Type A behavior pattern most predictive of CVD, namely, hostility and anger. A full range of hostility and anger scales have been administered to children as young as 7 and have demonstrated adequate test-retest and internal reliability in child and adolescent samples (see [76] for a review). Individual differences in hostility and anger, as well as components of anger such as trait anger, anger-in, anger-out, and anger control track into adulthood [76]. Finally, hostility and anger in children and adolescents have been linked to a number of CVD risk factors such as blood pressure, although the findings are not entirely consistent, and often these relationships are explained by poor health behaviors [76]. It is also important to note that antisocial behavior in children, for which aggression is a defining component, is linked to lower resting heart rate and attenuated heart rate reactivity [131] suggesting that more pathological or “cold-tempered” forms of aggression may not exhibit typical relationships with CVD risk. Once hostility and anger were established as reliable individual differences in youth, research turned to a focus on determining the childhood origins of these predispositions. This research has focused on a number of factors including caregiver attachment, infant temperament, parenting style, and heritability. Early research suggests that children with an insecure attachment to their primary caregiver are reported as more hostile, angry, and aggressive than those with a secure attachment style [76]. Similarly, two components that may drive the quality of caregiver-child attachment – caregivers’ hostile parenting style and children’s difficult temperament – predict children’s aggression and hostility [146, 167]. Interestingly, the relative importance of these two predictors may differ for boys and girls such that girls’ hostility may be more related to a difficult temperament, whereas boys’ hostility may be more related to parenting style [146]. Twin studies suggest that heritability and environment both explain childhood individual differences in anger, although each may differentially explain select components of anger. Specifically, anger control may be best explained by heritability, anger-in may be best explained by shared environmental factors, and anger-out and overall anger expression may be best explained by a combination of heritability and environment [171]. Overall the research suggests that hostility, anger, and aggression are the result of both genetic and environmental influences that transmit through childhood temperament and parental rearing practices.

Depression Hostility and anger are not the only negative emotional states that confer risk for CVD. Similar to research on hostility and anger, research on depression has

5

Childhood Factors in Adult Risk for Cardiovascular Disease

107

primarily focused on individual differences, rather than examining transient emotional states. It is widely accepted that among adults, depression predicts future incidence of CVD (e.g., [178]), thus solidifying depression as a primary psychological CVD risk factor, along with hostility and anger. Depressed mood and clinically relevant depressive episodes in adults are associated with a host of biological and physiological dysfunctions associated with CVD such as reduced baroreflex control, reduced heart rate variability, increased C-reactive protein, impaired endothelial dysfunction, as well as unhealthy lifestyle habits. However, depressive disorders are not only an “adult” disease. In adolescence, depressive disorders have a prevalence rate of 11.7% [122] and are the most disabling condition among youth aged 10–19 years [75]. Furthermore, juvenile-onset depression signals a chronic or relapsing disorder with debilitating adult outcomes [98, 157], suggesting that the link between depression and CVD risk may have origins in childhood and adolescence. Thus, it is reasonable to assume that depression’s effects on the cardiovascular system may begin to appear in childhood. The literature linking depression to CVD risk in youth is small yet growing. Most of the published work indicates a relationship between depression and vascular dysfunction, a subclinical measure of atherosclerotic acceleration. For example, elevated depressive symptoms are associated with greater vascular stiffness among adolescent boys and girls [48, 169] and poorer endothelial function among adolescent girls [132, 158, 159]. Researchers have examined a number of possible mechanisms for this relationship including altered autonomic dysfunction, increased inflammation, and lifestyle. In regard to autonomic function, depressed youth have been shown to have higher heart rates at rest [27] and higher blood pressure during sleep [170]. Elevated inflammatory markers are also associated with depression in youth [70, 83, 86, 123] although not all studies find an association [20, 21, 31]. Finally, youth with major depressive disorder, compared to nondepressed youth, have a higher prevalence of smoking, engage in less physical activity, and have higher rates of parental CVD [151]. Overall, this evidence suggests that the relationships between depressed mood or depressive disorder and CVD risk factors seen in adults are evident earlier in life. The number of existing studies is small, and thus more research examining depression and CVD risk in youth is needed, particularly research aimed at identifying biological and physiological mechanisms, as well as longitudinal studies that track depression and cardiovascular outcomes into adulthood. The appearance of depression in childhood and its link to CVD risk suggest that successful treatment of juvenile depression is important not only for future mental health but also physical health.

Cardiovascular Stress Responses Thus far, the risk factors reviewed have consisted of demographic factors or psychological traits. However, cardiovascular behavioral medicine researchers have also sought to identify physiological trait differences, particularly those within the cardiovascular system. While differences in resting cardiovascular levels (e.g., blood

108

K. Salomon et al.

pressure and heart rate) are commonly considered as risk factors and outcomes, researchers interested in a more psychosocial approach have focused on cardiovascular responses to stressful, demanding, and/or challenging situations. Much of this research focuses on responses to laboratory tasks in an effort to control the objective demands of the demanding situation across individuals. With the advent of ambulatory monitoring, researchers were more easily able to examine cardiovascular responses during real-life demands, albeit with a loss of control over the what, when, and where of these situations. The present discussion of cardiovascular stress responses in youth will address both of these paradigms in turn – cardiovascular reactivity to laboratory tasks and ambulatory monitoring during daily life – with the assumption that both provide a short-term window into individuals’ stress response tendencies throughout life. Adult cardiovascular responses to laboratory tasks are established to confer risk for CVD. Research with children and adolescents has sought to identify how early individual differences in cardiovascular reactivity emerge and whether these differences in youth predict adult risk for CVD. Overall, research supports the validity and predictive utility of cardiovascular reactivity paradigms in youth. Children and adolescents exhibit stability of not only blood pressure and heart rate responses but also underlying hemodynamic responses derived from impedance cardiography [94, 113, 119, 126]. Children’s cardiovascular reactivity, including blood pressure, heart rate, and hemodynamics, is also predictive of future levels of blood pressure [114]. In addition, many of the psychosocial factors associated with reactivity in adults are linked to reactivity in youth. For example, male gender, black ethnicity, hostility, and low familial socioeconomic status have all been associated with greater blood pressure reactivity in childhood [89, 93, 125, 160–162]. The primary challenge in this endeavor is not one of obtaining measures of cardiovascular function in children as impedance cardiography can be utilized with children as young as 6 months [4]. Instead, the challenge lies in finding age-appropriate tasks that elicit similar psychological and physiological stress responses as those seen among adults. With older children and adolescents, many of the traditional adult reactivity tasks such as serial subtraction, handgrip, cold pressor, mirror-image tracing, and speech preparation are appropriate. While some of these may be appropriate with younger children (e.g., mirror tracing and cold pressor), tasks that require higher-order cognitive functions may not. Therefore, researchers have developed tasks specific to younger children. For example, repeating sequences of numbers with very young children [3] can serve as an analogue to mental arithmetic, and reaction-time tasks can require minimal cognitive processing [5]. The key to developing tasks for use with children is that the knowledge and skills needed for the task are age appropriate and that the task engenders sufficient engagement and/or motivation for the child. Incentives may be needed to engage a child, such as winning small amounts of money or prizes for performance, whereas adolescents and adults may find sufficient motivation through evaluation apprehension and self-esteem motives. As with adults, researchers have also been interested in the distinction between responses to asocial stressors (e.g., cold pressor, video games) and social stressors

5

Childhood Factors in Adult Risk for Cardiovascular Disease

109

(e.g., those primarily involving interacting with others). Again, the age of the child may determine the type of social task used. One task, the Social Competence Interview (SCI), was developed specifically for adolescents to serve both as a challenging laboratory social stressor and as a measure of social competence [62, 64]. The interview is geared toward drawing out adolescents’ goal-oriented strivings and emotion expression styles while simultaneously eliciting a cardiovascular response. Children are asked to describe a recurring stressor in their lives and then are prompted to discuss their coping strategies and preferred outcomes. The interviews can then be coded for participants’ interpersonal skills and goal-oriented strivings [62, 64]. As a reactivity task, the SCI has been shown to elicit cardiovascular reactivity similar to traditional tasks [62], and the coded interpersonal skills and strivings have been shown to relate to the degree of reactivity [35, 60, 61]. Thus, the SCI has proven a useful tool with adolescents, but much younger children may have difficulty expressing their circumstances and coping responses. Other tasks may be useful younger children, such as watching and/or listening to two adults argue which has been shown to elicit reactivity among younger elementary school-aged children and preschoolers [51–53]. Other social tasks that can be used with younger children include responding to questions about family, school and likes/dislikes, and viewing emotional films [3]. While laboratory tasks elicit reliable cardiovascular responses in youth, ambulatory monitoring provides an ecologically valid assessment of responses to wider range of personally relevant stress and daily hassles. Ambulatory blood pressure measurement, in particular, is established as a reliable and valid paradigm in youth, with published guidelines [164]. Considerations when performing ambulatory measurements on children include the size and weight of the monitor, whether it comes equipped with cuffs appropriately sized for children, the monitor’s resistance to movement artifact, and whether or not the monitor has been validated in children. Depending on age, children may not be able to complete diary information required to control for activity level and food and caffeine consumption and, similarly, may not be able to provide self-reports of mood and stress. While studies assessing coincident ecological assessment of self-reported physical and psychological states have been conducted with adolescents (e.g., [115]), some even using electronic devices for diary assessments, studies with young children may require the use of an additional actigraphy monitor to provide some control for movement. Ambulatory monitoring in youth has proven useful in understanding the relationships between stress responses and CVD risk. Differences in 24-h blood pressure levels as a function of race, gender, and parental hypertension are evident in youth [6, 81, 173]. Studies with adolescents have shown that ambulatory blood pressure is predicted by laboratory stress reactivity [63], perceived unfair treatment [115], chronic background stress [23], socioeconomic status [120], pessimism [145], and trait anger [13]. While these findings are varied, they are mostly from the same sample. The lack of ambulatory monitoring in youth may be due to additional challenges presented by studying this age group during daily activities. In particular, children and adolescents spend most of their day, for most of the year, in school. Ambulatory studies of youth likely need to obtain not just permission but also the

110

K. Salomon et al.

active support of school administrators and staff to allow children to be interrupted by repeated blood pressure measurements and diary completion. Ambulatory measurement may also preclude participation in some school activities (e.g., physical education). While much of these challenges can be avoided by conducting the study in summer months, this solution likely limits the diversity of daily demands experienced by youth. Despite these challenges, more research examining utilizing ambulatory monitoring in youth is needed to better understand the relationships between psychosocial factors and CVD risk. In sum, the research examining “adult” risk factors in youth indicates that many of the primary adult psychosocial risk factors emerge in youth. Further, these risk factors are related to elevations in subclinical disease markers and biological risk factors during childhood and/or adolescence and also predict adult CVD outcomes. Much of this research adapts methods developed with adults for use with younger samples, which can be challenging, and often requires independent validation of measures in youth samples. Some researchers have developed methods specific to children and adolescents (e.g., the Social Competence Interview [62, 64] and videos depicting ambiguous social scenarios [33]), which has allowed researchers to examine psychosocial factors in a way that is relevant to children’s and adolescents’ unique social contexts. It is a focus on these unique social contexts that has led researchers away from examining “adult” risk factors to instead examine risk factors that are specific to childhood and adolescence.

Risk Factors Specific to Childhood Childhood Adversity Adverse experiences during childhood have recently emerged as a potent predictor of poor health in adulthood [24]. Early life adversity sets in motion a constellation of social, psychological, biological, and behavioral processes, which in turn contribute to intermediary pathways in a chain of physical health risks becoming evident in adulthood. Childhood adversity is typically defined as low SES or child maltreatment. While adults’ personal SES has long been examined as a predictor of adult health, it is only more recently that researchers have considered the socioeconomic environment in which individuals are raised as predicting adult health. Childhood SES can be assessed using any of a number of indices including poverty status, parental education and occupation, family income, the ratio of the number of rooms to the number of people living in the home, and other markers of financial strain. These indices provide information about resource availability in the form of food, safety, and shelter. Additionally, lower childhood SES can create “cold” environments in which children’s’ psychosocial, emotional, and behavioral needs for development, learning, and growth are either unmet or not optimally supported [55, 149]. Indices of maltreatment include emotional and physical neglect and abuse, sexual abuse, and other extreme stressors such as parental divorce, death of a parent or

5

Childhood Factors in Adult Risk for Cardiovascular Disease

111

sibling, and living with a person abusing substances or with a criminal background. While there are a host of other ways to conceptualize childhood adversity (e.g., social isolation), the overarching theme to adversity in childhood is that it contributes to stressful circumstances for the child beyond what might be considered a normative aspect of childhood. Similar to the broader literature, evidence for the association between childhood adversity and CVD risk will be discussed separately for SES and maltreatment.

Socioeconomic Status The literature on the socioeconomic environment of childhood consistently demonstrates a relationship with adult CVD endpoints. In systematic reviews, multiple indices of childhood SES were inversely related to multiple subtypes of CVD, including stroke, coronary heart disease (CHD), angina, atherosclerosis, and CVD-related mortality [40, 71, 72]. These findings were largely independent of adult SES, and most were prospective in nature. In early studies, the main predictor was often the fathers’ occupation, social class, or education. However, other SES indices such as number of persons per bedroom and household amenities (e.g., telephone, running water, sewer, electric) were often predictive as well. Similarly, in a large prospective cohort study of medical school graduates, lower childhood SES conferred a 2.4-fold increased hazard of developing coronary heart disease over an approximately 40-year follow-up, even after adjustment for traditional risk factors [96]. Noticeably, this study assessed father’s and mother’s SES, and findings did not differ across the two indicators. In line with the broader literature, these findings demonstrate not only the long reach of childhood adversity into adulthood but also that upward shifts in SES do not “undo” the effects of childhood SES. Further, in line with more recent research, both parents’ SES contributes to the link between childhood SES and CVD risk. Childhood SES not only prospectively predicts adult CVD incidence but also the development and progression of adult cardiovascular disease risk factors. For example, parental occupational status assessed in childhood predicts increased body mass index, increased waist-to-hip ratio, increased waist circumference, high blood pressure, diabetes, high cholesterol, low HDL cholesterol, high glycostatic hemoglobin concentration, and low cardiorespiratory fitness [97, 121, 140]. Childhood family income is also associated with increased risk for impaired fasting glucose, metabolic syndrome, and type 2 diabetes [143]. Overall, the evidence is conclusive that the socioeconomic environment of childhood confers risk for adult CVD. While prospective evidence supports the role of childhood SES in predicting CVD risk and outcomes, much of the research in this area demonstrates these relationships by retrospective assessment of SES factors. While this may lead to questions regarding memory bias and the direction of causality, findings using retrospective reports largely confirm the findings from prospective studies where childhood SES is assessed in childhood [71, 72, 80, 82, 87, 102, 142]. A high

112

K. Salomon et al.

agreement for accuracy of recall of parent education has been demonstrated in studies using a twin model (91% concordance; [100]) and when using historical records to validate participant reports (83% record agreement; [17]). Thus, research assessing childhood SES in childhood or retrospectively can be considered equally valid in demonstrating effects on CVD risk. In light of the racial/ethnic disparities in childhood SES and adult CVD risk in the USA, studies have examined the association between these factors within the context of racial/ethnic minority status and found the same associations among black Americans. In a cross-sectional analysis of data from the Jackson Heart Study (a prospective, epidemiologic investigation of cardiovascular disease among black Americans), a mother’s lower educational attainment and fewer household assets were related to greater carotid intima-media thickness [45]. Similarly, in this study, lower parent education, fewer numbers of rooms in the home, and lack of household amenities were related to greater hypertension risk in women [156]. Studies making direct comparisons across racial groups do not necessarily find racial differences in the relationship between childhood SES and adult CVD risk. For example, in a population-based study of young adult men, parental education and occupation were related to less heart rate and systolic blood pressure recovery from stress (a risk factor for CVD; see [36, 133]), although childhood SES was not related to resting levels or reactivity [22]. Importantly, findings did not differ by racial group. Similarly, in a sample of black and white women, those reporting lower childhood SES had elevated levels of hemostasis and inflammation in adulthood across a 7-year follow-up, and findings did not differ by race [116]. Alternately, in a large cohort study, low childhood SES was associated with inflammatory risk factors for CVD among white participants but less consistently so among black participants [139]. The paucity and inconsistency of findings examining racial differences suggest the need for more research. Comprehensive assessments of childhood factors that consider intergenerational transmissions of SES patterning may shed more light given the sociohistorical contexts of race, minority status, and the legal and social regulations that affect access to resources (e.g., jobs and education) among black Americans. Further, little work on childhood SES examines racial and ethnic categories other than black and white. Thus, more research is warranted before a full understanding of the role of race and ethnicity in moderating relationships between childhood SES and CVD risk is attained. Childhood socioeconomic conditions not only predict adult CVD risk factors but also adult SES, which confers independent risk for CVD. Some studies show the association between childhood SES and adult CVD risk factors can be attenuated by adult SES, lifespan SES, and adult risk factors [130, 147]. Much, but not all, of the research in this area examines the effects of childhood SES independent of adult SES. Vital to this work are assessments of the independent associations between childhood SES and adult CVD risk, in particular those which employ comprehensive approaches to capturing proxies of childhood SES. Additionally, this work may be complimented by consideration of lifespan indices of SES to understand whether childhood SES continues to exert a unique and direct effect or operates through its impact on other indices of SES into adulthood.

5

Childhood Factors in Adult Risk for Cardiovascular Disease

113

Adverse Childhood Experiences Considerable research indicates a relationship between adverse childhood experiences (ACEs) and CVD risk. Generally, CVD risk is significantly greater for those who have experienced adverse events such as abuse, neglect, trauma, and maltreatment in childhood [12, 49, 66]. The majority of the research in this area utilizes retrospective methods for assessing ACEs in childhood, given the sensitivity of assessing these factors in child participants, although childhood report and longitudinal methods may be preferable. The studies that do assess ACEs are consistent with adult, retrospective studies. For example, a recent systematic review examined the association between ACEs and CVD, and roughly one-third of the studies were prospective. Despite the heterogeneity in adversity measurement, most studies found a positive association between ACEs and CVD risk [8]. Similarly, children show a positive relationship between adversity, including a number of adverse events and severity of social isolation in childhood, and adult CVD risk, including inflammatory markers and unhealthy metabolic risk biomarkers [44]. Childhood maltreatment is also associated with unfavorable outcomes in the nervous (e.g., smaller prefrontal cortex and amygdala), endocrine (e.g., hypothalamic pituitary adrenocortical (HPA) axis activation), and immune systems (e.g., elevated inflammation levels) in adulthood [43]. Research also points to a graded or cumulative effect of ACEs on risk for CVD. That is, the greater the number of adverse events experienced [44, 49, 66] and the more chronic the experiences [29], the higher the odds of developing CVD later in life. For example, ACEs across seven categories (psychological, physical, or sexual abuse; violence against mother; and living with household members who were substance abusers, mentally ill or suicidal, or ever imprisoned) exhibit a graded relationship to the presence of adult ischemic heart disease. Further, people who experienced four or more categories of childhood exposure, compared to those who experienced none, had a two- to fourfold increase in poor self-rated health and a 1.4- to 1.6-fold increase in obesity [66]. ACEs also directly impact lifespan trajectories of symptoms that are more proximal risk factors for CVD development. For example, those who experience multiple ACEs exhibit a faster rise in blood pressure levels across 30 years [155]. ACEs are also associated with a graded increase in plasma endothelin-1 levels [154]. Furthermore, those with moderate to severe exposure to ACEs (2) exhibit higher vascular resistance, diastolic blood pressure, and arterial stiffness compared to those without any ACEs [154]. Thus, exposure to adversity due to abuse or neglect in multiple domains in childhood is associated with an increase in biological risk factors for CVD later in life.

Moderators and Mediators of Childhood Adversity Sex has been shown to moderate the relationship between childhood adversity and CVD risk. Childhood maltreatment, including sexual abuse, physical abuse, and neglect, has been associated with CVD risk in women, but not men [12].

114

K. Salomon et al.

Interestingly, the association between maltreatment and depression in this sample was stronger for men than for women, and depression did not confer CVD risk for women when childhood maltreatment was taken into account. These findings are interesting considering that before age 75, the overall risk for CVD is lower among women than men, and women generally have a higher incidence of depression than men [95]. These findings not only suggest that experiencing childhood adversity seems to decrease the protective effects of female gender on risk for CVD but also that certain forms of childhood adversity may be more pernicious for CVD outcomes in women than in men. Three factors have primarily been examined as mediators of the childhood adversity-adult CVD relationship. The first includes alterations in cardiovascular and neuroendocrine functioning that indicate allostatic load. The second includes health-compromising behavioral and emotional responses that increase risk for CVD. The third factor can be defined as resource limitations that significantly impair functioning. These three factors are outlined further below. Childhood adversity is associated with alterations in cardiovascular and neuroendocrine responses that indicate allostatic load, defined as alterations in physiological systems indicative of repeated, prolonged, or inadequate adaptation to stress [117]. For example, childhood adversity is associated with elevated basal levels of blood pressure, cortisol, epinephrine and norepinephrine, as well as an overall index of allostatic load [54, 109]. While exaggerated cardiovascular stress reactivity confers risk for CVD, evidence suggests that childhood adversity is associated with blunted cardiovascular reactivity and impaired post-stressor recovery [57, 58, 77, 110]. However, relatively small or inadequate reactivity also confers risk for poor health outcomes [28]. Adults with a history of childhood maltreatment also show elevated basal levels of inflammation and a greater inflammatory response as a result of psychosocial stressors in a laboratory setting [43]. Thus, it appears that those who experience childhood adversity may also be at risk for multiple deregulatory stress response patterns. As noted earlier, childhood adversity is also associated with behavioral and emotional responses that may be health compromising which, in turn, increase risk for CVD. Researchers have found that childhood adversity is associated with smoking and increased alcohol use [7, 50, 66, 106], and these behavioral factors mediate the relationship between childhood adversity and risk for CVD [49, 106, 141, 147]. Thus, children who experience adversity tend to engage in poor health behaviors that put them at greater risk for developing CVD later in life. These health behaviors may reflect maladaptive coping responses suggesting that interventions aimed at teaching healthy behaviors and adaptive coping, as well as interventions that alleviate the stress of adverse circumstances could reduce risk for CVD. For example, a longitudinal, randomized control trial of a support program for rural youth demonstrated that ACEs were associated with increased prediabetes risk at age 25 among participants control group only. ACEs were not associated with risk for prediabetes among participants in the treatment condition. Finally, childhood adversity often limits the number of emotional, financial, physical, and psychological resources that are available for an individual, which

5

Childhood Factors in Adult Risk for Cardiovascular Disease

115

subsequently may put him/her at greater risk for CVD. For example, children of divorced families exhibited blunted cortisol reactivity during a laboratory stress task compared to those from intact families but lower parental income partially mediated this relationship [99], suggesting that the adverse event of divorce may serve to limit financial resources and, hence, affect SES. Low SES, as measured by parental income in and of itself, indicates childhood adversity. Thus, these findings also suggest that childhood adversity may involve a cascade whereby one adversity leads to more adverse factors. In regard to emotional resources, higher parental conflict is associated with greater emotional distress, which negatively impacts self-reported health status among children from divorced parents [65]. Thus, adversity may affect parent-child relationships and remove important emotional resources that impact health status later in life. Similarly, among those living in poverty, the relationship between low SES and poor health outcomes can be attributed to significant limitations in educational, living, and stability resources [55, 56, 59]. Taken together, these studies suggest that limitations in resources in childhood may help to explain the relationship between childhood adversity and CVD risk later in life.

Low Birth Weight Low birth weight predicts elevated cardiovascular risk factors, subclinical disease, and incidence of CVD. A variety of CVD outcomes are associated with low birth weight including ischemic heart disease, coronary heart disease, and atherosclerotic cardiovascular disease [153]. Low birth weight is also a risk factor for hypertension, type 2 diabetes, and obesity in adulthood [11, 42]. Low birth weight has also been associated with higher resting heart rate in childhood [1] and impaired endothelial function in childhood and early adulthood [107, 108, 129]. As for CVD risk factors, reviews of the literature suggest that a 1 kg increase in birth weight is associated with a 1–4 mmHg decrease in blood pressure in children, adolescents, and adults, after adjustment for current body size [88, 104]. Further, birth weight is inversely associated with adult insulin resistance, triglycerides, and total cholesterol [127]. Similarly, low birth weight is also associated with increased risk for atrial fibrillation, low heart rate variability, elevated leptin levels, obesity, diabetes, and metabolic syndrome [92, 105, 137]. Thus, the preponderance of evidence suggests that low birth weight can itself be considered a risk factor for CVD. Associations between birth weight and CVD risk seem to be the result of intrauterine growth restriction, rather than a consequence off premature birth [74]. As such, a number of mechanisms have been proposed to explain the relationship between low birth weight and CVD risk including fetal programming of metabolic functions affecting blood pressure, glucose lipid regulation in later life, restricted kidney development due to protein restriction in utero, and rapid postnatal “catchup” growth leading to excess adiposity [165]. The relevance of this association for cardiovascular behavioral medicine researchers emanates from findings suggesting that many of the intrauterine growth factors may be the result of maternal stress. Much of this research has come from work with animal models, although research in

116

K. Salomon et al.

humans suggests the same phenomenon. Chronic psychosocial stress, including crowding in the home, unemployment, a child with a chronic illness in the home, and poor coping skills, has been associated with low birth weight [19]. Furthermore, low birth weight may subsequently affect cardiovascular responses to stress [90, 144, 174]. Similar to resting differences in cardiovascular function, associations with cardiovascular reactivity are seen with birth weight, independent of gestational age. Interestingly, birth weight’s effects on cardiovascular function are more consistently found among women, suggesting that intrauterine growth restriction and prenatal programming may remove the protective effects of estrogen on the cardiovascular system. However, consistent with the literature examining the relationship between low birth weight and stress responses in animals, gender differences remain largely unexplained. Additional research supporting the role of stress in the association of low birth weight to CVD risk is the finding that birth weight is positively associated with maternal/familial SES [135]. Further, psychosocial factors associated with stress, lower SES, and CVD risk have also been linked to low birth weight. For example, high anxiety and low optimism among pregnant women are related to lower birth weight of their offspring [30, 67]. Social support may moderate the stress-birth weight relationship such that support from close others during pregnancy ameliorates the negative effects of maternal stress on birth weight [85]. Also, maternal stress has been shown to increase depression and anxiety in offspring, both of which serve as psychosocial predictors of CVD [103]. Finally, the relationship between childhood SES and CVD risk is stronger among women than men [78, 150], just as the relationship between low birth weight and CVD risk may be stronger among girls [91]. Given that low SES and living in low SES neighborhoods are often associated with greater stress, SES and other adverse conditions may begin to influence child health prenatally. Further, psychosocial risk factors such as anxiety and depression may also have their origins in the prenatal environment. Little research has attempted to directly link SES, maternal stress, birth weight, and risk for CVD, but these findings suggest this may be a rich area for future research. Further, given that that low birth weight may be a result of maternal stressors (e.g., low SES, anxiety, or depression), it may also qualify as an adverse childhood experience that can lead to the development of CVD in adulthood.

Conclusion Childhood represents an important period in which to study risk for future CVD. The primary benefit of this research is that it can provide strong evidence for the etiology of cardiovascular disease. Often, causality is difficult to infer in studies of CVD risk because manipulation of risk factors is unfeasible and/or unethical. Examining the emergence of risk factors in childhood can help to establish the temporal precedence of some factors over others, providing some evidence for causal etiological pathways. Much of this research suggests that early familial environments, including socioeconomic status, exposure to adversity, and parenting styles, set the stage for

5

Childhood Factors in Adult Risk for Cardiovascular Disease

117

the development of risk factors such as anger, hostility, depression, perceptions of discrimination, and cardiovascular stress responses. Further, these early familial factors may begin to influence adult health and disease in the prenatal environment, suggesting that efforts to prevent risk for CVD can begin before birth. Thus, understanding the origins of “adult” risk factors and identifying novel “childhood” risk factors can lead to a greater understanding of the mechanisms involved in the etiology of CVD, thus informing prevention and treatment.

References 1. Abe C, Minami J, Ohrui M, Ishimitsu T, Matsuoka H (2007) Lower birth weight is associated with higher resting heart rate during boyhood. Hypertens Res 30:945–950 2. Ahimastos AA, Formosa M, Dart AM, Kingwell BA (2003) Gender differences in large artery stiffness pre- and post puberty. J Clin Endocrinol Metab 88:5375–5380 3. Alkon A, Goldstein LH, Smider N, Essex MJ, Kupfer DJ, Boyce WT, MacArthur Assessment Battery W (2003) Developmental and contextual influences on autonomic reactivity in young children. Dev Psychobiol 42:64–78 4. Alkon A, Lippert S, Vujan N, Rodriquez ME, Boyce WT, Eskenazi B (2006) The ontogeny of autonomic measures in 6-and 12-month-old infants. Dev Psychobiol 48:197–208 5. Allen MT, Matthews KA (1997) Hemodynamic responses to laboratory stressors in children and adolescents: the influences of age, race, and gender. Psychophysiology 34:730–730 6. Alpay H, Ozdemir N, Wuhl E, Topuzoglu A (2009) Ambulatory blood pressure monitoring in healthy children with parental hypertension. Pediatr Nephrol 24:155–161 7. Anda RF, Croft JB, Felitti VJ, Nordenberg D, Giles WH, Williamson DF, Giovino GA (1999) Adverse childhood experiences and smoking during adolescence and adulthood. JAMA 282:1652–1658 8. Appleton AA, Holdsworth E, Ryan M, Tracy M (2017) Measuring childhood adversity in life course cardiovascular research: a systematic review. Psychosom Med 79:434–440 9. Atabek ME, Pirgon O, Kivrak AS (2007) Evidence for association between insulin resistance and premature carotid atherosclerosis in childhood obesity. Pediatr Res 61:345–349 10. Bao WH, Threefoot SA, Srinivasan SR, Berenson GS (1995) Essential hypertension predicted by tracking of elevated blood pressure from childhood to adulthood – the Bogalusa Heart Study. Am J Hypertens 8:657–665 11. Barker DJP, Bull AR, Osmond C, Simmonds SJ (1990) Fetal and placental size and risk of hypertension in adult life. Br Med J 301:259–262 12. Batten SV, Aslan M, Maciejewski PK, Mazure CM (2004) Childhood maltreatment as a risk factor for adult cardiovascular disease and depression. J Clin Psychiatry 65:249–254 13. Beatty DL, Matthews KA (2009) Unfair treatment and trait anger in relation to nighttime ambulatory blood pressure in African American and White adolescents. Psychosom Med 71:813–820 14. Beauloye V, Zech F, Tran HT, Clapuyt P, Maes M, Brichard SM (2007) Determinants of early atherosclerosis in obese children and adolescents. J Clin Endocrinol Metab 92:3025–3032 15. Belcher JD, Ellison RC, Shepard WE, Bigelow C, Webber LS, Wilmore JH, Parcel GS, Zucker DM, Luepker RV (1993) Lipid and lipoprotein distributions in children by ethnic group, gender, and geographic location – preliminary findings of the Child and Adolescent Trial for Cardiovascular Health (CATCH). Prev Med 22:143–153 16. Berenson GS, Wattigney WA, Tracy RE, Newman WP, Srinivasan SR, Webber LS, Dalferes ER, Strong JP (2009) Atherosclerosis of the aorta and coronary arteries and cardiovascular risk factors in persons aged 6 to 30 years and studied at necropsy (The Bogalusa Heart Study). Am J Cardiol 70:851–858

118

K. Salomon et al.

17. Berney LR, Blane DB (1997) Collecting retrospective data: accuracy of recall after 50 years judged against historical records. Soc Sci Med 45:1519–1525 18. Boehma B, Hartmann K, Buck M, Oberhoffer R (2009) Sex differences of carotid intimamedia thickness in healthy children and adolescents. Atherosclerosis 206:458–463 19. Borders AEB, Grobman WA, Amsden LB, Holl JL (2007) Chronic stress and low birth weight neonates in a low-income population of women. Obstet Gynecol 109:331–338 20. Bosch NM, Riese H, Dietrich A, Ormel J, Verhulst FC, Oldehinkel AJ (2009) Preadolescents’ somatic and cognitive-affective depressive symptoms are differentially related to cardiac autonomic function and cortisol: the TRAILS Study. Psychosom Med 71:944–950 21. Bosch NM, Riese H, Ormel J, Verhulst F, Oldehinkel AJ (2009) Stressful life events and depressive symptoms in young adolescents: modulation by respiratory sinus arrhythmia? The TRAILS study. Biol Psychol 81:40–47 22. Boylan JM, Jennings JR, Matthews KA (2016) Childhood socioeconomic status and cardiovascular reactivity and recovery among Black and White men: mitigating effects of psychological resources. Health Psychol 35:957–966 23. Brady SS, Matthews KA (2006) Chronic stress influences ambulatory blood pressure in adolescents. Ann Behav Med 31:80–88 24. Braveman P, Barclay C (2009) Health disparities beginning in childhood: a life-course perspective. Pediatrics 124:S163–S175 25. Brody GH, Chen YF, Murry VM, Ge XJ, Simons RL, Gibbons FX, Gerrard M, Cutrona CE (2006) Perceived discrimination and the adjustment of African American youths: a five-year longitudinal analysis with contextual moderation effects. Child Dev 77:1170–1189 26. Burke GL, Arcilla RA, Culpepper WS, Webber LS, Chiang YK, Berenson GS (1987) Blood pressure and echocardiographic measures in children – the Bogalusa Heart Study. Circulation 75:106–114 27. Byrne ML, Sheeber L, Simmons JG, Davis B, Shortt JW, Katz LF, Allen NB (2010) Autonomic cardiac control in depressed adolescents. Depress Anxiety 27:1050–1056 28. Carroll D, Phillips AC, Lovallo WR (2012) The behavioral and health corollaries of blunted physiological reactions to acute psychological stress: revising the reactivity hypothesis. In: Gendolla R (ed) How motivation affects cardiovascular response: mechanisms and applications. American Psychological Association, Washington, DC, pp 243–263 29. Caspi A, Harrington H, Moffitt TE, Milne BJ, Poulton R (2006) Socially isolated children 20 years later: risk of cardiovascular disease. Arch Pediatr Adolesc Med 160:805–811 30. Catov JM, Abatemarco DJ, Markovic N, Roberts JM (2010) Anxiety and optimism associated with gestational age at birth and fetal growth. Matern Child Health J 14:758–764 31. Chaiton M, O’Loughlin J, Karp I, Lambert M (2010) Depressive symptoms and c-reactive protein are not associated in a population-based sample of adolescents. Int J Behav Med 17:216–222 32. Chambers EC, Tull ES, Fraser HS, Mutunhu NR, Sobers N, Niles E (2004) The relationship of internalized racism to body fat distribution and insulin resistance among African adolescent youth. J Natl Med Assoc 96:1594–1598 33. Chen E, Matthews KA (2001) Cognitive appraisal biases: an approach to understanding the relation between socioeconomic status and cardiovascular reactivity in children. Ann Behav Med 23:101–111 34. Chen X, Wang Y (2008) Tracking of blood pressure from childhood to adulthood – a systematic review and meta-regression analysis. Circulation 117:3171–3180 35. Chen E, Matthews KA, Salomon K, Ewart CK (2002) Cardiovascular reactivity during social and nonsocial stressors: do children’s personal goals and expressive skills matter? Health Psychol 21:16–24 36. Chida Y, Steptoe A (2010) Greater cardiovascular responses to laboratory mental stress are associated with poor subsequent cardiovascular risk status: a meta-analysis of prospective evidence. Hypertension 55:1026–U1368

5

Childhood Factors in Adult Risk for Cardiovascular Disease

119

37. Clark R (2006) Interactive but not direct effects of perceived racism and trait anger predict resting systolic and diastolic blood pressure in black adolescents. Health Psychol 25:580–585 38. Clark R, Gochett P (2006) Interactive effects of perceived racism and coping responses predict a school-based assessment of blood pressure in Black youth. Ann Behav Med 32:1–9 39. Clark R, Benkert RA, Flack JM (2006) Violence exposure and optimism predict task-induced changes in blood pressure and pulse rate in a normotensive sample of inner-city Black youth. Psychosom Med 68:73–79 40. Cohen S, Janicki-Deverts D, Chen E, Matthews KA (2010) Childhood socioeconomic status and adult health. Ann N Y Acad Sci 1186:37–55 41. Culpepper WS, Sodt PC, Messerli FH, Ruschhaupt DG, Arcilla RA (1983) Cardiac status in juvenile borderline hypertension. Ann Intern Med 98:1–7 42. Curhan GC, Willett WC, Rimm EB, Spiegelman D, Ascherio AL, Stampfer MJ (1996) Birth weight and adult hypertension, diabetes mellitus, and obesity in US men. Circulation 94:3246– 3250 43. Danese A, McEwen BS (2012) Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol Behav 106:29–39 44. Danese A, Moffitt TE, Harrington H, Milne BJ, Polanczyk G, Pariante CM, Poulton R, Caspi A (2009) Adverse childhood experiences and adult risk factors for age-related disease: depression, inflammation, and clustering of metabolic risk markers. Arch Pediatr Adolesc Med 163:1135–1143 45. Deere B, Griswold M, Lirette S, Fox E, Sims M (2016) Life course socioeconomic position and subclinical disease: the Jackson Heart Study. Ethn Dis 26:355–362 46. Dekkers JC, Snieder H, van den Ord E, Treiber FA (2002) Moderators of blood pressure development from childhood to adulthood: a 10-year longitudinal study. J Pediatr 141: 770–779 47. Delgado MY, Updegraff KA, Roosa MW, Umana-Taylor AJ (2011) Discrimination and Mexican-origin adolescents’ adjustment: the moderating roles of adolescents’, mothers’, and fathers’ cultural orientations and values. J Youth Adolesc 40:125–139 48. Dietz LJ, Matthews KA (2011) Depressive symptoms and subclinical markers of cardiovascular disease in adolescents. J Adolesc Health 48:579–584 49. Dong M, Anda RF, Felitti VJ, Dube SR, Williamson DF, Thompson TJ, Loo CM, Giles WH (2004) The interrelatedness of multiple forms of childhood abuse, neglect, and household dysfunction. Child Abuse Negl 28:771–784 50. Edwards VJ, Anda RF, Gu D, Dube SR, Felitti VJ (2007) Adverse childhood experiences and smoking persistence in adults with smoking-related symptoms and illness. Perm J 11:5–13 51. El-Sheikh M (1994) Childrens emotional and physiological responses to interadult angry behavior – the role of history of interparental hostility. J Abnorm Child Psychol 22:661–678 52. El-Sheikh M, Harger J (2001) Appraisals of marital conflict and children’s adjustment, health, and physiological reactivity. Dev Psychol 37:875–885 53. El-Sheikh M, Cummings EM, Goetsch VL (1989) Coping with adults angry behavior: behavioral, physiological, and verbal responses in preschoolers. Dev Psychol 25:490–498 54. Evans GW (2003) A multimethodological analysis of cumulative risk and allostatic load among rural children. Dev Psychol 39:924–933 55. Evans GW (2004) The environment of childhood poverty. Am Psychol 59:77–92 56. Evans GW, Kantrowitz E (2002) Socioeconomic status and health: the potential role of environmental risk exposure. Annu Rev Public Health 23:303–331 57. Evans GW, Kim P (2007) Childhood poverty and health: cumulative risk exposure and stress dysregulation. Psychol Sci 18:953–957 58. Evans GW, Hygge S, Bullinger M (1995) Chronic noise and psychological stress. Psychol Sci 6:333–338 59. Evans GW, Gonnella C, Marcynyszyn LA, Gentile L, Salpekar N (2005) The role of chaos in poverty and children’s socioemotional adjustment. Psychol Sci 16:560–565

120

K. Salomon et al.

60. Ewart CK (2002) Social competence, interpersonal stress and ambulatory blood pressure in black and white youth. Int J Psychophysiol 45:41–41 61. Ewart CK, Jorgensen RS (2004) Agonistic interpersonal striving: social-cognitive mechanism of cardiovascular risk in youth? Health Psychol 23:75–85 62. Ewart CK, Kolodner KB (1991) Social competence interview for assessing physiological reactivity in adolescents. Psychosom Med 53:289–304 63. Ewart CK, Kolodner KB (1993) Predicting ambulatory blood pressure during school – effectiveness of social and nonsocial reactivity tasks in black and white adolescents. Psychophysiology 30:30–38 64. Ewart CK, Jorgensen RS, Suchday S, Chen E, Matthews KA (2002) Measuring stress resilience and coping in vulnerable youth: the social competence interview. Psychol Assess 14:339–352 65. Fabricius WV, Luecken LJ (2007) Postdivorce living arrangements, parent conflict, and longterm physical health correlates for children of divorce. J Fam Psychol 21:195–205 66. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, Marks JS (1998) Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med 14:245–258 67. Field T, Diego M, Hernandez-Reif M, Schanberg S, Kuhn C, Yando R, Bendel D (2003) Pregnancy anxiety and comorbid depression and anger: effects on the fetus and neonate. Depress Anxiety 17:140–151 68. Flores E, Tschann JM, Dimas JM, Pasch LA, de Groat CL (2010) Perceived racial/ethnic discrimination, posttraumatic stress symptoms, and health risk behaviors among Mexican American adolescents. J Couns Psychol 57:264–273 69. Frerichs RR, Srinivasan SR, Webber LS, Berenson G (1976) Serum cholesterol and triglyceride levels in 3,446 children from a biracial community: the Bogalusa Heart Study. Circulation 54:302 70. Gabbay V, Klein RG, Alonso CM, Babb JS, Nishawala M, De Jesus G, Hirsch GS, HottingerBlanc PMZ, Gonzalez CJ (2009) Immune system dysregulation in adolescent major depressive disorder. J Affect Disord 115:177–182 71. Galobardes B, Davey SG, Lynch J (2006) Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Ann Epidemiol 16:91–104 72. Galobardes B, Lynch J, Davey Smith G (2008) Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. J Epidemiol Community Health 62:387–390 73. Gee GC, Walsemann KM, Brondolo E (2012) A life course perspective on how racism may be related to health inequities. Am J Public Health 102:967–974 74. Godfrey KM, Barker DJP (2000) Fetal nutrition and adult disease. Am J Clin Nutr 71:1344S– 1352S 75. Gore FM, Bloem PJN, Patton GC, Ferguson J, Joseph V, Coffey C, Sawyer SM, Mathers CD (2011) Global burden of disease in young people aged 10–24 years: a systematic analysis. Lancet 377:2093–2102 76. Grunbaum JA, Vernon SW, Clasen CM (1997) The association between anger and hostility and risk factors for coronary heart disease in children and adolescents: a review. Ann Behav Med 19:179–189 77. Hagan MJ, Luecken LJ, Sandler IN, Tein JY (2010) Prospective effects of post-bereavement negative events on cortisol activity in parentally bereaved youth. Dev Psychobiol 52:394–400 78. Hamil-Luker J, O’Rand AM (2007) Gender differences in the link between childhood socioeconomic conditions and heart attack risk in adulthood. Demography 44:137–158 79. Hanevold C, Waller J, Daniels S, Portman R, Sorof J (2004) The effects of obesity, gender, and ethnic group on left ventricular hypertrophy and geometry in hypertensive children: a collaborative study of the international pediatric hypertension association. Pediatrics 113:328–333

5

Childhood Factors in Adult Risk for Cardiovascular Disease

121

80. Harper S, Lynch J, Hsu WL, Everson SA, Hillemeier MM, Raghunathan TE, Salonen JT, Kaplan GA (2002) Life course socioeconomic conditions and adult psychosocial functioning. Int J Epidemiol 31:395–403 81. Harshfield GA, Alpert BS, Willey ES, Somes GW, Murphy JK, Dupaul LM (1989) Race and gender influence ambulatory blood pressure patterns of adolescents. Hypertension 14:598–603 82. Heinonen K, Räikkönen K, Matthews KA, Scheier MF, Raitakari OT, Pulkki L, KeltikangasJärvinen L (2006) Socioeconomic status in childhood and adulthood: associations with dispositional optimism and pessimism over a 21-year follow-up. J Pers 74:1111–1126 83. Henje Blom E, Lekander M, Ingvar M, Asberg M, Mobarrez F, Serlachius E (2012) Proinflammatory cytokines are elevated in adolescent females with emotional disorders not treated with SSRIs. J Affect Disord 136:716–723 84. Hlaing WWM, Prineas RJ (2006) Arterial stiffness variations by gender in African-American and Caucasian children. J Natl Med Assoc 98:181–189 85. Hoffman S, Hatch MC (1996) Stress, social support and pregnancy outcome: a reassessment based on recent research. Paediatr Perinat Epidemiol 10:380–405 86. Hood KK, Lawrence JM, Anderson A, Bell R, Dabelea D, Daniels S, Rodriguez B, Dolan LM, Grp SDYS (2012) Metabolic and inflammatory links to depression in youth with diabetes. Diabetes Care 35:2443–2446 87. Huurre T, Eerola M, Rahkonen O, Aro H (2007) Does social support affect the relationship between socioeconomic status and depression? A longitudinal study from adolescence to adulthood. J Affect Disord 100:55–64 88. Huxley RR, Shiell AW, Law CM (2000) The role of size at birth and postnatal catch-up growth in determining systolic blood pressure: a systematic review of the literature. J Hypertens 18:815–831 89. Jackson RW, Treiber FA, Turner JR, Davis H, Strong WB (1999) Effects of race, sex, and socioeconomic status upon cardiovascular stress responsivity and recovery in youth. Int J Psychophysiol 31:111–119 90. Jones A, Beda A, Ward AMV, Osmond C, Phillips DIW, Moore VM, Simpson DM (2007) Size at birth and autonomic function during psychological stress. Hypertension 49:548–555 91. Jones A, Beda A, Osmond C, Godfrey KM, Simpson DM, Phillips DIW (2008) Sex-specific programming of cardiovascular physiology in children. Eur Heart J 29:2164–2170 92. Jornayvaz FR, Vollenweider P, Bochud M, Mooser V, Waeber G, Marques-Vidal P (2016) Low birth weight leads to obesity, diabetes and increased leptin levels in adults: the CoLaus study. Cardiovasc Diabetol 15:73 93. Kapuku GK, Treiber FA, Davis HC (2002) Relationships among socioeconomic status, stress induced changes in cortisol, and blood pressure in African American males. Ann Behav Med 24:320–325 94. Kelsey RM, Ornduff SR, Alpert BS (2007) Reliability of cardiovascular reactivity to stress: internal consistency. Psychophysiology 44:216–225 95. Kessler RC, McGonagle KA, Swartz M, Blazer DG, Nelson CB (1993) Sex and depression in the National Comorbidity Survey. I: lifetime prevalence, chronicity and recurrence. J Affect Disord 29:85–96 96. Kittleson MM, Meoni LA, Wang N, Chu AY, Ford DE, Klag MJ (2006) Association of childhood socioeconomic status with subsequent coronary heart disease in physicians. Arch Intern Med 166:2356–2361 97. Kivimaki M, Smith GD, Juonala M, Ferrie JE, Keltikangas-Jarvinen L, Elovainio M, PulkkiRaback L, Vahtera J, Leino M, Viikari JS et al (2006) Socioeconomic position in childhood and adult cardiovascular risk factors, vascular structure, and function: cardiovascular risk in young Finns study. Heart 92:474–480 98. Klein DN, Schatzberg AF, McCullough JP, Dowling F, Goodman D, Howland RH, Markowitz JC, Smith C, Thase ME, Rush AJ et al (1999) Age of onset in chronic major depression: relation to demographic and clinical variables, family history, and treatment response. J Affect Disord 55:149–157

122

K. Salomon et al.

99. Kraft AJ, Luecken LJ (2009) Childhood parental divorce and cortisol in young adulthood: evidence for mediation by family income. Psychoneuroendocrinology 34:1363–1369 100. Krieger N, Okamoto A, Selby JV (1998) Adult female twins’ recall of childhood social class and father’s education: a validation study for public health research. Am J Epidemiol 147: 704–708 101. Kupper N, Ge DL, Treiber FA, Snieder H (2006) Emergence of novel genetic effects on blood pressure and hemodynamics in adolescence – The Georgia cardiovascular twin study. Hypertension 47:948–954 102. Laaksonen M, Silventoinen K, Martikainen P, Rahkonen O, Pitkäniemi J, Lahelma E (2007) The effects of childhood circumstances, adult socioeconomic status, and material circumstances on physical and mental functioning: a structural equation modelling approach. Ann Epidemiol 17:431–439 103. Lahti J, Raikkonen K, Pesonen AK, Heinonen K, Kajantie E, Forsen T, Osmond C, Barker DJP, Eriksson JG (2010) Prenatal growth, postnatal growth and trait anxiety in late adulthood – the Helsinki Birth Cohort Study. Acta Psychiatr Scand 121:227–235 104. Law CM, Shiell AW (1996) Is blood pressure inversely related to birth weight? The strength of evidence from a systematic review of the literature. J Hypertens 14:935–941 105. Lawani SO, Demerath EW, Lopez FL, Soliman EZ, Huxley RR, Rose KM, Alonso A (2014) Birth weight and the risk of atrial fibrillation in whites and African Americans the Atherosclerosis Risk In Communities (ARIC) study. BMC Cardiovasc Disord 14:69 106. Lawlor DA, Batty D, Morton SMB, Clark H, Macintyre S, Leon DA (2005) Childhood socioeconomic position, educational attainment, and adult cardiovascular risk factors: the Aberdeen Children of the 1950s Cohort Study. Am J Public Health 95:1245–1251 107. Leeson CPM, Whincup PH, Cook DG, Donald AE, Papacosta O, Lucas A, Deanfield JE (1997) Flow-mediated dilation in 9- to 11-year-old children – the influence of intrauterine and childhood factors. Circulation 96:2233–2238 108. Leeson CPM, Kattenhorn M, Morley R, Lucas A, Deanfield JE (2001) Impact of low birth weight and cardiovascular risk factors on endothelial function in early adult life. Circulation 103:1264–1268 109. Luecken LJ, Appelhans BM (2006) Early parental loss and salivary cortisol in young adulthood: the moderating role of family environment. Dev Psychopathol 18:295–308 110. Luecken LJ, Kraft A, Hagan MJ (2009) Negative relationships in the family-of-origin predict attenuated cortisol in emerging adults. Horm Behav 55:412–417 111. Manatunga AK, Jones JJ, Pratt JH (1993) Longitudinal assessment of blood pressures in black and white children. Hypertension 22:84–89 112. Matthews KA, Angulo J (1980) Measurement of the Type-A behavior pattern in children – assessment of childrens’ competitiveness, impatience-anger, and agression. Child Dev 51:466–475 113. Matthews KA, Salomon K, Kenyon K, Allen MT (2002) Stability of children’s and adolescents’ hemodynamic responses to psychological challenge: a three-year longitudinal study of a multiethnic cohort of boys and girls. Psychophysiology 39:826–834 114. Matthews KA, Salomon K, Brady SS, Allen MT (2003) Cardiovascular reactivity to stress predicts future blood pressure in adolescence. Psychosom Med 65:410–415 115. Matthews KA, Salomon K, Kenyon K, Zhou F (2005) Unfair treatment, discrimination, and ambulatory blood pressure in black and white adolescents. Health Psychol 24:258–265 116. Matthews KA, Chang Y, Bromberger JT, Karvonen-Gutierrez CA, Kravitz HM, Thurston RC, Montez JK (2016) Childhood socioeconomic circumstances, inflammation, and hemostasis among midlife women: study of women’s health across the nation. Psychosom Med 78: 311–318 117. McEwen BS (1998) Stress, adaptation, and disease – allostasis and allostatic load. In: SM MC, Lipton JM, Sternberg EM, Chrousos GP, Gold PW, Smith CC (eds) Neuroimmunomodulation: molecular aspects, integrative systems, and clinical advances, vol 840, pp 33–44

5

Childhood Factors in Adult Risk for Cardiovascular Disease

123

118. McGill HC, Strong JP, Tracy RE, McMahan CA, Oalmann MC (1995) Relation of a postmortem renal index of hypertension to atherosclerosis in youth. Arterioscler Thromb Vasc Biol 15:2222–2228 119. McGrath JJ, O’Brien WH (2001) Pediatric impedance cardiography: temporal stability and intertask consistency. Psychophysiology 38:479–484 120. McGrath JJ, Matthews KA, Brady SS (2006) Individual versus neighborhood socioeconomic status and race as predictors of adolescent ambulatory blood pressure and heart rate. Soc Sci Med 63:1442–1453 121. Melchior M, Moffitt TE, Milne BJ, Poulton R, Caspi A (2007) Why do children from socioeconomically disadvantaged families suffer from poor health when they reach adulthood? A life-course study. Am J Epidemiol 166:966–974 122. Merikangas KR, He J-p, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, Swendsen J (2010) Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 49:980–989 123. Mills NT, Scott JG, Wray NR, Cohen-Woods S, Baune BT (2013) Research review: the role of cytokines in depression in adolescents: a systematic review. J Child Psychol Psychiatry 54:816–835 124. Moskowitz WB, Schwartz PF, Schicken RM (1999) Childhood passive smoking, race, and coronary artery disease risk – the MCV twin study. Arch Pediatr Adolesc Med 153:446–453 125. Murphy JK, Alpert BS, Walker SS (1994) Consistency of ethnic differences in children’s pressor reactivity – 1987 to 1992. Hypertension 23:I152–I155 126. Musante L, Treiber FA, Davis H, Levy M, Strong WB (1995) Temporal stability of children’s cardiovascular (CV) reactivity – role of ethnicity, gender and family history of mycardial infarction. Int J Psychophysiol 19:281–286 127. Mzayek F, Cruickshank JK, Amoah D, Srinivasan S, Chen W, Berenson GS (2016) Birth weight was longitudinally associated with cardiometabolic risk markers in mid-adulthood. Ann Epidemiol 26:643–647 128. Newman WP, Freedman DS, Voors AW, Gard PD, Srinivasan SR, Cresanta JL, Williamson GD, Webber LS, Berenson GS (1986) Relation of serum lipoprotein levels and systolic blood pressure to early atherosclerosis – the Bogalusa Heart Study. N Engl J Med 314: 138–144 129. Nilsson PM, Lurbe E, Laurent S (2008) The early life origins of vascular ageing and cardiovascular risk: the EVA syndrome. J Hypertens 26:1049–1057 130. O’Rand AM, Hamil-Luker J (2005) Processes of cumulative adversity: childhood disadvantage and increased risk of heart attack across the life course. J Gerontol Ser B Psychol Sci Soc Sci 60:117–124 131. Ortiz J, Raine A (2004) Heart rate level and antisocial behavior in children and adolescents: a meta-analysis. J Am Acad Child Adolesc Psychiatry 43:154–162 132. Osika W, Montgomery SM, Dangardt F, Wahrborg P, Gan LM, Tideman E, Friberg P (2011) Anger, depression and anxiety associated with endothelial function in childhood and adolescence. Arch Dis Child 96:38–43 133. Panaite V, Salomon K, Jin A, Rottenberg J (2015) Cardiovascular recovery from psychological and physiological challenge and risk for adverse cardiovascular outcomes and all-cause mortality. Psychosom Med 77:215–226 134. Paradies Y (2006) A systematic review of empirical research on self-reported racism and health. Int J Epidemiol 35:888–901 135. Parker JD, Schoendorf KC, Kiely JL (1994) Associations between measures of socioeconomic status and low birth weight, small for gestational age, and premature delivery in the United States. Ann Epidemiol 4:271–278 136. Pascoe EA, Smart Richman L (2009) Perceived discrimination and health: a meta-analytic review. Psychol Bull 135:531–554

124

K. Salomon et al.

137. Perkiömäki N, Auvinen J, Tulppo MP, Hautala AJ, Perkiömäki J, Karhunen V, Karhunen V, Keinänen-Kiukaanniemi S, Puukka K, Ruokonen A et al (2016) Association between birth characteristics and cardiovascular autonomic function at mid-life. PLoS One 11: e0161604 138. Phinney JS (1989) Stages of ethnic identity development in minority group adolescents. J Early Adolesc 9:34–49 139. Pollitt RA, Kaufman JS, Rose KM, Diez-Roux AV, Zeng D, Heiss G (2007) Early-life and adult socioeconomic status and inflammatory risk markers in adulthood. Eur J Epidemiol 22:55–66 140. Poulton R, Caspi A, Milne BJ, Thomson WM, Taylor A, Sears MR, Moffitt TE (2002) Association between children’s experience of socioeconomic disadvantage and adult health: a life-course study. Lancet 360:1640–1645 141. Power C, Hypponen E, Smith GD (2005) Socioeconomic position in childhood and early adult life and risk of mortality: a prospective study of the mothers of the 1958 British birth cohort. Am J Public Health 95:1396–1402 142. Power C, Atherton K, Strachan DP, Shepherd P, Fuller E, Davis A, Gibb I, Kumari M, Lowe G et al (2007) Life-course influences on health in British adults: effects of socioeconomic position in childhood and adulthood. Int J Epidemiol 36:532–539 143. Puolakka E, Pahkala K, Laitinen TT, Magnussen CG, Hutri-Kähönen N, Tossavainen P, Jokinen E, Sabin MA, Laitinen T et al (2016) Childhood socioeconomic status in predicting metabolic syndrome and glucose abnormalities in adulthood: the Cardiovascular Risk in Young Finns Study. Diabetes Care 39:2311–2317 144. Pyhala R, Raikkonen K, Feldt K, Andersson S, Hovi P, Eriksson JG, Jarvenpaa AL, Kajantie E (2009) Blood pressure responses to psychosocial stress in young adults with very low birth weight: Helsinki Study of very low birth weight adults. Pediatrics 123:731–734 145. Raikkonen K, Matthews KA (2008) Do dispositional pessimism and optimism predict ambulatory blood pressure during schooldays and nights in adolescents? J Pers 76:605–629 146. Raikkonen K, Katainen S, Keskivaara P, Keltikangas-Jarvinen L (2000) Temperament, mothering, and hostile attitudes: a 12-year longitudinal study. Personal Soc Psychol Bull 26:3–12 147. Ramsay SE, Whincup PH, Morris RW, Lennon LT, Wannamethee SG (2007) Are childhood socio-economic circumstances related to coronary heart disease risk? Findings from a population-based study of older men. Int J Epidemiol 36:560–566 148. Reinehr T, Kiess W, de Sousa G, Stoffel-Wagner B, Wunsch R (2006) Intima media thickness in childhood obesity: relations to inflammatory marker, glucose metabolism, and blood pressure. Metabolism 55:113–118 149. Repetti RL, Taylor SE, Seeman TE (2002) Risky families: family social environments and the mental and physical health of offspring. Psychol Bull 128:330–366 150. Rosvall M, Ostergren PO, Hedblad B, Isacsson SO, Janzon L, Berglund G (2002) Life-course perspective on socioeconomic differences in carotid atherosclerosis. Arterioscler Thromb Vasc Biol 22:1704–1711 151. Rottenberg J, Yaroslavsky I, Carney RM, Freedland KE, George CJ, Baji I, Dochnal R, Gadoros J, Halas K, Kapornai K et al (2014) The association between major depressive disorder in childhood and risk factors for cardiovascular disease in adolescence. Psychosom Med 76:122–127 152. Schieken RM (1987) Measurement of left ventricular wall mass in pediatric populations. Hypertension 9:47–52 153. Smith C, Ryckman K, Barnabei VM, Howard B, Isasi CR, Sarto G, Tom SE, Van Horn L, Wallace R, Robinson JG (2016) The impact of birth weight on cardiovascular disease risk in the Women’s Health Initiative. Nutr Metab Cardiovasc Dis 26:239–245 154. Su S, Wang X, Kapuku GK, Treiber FA, Pollock DM, Harshfield GA, McCall WV, Pollock JS (2014) Adverse childhood experiences are associated with detrimental hemodynamics and elevated circulating endothelin-1 in adolescents and young adults. Hypertension 64:201

5

Childhood Factors in Adult Risk for Cardiovascular Disease

125

155. Su S, Wang X, Pollock JS, Treiber FA, Xu X, Snieder H, McCall WV, Stefanek M, Harshfield GA (2015) Adverse childhood experiences and blood pressure trajectories from childhood to young adulthood: the Georgia stress and Heart study. Circulation 131:1674–1681 156. Subramanyam MA, James SA, Diez-Roux AV, Hickson DA, Sarpong D, Sims M, Taylor HA, Wyatt SB (2013) Socioeconomic status, John Henryism and blood pressure among AfricanAmericans in the Jackson Heart Study. Soc Sci Med 93:139–146 157. Thapar A, Collishaw S, Pine DS, Thapar AK (2012) Depression in adolescence. Lancet 379:1056–1067 158. Tomfohr LM, Martin TM, Miller GE (2008) Symptoms of depression and impaired endothelial function in healthy adolescent women. J Behav Med 31:137–143 159. Tomfohr LM, Murphy MLM, Miller GE, Puterman E (2011) Multiwave associations between depressive symptoms and endothelial function in adolescent and young adult females. Psychosom Med 73:456–461 160. Treiber FA, Musante L, Riley W, Mabe PA, Carr T, Levy M, Strong WB (1989) The relationship between hostility and blood pressure in children. Behav Med 15:173–178 161. Treiber FA, Musante L, Strong WB, Levy M (1989) Racial differences in young children’s blood pressure responses to dynamic exercise. Am J Dis Child 143:720–723 162. Treiber FA, Davis H, Musante L, Raunikar RA, Strong WB, McCaffrey F, Meeks MC, Vandernoord R (1993) Ethnicity, gender, family history of myocardial infarction, and hemodynamic responses to laboratory stressors in children. Health Psychol 12:6–15 163. Treiber FA, Musante L, Kapuku G, Davis C, Litaker M, Davis H (2001) Cardiovascular (CV) responsivity and recovery to acute stress and future CV functioning in youth with family histories of CV disease: a 4-year longitudinal study. Int J Psychophysiol 41:65–74 164. Urbina E, Alpert B, Flynn J, Hayman L, Harshfield GA, Jacobson M, Mahoney L, McCrindle B, Mietus-Snyder M, Steinberger J et al (2008) Ambulatory blood pressure monitoring in children and adolescents: recommendations for standard assessment – a scientific statement from the American heart association atherosclerosis, hypertension, and obesity in youth committee of the council on cardiovascular disease in the young and the council for high blood pressure research. Hypertension 52:433–451 165. Varvarigou AA (2010) Intrauterine growth restriction as a potential risk factor for disease onset in adulthood. J Pediatr Endocrinol Metab 23:215–224 166. Visintainer PF, Matthews KA (1987) Stability of overt type a behaviors in children: results from a two- and five-year longitudinal study. Child Dev 58:1586–1591 167. Vitaro F, Barker ED, Boivin M, Brendgen M, Tremblay RE (2006) Do early difficult temperament and harsh parenting differentially predict reactive and proactive aggression? J Abnorm Child Psychol 34:685–695 168. Voors AW, Webber LS, Berenson GS (1982) Resting heart rate and pressure-rate product of children in a total biracial community – the Bogalusa Heart Study. Am J Epidemiol 116:276–286 169. Waloszek JM, Byrne ML, Woods MJ, Nicholas CL, Bei B, Murray G, Raniti M, Allen NB, Trinder J (2015) Early physiological markers of cardiovascular risk in community based adolescents with a depressive disorder. J Affect Disord 175:403–410 170. Waloszek JM, Woods MJ, Byrne ML, Nicholas CL, Bei B, Murray G, Raniti M, Allen NB, Trinder J (2016) Nocturnal indicators of increased cardiovascular risk in depressed adolescent girls. J Sleep Res 25:216–224 171. Wang XL, Trivedi R, Treiber F, Snieder H (2005) Genetic and environmental influences on anger expression, John Henryism, and stressful life events: the Georgia cardiovascular twin study. Psychosom Med 67:16–23 172. Wang XL, Treiber FA, Harshfield GA, Kapuku G, Snieder H (2006) A 15-year longitudinal study on ambulatory blood pressure from childhood to early adulthood: tracking efficiency and prediction of left ventricular mass. Circulation 113:E308–E308 173. Wang X, Poole JC, Treiber FA, Harshfield GA, Hanevold CD, Snieder H (2006) Ethnic and gender differences in ambulatory blood pressure trajectories – results from a 15-year longitudinal study in youth and young adults. Circulation 114:2780–2787

126

K. Salomon et al.

174. Ward AMV, Moore VM, Steptoe A, Cockington RA, Robinson JS, Phillips DIW (2004) Size at birth and cardiovascular responses to psychological stressors: evidence for prenatal programming in women. J Hypertens 22:2295–2301 175. Webber LS, Osganian V, Luepker RV, Feldman HA, Stone EJ, Elder JP, Perry CL, Nader PR, Parcel GS, Broyles SL et al (1995) Cardiovascular risk factors among third grade children in 4 regions of the United States – the CATCH Study. Am J Epidemiol 141:428–439 176. Winkleby MA, Robinson TN, Sundquist J, Kraemer HC (1999) Ethnic variation in cardiovascular disease risk factors among children and young adults – findings from the Third National Health and Nutrition Examination Survey, 1988–1994. JAMA 281:1006–1013 177. Woo KS, Chook P, Yu CW, Sung RY, Qiao M, Leung SS, Lam CW, Metreweli C, Celermajer DS (2004) Overweight in children is associated with arterial endothelial dysfunction and intima-media thickening. Int J Obes Relat Metab Disord 28:852–857 178. Wulsin LR, Singal BM (2003) Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review. Psychosom Med 65:201–210 179. Zeiders KH, Doane LD, Roosa MW (2012) Perceived discrimination and diurnal cortisol: examining relations among Mexican American adolescents. Horm Behav 61:541–548

6

Aging Changes in Cardiovascular Structure and Function Jerome L. Fleg and Daniel E. Forman

Contents Demographics of Aging and Impact of Age on Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . Aging Changes in the Vasculature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Morphology of Aging Vasculature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physiological and Clinical Effects of Vascular Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aging Changes in Cardiac Structure and Resting Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiovascular Response to Exercise and Other Stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orthostatic Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pressor Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aerobic Exercise Capacity and Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mechanisms of Impaired LV Ejection During Maximal Aerobic Exercise in Healthy Older Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sympathetic Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deficits in Cardiac Beta-Adrenergic Receptor Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrocardiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sinus Node Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P-Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P-R Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QRS Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Repolarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arrhythmias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atrial Arrhythmias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atrial Fibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

128 130 131 131 136 140 140 141 141 144 145 145 146 146 147 147 148 149 150 150 151

J. L. Fleg (*) Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA e-mail: fl[email protected] D. E. Forman Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA VA Boston Healthcare System, Boston, MA, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_6

127

128

J. L. Fleg and D. E. Forman

Paroxysmal Supraventricular Tachycardia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Ventricular Arrhythmias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Abstract

With aging there is an increasing prevalence of cardiovascular disease (CV) which includes a group of diseases and disorders such coronary heart disease (CHD), hypertension, congestive heart failure, valvular heart disease, cardiac arrhythmias, peripheral vascular disease, and stroke. The etiology of the age-associated increase in CV is attributed to primary biological aging, adverse lifestyle, the environment, disease, socioeconomics, and other external factors that interact detrimentally with the biological aging. This chapter reviews a number of age-associated changes in the cardiovascular system including aging changes in the vasculature, physiological and clinical effects of vascular aging, aging changes in cardiac structure and resting function, impact of age and disease on the cardiovascular response to exercise and other stressors, aerobic exercise capacity and aging, mechanisms of impaired left ventricular (LV) function during maximal aerobic exercise in older adults, age-associated changes in the electrocardiogram (ECG), and propensity for development of arrythmias such as atrial fibrillation in older adults. A better understanding of the age-associated alterations in cardiac and vascular structure and function may lead to the development of new novel therapies to decrease the prevalence of CV disease and to attenuate the changes in cardiac and vascular function that accompany advancing age. Keywords

Aging · Cardiovascular disease · Heart aging · Vascular aging · Electrocardiogram

Demographics of Aging and Impact of Age on Cardiovascular Disease The US Census Bureau estimates that there are now 34.8 million Americans aged over 65 years (58.5% female) and 9.2 million aged over 80 years (65.7% female). By 2030, it is projected that there will be 70.3 million adults aged over 65 years, of whom over 18% will be aged over 80 [120]. Not only does aging prompt intrinsic physiological changes that predispose older adults to cardiovascular (CV) disease [104, 150], but CV risk factors also accumulate over a lifetime such that most seniors eventually have multiple risk factors, with mounting injurious effects as these risk factors persist over years and decades [92]. Consistently, aging is associated with a high prevalence of CV disease and also with high CV morbidity and mortality [86, 88]. Such predisposition is a prominent concern in regard to the caregiving burden for our growing elderly population. Coronary heart disease (CHD) is three times greater among adults aged 65 years and older than those aged 45–64 (22%

6

Aging Changes in Cardiovascular Structure and Function

129

vs. 7%) [119]. Hypertension, heart failure, and arrhythmias increase similarly with aging [87]. Age-associated risks for CV disease extend beyond specific CV events and diagnoses, as the impact of aging can also be more subtle [20, 51]. Aging can, for example, underlie vascular-mediated declines in cognition, exercise tolerance, susceptibility to falls, and other vulnerabilities stemming from insidious CV physiologic changes that occur with age but which do not reach the threshold of clear-cut CV diagnoses. Concomitant age-related declines in skeletal muscle mass and strength (decreasing mechanical work efficiency) [34], sleep (exacerbating hypertension, fatigue, and overall frailty) [33], and diet (with reduced vital proteins and vitamins) [34] often compound CV vulnerabilities, i.e., diminishing capacities to tolerate physiological perturbations that would have been bearable at a younger age. Therefore, infections, surgery, or acute stresses more often overwhelm older adults and trigger CV instability. Furthermore, CV disease complexity among elderly is also compounded by the high prevalence of comorbid conditions, including concurrent CV conditions (e.g., an acute coronary event in someone who already has hypertension, chronic angina, heart failure, arrhythmias, and peripheral vascular disease) and destabilizing non-CV diseases (e.g., gastrointestinal bleeding, anemia, chronic obstructive lung disease, diabetes, thyroid disease, kidney disease, affective disorders, infections, cancer, arthritis, and Alzheimer’s dementia). Socioeconomic constraints (lower income, reduced social supports, and reduced access to medical services) and polypharmacology are also common with aging, further exacerbating patient instability and management challenges. In analyzing the concept of age-related CV disease, it remains a challenge to discriminate risks attributable purely to age from those that are determined by the association of aging with lifestyle, environment, socioeconomics, and other external factors that interact (often detrimentally) with the biological substrate. The phrase “typical aging” is often used to describe aging patterns in which suboptimal lifestyle, environment, and socioeconomic factors contribute to overall aging effects. In contrast, the phrase “successful aging” distinguishes aging patterns in which external factors have been modified to achieve more successful aging patterns, such that function, health, and well-being can be preserved despite biological progression of age [86, 87]. Many have demonstrated that the process of aging is much less incapacitating if detrimental liabilities of “typical aging” are attenuated by exercise, diet, and other favorable lifestyle choices [16, 134]. A strong rationale is growing for preventive health priorities for adults who are middle aged and older, i.e., establishing and maintaining health measures that best insure successful aging [74]. However, there still remains enormous variability in aging patterns, with marked differences in lifestyle, environment, and socioeconomic dynamics blurring assessments focused on aging. Consistently, it becomes much more difficult to achieve assessments of younger versus older adults using cross-sectional methodology as many who survive into old age have benefited from differences in genetics, lifestyle, environment, and socioeconomic patterns that confound the analyses. Therefore, selection bias is a common criticism of cross-sectional study designs for age-based assessments. Alternatively, it makes much greater conceptual sense to study aging and health-promoting interventions in relation to aging using longitudinal analyses, in which individuals undergo repeated observations over time. The effects of specific

130

J. L. Fleg and D. E. Forman

lifestyle or medical interventions are best studied by randomized controlled trials. Nonetheless, the practical and cost challenges for such study designs are formidable and often prohibitive. Another major difficulty regarding studies of older adults is that the typical patterns of comorbidity and lifestyle-related aging limitations are often seen as contraindications for inclusion into clinical studies. Ironically, most “typical elderly” individuals are excluded from the majority of CV clinical trials; thus, the generalizability of such trial results is fundamentally limited [3].

Aging Changes in the Vasculature Based on longitudinal studies of a healthy population, age-related morphologic and physiological changes have been described [88, 104]. Typical aging includes a constellation of morphological and physiological changes in the vasculature that predispose to increased CV risk and events (Table 1) [86, 87, 104]. While, to some Table 1 Relationship of cardiovascular aging in healthy humans to cardiovascular disease Age-associated changes Plausible mechanisms CV structural remodeling " Vascular "VSMC migration and matrix production intimal thickness " Vascular Elastin fragmentation stiffness " Elastase activity " Collagen production and cross-linking Altered growth factor regulation and tissue repair " LV wall " LV myocyte size thickness # Myocyte number Focal collagen deposition " L. atrial size " L. atrial volume/pressure CV functional changes Altered # NO production/effects vascular tone # βAR responses #CV reserve "Vascular load

#Physical activity

#Intrinsic myocardial contractility # β-adrenergic modulation of heart rate, LV contractility, and vascular tone Comorbidities #Skeletal muscle mass

Possible relationship to disease Early stages of atherosclerosis

Systolic hypertension

Atherosclerosis # Early LV diastolic filling " LV filling pressure/dyspnea "Risk of atrial fibrillation Vascular stiffening/ hypertension Lower threshold for heart failure

Accelerated aging changes in CV structure and function "Risk of CV disease

Abbreviations: βAR beta adrenergic receptor; CV cardiovascular; LV left ventricular; NO nitric oxide; VSMC vascular smooth muscle cell

6

Aging Changes in Cardiovascular Structure and Function

131

extent, morphological and physiological changes occur in parallel, they are progressively interrelated as changes in structure exacerbate age-related physiological limitations and also contribute to atherosclerotic disease risk synergistically. However, morbidity is not inevitable; elders who enjoy successful aging retain good function and health despite age-related morphological and physiological changes.

Morphology of Aging Vasculature Constitutional changes in the vasculature occur in the endothelial cells (which align the vessel lumen) as well as within the vascular media. With advancing age, there is a progressive thickening of the intimal-medial (IM) vessel wall layers in large and medium sized vessels. A complex aging biology has been described, particularly involving vascular tissue concentrations of angiotensin II and series of downstream biological effects with progressive vascular fibrosis, increasing collagen and calcium accumulations, covalent cross-linking of collagen, and migration of smooth muscle cells from the medial or middle layers to the subintimal layer [104]. Progressive shortening of telomeres in the DNA of endothelial cells over a lifetime of replications plays a key role provoking these changes [104]. Vascular ultrasound is commonly used to assess IM thickening, typically in the carotid artery. Epidemiological studies of individuals not initially screened to exclude the presence of occult CV disease have demonstrated age-related increases in carotid IM thickness [102, 108]. Such IM thickening has also been described as a prognostic indicator for atherosclerosis [108]. As seen in Fig. 1, the degree of CV risk varies with the degree of IM thickness, with the greatest risk occurring in the upper quintile [108]. A key point is that while such vascular thickening is associated with increased risk of CV, disease and clinical CV events are not inevitable. A monolayer of endothelial cells lines the luminal surface of arteries where they synthesize peptides such as nitric oxide (NO) that promote vital vasodilatory, antiinflammatory, anti-atherosclerotic, and antiplatelet properties. Aging leads to intrinsic endothelial changes: a higher proportion of cells have abnormal nuclei and alterations to their cytoskeleton. Laminar flow dynamics and flow-mediated vasodilator capacity progressively erode. Vital nitric oxide (NO) synthesis, antioxidative, and other key aspects of vascular homeostasis decrease, with increased vasoconstrictive (e.g., endothelin) and procoagulant (e.g., plasminogen activator inhibitor-1) peptide synthesis instead [104]. While these changes are omnipresent, they are relatively more pronounced in those who are sedentary and/or who suffer from the compounding effects of CV risk factors and/or atherosclerosis [75, 86, 87].

Physiological and Clinical Effects of Vascular Aging Brachial arterial flow-mediated dilation (FMD) is a commonly used ultrasound technique to assess endothelial function. In particular, FMD gauges vasodilatory

132 (A) 0.12 Male 0.10 Intimal Medial Thickness (cm)

Fig. 1 Panel A. Common carotid artery intimal-medial thickness as a function of age in healthy volunteers [102]. Panel B. Carotid intimalmedial thickness as a prognostic index of future cardio-vascular events [108]

J. L. Fleg and D. E. Forman

Female 0.08

0.06

0.04

0.02

0.00 20

30

40

50 60 Age (years)

70

90

80

100

(B)

Cumulative Event-Free Rate (%)

100 95

1st Quintile 2nd Quintile

90

3rd Quintile

85

4th Quintile

80 75

5th Quintile

70 0

1

2

3

4

5

6

7

Years

responses that are mediated in large part by endothelial NO. Brachial FMD declines with age in both sexes, even in the absence of other CV risk factors [17]. However, FMD declines more rapidly in those with CV risk factors and/or CVD. A decline of ~75% in FMD occurs in men between ages 40 and 70 years (Fig. 2) [17]. This decline begins approximately a decade later in women, perhaps because of the protective effect of estrogen, but the slope of decline is then 2.5 times steeper than the longer and more gradual age-related decline among men. Diminished NO and other age-related endothelial peptide shifts predispose to increased oxidative stress and inflammation, with greater adherence of low-density lipoprotein cholesterol, white blood cells, platelets, and other constitutive features of atherosclerosis. Atherosclerotic lesions are more likely to form and are also more likely to be complex and unstable [10]. Age-related endothelial responses and arterial thickening are also linked to widespread vascular stiffening. Stiffness accrues, in part because of the increasing collagen content (particularly in the medial layer of vessels), cross-linking of the collagen into a stiffer subtype, fragmentation of elastin content, and increased

Aging Changes in Cardiovascular Structure and Function

Fig. 2 Decline in flowmediated dilation with age in healthy volunteers. The decline begins approximately one decade earlier in men (top panel) than in women (bottom panel), though the decline is much steeper in women [17]

133

FLOW MEDIATED DILATATION

20

Male Female

15 Percent

6

10 5 0

Percent

20 15 10 5 0 10

20

30 40 50 Age (years)

60

70

calcifications [86, 104]. Moreover, there is a vicious cycle such that age-related changes in the mechanical properties of the vessel wall predispose to atherosclerotic risks. In turn, the lesions of atherosclerosis add to arterial stiffness with greater vascular wall thickening, worsening endothelial cell dysfunction, and worsened intrinsic stiffening. These tissue characteristics relate to endothelial regulation of vascular smooth muscle tone as well as to endothelial regulation of vascular wall structure/function [26, 86, 87, 99, 104]. Atherosclerosis is ultimately more common with age, and lesions may progress relatively rapidly [136]. However, beyond the atherosclerotic implications of age-associated endothelial and arterial medial changes, there are vascular implications in terms of the workload on the heart and related potential for ischemia. Each ventricular systolic contraction generates a pressure wave that propagates centrifugally down the arterial tree, slightly preceding the luminal flow wave generated during systole. The propagation velocity of this wave is proportional to the stiffness of the arterial wall. The velocity

134

J. L. Fleg and D. E. Forman

of the pulse wave in vivo is determined not only by the intrinsic stress/strain relationship (stiffness) of the vascular wall but also by the vascular smooth muscle tone (i.e., which is measured as blood pressure). Noninvasive measures of the vascular pulse wave velocity (PWV) provide a convenient means to gauge this index of vascular mechanical stiffness [106]. In both rigorously screened normal subjects and populations with varying prevalence of CV disease [65, 81, 109, 149], a significant age-associated increase in PWV has been observed in men and women (Fig. 3). Furthermore, just as greater decrements in FMD with age also predict CV disease, higher PWV indicates increased CV risk [153]. In addition to the forward pulse wave, each cardiac cycle generates a reflected wave, i.e., the wave reflects back from the periphery (at areas of arterial impedance mismatch), to then travel back toward the central aorta. This reflected wave can be assessed noninvasively from recordings of the carotid [81] or radial [21] arterial pulse waveforms by arterial applanation tonometry and high-fidelity micromanometer probes. The reflected waves are recorded as a modest increase in central aortic pressures, as the forward-moving systolic pressure waveforms merge with the Fig. 3 Panel A. Aortofemoral pulsewave velocity as a function of age in healthy volunteers. Panel B. Carotid artery augmentation index as a function of age in healthy volunteers. The inset shows how augmentation index is calculated from a carotid pulse tracing [149]

6

Aging Changes in Cardiovascular Structure and Function

135

pressure waveforms that are reflecting back from the periphery. This characteristic is also described as the augmentation index; studies show that this measure [109] can also be used gauge vascular stiffening (Fig. 3). Those with relative higher augmentation indices also have greater CV risks [105]. Vascular stiffening not only correlates to atherosclerosis that impedes vital blood perfusion through the coronary arteries but also to reduced arterial flow as a result of timing differences in coronary artery flow dynamics. Blood flows through the epicardial coronary arteries to the underlying myocardium as the heart relaxes in ventricular diastole. In young healthy adults, the PWV through the central arteries is relatively slow, such that reflected waves return to the central vasculature in diastole, i.e., the characteristically slow pressure wave reflections are well timed to help perfuse the coronary arteries in diastole. However, as the arteries stiffen with age, PWV accelerates, and the reflected pressure waves return to the central vasculature faster, i.e., in late systole instead of diastole. This constitutes a critical timing shift, causing the natural benefit of reflected waves to propel blood through the coronary arteries in diastole to be lost or attenuated. Furthermore, since the reflected pressures waves augment systolic BP, the heart must pump against this increased physiologic resistance, increasing cardiac work demands [105]. Arterial blood pressure (BP) constitutes a measure of vascular function that depends on smooth tone plus as well as stiffness of the large and medium sized arteries. Whereas increased arterial tone raises both systolic and diastolic BP to a similar degree, stiffening of the large conduit arteries raises systolic but lowers diastolic BP. A rise in average systolic BP across adult age has been well documented, even in normotensive populations (Fig. 4) [52, 115]. Of note, this increase in systolic BP is substantially greater in women than men. In contrast, Fig. 4 Change in blood pressures with age in healthy men (~) and women (●) [115]

Male Female

Blood Pressure (mmHg)

140

120

Systolic

100 Diastolic 80

60 0

20

30

40 50 Age

60

70

80

136

J. L. Fleg and D. E. Forman

average diastolic pressure (Fig. 4) rises until about 50 years of age, levels off from age 50 to 60 years, and declines thereafter [52, 115]. Pulse pressure (systolic BP minus diastolic BP) is a useful indicator of conduit artery vascular stiffness and increases with age [52]. Both higher systolic BP and pulse pressure predict increased CV risk, especially heart failure and stroke [9, 64, 103, 148]. Because of the decline in diastolic BP in older men and women in whom systolic pressure is increased, isolated systolic hypertension emerges as the most common form of hypertension in individuals over the age of 50. Isolated systolic hypertension, even when mild in severity, is associated with significantly increased CV disease risk [107, 127]. While there remains some debate about the relative sensitivity of isolated systolic hypertension versus pulse pressure to discriminate CV risk, it is generally agreed that both reflect similar physiological relationships and constitute rationale for therapy [64]. Pulse pressure may be especially informative of coronary risk in older subjects [9] because of the “J”- or “U”-shaped association between diastolic BP and coronary risk. Thus, consideration of the systolic and diastolic BP in combination is preferable to consideration of either value alone.

Aging Changes in Cardiac Structure and Resting Function In parallel to the intrinsic changes in the vasculature, age-related cardiac changes include morphological, biological, and physiological effects that modify normal cardiac function and also increase risks of cardiac disease. Left ventricular mural thickening is common and is mediated by enlargement of myocytes. In patients without evidence of CV disease, Olivetti et al. [111] observed that myocytes enlarged with age, but an age-associated reduction of left ventricular (LV) mass was mediated by a decrease in myocyte number. In an animal study, apoptotic myocytes were more prevalent in the hearts of older males compared to females, paralleling the age-related decline of LV mass in men but not in women [112]. An autopsy study of 765 normal hearts from persons 20–99 years old who were free from hypertension and coronary artery disease (CAD) showed that heart weight indexed to body surface area was not age related in men but increased with age in women, primarily between the fourth and seventh decades [83]. Over the past three decades, a consistent echocardiographic finding in healthy normotensive populations is a modest increase in LV wall thickness with age [57, 58]. More recently, magnetic resonance imaging has been used to assess overall heart structure in healthy men and women in the Baltimore Longitudinal Study of Aging (BLSA). In this study, LV wall thickness also increased with age, but LV length declined with age in both sexes such that the LV became more spherical (Fig. 5) [68], a morphologic change which has been associated with increased CV risk. In part, the changes in myocardial anatomy are thought to be adaptive to the arterial changes that accompany aging [19]. Putative stimuli for cardiac cell enlargement with age are an increase in vascular load due to arterial stiffening and a stretching of cells due to dropout of neighboring apoptotic myocytes [5, 23].

6

Aging Changes in Cardiovascular Structure and Function

(A)

Women

Men 1.1

LV Wall Thickness / Ht (cm/m)

1.1 P 80 cm) had higher levels of fasting insulin, triglycerides, incidence of glucose intolerance, and systolic blood pressure compared to those with smaller waists, even after adjusting for BMI [46]. The reduction in endogenous estrogen at menopause also leads to adverse changes in lipoprotein metabolism, including elevated plasma cholesterol, elevated LDL cholesterol, and reduced HDL cholesterol, which further increases their IHD risk [123, 182]. Thus, certain risk factors are specific to women, and evidence suggest that menopause adversely changes the cardiovascular risk profile.

Psychosocial Factors and Risk for Incident IHD Type A, Hostility, and Anger The initial recognition of the Type A behavior pattern, characterized by extreme impatience, pervasive hostility, profound competitiveness, and quick proneness to anger, is based on clinical observations of young and middle-aged men with coronary disease [56]. The few studies that examined the Type A behavior pattern in women seemed to suggest that a positive association occurs in both sexes [69, 70, 142], but the inconsistent findings led researchers to search for the “toxic” ingredient, presumed to be the true culprit for IHD incidence. Hostility and anger were thought to be the components in the Type A behavior pattern responsible for the risk of IHD incidence. In men and women, the findings of hostility as IHD risk factors are mixed, with some studies evidencing positive findings [11] and others failing to demonstrate a connection [169, 179]. In one study, trait anger predicted an increased risk of combined IHD (acute MI, fatal IHD, silent MI, or cardiac revascularization procedures) and IHD events (acute MI and fatal IHD), and women and men did not differ in either outcome [115]. Instead of focusing on cardiac events as targeted outcomes, recent studies have begun employing noninvasive methods, such as intima-media thickness (IMT) by B-mode ultrasonography, to quantify the progression of underlying IHD or atherosclerosis. The Study of Women’s Health Across the Nation (SWAN) found a cross-sectional association between hostility and higher IMT scores in middle-aged women after adjusting for age, race, education, BMI, blood pressure, and smoking [47]. Furthermore, baseline anger suppression, but not trait anger, predicted higher IMT scores 10 years later in postmenopausal women after adjusting for baseline pulse pressure, smoking history, and triglycerides [107]. In the same study, higher hostility also predicted higher IMT scores over an average of 1.5 years. Taken together, there appears to be evidence that anger and hostility are associated with the levels of atherosclerotic progression in women. In summary, though results are mixed, evidence suggests that hostility and anger are implicated in the development of IHD in both men and women.

172

B.-J. Shen et al.

Depression Depression is among the most researched psychological constructs with regard to its association with IHD in the past two decades [23, 71, 95, 144, 145]. There is robust evidence indicating that depression independently and significantly predicts the onset of IHD for both men and women [50, 112, 133, 152]. Some studies found sex differences in the degree to which depression was associated with IHD incidence and mortality. In a mixed sex sample of adults ages 65 years or older, depressive symptoms significantly predicted IHD deaths and total IHD incidence over 9 years in women but not in men [112]. In the first National Health and Nutrition Examination Survey (NHANES I), depressed women and men were 73% and 71%, respectively, more likely to experience a nonfatal IHD incident than non-depressed individuals over 8 years after adjusting for poverty, diabetes, hypertension, smoking, and BMI [48]. When focusing on fatal IHD events, depressed men were 2.34 times as likely as non-depressed men to suffer a fatal IHD event, whereas depressed women did not differ from their non-depressed counterparts. Additionally, in a population-based case-control study, a history of depression diagnosis was associated with a threefold increase in the risk for IHD in men but not in women after adjusting for smoking, diabetes, hypertension, and socioeconomic status [72]. Finally, a meta-analysis of studies conducted between 1966 and 2000 concluded that depressive symptomatology is a significant risk factor for the onset of IHD in both men and women [186]. In summary, although there is abundant empirical evidence supporting a prospective association between depression and IHD incidence in women, this relationship seems weaker compared to that observed in men.

Anxiety Studies on anxiety and IHD in women are scarce, but available research appears to suggest a positive association in both men [66, 83, 89, 159] and women [4, 44]. One study of 749 women found that after adjusting for age, blood pressure, cholesterol, diabetes, smoking, and BMI, anxiety predicted MI or coronary death over 20 years only among homemakers but not among working women [44]. In another study of 72,359 female nurses, phobic anxiety was associated with an increased risk of fatal IHD and sudden cardiac death over 12 years, although it did not predict nonfatal MI [4]. In sum, the evidence associating anxiety with IHD risk in women is promising yet limited, and the interpretation is hampered by the lack of research.

Social Support Social support or social isolation, conceptualized and measured in a variety of manners, has received much attention for its salutary influence on health [36]. There is indication that lower social support may predict the onset of IHD in women [127] as well as men [128, 137]. One study following a mixed sex sample

7

Risk Factors for Ischemic Heart Disease in Women

173

for a mean of approximately 8 years demonstrated that low social support at work predicted increased risk of MI in women only, controlling for age, blood pressure, diabetes, and current smoking [6]. Considering these findings, social support appears to play a protective role against IHD in women.

Stress Several different types of stress appear to increase risk for IHD in both sexes. A review by Schnall et al. [150] indicated that most studies have demonstrated a positive link between job strain and development of IHD in men and women [150]. For example, a case-control study found that having a hectic and monotonous job rendered men at a 20% and women at a 40% higher risk for an MI incident [67]. In addition, the combination of a hectic job with few learning possibilities or low influence on planning work raised the MI risk by 30% for both men and women. Caregiving stress also appears to have a negative impact on women’s cardiac health. In a study of female nurses, caring for a disabled or ill spouse for at least 9 h per week significantly increased the risk of combined nonfatal MI and IHD death by 82% over 4 years after controlling for demographic and clinical risk factors [92]. The same research group also observed a 59% increase in IHD risk among women spending at least 21 h per week caring for healthy children and a 55% increase among those caring for grandchildren for at least 9 h per week, relative to women with no caregiving responsibility [93]. These studies strongly suggest that various sources of stress, either from professional responsibilities or from caregiving roles in the family, may constitute a significant risk for IHD in women. These findings may be especially illuminating for the women in today’s society who are increasingly joining the labor force while maintaining their caregiving roles at home. Between 1970 and 2007, the percentage of women in the labor force has increased from 43% to 59% [175], and the percentage of women in the labor force with children under 18 at home has increased from 47% to 71% from 1975 to 2007 [175]. Research has shown that women assuming multiple roles reported higher psychological distress [58, 132] and exhibited greater 24-h urinary cortisol excretion [105]. The dual or multiple roles assumed by women appear to emerge as a novel IHD risk factor that urgently deserves more research attention.

Psychosocial Factors in Women with IHD The literature has consistently shown that women with IHD have poorer quality of life than men, endorsing higher levels of perceived stress, lower levels of emotional functioning, lower scores in vitality and physical functioning, and reductions in social activities and activities of daily living [102, 124, 161]. Although these differences seem to suggest that women are at a disadvantage compared to men, it is also plausible that women are socialized to be more adept at expressing emotional matters, more willing to endorse emotional symptoms [24], and more perceptive of

174

B.-J. Shen et al.

somatic sensations and related symptoms [14, 185]. It remains unclear whether these sex disparities truly represent the deficits in women or merely reflect sex-specific response tendencies. While more research is needed to elucidate the meaning of these differences, the impact of psychosocial factors on women with IHD continues to be an important focus in current research. In the following section, we will review the studies that examine sex differences in the potential influences of psychosocial factors on the health of IHD patients.

Depression Research examining IHD patients has consistently reported that depression, assessed by validated measures or by diagnostic interviews, is more prevalent in women (26–47%) than in men (13–26%) [22, 49, 55, 172]. Furthermore, the depressive symptoms of these female patients tend to be more severe and persist for longer periods of time compared to those of men, even after controlling for sociodemographic variables [40]. Studies analyzing men and women together generally support the conclusion that depression raises the mortality risk among individuals with IHD [15]. Focusing exclusively on women with suspected IHD, the Women’s Ischemia Syndrome Evaluation Study (WISE) demonstrated that after controlling for age, disease severity, and IHD risk factors (diabetes, hypertension, cholesterol, BMI, smoking, and education), depression symptom severity was associated with increased mortality risk (relative risk [RR] ¼ 1.05) and that depression treatment history was associated with increased risk of hospitalization (RR ¼ 1.3) [148]. A separate analysis with 500 women from the WISE study showed that the combination of current depressive symptoms and history of depression treatment, rather than either one alone, was one of the strongest predictors of recurrent cardiac events in women (RR ¼ 3.1) [148, 149].

Anxiety Despite the fact that anxiety disorders are the most common mental health problems and are more prevalent in women, studies on anxiety and IHD in women have been sparse. The complexity in defining and assessing anxiety disorders, the confounding between somatic symptoms of anxiety and cardiac risk factors (e.g., elevated heart rates, shortness of breath), and comorbidity between anxiety and depression may have contributed to the difficulties in research. A recent study conducted comprehensive clinical diagnostic interviews to assess anxiety disorders using the DSM-IV criteria in men and women with IHD. The results indicated that 36% of all participants met criteria for at least one anxiety disorder at the time of interview and 54% did at some point during their lifetime [173]. Anxiety disorders were significantly more prevalent in women with IHD, of whom 58% met the current criteria and 71% met the lifetime criteria for at least one

7

Risk Factors for Ischemic Heart Disease in Women

175

anxiety disorder, whereas in men, the current and lifetime prevalence rates were 26% and 33%, respectively. While an association between anxiety symptomatology and cardiac events has been shown in men with IHD [53], similar associations have not been demonstrated among women with IHD. Curiously, studies from WISE found that in women with chest pain or suspected IHD, a prior history of treatment for anxiety disorders was associated with a decreased likelihood of a positive ischemia diagnosis, with an odds ratio of 2.74 [146]. However, caution should be exercised in interpreting these findings. Since only a fraction of individuals who experience anxiety actually seek treatment, women with a treatment history do not equate anxious women in general. The treatment-seeking action in these women appeared to have a protective effect against IHD, probably through increased health maintenance, proactive health behavior, and psychological hardiness. It is also possible that there was a selection bias in which women seeking anxiety treatment were more likely to report more somatic symptoms with psychological origins, which could produce false-positives in diagnostic tests for IHD [149]. In sum, the limited available studies suggest that anxiety symptoms and disorders appear to be elevated and prevalent among women with IHD. More research on anxiety with larger sample sizes, more rigorous designs, and a better defined anxiety construct is needed to shed more light on whether this prevalent psychological disturbance may affect the outcome of women with IHD.

Hostility, Anger, and Type A Behavior Several studies have documented that women with IHD show higher levels of anger, hostility, and Type A behavior than those free of IHD symptoms [68, 70, 88]. Prospective studies examining the prognostic value of these constructs in women with IHD have focused primarily on hostility. Higher levels of hostility among women with IHD appear to be associated with increased risk of recurrent events and mortality [28, 68, 70, 126]. For example, a study of postmenopausal women with IHD found that patients with high hostility were twice as likely to have had a MI as those with low hostility [28]. Similarly, a study of women with suspected IHD reported that those with above-median hostility scores had an increased risk of an adverse cardiovascular event (hazard ratio [HR] ¼ 1.5) [126]. However, a recent study of a mixed sex sample found that higher hostility significantly predicted IHD recurrence only in men (HR ¼ 2.41) but not in women (HR ¼ 0.69), after controlling for demographic and clinical risk factors [65]. The authors argued that their population-based study randomly selected women with IHD from the community, whereas other clinical studies recruited volunteers who could be more agreeable and less hostile and thus presented merely a selected range of all women. Although the evidence largely favors the link between hostility and poor prognosis in women with IHD, additional research is needed to provide further validation and to investigate the role of anger and Type A behavior in the progression of IHD.

176

B.-J. Shen et al.

Social Support The literature has consistently found that women with heart disease receive less social support in terms of size, density, or frequency of social networks and interactions than men [161, 183]. Since IHD usually has a later onset in women, female patients tend to be older and more likely to be widowed and living alone, or acting as a caregiver to an elderly spouse, which may further contribute to the lack of perceived or actual support [10, 37, 60]. An examination of 503 women in the WISE study found that larger social networks were associated with less severe IHD, and those in the lowest quartile of social network scores had a mortality risk 2.4 times that of those in the highest quartile [147]. Structural support or social ties, however, may entail different meanings for women. Some research indicates that structural support may have a weaker protective effect for women than it does for men [176]. It has been suggested that social ties may come with concomitant stress for women but not for men and that women tend to minimize their own illness to avoid burdening people in their social network [21, 59]. For example, women who are recovering from a cardiac event report feeling guilty when they must rely on family members to help with household tasks [18]. Among different sources of social support, the marital relationship appears to be essential for women. A comprehensive review of marriage and physical health concluded that marital conflict exerts greater detrimental impact on the health of women than on men [87]. In the Stockholm Female Coronary Risk Study, OrthGomer et al. tracked 292 women with IHD and showed that the lack of spousal support and distress in the spousal relationship predicted a 2.9-fold increase in recurrent events over a median of 4.8 years after adjusting for a comprehensive list of demographic, biomedical, and lifestyle factors [129]. The same research group also measured atherosclerosis progression using quantitative coronary angiography in 102 women and found that those lacking emotional support, social integration, and interpersonal social relations had significantly greater disease progression (with mean coronary artery luminal diameter narrowing by 0.13–0.15 mm) than those with higher levels of support (narrowing by 0.04–0.07 mm) [178]. Taken together, evidence suggests that sex differences may exist in the forms and meanings of social support. Social support, especially a harmonious marital relationship, appears to be associated with better health outcomes for women with IHD.

Psychosocial Intervention for Women with IHD Given the prevalence and detrimental effects of emotional distress experienced by patients with IHD, it is not only plausible but also logical for clinicians and researchers to contemplate whether providing psychosocial treatment would bring additional clinical benefits beyond what the standard medical care has achieved. The notion of psychosocial care for distressed patients with heart disease appears particularly intuitive and pertinent for women who are at a

7

Risk Factors for Ischemic Heart Disease in Women

177

significantly higher risk for depression, anxiety, and distress-related disorders [85]. In the past two decades, psychosocial interventions have been accepted and implemented in the treatment and rehabilitation of cardiac patients [97, 99], but women have been under-represented or left out in most of these studies. In the following section, we will describe sex differences in the results from major landmark trials of psychosocial interventions for IHD patients.

Sex Differences in Major Psychosocial Clinical Trials for IHD Patients The Montreal Heart Attack Readjustment Trial (M-HART) The M-HART was a psychosocial randomized trial that attempted to replicate the findings of the all-male Ischemic Heart Disease Life Stress Monitoring Program (IHDLSM) [97, 99] with a larger sample of post-MI patients including 473 women [54]. The results of this clinical trial were not only disappointing but also controversial. Overall, the intervention group did not differ from the control group in mortality over 12 months. However, when results were stratified by sex, women in the intervention showed a marginally significant increase in cardiac mortality than those in the control group (9.4% vs. 5.0%, p ¼ 0.064) and higher all-cause mortality (10.3% vs. 5.4%, p ¼ 0.051), while no differences were found in men (cardiac mortality, 2.4 vs. 2.5%; all-cause mortality, 3.1% vs. 3.1%). After 5 years, the main findings were largely unchanged. Women in treatment continued to show higher overall mortality than those in the control group, although this difference was attenuated after adjusting for age and smoking. Contrary to the previous IHDLSM, the M-HART trial not only failed to support stress monitoring and management through home visits by nurses, but it even cautioned the potential harm that could result in women. These results sparked a slew of criticisms, controversies, and attempts for explanations. Some questioned the effectiveness of the intervention which was considered to be undefined, un-standardized, and delivered by nurses who had little or no training in psychological counseling [96], while others contended that the home visit may not have been a common or ideal way of providing psychological intervention for distressed patients [109]. Certain specific characteristics of the women in the study may also provide clues for understanding the findings. Women in the study were likely to be older and more depressed, thus presenting more complicated clinical challenges to psychosocial treatment. It is questionable whether the specific intervention employed in the study was adequate or even appropriate for these women. Additionally, research of psychotherapy has shown that 5 to 10% of patients in treatment actually deteriorate due to a variety of reasons, including the attitudes and behaviors of the patients and therapists, as well as therapeutic techniques applied [90]. It is also possible that some patients in psychotherapy may deteriorate before achieving improvement, as they need to confront and work through the issues that are sources of their emotional difficulties. These results underscore our general lack of understanding of the psychological distress and related treatment processes in women with IHD.

178

B.-J. Shen et al.

The Enhancing Recovery in Coronary Heart Disease (ENRICHD) Trial The ENRICHD study is the largest multi-site randomized trial to date in the United States designed to test whether a psychosocial intervention of treating depression and low-perceived social support with cognitive-behavioral therapy (CBT) would reduce clinical events among post-MI patients [19]. The study randomized 2,481 post-MI patients, including 1,084 women (44%). The overall finding showed that although the treatment significantly alleviated depressive symptoms and increased social support at 6-month follow-up, psychosocial intervention did not reduce combined event endpoints and mortality. Women did not differ from men, and neither showed survival benefits from the treatment. In a later analysis that re-examined the effects in sex and ethnic groups [151], the results demonstrated that white men receiving psychosocial intervention had significantly lower combined MI incidents and cardiac deaths, whereas white women, minority women, and minority men did not benefit from the treatment. The authors speculated that medical background, cardiac care treatment history, and other elements of psychosocial profiles, such as personality, self-efficacy, and coping skills, may have contributed to the sex differences in treatment response. Without further investigations, these conjectures remain merely hypotheses. The ENRICHD trial demonstrated that, while psychosocial intervention was successful in reducing psychological distress, it might not have been sufficient to affect morbidity and mortality in women. These results again highlight our insufficient understanding of the psychosocial mechanisms and therapeutic processes in women with heart disease. Effective psychosocial interventions with cardiac health benefits remain elusive for women with IHD.

Meta-analyses of Psychosocial Interventional Studies in IHD Patients These landmark trials painted a fairly mixed and mostly bleak picture of psychosocial intervention for IHD patients, especially for women. There have been several comprehensive reviews and meta-analyses attempting to answer whether psychosocial intervention for cardiac patients provided added clinical benefits [43, 98, 99]. A meta-analysis by Linden et al. [99] applied more stringent inclusion criteria and tested whether women or men differed in outcomes. This review concluded that while men who participated in psychosocial interventions had a 27% reduction in mortality, women did not benefit from the treatment, even after adjusting for the differences in age. Considering the heterogeneity of the psychosocial interventions across studies, it is not possible to isolate the specific elements that may be beneficial for men or women or both. As the literature stands today, psychosocial treatments in general fail to provide survival benefits for women, although they reduce psychological distress and improve quality of life.

7

Risk Factors for Ischemic Heart Disease in Women

179

The reasons for the lack of treatment response in women with IHD still elude researchers and clinicians. In hindsight, these results may not seem as surprising if we acknowledge that most epidemiologic and clinical evidence of the link between psychosocial factors and IHD came from white men. Women have been grossly under-represented and under-studied in these studies. Major epidemiologic studies focusing on women and IHD, such as the WISE [113] and the SWAN [165], were not implemented until the 1990s. Results from these studies should be informative in pointing clinical researchers to promising directions for developing successful psychosocial preventive and interventional strategies.

Cardiac Rehabilitation Program Services Outpatient cardiac rehabilitation has been established as an effective and recommended program to assist cardiac patients to reduce their risk factors, regain functional capacity [3], and reduce cardiac mortality, particularly sudden cardiac death [32]. Several reviews have pointed out that women are less likely to participate in cardiac rehabilitation [20, 171]. The lower referral and less encouragement from physicians and the lack of spousal support constitute two major obstacles among other issues, including older age, transportation difficulty, and family or household obligations (e.g., taking care of a dependent spouse or relative). Although women under-utilize cardiac rehabilitation, they clearly benefit from such programs, with the most salient improvements in exercise tolerance and capacity as well as quality of life. Despite the lower exercise capability at entry, women achieve a similar percentage of improvement as do men in cardiac rehabilitation [171]. In studies examining sex differences in the improvement of quality of life after cardiac rehabilitation, women, as well as men, showed significant progress [91, 108, 125]. One study actually showed that women showed greater improvement than men immediately after the rehabilitation [108]. In the areas of psychosocial functioning, most studies combined men and women in analyses and showed that anxiety and depression were alleviated significantly after the participation [20, 171]. One study compared women and men directly and found that both groups had similar decreases in anxiety, but women appeared less likely to experience reduction in depressive symptoms right after cardiac rehabilitation [91].

Recent Developments The American Heart Association has recently published a scientific statement regarding sex differences in myocardial infarction [188, 189, 190, 191, 192, 193], documenting sex-specific differences in the pathophysiological mechanisms, clinical presentation, and outcomes, consistent with the data presented in this chapter. Epidemiological studies continue to show that the disproportionate

180

B.-J. Shen et al.

adverse risk for recurrent cardiac events in women continues to be a clinical problem [e.g., 189, 190]. In a prospective study of 3536 patients (33% women) hospitalized for myocardial infarction, women had a 29% higher risk of all-cause re-hospitalization (hazard ratio ¼ 1.29, 95% confidence interval ¼ 1.12–1.48). Additional analyses of this study showed that psychosocial factors and poorer health status partially accounted for the elevated re-hospitalization risk in women [189]. A population-based study of 49,556 patients with acute coronary syndrome or stable angina showed similar results [190]. In line with the important role of psychosocial factors are findings by Vaccarino and colleagues (2016), demonstrating that women 50 years or younger, but not older women, have a relative adverse psychosocial profile and more severe mental stress-induced ischemia compared to age-matched men, whereas they did not differ in conventional risk factors [191]. In addition to advances in our understanding of biological sex in IHD and other cardiovascular disease outcomes, it is becoming increasingly clear that a feminine gender role is associated with an adverse cardiovascular risk profile, independent of biological sex [192]. The adverse IHD risk in women may in part be explained by pathophysiological processes that differ across sexes, including coronary microvascular dysfunction and inflammation-related processes described here and in a recent review of the literature [193].

Conclusions Research has identified sex differences in IHD ranging from risk factors, symptom presentations, physicians’ perceptions and attitudes, diagnostic considerations, and optimal treatment strategies. In this chapter, we reviewed the current literature on sex differences in biological and psychosocial factors and IHD. A repeated lesson throughout this review is that women are under-represented in almost all areas of research. Many established findings in men have not been sufficiently replicated or examined in women. The majority of psychosocial factors that increase the risk of IHD onset and recurrence in men, including depression, hostility, stress, and lack of social support, appears to have similar influences on women. Existing studies, however, rarely investigate whether these factors differentially affect women and men to varying degrees and whether certain psychosocial constructs, such as social support, entail different meanings to women as they are to men. The fact that depression and anxiety are more prevalent among female than male patients with IHD calls for more careful evaluation of and clinical attention to women receiving cardiac care. Psychosocial interventions for individuals with IHD, while promising and beneficial for men in general, remain ineffective for women in reducing recurrent adverse cardiac events. This is likely the consequence of the fact that current psychosocial interventions are built on the rationale and knowledge primarily developed in male cardiac patients. Further investigations into these lines of research reviewed in this chapter will undoubtedly shed more light on the role that psychosocial factors play and the mechanism by which they may affect the process of onset, progression, and recovery of women with heart disease.

7

Risk Factors for Ischemic Heart Disease in Women

181

References 1. Abbott RD, Donahue RP, Kannel WB, Wilson PW (1988) The impact of diabetes on survival following myocardial infarction in men vs women. The Framingham Study. JAMA 260 (23):3456–3460 2. Adamson DL, Webb CM, Collins P (2001) Esterified estrogens combined with methyltestosterone improve emotional well-being in postmenopausal women with chest pain and normal coronary angiograms. Menopause (New York) 8(4):233–238 3. Ades PA (2001) Cardiac rehabilitation and secondary prevention of coronary heart disease. N Engl J Med 345(12):892–902 4. Albert CM, Chae CU, Rexrode KM, Manson JE, Kawachi I (2005) Phobic anxiety and risk of coronary heart disease and sudden cardiac death among women. Circulation 111(4):480–487. https://doi.org/10.1161/01.CIR.0000153813.64165.5D. 111/4/480 [pii] 5. Anderson GL, Limacher M, Assaf AR, Bassford T, Beresford SA, Black H, Bonds D, Brunner R, Brzyski R, Caan B, Chlebowski R, Curb D, Gass M, Hays J, Heiss G, Hendrix S, Howard BV, Hsia J, Hubbell A, . . ., Wassertheil-Smoller S (2004) Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women’s Health Initiative randomized controlled trial. JAMA 291(14):1701–1712 6. Andre-Petersson L, Hedblad B, Janzon L, Ostergren PO (2006) Social support and behavior in a stressful situation in relation to myocardial infarction and mortality: who is at risk? Results from prospective cohort study “Men born in 1914,” Malmo, Sweden. Int J Behav Med 13(4):340–347. https://doi.org/10.1207/s15327558ijbm1304_9 7. Bairey Merz CN, Johnson BD, Sharaf BL, Bittner V, Berga SL, Braunstein GD, Hodgson TK, Matthews KA, Pepine CJ, Reis SE, Reichek N, Rogers WJ, Pohost GM, Kelsey SF, Sopko G (2003) Hypoestrogenemia of hypothalamic origin and coronary artery disease in premenopausal women: a report from the NHLBI-sponsored WISE study. J Am Coll Cardiol 41(3):413–419 8. Bairey Merz CN, Shaw LJ, Reis SE, Bittner V, Kelsey SF, Olson M, Johnson BD, Pepine CJ, Mankad S, Sharaf BL, Rogers WJ, Pohost GM, Lerman A, Quyyumi AA, Sopko G (2006) Insights from the NHLBI-sponsored Women’s Ischemia Syndrome Evaluation (WISE) Study: Part II: gender differences in presentation, diagnosis, and outcome with regard to gender-based pathophysiology of atherosclerosis and macrovascular and microvascular coronary disease. J Am Coll Cardiol 47(3 Suppl):S21–S29. https://doi.org/10.1016/j.jacc.2004.12.084 9. Bairey Merz CN, Johnson BD, Berga S, Braunstein G, Reis SE, Bittner V (2006) Past oral contraceptive use and angiographic coronary artery disease in postmenopausal women: data from the National Heart, Lung, and Blood Institute-sponsored Women’s Ischemia Syndrome Evaluation. Fertil Steril 85(5):1425–1431 10. Barber K, Stommel M, Kroll J, Holmes-Rovner M, McIntosh B (2001) Cardiac rehabilitation for community-based patients with myocardial infarction: factors predicting discharge recommendation and participation. J Clin Epidemiol 54(10):1025–1030 11. Barefoot JC, Larsen S, von der Lieth L, Schroll M (1995) Hostility, incidence of acute myocardial infarction, and mortality in a sample of older Danish men and women. Am J Epidemiol 142(5):477–484 12. Barrett-Connor E, Bush TL (1991) Estrogen and coronary heart disease in women. JAMA 265(14):1861–1867 13. Barron HV, Bowlby LJ, Breen T, Rogers WJ, Canto JG, Zhang Y, Tiefenbrunn AJ, Weaver WD (1998) Use of reperfusion therapy for acute myocardial infarction in the United States: data from the National Registry of Myocardial Infarction 2. Circulation 97(12):1150–1156 14. Barsky AJ, Peekna HM, Borus JF (2001) Somatic symptom reporting in women and men. J Gen Intern Med 16(4):266–275. jgi00229 [pii] 15. Barth J, Schumacher M, Herrmann-Lingen C (2004) Depression as a risk factor for mortality in patients with coronary heart disease: a meta-analysis. Psychosom Med 66(6):802–813 16. Beery TA (1995) Gender bias in the diagnosis and treatment of coronary artery disease. Heart Lung 24(6):427–435

182

B.-J. Shen et al.

17. Bello N, Mosca L (2004) Epidemiology of coronary heart disease in women. Prog Cardiovasc Dis 46(4):287–295 18. Benson G, Arthur H, Rideout E (1997) Women and heart attack: a study of women’s experiences. Can J Cardiovasc Nurs 8(3):16–23 19. Berkman LF, Blumenthal J, Burg M, Carney RM, Catellier D, Cowan MJ, Czajkowski SM, DeBusk R, Hosking J, Jaffe A, Kaufmann PG, Mitchell P, Norman J, Powell LH, Raczynski JM, Schneiderman N (2003) Effects of treating depression and low perceived social support on clinical events after myocardial infarction: the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Randomized Trial. JAMA 289(23):3106–3116. https://doi.org/10.1001/ jama.289.23.3106. 289/23/3106 [pii] 20. Bittner V, Sanderson BK (2003) Women in cardiac rehabilitation. J Am Med Wom Assoc 58(4):227–235 21. Bjarnason-Wehrens B, Grande G, Loewel H, Voller H, Mittag O (2007) Gender-specific issues in cardiac rehabilitation: do women with ischaemic heart disease need specially tailored programmes? Eur J Cardiovasc Prev Rehabil 14(2):163–171 22. Bjerkeset O, Nordahl HM, Mykletun A, Holmen J, Dahl AA (2005) Anxiety and depression following myocardial infarction: gender differences in a 5-year prospective study. J Psychosom Res 58(2):153–161 23. Blumenthal JA (2008) Depression and coronary heart disease: association and implications for treatment. Cleve Clin J Med 75(Suppl 2):S48–S53 24. Bordy LR (2000) The socialization of gender differences in emotional expression: display rules, infant temperament, and differentiation. In: Fischer AH (ed) Gender and emotion: social psychological perspectives. Cambridge University Press, pp 24–47 25. Brezinka V, Kittel F (1996) Psychosocial factors of coronary heart disease in women: a review. Soc Sci Med 42(10):1351–1365 26. Brinton EA, Hodis HN, Merriam GR, Harman SM, Naftolin F (2008) Can menopausal hormone therapy prevent coronary heart disease? Trends Endocrinol Metab 19(6):206–212 27. Burt VL, Whelton P, Roccella EJ, Brown C, Cutler JA, Higgins M, Horan MJ, Labarthe D (1995) Prevalence of hypertension in the US adult population. Results from the Third National Health and Nutrition Examination Survey, 1988–1991. Hypertension 25(3):305–313 28. Chaput LA, Adams SH, Simon JA, Blumenthal RS, Vittinghoff E, Lin F, Loh E, Matthews KA (2002) Hostility predicts recurrent events among postmenopausal women with coronary heart disease. Am J Epidemiol 156(12):1092–1099 29. Cherry N, Gilmour K, Hannaford P, Heagerty A, Khan MA, Kitchener H, McNamee R, Elstein M, Kay C, Seif M, Buckley H (2002) Oestrogen therapy for prevention of reinfarction in postmenopausal women: a randomised placebo controlled trial. Lancet 360(9350): 2001–2008 30. Chiaramonte GR, Friend R (2006) Medical students’ and residents’ gender bias in the diagnosis, treatment, and interpretation of coronary heart disease symptoms. Health Psychol 25(3):255–266 31. Cho L, Hoogwerf B, Huang J, Brennan DM, Hazen SL (2008) Gender differences in utilization of effective cardiovascular secondary prevention: a Cleveland clinic prevention database study. J Women’s Health 17(4):515–521 32. Clark AM, Hartling L, Vandermeer B, McAlister FA (2005) Meta-analysis: secondary prevention programs for patients with coronary artery disease. Ann Intern Med 143(9):659–672. 143/9/659 [pii]. https://www.ncbi.nlm.nih.gov/pubmed/16263889 33. Clarke SC, Kelleher J, Lloyd-Jones H, Slack M, Schofiel PM (2002) A study of hormone replacement therapy in postmenopausal women with ischaemic heart disease: the Papworth HRT atherosclerosis study. BJOG 109(9):1056–1062 34. Clarkson TB (2007) Estrogen effects on arteries vary with stage of reproductive life and extent of subclinical atherosclerosis progression. Menopause (New York) 14(3 Pt 1):373–384 35. Clarkson TB (2008) Can women be identified that will derive considerable cardiovascular benefits from postmenopausal estrogen therapy? J Clin Endocrinol Metab 93(1):37–39

7

Risk Factors for Ischemic Heart Disease in Women

183

36. Cohen S (2004) Social relationships and health. Am Psychol 59(8):676–684. https://doi.org/ 10.1037/0003-066X.59.8.676. 2004-20395-002 [pii] 37. Cooper AF, Jackson G, Weinman J, Horne R (2002) Factors associated with cardiac rehabilitation attendance: a systematic review of the literature. Clin Rehabil 16(5):541–552 38. DeStefano F, Merritt RK, Anda RF, Casper ML, Eaker ED (1993) Trends in nonfatal coronary heart disease in the United States, 1980 through 1989. Arch Intern Med 153(21): 2489–2494 39. Douglas PS (1986) Gender, cardiology, and optimal medical care. Circulation 74(5):917–919 40. Drory Y, Kravetz S, Hirschberger G (2003) Long-term mental health of women after a first acute myocardial infarction. Arch Phys Med Rehabil 84(10):1492–1498. S0003999303003 162 [pii] 41. Dubey RK, Oparil S, Imthurn B, Jackson EK (2002) Sex hormones and hypertension. Cardiovasc Res 53(3):688–708 42. Dubey RK, Imthurn B, Barton M, Jackson EK (2005) Vascular consequences of menopause and hormone therapy: importance of timing of treatment and type of estrogen. Cardiovasc Res 66(2):295–306 43. Dusseldorp E, van Elderen T, Maes S, Meulman J, Kraaij V (1999) A meta-analysis of psychoeduational programs for coronary heart disease patients. Health Psychol 18(5): 506–519 44. Eaker ED, Pinsky J, Castelli WP (1992) Myocardial infarction and coronary death among women: psychosocial predictors from a 20-year follow-up of women in the Framingham Study. Am J Epidemiol 135(8):854–864 45. Eastwood JA, Doering LV (2005) Gender differences in coronary artery disease. J Cardiovasc Nurs 20(5):340–351; quiz 352–343. 00005082-200509000-00008 [pii] 46. Eisenberg E (2004) Menopause and diabetes. In: Reece EA, Coustan DR, Gabbe SG (eds) Diabetes in women: adolescence, pregnancy, and menopause, 3rd edn. Lippincott Williams & Wilkins 47. Everson-Rose SA, Lewis TT, Karavolos K, Matthews KA, Sutton-Tyrrell K, Powell LH (2006) Cynical hostility and carotid atherosclerosis in African American and white women: the Study of Women’s Health Across the Nation (SWAN) Heart Study. Am Heart J 152(5):982. e987–982.e913. https://doi.org/10.1016/j.ahj.2006.08.010. S0002-8703(06)00750-2 [pii] 48. Ferketich AK, Schwartzbaum JA, Frid DJ, Moeschberger ML (2000) Depression as an antecedent to heart disease among women and men in the NHANES I study. National Health and Nutrition Examination Survey. Arch Intern Med 160(9):1261–1268 49. Fleury J, Sedikides C, Lunsford V (2001) Women’s experience following a cardiac event: the role of the self in healing. J Cardiovasc Nurs 15(3):71–82 50. Ford DE, Mead LA, Chang PP, Cooper-Patrick L, Wang NY, Klag MJ (1998) Depression is a risk factor for coronary artery disease in men: the precursors study. Arch Intern Med 158(13): 1422–1426 51. Ford ES, Giles WH, Dietz WH (2002) Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA 287(3):356–359 52. Ford ES, Giles WH, Mokdad AH (2004) The distribution of 10-year risk for coronary heart disease among US adults: findings from the National Health and Nutrition Examination Survey III. J Am Coll Cardiol 43(10):1791–1796. https://doi.org/10.1016/j.jacc.2003.11.061 53. Frasure-Smith N, Lesperance F (2008) Depression and anxiety as predictors of 2-year cardiac events in patients with stable coronary artery disease. Arch Gen Psychiatry 65(1):62–71 54. Frasure-Smith N, Lesperance F, Prince RH, Verrier P, Garber RA, Juneau M, Wolfson C, Bourassa MG (1997) Randomised trial of home-based psychosocial nursing intervention for patients recovering from myocardial infarction. Lancet 350(9076):473–479. https://doi.org/10. 1016/S0140-6736(97)02142-9. S0140-6736(97)02142-9 [pii] 55. Frasure-Smith N, Lesperance F, Juneau M, Talajic M, Bourassa MG (1999) Gender, depression, and one-year prognosis after myocardial infarction. Psychosom Med 61(1):26–37

184

B.-J. Shen et al.

56. Friedman M, Rosenman RH (1959) Association of specific overt behavior pattern with blood and cardiovascular findings; blood cholesterol level, blood clotting time, incidence of arcus senilis, and clinical coronary artery disease. J Am Med Assoc 169(12):1286–1296 57. Gierach GL, Johnson BD, Bairey Merz CN, Kelsey SF, Bittner V, Olson MB, Shaw LJ, Mankad S, Pepine CJ, Reis SE, Rogers WJ, Sharaf BL, Sopko G (2006) Hypertension, menopause, and coronary artery disease risk in the Women’s Ischemia Syndrome Evaluation (WISE) Study. J Am Coll Cardiol 47(3 Suppl):S50–S58. https://doi.org/10.1016/j.jacc.2005. 02.099. S0735-1097(05)02511-8 [pii] 58. Glynn K, Maclean H, Forte T, Cohen M (2009) The association between role overload and women’s mental health. J Women’s Health 18(2):217–223. https://doi.org/10.1089/jwh.2007. 0783. [pii] 59. Gore S, Colten ME (1991) Gender, stress, and distress: social-relationship influences. In: Eckenrode J (ed) The social context of coping. Plenum Press, pp 139–163 60. Grace SL, Abbey SE, Shnek ZM, Irvine J, Franche RL, Stewart DE (2002) Cardiac rehabilitation II: referral and participation. Gen Hosp Psychiatry 24(3):127–134. S01638343 02001792 [pii] 61. Grodstein F, Stampfer M (1995) The epidemiology of coronary heart disease and estrogen replacement in postmenopausal women. Prog Cardiovasc Dis 38(3):199–210 62. Grodstein F, Stampfer MJ (1998) Estrogen for women at varying risk of coronary disease. Maturitas 30(1):19–26 63. Grundy SM, D’Agostino RB Sr, Mosca L, Burke GL, Wilson PW, Rader DJ, Cleeman JI, Roccella EJ, Cutler JA, Friedman LM (2001) Cardiovascular risk assessment based on US cohort studies: findings from a National Heart, Lung, and Blood institute workshop. Circulation 104(4):491–496 64. Gurwitz JH, Nananda F, Auorn J (1992) The exclusion of the elderly and women from clinical trials in acute myocardial infarction. JAMA 268:1417–1422 65. Haas DC, Chaplin WF, Shimbo D, Pickering TG, Burg M, Davidson KW (2005) Hostility is an independent predictor of recurrent coronary heart disease events in men but not women: results from a population based study. Heart 91(12):1609–1610 66. Haines AP, Imeson JD, Meade TW (1987) Phobic anxiety and ischaemic heart disease. Br Med J (Clin Res Ed) 295(6593):297–299 67. Hammar N, Alfredsson L, Theorell T (1994) Job characteristics and the incidence of myocardial infarction. Int J Epidemiol 23(2):277–284 68. Haynes SG, Feinleib M (1980) Women, work and coronary heart disease: prospective findings from the Framingham heart study. Am J Public Health 70(2):133–141 69. Haynes SG, Feinleib M, Levine S, Scotch N, Kannel WB (1978) The relationship of psychosocial factors to coronary heart disease in the Framingham study. II. Prevalence of coronary heart disease. Am J Epidemiol 107(5):384–402 70. Haynes SG, Feinleib M, Kannel WB (1980) The relationship of psychosocial factors to coronary heart disease in the Framingham Study. III. Eight-year incidence of coronary heart disease. Am J Epidemiol 111(1):37–58 71. Hemingway H, Marmot M (1999) Evidence based cardiology: psychosocial factors in the aetiology and prognosis of coronary heart disease. Systematic review of prospective cohort studies. BMJ (Clin Res Ed) 318(7196):1460–1467 72. Hippisley-Cox J, Fielding K, Pringle M (1998) Depression as a risk factor for ischaemic heart disease in men: population based case-control study. BMJ (Clin Res Ed) 316(7146): 1714–1719 73. Hodis HN, Mack WJ (2008) Postmenopausal hormone therapy and cardiovascular disease in perspective. Clin Obstet Gynecol 51(3):564–580 74. Hsia J, Langer RD, Manson JE, Kuller L, Johnson KC, Hendrix SL, Pettinger M, Heckbert SR, Greep N, Crawford S, Eaton CB, Kostis JB, Caralis P, Prentice R (2006) Conjugated equine estrogens and coronary heart disease: the Women’s Health Initiative. Arch Intern Med 166(3):357–365

7

Risk Factors for Ischemic Heart Disease in Women

185

75. Hubbard LD, Brothers RJ, King WN, Clegg LX, Klein R, Cooper LS, Sharrett AR, Davis MD, Cai J (1999) Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology 106(12):2269–2280 76. Hulley S, Grady D, Bush T, Furberg C, Herrington D, Riggs B, Vittinghoff E (1998) Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women. Heart and Estrogen/progestin Replacement Study (HERS) Research Group. JAMA 280(7):605–613 77. Jackson L, Leclerc J, Erskine Y, Linden W (2005) Getting the most out of cardiac rehabilitation: a review of referral and adherence predictors. Heart 91(1):10–14 78. Johansson S, Bergstrand R, Schlossman D, Selin K, Vedin A, Wilhelmsson C (1984) Sex differences in cardioangiographic findings after myocardial infarction. Eur Heart J 5(5): 374–381 79. Johnson BD, Kelsey SF, Bairey Merz CN (2003) Clinical risk assessment in women: chest discomfort. Results from the NHLBI-sponsored Women’s Ischemia Syndrome Evaluation (WISE) study. In: Shaw L, Hachamovitch R, Redberg R, Culler S (eds) Coronary disease in women: evidence-based diagnosis and treatment. Humana Press, pp 129–141 80. Johnson BD, Shaw LJ, Buchthal SD, Bairey Merz CN, Kim HW, Scott KN, Doyle M, Olson MB, Pepine CJ, den Hollander J, Sharaf B, Rogers WJ, Mankad S, Forder JR, Kelsey SF, Pohost GM (2004) Prognosis in women with myocardial ischemia in the absence of obstructive coronary disease: results from the National Institutes of Health-National Heart, Lung, and Blood Institute-sponsored Women’s Ischemia Syndrome Evaluation (WISE). Circulation 109(24):2993–2999. https://doi.org/10.1161/01.CIR.0000130642.79868.B2 81. Jong P, Sternberg L (1998) Assessing coronary artery disease in women: how useful is coronary angiography? Medscape Women Health 3(3):1 82. Kaski JC (2002) Overview of gender aspects of cardiac syndrome X. Cardiovasc Res 53(3):620–626 83. Kawachi I, Sparrow D, Vokonas PS, Weiss ST (1994) Symptoms of anxiety and risk of coronary heart disease. The Normative Aging Study. Circulation 90(5):2225–2229 84. Kemp HG Jr, Vokonas PS, Cohn PF, Gorlin R (1973) The anginal syndrome associated with normal coronary arteriograms. Report of a six year experience. Am J Med 54(6): 735–742 85. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE (2005) Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 62(6):593–602. https://doi.org/10.1001/archpsyc. 62.6.593. 62/6/593 [pii] 86. Khaw KT (1993) Where are the women in studies of coronary heart disease? BMJ (Clin Res Ed) 306(6886):1145–1146 87. Kiecolt-Glaser JK, Newton TL (2001) Marriage and health: his and hers. Psychol Bull 127(4):472–503 88. Krantz DS, Olson MB, Francis JL, Phankao C, Bairey Merz CN, Sopko G, Vido DA, Shaw LJ, Sheps DS, Pepine CJ, Matthews KA (2006) Anger, hostility, and cardiac symptoms in women with suspected coronary artery disease: the Women’s Ischemia Syndrome Evaluation (WISE) Study. J Women’s Health 15(10):1214–1223 89. Kubzansky LD, Kawachi I, Spiro A 3rd, Weiss ST, Vokonas PS, Sparrow D (1997) Is worrying bad for your heart? A prospective study of worry and coronary heart disease in the Normative Aging Study. Circulation 95(4):818–824 90. Lambert MJ, Ogles BM (2005) The efficacy and effectiveness of psychotherapy. In: Lambert MJ (ed) Bergin and Garfield’s handbook of psychotherapy and behavior change, 5th edn. Wiley, pp 139–193 91. Lavie CJ, Milani RV (1995) Effects of cardiac rehabilitation and exercise training on exercise capacity, coronary risk factors, behavioral characteristics, and quality of life in women. Am J Cardiol 75(5):340–343. S0002914999805505 [pii]

186

B.-J. Shen et al.

92. Lee S, Colditz GA, Berkman LF, Kawachi I (2003) Caregiving and risk of coronary heart disease in U.S. women: a prospective study. Am J Prev Med 24(2):113–119. S07493 79702005822 [pii] 93. Lee S, Colditz G, Berkman L, Kawachi I (2003) Caregiving to children and grandchildren and risk of coronary heart disease in women. Am J Public Health 93(11):1939–1944 94. Lerner DJ, Kannel WB (1986) Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. Am Heart J 111(2):383–390 95. Lett HS, Blumenthal JA, Babyak MA, Sherwood A, Strauman T, Robins C, Newman MF (2004) Depression as a risk factor for coronary artery disease: evidence, mechanisms, and treatment. Psychosom Med 66(3):305–315 96. Lewin RJ, Thompson DR, Johnston DW, Mayou RA (1997) Cardiac rehabilitation. Lancet 350(9088):1400; author reply 1401 97. Linden W (2000) Psychological treatments in cardiac rehabilitation: review of rationales and outcomes. J Psychosom Res 48(4–5):443–454. S0022-3999(99)00094-X [pii] 98. Linden W, Stossel C, Maurice J (1996) Psychosocial interventions for patients with coronary artery disease: a meta-analysis. Arch Intern Med 156(7):745–752 99. Linden W, Phillips MJ, Leclerc J (2007) Psychological treatment of cardiac patients: a metaanalysis. Eur Heart J 28(24):2972–2984. https://doi.org/10.1093/eurheartj/ehm504. ehm504 [pii] 100. Lloyd-Jones DM, Leip EP, Larson MG, D’Agostino RB, Beiser A, Wilson PW, Wolf PA, Levy D (2006) Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation 113(6):791–798. https://doi.org/10.1161/CIRCULATIONAHA.105. 548206 101. Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, Ford E, Furie K, Go A, Greenlund K, Haase N, Hailpern S, Ho M, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, . . ., Hong Y (2009) Heart disease and stroke statistics – 2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 119(3):e21–e181 102. Loose MS, Fernhall B (1995) Differences in quality of life among male and female cardiac rehabilitation participants. J Cardpulm Rehabil 15(3):225–231 103. Lovejoy JC (2009) Weight gain in women at midlife: the influence of menopause. Obes Manag 5(2):52–56 104. Lovejoy JC, Champagne CM, de Jonge L, Xie H, Smith SR (2008) Increased visceral fat and decreased energy expenditure during the menopausal transition. Int J Obes 32(6):949–958. https://doi.org/10.1038/ijo.2008.25 105. Luecken LJ, Suarez EC, Kuhn CM, Barefoot JC, Blumenthal JA, Siegler IC, Williams RB (1997) Stress in employed women: impact of marital status and children at home on neurohormone output and home strain. Psychosom Med 59(4):352–359 106. Manson JE, Hsia J, Johnson KC, Rossouw JE, Assaf AR, Lasser NL, Trevisan M, Black HR, Heckbert SR, Detrano R, Strickland OL, Wong ND, Crouse JR, Stein E, Cushman M (2003) Estrogen plus progestin and the risk of coronary heart disease. N Engl J Med 349(6): 523–534 107. Matthews KA, Owens JF, Kuller LH, Sutton-Tyrrell K, Jansen-McWilliams L (1998) Are hostility and anxiety associated with carotid atherosclerosis in healthy postmenopausal women? Psychosom Med 60(5):633–638 108. McEntee DJ, Badenhop DT (2000) Quality of life comparisons: gender and population differences in cardiopulmonary rehabilitation. Heart Lung 29(5):340–347. https://doi.org/10. 1067/mhl.2000.109695. S0147-9563(00)61374-2 [pii] 109. McGee HM, Horgan JH (1997) Cardiac rehabilitation. Lancet 350(9088):1401–1402 110. McSweeney JC, Cody M, Crane PB (2001) Do you know them when you see them? Women’s prodromal and acute symptoms of myocardial infarction. J Cardiovasc Nurs 15(3):26–38 111. McSweeney JC, Cody M, O’Sullivan P, Elberson K, Moser DK, Garvin BJ (2003) Women’s early warning symptoms of acute myocardial infarction. Circulation 108(21):2619–2623

7

Risk Factors for Ischemic Heart Disease in Women

187

112. Mendes de Leon CF, Krumholz HM, Seeman TS, Vaccarino V, Williams CS, Kasl SV, Berkman LF (1998) Depression and risk of coronary heart disease in elderly men and women: New Haven EPESE, 1982–1991. Established populations for the epidemiologic studies of the elderly. Arch Intern Med 158(21):2341–2348 113. Merz CN, Kelsey SF, Pepine CJ, Reichek N, Reis SE, Rogers WJ, Sharaf BL, Sopko G (1999) The Women’s Ischemia Syndrome Evaluation (WISE) study: protocol design, methodology and feasibility report. J Am Coll Cardiol 33(6):1453–1461 114. Merz CN, Olson MB, McClure C, Yang YC, Symons J, Sopko G, Kelsey SF, Handberg E, Johnson BD, Cooper-DeHoff RM, Sharaf B, Rogers WJ, Pepine CJ (2010) A randomized controlled trial of low-dose hormone therapy on myocardial ischemia in postmenopausal women with no obstructive coronary artery disease: results from the National Institutes of Health/National Heart, Lung, and Blood Institute-sponsored Women’s Ischemia Syndrome Evaluation (WISE). Am Heart J 159(6):987.e981–987.e987. https://doi.org/10.1016/j.ahj.2010.03.024 115. Michos ED, Vasamreddy CR, Becker DM, Yanek LR, Moy TF, Fishman EK, Becker LC, Blumenthal RS (2005) Women with a low Framingham risk score and a family history of premature coronary heart disease have a high prevalence of subclinical coronary atherosclerosis. Am Heart J 150(6):1276–1281. https://doi.org/10.1016/j.ahj.2005.02.037 116. Michos ED, Nasir K, Braunstein JB, Rumberger JA, Budoff MJ, Post WS, Blumenthal RS (2006) Framingham risk equation underestimates subclinical atherosclerosis risk in asymptomatic women. Atherosclerosis 184(1):201–206. https://doi.org/10.1016/j.atherosclerosis. 2005.04.004 117. Mosca L, Manson JE, Sutherland SE, Langer RD, Manolio T, Barrett-Connor E (1997) Cardiovascular disease in women: a statement for healthcare professionals from the American Heart Association Writing Group. Circulation 96(7):2468–2482 118. Mosca L, Grundy SM, Judelson D, King K, Limacher M, Oparil S, Pasternak R, Pearson TA, Redberg RF, Smith SC Jr, Winston M, Zinberg S (1999) Guide to preventive cardiology for women. AHA/ACC Scientific Statement Consensus panel statement. Circulation 99 (18):2480–2484 119. Mosca L, Jones WK, King KB, Ouyang P, Redberg RF, Hill MN (2000) Awareness, perception, and knowledge of heart disease risk and prevention among women in the United States. American Heart Association Women’s Heart Disease and Stroke Campaign Task Force. Arch Fam Med 9(6):506–515 120. Mosca L, Ferris A, Fabunmi R, Robertson RM (2004) Tracking women’s awareness of heart disease: an American Heart Association national study. Circulation 109(5):573–579 121. Mosca L, Banka CL, Benjamin EJ, Berra K, Bushnell C, Dolor RJ, Ganiats TG, Gomes AS, Gornik HL, Gracia C, Gulati M, Haan CK, Judelson DR, Keenan N, Kelepouris E, Michos ED, Newby LK, Oparil S, Ouyang P, . . ., Wenger NK (2007) Evidence-based guidelines for cardiovascular disease prevention in women: 2007 update. Circulation 115(11):1481–1501. https://doi.org/10.1161/CIRCULATIONAHA.107.181546 122. Nasir K, Michos ED, Blumenthal RS, Raggi P (2005) Detection of high-risk young adults and women by coronary calcium and National Cholesterol Education Program Panel III guidelines. J Am Coll Cardiol 46(10):1931–1936. https://doi.org/10.1016/j.jacc.2005.07.052 123. Nasr A, Breckwoldt M (1998) Estrogen replacement therapy and cardiovascular protection: lipid mechanisms are the tip of an iceberg. Gynecol Endocrinol 12(1):43–59 124. Norris CM, Ghali WA, Galbraith PD, Graham MM, Jensen LA, Knudtson ML (2004) Women with coronary artery disease report worse health-related quality of life outcomes compared to men. Health Qual Life Outcomes 2:21 125. O’Farrell P, Murray J, Huston P, LeGrand C, Adamo K (2000) Sex differences in cardiac rehabilitation. Can J Cardiol 16:319–325 126. Olson MB, Krantz DS, Kelsey SF, Pepine CJ, Sopko G, Handberg E, Rogers WJ, Gierach GL, McClure CK, Merz CN (2005) Hostility scores are associated with increased risk of cardiovascular events in women undergoing coronary angiography: a report from the NHLBISponsored WISE Study. Psychosom Med 67(4):546–552

188

B.-J. Shen et al.

127. Orth-Gomer K, Johnson JV (1987) Social network interaction and mortality. A six year followup study of a random sample of the Swedish population. J Chronic Dis 40(10):949–957 128. Orth-Gomer K, Rosengren A, Wilhelmsen L (1993) Lack of social support and incidence of coronary heart disease in middle-aged Swedish men. Psychosom Med 55(1):37–43 129. Orth-Gomer K, Wamala SP, Horsten M, Schenck-Gustafsson K, Schneiderman N, Mittleman MA (2000) Marital stress worsens prognosis in women with coronary heart disease: the Stockholm Female Coronary Risk Study. JAMA 284(23):3008–3014 130. Panting JR, Gatehouse PD, Yang GZ, Grothues F, Firmin DN, Collins P, Pennell DJ (2002) Abnormal subendocardial perfusion in cardiac syndrome X detected by cardiovascular magnetic resonance imaging. N Engl J Med 346(25):1948–1953. https://doi.org/10.1056/ NEJMoa012369 131. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB (2003) The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med 163(4):427–436 132. Pearson QM (2008) Role overload, job satisfaction, leisure satisfaction, and psychological health among employed women. J Couns Dev 86(1):57–63 133. Penninx BW, Beekman AT, Honig A, Deeg DJ, Schoevers RA, van Eijk JT, van Tilburg W (2001) Depression and cardiac mortality: results from a community-based longitudinal study. Arch Gen Psychiatry 58(3):221–227. yoa20038 [pii] 134. Prentice RL, Langer RD, Stefanick ML, Howard BV, Pettinger M, Anderson GL, Barad D, Curb JD, Kotchen J, Kuller L, Limacher M, Wactawski-Wende J (2006) Combined analysis of Women’s Health Initiative observational and clinical trial data on postmenopausal hormone treatment and cardiovascular disease. Am J Epidemiol 163(7):589–599 135. Ray JG, Vermeulen MJ, Schull MJ, Redelmeier DA (2005) Cardiovascular health after maternal placental syndromes (CHAMPS): population-based retrospective cohort study. Lancet 366(9499):1797–1803. https://doi.org/10.1016/S0140-6736(05)67726-4 136. Redberg RF, Cannon RO 3rd, Bairey Merz N, Lerman A, Reis SE, Sheps DS (2004) Women’s Ischemic Syndrome Evaluation: current status and future research directions: report of the National Heart, Lung and Blood Institute workshop: October 2–4, 2002: section 2: stable ischemia: pathophysiology and gender differences. Circulation 109(6):e47–e49. https://doi. org/10.1161/01.CIR.0000116207.38349.FF 137. Reed D, McGee D, Yano K, Feinleib M (1983) Social networks and coronary heart disease among Japanese men in Hawaii. Am J Epidemiol 117(4):384–396 138. Ridker PM, Cook NR, Lee IM, Gordon D, Gaziano JM, Manson JE, Hennekens CH, Buring JE (2005) A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 352(13):1293–1304. https://doi.org/10.1056/NEJMoa 050613 139. Ridker PM, Buring JE, Rifai N, Cook NR (2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 297(6):611–619. https://doi.org/10.1001/jama.297.6.611 140. Roger VL, Farkouh ME, Weston SA, Reeder GS, Jacobsen SJ, Zinsmeister AR, Yawn BP, Kopecky SL, Gabriel SE (2000) Sex differences in evaluation and outcome of unstable angina. JAMA 283(5):646–652 141. Rosano GM, Peters NS, Lefroy D, Lindsay DC, Sarrel PM, Collins P, Poole-Wilson PA (1996) 17-beta-Estradiol therapy lessens angina in postmenopausal women with syndrome X. J Am Coll Cardiol 28(6):1500–1505 142. Rosenman RH, Friedman M (1961) Association of specific behavior pattern in women with blood and cardiovascular findings. Circulation 24:1173–1184 143. Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, Jackson RD, Beresford SA, Howard BV, Johnson KC, Kotchen JM, Ockene J (2002) Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. JAMA 288(3):321–333

7

Risk Factors for Ischemic Heart Disease in Women

189

144. Rozanski A, Blumenthal JA, Kaplan J (1999) Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation 99(16):2192–2217 145. Rugulies R (2002) Depression as a predictor for coronary heart disease. A review and metaanalysis. Am J Prev Med 23(1):51–61. S0749379702004397 [pii] 146. Rutledge T, Reis SE, Olson M, Owens J, Kelsey SF, Pepine CJ, Reichek N, Rogers WJ, Merz CN, Sopko G, Cornell CE, Sharaf B, Matthews KA (2001) History of anxiety disorders is associated with a decreased likelihood of angiographic coronary artery disease in women with chest pain: the WISE study. J Am Coll Cardiol 37(3):780–785 147. Rutledge T, Reis SE, Olson M, Owens J, Kelsey SF, Pepine CJ, Mankad S, Rogers WJ, Bairey Merz CN, Sopko G, Cornell CE, Sharaf B, Matthews KA (2004) Social networks are associated with lower mortality rates among women with suspected coronary disease: the National Heart, Lung, and Blood Institute-Sponsored Women’s Ischemia Syndrome Evaluation study. Psychosom Med 66(6):882–888 148. Rutledge T, Reis SE, Olson M, Owens J, Kelsey SF, Pepine CJ, Mankad S, Rogers WJ, Sopko G, Cornell CE, Sharaf B, Merz CN (2006) Depression is associated with cardiac symptoms, mortality risk, and hospitalization among women with suspected coronary disease: the NHLBI-sponsored WISE study. Psychosom Med 68(2):217–223 149. Rutledge T, Reis SE, Olson MB, Owens J, Kelsey SF, Pepine CJ, Mankad S, Rogers WJ, Merz CN, Sopko G, Cornell CE, Sharaf B, Matthews KA, Vaccarino V (2006) Depression symptom severity and reported treatment history in the prediction of cardiac risk in women with suspected myocardial ischemia: the NHLBI-sponsored WISE study. Arch Gen Psychiatry 63(8):874–880 150. Schnall PL, Landsbergis PA, Baker D (1994) Job strain and cardiovascular disease. Annu Rev Public Health 15:381–411. https://doi.org/10.1146/annurev.pu.15.050194.002121 151. Schneiderman N, Saab PG, Catellier DJ, Powell LH, DeBusk RF, Williams RB, Carney RM, Raczynski JM, Cowan MJ, Berkman LF, Kaufmann PG (2004) Psychosocial treatment within sex by ethnicity subgroups in the Enhancing Recovery in Coronary Heart Disease clinical trial. Psychosom Med 66(4):475–483. https://doi.org/10.1097/01.psy.0000133217.96180.e8. 66/4/ 475 [pii] 152. Sesso HD, Kawachi I, Vokonas PS, Sparrow D (1998) Depression and the risk of coronary heart disease in the Normative Aging Study. Am J Cardiol 82(7):851–856. S0002914998004913 [pii] 153. Sharaf BL, Pepine CJ, Kerensky RA, Reis SE, Reichek N, Rogers WJ, Sopko G, Kelsey SF, Holubkov R, Olson M, Miele NJ, Williams DO, Merz CN (2001) Detailed angiographic analysis of women with suspected ischemic chest pain (pilot phase data from the NHLBIsponsored Women’s Ischemia Syndrome Evaluation [WISE] Study Angiographic Core Laboratory). Am J Cardiol 87(8):937–941; A933. S0002-9149(01)01424-2 [pii] 154. Shaw LJ, Miller DD, Romeis JC, Kargl D, Younis LT, Chaitman BR (1994) Gender differences in the noninvasive evaluation and management of patients with suspected coronary artery disease. Ann Intern Med 120(7):559–566 155. Shaw LJ, Gibbons RJ, McCallister B et al (2002) Gender differences in extent and severity of coronary disease in the ACC-National Cardiovascular Data Registry (abstract). J Am Coll Cardiol 39:321A 156. Shaw LJ, Lewis JF, Hlatky MA, Hsueh WA, Kelsey SF, Klein R, Manolio TA, Sharrett AR, Tracy RP (2004) Women’s Ischemic Syndrome Evaluation: current status and future research directions: report of the National Heart, Lung and Blood Institute workshop: October 2–4, 2002: section 5: gender-related risk factors for ischemic heart disease. Circulation 109(6):e56– e58. https://doi.org/10.1161/01.CIR.0000116210.70548.2A 157. Shaw LJ, Bairey Merz CN, Pepine CJ, Reis SE, Bittner V, Kelsey SF, Olson M, Johnson BD, Mankad S, Sharaf BL, Rogers WJ, Wessel TR, Arant CB, Pohost GM, Lerman A, Quyyumi AA, Sopko G (2006) Insights from the NHLBI-sponsored Women’s Ischemia Syndrome Evaluation (WISE) Study: Part I: gender differences in traditional and novel risk factors, symptom evaluation, and gender-optimized diagnostic strategies. J Am Coll Cardiol 47(3 Suppl):S4–S20

190

B.-J. Shen et al.

158. Sheldon WC (1993) Indications for coronary arteriography. Heart Dis Stroke 2(3):192–197 159. Shen BJ, Avivi YE, Todaro JF, Spiro A 3rd, Laurenceau JP, Ward KD, Niaura R (2008) Anxiety characteristics independently and prospectively predict myocardial infarction in men the unique contribution of anxiety among psychologic factors. J Am Coll Cardiol 51(2): 113–119. https://doi.org/10.1016/j.jacc.2007.09.033. S0735-1097(07)03357-8 [pii] 160. Shufelt CL, Bairey Merz CN (2009) Contraceptive hormone use and cardiovascular disease. J Am Coll Cardiol 53(3):221–231 161. Shumaker SA, Brooks MM, Schron EB, Hale C, Kellen JC, Inkster M, Wimbush FB, Wiklund I, Morris M (1997) Gender differences in health-related quality of life among postmyocardial infarction patients: brief report. CAST Investigators. Cardiac Arrhythmia Suppression Trials. Women Health (Hillsdale, NJ) 3(1):53–60 162. Sibley C, Blumenthal RS, Merz CN, Mosca L (2006) Limitations of current cardiovascular disease risk assessment strategies in women. J Women’s Health 15(1):54–56. https://doi.org/ 10.1089/jwh.2006.15.54 163. Solomon CG, Hu FB, Dunaif A, Rich-Edwards JE, Stampfer MJ, Willett WC, Speizer FE, Manson JE (2002) Menstrual cycle irregularity and risk for future cardiovascular disease. J Clin Endocrinol Metab 87(5):2013–2017 164. Sowers JR (1998) Diabetes mellitus and cardiovascular disease in women. Arch Intern Med 158(6):617–621 165. Sowers MF, Crawford S, Sternfeld B, Morganstein D, Gold E, Greendale G, Evans D, Neer R, Matthews K, Sherman S, Lo A, Weiss G, Kelsey J (2000) Design, survey sampling and recruitment methods of SWAN: a multi-center,multi-ethnic, community-based cohort study of women and the menopausal transition. In: Lobo R, Marcus R, Kelsey J (eds) Menopause: biology and pathobiology. Academic, pp 175–188 166. Sowers M, Zheng H, Tomey K, Karvonen-Gutierrez C, Jannausch M, Li X, Yosef M, Symons J (2007) Changes in body composition in women over six years at midlife: ovarian and chronological aging. J Clin Endocrinol Metab 92(3):895–901. https://doi.org/10.1210/jc.2006-1393 167. Stampfer MJ, Willett WC, Colditz GA, Speizer FE, Hennekens CH (1988) A prospective study of past use of oral contraceptive agents and risk of cardiovascular diseases. N Engl J Med 319(20):1313–1317 168. Stewart DE, Abbey SE, Shnek ZM, Irvine J, Grace SL (2004) Gender differences in health information needs and decisional preferences in patients recovering from an acute ischemic coronary event. Psychosom Med 66(1):42–48 169. Surtees PG, Wainwright NWJ, Luben R, Day NE, Khaw KT (2005) Prospective cohort study of hostility and the risk of cardiovascular disease mortality. Int J Cardiol 100:155–166 170. Tobin JN, Wassertheil-Smoller S, Wexler JP, Steingart RM, Budner N, Lense L, Wachspress J (1987) Sex bias in considering coronary bypass surgery. Ann Intern Med 107(1):19–25 171. Todaro JF, Shen BJ, Niaura R, Tilkemeier PL, Roberts BH (2004) Do men and women achieve similar benefits from cardiac rehabilitation? J Cardpulm Rehabil 24(1):45–51 172. Todaro JF, Shen BJ, Niaura R, Tilkemeier PL (2005) Prevalence of depressive disorders in men and women enrolled in cardiac rehabilitation. J Cardpulm Rehabil 25(2):71–75; quiz 76–77 173. Todaro JF, Shen BJ, Raffa SD, Tilkemeier PL, Niaura R (2007) Prevalence of anxiety disorders in men and women with established coronary heart disease. J Cardiopulm Rehabil Prev 27 (2):86–91. https://doi.org/10.1097/01.HCR.0000265036.24157.e7. 01273116–20070300000006 [pii] 174. Toth MJ, Tchernof A, Sites CK, Poehlman ET (2000) Effect of menopausal status on body composition and abdominal fat distribution. Int J Obes Relat Metab Disord 24(2):226–231 175. U.S. Bureau of Labor Statistics (2008) Women in the labor force: a databook. U.S. Department of Labor 176. Unger JB, McAvay G, Bruce ML, Berkman L, Seeman T (1999) Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies of Successful Aging. J Gerontol 54(5):S245–S251

7

Risk Factors for Ischemic Heart Disease in Women

191

177. Vaccarino V, Parsons L, Every NR, Barron HV, Krumholz HM (1999) Sex-based differences in early mortality after myocardial infarction. National Registry of Myocardial Infarction 2 Participants. N Engl J Med 341(4):217–225 178. Wang HX, Mittleman MA, Orth-Gomer K (2005) Influence of social support on progression of coronary artery disease in women. Soc Sci Med 60(3):599–607 179. Whiteman MC, Deary IJ, Lee AJ, Fowkes FG (1997) Submissiveness and protection from coronary heart disease in the general population: Edinburgh Artery Study. Lancet 350(9077): 541–545. S0140673696031418 [pii] 180. Wild RA, Carmina E, Diamanti-Kandarakis E, Dokras A, Escobar-Morreale HF, Futterweit W, Lobo R, Norman RJ, Talbott E, Dumesic DA (2010) Assessment of cardiovascular risk and prevention of cardiovascular disease in women with the polycystic ovary syndrome: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome (AE-PCOS) Society. J Clin Endocrinol Metab 95(5):2038–2049. https://doi.org/10.1210/jc.2009-2724 181. Wilson BJ, Watson MS, Prescott GJ, Sunderland S, Campbell DM, Hannaford P, Smith WC (2003) Hypertensive diseases of pregnancy and risk of hypertension and stroke in later life: results from cohort study. BMJ (Clin Res Ed) 326(7394):845. https://doi.org/10.1136/bmj.326. 7394.845 182. Witteman JC, Grobbee DE, Kok FJ, Hofman A, Valkenburg HA (1989) Increased risk of atherosclerosis in women after the menopause. BMJ (Clin Res Ed) 298(6674):642–644 183. Woloshin S, Schwartz LM, Tosteson AN, Chang CH, Wright B, Plohman J, Fisher ES (1997) Perceived adequacy of tangible social support and health outcomes in patients with coronary artery disease. J Gen Intern Med 12(10):613–618 184. Wong TY, Klein R, Sharrett AR, Duncan BB, Couper DJ, Tielsch JM, Klein BE, Hubbard LD (2002) Retinal arteriolar narrowing and risk of coronary heart disease in men and women. The Atherosclerosis Risk in Communities Study. JAMA 287(9):1153–1159 185. Wool CA, Barsky AJ (1994) Do women somatize more than men? Gender differences in somatization. Psychosomatics 35(5):445–452 186. Wulsin LR, Singal BM (2003) Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review. Psychosom Med 65(2):201–210 187. Yarnoz MJ, Curtis AB (2008) More reasons why men and women are not the same (gender differences in electrophysiology and arrhythmias). Am J Cardiol 101(9):1291–1296. https:// doi.org/10.1016/j.amjcard.2007.12.027

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention Trial in Coronary Heart Disease Study Kristina Orth-Gome´r, May Blom, and Christina Walldin

Contents Clinical Picture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Why Are Women Under-Represented in the Coronary Care Units (CCUs)? . . . . . . . . . . . . . . . . . . Age Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sex Hormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Stockholm Female Coronary Risk Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Stockholm Women’s Intervention Trial in Coronary Heart Disease (SWITCHD) . . . . . . . Topics Discussed at the Group Sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results from the SWITCHD Intervention Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subjective Experiences of Women’s (Group) Interactive Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

194 196 197 198 199 201 202 203 204 205 205

Abstract

This chapter reviews the psychological issues that play a role in women with coronary heart disease. The unique perspectives in women are highlighted from a general scientific perspective and also based on the experiences of the Stockholmbased intervention studies. A detailed description of the intervention procedure in the Stockholm Women’s Intervention Trial in Coronary Heart Disease (SWITCHD) is presented with examples of patient experiences with the group sessions.

Kristina Orth-Gomér: deceased. K. Orth-Gomér (*) Karolinska Institutet, Stockholm, Sweden Charité Universitätsmedizin, Campus Benjamin Franklin, Berlin, Germany M. Blom · C. Walldin Division of Evidence-Based Medicine, Stockholm County Council, Stockholm, Sweden © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_8

193

194

K. Orth-Gome´r et al.

Keywords

Women · Coronary heart disease · Myocardial infarction · Psychosocial risk factors · Intervention

Women with ischemic heart disease often have a multitude of symptoms, and many of them have atypical symptoms, like nausea, fatigue, and feelings of weakness. However, in many women the acute myocardial infarction symptoms are as “typical” as those observed in men, with sharp central chest pain, radiating to the left arm, neck, and jaw. “One feels as if an elephant is standing on the chest”; one female patient said about the pain. The diagnosis of acute myocardial infarction from symptoms alone is difficult in men but even more so in women. The same or similar symptoms can result from disorders of the gastro-intestinal system, the muscles, or the joints. Such disorders can cause very substantial pain, although they are less acute and not life threatening. The present chapter primarily focuses on the patient perspective in ischemic heart disease as related to bio-behavioral processes and the clinical manifestations of coronary heart disease in women. This research field has received considerable attention. Below is a case report presenting the clinical picture that is rather typical of heart disease in women (see Table 1 and Fig. 1).

Clinical Picture “Women do not get heart disease. At least not before age 50. You are much too young (45 years). This is not from your heart – it’s probably your joints, your muscles, or just too much of that aerobic training you did last week.” What the physician in the emergency room had told her made sense and seemed right. It could not be from her heart. So, our patient went home, driving her own car, took two aspirins, and went to bed early. She slept through the night but woke up with a headache and a slightly elevated temperature. It must be a flu; she thought. When she got out of bed, all of a sudden, she felt nauseated and fainted right in front of the bathroom door. Luckily, her husband woke up, got her into the car, and drove her directly back to that county hospital emergency room where she had been the night before. Table 1 Symptoms suggestive of acute myocardial infarction -ECG and S-enzymes will clarify the diagnosis -Immediate diagnostic and therapeutic investigation by percutaneous catheterization Symptoms in all patients: Central chest pain irradiating to the left arm, left jaw, and the back Symptoms in women: Central chest pain irradiating to the neck Nausea Tiredness Headaches

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention. . .

195

Fig. 1 The coronary arteries

This time an electrocardiogram (ECG) was obtained and was conclusive. She had a transmural myocardial infarction of the anterior wall. She was put into bed, told she was not allowed to get up, and was taken straight to the cardiac catheterization laboratory for a coronary angiographic examination. Her coronary arteries were not as compromised and clogged as one might have thought, given her serious symptoms and clear ECG picture. She had ECG changes that suggested a large and definite myocardial damage. However, her coronary

196

K. Orth-Gome´r et al.

Fig. 2 Cognitive Behavioral Therapy (CBT) in women with coronary heart disease. Relationship with 7 year cumulative all cause mortality

arteries, even the left anterior descending branch which would be expected to be affected, were almost normal (see Fig. 2).

Why Are Women Under-Represented in the Coronary Care Units (CCUs)? The patient described above was recruited as one of our first patients into the Stockholm Female Coronary Risk Study, now approximately 20 years ago. At that time, her case was not commonly known as typical and characteristic. It was in fact generally believed, even among physicians and other healthcare providers, that women didn’t get heart disease. Possibly as a consequence of this bias, women had not been very visible in the coronary intensive care units in the 1980s and early 1990s. Female cardiac patients had not had full access to and benefited from the formidable diagnostic and therapeutic pharmaceutical and technology progress which was available to male patients. This was found in many different types of clinical settings, in small units in the countryside as well as in urban academic institutions.

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention. . .

197

In Göteborg, Dr. Mats Dellborg and colleagues showed that women were less often treated with thrombolysis in order to limit the infarct process and reduce the extent of myocardial damage. He observed that women’s ECG responses to ischemia were weaker than men’s ECG responses, which might explain why women were less aggressively treated in cardiac intensive care [4]. In the United States, the late Dr. Bernadine Healy, the first woman director of the National Institutes of Health, commented in an editorial of the New England Journal of Medicine (1991) “The Yentl Syndrome,” stating that women needed to pretend to be men in order to receive the same diagnostic and therapeutic arsenal as men did. She compared the female patient situation with the situation of the Jewish girl, Yentl, who disguised herself as a boy in order to be able to be educated about Talmud [6, 11]. The reasons why women were not found in the CCUs were not known – only that women were admitted to the hospital for coronary disease almost as frequently as men did. However, they were not as frequently found in the CCU. One explanation may be that women get their coronary disease at a later stage in life, about ten years later in average. As they were older at admission, often they were not admitted to the CCU, because of priority reasons. Elderly women are usually not productive and – although it should not be – it may be more difficult to initiate complicated interventions in the elderly with complex comorbid conditions and a relatively short remaining life span. Women may also have been less visible because they tend to be delayed at home longer than men, before they get to acute hospital care. Women seem to hesitate or wait a little longer before they seek medical care. Alternatively, women might receive suboptimal or even inaccurate medical advice and therefore do not seek immediate care when they should. If a patient is delayed going to the emergency room, arriving several hours, or days after onset of symptoms, with a suspicion of an acute coronary event, acute treatments and interventions may become less efficient. Pharmacological interventions may be administered too late in the disease process, or they may not be administered at all. In summary, although women have a wider range of symptoms that may be more severe and involve other complaints than chest pain, they tend to come too late to emergency care for optimal acute and aggressive care.

Age Characteristics One important issue related to female sex is age. Often, younger patients, e.g., those below age 65, are targeted in population-based studies. The argument then is often that acquiring atherosclerotic heart disease at an older age is merely the natural course of aging. The truly premature cases, however, would be important to treat or to prevent. Since women get their heart disease about ten years later in life than men do, at the age of 65 years, heart disease is about three to five times more common in males versus females. But once women get older, their incidence of coronary heart disease (CHD) is as high as in men. When consecutive cardiac patients are recruited, the sex ratio will therefore depend upon the upper age limit chosen. The older the

198

K. Orth-Gome´r et al.

group the higher is the proportion of women. If one aims to include as many women as men, then the women will necessarily be older than the men. If one aims to study only the truly premature coronary disease cases among women, they will be difficult to find. On the other hand, these younger and supposedly premature female patients, who are under age 65, have a higher mortality and a worse prognosis than their male counterparts. This was one of the main reasons for us to start investigating women cardiac patients in the Stockholm Female Coronary Risk (FemCorRisk) Study. This was also one of the main reasons for designing this study as a rather small scale, but intensive community-based case referent study [11]. In the FemCorRisk Study, we attempted to recruit all women in the greater Stockholm area who were hospitalized for an acute coronary event during a threeyear period. For each of the about 300 cases that we found, a control woman was obtained from the census registry, the woman with the nearest possible birth date. This procedure was feasible because of the well-kept and accurate census registries in Sweden. It was commented in an early editorial that the “investigation used a sound approach to the study of women below age 65. A community-based case control approach such as the one used is well motivated in such a rare disease as CHD in women under 65 years” [5]. We will present the design and results of the FemCorRisk Study in the sections below.

Sex Hormones It is generally assumed that women’s endogenous sex hormones are protective for cardiovascular disease progression. With women’s endogenous secretion of estradiol and progesterone until the age of menopause (on average at the age of 51), much of the sex gradient in “younger” age can be explained. After age 51, the secretion rates gradually decrease with slowly increasing incidence and mortality rates of coronary artery disease in women. The female sex hormones have favorable influences on almost all standard risk factors, including lipid profile, clotting mechanisms, atherosclerosis, blood pressure, and psychopathology [18]. This is more likely to be true for endogenous hormones, produced by the patient’s own body, than for exogenous therapy with hormones (i.e., hormone replacement therapy: HRT). In the Women’s Health Initiative, a multi-site, clinical trial of HRT, around 60,000 US women were randomized to receive HRT and compared to controls. The expected beneficial HRT effect on CHD risk in these women, however, was not confirmed. Instead, an increased risk for both recurrent coronary events and for stroke was found in women who were taking HRT. The trial had to close before completion, and frustration was high [7]. Even among gynecologists, the enthusiasm for HRT as a remedy for almost every medical problem in women has now subsided. Time trends in the side effects of HRT

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention. . .

199

have been closely and carefully observed. Even if given in appropriate doses and in appropriate combinations of hormones, exogenous estrogen was found to increase the risk of breast cancer [8]. As fewer women are now given HRT, the incidence of breast cancer is falling for the first time since several decades [8].

The Stockholm Female Coronary Risk Study We initiated the studies of Stockholm women in 1991. For reasons already mentioned, we decided to study only women. With support from the US National Institutes of Health, we investigated women below age 65 who were consecutively admitted to the CCUs in the greater Stockholm area with acute myocardial infarction or unstable angina pectoris. We compared 292 women patients to the same number of healthy women randomly obtained from the census register and born on the same day as the respective patient. The youngest patient was 30 years, one-fifth of patients were below age 50 years, 40% were aged 50 to 60, and the remaining 40% were 60 to 65. Each of the women was examined with behavioral and psychophysiological methods for two days to yield a complete baseline behavioral cardiology evaluation. A theoretical model and flow chart of hypothesized mechanisms has been previously published (Fig. 2) [10]. All women were followed up for ten years. When examining the women in the observational study, we addressed general and total stress exposure. We asked the women to describe their self-rated most stressful periods in their lives. The following dimensions of strain were identified: educational, occupational, financial, and residential area problems on the one hand and on the other hand family-related strains concerning the spouse, children, grandchildren, parents, and siblings, among other issues. In a comprehensive analyses of all sources of distress, those related to spousal relationships were the most prominent and showed the strongest association with cardiac events, both retrospectively and prospectively (see Fig. 3) [14]. Those women who reported to have a stressful marital relationship, according to the Stockholm marital stress scale, had three times the risk of a recurrent cardiac event as compared to women with a happy marriage. Further analyses revealed that marital stress was strongly associated with the emotional responses of negative affect. Women with both CHD and marital stress had an average of more than five symptoms of depression, whereas healthy women without marital stress had reported an average of less than one depressive symptom, with intermediate depression values for the groups in between [13, 14]. Attachment and social support estimates also differed between women with high and women with low marital stress. In contrast to previous studies of men showing that lack of emotional support worsened prognosis [19] in women patients, the lack

K. Orth-Gome´r et al.

200 has fewer obligations

the group was a life line

learnt not to get hurt not afraid any more

was allowed to dwell on heart disease

increased self esteem

felt comparisonship

courage to say no

felt support

stonger psyche

Worried less

become aware

felt social cohesion

courage to talk in the group

developed more patience

become more egocentered

could talk about things one don’t really want to talk about

not as angy as previously

enjoys life more

Fig. 3 Selective report of women’s interactive patterns after the group therapy

of close emotional support was not a predictor of poor prognosis. In both men and women, the size of the large and more peripheral network contacts was the most important characteristic for maintaining good health, whereas close, emotional support from a family member or a very close friend was a predictor of good health in men but not in women. We hypothesized that close emotional ties might potentially be harmful rather than beneficial for women’s health. In an earlier study, eight out of ten Swedish male patients had ascribed their stressful experiences that preceded their cardiac events, to their jobs [9]. In female patients, we found that stress and strain in the family, in particular in marriage, was the most important predictor of poor prognosis [16]. Likewise, using quantitative evaluations of coronary angiography (QCA), we found family stress, rather than job stress, to be responsible for accelerated progression of coronary atherosclerosis [20]. This was the more surprising as almost all women were working outside the home and only two female homemakers participated in the study. The worst cardiac prognosis was found among women who reported both stress within family and at work, whereas in women patients, who had a happy marriage and a satisfying job, the mean luminal diameter change over three years increased, suggesting a regression of their coronary artery disease [16, 20]. These epidemiological findings and the continuous and repeated demands from the

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention. . .

201

patients themselves that we should do something about their situation determined the design and timing of the intervention study.

The Stockholm Women’s Intervention Trial in Coronary Heart Disease (SWITCHD) We used a psychosocial intervention program based on cognitive behavioral therapy principles, in which the emphasis was on reducing stress and improving coping with stress, whether the stress originated from marriage or work. We used group meetings as the main therapeutic modality, and we conducted the study with women only, thus we ran only same sex groups. A total of 237 patients, below age 75 and hospitalized for an acute coronary syndrome, were recruited, screened, assessed, and then randomized to either usual cardiologic outpatient care with the stress reduction program, or to usual care only. Further clinical data were obtained from standardized hospital charts, following patient consent. The behavioral assessments followed standard behavioral risk factor models and were repeated before, during, and after the intervention program. Two nurses with experience in intensive coronary care settings and independent consultation work with cardiac patients performed the intervention. They received intensive training from an expert clinical psychologist. The contents of the program were focused on women’s psychosocial risk-factor profile and attempted to control behavioral risk factors, attenuate negative emotions, improve coping skills, reduce stress, and improve social support [3, 15]. The intervention methodology followed basic principles of cognitive behavioral intervention programs, focusing on: 1. 2. 3. 4.

Communication of cardiovascular health knowledge. Methods for self-monitoring. Recognizing cognitive distortions – cognitive restructuring. Skills training and role playing.

The intervention was provided in 20 sessions, each 2 to 2.5 hours long. Groups consisting of 4 to 8 female patients met weekly for 10 weeks and thereafter monthly. They met during the day or in the evening to make participation possible for female patients who were working and could not get time off. Groups met in the same location throughout the program, and the composition of the group was largely preserved. Close to the main hospital entrance, we had furnished a special room, which we called the Women’s Room for the purpose of the group meetings. Sessions began with 5 to 10 minutes of relaxation, a technique to decrease arousal. Each session was focused on a given topic, with prepared educational material. Topics ranged from the cardiovascular system and its pathophysiology to clinical and behavioral risk factors. Opportunities for smoking cessation, for physical exercise, and for weight change were also offered.

202

K. Orth-Gome´r et al.

The therapist made sure that every patient talked at each session. Some of these female patients reported never having talked freely in groups before. Within each session, patients were made aware of and practiced self-monitoring skills, recognition of cognitive distortions, and cognitive restructuring in their interactions. Metaphors were frequently used to illustrate and facilitate understanding of adverse behavioral contexts. These were intended to help the patient to become aware of alternative interpretations in her own life context and facilitate reinterpretations of life situations that were less threatening and emotionally loaded. Further topics were concerned with the negative emotional consequences of heart disease, hostility, depression, exhaustion, and stressful events at work and in the family and were discussed along with strategies to cope with such emotions. Social relations, social roles, and social support were highlighted, and traditional male and female roles in the work and family spheres were discussed. Finally, existential questions about life and death were raised along with strengths and weaknesses in each patient’s personal life. By the end of the program, patient groups had become cohesive and mutually supportive of their participants, and many of them continued to meet socially long after the course. In all, we ran 20 groups, each group for a period of one year. A total of 400 sessions were prepared, conducted, and monitored.

Topics Discussed at the Group Sessions Each of the 20 sessions had a specific topic and predefined content. Before starting, sufficient time was taken for introduction and presentation of group members. For each session, written materials were prepared, either a comic strip, a citation from a newspaper,or any other illustration relevant to the topic. The topics were the following: – Atherosclerosis and risk factors for cardiovascular disease. – Psychological consequences of experiencing clinical heart disease. – Stress and physiological reactions. Recognizing multiple sources of stress – at work, at home, and elsewhere. – Individual identification of standard risk factors for cardiovascular disease – monitoring of the individual risk factor profile. – Anger and hostility in response to daily stress exposure. Problem solving, cognitive strategies. – Coronary prone stress behavior. Reviewing and testing patients’ Type A and stress behavior. A video film was used to identify this behavior. – Worry, depression, low-spiritedness. Problem-solving cognitive strategies. – Communication training, improving skills of effective communication. – Interpersonal conflict situations. The patients were presented with models of conflict situations and asked to deal with them. Real experiences from the patients’ daily lives were presented. – Positive and negative emotions. Home assignments included starting an exercise book with daily, concrete reports and efforts to recognize one’s own positive and negative emotions. This book was maintained throughout the course.

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention. . .

203

– Daily practices of altered behavior. Walking slowly, choosing the longest queue in the grocery store, driving in the right lane without unnecessary passing of other cars, and avoiding going angry were some of the practices. A further exercise was to keep smiling at other people, etc. The patients were to return to report about the results to the group. – Patterns/roles of life, social support, strong and weak “legs” to stand on – “legs” symbolizing “parent,” “child,” “professional pride,” etc. Patients were asked to focus on their strong legs, and to try to get more of important relations and avoid “standing” on just one leg. – Stress and personality. Describing one’s own personal strengths in the working situation, within professional and family life. Exercises to increase the understanding of how people function and think differently, why this may cause conflicts at work but also privately. Furthermore, stress and “burn-out” reactions when a person ends up in the “wrong” professional place, e.g., is forced to deal with detailed questions, if his/her talent is to deal with global tasks. – Describing one’s own life-situation: “How is my life now?” “How would I like it to be?” “How much time do I get to myself?” “What is a good balance between the different life domains?” “What is important in life?” – 14–20. Repeat and strengthen knowledge base, cognition, and skills training. We continuously monitored, discussed, and evaluated the sessions. At several points, before, during, and after the group sessions, the issue of women-specific topics was raised. It turned out that, if we did not specifically address the choice of topics, women talked mostly about women-specific problems. Thus, in same sex women’s groups, in our experience, women will focus on their gender-specific issues without any further guidance.

Results from the SWITCHD Intervention Trial Most of the 20 groups finished their program within one year. A few groups took a bit longer, depending on their other time constraints. Although many women were working outside home, attendance rates were surprisingly high; 85% of the participants attended a minimum of 15 sessions. Groups were highly cohesive, and we observed that participants developed considerable skills of supporting each other. Once they had started and become involved, they stayed in the group. Among psychological assessments, self-reported stressful daily behavior was positively influenced, decreasing more steeply in the intervention than in the control group [1]. We have focused the outcome analyses on the effects on hard endpoints. Over a mean of 7 years after entering the study, women in usual care had a overall mortality of 20%, whereas those in the psychosocial intervention had a mortality of 7% (p < 0.01). No woman was lost to follow-up [17]. The differences in mortality could not be attributed to baseline differences in clinical prognostic variables such as age, education, severity of diagnosis, signs of heart failure, medication use,

204

K. Orth-Gome´r et al.

adiposity, family history of CHD, and smoking history or lipid profile. In general, as a consequence of successful randomization, the risk profiles and pharmacological treatment conditions were evenly distributed.

Subjective Experiences of Women’s (Group) Interactive Patterns When running the 400 sessions in the 20 groups, we had frequent discussions about the participants’ subjective experiences of the program, what they liked and what they did not like. When the patients were asked which part of the program was the most important, many pointed at (out) the group concept, (to have) at the homework, and at the awareness of one’s own unhealthy and adverse behaviors and learning how to change them (and that the program addressed many of the issues important to women). Also, the importance of meeting other women with similar experiences (and problems) and to discuss women-specific issues for a sufficiently long time was well liked. In the group sessions, women appreciated meeting with other women with the same illness and symptoms and the same problems and to be truly understood by each other. The members of the groups were all different in terms of age and social background, but that didn’t seem to matter; they were well integrated and group cohesion was high. Participants often expressed that they didn’t get enough support from their spouses. They said, “at home they are tired of hearing about my worries and distress,” and therefore they felt very lonely with their problems and were happy to meet women in the same position and (where they could) talk about their problems. Additional direct transcriptions of the interviews have been published previously [2]. Many women had very low self-esteem and problems to recognize their own (importance and) needs. They could never say no; instead it was their children, the husband, old parents, or relatives that had priority and always came first. Coronary heart disease is known to be associated with an unhealthy lifestyle. For men it is widely accepted to engage in unhealthy behaviors such as a poor diet and too much work. For women the situation is different. They feel personally responsible and blame themselves for their illness, with accompanying feelings of shame. The women often reported that they were expected to be healthy, take care of their families, and not complain about their concerns and worries. One of the advantages with group sessions is that group participants have similar problems, and therefore each participant can inspire the other group members to solve her problems. This is one example In one group one woman told how she handled a difficult situation. When she wanted to be alone, she just told her husband very clearly: “I don’t want to talk to you today, I need to be alone.” The husband usually accepted and left her alone, and the next day she felt much better. For the other women in this group, this was far removed from what they thought they would ever say or do. Although many times they would like to have their own time and just be alone, they had learnt never to express what they really wanted.

8

Stress and Heart Disease in Women: The Stockholm Women’s Intervention. . .

205

At the same time, they were often angry and frustrated. Another example is the following situation: One woman who was not yet 50 years and had had a bypass operation told the group that when she was cooking, she had to put cream in every food item, otherwise her husband refused to eat. The advice from another woman was to tell her husband that he probably didn’t have enough knowledge of healthy food and that she should try and explain to him what the reason was for her to go on working on learning a better lifestyle in order to prevent further heart problems. It was also important to involve the whole family; consequently, if she felt healthier, the rest of the family would feel healthy too. Our experience was that when the women started thinking about their life, how they could act and think differently in order to feel better, but also by listening to other women’s experiences and reflections, then the changes started to happen. Some of them changed their life situations, their work situation, and their personal relations; most women stopped smoking and started a happier and healthy lifestyle. Many women said they: “had never survived without the group sessions!”

Conclusions This chapter reviews the unique characteristics of cardiovascular risk factors in women and issues related to the risk and progression of coronary heart disease. We use our experiences from the SWITCHD trial to further clarify patient-centered aspects of having coronary heart disease in women who survived a myocardial infarction or those who were admitted with unstable angina. The Stockholm Women’s Intervention Trial for Coronary Heart Disease, SWITCHD, using a group-based psychosocial intervention program for women, is the first major clinical trial to show a “hard” beneficial health effect in women and to improve their survival. It will be very important to further follow the patients and to create psychological interventions targeted at women to reduce cardiovascular risk and improve quality of life. Acknowledgments We are indebted to all participants/patients, who devoted their time and interest and to Prof. Dr. Hans-Christian Deter, Berlin, for his comments to the manuscript. This chapter was adapted, in part, from: Orth-Gomér, K (2011) A look at women with coronary heart disease and the Stockholm Women’s Intervention Trial for Coronary Heart Disease. In R. Allan & J. Fisher, Heart and Mind: The Practice of Cardiac Psychology (p. 355–363). American Psychological Association. https://doi.org/10.1037/13086-017.

References 1. Blom M, Georgiades A, Janszky I, Alinaghizadeh H, Lindvall B, Ahnve S (2009) Daily stress and social support among women with CAD: results from a 1-year randomized controlled stress management intervention study. Int J Behav Med 16(3):227–237 2. Blom M, Deter HC, Orth-Gomér K (2015) How did the stress reduction program help women to survive? The patients view in the SWITCHD study. In: Orth-Gomer K et al (eds) Psychosocial

206

K. Orth-Gome´r et al.

stress and cardiovascular disease in women. Concepts, findings, future directions. Springer, Cham, pp 251–262 3. Burell G, Granlund B (2002) Women’s hearts need special treatment. Int J Behav Med 9:228–242 4. Dellborg M (1998) Less prominent electrocardiographic changes during myocardial ischemia in women may explain differences in treatment as compared to men. In: Orth-Gomer K, Chesney M, Wenger N (eds) Women, stress and heart disease. Erlbaum, New York 5. Fortmann SP, Marcovina SM (1997) Lipoprotein(a), a clinically elusive lipoprotein particle. Circulation 95(2):295–296 6. Healy B (1991) The Yentl syndrome. N Engl J Med 325:274–275 7. Hulley S, Grady D, Bush T, Furberg C, Herrington D, Riggs B, Vittinghoff E (1998) Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women. Heart and Estrogen/Progestin Replacement Study (HERS) Research Group. JAMA 280(7):605–613 8. Lambe M, Wigertz A, Holmqvist M et al (2009) Reductions in use of hormone replacement therapy: effects on Swedish breast cancer incidence trends only seen after several years. Breast Cancer Res Treat 121:679–683 9. Orth-Gomér K (1979) Ischemic heart disease and psychological stress in Stockholm and New York. J Psychosom Res 23(3):165–173 10. Orth-Gomér K (1994) Use of a theoretical framework to study psychosocial factors and cardiovascular disease in women: the Swedish experience. National Institutes of Health publ. no 94-3309, Bethesda 11. Orth-Gomér K (2000) New light on the Yentl syndrome. Eur Heart J 21(11):874–875 12. Orth-Gomér K (2001) Stress and coronary heart disease in women. In: Deter H-C (ed) Psychosomatische Medizin am Beginn des 20. Jahrhunderts. Huber, Bern, pp 337–347 13. Orth-Gomér K, Leineweber C (2005) Multiple stressors and coronary disease in women. The Stockholm Female Coronary Risk Study. Biol Psychol 69(1):57–66 14. Orth-Gomér K, Burell G, Perk J, Ornish D, Benesch L, Roquebrune JP (1994) [Fresh start after heart disease. Changed life style is an important part of rehabilitation]. Lakartidningen (J Swed Soc Med) 91(5):379–384 15. Orth-Gomér K, Moser V, Blom M, Wamala SP, Schenck-Gustafsson K (1997) Survey of stress in women. Heart disease in Stockholm women is caused by both family- and work-related stress. Lakartidningen (J Swed Soc Med) 94(8):632, 635–638 16. Orth-Gomér K, Wamala SP, Horsten M, Schenck-Gustafsson K, Schneiderman N, Mittleman MA (2000) Marital stress worsens prognosis in women with coronary heart disease: the Stockholm Female Coronary Risk Study. J Am Med Assoc 284(23):3008–3014 17. Orth-Gomér K, Schneiderman N, Wang HX, Walldin C, Blom M, Jernberg T (2009) Stress reduction prolongs life in women with coronary disease: the Stockholm Women’s Intervention Trial for Coronary Heart Disease (SWITCHD). Circ Cardiovasc Qual Outcomes 2(1):25–32 18. Pripp U, Eriksson-Berg M, Orth-Gomér K, Schenck-Gustafsson K, Landgren BM (2005) Does body mass index, smoking, lipoprotein levels, surgically induced menopause, hormone replacement therapy, years since menopause, or age affect hemostasis in postmenopausal women? Gend Med 2(2):88–95 19. Rosengren A, Wilhelmsen L, Orth-Gomér K (2004) Coronary disease in relation to social support and social class in Swedish men. A 15-year follow-up of the study of men born in 1933. Eur Heart J 25(1):56–63 20. Wang HX, Leineweber C, Kirkeeide R, Svane B, Schenck-Gustafsson K, Theorell T, Orth Gomér K (2007) Psychosocial stress and atherosclerosis: family and work stress accelerate progression of coronary disease in women. The Stockholm Female Coronary Angiography Study. J Intern Med 261(3):245–254

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and Measures Camara Jules P. Harrell, Tanisha I. Burford, and Renee Davis

Contents Race and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Races Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Variation, Genetics, and Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Whither the Study of Race and Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Racism and Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Racism Conceptualized . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Study of Racism and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

209 209 213 217 218 218 219 224 225 226

Abstract

Even though the prevalence of a number of cardiovascular diseases differs across self-identified racial groups, dividing human populations into subspecies or races is not defensible from a biological perspective. Evidence points to a relatively recent origin of the human species and to a substantial level of genetic exchanges between migrating human populations. The persistent health disparities between populations of varying ethnic origins that currently inhabit the same geographical area may reflect differences in rates of exposure to disease-generating circumstances, rather than underlying biological differences. Thus, studies of the impact of racism on health may assist in identifying the source of between-group differences in health status that were formally attributed to race. Empirical studies of racism are becoming increasingly sophisticated. This chapter includes a C. J. P. Harrell (*) · R. Davis Department of Psychology, Howard University, Washington, DC, USA e-mail: [email protected] T. I. Burford Department of Psychology, Hampton University, Hampton, VA, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_9

207

208

C. J. P. Harrell et al.

discussion of several paradigms that take a multilevel approach to the study of racism and health outcomes. The promise of these studies is the uncovering of the pathways that connect environmental events to cardiovascular diseases in groups negatively impacted by racism. Keywords

Race/subspecies · Human origins · Health disparities · Racism · Multilevel studies

“But race is the child of racism, not the Father.” Ta Nehisi Coates, Between the world and me (2015).

When evidence reveals differences in health outcomes among “racial” groups [13], the subsequent interpretations that scholars advance can be placed on a continuum anchored by two theoretical positions. (Discussions regarding the racial disparities indicated by prevalence rates of cardiovascular disease can be found in data reported by the Centers for Disease Control and Prevention (CDC). Relevant statistics indicate that while the percentage of Blacks diagnosed with heart disease is quite comparable to Whites, the difference in diagnoses of hypertension between the two ethnicities (i.e., Blacks, 32.9%; Whites, 22.9%) is quite drastic (CDC, [13]). Kurian and Cardarelli [57] point out similar statistics in their systemic review of cardiovascular disease, which indicated that the rate of hypertension among Blacks was significantly higher than among Whites. Viewpoints shading toward one pole see health disparities as signaling the presence of underlying biological difference between races. The opposing theory surmises that variations in health outcomes are due largely to differences in the likelihood of encountering environmental and psychosocial causes of poor health. The tension between the perspectives that lead to these varying interpretations is long-standing. For example, in 1896, the statistician Frederick Hoffman [41]—who at that time was merely thirty-five years removed from enslavement—published a comprehensive study of the health and physiology of African Americans. Hoffman asserted that his German background would free him from “personal bias” in executing the study, but his use of the words “race traits” in the title presages his dire conclusions. He projected that death, disease, crime, and immoral behavior would continue to ravage African Americans, due largely to the dispositions and inclinations of the race. Kelly Miller [71], a Howard University sociologist, found little in the evidence presented in Hoffman’s report to support concocting the notion of race traits or concluding that a general lack of vitality in African Americans would fuel their interminable decline. Du Bois [23], in his study of the health of African Americans, also rebutted Hoffman’s interpretations, citing evidence that poverty and squalid social conditions profoundly affect health outcomes in American and European populations. He reached a more balanced conclusion than Hoffman, asserting that “. . .with improved sanitary conditions, improved education, and better economic opportunities, the mortality of the race. . .will steadily decrease until it becomes normal” (p. 90).

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

209

Over a century after Hoffman’s study, Hirsch et al. [40] identified a correlate of heart disease in the form of a latent disposition termed frailty. The index was comprised of a lack of physical robustness, slowness, weight loss, and exhaustion. They reported Blacks scored higher on a measure of frailty than Whites, even after controlling for social economic levels, inflammatory markers, and subclinical indices of cardiovascular disease. The authors proposed that genetic factors accounted for racial differences on this dimension. Indeed, Hoffman’s perspective casts a long shadow, and its pessimistic tone persists. Tension remains to this day between the race-traits approach and the perspectives Miller and Du Bois articulated. The acceptance of the biological formulation of race is central to the belief in racial predispositions. Initially, in this chapter, we examine some recent contributions from molecular genetics to the status of the concept of human races and to the development of metaphors depicting human origins. Subsequently, we discuss the yield of studies of genetic causes of health outcomes and consider lessons from research focusing on race and hypertension. A general review on health disparities in cardiovascular disease is provided in Chap. ▶ 11 of this volume. The relationship between genes and population rates of diseases is proving to be more complex than anticipated. Therefore, it is not surprising, given problems with the biogenetic concept of race, that it has been difficult to isolate genetic causes of differences between human populations in cardiovascular disease. We highlight the strengths of two alternative perspectives on examining human variability in disease outcomes. These perspectives depart significantly from the standard paradigm that compares “racial” groups on risk factors, disease outcomes, or physiological measures. They require investigators to merge genomic studies with thorough analyses of toxic environmental conditions and the unique circumstances that result from ascribed group membership based on socially constructed race categories. Thus, as Du Bois predicted, the study of race and disease gravitates toward a focus on aspects of the environment that adversely affect development. One environmental factor that must be examined is racism. The second section of this chapter examines conceptual and empirical frameworks that promise to advance the study of racism and cardiovascular diseases. We consider mounting evidence that suggests multilevel approaches will reveal how racism becomes part of the biological status of the oppressed.

Race and Disease Human Races Revisited Conceptual Distinctions When applied to human beings in scientific or in popular settings, the term race lacks precise meaning. In the tenth edition of Systema Naturae, Linnaeus [63] discussed four “varieties” within the species Homo sapiens. His taxonomy and subsequent attempts to subdivide humans into definitive categories have been met with

210

C. J. P. Harrell et al.

controversy, calls for revision, and skepticism [31]. Is it possible to define race in a manner that will facilitate the study of human variation? Will the centuries-long debate over the biological basis of race finally be settled by advances in molecular genetics? Finally, how might we view human variability other than in terms of the traditional racial cleavages that taxonomists have handed down over the centuries? We address each of these questions presently. Though race and subspecies are often used interchangeably, Keita and colleagues [52] noted that sometimes in scientific practice, race is employed as a more restrictive term, referring to local groups of organisms that tend to interbreed. Therefore, as Keita et al. observed, applying race to human beings would permit calling cloistered religious or cultural groups like the Amish racially distinct, when clearly this is not what is intended by the concept. Subspecies are clusters of geographically proximal breeding populations that share distinct characteristics. The designation subspecies requires that organisms share consistently multiple traits of a genetic origin [4]. Subspecies emerge along distinct phylogenetic paths, though they can interbreed successfully with groups outside of their subspecies. Indeed, to show that human races or subspecies exist, one must establish more than phenotypic variations between geographically distinct groups. Keita et al. [52] suggested substantial genotypic divergence from other groups must be evident, as well as distinct genetic markers of common, private lineages. Similarly, Templeton [90] distinguished between classification systems that divide human population into major subdivisions and lower level taxons used to classify smaller, more localized groups. Biologists divide populations into demes, which are groups that tend to breed locally. This designation is far from a grand partitioning of humanity into major subtypes, since even local ethnic groups can be further divided into demes. Another term used in evolutionary biology is ecotype—groups of individuals who share one or more adaptations that provide an advantage in a specific environment. Demes and ecotypes are significantly more restrictive and finer level cleavages than global designations of races or subspecies [90]. When applied to humans, they are more logically and empirically defensible.

Empirical Findings Modern research in molecular genetics is providing critical tests of the “race” categories used in scientific studies and in popular discourse [15]. Evidence from studies of large portions of the human genome can now be added to that provided by traditional studies of allele frequencies and blood groupings. People differ with respect to single nucleotide polymorphisms (SNPs), the basic unit of genetic coding. They also vary with respect to haplotypes—large units of analysis comprised of sizable areas on chromosomes. Presently, populations of humans can be identified by SNPs within haplotypes and by the presence of one or more haplotype. The inheriting of blocks of DNA variants results in a tendency for closely related people who have a particular variant at one locus, to share variants at loci that are not in close proximity. Accordingly, Cooper [15] contended that a robust test of the genetic basis of subspecies in humans is the presence of variants at unlinked loci in

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

211

individuals considered to fall within and outside of the subspecies. Research has examined SNPs and haplotypes in various human and primate populations. Molecular genetic analyses have impacted primate taxonomy. The lessons learned from this research are instructive when one considers notions of human subspecies. They should inform scientific as well as popular notions of the genetic similarities between humans and other primates and differences within species of primates. Studies of mitochondrial DNA revealed that chimpanzees, bonobos, and gorillas are more closely related to humans than they are to orangutans and that chimps and bonobos are more closely related to humans than they are to gorillas [29]. More importantly, within chimpanzee and gorilla populations, species that are much older than homo sapiens, several subspecies have been identified. The within-population genetic variance in selected gorilla and chimpanzee populations represents only a small proportion of the total genetic variance of each species. If the local withinpopulation genetic variance accounts for only a small portion of genetic variance of the species as a whole, a distinct subspecies or race may be distinguishable. Indeed, the within-population variance for eastern chimpanzees is 28% of the total genetic variance of chimpanzees, and the mountain gorilla within-population variance is only 10% of the total genetic variance for gorillas [88]. In contrast, Lewontin’s [60] classic estimate of human genetic variance, based on blood groupings and allele frequencies, set the within-population variance for human subgroups at 86% of the total human genetic variance. The data from ongoing molecular genetic studies of human populations across the world provide little evidence for the existence of subspecies as delineated in the accepted race taxonomies. Romualdi and colleagues [86] examined rapidly mutating and relatively stable DNA polymorphisms obtained from 32 populations from five continents. On the average, around 80% of the genetic variance over the sites occurred between individuals within the same population. Though the frequency of polymorphisms at some sites differed substantially between groups, error rates for assigning individuals to continents by polymorphisms were consistently at or above 27%. Similarly, Li et al. [61] in a study of 650,000 SNPs from 51 populations reported that within-population differences accounted for 89% of the genetic variance. Additionally, Li et al. determined that the farther populations resided geographically from Addis Ababa, Ethiopia, the lower the mean haplotype heterozygosity. The latter finding is consistent with theories that small groups of modern humans traveled out of Africa in several waves and established new populations. To be sure, differences in genetic variants between populations identified in the findings of Romualdi et al. and Li et al. are not negligible. Additionally, the genetic diversity within populations is not equal across groups, with greater diversity consistently found in Sub-Saharan African populations. In fact, Long et al. [64] tested statistical models of the fit between SNPs as well as larger genetic markers, and classical race designations of samples drawn from Africa, Europe, and Asia. The Sub-Saharan African genetic samples were so diverse that they could be included in a single subgroup only if non-Sub-Saharan African populations were part of the same group.

212

C. J. P. Harrell et al.

Kaufman and Cooper [50] and Cooper [15] noted that though the crucial molecular genetic studies of human variability are still in their initial phases, at this time the findings underscore what was known or strongly suspected on the basis of earlier research. First, mounting evidence suggests that between 100 and 125 thousand years ago (kya), not a long time in evolutionary terms, small groups of Africans migrated to southern Asia and later to Europe. These people had originated in eastern and possibly northern Africa from 150 to 190 kya (see [84]). Second, evidence suggests that genetic diversity is greatest among Sub-Saharan African populations and that extensive variability within human populations is rooted in Sub-Saharan populations, though on each continent local and more recent changes in haplotypes have occurred [64, 102]. Human variation is clinal (see [94]), that is, traits change gradually over geographical regions and cut-offs for racial categories are arbitrary. Templeton [89, 90] provided useful explications and comments on two empirical strategies for establishing the existence of mammalian subspecies. He noted that the first approach, an extension of the methods employed in several studies cited above, gauges the degree to which the genetic differentiation between two populations exceeds the genetic differentiation within the local population. Racial or subspecies distinctions imply a sharp boundary between groups, so by convention, the presence of 25% or greater differentiation between groups signals the existence of a race of subspecies. The second strategy examines the evolutionary lineage of populations; it determines if there is genetic evidence for a continuing line of descent. When genetic lines split, as time passes genetic differences between groups accumulate. The second strategy uses a statistical goodness-of-fit model to test if the patterns of genetic changes within populations match a tree-like structure, where populations branch off in distinct evolutionary lineages. Consistent with previous studies, Templeton [90] showed that when both methods were applied to genetic samples from chimpanzee populations, results revealed the presence of three rather than the five recognized subspecies or races of chimpanzees. When these methods were employed to examine genetic samples from human populations, neither sharp genetic distinctions nor distinct evolutionary lineages was evident.

Family Trees, Venns, and Trellises Cooper [15] called for a new metaphor when conceiving human variability, one that will replace the tree depiction of the human family [42, 85]. He noted that the often used illustration of phylogenetic trees for human populations on different branches “subverts” (p. 291) notions of the similarity and continuity among all human groups that the molecular genetic data reveals. Similarly, the molecular evidence debunks an alternative conception of race as an identifier of genetically based traits related to health or to human abilities [87]. Cooper’s [15] alternative metaphor used a series of overlapping circles, akin to a Venn diagram, to illustrate haplotypes from African, European, and Asian populations (p. 298). The circles are of comparable size, but the ones representing the Asian and European variants are largely a subset of a nearly encompassing circle illustrating African variants. Still each circle contains a small portion of separate variance. For the Asian and European circles, this represents lost and exaggerated haplotypes emerging after separation from the African parent

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

213

population. This illustration emphasizes not a separation of humanity into subspecies but the continuous nature of human variation, enormous within-population variability, as well as the non-negligible portions of between-population differences that reside distinctly below the subspecies threshold. Indeed, statistical tests of shared genetic lineages and admixtures should impact the metaphors we employ to depict human variability. Templeton [89, 90] employed multiple locus nested clade phylogeographical analyses (MLNCPA) to map the historical flow of late hominid and early human populations. Evolutionary biologists employing this procedure select multiple genetic loci—those that show little or no recombination, in groups known to show varying level of direct common ancestry (clades), across a number of geographical locations. Mutations in the selected genetic regions allow researchers to track the genetic history of the populations. Templeton [90] reported that MLNCPA confirmed the conclusion based on the skeletal evidence of a migration of some Homo erectus individuals out of Africa into Eurasia 1.9 million years ago. The Southern European and Southern Asian populations that emerged after these migrations remained relatively isolated by distance, with negligible genetic admixtures between them. A second migration of early humans occurred 650 thousand years ago, marking the spread of the Acheulean tool culture from Africa to Eurasia. Genetic admixtures between resultant African, Southern European, and Southern Asian populations are evident during this period, and significant genetic crossflows took place following the third migration of some modern humans out of Africa 130 thousand years ago. The persistent genetic flow between groups militated against the development of distinct subspecies or races during these later periods. Depicting early migratory human history as a branching into relatively isolated groups belies the genetic evidence that shows persistent gene flow between geographical populations. These tree-like structures are misleading. Templeton [90] called for a trellis model; a lattice framework that depicts the three major migrations out of Africa to various parts of Eurasia, but includes, for the latter two migrations, lines connecting disparate populations, reflecting the continual genetic flow between these groups. The advantage of Templeton’s trellis over the Cooper’s Venn model, and the tree that Cooper cogently critiques, is that it accounts for the temporal and special movement of various populations. Homo erectus and modern humans were of African origin, and they were intercontinental sojourners. However, the itinerary for their journeys included genetic exchanges with groups that they encountered. The trellis depiction reflects the time and nature of human migrations and interactions and illustrates why the subspecies or race designation is not applicable to humans.

Human Variation, Genetics, and Cardiovascular Disease In most societies, as improved sanitation reduces the level of mortality due to infections, diseases of the heart begin to emerge among the leading causes of deaths [83]. This has been true of the United States since 1920, when heart disease permanently replaced influenza and pneumonia as the leading cause of death. The

214

C. J. P. Harrell et al.

rates of stroke, coronary artery disease, and hypertension reach their peak in the African American population [75], which fueled sustained research into underlying causal mechanisms.

“Race” and Hypertension Quests to find genetic causes of the differing prevalence of cardiovascular diseases as well as other maladies in African and European Americans are legacies of the traditional “race” concept. Such efforts were successful in the case of sickle cell anemia, a monogenetic condition that occurs at much higher rates in groups of Sub-Saharan African origin. Accompanying the advances in knowledge of this rare disease was an intriguing explanation contending that the crescent-shaped red blood cells that characterize the disease are adaptive in settings where malaria is rampant. Perhaps the successes in solving the riddle of sickle cell encouraged the search for a single gene that causes high rates of cardiovascular diseases among African Americans. Indeed, some interesting stories have been attached to genetic models of disease suffered by populations outside of Europe [22]. One narrative proposed that rates of hypertension among Blacks are high because they descended from uniquely adapted African women and men. Africans who survived the middle passage and the harsh conditions of enslavement were those whose kidneys, because of salt shortages in Africa, tended to retain sodium (see also [99]). Another hypothesis, Neel’s [77, 78] thrifty gene scenario proposed that those who move from less developed societies, where famine or periods of low food supply are common, are able to store fat more efficiently. This would include Native Americans, Africans, and Pacific Islanders. When these individuals migrate or are forced into modern, more sedentary settings, they tend to suffer from obesity and diabetes. These theories represent an odd mixture of historical and biological speculation. Historian Philip Curtin [20] extensively criticized the sodium retention or “slavery hypothesis of hypertension.” He noted that records indicate there existed no general shortage of salt in West Africa, and diseases of the intestines that might cause diarrhea and deplete sodium were probably far less common causes of death among Africans than tuberculosis and pneumonia. Others have observed [15, 82] that the epidemic rates of obesity in a diverse array of the world’s populations suggest that a thrifty gene would have to be a human attribute, not merely an attribute of people from the developing world. It is becoming clear that many cardiovascular diseases, including hypertension and heart disease, are multiple determined phenotypes. These complex diseases will not be explained through convenient narratives and a simple genetic profile. In fact, the sodium retention or slavery hypothesis generated a large body of research devoted to the study of differences in salt excretion between various populations in the northern and southern hemispheres. Gliebermann’s [26] review of this literature included recent data revealing differences between samples of African descent and individuals from colder climates in the frequencies in alleles associated with salt retention. For example, Gliebermann [26] tabled evidence showing that Nigerians and African Americans versus Caucasians or Japanese

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

215

have a much higher frequency of the T allele variant of the angiotensinogen gene, a variant that has been associated with hypertension. The present critique does not argue that the null hypothesis holds with respect to the status of biological dispositions of samples designated racially distinct. Differences between African Americans and Caucasians have been reported for many measures putatively related to cardiovascular disease, including cardiac and vascular responses to stress [3]. Our position is that the race comparative strategy that the sodium retention hypothesis encouraged tends to gloss over the question of the differential validity of the measure of focus as a predictor of the disease outcomes within groups. Thus, although Nigerians and African Americans share the high frequency of the T allele, their rates of hypertension differ substantially. Evidence suggests rates for Nigerians are comparable to those found among European Americans [17]. Important variables may moderate the predictive efficacy of this gene in the two populations, and researchers should be wary of simple “race” comparative strategy for understanding hypertension rates in these populations. In fact, a first step in better understanding the genetic contribution to disparities between populations in the rates of cardiovascular diseases is to identify the genes that contribute to the illnesses. Mapping of the human genome promised to facilitate the identification of specific genetic causes of disease. Family history studies and twin research point to a strong genetic contribution to the development of hypertension (see [27]), but genes that are causally linked to elevated blood pressure have proved elusive. Still, studies based on vast datasets composed of genotyped individuals from around the world are beginning to report progress. Wu and colleagues [101] reviewed evidence that identified two genetic markers that were consistently linked to hypertension and blood pressure based on four samples of a total of 12,000 individuals. Adeyemo et al. [1] in a genome-wide scan of African Americans located several SNPs consistently linked to systolic blood pressure. Two of these were of particular interest because of their association with genes related to blood pressure regulatory mechanisms. Hubner et al. [43] encouraged researchers who focus on the genetic mechanisms in hypertension to identify “regulatory” processes as well as direct causal genes. The regulatory genes control the expression of genes that prove to relate to blood pressure control. Clearly, “epigenetic” effects are not limited to interactions among genes and include any factor, genetic or environmental, that influences genetic expression [30]. Moore [72] described a developmental systems perspective that can be applied to the study of the etiology of cardiovascular diseases. It asserts that “dependent interactions” between genes and environment control the expression of simple as well as complex traits. Modern conceptualizations of genetic influences speak to the fallacy of thinking of genetic and environmental effects working in isolation from the time of conception forward [30, 46]. Thus, genetic influences on hypertension will be expressed through interactions among genes and between genes and nutritional, psychosocial, and ecological factors. The specific genes responsible for hypertension have been difficult to detect, and it should come as no surprise that to date, research is yet to uncover a purely genetic basis for differential rates of hypertension between African Americans and European

216

C. J. P. Harrell et al.

Americans. Diuretics and calcium channel blocking drugs are seen as more effective for Black hypertensive individuals than drugs that inhibit the renin-angiotensin system [14]. However, recent studies call into question prescribing of medication for hypertension based on race. A meta-analysis of six studies revealed no differential effectiveness of calcium channel blocking drugs by race [79]. Indeed, the rates of hypertension vary considerably within populations of both African [16] and European [100] descent. The prevalence of hypertension was reported to be higher in Spain, Finland, and Germany than in the African American population and lower in Nigeria than among eight Western European countries and European Americans [17]. Thus, the evidence does not favor racializing causes or therapies for hypertension. Genetic and environmental causes interact resulting in differences within and between populations in the prevalence of hypertension.

Race and Genetic Markers of Health An intense search for specific genetic markers of complex diseases has identified more than 1200 genetic loci linked to over 165 illnesses [103]. Still, as Hurdoff et al. [39] showed, these trait/disease associated SNP’s (TAS) account for only modest proportions of the variability in the rates of diseases within populations. As noted earlier, the sequencing of the human genome had raised expectations related to the understanding of the genetic basis of health. Perhaps these elevated expectations contributed to the angst that emerged among investigators when genetic markers failed to account for rates of disease. Scholars scrambled to solve what was called a “missing heritability” mystery [67]. Perhaps, as Zuk et al. [103] proposed, more accurate estimates of the effects of TAS on population rates of disease may be generated from statistical models that consider the effects of interactions among genes at different loci (epistasis) on gene expression. Unraveling riddles of epistasis is but one task facing those charting the paths and channels of genetic expression. In addition, several authors have noted that even with improved success in identifying genetic markers for various diseases and the mechanisms of their expression, the assumption that global designations of racial background will signal the presence or absence of these markers will remain untenable [28, 65]. Improved understanding of genetics in no way guarantees that race will become a viable biological marker. As investigators explicate the many sources and levels of environmental input to the developing and functioning human organism, the interaction between biological and environmental causes of diseases will be more fully appreciated. Wild [95], in considering the etiology of cancer, employed the term exposome to encapsulate the total exposure to facets of the internal and external environment throughout the lifespan. Social, political, and economic conditions are part of the external facets of the exposome. As several authors [28, 65] point out, “race,” rather than having a biological referent, serves as a proxy for increased likelihood of encountering a portion of the exposome that contributes to disease through the operations of racism in society.

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

217

Whither the Study of Race and Cardiovascular Disease A methodological caution and an intriguing new paradigm should guide future studies of race and disease. The caution, advanced by Kaufman and colleagues [49–51], urged researchers to avoid an all-to-common practice of first locating differences on a disease indicator between samples identified as racially distinct. As part of this strategy, studies use covariates, including socioeconomic status, education, and assorted demographic variables to control statistically for environmental differences between the groups. Investigators attribute any differences on the disease indicators that persist after introducing the covariates to genetic factors. Kaufman et al. [51] are among those who argued that self-identifications of race membership, while capturing some genetic variability also encompass an array of environmental and psychological factors that differ as a function of ethnicity and racial group membership in societies where racial hierarchies and inequities exist. Among these factors are access to opportunity and resources and one’s personal sense of cultural and political leverage. Often the covariates strategy employs crude and abridged measures to capture complex environmental circumstances while employing the race category as a proxy for “unmeasured genetic factors” [50]. Race is saturated with content reflecting the life experiences of the individuals. The interpretations based on this approach are flawed and misleading. Kaufman and Cooper [50] applied this critiqued to the frailty construct of heart disease [40] cited earlier. Jackson [45] described an interesting alternative paradigm, called ethnogenetic layering. It fuses detailed genetic analyses of regional subpopulations with information about biologically related facets of the history, social, and cultural practices of these groups. Combining genetic information with social and historical knowledge allows researchers to create maps that provide useful predictions of disease prevalence in microethnic groups. The point of departure for this strategy is the dismissal of the macro groupings of human variability created in traditional race theory. It then proposes that the history of most of the world’s habitable geographical regions is marked by serial influxes of various populations with varying degrees of interactions among these groups. Thus, a layered historical, social, and genetic fabric is created. Once the contours of the social, cultural, and historical fabric are charted, the method superimposes “on these depictions, the geographical distribution of specific genes, gene clusters and health outcomes” (p. 231). One formidable aspect of Jackson’s approach is that its emphasis on more local lineages provides a gauge of the within-group genetic and cultural variation known to exist in “racial” groups. A second advantage the method has over traditional race approaches is its focus on gene-environmental interaction. This perspective promises better explanations of the variability in phenotype than approaches that search for main effects genes or those that measure selected social indicators void of an understanding of the genetic context. Ethnogenetic layering and the critique of the simplified use of covariates in race comparative studies challenge future research to boost the magnification on environmental factors. Genetic patterning exists and contributes to human variation but

218

C. J. P. Harrell et al.

should be understood in a much more sophisticated fashion in terms of the profound and significant differences in the lived experiences of different racial groups. Conditions stemming from being assigned to a particular racial group influences opportunities, access to resources (e.g., health care, education), and exposure or protection from environmental toxin and stressors. These circumstances ultimately map onto human biology. Krieger [53, 55] asserted that social inequities (e. g., gender discrimination and racism) are expressed biologically by those on the receiving end of oppression, and often the impact assumes the form of disease. In her words, the ethnic differences that persist in health outcomes “literally embody and biologically express experiences of racial oppression and resistance from conception to death” ([53], p. 331). In summary, there is a growing recognition that the lived experiences of race become entangled in human biology. The deleterious effects of racism are very prominent features of what individuals who are considered to be other than White encounter throughout the lifespan in highly racialized societies across the world. As will be discussed in the next section, a defining characteristic of these societies is that “economic, political, social, and ideological levels are partially structured by the placement of actors in racial categories or races” p. 37 [8]. The hierarchical arrangements within these racialized structures result in inequities. Ultimately, health and the risks and burdens of many diseases become partially racialized. This occurs because for people of European descent, racialization is often experienced in a way that permits significant advantages to accrue (e.g., access to quality health care, exposure to less toxic physical and social environments). These are designated best as privilege. However, people of color tend to experience racialization in the form of disadvantages, best labeled as historical and contemporary oppression, i.e., racism. Much of the meaning of the race concept is captured in the manner in which racism becomes biology.

Racism and Cardiovascular Disease Racism Conceptualized Racism is a multilevel entity whose manifestations saturate the life experiences of its victims and contribute negatively to their health and well-being [96]. Harrell [36] defined racism as “a system of dominance, power, and privilege, based on racial group designations that is rooted in the historical oppression. . . where members of the dominant group create or accept their privilege in a society by maintaining structures, ideology, values, and behaviors that have the intent or effect of non-dominant group members being relatively excluded from power, esteem status and/or equal access to societal resources” (p. 43). This definition highlights the complexity of racism and the key features of power differentials, oppression, privilege, (see [80]), punitive inclusion, and imbalanced exclusion. The complexity and nuances of racism are evidenced further in the myriad of ways in which racism is perpetuated and experienced. Jones [47] argued racism is

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

219

experienced in individual, cultural, and institutional domains. Individual racism is characterized by beliefs in the superiority of one’s racial group and inferiority of other racial groups and the resulting ideologies and behavior (unfair treatment) to support such beliefs. Institutional racism is broader and describes the ways in which laws, customs, and practices are enacted to restrict or deny access to rights and opportunities for groups of people based on their race. Cultural racism refers to the tendency of the dominant group to superimpose its beliefs, norms, and mores onto other racial groups in order to maintain the superiority of one’s culture. The varied manifestations of racism have evolved from vulgar, blatant, and gross injustices that were apparent during the past few centuries in the United States and other “Western” countries. Bonilla-Silva [7–9] has advanced a structural interpretation of racism. The structural approach posits that racism is integral to the foundation and operation of the social system. It challenges the dominant assumption that racism is a “freefloating” ideology by highlighting its organizing principles and systematic arrangement in society. The structural interpretation of racism emphasizes differences in outcomes that are deliberate and organized along racial lines. It is based on racialized social systems that are societal systems in which people’s racial classification significantly determines access to valued economic, political, and social resources. These societies differentially allocate economic, social, political, and even psychological rewards and penalties to groups along racial lines. In such systems, the life chances of the non-dominant members of a society are significantly lower than the dominant group. Therefore, the structured approach to racism emphasizes the centrality, pervasiveness, and organized nature of racial inequality in society. The forms of racism differ not only in content but also in terms of their pervasiveness, and ultimately their impact, both unconscious and conscious on individuals over the life course. While much of the current research on the health effects of racism focuses on individual forms of racism, the systematic nature of racism requires researchers to attend to both micro- (individual) and macrolevel analyses of racism (institutional, cultural, and structural form) [35]. The yield of a multilevel focus will be a more complete understanding of disparate pathways that converge to produce persistent health disparities.

The Study of Racism and Disease Methodological Advances Behavioral studies of the manner in which racism influences cardiovascular health rest on a continuum that ranges from carefully controlled experiments conducted in specialized psychophysiological laboratories to social epidemiological survey research that sometimes samples the health status, attitudes, and experiences of thousands of individuals. Often but not exclusively in survey research, as racism is operationalized more comprehensively, the findings reveal greater degrees of information about the manner in which experiences with different aspects of racism are related to health outcomes. The experimental laboratory studies construct analogs of racism that are in vitro forms designed to elicit physiological activation. The principal

220

C. J. P. Harrell et al.

value of laboratory studies is the exploration of neural and bio-behavioral mechanisms that underpin the responses to the analogs of racism. Progress in our understanding of the role racism plays in disease outcomes has followed the refinement of the research on all points along this continuum [11, 37, 98]. Indeed, the various empirical approaches benefit from sharing methodological features. For example, field studies often incorporate objective, sometimes sophisticated, and even obtrusive measures of physiological activity. Beatty and Mathews [5] obtained twenty-four-hour ambulatory cardiovascular assessments in a study of perceived mistreatment in Black and White adolescents. They found higher nighttime diastolic blood pressures in low-income African American youth who reported higher levels of unfair treatment. Cooper et al. [18] measured plasma levels of endothelin-1, a potent vasoconstrictor linked to cardiovascular and renal disease in a sample that included both Caucasian and African American adults. Higher social economic status was associated with lower levels of endothelin-1 in Whites. Among African Americans, perceived discrimination was correlated positively with endothelin-1 regardless of social economic status. In addition to benefiting from better measures of physiological activity, progress in survey studies has stemmed from improved tests and surveys for measuring racism. Williams and Muhammed [97] discussed the effects of separating survey queries that tap unfair treatment from the questions that probed perceptions of the source of the treatment. They argued that items that require participants to report the occurrence of unfair actions and to make judgments related to the cause of this behavior (e.g., race, economics, or gender) may result in an underestimate of the experiences of discrimination. Additionally, a number of scales assess more than interpersonal racism [35, 69]. However, comprehensive but lengthy instruments become less practical when large samples are studied or a variety of constructs and topics are the focus of the research. Paradies [80] questioned the reliability and validity of existing measures of racism and later described the validation of a measure developed specifically for the indigenous Australian population [36, 81]. They found that over two-thirds of the respondents reported encounters with interpersonal racism and acknowledged the existence of institutional forms. The levels of internalized racism were high in a third of the 312 indigenous Australians they measured [81]. Krieger and colleagues [56], in a computer-based study, measured discrimination using explicit and implicit techniques. To examine unconscious experiences with racism, the authors modified the Implicit Association Test [33], a widely used measure of unconscious prejudice. Among African Americans with lower education, they determined that reports of hypertension were related to both conscious and unconscious reports of discrimination. Improved instruments will take various forms leading to a more precise, multidimensional, and less global assessment of racism. The structural aspects of racism are assessed by research into the impact of neighborhood characteristics and macroeconomic conditions [73]. These studies track the availability of supermarkets, liquor stores, hospitals, and other institutions that might affect health. Investigation of the neighborhood factors as related to cardiovascular disease can reveal aggregated socioeconomic indices, which may

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

221

serve as proxies for variables known to be related to health outcomes [21]. The causes of disease that are partially captured in the socioeconomic indicators include the relative concentration of exercise facilities and markets that vend fresh fruits and vegetables and the levels of environmental toxins. Accordingly, Mujahid et al. [74] reported that hypertension, determined by seated blood pressure measurements, was less likely to occur among individuals who characterized their neighborhoods as having high social cohesion, numerous healthy food outlets, and high levels of personal safety. They tested several statistical models for determining the reliability of the ratings participants provide of neighborhood factors. Thus, conceptual and statistical advances have sharpened the survey-based studies of the relationship between structural social forces and cardiovascular disease. Most of the laboratory studies of the impact of racism have assessed blood pressure and heart rate changes that take place during and after viewing films or listening to vignettes of race-related material, imaging racist scenarios, or recalling and discussing racist events [10, 37]. Many studies focus on blatant and vulgar forms of interpersonal racism, and more ubiquitous forms of racism have also been used [48, 70]. Merritt et al. [70] found that African American men evidenced pronounced blood pressure reactions when they reported perceiving racism after being shown unfair treatment where racist cues were not presented. The authors concluded that situational elements that were ambiguous with respect to racism elicited larger blood pressure responses than those that are clearly racist. Institutional and structural forms of racism have been largely neglected in laboratory studies. However, Burford [12] described a psychophysiological study of the impact of structural racism on cardiovascular measures. The procedure involved the animated presentations of racialized outcomes in the form of incarceration rates. These depictions elicited increased levels of rumination and decreased heart rate variability in a sample of African American college students. Burford’s paradigm is consistent with the call [24, 37] for an expansion of the number and scope of studies of structural racism and the health of people of color. Gee and Ford [24] encouraged a multifaceted approach to structural racism and noted that its footprint is evident in vexing social problems including inequitable immigration policies and housing segregation. Clearly there is an urgent need for cross-disciplinary studies of structural aspects of racism; its toxic impact on health persists across generations. The nature of the physiological measures taken during the laboratory studies will continue to become more sophisticated. Psychophysiological research involving pharmacological blockades has identified the relative sympathetic and parasympathetic contributions to selected cardiac measures (see [6]). Several indices of heart rate variability (HRV) provide useful measures of fluctuations in vagal control of the heart [2]. Utsey and Hook [93] employed HRV, an index of parasympathetic activity, as a possible moderator of the effects of discrimination on mental health. The African American men who tended to have higher HRV did not show the deleterious effects on mental health ratings evidenced by those with lower HRV. Neblett and Ford [76], studying an African American college sample, examined the effects of blatant and subtle forms of racism enacted by either Black or White perpetrators on

222

C. J. P. Harrell et al.

HRV and the cardiac pre-ejection period (PEP), a measure of sympathetic cardiac activity. They found that the changes in both cardiac measures elicited by the various forms of racism were moderated by Private Regard, the extent to which participants internalize racist beliefs. Thayer and Friedman [92] argued that because unsettling encounters with racially charged events often generate perseverative cognitive processes that are antagonistic to cardiac vagal input, HRV may be a useful index of the impact of racism. Laboratory studies are singularly positioned to identify some of the mechanisms through which racism permanently alters physiological processes. Mays et al. [68] encouraged the use of imaging technology in laboratory studies of the neural pathways involved in processing racist input. Though they are extremely promising, neural imaging studies are in their infancy in the study of the impact of racism. An early study [62] found that both Black and White participants showed greater amygdala activation viewing Black versus White faces. This response, possibly reflecting fear, may represent the inculcation of racist values and attitudes (see [35, 80]). In the foreseeable future, neuroimaging used in conjunction with these peripheral measures (see [59]) will chart the neural circuitry activated by particular forms of racism.

The Promise of the Empirical Studies Figure 1 contains theoretical depictions of the substantive yield of empirical studies of racism. It shows that the levels of knowledge forthcoming from this research varies as a function of two parameters: the complexity of the operationalization of racism and the nature of the measures of health-related symptoms. The graphs in the middle and upper left panels show that we learn more about the neural pathways of racism by increasing the sophistication of the measures used to assess symptoms than by increasingly complex conceptualizations of racism. The right panels depict the increased knowledge gained about the facets of racism that impact health by operationalizing it in more complex terms (middle panel). The right upper panel illustrates the lesser, though not negligible, impact of using more sophisticated physiological measures on the knowledge acquired about racism’s health effects. The lower panels of Fig. 1 depict the combined impact of these parameters of research. Specifically, the lower right panel shows that as the conceptualization of racism becomes more complex (i.e., tests multiple facets of racism) in studies that measure self-reports of symptoms or medical histories, they tend to increase our understanding of the relationship between the terrain of racist manifestations and health outcomes, but to a smaller extent than when more refined physiological indices serve as dependent measures and complex approach to racism are taken. The kinds of studies that will provide the highest yield of knowledge about the physiological pathways that lead from environmental events to cardiovascular health outcomes are characterized in the lower left panel. The optimal studies assess cardiovascular parameters with known neural origins or employ functional brain imaging techniques, and they operationalize and expose participants to multiple forms of racism. Thus, as the right panels of the illustration suggests, survey research

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

223

Fig. 1 Theoretical depictions of the substantive yield of empirical studies of racism

studies that measure multiple facets of racism, even if limited to medical records or self-reports of health status, potentially provide a large amount of information about the forms of racism that might impact health. In the case of complex brain imaging experiments— represented at the bottom left illustration—even where the analogs of racist events reflect only one form of racism, the findings provide insight into the physiological pathways of racism. Though the laboratory and field studies can be viewed as lying on opposite ends of a research continuum, they are more than complementary. Crucial to Krieger’s [54] very valuable ecosocial approach to disease is the notion of the multilevel representation of factors in complex diseases. She illustrated the interaction among causal levels with a fractal model in which each factor level is represented at all

224

C. J. P. Harrell et al.

levels in a multi-factorial system. Accordingly, racism is at once cognitive and epigenetic, economic and neural; its cognitive effects have neural components, and its economic impact will have epigenetic implications. Understanding how racism impacts health status will result from examining multiple levels of its manifestations. The pathways that empirical studies of racism promise to uncover lead from encounters with racism to disease outcomes. These channels will vary across different forms of racism and as a function of individual differences in psychological and physiological predispositions. In the model proposed by Harrell et al. [38], racist events, only a portion of which will be processed consciously, activate both cortical and subcortical structures. Some will elicit significant changes in traditional stress axes, while others may not. Ultimately, the permanent record of living in a racialized society is written in implicit and declarative memory and in physiological systems, through mechanisms ranging from epigenetic to alterations in allostatic load.

Future Directions Three caveats accompany the promise of the future laboratory studies. First, it is likely that the brain circuits involved in responses to racism are being identified presently in neural imaging studies and that they involve the central autonomic network described by several investigators [25, 34, 91]. This network consists of cortical circuits in the anterior cingulate and ventromedial prefrontal cortices and a cavalcade of subcortical structures, among them the central nucleus of the amygdala and networks of hypothalamic nuclei that connect to medullary structures. Critchely [19] reviewed laboratory evidence that traced the cardiac and electrodermal responses occurring during cognitive and emotional processing to the activation of the anterior cingulate cortex and the amygdala. Various psychological mechanisms including stress and coping, rumination, negative emotions, and classically conditioned responses may operate in the racist context [38]. These mechanisms will share common pathways with other emotional and cognitive states and are not likely to be unique to racism. Thus, the sensory and efferent, cognitive and emotional pathways neural imaging studies are revealing are extremely important in the study of racism. As is the case with cognitive and emotional output, pathways mediating responses specific to the context of racism will be “flexibly recruited” [44] from existing circuitry that assists humans in adapting to complex environments. The uniqueness of responses to racism will reside in the temporal dimensions and configurations of the recruitment of the neural networks that mediate peripheral responses. Ito and Bartholow [44] presented a neural model of race perception that includes loci for encoding facial information (fusiform gyri), knowledge (cortical), evaluation (e.g., amygdala), and regulatory activity (prefrontal cortical areas). It is possible to develop similar models of the processing of racism as the various facets of racism are delineated. Secondly, very small sample sizes even for psychophysiological studies are often employed in neuroimaging research. Consistent with evidence of marked

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

225

individual differences in the susceptibility to the impact of racism, Critchely [19] reviewed mounting evidence pointing to individual differences in interoception of emotional cues that might significantly affect emotional reactions. A comprehensive understanding of the moderating effects personality variables may have on responses to racism requires data from a larger number of participants. Tension exists between studies of the developing knowledge of the neural mechanisms involved in response to racism and the appreciation of important individual differences in these responses. Some reconciliation may be possible if personality measures are obtained in the neural imaging studies, permitting reviewers to execute meta-analyses of the impact of personality on neural circuits. Finally, measuring racism accurately and comprehensively will also require the use of qualitative methods. Qualitative strategies enhance the understanding of individual differences in perception, response, and coping with racism and will potentially aid researchers in the development of theories and models that explain the effects of racism on health. Obtaining information from individuals who are targets of racism via written or oral account of their experiences will be an important aspect of future research [or equivalent].

Conclusion Gravlee [32] distinguished between scientific perspectives that emphasize race as biology and those that study how race becomes biology. The study by Hoffman in the late 1800s [41] is extended by research that conceptualizes race from a biologic perspective. This tradition has resulted in a large literature, including race-based health disparities and the search for genetic factors (see [80]). The biological legacy has generated countless tests of various forms of hypotheses positing racial frailty of the non-dominant groups in a society. In contrast, the early critiques of Miller [71] and Du Bois [23] live on in investigations of how race becomes biology. These pioneers would be pleased that the mounting field and laboratory evidence of racism’s relationship to cardiovascular disease now merits frequent if not yearly reviews [97]. Currently, multilevel models emerging from several disciplines describe nonlinear and reciprocal causal paths that transcribe environmental events onto the physiology of individuals [66, 68, 96]. More sophisticated conceptualizations of racism serve as beacons for examinations of social conditions and health. They make it clear that racism, with its lethal pantheon of neglects, insults, toxins, and obfuscations, saturates every level of life. Its writing on the physiological parchment begins prenatally [58] and continues across the lifespan. Ironically, the rejection of the race-as-biology perspective intensifies the search for the source of racialized health outcomes. It charges researchers to sharpen the focus on subtle and intransigent aspects of racism and challenges them to operationalize each manifestation for use in field and laboratory investigations. The yield of this research will be an understanding of the interplay between various

226

C. J. P. Harrell et al.

aspects of racism and the activation of biological systems. Ultimately, we will learn less of “race traits” and more of the disease-nurturing dispositions of societies that have been sick with racism far too long.

References 1. Adeyemo A, Gerry N, Chen G et al (2009) A genome-wide association study of hypertension and blood pressure in African Americans. PLoS Genet 5(7):e1000564 2. Allen JJB, Chambers AS, Towers DN (2007) The many metrics of cardiac chronotropy: a pragmatic primer and a brief comparison of metrics. Biol Psychol 74:243–262 3. Anderson NB (1989) Racial differences in stress-induced cardiovascular reactivity and hypertension: current status and substantive issues. Psychol Bull 105:89–8105 4. Avise JC, Ball RM (1990) Principles of genealogical concordance in species concepts and biological taxonomy. Oxf Surv Evol Biol 7:45–67 5. Beatty DL, Matthews KA (2009) Unfair treatment and trait anger in relation to nighttime ambulatory blood pressure in African American and white adolescents. Psychosom Med 71:813–820 6. Berntson GG, Cacioppo JT, Binkley PF et al (1994) Autonomic cardiac control: III Psychological stress and cardiac response in autonomic space as revealed by pharmacological blockades. Psychophysiology 31:599–608 7. Bonilla-Silva E (1997) Rethinking racism: toward a structural interpretation. Am Sociol Rev 62:465–480 8. Bonilla-Silva E (2001) White supremacy and racism in the post-civil rights era. Lynne Rienner Publishers, Boulder 9. Bonilla-Silva E (2006) Racism without racists: color-blind racism and the persistence of racial inequality in the United States. Rowman and Littlefield Publishers Inc, Lanham 10. Brondolo E, Rieppi R, Kelly KP, Gerin W (2003) Perceived racism and blood pressure: a review of the literature and conceptual and methodological critique. Ann Behav Med 25:55–65 11. Brondolo E, Gallo LC, Myers HF (2009) Race, racism and health: disparities, mechanisms, and interventions. J Behav Med 32:1–8 12. Burford TI (2009) Structural racism cardiovascular activity and affect: the role of rumination and personality. Unpublished dissertation, Howard University 13. Centers for Disease Control and Prevention (2012). Summary health statistics for US Adults: National health interview survey 2012 [Data file]. Retrieved from http://www.cdc.gov/nchs/ data/series/sr_10/sr10_260.pdf 14. Chobanian A, Bakris G, Black H et al (2003) Seventh report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension 42:1206–1252 15. Cooper RS (2004) Genetic factors in the ethnic disparities in health. In: Anderson NB, Bulatao RA, Cohen B (eds) Critical perspectives on racial and ethnic differences in health in late life. The National Academies Press, Washington, DC, pp 269–309 16. Cooper RS, Rotimi C, Ataman S, McGee D, Osotimehin B, Kadiri S (1997) Hypertension prevalence in seven populations of African origin. Am J Public Health 87:160–168 17. Cooper RS, Wolf-Maier K, Luke J, Adeyemo A, Banegas JR, Forrester T et al (2005) An international comparative study of blood pressure in populations of European and African descent. Biomed Cent Med 3:2 18. Cooper DC, Mills PJ, Bardwell WA et al (2009) The effects of ethnic discrimination and socioeconomic status on endothelin-1 among blacks and whites. Am J Hypertens 22:698–704 19. Critchley HD (2009) Psychophysiology of neural cognitive and affective integration: fMRI and autonomic indicants. Int J Psychophysiol 73:88–94

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

227

20. Curtin PD (1992) The slavery hypothesis for hypertension among African Americans: the historical evidence. Am J Public Health 82:1681–1686 21. Diez-Roux AV (2004) Estimating neighborhood effects: the challenges of causal inferences in a complex world. Soc Sci Med 58:1953–1960 22. Dimsdale J (2002) Stalked by the past: the influence of ethnicity on health. Psychosom Med 62:161–170 23. Du Bois WB (1906) The health and physique of the American Negro: a sociological study made under the direction of Atlanta University by the eleventh Atlanta Conference. Atlanta University Press, Atlanta 24. Gee GC, Ford CL (2011) Structural racism and health inequities: old issues, new directions. Du Bois Rev 8:115–122 25. Gianaros PJ, Sheu LK (2009) A review of neuroimaging studies of stressor-evoked blood pressure reactivity: emerging evidence for a brain-body pathway to coronary heart disease risk. NeuroImage 47:922–936 26. Gliebermann L (2009) Sodium blood pressure and ethnicity: what have we learned. Am J Hum Biol 21:679–686 27. Goldstein IB, Shapiro D, Guthrie D (2006) Ambulatory blood pressure and family history of hypertension in healthy men and women. Am J Hypertens 19:486–491 28. Goodman AH (2000) Why genes don’t count (for racial differences in health). Am J Public Health 90:1699–1702 29. Goodman M, Grossman LI, Wildman DI (2005) Moving primate genomics beyond the chimpanzee genome. Trends Genet 21:511–517 30. Gottlieb G (1998) Normally occurring environmental and behavioral influences on gene activity: from central dogma to probabilistic epigenesis. Psychol Rev 105:792–802 31. Graves JL (2001) The emperor's new clothes: biological theories of race at the millenium. Rutgers University Press, New Brunswick 32. Gravlee CC (2009) How race becomes biology: embodiment of social inequality. Am J Phys Anthropol 139:47–57 33. Greenwald A, McGhee D, Schwartz J (1998) Measuring individual differences in implicit cognition: the implicit association test. J Pers Soc Psychol 74:1464–1480 34. Hagemann D, Waldstein SR, Thayer JF (2003) Central and autonomic nervous system integration in emotion. Brain Cogn 52:79–87 35. Harrell CJP (1999) Manichean psychology: racism and the minds of people of African descent. Howard University Press, Washington, DC 36. Harrell SP (2000) A multidimensional conceptualization of racism-related stress: implications for the well-being of people of color. Am J Orthopsychiatry 70:42–57 37. Harrell J, Hall S, Taliaferro J (2003) Physiological responses to racism and discrimination: an assessment of the evidence. Am J Public Health 93:243–248 38. Harrell CJP, Burford TI, Cage B, Nelson TM, Shearon S, Thompson A, Green S (2011) Multiple mechanisms linking racism to health outcomes: recommended interventions. Du Bois Rev 8:143–157 39. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. PNAS 106:9362–9367 40. Hirsch C, Anderson ML, Newman A, Kop W, Jackson S, Gottdiener J et al (2006) The association of race with frailty: the Cardiovascular Health Study. Ann Epidemiol 16:545–553 41. Hoffman F (1896) Race traits and tendencies of the American Negro. American Economic Association, New York 42. http://humanorigins.si.edu/evidence/human-family-tree 43. Hubner N, Yagil C, Yagil Y (2006) Novel integrative approaches to identification of candidate genes in hypertension. Hypertension 47:1–5 44. Ito TA, Bartholow BD (2009) The neural correlates of race. Trends Cogn Sci 13:524–531

228

C. J. P. Harrell et al.

45. Jackson FLC (2004) Human genetic layering and health: new assessment approaches based on ethnogenetic layering. Br Med Bull 69:215–235 46. Johnston TD, Edwards L (2002) Genes, interactions, and the development of behavior. Psychol Rev 109:26–34 47. Jones JM (1997) Prejudice and Racism, 2nd edn. McGraw-Hill, New York 48. Jones DR, Harrell JP, Morris-Prather CE et al (1996) Affective and physiological responses to racism: the roles of afrocentrism and mode of presentation. Ethn Dis 6:109–122 49. Kaufman JS, Cooper RS (2007) RE: Hirsch C, Anderson ML, Newman A, Kop W, Jackson S, Gottdiener J, et al, for the Cardiovascular Health Study Research Group. The association of race with frailty: the Cardiovascular Health Study. Ann Epidemiol 2006;16:545-553. Ann Epidemiol 17:157–158 50. Kaufman JS, Cooper RS (2008) Race in epidemiology: new tools, old problems. Ann Epidemiol 18:119–123 51. Kaufman JS, Cooper RS, McGee DL (1997) Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race. Epidemiology 8:621– 628 52. Keita SOY, Kittles RA, Royal CDM et al (2004) Conceptualizing human variation. Nat Genet 36:17–20 53. Krieger N (1999) Embodying inequality: a review of concepts, measures, and methods for studying health consequences of discrimination. Int J Health Serv 29:295–352 54. Krieger N (2011) Epidemiology and the people’s health: theory andcontext. Oxford University Press, New York 55. Krieger N, Lowy I, Aronowitz R et al (2005) Hormone replacement therapy, cancer, controversies, and women's health: historical, epidemiological, biological, clinical, and advocacy perspectives. J Epidemiol Community Health 59:740–748 56. Krieger N, Carney D, Lancaster K et al (2010) Combining explicit and implicit measures of racial discrimination in health research. Am J Public Health 100:1485–1492 57. Kurian and Cardarelli (2007). Racial and ethnic differences in cardiovascular disease risk factors: A systemic review. Ethnicity & Disease, 17, 143–152 58. Kuzawa C, Sweet E (2009) Epigenetics and the embodiment of race: developmental origins of US racial disparities in cardiovascular health. Am J Hum Biol 21:2–15 59. Lane R, Waldstein SR, Chesney M et al (2009) The rebirth of neuroscience in psychosomatic medicine, part I: historical context, methods and relevant basic science. Psychosom Med 71:117–134 60. Lewontin R (1972) The apportionment of human diversity. Evol Biol 6:1–398 61. Li JZ, Absher DM, Tang H et al (2008) Worldwide human relationships inferred from genomewide patterns of variation. Science 319:1100–1104 62. Lieberman MD, Hariri A, Jarcho JM et al (2005) An fMRI investigation of race-related amygdala activity in African-American and Caucasian-American individuals. Nat Neurosci 8:720–722 63. Linnaeus C (1758) Systema naturae, 10th edn. Holmiae Impensis Direct Laurentii Salvii 64. Long JC, Li J, Healy ME (2009) Human DNA sequences: more variation and less race. Am J Phys Anthropol 139:23–34 65. Lorusso L, Bacchini F (2015) A reconsideration of the role of self-identified races in epidemiology and biomedical research. Stud Hist Phil Biol Biomed Sci 52:56–64. https://doi.org/ 10.1016/j.shpsc.2015.02.004 66. Madhere S, Harrell J, Royal C (2009) Social ecology, genomics, and African American health: a nonlinear dynamical perspective. J Black Psychol 35:154 67. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ et al (2009) Finding the missing heritability of complex diseases. Nature 461(7265):747–753 68. Mays VM, Cochran SD, Barnes NW (2007) Race, race-based discrimination, and health outcomes among African Americans. Annu Rev Psychol 58:201–225

9

From Race to Racism in the Study of Cardiovascular Diseases: Concepts and. . .

229

69. McNeilly MD, Anderson NB, Armstead CA et al (1996) The perceived racism scale: a multidimensional assessment of the experience of white racism among African Americans. Ethn Dis 6:154–166 70. Merritt MM, Bennett GG, Williams RB et al (2006) Perceived racism and cardiovascular reactivity and recovery to personally relevant stress. Health Psychol 25:364–369 71. Miller K (1897) A review of Hoffman’s Race traits and tendencies of the American Negro. The American Negro Academy, Washington, DC 72. Moore DS (2001) The dependent gene: the fallacy of nature vs nurture. Henry Holt and Company, New York 73. Morenoff JD, Lynch JW (2004) What makes a place healthy: neighborhood influences on racial/ethnic disparities over the life course. In: Anderson NB, Bulatao RA, Cohen B (eds) Critical perspectives on racial and ethnic differences in health in late life. The National Academies Press, Washington, DC, pp 406–449 74. Mujahid MS, Diez-Roux AV, Morenoff JD et al (2008) Neighborhood characteristics and hypertension. Epidemiology 19:590–598 75. National Center for Health Statistics (2007) Health United States 2007 with Chartbook. National Center for Health Statistics, Hyattsville 76. Neblett EW Jr, Roberts SO (2013) Racial identity and autonomicresponses to racial discrimination. Psychophysiology 50:943–953. https://doi.org/10.1111/psyp.12087 77. Neel JV (1962) Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? Am J Hum Genet 14:353–362 78. Neel J (1999) The “thrifty genotype” in 1998. Nutr Rev 57(5 Pt 2):S2–S9 79. Nguyen TT, Kaufman JS, Whitsel EA, Cooper RS (2009) Racial differences in blood pressure response to calcium channel blocker monotherapy: a meta-analysis. Am J Hypertens 22:911–917 80. Paradies YC (2006) Defining conceptualizing and categorizing racism in health research. Crit Public Health 16:143–157 81. Paradies Y, Cunningham J (2009) Experiences of racism among urban Indigenous Australians: findings from the DRUID study. Ethn Racial Stud 32:548–573 82. Paradies Y, Montoya M, Fullerton S (2007) Racialized genetics and the study of complex diseases. Perspect Biol Med 50:203–227 83. Ray O (2004) How the mind hurts and heals the body. Am Psychol 59:29–40 84. Reed FA, Tishkoff SA (2006) African human diversity, origins and migrations. Curr Opin Genet Dev 16:597–605 85. Reich D, Green RE, Kircher M, Krause J, Patterson N, Durand EY et al (2010) Genetic history of an archaic hominin group from Denisova Cave inSiberia. Nature 468:1053–1060. https:// doi.org/10.1038/nature09710. http://www.nature.com/nature/journal/v468/n7327/abs/ nature09710.html#supplementary-information 86. Romualdi C, Balding D, Nasidze IS et al (2002) Patterns of human diversity, within and among continents, inferred from biallelic DNA polymorphisms. Genome Res 12:602–612 87. Rushton J, Jensen A (2005) Thirty years of research on race differences in cognitive ability. Psychol Public Policy Law 11:235–294 88. Ruvolo M (1997) Genetic diversity in hominoid primates. Annu Rev Anthropol 26:515–540 89. Templeton A (2005) Haplotype trees and modern human origins. Yearb Phys Anthropol 48:33–59 90. Templeton A (2013) Biological races in humans. Stud Hist Phil Biol Biomed Sci 44:262–241. https://doi.org/10.1016/j.shpsc.2013.04.010 91. Thayer J (2007) What the heart says to the brain (and vice versa) and why we should listen. Psychol Top 16:241–250 92. Thayer JF, Friedman BH (2004) A neurovisceral integration model of health disparities in aging. In: Anderson NB, Bulatao RA, Cohen B (eds) Critical perspectives on racial and ethnic differences in health in late life. The National Academies Press, Washington, DC, pp 567–603

230

C. J. P. Harrell et al.

93. Utsey S, Hook J (2007) Heart rate variability as a physiological moderator of the relationship between race-related stress and psychological distress in African Americans. Cult Divers Ethn Minor Psychol 13:250–253 94. Wang V, Sue S (2005) In the eye of the storm: race and genomics in research and practice 1. Am Psychol 60:37–45 95. Wild CP (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev 14:1847–1850 96. Williams DR (1997) Race and health: basic questions, emerging directions. Ann Epidemiol 7:322–333 97. Williams DR, Mohammed SA (2009) Discrimination and racial disparities in health: evidence and needed research. J Behav Med 32:20–47 98. Williams DR, Neighbors HW, Jackson JS (2003) Racial/ethnic discrimination and health: findings from community studies. Am J Public Health 93:200–208 99. Wilson TW, Hollifield LR, Grim CE (1991) Systolic blood pressure levels in black populations in sub-Sahara Africa, the West Indies, and the United States: a meta-analysis. Hypertension 18:87–91 100. Wolf-Maier K, Cooper R, Banegas J et al (2003) Hypertension prevalence and blood pressure levels in 6 European countries, Canada, and the United States. JAMA 289:2363–2369 101. Wu X, Kan D, Province M et al (2006) An updated meta-analysis of genome scans for hypertension and blood pressure in the NHLBI Family Blood Pressure Program (FBPP). Am J Hypertens 19:122–127 102. Zietkiewicz E, Yotova V, Gehl D et al (2003) Haplotypes in the dystrophin DNA segment point to a mosaic origin of modern human diversity. Am J Hum Genet 73:994–1015 103. Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. PNAS 109:1193–1198. https://doi.org/10.1073/pnas. 1119675109

Socioeconomic Status and Cardiovascular Disease

10

Linda C. Gallo, Steven D. Barger, Addie L. Fortmann, and Smriti Shivpuri

Contents Conceptualization and Measurement of SES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traditional Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Childhood Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Course Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Area-Based Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subjective Social Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socioeconomic Gradients in CVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indicators of Adult SES and CVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indicators of Childhood SES and CVD Later in Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Course Approaches to Evaluating the SES and CVD Association . . . . . . . . . . . . . . . . . . . . Variability in the Pattern of the Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Are the Pathways Connecting SES with CVD and What Possibilities Exist for Intervention? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Upstream/Structural Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental Influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 235 235 236 237 237 238 238 241 241 242 243 243 244 245

L. C. Gallo (*) · S. Shivpuri Department of Psychology, San Diego State University, San Diego, CA, USA e-mail: [email protected] S. D. Barger Department of Psychological Sciences, Northern Arizona University, Flagstaff, AZ, USA A. L. Fortmann Scripps Whittier Diabetes Institute, Scripps Health, San Diego, CA, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_10

231

232

L. C. Gallo et al.

Healthcare Access and Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biobehavioral Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychosocial Risk and Resilience Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

246 247 248 250 251 252

Abstract

Substantial research suggests that individuals with low socioeconomic status (SES) are at elevated risk for cardiovascular disease (CVD) morbidity and mortality. The current chapter focuses on the relationship between SES and CVD and the antecedents of this association. First, the article discusses issues in conceptualization and measurement of SES in CVD research. Second, the literature concerning SES and CVD is summarized, drawing substantially on available resources and comprehensive reviews while highlighting notable recent and novel studies. Several factors viewed as key determinants of SES-related disparities in CVD are then discussed, including upstream, structural/material and environmental influences, biobehavioral pathways, and psychosocial risk processes. Finally, in this context, the chapter addresses how information regarding each candidate pathway can be conveyed into prevention and intervention programs to reduce or eliminate SES-related CVD disparities. Keywords

Cardiovascular disease · Coronary heart disease · Social status · Socioeconomic status · Stroke

Health disparities (i.e., preventable gaps in the health status of population groups) have existed for centuries and have been recognized by the scientific and healthcare communities for many decades [107]. Currently, an abundance of research indicates that individuals of minority ethnicity (e.g., African American/Black; Native American; Hispanic/Latino) and low socioeconomic status (SES) suffer worse health than do other population groups. The association between SES and health takes a graded form, so that each incremental increase in SES brings health advantages, although the gradient is typically steepest at the lowest income levels [4, 7]. This relationship does not appear to be a primary result of reverse causation (aka “endogeneity” bias) or the influence of poor health on SES; rather, the influence of SES on health appears to be the more salient directional component [4]. Although some variability in the strength or pattern of the gradient has been observed, SES-related health disparities have been documented across sex, age, and ethnic groups. Furthermore, SES relates to health in countries with and without socialized healthcare programs and to health outcomes that are and are not amenable to medical treatment, suggesting that healthcare access or quality – while certainly a contributing factor – does not explain the SES health gradient [6, 7]. Rather, a complex web of interrelated material determinants and intervening factors appears to

10

Socioeconomic Status and Cardiovascular Disease

233

underlie health disparities [4, 33, 166]. A clear understanding of how we can address these factors to eliminate inequities in health continues to elude researchers and policy makers alike. In fact, despite overall improvements in population health over the course of the last century, and increasing public health efforts to eliminate them, SES and ethnicity-related health disparities persist and may even be widening [95, 140]. The current chapter will focus on the relationship between SES and cardiovascular disease (CVD) and the antecedents of this association. First, we will address issues in conceptualization and measurement of SES in CVD research. Consistent with the broad and complex nature of the construct, we discuss a variety of measurement approaches, their applications, and associated challenges and limitations. Second, we will summarize the literature concerning SES and CVD, drawing substantially on available resources and comprehensive reviews, while highlighting notable recent and novel studies. Next, we will examine several factors viewed as key determinants of SES-related disparities in CVD, including upstream, structural/ material and environmental influences, biobehavioral pathways, and psychosocial risk processes. Finally, in this context, we also address how information regarding each candidate pathway can be conveyed into prevention and intervention programs that seek to eliminate health disparities.

Conceptualization and Measurement of SES The term socioeconomic status (also socioeconomic position or social status) refers to an individual or group’s relative standing in a social and economic hierarchy. Consistent with the complex and multi-dimensional nature of the construct, SES has been conceptualized and measured in many [72, 73, 75, 108, 174]. Common indicators can broadly be organized according to whether they tap access to material and social resources or prestige and power differentials that indicate rank within a social hierarchy [12, 108]. SES may be measured at the individual (e.g., years of education), household (household income), or neighborhood/community level (percent below the poverty line) and can be assessed from various temporal standpoints including socioeconomic circumstances in childhood, adolescence, adulthood, or across the life course. Research suggests that discrete measures of SES are only moderately correlated and that each may show differential relationships with health outcomes, through distinct pathways [22, 204]. Below we describe common approaches to SES measurement and discuss their strengths, limitations, and relevance to understanding CVD risks and outcomes.

Traditional Indicators Education, income, and occupation – factors that can be viewed as markers of both rank and access to resources – are the three most common SES indicators in health research. Although correlated, each measure captures distinct aspects of SES, which

234

L. C. Gallo et al.

may be more or less relevant to different health outcomes and at varied points in the life course [73, 167, 212]. Due to its influence on occupational opportunities and earnings potential, education is often considered the most fundamental component of SES [3]. Education also provides a set of cognitive resources [136] that may make people more receptive to health promotion messages and better able to access health services. Education can be measured quantitatively (i.e., total number of years of education attained) or categorically (e.g., completion of high school), is relatively easy to assess, has low refusal rates, and is relevant to all adults regardless of age or activity in the labor force [75, 108]. Income information can be continuous (i.e., absolute income) or categorical (e.g., predefined income brackets) in form and can also be represented as a relative indicator, for example, a family or individual’s standing in relation to the official poverty level [138]. Household income, corrected for the number of individuals supported, tends to more accurately reflect standard of living and access to healthrelated resources than individual income [75, 108]. Income is believed to influence health primarily through its conversion into health-promoting environments (e.g., better housing), commodities (e.g., nutrition, exercise), and services (e.g., healthcare). Because households with comparable incomes may differ greatly in their total net worth [108, 136], measures of wealth, or accumulated assets, can provide an important supplement to income information, especially among older or retired populations [92, 118, 193]. Current or longest held occupation is another commonly used SES indicator in CVD research. In this approach, individuals are categorized into occupational status groups, based on prestige, skills, social influence, and/or power [22]. Occupational status positions individuals within the hierarchical social structure, which defines access to health-related resources and services. Each occupation also has its own unique characteristics, including physical and psychological demands, decisionmaking latitude, environmental conditions (e.g., exposure to toxins), economic returns, and effects on lifestyle factors (e.g., alcohol consumption and smoking) – with important implications for health [139, 204]. Although education, income, and occupation have exhibited consistent associations with cardiovascular health, each has limitations that should be considered. For example, the meaning of education varies across birth cohorts [108], the economic returns on education may differ markedly across ethnic and gender groups, the quality of instruction received is unknown, and it can be difficult to compare education obtained in different countries [72, 93, 136, 231]. Income information is sensitive, resulting in frequent inaccurate reporting and high non-response rates (i.e., 10–25%; [224]). In addition, household measures assume that income is equally distributed among individuals within a household [75, 108] and do not contain information about purchasing power, which may vary by area [108]. Occupational categorization systems have been criticized for their inherent subjectivity [97]; are often complicated by within-category variance in prestige, skills, power, and/or earnings [22, 104]; and cannot be used for individuals who are not in the labor force. Lastly, income, occupation, and (to a lesser extent) education may be influenced by reverse causation, resulting from the effects of illness on SES. Despite

10

Socioeconomic Status and Cardiovascular Disease

235

the limitations inherent to these indicators [30, 45], education, income, and occupational status are useful and widely used markers in studies of SES-CVD associations [84, 97].

Childhood Indicators Considering the important influence adulthood SES has on CVD, it is not surprising that socioeconomic conditions experienced during childhood also relate to health, both in youth [34, 200] and later in life [71]. Father’s occupation is the most frequently used indicator of childhood SES [72]; however, many other indicators, such as mother’s occupation, mother or father’s education or an aggregate of both, indicators of wealth (e.g., home or car ownership), housing conditions, and crowding during childhood, have also been considered [71]. Reports of these factors collected during childhood have generally demonstrated stronger associations with adult health than retrospective accounts [74]. Importantly, studies that have investigated the relative contribution of adult and childhood SES have found childhood markers to be a much weaker predictor [25, 150, 187]. Therefore, although the inclusion of childhood SES provides additional explanatory power, socioeconomic conditions experienced during childhood should not be considered a replacement for adulthood SES in investigating SES health disparities.

Life Course Approaches Other studies have incorporated a life course perspective to investigate how SES at various developmental phases relates to CVD and other physical health outcomes [186]. This approach pays explicit attention to timing and duration of exposures in explaining associations between risk factors and health endpoints and includes consideration of socioeconomic factors experienced during gestation, infancy, childhood, adolescence, and/or adulthood, in combination [113]. The underlying premise is that a single SES measurement taken at only one time point is inadequate to fully capture the evolving social and biological implications of SES across the life course. For instance, an investigator might examine parental poverty status at several critical periods during development (e.g., gestation; onset of formal schooling; adolescence), as well as poverty status in adulthood. The specific indicators chosen, and the points at which they are measured, should reflect the investigator’s conceptual perspective regarding how early life circumstances connect with a given health outcome, as well as his/her understanding of historical and geographical contextual factors relevant to the sample [35]. An excellent example of this approach can be found in the prospective-cohort Newcastle Thousand Families Study, in which multiple measures of SES (i.e., parental occupational status and housing conditions, defined according to indicators of crowding, available hot water, shared bath, and presence of dampness or disrepair) were collected from parents of child-participants in the study at their

236

L. C. Gallo et al.

birth and at 5 years and 10 years of age. In later surveys, measures of SES (i.e., educational attainment and occupational status) were also collected from participants as adults [178]. In one study from this cohort, birth weight and childhood SES had a small independent association with participants’ extent of atherosclerosis at age 50, whereas adult socioeconomic circumstances had a relatively larger impact [116]. Although prospective accounts are certainly preferable, various measures of socioeconomic circumstances in childhood, adolescence, and adulthood can be collected in any study to allow evaluation of specific hypotheses regarding how SES at specific points in time, or across the life course, relate to CVD.

Area-Based Measures In addition to individual and family SES markers, growing evidence suggests that area-based socioeconomic indicators, such as community or neighborhood level SES, may have independent cardiovascular health relevance (for reviews, see [40, 45, 48, 120, 159, 184]). Area-based measures can provide important contextualizing information as individuals with similar levels of income or education could live in very disparate socioeconomic environments. For example, in the USA, ethnic/racial minorities may live in more impoverished, segregated neighborhoods when compared with their non-Hispanic White counterparts, even when levels of individual SES are comparable [117, 232]. Socioeconomic characteristics of communities are generally expressed through aggregate indicators of the SES of individuals living in the area, typically obtained from census data reports. Examples include proportion of residents with a high school education or greater or proportion of individuals living below the poverty line. Most research suggests that census tract and census block data perform similarly, whereas larger geographic units (e.g., zip code areas) may underestimate area SES effects [109, 110]. More recent approaches that provide a household-specific conceptualization of neighborhoods by examining characteristics of a geographically defined “buffer” surrounding an address may provide even more refined information regarding social environmental impact on health [58]. There are several limitations and challenges inherent to area-based measures that should be noted. For instance, census information can quickly become outdated in rapidly changing communities, and due to the error involved with the area-based approach (i.e., “ecological fallacy” [1]), studies that rely on these measures as proxies for person or household-level SES can substantially over- or underestimate the health impact of SES. Moreover, area versus individual indicators may in fact be tapping distinct constructs with differential pathways to health [77, 78]. In addition, although studies that have explored the association between area-based SES indicators and CVD have generally found that their independent effects are small to moderate (e.g., [120, 159]), many have failed to control for multiple individual SES indicators, which may result in residual confounding [159, 184]. Neighborhood selection, related to the non-random sorting of individuals into neighborhoods, may also contribute to area-based effects, although a recent study from the Health and Nutrition Examination Survey found that area socioeconomic status predicted

10

Socioeconomic Status and Cardiovascular Disease

237

10-year cardiovascular risk after accounting for the estimated influence of neighborhood selection [185]. Despite limitations and challenges associated with area models [30, 44, 45], such indicators of SES can be an important adjunct in research that seeks to establish the diverse pathways through which socioeconomic factors relate to CVD and other health outcomes.

Subjective Social Status In addition to objective indicators, some researchers have begun to address how subjective impressions of social status may relate to health. Such measures are believed to represent the “cognitive averaging of standard markers of socioeconomic situation” [207] and are based on the premise that perceived status within a selfdefined societal group may be as important to health as objectively measured status. Scores on subjective social status (SSS) measures have been associated with CVD risk factors [8, 39, 79, 122, 146, 207, 216], although many studies concerning SSS have examined less objective health outcomes, such as self-rated health [27, 163, 177], mental health [27, 171, 201], and behavioral risk factors [189, 192, 230]. A methodological limitation of findings concerning subjective or self-reported health outcomes is that a common response bias, or an unmeasured variable such as negative affectivity, may contribute to ratings on both SSS and perceived health, although at least some evidence supports the tenet that SSS relates to physical health outcomes beyond its association with negative affect [8, 41, 207]. Importantly, there is currently relatively little information regarding the construct validity of subjective measures [41], and some research indicates that subjective status may be defined inconsistently, or rated using different information, across ethnic/racial groups [9, 13, 176, 234]. A further limitation of these measures is their lack of integration into archival studies or ongoing surveillance studies, which prohibits comparisons across time. Thus, additional evidence is needed regarding the meaningfulness of SSS, the applicability of this construct across groups, and whether social status (SS) relates to objective health outcomes and clinical CVD morbidity and mortality.

Conclusions As the above discussion suggests, researchers are faced with many choices when selecting measures of SES for research concerning CVD risks and outcomes, including the level (i.e., individual, family, or area-based) and timing (i.e., childhood, adulthood, or life course approaches) of measurement, and whether to include objective or subjective indicators, or both. In general, investigations of the associations between SES and health should assume an outcome- and populationspecific approach to SES measurement, collect as much relevant socioeconomic information as possible at various time points, acknowledge the limitations inherent to the studied indicators, and carefully consider how conclusions may be influenced by unmeasured socioeconomic factors.

238

L. C. Gallo et al.

Socioeconomic Gradients in CVD A number of comprehensive reviews have concluded that low SES is associated with greater CVD risk, morbidity, and mortality [2, 74, 84, 91, 120, 145, 186]. This inverse SES-CVD gradient initially emerged in industrialized nations in the 1970s (whereas prior to this time lower SES was associated with lower CVD risk) and continued to increase in magnitude throughout the twentieth century [84, 97]. Below we summarize conclusions from previous research syntheses and also highlight illustrative and/or recent findings. We emphasize studies concerning clinical endpoints (i.e., CVD morbidity and mortality), since these are of greatest interest from a public health perspective and because reviewing the association between SES and all indicators of cardiovascular risk, functioning, and health is beyond the scope of this chapter. However, it should be noted that SES has been associated with every stage of the CVD continuum, including inflammation and hemostatic dysregulation [67, 87, 218], subclinical atherosclerosis and atherosclerotic progression [50, 195, 222], and various cardiovascular disease risk factors [97, 219].

Indicators of Adult SES and CVD One of the first reviews to critically evaluate the research examining CVD and adult SES markers (education, occupation, income, housing ownership, or combinations thereof) was published by Kaplan and Keil in 1993 [97]. The authors surveyed prospective and retrospective cohort studies examining the link between SES and CVD mortality and risk factors, conducted in the USA, the UK, and elsewhere between 1956 and the early 1990s. They identified substantial support for a relationship between higher SES (variously defined) and lower CVD mortality, as well as several CVD risk factors, especially smoking and hypertension. Although the available evidence was relatively sparse, some studies suggested that higher SES was associated with greater longevity, specifically in populations with coronary heart disease (CHD). Investigations controlling for traditional CVD risks showed that SES appeared to be an independent risk factor for CVD. In 1998, Gonzalez and colleagues [84] performed a systematic review of 34 cohort and case control studies of the association between adult SES markers (specifically, educational attainment or occupational status) and CHD morbidity and mortality, conducted between 1960 and 1993 in the USA, the UK, and other locations. The evidence supported the presence of inverse SES gradients in CHD in industrialized nations since the 1970s, which increased in magnitude during the latter years of the twentieth century. For example, all but one study demonstrated a monotonic association between education and CHD during this time period, so that each incremental advance in education was associated with a concomitant decrease in CHD morbidity or mortality. An important limitation of the studies reviewed, however, is that nearly all examined were men exclusively, and all but one included non-Hispanic White participants only.

10

Socioeconomic Status and Cardiovascular Disease

239

Other reviews examining more specific CVD outcomes lend additional support for an association with SES. One recent review demonstrated an association of SES with stroke [2], concluding that individuals and populations with lower SES suffer a higher incidence of stroke, stroke risk factors, and elevated stroke mortality relative to their higher SES counterparts. A 2011 systematic review and meta-analysis of 70 studies found an increased risk of acute myocardial infarction associated with lower income (pooled RR 1.71, 95% CI 1.43 to 2.05), occupation (pooled RR 1.35, 95% CI 1.19 to 1.53) and education (pooled RR 1.34, 95% CI 1.22 to 1.47) [145]. Of interest, the association between SES and incident myocardial infarction was found to be clearest among higher-income countries (e.g., USA, Canada, Europe) and less consistent for low- to middle-income countries. Finally, a systematic review of 28 studies concluded that low SES is associated with congestive heart failure incidence, prevalence, and adverse outcomes among individuals with heart failure [91]. This association was shown to be independent of a variety of confounding factors. Large surveillance studies conducted in the USA continue to document robust disparities in CVD risk factors, morbidity, and mortality according to adult socioeconomic circumstances [99, 206]. As shown in Fig. 1, data from the National Health Interview Survey (NHIS) [168], an interview-based study of the civilian non-institutionalized population of the USA, showed that both educational attainment and family income were significantly, inversely associated with CVD mortality

Fig. 1 Cox proportional mortality hazard ratios and 95% confidence intervals (CI) for the association of educational attainment and family income with mortality from cardiovascular diseases, 2001 US National Health Interview Survey [168]. Vital status was completed through December 31, 2011 [169, 170]. Estimates are stratified on 5-year birth cohort, adjusted for sex and race/ethnicity (non-Hispanic White, non-Hispanic Black, other non-Hispanic race) and incorporate the complex survey design. CIs that do not include 1.0 are statistically significant. Analyses are based upon 28,3310 and 22,669 participants (919 and 637 deaths) for education and income, respectively. Cardiovascular deaths included combined deaths from heart disease (International Classification of Diseases; ICD-10 codes I00–I09, I11, I13, I20–I51) and cerebrovascular disease (ICD-10 codes I60–I69). CDC, Centers for Disease Control and Prevention; NCHS, National Center for Health Statistics

240

L. C. Gallo et al.

Fig. 2 Crude prevalence and 95% confidence intervals (CI) for coronary heart disease (told by a physician you had coronary heart disease, a heart attack, or other heart disease) among a probability sample of noninstitutionalized adults in the USA, 2014, according to educational achievement and income. Estimates are adjusted for the complex survey design. Sample sizes are 36,534 and 33,726 for education and income, respectively

across 10 years of follow-up (using vital statistics data) [169]. In addition, education and income related inversely to diagnosed CHD prevalence in the 2014 NHIS (See Fig. 2) [170]. Another report from the National Longitudinal Mortality Study examined temporal inequalities related to SES and race/ethnicity in CVD mortality in the USA from 1969 and 2013 and concluded that both racial disparities and socioeconomic gradients in CVD mortality have increased substantially over time [206]. Higher educational attainment, income, and occupational status were related to lower CVD mortality in both men and women. For instance, CVD mortality rates were 46–76% higher in men and women with lower education and income levels than in their better-educated or more affluent counterparts [206]. Research conducted in other industrialized nations has also documented consistent inverse associations between adult SES and CVD. Mackenbach and colleagues [142] investigated socioeconomic health inequalities in 22 European countries and uncovered pervasive socioeconomic gradients in all-cause mortality, as well as mortality from CVD and other specific causes. Although the magnitude varied widely, the slope index of inequality (i.e., the absolute difference in number of deaths/100,000 person-years between those with the lowest and highest level of education) was reported to be positive for CVDs in all countries and tended to be markedly higher in Eastern than in Southern European nations. Inequalities in CVD contributed significantly to overall educational disparities in all-cause mortality, accounting for 34% and 51% of education-related mortality differentials in men and women, respectively. As described in the measurement section, other research has focused on how non-traditional indicators of SES relate to CVD. For example, a growing body of research suggests that lower area-based SES is associated with higher CHD

10

Socioeconomic Status and Cardiovascular Disease

241

prevalence [49, 211] CHD incidence [47, 217], CVD mortality [20, 188], subclinical atherosclerosis [172], and incident stroke [129]. To our knowledge, the relationships between subjective social status and CVD morbidity or mortality have yet to be investigated, although subjective SES has been associated with cardiovascular risk and metabolic syndrome prevalence in prior research [42, 79, 146].

Indicators of Childhood SES and CVD Later in Life A systematic review examined the associations of childhood SES (primarily father’s occupation) with adult CVD morbidity and mortality in 40 prospective, case-control, or cross-sectional studies performed through 2004, in the USA, UK, Northern European countries, and elsewhere [74]. CVD outcomes included CHD, cerebrovascular disease (hemorrhagic and ischemic stroke), peripheral vascular disease, measures of atherosclerosis, and rheumatic heart disease. Approximately 78% of all studies and 80% of prospective studies reviewed identified an inverse association between childhood SES and adult CVD; however, the magnitude of the relationship varied by sex, SES indicator, and specific CVD subtype, with associations observed to be stronger for cerebrovascular disease than for CHD. In an update to this review, 10 of 11 new studies provided additional evidence supporting the link between childhood SES and CVD, as well as other causes of death [76]. A 2014 synthesis of the literature corroborated the inverse association between childhood SES and adult CVD risk and also cited a paradoxical relationship between higher childhood SES and higher adult CVD risk in low- and middle-income countries [102]. The impact of childhood SES may also reflect a similar life course social patterning of traditional CVD risk factors, such as obesity and smoking [74]. In many studies, the effects of childhood SES are reduced in part or completely with control for adult SES [74, 76]. However, since SES often remains consistent throughout the lifespan [113], effects of childhood SES may still be relevant even if its effects are attenuated with control for adult circumstances.

Life Course Approaches to Evaluating the SES and CVD Association In addition to childhood SES, many researchers have become interested in how SES over the lifespan relates to health [38]. A review by Pollitt and colleagues [186] examined 49 observational studies connecting SES measures in childhood or adolescence with CVD endpoints including morbidity, mortality, measures of atherosclerosis, and risk factors later in life. Moderate support was identified for the hypothesis that early life experiences of low SES relate to CVD risk in adulthood, and strong support was revealed for the cumulative exposure perspective, such that aggregate or summative experiences of poverty, unemployment, etc., across the lifespan had an incremental association with CVD risk. More recent research has also supported the cumulative exposure perspective [102]. For example, in the Framingham Offspring Study, childhood SES (father’s educational attainment,

242

L. C. Gallo et al.

elicited directly from parents) and adulthood SES (own occupation and educational attainment) jointly predicted incident CHD and CVD across 30 years [132]. Similarly, in a longitudinal study of a population-based Finnish cohort, SES in both childhood (occupation of the head of the family, highest level of parental education) and in adulthood (own occupation and educational attainment), measured through census records, predicted CVD mortality derived from a national death registry [53].

Variability in the Pattern of the Gradient The persistence of the SES-gradient across virtually all socioeconomic indicators, CVD outcomes, and countries suggests that SES has a pervasive influence on cardiovascular health via multiple pathways. Nonetheless, a close look at the literature reveals at least some variability in the pattern of the gradient across and within subpopulations. For example, most evidence supporting an inverse association between SES and CVD derives from industrialized nations with relatively highincome levels. In middle- or lower-income countries, the SES-gradients are typically flattened or even reversed [175, 237]. However, whereas CVD morbidity and mortality rates continue to decline in more affluent countries, they are increasing rapidly in many low- and middle-income countries [26]. Moreover, as described above, even industrialized nations with roughly similar affluence levels show CVD-SES relationships of varying magnitude, with the USA and Northern European countries generally exhibiting larger gradients than those observed in Southern European countries [142, 143]. Some studies have also identified steeper socioeconomic gradients for various CVD disease endpoints in women than in men [183, 194, 199, 221] (for review and discussion, see [182]). However, few studies have tested sex differences statistically, prospectively, or in large national samples, and many studies concerning SES and CVD have included only one sex group (for discussion, see [221]). Additional studies suggest that SES inequalities in CVD may vary by ethnic group, with gradients sometimes attenuated among ethnic minorities relative to non-Hispanic whites. For example, in the Atherosclerosis Risk in Communities (ARIC) study, the inverse associations between education and CHD prevalence and subclinical atherosclerosis were significantly stronger in non-Hispanic Whites than in Blacks [46]. The Multi-Ethnic Study of Atherosclerosis (MESA) also found evidence of ethnicity/race-related heterogeneity in socioeconomic gradients for atherosclerosis. Specifically, adult SES showed a small positive association with carotid intima-media thickness in black men but no association in Hispanic men [121]. On the other hand, black women exhibited a stronger association between neighborhood SES and carotid atherosclerosis relative to White women, and Hispanic women showed a positive association between area SES and extent of carotid disease. Other studies from MESA have shown that lower SES was related to more extensive carotid atherosclerosis [135] and CVD risk factors [21] in non-Hispanic Whites and in Blacks but was not associated with these outcomes in participants of Hispanic/Latino or Chinese descent. Still other studies suggest a flattening of SES health gradients in immigrant or low-acculturated populations [66, 104].

10

Socioeconomic Status and Cardiovascular Disease

243

Importantly, the ability to elucidate between-group differences in the nature of SES gradients is hampered by measurement limitations since, as noted above, the meaning and implications of SES indicators may vary across demographic groups. Moreover, SES and other demographic characteristics are confounded, so that in the USA and many other industrialized nations, immigrants and individuals with ethnic/ racial minority status are more likely to be found in low SES strata. In this regard, range restriction could contribute to attenuated SES gradients in ethnic minority groups or immigrant populations. Immigration patterns (e.g., the tendency for immigrants to be physically healthy) further complicate the ability to explore ethnic and nativity-based patterns of SES inequalities. Since the evidence concerning demographically driven SES-CVD gradients is limited, additional research, particularly including large, diverse, representative samples, examining clinical outcomes such as CVD incidence and prevalence, and including multiple SES indicators, is needed to further explore these patterns and underlying explanations.

Conclusions As described in a number of comprehensive reviews published across the last two decades, and as reported in large surveillance studies conducted in the USA and elsewhere, SES in adulthood, represented by a variety of markers including education, income, occupation, housing tenure, as well as non-traditional measures such as area-based measures, has a robust, significant, inverse, graded association with CVD (both CHD and cerebrovascular disease) incidence, prevalence, mortality, risk factors, events, and indicators of subclinical disease [2, 84, 91, 97, 145, 206]. In addition, research suggests that individuals who experience socioeconomic deprivation in childhood are more vulnerable to cardiovascular health problems later in life [74, 76]. Studies that have examined life course models support a cumulative, doseresponse effect of exposure to adverse socioeconomic circumstances in relation to later CVD risk [186]. SES gradients occur across many populations [142, 143] although there is some variability between nations and demographic subgroups. We now turn our attention to the myriad pathways through which SES and CVD relationships emerge, and the potential intervention avenues that these pathways highlight.

What Are the Pathways Connecting SES with CVD and What Possibilities Exist for Intervention? Efforts to elucidate the pathways linking SES with CVD and other major health outcomes have identified diverse candidate mechanisms ranging from broad material or structural factors (e.g., fundamental resources; access to healthcare; environmental exposures), to interrelated behavioral and biological risk factors (e.g., hypertension, obesity, smoking), to psychosocial factors (e.g., stress, control in important life areas, including work, and social resources). The next sections will provide an

244

L. C. Gallo et al.

overview of the major mechanisms that have received the greatest consideration in the literature to date. In addition, we consider the implications of these pathways for possible intervention targets that may contribute to efforts to eliminate SES-related health disparities.

Upstream/Structural Determinants At the broadest level, SES may impact health by shaping access to critical resources, including knowledge, money, power, and beneficial social connections. In turn, these resources can distribute access to healthy living and social environments and influence individual health behaviors, thereby pervasively impacting health and well-being [126, 128, 148]. Phelan and Link have proposed the “fundamental causes” perspective as a way of explaining how SES, via its influence on such basic resources, has continued to impact health across time and disease trends [181]. For example, communicable diseases, poor working and housing conditions, sanitation problems, and similar factors explained much of the morbidity and mortality burden in the nineteenth century. In developed countries, these “risk factors” have been drastically reduced over the last century, yet they have been replaced by new ones, such as smoking, physical inactivity, and poor diet [164]. Because the fundamental resources that are molded by SES can be flexibly applied to minimize changing health risks and capitalize on emerging health protective opportunities, the SES health gradient has endured through these dynamic patterns of health. Attending to risk factors while excluding the resources that “. . .strongly influence people’s ability to avoid risks and to minimize the consequences of disease once it occurs” [127] is therefore unlikely to successfully prevent the disproportionate vulnerability to CVD and other major health problems experienced by those with low SES. From this perspective, policy changes that seek to reduce socioeconomic disadvantage at the population level are needed to eliminate SES-related health disparities. Such programs might incorporate a variety of tactics to redistribute fundamental resources, by improving educational or occupational opportunities and quality in low SES communities, enhancing neighborhood development in disadvantaged areas, or implementing tax credits and income augmentation plans. These types of intervention programs have the potential to save substantially more lives than medical advances [235]. Consistent with this theme is evidence that adverse economic cycles are associated with unhealthy dietary changes in the population [18]. In turn, interventions focusing on “upstream” factors can favorably alter specific health pathways, despite not specifically targeting health risks [17, 18, 101, 123, 131]. Although not directly focusing on the development of slowly developing processes such as CVD, such intervention findings provide support for the efficacy of targeting more distal contributors to CVD. Thus, despite formidable political and practical challenges, policy strategies that confront socioeconomic disadvantage or related disparities in fundamental resources represent a promising approach to addressing socioeconomic disparities in CVD and health in general.

10

Socioeconomic Status and Cardiovascular Disease

245

Environmental Influences Contextual or environmental influences at multiple levels form another candidate pathway in the relationship between SES and CVD incidence and course. For example, ecologic studies have revealed a higher stroke prevalence in the southeastern USA that appears to be due to lower SES and higher resulting risk factor exposure in the region [125]. In addition, as described above, CHD is more common in disadvantaged communities, and this association is independent of individual level SES and key CVD risk factors [47, 188]. Such relationships may be due to a number of environmental influences that are linked to SES. For instance, healthy food is less available in poorer neighborhoods [124], and anti-smoking policies are more prevalent in communities with higher education and per-capita income [210]. Higher area-based SES is associated with greater measured physical fitness, and this effect is independent of individual SES ([205]; for a review, see [133]). Thus, the environmental context can shape diet, physical activity, and smoking – all potent CVD risk behaviors. Lower SES environments (e.g., communities, occupational settings) may also bring increased exposure to environmental risk factors such as poor air quality, chemical toxins, and ambient noise (for reviews, see: [55, 141]) particularly in the developing world [19]. In a novel but related line of research, studies have shown that heart attack victims in private residences are more likely to receive bystander CPR in high-resource neighborhoods – a pattern that appears to be independent of population density, patient age and gender, and the per capita rate of cardiac arrest [162, 225]. Such studies provide persuasive illustrations of how contextual factors can shape health risks through a myriad of pathways and can help identify novel prevention and intervention approaches. For example, substantial evidence shows that public policies limiting indoor smoking are associated with decreased CVD events [180] – although disadvantaged public spaces have been shown to have higher particulate counts under similar policy coverage [52]. Environmental interventions focused on simple structural changes can also effectively impact physical health outcomes. Farley and colleagues [57] found that opening a supervised schoolyard for play during non-school hours led to an overall increase in children’s activity levels in a low SES community. As another example, attempts to address the association between housing quality and blood pressure have shown that simple interventions to “weatherproof” apartments (i.e., by providing better insulation, preventing drafts, and providing constant household temperatures) have led to clinically significant decreases in occupants’ systolic and diastolic blood pressure [130] (see also [220]). A Centers for Disease Control sponsored task force on Community Preventive Services identified strong evidence for multiple approaches including enhancing physical education in schools, implementing excise taxes on tobacco and public smoking bans, and reducing patient co-payments for smoking cessation products. Moderate evidence was found for additional intervention strategies such as redesigning the environment to promote physical activity [24]. A Centers for Disease Control sponsored task force on Community Preventive Services [238] identified strong evidence for multiple approaches including enhancing physical

246

L. C. Gallo et al.

education in schools, implementing excise taxes on tobacco and public smoking bans, and reducing patient co-payments for smoking cessation products. Moderate evidence was found for additional intervention strategies such as redesigning the environment to promote physical activity. Despite the apparent success and relative low cost of these types of interventions, they are not yet frequently implemented due to obstacles such as ineffective communication of research findings to policy makers, resistance from business interests [100], and public beliefs that individuallevel choices are the primary determinants of health [229]. Contextual influences on CVD risk are potent [223], and attending to these influences is likely to reduce health disparities at the population level.

Healthcare Access and Quality Healthcare access and quality differentials form another fundamental contributor to SES disparities in CVD and many other health outcomes. Lack of health insurance is associated with higher all-cause mortality [233], and low SES is associated with being uninsured. In fact, 30% of those without a high school education report being uninsured for more than a year versus 7% of those with education beyond high school [36]. Similar gradients exist when stratifying by employment or poverty status [36]. In prospective studies, both loss of health insurance and being uninsured are associated with poorer hypertension control [62, 134], and being uninsured is associated with higher stroke incidence and all-cause mortality [62]. Despite these clear-cut trends, determining the degree to which healthcare access underlies SES-related disparities is difficult, since SES and insurance status are closely related. Moreover, for some preventive measures, such as cholesterol screening, research has shown that healthcare access does not explain educational differences [160]. On the other hand, another study found no education gradient for CVD risk changes among cardiac rehabilitation participants undergoing lifestyle intervention where all participants had insurance to cover the treatment [85]. Similarly, adherence to lipid-lowering medications among post-MI patients was unrelated to a wide variety of SES markers [173]. Thus, it is possible that healthcare access in the secondary context may equalize socioeconomic differentials in risk reduction. One strategy for clarifying the extent to which SES-CVD associations can be explained by healthcare access is to examine the gradient in settings with universal coverage. For example, both Sweden and the UK provide near-universal healthcare, yet an education gradient for incident CHD has been observed among Swedish women [114], and economic difficulties predict incident coronary events in British men independent of occupational class and a large number of established risk factors [59]. There is further variation in the SES gradient across incident versus recurrent events within universal coverage settings. Examination of a very large Italian stroke registry (>10,000 events) identified a SES gradient in stroke incidence but no gradient in post-stroke mortality over 1 year [29]. This pattern suggests that universal coverage could offset the SES gradient in the secondary prevention context. However, other data comparing SES gradients for incident and recurrent events are

10

Socioeconomic Status and Cardiovascular Disease

247

less clear. Specifically, both 30-day and 1-year post-stroke mortality gradients were observed in Canada (where there is near-universal coverage) using neighborhood income as the SES marker [98]. Similarly, a twofold greater risk of 5-year mortality has been observed among lower-income participants in a population-based study of acute MI patients in Sweden [196], and in Finland the low-income population attributable risk of death from first ischemic stroke is estimated at 56% [94]. These post-event patterns of SES stratification imply that healthcare access does not consistently offset the SES gradient, but other factors complicate interpretation. Incident CVD events are more severe among low-income groups, although this severity does not seem to explain the gradient in post-event survival [14]. In Canada, low-income stroke patients are more likely to be admitted to “low-volume” hospitals that are less successful in treating acute CVD events [198], but incomerelated MI mortality gradients persist within high- and low-volume hospitals [11]. In the USA, functional decline after acute stroke is greater among those with Medicaid (need-based coverage) or no health insurance versus Medicare (age-based coverage) or private insurance. These differences are not explained by age or stroke severity [43]. Overall, the evidence that insurance is health protective is robust, and the salutary effects of coverage extend well beyond CVD [157, 203, 233]. Universal healthcare coverage is unlikely to eliminate SES-CVD event disparities but would address one source of this differential. Universal coverage may also be cost-effective in the long run because CVD-related expenses are lower for continuously insured persons transitioning to age-based universal insurance coverage (e.g., Medicare) versus those who were uninsured before public insurance eligibility [158]. The evidence seems to suggest that healthcare reform in the USA [37] and other populations, if successful, would contribute at least in part to efforts to alleviate CVD and other health problems experienced disproportionately by those with low SES (or ethnic/ racial minority status) [203].

Biobehavioral Risk Factors Moving downward from these higher-order constructs, known, interrelated biological and behavioral risk factors comprise plausible intervening factors between SES and CVD. In general, CVD risk factors such as smoking, hyperlipidemia, hypertension, and diabetes [86] are stratified by both education and income [28, 165, 237] and by other markers of SES, including childhood socioeconomic circumstances [105, 147] and area-based SES [48]. Socioeconomic gradients have also been observed for a broader range of putative CVD risk factors such as C-reactive protein [82, 187] and waist-to-hip ratio [105, 202]. Although in the USA risk factor prevalence has generally decreased from the 1970s to the early twenty-first century, the distribution of these improvements varied by SES. Specifically, larger declines in lipid abnormalities and smoking were observed in the highest SES categories, whereas hypertension prevalence decreased most dramatically among those with lower SES [96]. Diabetes was the only risk factor that increased over that period, and

248

L. C. Gallo et al.

this occurred only among lower SES groups. Thus, CVD risk factor disparities have increased against a backdrop of general population improvements in CVD risk. Despite the low SES disadvantage for CVD risk factors, statistical control for these variables typically reduces but does not eliminate the gradient in CHD [114, 155, 208], stroke [115, 156], or aggregate CVD event prevalence [10]. However, mediation of the SES gradient by traditional risk factors appears to be more complete in developing countries and varies by the operational definition of SES [31]. Risk factors may also play a greater role in developed nations where healthcare is universal and income stratification is less severe relative to the USA [137, 179]. These conditions may naturally minimize other SES-related sources of variance, thus leading to fuller explication by risk factors and concomitantly revealing possible social determinants of CVD. Scientific understanding of the socioeconomic gradient in CVD risk factors presents opportunities to improve population health. For example, targeting lower SES groups improves identification of persons with elevated CVD risk. This was shown in a UK simulation study where CVD risk screening in deprived communities required fewer screenings and cost 2.6 times less to detect one person at elevated risk relative to population-based screening [119]. Similarly, incorporation of SES indicators improves CVD risk prediction using Framingham risk scores [60]. Thus, the association between SES and CVD risk can be co-opted to increase the efficiency and cost-effectiveness of primary prevention efforts. It is also notable that SES can moderate CVD risk reduction interventions, in both favorable and unfavorable directions. In primary prevention efforts, such as those designed to decrease childhood obesity, stronger effects in low SES groups have been reported [54]. In secondary contexts, such as self-care for diabetes management, low education is associated with poorer glycemic control. However, utilizing more intensive treatment regimens can eliminate this SES adherence gradient, a notable intervention success to reduce SES disparities [83]. Finally, several promising primary CVD risk reduction strategies appear to be effective across and adaptable to diverse populations. These include efforts to increase physical activity, decrease sedentary activity, and promote smoking cessation among children and adolescents, respectively (for overview, see [63]). Such contexts provide optimism for reducing SES-related disparities in CVD risk, although they probably represent “downstream” factors in the chain of causation.

Psychosocial Risk and Resilience Factors Psychosocial pathways may be another important factor linking low SES with negative health outcomes [5, 33, 64, 111, 112, 153, 166]. Several integrative theoretical models have been presented to explain these associations [64, 166]. In general, these frameworks posit that low socioeconomic circumstances foster heighted exposure to stress, adversity, and distress while limiting opportunities to build and maintain health-protective psychosocial resources. In turn, a great deal of literature links psychosocial risk factors including stress and negative emotions and

10

Socioeconomic Status and Cardiovascular Disease

249

protective factors, such as social (social support) and personal resources (e.g., optimism), with risk for CVD and other negative health outcomes [51, 56, 103, 106, 213, 214, 226]. Psychosocial factors are believed to affect physical health via their influences on biological and behavioral regulation. Two recent reviews examined the tenet that psychosocial pathways (specifically, stress, emotional factors, and psychosocial resources) contribute to associations between SES and objective health outcomes, including CVD and all-cause mortality, and found that the literature was notable mainly for its heterogeneous findings and scarcity of direct tests [151]. In particular, limited support was identified for an intervening role of stress, with the possible exception of job strain. It may be that the strength and direction of the SES-stress association varies according other sociodemographic characteristics (sex, race/ethnicity) and the domains and indicators of stress examined (e.g., [69]; for discussion, [151]). Greater support was found for the roles of psychosocial resources as intervening variables connecting low SES with higher CVD risk. For example, Marmot et al. [149] found that perceptions of control in the work context explained more than half of the excess CHD risk associated with having low occupational status. Another study found that statistical control for variations in social integration explained approximately one-third of the excess stroke risk associated with low income and education [15]. In a 12-year prospective study, SES related to higher incident metabolic syndrome directly, and indirectly, via pathways from low SES to elevated negative emotions, to lower aggregate psychosocial resources (optimism, social support, self-esteem), and to the metabolic syndrome [152]. Two recent cross-sectional studies in Mexican American women have also shown that in combination, lower psychosocial resources and higher negative emotional and cognitive factors partially explained associations of lower SES with elevated cardiometabolic risk [68] and reduced overnight ambulatory blood pressure dipping (indicating higher CVD risk) [61]. Additional studies are needed to determine the extent to which psychosocial factors contribute to SES-related disparities in cardiovascular health and to establish which psychosocial variables are of greater or lesser importance. Further research is also needed to examine contextual factors and individual differences that may influence the nature of associations among SES, psychosocial factors, and health. For example, a recent conceptual model proposes that some individuals with low SES may continue to thrive psychosocially due to adaptive strategy of “shifting” (accepting stressors and adapting stress appraisals and responses) and “persisting” (maintaining strength, optimism, and a sense of propose despite adversity) [32]. These resilient individuals are theorized to have benefited in childhood from positive role models who conveyed a sense of trust-fostered optimism about the future and modeled adaptive emotion regulation [32]. Other researchers have posited that protective cultural processes may foster psychosocial resources (e.g., higher support, family cohesion, religiosity) that protect against the impact of low SES in certain ethnic/racial groups, such as Hispanics/Latinos [65, 70]. Importantly, the degree to which interventions targeting psychosocial factors can protect against CVD and adverse outcomes also remains an open question [144].

250

L. C. Gallo et al.

The Enhancing Recovery in Coronary Heart Disease (ENRICHD) trial, the largest study to test a psychological risk reduction intervention (specifically, targeting social isolation and depression) among persons with CHD found no difference in clinical outcomes between cognitive behavioral intervention participants and control participants [236]. However, a post-hoc analysis showed that ENRICHD participants who showed a reduction in depression in response to intervention did in fact experience reduced mortality [16]. A 2011 Cochrane review and meta-analysis of psychological interventions for CHD concluded that these treatments appear to be effective in alleviating psychological distress in CHD patients [228] and, based on a limited number of studies, may have a small protective effect in relation to cardiovascular mortality [228]. In contrast, the review failed to support the hypotheses that psychological interventions would reduce total deaths, risk of revascularization, or non-fatal infarction [228]. Another recent review and meta-analysis of 18 studies testing effects of mental health treatments for reducing recurrent events in CHD patients showed no effect on total mortality but moderate efficacy for reducing CHD events [197]. Whether or not interventions targeting psychosocial factors can reduce cardiovascular health problems specifically in low-SES individuals with high-risk psychosocial profiles is an area for future research. Examples of such interventions might include community-based interventions designed to foster group advocacy and social capital in low-SES neighborhoods, school-based interventions to teach problem solving and promote child bonding with schools and community [191], or workplace interventions focused on job redesign to increase perceptions of control and reduce stress associated with low SES occupations. Given the research suggesting that the pathways from low SES to psychosocial and health risks have their origins early in life [161, 190], prevention and intervention programs that target at-risk children may be especially effective.

Summary In summary, the literature reveals a myriad of potential pathways that may connect low SES with worse health outcomes, including higher-order factors, such as fundamental resources that are differentially distributed according to SES and broadly applicable to health and disease, environmental exposures, and healthcare access, as well as traditional biobehavioral risk factors and psychosocial risk factors, both of which are likely to be shaped by broader influences. Research examining the relative contribution of these factors to SES-related gradients in objective health outcomes is relatively sparse. However, the available literature suggests that any of these given risk pathways is likely to play only a partial role in explaining SES health disparities, and material factors appear to account for a greater relative burden of risk (e.g., [209, 227]). Consideration of alternative mechanisms, and the extent to which they help explain SES-disparities in health, may be informative. For instance, recent evidence

10

Socioeconomic Status and Cardiovascular Disease

251

suggests that the socioeconomic environment may affect brain structure and function in a manner that influences health and well-being [23, 80, 81, 88, 89, 154]. Epigenetic processes, such as the DNA methylation of pro-inflammatory genes, may likewise contribute to the biological embedding and health implications of low socioeconomic status and other social factors [161, 215]. Knowledge about such novel mechanisms can help to further elucidate the relationship between SES and health risks and outcomes and to guide more effective prevention and intervention efforts [100]. The research concerning underlying mechanisms reveals a variety of intervention targets, and the available research regarding interventions to reduce socioeconomic health disparities seems promising. However, little to no research has attempted to examine how interventions that seek to reduce socioeconomic disadvantage, or to intervene with mediating mechanisms, might influence cardiovascular risks or disease in particular. As the importance of social factors in CVD and health becomes more widely accepted [90], additional intervention and policy efforts may shed greater light on these issues.

Concluding Comments Research performed since the 1970s has revealed a profound, inverse association between diverse indicators of SES and CVD (as well as other physical health outcomes) that appears to be robust, though not invariant, across many populations and disease indicators. Despite reductions in population level CVD morbidity and mortality, SES-related disparities persist and even appear to be widening. Attempts to examine these associations and their origins are hampered by complexities in measurement and conceptualization. In this regard, it is recommended that the choice of SES indicator(s) be tailored to the population and health outcome under investigation and that future research employ a variety of SES indicators measured at different levels and points across the lifespan in order to fully capture the nature of the SES-CVD gradient or the pathways that underlie it. Researchers have identified a variety of mechanisms that may contribute to SES disparities in CVD and other health outcomes, but additional research is needed to determine their relative roles and to examine alternative or novel mechanisms. Convincing data regarding the origins of health disparities are critical to inform more extensive policy and intervention efforts. To date, despite the tremendous increase in scholarly interest regarding health disparities across the last two decades, efforts to develop or test how interventions designed to reduce socioeconomic disadvantage in the population, or to intervene with mediating mechanisms, may affect physical health outcomes including CVD risks and incidence have been notably scarce. Given the evidence suggesting that a myriad of pathways is at work and that both upstream and downstream processes must be considered simultaneously to effect meaningful changes, it is clear that interventions involving multiple levels and systems will be necessary if we are to achieve the public health goals of eliminating SES-related health disparities.

252

L. C. Gallo et al.

References 1. Achen CH, Shively WP (1995) Cross-level inference. The University of Chicago Press, Chicago 2. Addo J, Ayerbe L, Mohan KM, Crichton S, Sheldenkar A, Chen R, Wolfe CDA, Mckevitt C (2012) Socioeconomic status and stroke: an updated review. Stroke 43:1186–1191 3. Adler NE, Newman K (2002) Socioeconomic disparities in health: pathways and policies. Inequality in education, income, and occupation exacerbates the gaps between the health “haves” and “have-nots”. Health Aff 21:60–76 4. Adler NE, Rehkopf DH (2008) U.S. disparities in health: descriptions, causes, and mechanisms. Annu Rev Public Health 29:235–252 5. Adler NE, Snibbe AC (2003) The role of psychosocial processes in explaining the gradient between socioeconomic status and health. Curr Dir Psychol Sci 12:119–123 6. Adler NE, Boyce WT, Chesney MA, Folkman S, Syme SL (1993) Socioeconomic inequalities in health. No easy solution. JAMA 269:3140–3145 7. Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, Syme SL (1994) Socioeconomic status and health: the challenge of the gradient. Am Psychol 49:15–24 8. Adler NE, Epel ES, Castellazzo G, Ickovics JR (2000) Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol 19:586–592 9. Adler N, Singh-Manoux A, Schwartz J, Stewart J, Matthews K, Marmot MG (2008) Social status and health: a comparison of British civil servants in Whitehall-II with European- and African-Americans in CARDIA. Soc Sci Med 66:1034–1045 10. Albert MA, Glynn RJ, Buring J, Ridker PM (2006) Impact of traditional and novel risk factors on the relationship between socioeconomic status and incident cardiovascular events. Circulation 114:2619–2626 11. Alter DA, Naylor CD, Austin P, Tu JV (1999) Effects of socioeconomic status on access to invasive cardiac procedures and on mortality after acute myocardial infarction. NEJM 341:1359–1367 12. American Psychological Association Task Force on Socioeconomic Status (2007) Report of the APA task force on socioeconomic status. American Psychological Association, Washington, DC 13. Andersson MA (2015) How do we assign ourselves social status? A cross-cultural test of the cognitive averaging principle. Soc Sci Res 52:317–329 14. Arrich J, Lalouschek W, Mullner M (2005) Influence of socioeconomic status on mortality after stroke: retrospective cohort study. Stroke 36:310–314 15. Avendano M, Kawachi I, Van LF, Boshuizen HC, Mackenbach JP, Van Den Bos GA, Fay ME, Berkman LF (2006) Socioeconomic status and stroke incidence in the US elderly: the role of risk factors in the EPESE study. Stroke 37:1368–1373 16. Banankhah SK, Friedmann E, Thomas S (2015) Effective treatment of depression improves post-myocardial infarction survival. World J Cardiol 7:215–223 17. Banerjee A, Duflo E, Goldberg N, Karlan D, Osei R, Pariente W, Shapiro J, Thuysbaert B, Udry C (2015) Development economics. A multifaceted program causes lasting progress for the very poor: evidence from six countries. Science 348:1260799 18. Bonaccio M, Di Castelnuovo A, Bonanni A, Costanzo S, De Lucia F, Persichillo M, Zito F, Donati MB, De Gaetano G, Iacoviello L (2014) Decline of the Mediterranean diet at a time of economic crisis. Results from the Moli-sani study. Nutr Metab Cardiovasc Dis 24:853–860 19. Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Pruss-Ustun A, Lahiff M, Rehfuess EA, Mishra V, Smith KR (2013) Solid fuel use for household cooking: country and regional estimates for 1980–2010. Environ Health Perspect 121:784–790 20. Borrell LN, Diez Roux AV, Rose K, Catellier D, Clark BL, Atherosclerosis Risk in Communities S (2004) Neighbourhood characteristics and mortality in the atherosclerosis risk in communities study. Int J Epidemiol 33:398–407

10

Socioeconomic Status and Cardiovascular Disease

253

21. Boykin S, Diez-Roux AV, Carnethon M, Shrager S, Ni H, Whitt-Glover M (2011) Racial/ethnic heterogeneity in the socioeconomic patterning of CVD risk factors in the United States: the multi-ethnic study of atherosclerosis. J Health Care Poor Underserved 22:111–127 22. Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, Posner S (2005) Socioeconomic status in health research: one size does not fit all. JAMA 294:2879–2888 23. Brito NH, Noble KG (2014) Socioeconomic status and structural brain development. Front Neurosci 8:276 24. Brownson RC, Haire-Joshu D, Luke DA (2006) Shaping the context of health: a review of environmental and policy approaches in the prevention of chronic diseases. Annu Rev Public Health 27:341–370 25. Brunner E, Shipley MJ, Blane D, Smith GD, Marmot MG (1999) When does cardiovascular risk start? Past and present socioeconomic circumstances and risk factors in adulthood. J Epidemiol Community Health 53:757–764 26. Celermajer DS, Chow CK, Marijon E, Anstey NM, Woo KS (2012) Cardiovascular disease in the developing world: prevalences, patterns, and the potential of early disease detection. J Am Coll Cardiol 60:1207–1216 27. Cene CW, Halladay JR, Gizlice Z, Roedersheimer K, Hinderliter A, Cummings DM, Donahue KE, Perrin AJ, Dewalt DA (2015) Associations between subjective social status and physical and mental health functioning among patients with hypertension. J Health Psychol 21 (11):2624–2635. https://doi.org/10.1177/1359105315581514 28. Centers for Disease C (2005) Racial/ethnic and socioeconomic disparities in multiple risk factors for heart disease and stroke – United States, 2003. MMWR 54:113–117 29. Cesaroni G, Agabiti N, Forastiere F, Perucci CA (2009) Socioeconomic differences in stroke incidence and prognosis under a universal healthcare system. Stroke 40:2812–2819 30. Chaix B, Leal C, Evans D (2010) Neighborhood-level confounding in epidemiologic studies: unavoidable challenges, uncertain solutions. Epidemiology 21:124–127 31. Chang CL, Shipley MJ, Marmot MG, Poulter NR (2002) Can cardiovascular risk factors explain the association between education and cardiovascular disease in young women? J Clin Epidemiol 55:749–755 32. Chen E, Miller GE (2012) “Shift-and-persist” strategies: why low socioeconomic status isn’t always bad for health. Perspect Psychol Sci 7:135–158 33. Chen E, Miller GE (2013) Socioeconomic status and health: mediating and moderating factors. Annu Rev Clin Psychol 9:723–749 34. Chen E, Matthews KA, Boyce WT (2002) Socioeconomic differences in children’s health: how and why do these relationships change with age? Psychol Bull 128:295–329 35. Chittleborough CR, Baum FE, Taylor AW, Hiller JE (2006) A life-course approach to measuring socioeconomic position in population health surveillance systems. J Epidemiol Community Health 60:981–992 36. Cohen RA, Martinez ME (2009) Health insurance coverage: early release of estimates from the National Health Interview Survey, January-March 2009. National Center for Health Statistics, Hyatsville 37. Cohen R, Martinez M (2015) Health insurance coverage: early release of estimates from the National Health Interview Survey, January-March 2015. In: Division of Health Interview Statistics NCFHS (ed) . Centers for Disease Control and Prevention, Hyattsville 38. Cohen S, Janicki-Deverts D, Chen E, Matthews KA (2010) Childhood socioeconomic status and adult health. Ann N Y Acad Sci 1186:37–55 39. Cooper DC, Milic MS, Mills PJ, Bardwell WA, Ziegler MG, Dimsdale JE (2010) Endothelial function: the impact of objective and subjective socioeconomic status on flow-mediated dilation. Ann Behav Med 39:222–231 40. Cubbin C, Winkleby MA (2005) Protective and harmful effects of neighborhood-level deprivation on individual-level health knowledge, behavior changes, and risk of coronary heart disease. Am J Epidemiol 162:559–568

254

L. C. Gallo et al.

41. Cundiff JM, Smith TW, Uchino BN, Berg CA (2013) Subjective social status: construct validity and associations with psychosocial vulnerability and self-rated health. Int J Behav Med 20:148–158 42. Demakakos P, Nazroo J, Breeze E, Marmot M (2008) Socioeconomic status and health: the role of subjective social status. Soc Sci Med 67:330–340 43. Dhamoon MS, Moon YP, Paik MC, Boden-Albala B, Rundek T, Sacco RL, Elkind MSV (2009) Long-term functional recovery after first ischemic stroke: the northern Manhattan study. Stroke 40:2805–2811 44. Diez Roux AV (2004) Estimating neighborhood health effects: the challenges of causal inference in a complex world. Soc Sci Med 58:1953–1960 45. Diez Roux AV, Mair C (2010) Neighborhoods and health. Ann N Y Acad Sci 1186:125–145 46. Diez Roux AV, Nieto FJ, Tyroler HA, Crum LD, Szklo M (1995) Social inequalities and atherosclerosis. The atherosclerosis risk in communities study. Am J Epidemiol 141:960–972 47. Diez Roux AV, Merkin SS, Arnett D, Chambless L, Massing M, Nieto FJ, Sorlie P, Szklo M, Tyroler HA, Watson RL (2001) Neighborhood of residence and incidence of coronary heart disease. NEJM 345:99–106 48. Diez Roux A, Kershaw K, Lisabeth L (2008) Neighborhoods and cardiovascular risk: beyond individual-level risk factors. Curr Cardiovasc Risk Rep 2:175–180 49. Diez-Roux AV, Nieto FJ, Muntaner C, Tyroler HA, Comstock GW, Shahar E, Cooper LS, Watson RL, Szklo M (1997) Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol 146:48–63 50. Dragano N, Verde PE, Moebus S, Stang A, Schmermund A, Roggenbuck U, Möhlenkamp S, Peter R, Jöckel KH, Erbel R, Siegrist J, For the Heinz Nixdorf Recall S (2007) Subclinical coronary atherosclerosis is more pronounced in men and women with lower socio-economic status: associations in a population-based study: coronary atherosclerosis and social status. Eur J Cardiovasc Prev Rehabil 14:568–574 51. Dubois CM, Beach SR, Kashdan TB, Nyer MB, Park ER, Celano CM, Huffman JC (2012) Positive psychological attributes and cardiac outcomes: associations, mechanisms, and interventions. Psychosomatics 53:303–318 52. Edwards R, Hasselholdt C, Hargreaves K, Probert C, Holford R, Hart J, Van Tongeren M, Watson A (2006) Levels of second hand smoke in pubs and bars by deprivation and foodserving status: a cross-sectional study from north West England. BMC Public Health 6:42 53. Elo IT, Martikainen P, Myrskyla M (2014) Socioeconomic status across the life course and allcause and cause-specific mortality in Finland. Soc Sci Med 119:198–206 54. Epstein LH, Roemmich JN, Robinson JL, Paluch RA, Winiewicz DD, Fuerch JH, Robinson TN (2008) A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children. Arch Pediatr Adolesc Med 162:239–245 55. Evans GW, Kantrowitz E (2002) Socioeconomic status and health: the potential role of environmental risk exposure. Annu Rev Public Health 23:303–331 56. Everson-Rose SA, Lewis TT (2005) Psychosocial factors and cardiovascular diseases. Annu Rev Public Health 26:469–500 57. Farley TA, Meriwether RA, Baker ET, Watkins LT, Johnson CC, Webber LS (2007) Safe play spaces to promote physical activity in inner-city children: results from a pilot study of an environmental intervention. Am J Public Health 97:1625–1631 58. Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS (2010) The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place 16:175–190 59. Ferrie JE, Martikainen P, Shipley MJ, Marmot MG (2005) Self-reported economic difficulties and coronary events in men: evidence from the Whitehall II study. Int J Epidemiol 34:640–648 60. Fiscella K, Tancredi D, Franks P (2009) Adding socioeconomic status to Framingham scoring to reduce disparities in coronary risk assessment. Am Heart J 157:988–994 61. Fortmann AL, Gallo LC, Roesch SC, Mills PJ, Barrett-Connor E, Talavera GA, Elder JP, Matthews KA (2012) Socioeconomic status, nocturnal blood pressure dipping, and

10

Socioeconomic Status and Cardiovascular Disease

255

psychosocial factors: a cross-sectional investigation in Mexican-American women. Ann Behav Med 44:389–398 62. Fowler-Brown A, Corbie-Smith G, Garrett J, Lurie N (2007) Risk of cardiovascular events and death: does insurance matter? J Gen Intern Med 22:502–507 63. Franks AL, Kelder SH, Dino GA, Horn KA, Gortmaker SL, Wiecha JL, Simoes EJ (2007) School-based programs: lessons learned from CATCH, planet health, and not-on-tobacco. Prev Chronic Dis 4:A33 64. Gallo LC, Matthews KA (2003) Understanding the association between socioeconomic status and physical health: do negative emotions play a role? Psychol Bull 129:10–51 65. Gallo LC, Penedo FJ, De Los E, Monteros K, Arguelles W (2009) Resiliency in the face of disadvantage: do Hispanic cultural characteristics protect health outcomes? J Pers 77: 1707–1746 66. Gallo LC, De Los Monteros KE, Allison M, Diez Roux A, Polak JF, Watson KE, Morales LS (2009) Do socioeconomic gradients in subclinical atherosclerosis vary according to acculturation level? Analyses of Mexican-Americans in the multi-ethnic study of atherosclerosis. Psychosom Med 71:756–762 67. Gallo LC, Fortmann AL, De Los Monteros KE, Mills PJ, Barrett-Connor E, Roesch SC, Matthews KA (2012) Individual and neighborhood socioeconomic status and inflammation in Mexican American women: what is the role of obesity? Psychosom Med 74:535–542 68. Gallo LC, Fortmann AL, Roesch SC, Barrett-Connor E, Elder JP, De Los E, Monteros K, Shivpuri S, Mills PJ, Talavera GA, Matthews KA (2012) Socioeconomic status, psychosocial resources and risk, and cardiometabolic risk in Mexican-American women. Health Psychol 31:334–342 69. Gallo L, Shivpuri S, Gonzalez P, Fortmann A, De Los MK, Roesch S, Talavera G, Matthews K (2013) Socioeconomic status and stress in Mexican–American women: a multi-method perspective. J Behav Med 36:379–388 70. Gallo LC, Penedo FJ, Carnethon M, Isasi C, Sotrez-Alvarez D, Malcarne VL, Daviglus ML, Gonzalez P, Talavera GP (2014) The Hispanic community health study/study of Latinos sociocultural ancillary study: sample, design, and procedures. Ethnic Discrimination 24:77–83 71. Galobardes B, Lynch JW, Davey Smith G (2004) Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev 26:7–21 72. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G (2006) Indicators of socioeconomic position (part 1). J Epidemiol Community Health 60:7–12 73. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG (2006) Indicators of socioeconomic position (part 2). J Epidemiol Community Health 60:95–101 74. Galobardes B, Smith GD, Lynch JW (2006) Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Ann Epidemiol 16:91–104 75. Galobardes B, Lynch J, Davey Smith G (2007) Measuring socioeconomic position in health research. Br Med Bull 81–82:21–37 76. Galobardes B, Lynch JW, Smith GD (2008) Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. J Epidemiol Community Health 62:387–390 77. Geronimus AT, Bound J (1998) Use of census-based aggregate variables to proxy for socioeconomic group: evidence from National Samples. Am J Epidemiol 148:475–486 78. Geronimus AT, Bound J, Neidert LJ (1996) On the validity of using census geocode characteristics to proxy individual socioeconomic characteristics. J Am Stat Assoc 91:529–537 79. Ghaed SG, Gallo LC (2007) Subjective social status, objective socioeconomic status, and cardiovascular risk in women. Health Psychol 26:668–674 80. Gianaros PJ, Manuck SB (2010) Neurobiological pathways linking socioeconomic position and health. Psychosom Med 72:450–461

256

L. C. Gallo et al.

81. Gianaros PJ, Horenstein JA, Hariri AR, Sheu LK, Manuck SB, Matthews KA, Cohen S (2008) Potential neural embedding of parental social standing. Soc Cogn Affect Neurosci 3:91–96 82. Gimeno D, Brunner E, Lowe G, Rumley A, Marmot M, Ferrie J (2007) Adult socioeconomic position, C-reactive protein and interleukin-6 in the Whitehall II prospective study. Eur J Epidemiol 22:675–683 83. Goldman DP, Smith JP (2002) Can patient self-management help explain the SES health gradient? Proc Natl Acad Sci U S A 99:10929–10934 84. Gonzalez MA, Rodriguez AF, Calero JR (1998) Relationship between socioeconomic status and ischaemic heart disease in cohort and case-control studies: 1960–1993. Int J Epidemiol 27:350–358 85. Govil SR, Weidner G, Merritt-Worden T, Ornish D (2009) Socioeconomic status and improvements in lifestyle, coronary risk factors, and quality of life: the multisite cardiac lifestyle intervention program. Am J Public Health 99:1263–1270 86. Greenland P, Knoll MD, Stamler J, Neaton JD, Dyer AR, Garside DB, Wilson PW (2003) Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA 290:891–897 87. Gruenewald TL, Cohen S, Matthews KA, Tracy R, Seeman TE (2009) Association of socioeconomic status with inflammation markers in black and white men and women in the coronary artery risk development in young adults (CARDIA) study. Soc Sci Med 69:451–459 88. Hackman DA, Farah MJ (2009) Socioeconomic status and the developing brain. Trends Cogn Sci 13:65–73 89. Hackman DA, Farah MJ, Meaney MJ (2010) Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat Rev Neurosci 11:651–659 90. Havranek EP, Mujahid MS, Barr DA, Blair IV, Cohen MS, Cruz-Flores S, Davey-Smith G, Dennison-Himmelfarb CR, Lauer MS, Lockwood DW, Rosal M, Yancy CW (2015) Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation 132:873–898 91. Hawkins NM, Jhund PS, Mcmurray JJV, Capewell S (2012) Heart failure and socioeconomic status: accumulating evidence of inequality. Eur J Heart Fail 14:138–146 92. House JS, Lepkowski JM, Kinney AM, Mero RP, Kessler RC, Herzog AR (1994) The social stratification of aging and health. J Health Soc Behav 35:213–234 93. Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, Patel R, Webb EA, Lawlor DA, Hargreaves JR (2012) Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol 41:871–886 94. Jakovljevic KK, Tuomilehto J, Puska P, Salomaa V (2001) Socioeconomic status and ischemic stroke: the FINMONICA stroke register. Stroke 32:1492–1498 95. Jemal A, Ward E, Anderson RN, Murray T, Thun MJ (2008) Widening of socioeconomic inequalities in U.S. death rates, 1993–2001. PLoS One 3:e2181 96. Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelson DE, Mensah G, Beckles GLA (2006) Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971–2002. Arch Intern Med 166:2348–2355 97. Kaplan GA, Keil JE (1993) Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 88:1973–1998 98. Kapral MK, Wang H, Mamdani M, Tu JV, Boden-Albala B, Sacco RL (2002) Effect of socioeconomic status on treatment and mortality after stroke. Stroke 33:268–275 99. Karlamangla AS, Merkin SS, Crimmins EM, Seeman TE (2010) Socioeconomic and ethnic disparities in cardiovascular risk in the United States, 2001–2006. Ann Epidemiol 20:617–628 100. Katz MH (2009) Structural interventions for addressing chronic health problems. JAMA 302:683–685 101. Kehrer BH, Wolin CM (1979) Impact of income maintenance on low birth weight: evidence from the Gary experiment. J Hum Resour 14:434–462

10

Socioeconomic Status and Cardiovascular Disease

257

102. Kelishadi R, Poursafa P (2014) A review on the genetic, environmental, and lifestyle aspects of the early-life origins of cardiovascular disease. Curr Probl Pediatr Adolesc Health Care 44:54–72 103. Kiecoltglaser JK, Mcguire L, Robles TF, Glaser R (2002) Emotions, morbidity, and mortality: new perspectives from psychoneuroimmunology. Annu Rev Public Health 53:83–107 104. Kimbro RT, Bzostek S, Goldman N, Rodriguez G (2008) Race, ethnicity, and the education gradient in health. Health Aff 27:361–372 105. Kivimaki M, Smith GD, Juonala M, Ferrie JE, Keltikangas-Jarvinen L, Elovainio M, PulkkiRaback L, Vahtera J, Leino M, Viikari JS, Raitakari OT (2006) Socioeconomic position in childhood and adult cardiovascular risk factors, vascular structure, and function: cardiovascular risk in young Finns study. Heart 92:474–480 106. Krantz DS, Mcceney MK (2002) Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease. Annu Rev Public Health 53:341–369 107. Krieger N, Fee E (1996) Measuring social inequities in health in the United States: a historical review, 1900–1950. Int J Health Serv 26:391–418 108. Krieger N, Williams DR, Moss NE (1997) Measuring social class in U.S. public health research: concepts, methodologies, and guidelines. Annu Rev Public Health 18:341–378 109. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R (2002) Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter? The public health disparities geocoding project. Am J Epidemiol 156:471–482 110. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R (2003) Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: the public health disparities geocoding project (US). J Epidemiol Community Health 57:186–199 111. Kristenson M, Eriksen HR, Sluiter JK, Starke D, Ursin H (2004) Psychobiological mechanisms of socioeconomic differences in health. Soc Sci Med 58:1511–1522 112. Kroenke C (2008) Socioeconomic status and health: youth development and neomaterialist and psychosocial mechanisms. Soc Sci Med 66:31–42 113. Kuh D, Ben-Shlomo Y (2004) A life course approach to chronic disease epidemiology. Oxford University Press, Oxford 114. Kuper H, Adami HO, Theorell T, Weiderpass E (2006) Psychosocial determinants of coronary heart disease in middle-aged women: a prospective study in Sweden. Am J Epidemiol 164:349–357 115. Kuper H, Adami HO, Theorell T, Weiderpass E (2007) The socioeconomic gradient in the incidence of stroke: a prospective study in middle-aged women in Sweden. Stroke 38:27–33 116. Lamont D, Parker L, White M, Unwin N, Bennett SMA, Cohen M, Richardson D, Dickinson HO, Adamson A, Alberti KGMM, Craft AW (2000) Risk of cardiovascular disease measured by carotid intima-media thickness at age 49–51: lifecourse study. BMJ (Clin Res ed) 320: 273–278 117. Landrine H, Corral I (2009) Separate and unequal: residential segregation and black health disparities. Ethn Dis 19:179–184 118. Lantz PM, Lynch JW, House JS, Lepkowski JM, Mero RP, Musick MA, Williams DR (2001) Socioeconomic disparities in health change in a longitudinal study of US adults: the role of health-risk behaviors. Soc Sci Med 53:29–40 119. Lawson KD, EaL F, Pell ACH, Pell JP (2009) Comparison of mass and targeted screening strategies for cardiovascular risk: simulation of the effectiveness, cost-effectiveness and coverage from a cross-sectional survey of 3,921 people. Heart 96:208–212 120. Leal C, Chaix B (2011) The influence of geographic life environments on cardiometabolic risk factors: a systematic review, a methodological assessment and a research agenda. Obes Rev 12:217–230

258

L. C. Gallo et al.

121. Lemelin ET, Diez Roux AV, Franklin TG, Carnethon M, Lutsey PL, Ni H, O’meara E, Shrager S (2009) Life-course socioeconomic positions and subclinical atherosclerosis in the multiethnic study of atherosclerosis. Soc Sci Med 68:444–451 122. Lemeshow AR, Fisher L, Goodman E, Kawachi I, Berkey CS, Colditz GA (2008) Subjective social status in the school and change in adiposity in female adolescents: findings from a prospective cohort study. Arch Pediatr Adolesc Med 162:23–28 123. Leventhal T, Brooks-Gunn J (2003) Moving to opportunity: an experimental study of neighborhood effects on mental health. Am J Public Health 93:1576–1582 124. Lewis LB, Sloane DC, Nascimento LM, Diamant AL, Guinyard JJ, Yancey AK, Flynn G, For The RCOTaaB (2005) African Americans’ access to healthy food options in South Los Angeles restaurants. Am J Public Health 95:668–673 125. Liao Y, Greenlund KJ, Croft JB, Keenan NL, Giles WH (2009) Factors explaining excess stroke prevalence in the US stroke belt. Stroke 40:3336–3341 126. Link BG, Phelan J (1995) Social conditions as fundamental causes of disease. J Health Soc Behav 1995:80–94 127. Link BG, Phelan JC (1996) Understanding sociodemographic differences in health: the role of fundamental social causes. Am J Public Health 86:471–473 128. Link BG, Phelan JC (2002) McKeown and the idea that social conditions are fundamental causes of disease. Am J Public Health 92:730–732 129. Lisabeth LD, Diez Roux AV, Escobar JD, Smith MA, Morgenstern LB (2007) Neighborhood environment and risk of ischemic stroke: the brain attack surveillance in Corpus Christi (BASIC) project. Am J Epidemiol 165:279–287 130. Lloyd EL, Mccormack C, Mckeever M, Syme M (2008) The effect of improving the thermal quality of cold housing on blood pressure and general health: a research note. J Epidemiol Community Health 62:793–797 131. Lonczak HS, Abbott RD, Hawkins JD, Kosterman R, Catalano RF (2002) Effects of the Seattle social development project on sexual behavior, pregnancy, birth, and sexually transmitted disease outcomes by age 21 years. Arch Pediatr Adolesc Med 156:438–447 132. Loucks EB, Lynch JW, Pilote L, Fuhrer R, Almeida ND, Richard H, Agha G, Murabito JM, Benjamin EJ (2009) Life-course socioeconomic position and incidence of coronary heart disease: the Framingham offspring study. Am J Epidemiol 169:829–836 133. Lovasi GS, Hutson MA, Guerra M, Neckerman KM (2009) Built environments and obesity in disadvantaged populations. Epidemiol Rev 31:7–20 134. Lurie N, Ward NB, Shapiro MF, Brook RH (1984) Termination from Medi-Cal: does it affect health? NEJM 311:480–484 135. Lutsey PL, Diez Roux AV, Jacobs DR Jr, Burke GL, Harman J, Shea S, Folsom AR (2008) Associations of acculturation and socioeconomic status with subclinical cardiovascular disease in the multi-ethnic study of atherosclerosis. Am J Public Health 98:1963–1970 136. Lynch J, Kaplan GA (2000) Socioeconomic position. In: Berkman LF, Kawachi I (eds) Social epidemiology. Oxford University Press, New York 137. Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT (1996) Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction? Am J Epidemiol 144:934–942 138. Lynch JW, Kaplan GA, Shema SJ (1997) Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. NEJM 337:1889–1895 139. Lynch JW, Smith GD, Kaplan GA, House JS (2000) Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. BMJ 320:1200–1204 140. Ma J, Xu J, Anderson RN, Jemal A (2012) Widening educational disparities in premature death rates in twenty six states in the United States, 1993–2007. PLoS One 7:e41560 141. Macdonald LA, Cohen A, Baron S, Burchfiel CM (2009) Occupation as socioeconomic status or environmental exposure? A survey of practice among population-based cardiovascular studies in the United States. Am J Epidemiol 169:1411–1421

10

Socioeconomic Status and Cardiovascular Disease

259

142. Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, Kunst AE, The European Union Working Group on Socioeconomic Inequalities In H (2008) Socioeconomic inequalities in health in 22 European countries. NEJM 358:2468–2481 143. Mackenbach JP, Kulhánová I, Menvielle G, Bopp M, Borrell C, Costa G, Deboosere P, Esnaola S, Kalediene R, Kovacs K, Leinsalu M, Martikainen P, Regidor E, Rodriguez-Sanz M, Strand BH, Hoffmann R, Eikemo TA, Östergren O, Lundberg O (2015) Trends in inequalities in premature mortality: a study of 3.2 million deaths in 13 European countries. J Epidemiol Community Health 69:207–217 144. Macleod J, Davey Smith G (2003) Psychosocial factors and public health: a suitable case for treatment? J Epidemiol Community Health 57:565–570 145. Manrique-Garcia E, Sidorchuk A, Hallqvist J, Moradi T (2011) Socioeconomic position and incidence of acute myocardial infarction: a meta-analysis. J Epidemiol Community Health 65:301–309 146. Manuck SB, Phillips JE, Gianaros PJ, Flory JD, Muldoon MF (2010) Subjective socioeconomic status and presence of the metabolic syndrome in midlife community volunteers. Psychosom Med 72:35–45 147. Marin TJ, Chen E, Miller GE (2008) What do trajectories of childhood socioeconomic status tell us about markers of cardiovascular health in adolescence? Psychosom Med 70:152–159 148. Marmot MG (2004) The status syndrome: how social standing affects our health and longevity. Owl Books, Henry Holt and Company, New York 149. Marmot MG, Bosma H, Hemingway H, Brunner E, Stansfeld S (1997) Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet 350:235–239 150. Marmot M, Shipley M, Brunner E, Hemingway H (2001) Relative contribution of early life and adult socioeconomic factors to adult morbidity in the Whitehall II study. J Epidemiol Community Health 55:301–307 151. Matthews KA, Gallo LC (2010) Psychological perspectives on pathways linking socioeconomic status and physical health. Annu Rev Psychol 62:501–530 152. Matthews KA, Räikkönen K, Gallo LC, Kuller LH (2008) Association between socioeconomic status and metabolic syndrome in women: testing the reserve capacity model. Health Psychol 27:576–583 153. Matthews KA, Gallo LC, Taylor SE (2010) Are psychosocial factors mediators of socioeconomic status and health connections? Ann N Y Acad Sci 1186:146–173 154. Mcewen BS, Gianaros PJ (2010) Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann N Y Acad Sci 1186:190–222 155. Mcfadden E, Luben R, Wareham N, Bingham S, Khaw KT (2008) Occupational social class, risk factors and cardiovascular disease incidence in men and women: a prospective study in the European prospective investigation of cancer and nutrition in Norfolk (EPIC-Norfolk) cohort. Eur J Epidemiol 23:449–458 156. Mcfadden E, Luben R, Wareham N, Bingham S, Khaw KT (2009) Social class, risk factors, and stroke incidence in men and women: a prospective study in the European prospective investigation into cancer in Norfolk cohort. Stroke 40:1070–1077 157. Mcwilliams JM (2009) Health consequences of uninsurance among adults in the United States: recent evidence and implications. Milbank Q 87:443–494 158. Mcwilliams JM, Meara E, Zaslavsky AM, Ayanian JZ (2009) Medicare spending for previously uninsured adults. Ann Intern Med 151:757–766 159. Meijer M, Röhl J, Bloomfield K, Grittner U (2012) Do neighborhoods affect individual mortality? A systematic review and meta-analysis of multilevel studies. Soc Sci Med 74:1204–1212 160. Merkin S, Karlamangla A, Crimmins E, Charette S, Hayward M, Kim J, Koretz B, Seeman T (2009) Education differentials by race and ethnicity in the diagnosis and management of hypercholesterolemia: a national sample of U.S. adults (NHANES 1999–2002). Int J Public Health 54:166–174

260

L. C. Gallo et al.

161. Miller GE, Chen E, Parker KJ (2011) Psychological stress in childhood and susceptibility to the chronic diseases of aging: Moving toward a model of behavioral and biological mechanisms. Psychol Bull 137:959–997 162. Mitchell MJ, Stubbs BA, Eisenberg MS (2009) Socioeconomic status is associated with provision of bystander cardiopulmonary resuscitation. Prehosp Emerg Care 13:478–486 163. Miyakawa M, Magnusson Hanson LL, Theorell T, Westerlund H (2012) Subjective social status: its determinants and association with health in the Swedish working population (the SLOSH study). Eur J Pub Health 22:593–597 164. Mokdad AH, Marks JS, Stroup DF, Gerberding JL (2004) Actual causes of death in the United States, 2000. JAMA 291:1238–1245 165. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, De Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Judd SE, Kissela BM, Lackland DT, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Matchar DB, Mcguire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Willey JZ, Woo D, Yeh RW, Turner MB (2015) Heart disease and stroke statistics – 2015 update: a report from the American Heart Association. Circulation 131:e299–e322 166. Myers HF (2009) Ethnicity- and socio-economic status-related stresses in context: An integrative conceptual model. J Behav Med 32:9–19 167. Naess O, Claussen B, Davey Smith G (2004) Relative impact of childhood and adulthood socioeconomic conditions on cause specific mortality in men. J Epidemiol Community Health 58:597–598 168. National Center for Health Statistics (2002) National Health Interview Survey, 2001. Publicuse data file and documentation. National Center for Health Statistics, Hyattsville 169. National Center for Health Statistics (2015) Public-use linked mortality file, 2015. Office of Analysis and Epidemiology, Hyattsville 170. National Center for Health Statistics (2015) National Health Interview Survey, 2014. Publicuse Data File and Documentation, Hyattsville 171. Nicklett EJ, Burgard SA (2009) Downward social mobility and major depressive episodes among Latino and Asian-American immigrants to the United States. Am J Epidemiol 170:793–801 172. Nordstrom CK, Diez Roux AV, Jackson SA, Gardin JM, Cardiovascular HS (2004) The association of personal and neighborhood socioeconomic indicators with subclinical cardiovascular disease in an elderly cohort. The Cardiovascular health study. Soc Sci Med 59:2139–2147 173. O’brien EC, Mccoy LA, Thomas L, Peterson ED, Wang TY (2015) Patient adherence to generic versus brand statin therapy after acute myocardial infarction: insights from the can rapid stratification of unstable angina patients suppress adverse outcomes with early implementation of the American College of Cardiology/American Heart Association guidelines registry. Am Heart J 170:55–61 174. Oakes JM, Rossi PH (2003) The measurement of SES in health research: current practice and steps toward a new approach. Soc Sci Med 56:769–784 175. Okrainec K, Banerjee DV, Eisenberg MJ (2004) Coronary artery disease in the developing world. Am Heart J 148:7–15 176. Ostrove JM, Adler NE, Kuppermann M, Washington AE (2000) Objective and subjective assessments of socioeconomic status and their relationship to self-rated health in an ethnically diverse sample of pregnant women. Health Psychol 19:613–618 177. Page R, Simonek J, Ihász F, Hantiu I, Uvacsek M, Kalabiska I et al (2009) Self-rated health, psychosocial functioning, and other dimensions of adolescent health in central and Eastern Europe adolescents. Eur J Psychiatry 23:101–114 178. Pearce MS, Unwin NC, Parker L, Craft AW (2009) Cohort profile: the Newcastle thousand families 1947 birth cohort. Int J Epidemiol 38:932–937 179. Pekkanen J, Tuomilehto J, Uutela A, Vartiainen E, Nissinen A (1995) Social class, health behaviour, and mortality among men and women in eastern Finland. BMJ (Clin Res ed) 311:589–593

10

Socioeconomic Status and Cardiovascular Disease

261

180. Pell JP, Haw S, Cobbe S, Newby DE, Pell ACH, Fischbacher C, Mcconnachie A, Pringle S, Murdoch D, Dunn F, Oldroyd K, Macintyre P, O’rourke B, Borland W (2008) Smoke-free legislation and hospitalizations for acute coronary syndrome. NEJM 359:482–491 181. Phelan JC, Link BG (2005) Controlling disease and creating disparities: a fundamental cause perspective. J Gerontol Ser B Psychol Sci Soc Sci 60:S27–S33 182. Phillips SP, Hamberg K (2015) Women’s relative immunity to the socio-economic health gradient: artifact or real? Glob Health Action 8:27259 183. Picciotto S, Forastiere F, Stafoggia M, Ippoliti D, Ancona C, Perucci CA (2006) Associations of area based deprivation status and individual educational attainment with incidence, treatment, and prognosis of first coronary event in Rome, Italy. J Epidemiol Community Health 60:37–43 184. Pickett KE, Pearl M (2001) Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health 55:111–122 185. Pollack CE, Slaughter ME, Griffin BA, Dubowitz T, Bird CE (2012) Neighborhood socioeconomic status and coronary heart disease risk prediction in a nationally representative sample. Public Health 126:827–835 186. Pollitt RA, Rose KM, Kaufman JS (2005) Evaluating the evidence for models of life course socioeconomic factors and cardiovascular outcomes: a systematic review. BMC Public Health 5(7):20 187. Pollitt R, Kaufman J, Rose K, Diez-Roux A, Zeng D, Heiss G (2007) Early-life and adult socioeconomic status and inflammatory risk markers in adulthood. Eur J Epidemiol 22:55–66 188. Ramsay SE, Morris RW, Whincup PH, Subramanian SV, Papacosta AO, Lennon LT, Wannamethee SG (2015) The influence of neighbourhood-level socioeconomic deprivation on cardiovascular disease mortality in older age: longitudinal multilevel analyses from a cohort of older British men. J Epidemiol Community Health 69(12):1224–1231. https://doi.org/10. 1136/jech-2015-205542 189. Reitzel LR, Nguyen N, Strong LL, Wetter DW, Mcneill LH (2013) Subjective social status and health behaviors among African Americans. Am J Health Behav 37:104–111 190. Repetti RL, Taylor SE, Seeman TE (2002) Risky families: family social environments and the mental and physical health of offspring. Psychol Bull 128:330–366 191. Reynolds AJ, Temple JA, Robertson DL, Mann EA (2001) Long-term effects of an early childhood intervention on educational achievement and juvenile arrest: a 15-year follow-up of low-income children in public schools. JAMA 285:2339–2346 192. Ritterman ML, Fernald LC, Ozer EJ, Adler NE, Gutierrez JP, Syme SL (2009) Objective and subjective social class gradients for substance use among Mexican adolescents. Soc Sci Med 68:1843–1851 193. Robert S, House JS (1996) SES differentials in health by age and alternative indicators of SES. J Aging Health 8:359–388 194. Rosvall M, Ostergren PO, Hedblad B, Isacsson SO, Janzon L, Berglund G (2002) Life-course perspective on socioeconomic differences in carotid atherosclerosis. Arterioscler Thromb Vasc Biol 22:1704–1711 195. Rosvall M, Ostergren PO, Hedblad B, Isacsson SO, Janzon L, Berglund G (2006) Socioeconomic differences in the progression of carotid atherosclerosis in middle-aged men and women with subclinical atherosclerosis in Sweden. Soc Sci Med 62:1785–1798 196. Rosvall M, Chaix B, Lynch J, Lindström M, Merlo J (2008) The association between socioeconomic position, use of revascularization procedures and five-year survival after recovery from acute myocardial infarction. BMC Public Health 8:44–44 197. Rutledge T, Redwine LS, Linke SE, Mills PJ (2013) A meta-analysis of mental health treatments and cardiac rehabilitation for improving clinical outcomes and depression among patients with coronary heart disease. Psychosom Med 75:335–349 198. Saposnik G, Jeerakathil T, Selchen D, Baibergenova A, Hachinski V, Kapral MK, For the Stroke Outcome Research Canada Working Group (2008) Socioeconomic status, hospital volume, and stroke fatality in Canada. Stroke 39:3360–3366 199. Saurel-Cubizolles MJ, Chastang JF, Menvielle G, Leclerc A, Luce D (2009) Social inequalities in mortality by cause among men and women in France. J Epidemiol Community Health 63:197–202

262

L. C. Gallo et al.

200. Schreier HM, Chen E (2013) Socioeconomic status and the health of youth: a multilevel, multidomain approach to conceptualizing pathways. Psychol Bull 139:606–654 201. Scott KM, Al-Hamzawi AO, Andrade LH, Borges G, Caldas-De-Almeida JM, Fiestas F, Gureje O, Hu C, Karam EG, Kawakami N, Lee S, Levinson D, Lim CC, Navarro-Mateu F, Okoliyski M, Posada-Villa J, Torres Y, Williams DR, Zakhozha V, Kessler RC (2014) Associations between subjective social status and DSM-IV mental disorders: results from the world mental health surveys. JAMA Psychiatry 71:1400–1408 202. Seeman T, Merkin SS, Crimmins E, Koretz B, Charette S, Karlamangla A (2008) Education, income and ethnic differences in cumulative biological risk profiles in a national sample of US adults: NHANES III (1988–1994). Soc Sci Med 66:72–87 203. Sehgal AR (2009) Universal health care as a health disparity intervention. Ann Intern Med 150:561–562 204. Shavers VL (2007) Measurement of socioeconomic status in health disparities research. J Natl Med Assoc 99:1013–1023 205. Shishehbor MH, Gordon P, Kiefe CI, Litaker D (2008) Association of neighborhood socioeconomic status with physical fitness in healthy young adults: the coronary artery risk development in young adults (CARDIA) study. Am Heart J 155:699–705 206. Singh GK, Siahpush M, Azuine RE, Williams SD (2015) Widening socioeconomic and racial disparities in cardiovascular disease mortality in the United States. Int J Matern Child Health (MCH) AIDS 3:106–118 207. Singh-Manoux A, Adler NE, Marmot MG (2003) Subjective social status: its determinants and its association with measures of ill-health in the Whitehall II study. Soc Sci Med 56: 1321–1333 208. Singh-Manoux A, Nabi H, Shipley M, Guéguen A, Sabia S, Dugravot A, Marmot M, Kivimaki M (2008) The role of conventional risk factors in explaining social inequalities in coronary heart disease: the relative and absolute approaches to risk. Epidemiology 19:599–605 209. Skalicka V, Van Lenthe F, Bambra C, Krokstad S, Mackenbach J (2009) Material, psychosocial, behavioural and biomedical factors in the explanation of relative socio-economic inequalities in mortality: evidence from the HUNT study. Int J Epidemiol 38:1272–1284 210. Skeer M, George S, Hamilton WL, Cheng DM, Siegel M (2004) Town-level characteristics and smoking policy adoption in Massachusetts: are local restaurant smoking regulations fostering disparities in health protection? Am J Public Health 94:286–292 211. Smith GD, Hart C, Watt G, Hole D, Hawthorne V (1998) Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley study. J Epidemiol Community Health 52:399–405 212. Smith GD, Hart C, Blane D, Hole D (1998) Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. BMJ (Clin Res ed) 316:1631–1635 213. Steptoe A, Kivimaki M (2012) Stress and cardiovascular disease. Nat Rev Cardiol 9:360–370 214. Steptoe A, Kivimaki M (2013) Stress and cardiovascular disease: an update on current knowledge. Annu Rev Public Health 34:337–354 215. Stringhini S, Polidoro S, Sacerdote C, Kelly RS, Van Veldhoven K, Agnoli C, Grioni S, Tumino R, Giurdanella MC, Panico S, Mattiello A, Palli D, Masala G, Gallo V, Castagné R, Paccaud F, Campanella G, Chadeau-Hyam M, Vineis P (2015) Life-course socioeconomic status and DNA methylation of genes regulating inflammation. Int J Epidemiol 44:1320–1330 216. Subramanyam MA, Diez-Roux AV, Hickson DA, Sarpong DF, Sims M, Taylor HA Jr, Williams DR, Wyatt SB (2012) Subjective social status and psychosocial and metabolic risk factors for cardiovascular disease among African Americans in the Jackson heart study. Soc Sci Med 74:1146–1154 217. Sundquist K, Winkleby M, Ahlen H, Johansson SE (2004) Neighborhood socioeconomic environment and incidence of coronary heart disease: a follow-up study of 25,319 women and men in Sweden. Am J Epidemiol 159:655–662 218. Tabassum F, Kumari M, Rumley A, Lowe G, Power C, Strachan DP (2008) Effects of socioeconomic position on inflammatory and hemostatic markers: a life-course analysis in the 1958 British birth cohort. Am J Epidemiol 167:1332–1341

10

Socioeconomic Status and Cardiovascular Disease

263

219. Tamashiro KLK (2011) Metabolic syndrome: links to social stress and socioeconomic status. Ann N Y Acad Sci 1231:46–55 220. Thomson H, Thomas S, Sellstrom E, Petticrew M (2013) Housing improvements for health and associated socio-economic outcomes. Cochrane Database Syst Rev 2:CD008657. https:// doi.org/10.1002/14651858.CD008657.pub2 221. Thurston RC, Kubzansky LD, Kawachi I, Berkman LF (2005) Is the association between socioeconomic position and coronary heart disease stronger in women than in men? Am J Epidemiol 162:57–65 222. Thurston RC, El Khoudary SR, Derby CA, Barinas-Mitchell E, Lewis TT, Mcclure CK, Matthews KA (2014) Low socioeconomic status over 12 years and subclinical cardiovascular disease: the study of women’s health across the nation. Stroke 45:954–960 223. Tu JV, Ko DT (2008) Ecological studies and cardiovascular outcomes research. Circulation 118:2588–2593 224. Turrell G (2000) Income non-reporting: implications for health inequalities research. J Epidemiol Community Health 54:207–214 225. Vaillancourt C, Lui A, De Maio VJ, Wells GA, Stiell IG (2008) Socioeconomic status influences bystander CPR and survival rates for out-of-hospital cardiac arrest victims. Resuscitation 79:417–423 226. Van Der Kooy K, Van Hout H, Marwijk H, Marten H, Stehouwer C, Beekman A (2007) Depression and the risk for cardiovascular diseases: systematic review and meta-analysis. Int J Geriatr Psychiatry 22:613–626 227. Van Oort FV, Van Lenthe FJ, Mackenbach JP (2005) Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in the Netherlands. J Epidemiol Community Health 59:214–220 228. Whalley B, Rees K, Davies P, Bennett P, Ebrahim S, Liu Z, West R, Moxham T, Thompson DR, Taylor RS (2011) Psychological interventions for coronary heart disease. Cochrane Database Syst Rev 2:CD002902 229. Wilcox LS (2007) Health, wealth, and Well-being. Prev Chronic Dis 4:A115 230. Wilkinson AV, Shete S, Vasudevan V, Prokhorov AV, Bondy ML, Spitz MR (2009) Influence of subjective social status on the relationship between positive outcome expectations and experimentation with cigarettes. J Adolesc Health 44:342–348 231. Williams DR, Collins C (1995) US socioeconomic and racial differences in health: patterns and explanations. Annu Rev Sociol 21:349–386 232. Williams DR, Jackson PB (2005) Social sources of racial disparities in health. Health Aff 24:325–334 233. Wilper AP, Woolhandler S, Lasser KE, Mccormick D, Bor DH, Himmelstein DU (2009) Health insurance and mortality in U.S. adults. Am J Public Health 99(12):2289–2295 234. Wolff LS, Acevedo-Garcia D, Subramanian SV, Weber D, Kawachi I (2010) Subjective social status, a new measure in health disparities research: do race/ethnicity and choice of referent group matter? J Health Psychol 15:560–574 235. Woolf SH, Johnson RE, Phillips RL Jr, Philipsen M (2007) Giving everyone the health of the educated: an examination of whether social change would save more lives than medical advances. Am J Public Health 97:679–683 236. Writing Committee for The Enhancing Recovery in Coronary Heart Disease Patients Investigators (ENRICHD) Trial (2003) Effects of treating depression and low perceived social support on clinical events after myocardial infarction: the enhancing recovery in coronary heart disease patients (ENRICHD) randomized trial. JAMA 289:3106–3116 237. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, Mcqueen M, Budaj A, Pais P, Varigos J, Lisheng L, Interheart SI (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 364:937–952 238. Zaza S, Briss PA, Harris KW (2005) The guide to community preventive services: what works to promote health? Task force on community preventive services. Oxford University, New York

Health Disparities and Cardiovascular Diseases

11

Kimberly M. Fordham, Michael Golden, Kolawole S. Okuyemi, and Susan A. Everson-Rose

Contents A Conceptual Approach to Studying Health Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Racial/Ethnic Disparities in Cardiovascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coronary Heart Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hypertension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recent Progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CVD Risk Factors and Racial Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socioeconomic Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traditional Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychosocial Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Racism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multiple CVD Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Does Where You Live Matter? Geographic Disparities in CVD Risk . . . . . . . . . . . . . . . . . . . . . . . . . Unequal Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

267 268 268 268 269 269 269 270 270 271 271 273 273 274 275 275 276

K. M. Fordham · S. A. Everson-Rose (*) Department of Family Medicine and Community Health and Program in Health Disparities Research, University of Minnesota Medical School, Minneapolis, MN, USA e-mail: [email protected] M. Golden Department of Family Medicine and Community Health and Program in Health Disparities Research, University of Minnesota Medical School, Minneapolis, MN, USA K. S. Okuyemi Department of Family Medicine and Community Health and Program in Health Disparities Research, and Center for Health Equity, University of Minnesota Medical Center, Minneapolis, MN, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_11

265

266

K. M. Fordham et al.

Future Directions: Closing the Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improving Socioeconomic Determinants and Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cultural Competency Among Healthcare Providers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Increased Awareness of Health Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diversity in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . More Research, Better Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

277 277 278 278 278 279 280 280

Abstract

Disparities in cardiovascular disease risk and outcomes by race/ethnicity, age, sex, and social position are long-standing and well documented, particularly in the public health sector. It is only relatively recently that the discipline of behavioral medicine has turned attention to this critical issue. This chapter reviews the extant epidemiologic evidence on disparities in cardiovascular health and healthcare, including initiatives and recommendations that have been implemented to address disparities, briefly outlines a conceptual framework for health disparities research, and then discusses important factors—from traditional cardiovascular risk factors to psychosocial factors and geographic patterning of cardiovascular risk—that contribute to cardiovascular health disparities. We argue that behavioral medicine, with its biopsychosocial perspective, is ideally positioned to advance our understanding of health disparities but, in particular, offers unique opportunities to develop methods and means of ameliorating health disparities. The chapter closes with recommendations for behavioral medicine investigators to reduce and ultimately eliminate disparities in cardiovascular health. Keywords

Cardiovascular diseases · Epidemiology · Health disparities · Race/ethnicity · Risk factors

Health disparities are significant and preventable differences in health between one population group and another—differences in the incidence, prevalence, mortality, and burden of diseases and other adverse health conditions existing between specific population groups, typically defined by key demographic characteristics. The literature on health disparities has exploded over the past 40 years [1]. A long history of scientific work has documented differences in health by race, age, sex, and social position; however, it is only in relatively recent years have these inequalities been given special attention beyond the public health sector. In this chapter, we argue that the field of behavioral medicine has a critical role to play in addressing health disparities even though disparities historically have not been a focus within the discipline. There has been exponential growth in the science of behavioral medicine in the last three decades; indeed, many important scientific advances have resulted due to the biopsychosocial perspective of behavioral medicine, which emphasizes

11

Health Disparities and Cardiovascular Diseases

267

that biological, psychological, and social factors all play significant roles in disease and illness. It is precisely this perspective as well as the holistic approach offered by socio-ecological models of disease [53, 84] that can help us understand health disparities and, more importantly, develop methods and means of ameliorating and ultimately eliminating health disparities. Most commonly, health disparities are discussed in relation to socioeconomic position (e.g., education, income, or other indicators of social position within society) or race/ethnicity. Other chapters within this volume specifically address socioeconomic factors (see Chap. ▶ 10, this volume), the historical conceptualization and use of “race” in health research (see Chap. ▶ 9, this volume), as well as racism and discrimination (see Chap. ▶ 26, this volume). Consequently, for this chapter, our goals are to provide readers with a general overview of current disparities in cardiovascular health and healthcare among the US population—focusing largely on racial/ethnic disparities—and to highlight initiatives and recommendations that have been proposed to close the gap on disparities. First, we briefly describe a conceptual framework or approach to studying health disparities that utilizes a biopsychosocial perspective; then we summarize the excess incidence and prevalence of cardiovascular disease (CVD) in US minorities. Next, we discuss some factors that may contribute to racial/ethnic disparities in CVD, including the differential burden of traditional risk factors for CVD, psychosocial factors, and geographic patterns in CVD risk. Then, we briefly summarize some of the literature documenting the unequal treatment that minorities receive in cardiovascular care, including inequalities in the quality, availability, and administration of care. Finally, we conclude the chapter with a brief summary and overview of recommendations for behavioral medicine investigators to address and eliminate health disparities.

A Conceptual Approach to Studying Health Disparities To fully understand the observed health disparities that are evident across so many diseases, health outcomes, and risk factors, investigators and clinicians need to consider the multiple influences on health, from microlevel to macrolevel processes. Indeed, moving beyond the traditional focus on individual-level biological and behavioral risk factors, and looking “upstream” to the psychological and social context of people’s lives, their communities and neighborhoods, and the broader social and economic policies that order our lives, a bigger and more complex picture unfolds. Empirical and theoretical efforts have been made to paint a cohesive picture of the complex pathways and relationships by which individuals progress toward disease. In a 2009 review, Myers utilized a biopsychosocial perspective in conceptualizing a multifactorial approach to examine how ethnicity and socioeconomic status (SES)-related stressors affect health over the lifespan [84]. Myers combines the popular reserve capacity model [41, 42, 78], which posits that socioeconomic position is linked to health through emotional states, stress, and biobehavioral processes and that psychosocial resiliency resources are protective, with ideas from scholars such as Williams and Kawachi, who emphasize the importance of modeling race and social

268

K. M. Fordham et al.

position simultaneously in order to identify and address the disparities in health downstream [55, 111]. Race and class are recognized as unique barriers that are fundamental to understanding how people function and process their daily experiences, as well as what physical and social resources are available to them [28, 46]. Proximal to and as a result of such external factors are the internal (i.e., biological, emotional, and psychological) responses that may ultimately determine our cardiovascular fate (health) [98]. Conceptual models such as that put forth by Myers and which utilize a biopsychosocial perspective to define the pathways connecting these broad indicators of health are important not only in understanding what sets certain groups apart but also on where to begin working toward equity in health.

Racial/Ethnic Disparities in Cardiovascular Diseases Below, we briefly present current epidemiologic findings documenting significant racial/ethnic disparities in prevalence and incidence of coronary heart disease, hypertension, heart failure, and stroke.

Coronary Heart Disease Coronary heart disease (CHD) remains the leading cause of death for all racial and ethnic groups in the United States, even though mortality rates from CHD have been declining for over a half century [83]. Causing approximately one of every seven US deaths in 2013 (the most recent year for which such data are available) and making up more than half of all cardiovascular deaths, CHD also is the largest component of CVD. Black Americans have the highest overall CHD mortality rate and the highest out-of-hospital coronary death rate of any other group in the United States, particularly at younger ages. Indeed, CHD mortality rates are 26% higher among Black females than among White females and 14% higher among Black males compared to White males [83].

Hypertension The higher rates of hypertension within the Black American population have been recognized for over 70 years [27, 81, 92]. Indeed, hypertension rates within this population are among the highest in the world. Hypertension prevalence has increased significantly since 1988 in the United States in both Blacks and Whites; however, prevalence is markedly higher in Blacks (45–46%) than in Whites (30–33%) [83]. Compared to Whites, Blacks develop hypertension earlier in life, have significantly higher average blood pressures, are less likely to have their blood pressure under control if they are hypertensive, and have hypertension mortality rates that are nearly three times higher. Hypertension is a major controllable risk factor for CHD, myocardial infarction, stroke, heart failure, various renal diseases,

11

Health Disparities and Cardiovascular Diseases

269

peripheral vascular disease, diabetes, and metabolic syndrome. The high rates of hypertension among Blacks contribute to their excess risk of these cardiovascular and metabolic disorders as well. Clearly, the disparities in hypertension experience by Blacks are a major public health concern.

Heart Failure Black Americans also suffer disproportionately from heart failure, compared to White Americans, with higher incident rates and higher 5-year case-fatality rates [83] and greater risk of developing heart failure before age 50 [10]. One study reported that during 20 years of follow-up in the Coronary Artery Risk Development in Young Adults (CARDIA) study, heart failure occurred in the Black participants at a rate 20 times the incidence of White participants and was predicted by the presence of hypertension, obesity, chronic kidney disease, and depressed systolic function 10 to 15 years earlier [10].

Stroke The disproportionate burden of disease and mortality from stroke on Black Americans has been described as one of the major public health problems in the United States [56]. The risk of incident stroke in Blacks is nearly twice as high as it is in Whites; data from the Atherosclerosis Risk in Communities study showed that between the ages of 45 and 84, the age-adjusted incident stroke rate was 6.6 per 1000 in Black men and 4.9 per 1000 in Black women compared to 3.6 per 1000 in White men and 2.3 per 1000 in White women, respectively [86]. Some estimates are that up to 28% of the Black-White difference in total mortality rates is due to the excess stroke mortality in Blacks [88]. Some of the racial/ethnic disparity in stroke risk may be attributable to the greater risk factor burden experienced by Blacks, relative to Whites—most notably the higher prevalence of hypertension, obesity, and diabetes among Blacks [106]. There is some evidence that these risk factors may differentially affect stroke risk by race/ethnicity [94]. However, available population-based data in the United States [47, 93] suggest that differences in established stroke risk factors cannot fully account for the excess stroke morbidity and mortality experienced by Blacks nor can the average lower SES of Blacks in the United States [47].

Recent Progress It is important and worthy to note that much progress has been made within the last several decades in reducing the burden of CVD in the nation as a whole. For example, the United States has experienced significant decreases in CHD and stroke mortality. Control of blood pressure and total cholesterol has improved dramatically

270

K. M. Fordham et al.

for adults with CVD. A recent analysis of White, Black, and Hispanic acute myocardial infarction patients at 443 hospitals participating in the national quality improvement program Get With the Guidelines–Coronary Artery Disease, found evidence-based care for acute myocardial infarction appeared to improve over time for patients of all races, and racial differences in care were reduced or eliminated, highlighting the success of a targeted quality improvement program [26]. However, even in light of these and other recent steps forward in preventing and treating CVD, notable disparities between certain populations persist and in some cases have widened.

CVD Risk Factors and Racial Disparities US minority groups (i.e., African Americans, Hispanics, American Indians/Alaska Natives, Native Hawaiian/Pacific Islanders, and some Asian groups) have a significantly greater number of CVD risk factors compared to non-Hispanic Whites [39, 60, 83, 102]. Indeed, some of the racial/ethnic differences in CVD morbidity and mortality can be explained by variations in the proportion of risk factors. Also, although strides have been taken by policy makers and public health organizations to eliminate the disproportionate burden of risk factors, recent and national data trends reveal areas where inequalities continue to expand [54, 83]. Moreover, it must be remembered that the underlying reasons for cardiovascular health disparities are multifactorial, and, furthermore, the significance of any risk factor’s role in disease occurrence can vary between populations and between individuals within specific populations. Nonetheless, it is instructive to consider the racial/ethnic differences known to exist in CVD risk factors. Below, we highlight six broad areas of risk, i.e., genetics, socioeconomic status, traditional risk factors, psychosocial factors, racism, and geographical location, which contribute to excess disease in racial/ethnic minorities. In addition, we address the added burden of risk factors that cluster in minorities, such as diabetes, obesity, and physical inactivity.

Genetics The last three decades have brought important discoveries in the genetic basis for specific cardiovascular diseases. Research findings accumulated during this time convincingly demonstrate that family history in a parent or sibling is associated with atherosclerotic CVD, manifesting as coronary heart disease, stroke, and/or peripheral arterial disease [70, 74, 110] Several Mendelian disorders contribute to CVD; however, these mutations are currently understood to be rare in occurrence. The most common forms of CVD are believed to be multifactorial and result from many different genes, each of which has a relatively small effect, working either alone or in combination with modifier genes and/or environmental factors [6]. Although genetics broadly influence nearly all aspects of health, extensive research suggests that the direct contribution of genetics to the current pattern of health disparities in

11

Health Disparities and Cardiovascular Diseases

271

the United States is minimal relative to social and environmental influences [85]. Consequently, we do not discuss the genetic basis of CVD in further detail in this text; instead, we focus our discussion below on social factors, traditional lifestyle/ behavioral factors, and psychological factors that have been identified and which may be modifiable influences on CVD.

Socioeconomic Status Consistent evidence supports the strong, inverse association of cardiovascular disease and lower socioeconomic status (SES) over the life course [2, 52]. Persons with lower educational achievement, lower income, and/or limited occupational opportunities tend to have worse behavioral and biological profiles, including higher rates of smoking, hypertension, diabetes, and physical inactivity [62, 63, 73]. Most ethnic minorities in the United States are more socioeconomically disadvantaged than the White majority—the exception being certain Asian groups [20, 31]. In addition, growing evidence suggests that race/ethnicity and SES interact to adversely impact cardiovascular health [84, 111]. One recent population-based study reported that SES explained 39% of the excess risk for stroke incidence in Blacks relative to Whites [57]. Moreover, growing evidence shows that social inequalities account for a sizable proportion of the excess risk in minorities [37, 111]. Empirical evidence supports the graded pattern of poor cardiovascular health between social strata within racial groups and the significant gaps between racial groups within each social stratum. In 2004, Sharma and colleagues found that Blacks were at least 30% less likely to have no CVD risk factors at all three levels of educational attainment (less than 12 years, 12 years, greater than 12 years) compared to their White counterparts [99]. Importantly, this study and others support that some minorities experience “diminished returns” from the cardio-protective benefits of socioeconomic betterment [37]. However, Hispanics and Mexican Americans may experience the same or better cardiovascular health at low and high levels of SES as non-Hispanic Whites, a phenomenon termed the “Hispanic paradox” [30]. Although social position is an important contributor of racial disparities, SES does not account for all the racial differences in cardiovascular health [54, 111]. Readers are referred to Chap. ▶ 10 (see Chap. ▶ 10, this volume) for a fuller discussion of the impact of socioeconomic factors on health outcomes.

Traditional Risk Factors Behavioral, lifestyle, and biologic factors, including high blood pressure, obesity, smoking, high cholesterol, and physical inactivity, have been the focus of CVD research in many disciplines for decades. Our understanding of these traditional CVD risk factors and the role they play in CVD outcomes such as ischemic heart disease and stroke morbidity and mortality has grown dramatically in this time. The

272

K. M. Fordham et al.

distribution of these risk factors in the population also tells us something about racial/ethnic disparities in CVD. Several systematic reviews have focused on the racial/ethnic variations in prevalence of CVD risk factors. Kurian and colleagues reviewed thirteen populationbased studies assessing racial/ethnic differences in at least two of the following CVD risk factors: hypertension, diabetes, obesity, hypercholesterolemia, smoking, and no leisure-time physical activity. The authors included US studies, most of which controlled for sociodemographic factors. On balance, the studies reviewed showed that hypertension and diabetes were significantly associated with minority status. Blacks had the highest prevalence of hypertension compared to Whites, which was an almost unanimous finding. Also, Black and Mexican American women had a higher prevalence of diabetes than White women. Mexican Americans were less likely to smoke than Whites and Blacks, and American Indians/Alaskan Natives (AIAN) were more likely to smoke than Whites. Lack of leisure-time physical activity was shown to be significantly more prevalent in Blacks, Mexican American women, and AIAN [60]. Other published articles emphasize and clarify the findings from this systematic review. AIANs have the highest prevalence of diabetes, obesity, and tobacco usage [7]. Whites have a greater risk of hyperlipidemia [39]; however, Blacks and Mexican Americans with hyperlipidemia are screened less often, more likely to be unaware of their condition and less likely to control their cholesterol with medication [17]. Several studies show major differences in prevalence of smoking by race as well as by gender. There is a higher prevalence of smoking in Black males compared to non-Hispanic Whites or Hispanics [54, 96], and higher prevalence in smoking among non-Hispanic White women in comparison to Black and Hispanic women [38, 54]. Alcohol consumption is a behavioral trait that has both positive and negative impacts on cardiovascular health; however, studies show that the influence of alcohol consumption on CVD risk factors may translate differently across race and gender. Low and moderate alcohol consumption is cardio-protective in White men and women and Black women. On the other hand, studies have shown that no level of alcohol consumption is beneficial to Black men; in fact, excess alcohol consumption can lead to an increased risk for CHD and incident hypertension [40, 91]. The increasing burden of obesity, poor nutrition, and physical inactivity in the United States shows an overall trend toward earlier onset and more severe health conditions over time [58, 90]. However, some racial/ethnic groups are projected to bear this burden disproportionately. Among adults, Blacks and Hispanics are more likely to be obese [45, 58, 71] and less likely to participate in physical activity [90] than non-Hispanic Whites and Asians. A study of children and adolescents ages 2–19 found that Hispanic boys were more likely to be obese than non-Hispanic boys and that Black girls were more likely to be obese compared to non-Hispanic White girls [87]. Unhealthy weight, poor eating habits, and lack of leisure-time physical activities are positively associated with diabetes and other CVD risk factors [45]. The prevalence of diabetes is higher in African Americans, Hispanics, Native Americans, and Alaskan Natives than in non-Hispanic Whites and Asians. Indeed, one study

11

Health Disparities and Cardiovascular Diseases

273

reported that Blacks were twice as likely as Whites to develop type 2 diabetes over almost 25 years of follow-up [79].

Psychosocial Factors For centuries, historical and clinical anecdotes and observations have considered personality and emotional factors etiologically important in the manifestation of CVD [34]. Poor social and psychological functioning is associated with increased risk of CVD and its indicators, including hypertension, atherosclerosis, diabetes, cigarette smoking, and obesity [5, 32, 33, 35, 76, 77]. Growing evidence suggests psychological and social factors are variable by race and that minorities may experience worse psychosocial profiles than Whites. For example, one prospective study found that Native Americans had the highest rate of major depressive disorder, followed by Blacks, Hispanics, Whites, and Asians [44]. Also, Blacks have been shown to have higher levels of chronic and acute stress and less social support than Whites, although some differences may diminish with age [8]. Some have argued that racial disparities in CVD may be due to the greater exposure of minorities to psychosocial stressors, including negative emotions, chronic stress, discrimination, and poverty [49, 109]. Compared with Whites, Blacks report higher levels of hostility, more depressive symptomatology, and greater exposure to discrimination and negative life events [48, 50, 97, 109]; such differential exposures to psychosocial stressors may lead to increased vulnerability to CVD and more severe outcomes [111]. For example, emerging evidence suggests that depressive symptoms may be more adversely related to cardiovascular health outcomes among Blacks than among Whites [11, 66–68, 104]. Several lines of evidence suggest that race influences psychological and social functioning and that psychosocial factors play an important role in the etiology of health disparities in cardiovascular well-being. Racial and ethnic minorities have often been underrepresented in study of psychosocial factors and health; therefore, the nature of these associations remains to be fully understood [34]. Inconsistencies in the literature [14] on the manifestation of psychosocial factors in minorities contribute to our lack of understanding. It is clear that more prospective studies are warranted to better understand the role that psychosocial factors play in the patterning of CVD and other health outcomes by race/ethnicity.

Racism Over the last 15–20 years, racism, in the form of residential segregation, perceived discrimination, and self-stereotyping, has gained growing interest as predictors of social and psychological stress. These dimensions of racism play fundamental roles in the prevalence and sustainability of racial disparities in health. We will touch only briefly on the role of racism in cardiovascular disparities related to race and ethnicity (see Chap. ▶ 9 and ▶ 26, this volume) for fuller discussions of the historic conceptualization and impact of race, racism, and discrimination on health in the United States.

274

K. M. Fordham et al.

Racism adversely affects several areas of life, determining the functionality and structure of living conditions, interpersonal relationships, economic opportunities, and quality healthcare, all of which increases the risk for poor health in the oppressed group [24, 95, 100]. At the fundamental level, residential segregation determines the quality and availability of social and economic resources. Persons living in underprivileged neighborhoods experience a scarcity of jobs, limited healthcare options, poor education systems, and lack of green spaces [3, 80]. In addition, evidence suggests that underprivileged neighborhoods have more alcohol and tobacco advertisements (e.g., billboards), liquor stores, and convenience stores than do more advantaged neighborhoods [3, 64, 80]. In the United States, Blacks are more likely than Asians and Hispanics to live in worse neighborhoods than Whites, regardless of income [75, 107]. The upstream effects of residential segregation in minorities translate into major consequences downstream. Ethnic and racial minorities living in segregated and economically deprived communities are at an increased risk of CVD morbidity and mortality [75, 98]. Feelings of unfair treatment in the form of perceived discrimination have been linked to negative emotions, occupational strain, and risky behaviors in Blacks. In the Multi-Ethnic Study of Atherosclerosis (MESA), Blacks reported higher levels of discrimination than Hispanics, Asians, or Whites [12]. The same study found that among adults who reported discrimination (adjusting for sociodemographics), Blacks and Whites were significantly more likely to smoke, while Blacks and Hispanics were significantly more likely to engage in heavy drinking, compared to individuals who did not experience discrimination. A recent report from MESA also demonstrated that participants reporting experiences of lifetime discrimination in two or more life domains were at significantly elevated risk of incident CVD in adjusted analyses after 10 years of follow-up [36]. Other studies show that Blacks who report chronic [66] or everyday [67] discrimination have higher markers for coronary artery calcification and C-reactive protein, respectively. In a review of the literature, Williams and Mohammed [108] highlight the detrimental role of internalized racism on health, emphasizing how external situations of oppression create patterns of distress when individuals act out the same oppression upon themselves and others within their racial group. In a study on data from the National Survey of American Life, Chae and colleagues found that increased levels of internalized racism, in the form of negative racial group attitudes, significantly predicted self-reported CVD in Black men [21]. Research into internalized racism is scarce; therefore, more quality research is needed to understand its role in racial disparities.

Multiple CVD Risk Factors Risk factors seem to cluster in individuals at risk for CVD, especially in racial and ethnic minorities. The components of those clusters vary by group. For example, Fryar and colleagues examined the co-occurrence and clustering of diabetes, hypertension, and hyperlipidemia in a diverse sample from the 1999–2006 National Health and Nutrition Examination Survey [39]. They found that Blacks were more

11

Health Disparities and Cardiovascular Diseases

275

likely to have all three conditions compared to Mexican Americans and non-Hispanic Whites. For the co-occurrence of disease, Mexican Americans were more likely to have diabetes and hyperlipidemia, Blacks were more likely to have hypertension and diabetes, and Whites were more likely to have hypertension and hyperlipidemia. Other research shows that obesity-related hypertension and obesityrelated diabetes are higher in African Americans and Mexican Americans, respectively [29]. Also, the clustering of obesity, physical inactivity, poor nutrition, and psychological and social distress in Blacks contribute to increased risk for markers of CVD. The combined effect of multiple CVD risk factors may significantly increase the risk of illness and lead to excess risk in minorities.

Does Where You Live Matter? Geographic Disparities in CVD Risk Geographic variations in important CVD risk factors are well documented. For example, smoking rates vary widely in the United States. Data from the Centers for Disease Control indicate that the proportion of adults who smoke ranges from less than 10% in Utah to approximately 15% in many New England states to more than 25% in Oklahoma, West Virginia, and Kentucky [19]. Additionally, the rates of overweight and obesity, while increasing everywhere, clearly show geographic differences— differences that have received a great deal of both scientific and mass media attention [18]. Not surprisingly, prevalence and incidence of diabetes and CVD similarly vary across the United States [19]. Perhaps one of the best-known geographic disparities in CVD relates to stroke. Geographic variations in stroke mortality rates were first widely recognized in the mid-1960s, with higher stroke mortality rates noted in the southern part of the country [11]. Through subsequent research, these states became known as the “Stroke Belt”—a region in the southeastern United States with approximately 50% higher stroke mortality rates than the rest of the United States that clearly are not attributable to different diagnostic or reporting practices [61]. While different definitions of this region exist, it usually includes the states of Alabama, Arkansas, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, and Tennessee. The higher stroke mortality rates in this region have been very well documented [15, 16]. The geographic patterning of CVD risk should prompt us to ask, “What is it about certain counties, or certain states or regions, which contribute to this patterning?” In other words, it points to the importance of considering social contexts and social norms for behaviors, as well as environmental factors and social and economic policies, when trying to understand and address health disparities in CVD. An understanding of these social processes clearly lies within the expertise of behavioral medicine investigators.

Unequal Treatment Racial/ethnic disparities in healthcare are well documented. Several reviews published over the last decade clearly document racial differences in cardiovascular treatments, including the use, availability, and quality of coronary care in the

276

K. M. Fordham et al.

United States. Kressin and colleagues reviewed research published between 1966 and 2000 and found that a clear majority of studies identified significant racial differences in the use of invasive cardiac procedures, including cardiac catheterization, percutaneous transluminal coronary angioplasty, and coronary artery bypass grafting (CABG), even after adjustment for disease severity [59]. Another comprehensive review of the most rigorous studies investigating racial and ethnic differences in angiography, angioplasty, CABG surgery, and thrombolytic therapy found that more than 80% of studies reviewed identified differences in cardiac care between Black and White patients after controlling for other factors known to affect care [69]. Racial differences in use of cardiovascular procedures and major technology have been documented in various healthcare systems. Groeneveld and colleagues reported that across hospital types within the Veterans Affairs Medical Center (VAMC) system, there were few differences in procedure rates between hospitals with larger Black inpatient populations versus predominantly White VAMCs, but that clear within-hospital racial differences in procedure rates were evident in those hospitals with larger Black populations, with Whites more likely to receive procedures [43]. In 2003, the Institute of Medicine (IOM) released the report Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare, at the request of the US Congress [100]. Specifically, the IOM’s goals were to: assess the extent of racial and ethnic differences in healthcare that are not otherwise attributable to known factors such as access to care; evaluate potential sources of racial and ethnic disparities in healthcare, including the role of bias, discrimination, and stereotyping at the individual (provider and patient), institutional, and health system levels; and provide recommendations regarding interventions to eliminate healthcare disparities. This report was particularly notable for declaring that health disparities are among this nation’s most serious healthcare problems. Other reports have corroborated this [4], and more than a decade later, it can be argued that this declaration holds true. Furthermore, by 2050, estimates are that approximately 50% of the total US population will be made up of individuals now considered racial or ethnic minorities [89]. Thus, there is continued need and even urgency for investigators within behavioral medicine and related disciplines to work toward greater understanding of the health needs of racial/ethnic minorities and factors that contribute to disparate health outcomes.

Summary With this chapter, we have highlighted the pervasive disparities in cardiovascular disease outcomes and risk factors that exist, particularly within the United States. Persons of color and socioeconomically disadvantaged groups clearly have worse cardiovascular risk profiles, increased risk of CVD, poorer prognosis, and greater mortality and morbidity due to CVD compared to Whites or more advantaged groups. We also drew attention to the unequal treatment that racial and ethnic minorities experience in their cardiovascular care. A growing literature

11

Health Disparities and Cardiovascular Diseases

277

consistently reveals areas where disadvantaged groups are exposed to a lower quality of care, limited treatment options, and decreased access, especially to specialized care. With our increased awareness of the racial disparities in CVD health and healthcare, we can begin to create interventions that will create balance and increase overall quality.

Future Directions: Closing the Gap Recommendations for executing projects and policies to minimize racial disparities in health are complex and require multidisciplinary perspectives and multifaceted strategies. Behavioral medicine, with its long-standing interdisciplinary approach and recognition that social processes, psychological factors, and biobehavioral processes are all critical to understanding health and illness, stands to make important contributions in this regard. In this section, we highlight five areas we believe are critical to address as we work toward reducing and ultimately eliminating health disparities in CVD.

Improving Socioeconomic Determinants and Policies Social determinants of health are key contributors of inequalities and have become a priority in the battle against racial/ethnic disparities in health [107, 111]. Significant shifts in the distribution of wealth, power, and quality resources are needed to create economic and social balance, but such shifts will only come with policy changes and/or interventions that improve chances for historically underserved populations to access education and employment opportunities. For example, there is a need for interventions that will increase the rate of high school graduation among underprivileged and minority students, increase the rate of underrepresented minorities that matriculate to college, generate occupational opportunities, and encourage minority involvement in politics. To the extent that behavioral medicine experts can advocate for policy and legislative changes that will address disparities, their efforts would be well-spent promoting access to known and effective strategies such as creating “green spaces” in neighborhoods and making neighborhoods safe for physical activity. More housing options are needed that are fair and affordable. Advocacy is needed to support funding to improve public resources in disadvantaged neighborhoods, e.g., wellmaintained recreational centers, parks, playgrounds, transportation systems, shopping stores, and health facilities [98]. Regulations should be set in place to reduce heavy advertising of alcohol and tobacco use, liquor and cigarette stores, and unhealthy food options. Policies need to be enacted and enforced that ensure that there are affordable and nutrition-rich foods in schools and grocery stores [64]. Behavioral medicine experts need to work together with community members and political leaders to identify where socioeconomic reform would be most beneficial and how to begin implementing change.

278

K. M. Fordham et al.

Cultural Competency Among Healthcare Providers The current literature suggests that increased cultural competency in healthcare has the capacity to redefine quality of care and outcomes in minority health [13]. In order to effectively eliminate racial/ethnic disparities in health, all networks of the healthcare system should approach patient care holistically and seek to understand the unique racial, cultural, lingual, and geographical backgrounds that encompass the US patient population. Techniques for improving cultural competency in the healthcare setting are multidimensional. Cultural training for healthcare providers should include information that focuses on sociocultural behaviors, environmental conditions, rituals, and alternative medicines, as well as patient’s attitudes and beliefs about the etiology of disease, medication usage, and family involvement. Among health professionals, culturally competent care training should be taught both during and after graduate education. Crucial partnerships should be established between health providers, patients, their families, and the community in order to facilitate mutual understandings and increased trust. Through cross-cultural education and patient-centeredness, clinicians and patients can begin to communicate more effectively which in turn may lead to improved satisfaction and ultimately better health [100].

Increased Awareness of Health Disparities There is also a call for increased awareness of health disparities and overall improved health literacy, especially in persons most afflicted by CVD and its risk factors. Research has shown that there are significant racial/ethnic gaps in CVD knowledge. Black and Mexican American women are less likely to be knowledgeable about heart disease and stroke symptoms, more likely to be unaware of having a chronic condition, and less aware of their treatment options, compared to non-Hispanic Whites [23]. Patients should be encouraged to participate in their healthcare process in order to learn about the risks factors, treatments, and conditions that affect their well-being. Studies also have found that many providers have a low awareness of health disparities. One study found that among 344 cardiologists, only about one-third believed that racial/ethnic disparities in healthcare existed overall or in cardiovascular care specifically [72]. Clinicians should be encouraged to stay current on the health disparities in the literature and practice in a manner that consciously works toward equality in care. Curriculum advances to promote knowledge of disparities and inequities in healthcare are needed in graduate education both within the disciplines that are a part of behavioral medicine and also in medical education.

Diversity in Practice Another way to combat issues associated with unfair treatment and racial disparities in healthcare is to increase racial/ethnic diversity in the public health and clinical

11

Health Disparities and Cardiovascular Diseases

279

workforce. Studies show that minority patients are more satisfied, have higher levels of trust, and are more likely to adhere to recommendations of clinicians with whom they share similar racial, ethnic, and language characteristics [28, 51, 101]. Racial and ethnic minorities working as health professionals are known for practicing in underserved and disadvantaged neighborhoods (e.g., with patients of lower SES, Medicaid recipients, and racial/ethnic minorities). Mechanisms underlying the success of concordant physician-patient relationships may include similarities in “personal beliefs, values, and communication” [101]. While racial/ethnic minority populations are growing rapidly in the United States., diversity in the healthcare workforce is lacking substantially in comparison [9]. Previous literature on the topic suggests federal funding will increase and sustain underrepresented minorities in public health and clinical and academic medicine [25]. Some improvements have come from efforts such as Project 3000 by 2000, a program sponsored by the Association of American Medical Colleges that sought to increase the number of underrepresented minorities in medical schools; however, educational campaigns are needed that will begin early and offer support throughout one’s educational journey [103].

More Research, Better Research Although there is a growing amount of research dedicated to assessing and addressing racial and socioeconomic disparities in cardiovascular health, there are areas where gaps remain, findings are inconsistent, and certain populations are overlooked. In general, there is a need for more quality research, including randomized, longitudinal, and population-based studies that include a sufficient number of racial and ethnic minorities [28]. Previous studies that sampled racial minorities have mostly included non-Hispanic Blacks, but more research is needed on AIAN, Mexican Americans, Pacific Islanders, Asians, and immigrants [100]. While the research linking traditional risk factors (e.g., diabetes, hyperlipidemia, and smoking) to CVD has been the platform for understanding racial disparities, as we move forward increased emphasis should be placed on other determinants of cardiovascular well-being, such as psychosocial, environmental, and emotional factors [34]. As we have already established, cultural competency plays a major role in eliminating health disparities; therefore, it is important to include cultural competency in research studies working with minority communities. Community-based participatory research (CBPR) is an emerging paradigm that combines cultural humility with community partnerships [82]. It has gained recognition in recent years as an effective strategy for increasing health literacy in minority communities, creating sustainable and successful partnerships between academic institutions and the community, encouraging the community to participate in the research process, producing more valid and accurate measurements and findings, and creating action plans that will continue to benefit the community even after the study is complete [65, 82, 100, 105]. One benefit of CBPR is that the research is performed within the social context of the environment with which participants are familiar. Investigators should develop their research teams based on a diverse panel of investigators from

280

K. M. Fordham et al.

various fields and backgrounds. Strategies for engaging the community and conducting quality research should be multifactorial, and studies should be culturally tailored to the community as well as culturally and linguistically appropriate. CBPR has a direct advantage for improving the health of the community, especially when health information is properly disseminated [22]. The interdisciplinary nature of behavioral medicine, together with its emphasis on biopsychosocial frameworks makes it well-suited to incorporate CBPR approaches.

Conclusion The disproportionate burden of CVD risk factors, morbidity, and mortality in racial and ethnic minorities adds great weight to disparities in health and life expectancy. Although researchers have attempted to piece together the complex web of pathways that connect race and SES to CVD, there are many mechanisms that remain to be understood. Nonetheless, the literature supports the fact that some minority groups have a higher risk of CVD morbidity and mortality, especially Blacks. This also is true for many of the risk factors associated with CVD, although clear evidence exists to show that these risk factors account for a good portion of the excess CVD risk in minorities, much more remains to be learned. Addressing racial disparities in health is just as complex as understanding the factors that creates these disparities; however, lessons learned from previous research steers investigators toward more effective approaches to eliminating disparities. We believe that behavioral medicine has the appropriate expertise, conceptual frameworks, and methodological tools to make important contributions in the twenty-first century toward reducing and ultimately eliminating health disparities. Acknowledgments Ms. Henderson was supported by NIH grant 3R01HL084209-S1; Mr. Golden and Drs. Okuyemi and Everson-Rose were supported in part by NIH grant P60MD003422; Dr. Everson-Rose additionally was supported by NIH grants R01HL084209, R21HL091290, and R01HL089862; Dr. Okuyemi additionally was supported by U54CA153603 and R01HL081522. Additional support comes from the Program in Health Disparities Research and Applied Clinical Research Program at the University of Minnesota. The content of this chapter is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References 1. Adler NE, Rehkopf DH (2008) U.S. disparities in health: descriptions, causes, and mechanisms. Annu Rev Public Health 29:235–252 2. Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, Syme SL (1994) Socioeconomic status and health. The challenge of the gradient. Am Psychol 49:15–24 3. Ahmed AT, Mohammed SA, Williams DR (2007) Racial discrimination & health: pathways & evidence. Indian J Med Res 126:318–327 4. AHRQ (2008) 2007 National Healthcare Disparities Report. U.S. Department of Health and Human Services. Agency for Healthcare Research and Quality, Rockville

11

Health Disparities and Cardiovascular Diseases

281

5. Anda RF, Williamson DF, Escobedo LG, Mast EE, Giovino GA, Remington PL (1990) Depression and the dynamics of smoking. A national perspective. JAMA 264:1541–1545 6. Arnett DK, Baird AE, Barkley RA, Basson CT, Boerwinkle E, Ganesh SK, Herrington DM, Hong Y, Jaquish C, McDermott DA, O’Donnell CJ (2007) Relevance of genetics and genomics for prevention and treatment of cardiovascular disease: a scientific statement from the American Heart Association Council on Epidemiology and Prevention, the Stroke Council, and the Functional Genomics and Translational Biology Interdisciplinary Working Group. Circulation 115:2878–2901 7. Barnes PM, Adams PF, Powell-Griner E (2010) Health characteristics of the American Indian or Alaska Native adult population: United States, 2004–2008. Natl Health Stat Rep 20:1–22 8. Bell CN, Thorpe RJ Jr, Laveist TA (2010) Race/Ethnicity and hypertension: the role of social support. Am J Hypertens 23:534–540 9. Betancourt JR, Green AR, Carrillo JE, Ananeh-Firempong O 2nd (2003) Defining cultural competence: a practical framework for addressing racial/ethnic disparities in health and health care. Public Health Rep 118:293–302 10. Bibbins-Domingo K, Pletcher MJ, Lin F, Vittinghoff E, Gardin JM, Arynchyn A, Lewis CE, Williams OD, Hulley SB (2009) Racial differences in incident heart failure among young adults. N Engl J Med 360:1179–1190 11. Borhani NO (1965) Changes and geographic distribution of mortality from cerebrovascular disease. Am J Public Health 55:673–681 12. Borrell LN, Roux AV, Jacobs DR Jr, Shea S, Jackson SA, Shrager S, Blumenthal RS (2010) Perceived racial/ethnic discrimination, smoking and alcohol consumption in the Multi-Ethnic Study of Atherosclerosis (MESA). Prev Med 51:307–312 13. Brach C, Fraser I (2000) Can cultural competency reduce racial and ethnic health disparities? A review and conceptual model. Med Care Res Rev 57:181–217 14. Bromberger JT, Harlow S, Avis N, Kravitz HM, Cordal A (2004) Racial/ethnic differences in the prevalence of depressive symptoms among middle-aged women: the Study of Women’s Health Across the Nation (SWAN). Am J Public Health 94:1378–1385 15. Casper ML, Wing S, Anda RF, Knowles M, Pollard RA (1995) The shifting stroke belt. Changes in the geographic pattern of stroke mortality in the United States, 1962 to 1988. Stroke 26:755–760 16. Casper ML, Barnett E, Williams GI Jr, Halverson JA, Braham VE, Greenlund KJ (2003) Atlas of stroke mortality: racial, ethnic, and geographic disparities in the United States. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta 17. CDC (2005) Disparities in screening for and awareness of high blood cholesterol – United States, 1999–2002. Morb Mortal Wkly Rep 54:117–119 18. CDC (2009) Estimated county-level prevalence of diabetes and obesity – United States, 2007. Morb Mortal Wkly Rep 58:1259–1263 19. CDC (2010) Tobacco control state highlights, 2010. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta 20. CensusBureau (2008) Statistical Abstract of the United States: 2009, 128th edn. U.S. Census Bureau, Washington, DC 21. Chae DH, Lincoln KD, Adler NE, Syme SL (2010) Do experiences of racial discrimination predict cardiovascular disease among African American men? The moderating role of internalized negative racial group attitudes. Soc Sci Med 71:1182–1188 22. Chen PG, Diaz N, Lucas G, Rosenthal MS (2010) Dissemination of results in communitybased participatory research. Am J Prev Med 39:372–378 23. Christian AH, Rosamond W, White AR, Mosca L (2007) Nine-year trends and racial and ethnic disparities in women’s awareness of heart disease and stroke: an American Heart Association national study. J Womens Health 16:68–81 24. Clark R, Anderson NB, Clark VR, Williams DR (1999) Racism as a stressor for African Americans – a biopsychosocial model. Am Psychol 54:805–816 25. Cohen JJ, Gabriel BA, Terrell C (2002) The case for diversity in the health care workforce. Health Aff (Millwood) 21:90–102

282

K. M. Fordham et al.

26. Cohen MG, Fonarow GC, Peterson ED, Moscucci M, Dai D, Hernandez AF, Bonow RO, Smith SC Jr (2010) Racial and ethnic differences in the treatment of acute myocardial infarction: findings from the Get With the Guidelines-Coronary Artery Disease program. Circulation 121:2294–2301 27. Comstock GW (1957) An epidemiologic study of blood pressure levels in a biracial community in the Southern United States. Am J Hyg 65:271–315 28. Cooper LA, Hill MN, Powe NR (2002) Designing and evaluating interventions to eliminate racial and ethnic disparities in health care. J Gen Intern Med 17(6):477–486 29. Cossrow N, Falkner B (2004) Race/ethnic issues in obesity and obesity-related comorbidities. J Clin Endocrinol Metab 89:2590–2594 30. Crimmins EM, Kim JK, Alley DE, Karlamangla A, Seeman T (2007) Hispanic paradox in biological risk profiles. Am J Public Health 97:1305–1310 31. DeNavas-Walt C, Proctor BD, Smith JC (2010) Current Population Reports, P60-238, income, povery, and health insurance coverage in the United States: 2009. U.S. Census Bureau, Washington, DC 32. Everson SA, Lynch JW, Chesney MA, Kaplan GA, Goldberg DE, Shade SB, Cohen RD, Salonen R, Salonen JT (1997) Interaction of workplace demands and cardiovascular reactivity in progression of carotid atherosclerosis: population based study. BMJ 314:553–558 33. Everson SA, Goldberg DE, Kaplan GA, Julkunen J, Salonen JT (1998) Anger expression and incident hypertension. Psychosom Med 60:730–735 34. Everson-Rose SA, Lewis TT (2005) Psychosocial factors and cardiovascular diseases. Annu Rev Public Health 26:469–500 35. Everson-Rose SA, Meyer PM, Powell LH, Pandey D, Torrens JI, Kravitz HM, Bromberger JT, Matthews KA (2004) Depressive symptoms, insulin resistance, and risk of diabetes in women at midlife. Diabetes Care 27:2856–2862 36. Everson-Rose SA, Lutsey PL, Roetker NS, Lewis TT, Kershaw KN, Alonso A, Diez Roux AV (2015) Perceived discrimination and incident cardiovascular events: the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol 182:225–234 37. Farmer MM, Ferraro KF (2005) Are racial disparities in health conditional on socioeconomic status? Soc Sci Med 60:191–204 38. Finkelstein EA, Khavjou OA, Mobley LR, Haney DM, Will JC (2004) Racial/ethnic disparities in coronary heart disease risk factors among WISEWOMAN enrollees. J Womens Health 13:503–518 39. Fryar CD, Hirsch R, Eberhardt MS, Yoon SS, Wright JD (2010) Hypertension, high serum total cholesterol, and diabetes: racial and ethnic prevalence differences in U.S. adults, 1999– 2006. NCHS Data Brief 36:1–8 40. Fuchs FD, Chambless LE, Whelton PK, Nieto FJ, Heiss G (2001) Alcohol consumption and the incidence of hypertension: the Atherosclerosis Risk in Communities Study. Hypertension 37:1242–1250 41. Gallo LC, Matthews KA (2003) Understanding the association between socioeconomic status and physical health: do negative emotions play a role? Psychol Bull 129:10–51 42. Gallo LC, Bogart LM, Vranceanu AM, Matthews KA (2005) Socioeconomic status, resources, psychological experiences, and emotional responses: a test of the reserve capacity model. J Pers Soc Psychol 88:386–399 43. Groeneveld PW, Kruse GB, Chen Z, Asch DA (2007) Variation in cardiac procedure use and racial disparity among Veterans Affairs Hospitals. Am Heart J 153:320–327 44. Hasin DS, Goodwin RD, Stinson FS, Grant BF (2005) Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 62:1097–1106 45. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM (2004) Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 291:2847–2850 46. Hofrichter R (2006) Tackling health inequities through public health practice: a handbook for action, vol 12. National Association of County and City Health Officials, Washington, DC

11

Health Disparities and Cardiovascular Diseases

283

47. Howard G, Howard VJ (2001) Ethnic disparities in stroke: the scope of the problem. Ethn Dis 11:761–768 48. Hughes JW, Sherwood A, Blumenthal JA, Suarez EC, Hinderliter AL (2003) Hostility, social support, and adrenergic receptor responsiveness among African-American and white men and women. Psychosom Med 65:582–587 49. Jackson JS, Brown TN, Williams DR, Torres M, Sellers SL, Brown K (1996) Racism and the physical and mental health status of African Americans: a thirteen year national panel study. Ethn Dis 6:132–147 50. Jackson-Triche ME, Greer Sullivan J, Wells KB, Rogers W, Camp P, Mazel R (2000) Depression and health-related quality of life in ethnic minorities seeking care in general medical settings. J Affect Disord 58:89–97 51. Johnson RL, Roter D, Powe NR, Cooper LA (2004) Patient race/ethnicity and quality of patient-physician communication during medical visits. Am J Public Health 94:2084–2090 52. Kaplan G, Keil J (1993) Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 88:1973–1998 53. Kaplan GA, Everson SA, Lynch JW (2000) The contribution of social and behavioral research to an understanding of the distribution of disease: a multilevel approach. In: Smedley BD, Syme SL (eds) Promoting health: intervention strategies from social and behavioral research. National Academy Press, Washington, DC, pp 37–80 54. Karlamangla AS, Merkin SS, Crimmins EM, Seeman TE (2010) Socioeconomic and ethnic disparities in cardiovascular risk in the United States, 2001–2006. Ann Epidemiol 20:617–628 55. Kawachi I, Daniels N, Robinson DE (2005) Health disparities by race and class: why both matter. Health Aff (Millwood) 24:343–352 56. Kissela B, Schneider A, Kleindorfer D, Khoury J, Miller R, Alwell K, Woo D, Szaflarski J, Gebel J, Moomaw C, Pancioli A, Jauch E, Shukla R, Broderick J (2004) Stroke in a biracial population: the excess burden of stroke among blacks. Stroke 35(2):426–431 57. Kleindorfer D (2009) Sociodemographic groups at risk: race/ethnicity. Stroke 40:S75–S78 58. Koh HK (2010) A 2020 vision for healthy people. N Engl J Med 362:1653–1656 59. Kressin NR, Petersen LA (2001) Racial differences in the use of invasive cardiovascular procedures: review of the literature and prescription for future research. Ann Intern Med 135:352–366 60. Kurian AK, Cardarelli KM (2007) Racial and ethnic differences in cardiovascular disease risk factors: a systematic review. Ethn Dis 17:143–152 61. Lanska DJ (1993) Geographic distribution of stroke mortality in the United States: 1939–1941 to 1979–1981. Neurology 43:1839–1851 62. Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J (1998) Socioeconomic factors, health behaviors, and mortality: results from a nationally representative prospective study of US adults. JAMA 279:1703–1708 63. Lantz PM, Lynch JW, House JS, Lepkowski JM, Mero RP, Musick MA, Williams DR (2001) Socioeconomic disparities in health change in a longitudinal study of US adults: the role of health-risk behaviors. Soc Sci Med 53:29–40 64. Larson NI, Story MT, Nelson MC (2009) Neighborhood environments: disparities in access to healthy foods in the U.S. Am J Prev Med 36:74–81 65. Leung MW, Yen IH, Minkler M (2004) Community based participatory research: a promising approach for increasing epidemiology’s relevance in the 21st century. Int J Epidemiol 33:499– 506 66. Lewis TT, Everson-Rose SA, Powell LH, Matthews KA, Brown C, Karavolos K, SuttonTyrrell K, Jacobs E, Wesley D (2006) Chronic exposure to everyday discrimination and coronary artery calcification in African-American women: the SWAN Heart Study. Psychosom Med 68:362–368 67. Lewis TT, Everson-Rose SA, Colvin A, Matthews K, Bromberger JT, Sutton-Tyrrell K (2009) Interactive effects of race and depressive symptoms on calcification in African American and white women. Psychosom Med 71:163–170

284

K. M. Fordham et al.

68. Lewis TT, Guo H, Lunos S, Mendes de Leon CF, Skarupski KA, Evans DA, Everson-Rose SA (2011) Depressive symptoms and cardiovascular mortality in older black and white adults: evidence for a differential association by race. Circ Cardiovasc Qual Outcomes 4:293–299 69. Lillie-Blanton M, Rushing OE, Ruiz S, Mayberry R, Boone L (2002) Racial/ethnic differences in cardiac care: the weight of the evidence. Henry J Kaiser Family Foundation, American College of Cardiology Foundation, Washington, DC 70. Lloyd-Jones D, Nam BH, D’Agostino RB, Levy D, Murabito JM, Wang TJ, Wilson PWF, O’Donnell CJ (2004) Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults – a prospective study of parents and offspring. JAMA 291:2204–2211 71. Lovejoy J, Sasagawa M (2010) Obesity, nutrition, and physical activity in blacks and whites: implications for cardiovascular disease. Curr Cardiovasc Risk Rep 4:202–208 72. Lurie N, Fremont A, Jain AK, Taylor SL, McLaughlin R, Peterson E, Kong BW, Ferguson TB Jr (2005) Racial and ethnic disparities in care: the perspectives of cardiologists. Circulation 111:1264–1126 73. Lynch JW, Kaplan GA, Salonen JT (1997) Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse. Soc Sci Med 44:809–819 74. Marenberg ME, Risch N, Berkman LF, Floderus B, de Faire U (1994) Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med 330:1041–1046 75. Massey DS (2004) Segregation and stratification: a biosocial perspective. Du Bois Rev 1 (01):7–25 76. Matsumoto Y, Uyama O, Shimizu S, Michishita H, Mori R, Owada T, Sugita M (1993) Do anger and aggression affect carotid atherosclerosis. Stroke 24:983–986 77. Matthews KA, Cottington EM, Talbott E, Kuller LH, Siegel JM (1987) Stressful work conditions and diastolic blood-pressure among blue collar factory-workers. Am J Epidemiol 126:280–291 78. Matthews KA, Raikkonen K, Gallo L, Kuller LH (2008) Association between socioeconomic status and metabolic syndrome in women: testing the reserve capacity model. Health Psychol 27:576–583 79. Maty SC, James SA, Kaplan GA (2009) Life-course socioeconomic position and incidence of diabetes mellitus among blacks and whites: the Alameda County Study, 1965–1999. Am J Public Health 100:137–145 80. Mays VM, Cochran SD, Barnes NW (2007) Race, race-based discrimination, and health outcomes among African Americans. Annu Rev Psychol 58:201–225 81. McDonough JR, Garrison GE, Hames CG (1967) Blood pressure and hypertensive disease among Negroes and Whites in Evans County, Georgia. In: Stamler J, Stamler R, Pullman TN (eds) The epidemiology of hypertension. Grune and Stratton, New York, pp 167–187 82. Minkler M (2005) Community-based research partnerships: challenges and opportunities. J Urban Health 82(2 Suppl 2):ii3–ii12 83. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, Ferranti S, Després JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jiménez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, DK MG, Mohler ER III, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB, American Heart Association Statistics Committee and Stroke Statistics Subcommittee (2016) Heart disease and stroke statistics–2016 update: a report from the American Heart Association. Circulation 133:e38–e360 84. Myers HF (2009) Ethnicity- and socio-economic status-related stresses in context: an integrative review and conceptual model. J Behav Med 32:9–19 85. Nelson A (2002) Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc 94:666–668 86. NHLBI (2006) Incidence and prevalence: 2006 chart book on cardiovascular and lung diseases. National Heart, Lung, and Blood Institute, Bethesda

11

Health Disparities and Cardiovascular Diseases

285

87. Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM (2010) Prevalence of high body mass index in US children and adolescents, 2007–2008. JAMA 303:242–249 88. Otten MW Jr, Teutsch SM, Williamson DF, Marks JS (1990) The effect of known risk factors on the excess mortality of black adults in the United States. JAMA 263:845–850 89. Passel JS, Cohn D (2008) U.S. population projections 2005–2050. Pew Research Center Social & Demographic Trends. Pew Research Center, Washington, DC 90. Pleis JR, Lucas JW, Ward BW (2009) Summary health statistics for U.S. adults: National Health Interview Survey, 2008. Vital Health Stat 10(242):1–157 91. Pletcher MJ, Varosy P, Kiefe CI, Lewis CE, Sidney S, Hulley SB (2005) Alcohol consumption, binge drinking, and early coronary calcification: findings from the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Epidemiol 161:423–433 92. Prineas R, Gillum R (1985) US epidemiology of hypertension in Blacks. In: Hall WD, Saunders E, Shulman NB (eds) Hypertension in blacks: epidimiology, pathophysiology and treatment. Year Book Medica Pubishers, Chicago, pp 17–37 93. Rosamond WD, Folsom AR, Chambless LE, Wang CH, McGovern PG, Howard G, Copper LS, Shahar E (1999) Stroke incidence and survival among middle-aged adults – 9-year followup of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke 30:736–743 94. Sacco RL, Boden-Albala B, Abel G, Lin IF, Elkind M, Hauser WA, Paik MC, Shea S (2001) Race-ethnic disparities in the impact of stroke risk factors: the northern Manhattan stroke study. Stroke 32:1725–1731 95. Sarrazin MS, Campbell ME, Richardson KK, Rosenthal GE (2009) Racial segregation and disparities in health care delivery: conceptual model and empirical assessment. Health Serv Res 44:1424–1444 96. Schoenborn CA, Adams PE (2010) Health behaviors of adults: United States, 2005–2007. Vital Health Stat 10(245):1–132 97. Schulz A, Israel B, Williams D, Parker E, Becker A, James S (2000) Social inequalities, stressors and self reported health status among African American and white women in the Detroit metropolitan area. Soc Sci Med 51:1639–1653 98. Schulz AJ, Kannan S, Dvonch JT, Israel BA, Allen A 3rd, James SA, House JS, Lepkowski J (2005) Social and physical environments and disparities in risk for cardiovascular disease: the healthy environments partnership conceptual model. Environ Health Perspect 113:1817–1825 99. Sharma S, Malarcher AM, Giles WH, Myers G (2004) Racial, ethnic and socioeconomic disparities in the clustering of cardiovascular disease risk factors. Ethn Dis 14:43–48 100. Smedley BD, Stith AY, Nelson AR (2003) Unequal treatment: confronting racial and ethinic disparities in healthcare. The National Academies Press, Washington, DC 101. Street RL Jr, O’Malley KJ, Cooper LA, Haidet P (2008) Understanding concordance in patient-physician relationships: personal and ethnic dimensions of shared identity. Ann Fam Med 6:198–205 102. Sundquist J, Winkleby MA, Pudaric S (2001) Cardiovascular disease risk factors among older black, Mexican-American, and white women and men: an analysis of NHANES III, 1988– 1994. Third National Health and Nutrition Examination Survey. J Am Geriatr Soc 49:109–116 103. Terrell C, Beaudreau J (2003) 3000 by 2000 and beyond: next steps for promoting diversity in the health professions. J Dent Educ 67:1048–1052 104. Troxel WM, Matthews KA, Bromberger JT, Sutton-Tyrrell K (2003) Chronic stress burden, discrimination, and subclinical carotid artery disease in African American and Caucasian women. Health Psychol 22:300–309 105. Wallerstein NB, Duran B (2006) Using community-based participatory research to address health disparities. Health Promot Pract 7:312–323 106. White H, Albala BB, Wang CL, Elkind MSV, Rundek T, Wright CB, Sacco RL (2005) Ischemic stroke subtype incidence among whites, blacks, and Hispanics – the northern Manhattan study. Circulation 111:1327–1331 107. Williams DR, Jackson PB (2005) Social sources of racial disparities in health. Health Aff (Millwood) 24:325–334

286

K. M. Fordham et al.

108. Williams DR, Mohammed SA (2009) Discrimination and racial disparities in health: evidence and needed research. J Behav Med 32:20–47 109. Williams D, Yu Y, Jackson J, Anderson N (1997) Racial differences in physical and mental health. J Health Psychol 3:335–351 110. Williams RR, Hunt SC, Heiss G, Province MA, Bensen JT, Higgins M, Chamberlain RM, Ware J, Hopkins PN (2001) Usefulness of cardiovascular family history data for populationbased preventive medicine and medical research (the Health Family Tree Study and the NHLBI Family Heart Study). Am J Cardiol 87:129–135 111. Williams DR, Mohammed SA, Leavell J, Collins C (2010) Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities. Ann N Y Acad Sci 1186:69–101

Nicotine Dependence and Cardiovascular Diseases: Biobehavioral and Psychosocial Correlates

12

Mustafa al’Absi, Motohiro Nakajima, Paige Green, Karen Petersen, and Lorentz Wittmers

Contents Measurement Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acute Effects of Smoking on Cardiovascular System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interactive Effects of Nicotine and Acute Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smoking and Cardiovascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coronary Artery Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stroke and Cerebrovascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peripheral Vascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mechanisms of Tobacco Effects on Cardiovascular Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

289 289 290 290 294 294 294 295

Lorentz Wittmers: deceased. M. al’Absi (*) Department of Family Medicine and Biobehavioral Health, University of Minnesota Medical School, Duluth, MN, USA e-mail: [email protected] M. Nakajima Department of Family Medicine and Biobehavioral Health, University of Minnesota Medical School, Duluth, MN, USA P. Green Basic Biobehavioral and Psychological Sciences Branch, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA K. Petersen Department of Psychology, The College of St. Scholastica, Duluth, MN, USA L. Wittmers Department of Physiology and Pharmacology, University of Minnesota Medical School, Duluth, MN, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_12

287

288

M. al’Absi et al.

Psychosocial Correlates/Determinant of Smoking Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethnic and Racial Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socioeconomic Status (SES) and Smoking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weight Concern After Smoking Cessation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment Option for Smoking Cessation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behavioral Intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacologic Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Cessation on Reversing Cardiovascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

296 296 297 298 299 299 300 301 303 304 304

Abstract

Tobacco use is the most preventable cause of premature death in the United States. Cigarette smoking increases the risk of coronary heart disease, stroke, and peripheral arterial disease. The link between pathology and tobacco use has been established through accumulating evidence over four decades demonstrating a dose-response relationship between cigarette smoking (amount and duration) and risk for cardiovascular diseases. This chapter will review examples of research demonstrating acute and chronic effects of smoking on cardiovascular function, the interaction of nicotine effects and stress, long-term effects of cigarette smoking on cardiovascular disease risk, and mechanisms mediating effects of cigarettes smoking on cardiovascular disease. In addition, a review of moderating factors, available intervention options to address smoking, and benefits of cessation on cardiovascular health will be undertaken. The article concludes with a summary of current research findings of the link between tobacco use and cardiovascular disease and future directions.

Keywords

Tobacco use · Smoking · Cardiovascular diseases · Stress · Cessation

Tobacco use is the most preventable cause of premature death in the United States and many other countries, accounting for more than 440,000 annual deaths in the United States alone [36]. In the United States, the overall mortality is approximately as three times as high in smokers than in those who never smoked. Current smokers have a life expectancy which is at least 10 years shorter than never smokers [75, 146]. Cigarette smoking increases the risk of coronary heart disease, stroke, and peripheral arterial disease. Cigarette smokers are two to four times more likely to develop coronary heart disease and stroke than nonsmokers, and they are more than ten times as likely as nonsmokers to develop peripheral vascular disease. The link between pathology and tobacco use has been established through accumulating evidence over four decades demonstrating a dose-response relationship between cigarette smoking (amount and duration) and risk for cardiovascular diseases. This chapter will review examples of research demonstrating acute and chronic effects of

12

Nicotine Dependence and Cardiovascular Diseases: Biobehavioral and. . .

289

smoking on cardiovascular function, the interaction of nicotine effects and stress, long-term effects of cigarette smoking on cardiovascular disease risk, and mechanisms mediating effects of cigarette smoking on cardiovascular disease. In addition, a review of moderating factors, available intervention options to address smoking, and benefits of cessation on cardiovascular health will be undertaken.

Measurement Issues Acute Effects of Smoking on Cardiovascular System One of the most studied chemicals in cigarette smoke is nicotine. Although the halflife of nicotine is approximately 2–4 h, it remains active in the system for 6–8 h, especially with the regular and repeated intake that occurs in chronic smokers. Absorption of nicotine and other constituents of cigarette smoke occurs in the lungs, where nicotine enters the bloodstream and binds to plasma proteins and is distributed to all body tissues, including the vasculature, the heart, the brain, and the liver [19]. Nicotine that is not metabolized remains in the system and exerts its effects on various neurotransmitters and other peripheral systems. These effects contribute to the harmful sequelae of tobacco use and also play a role in the addictive properties of nicotine. Acute effects of low doses of nicotine (i.e., similar doses to that consumed by smokers) include an increase in heart rate and blood pressure, increased force of myocardial contraction, constriction in the skin and the heart, relaxation of the skeletal muscles, increased whole body metabolism, and the release of a variety of hormones [18]. Acute smoking also increases peripheral vascular resistance, and studies have shown this increase takes place immediately after smoking one cigarette. This effect seems to be pronounced in older smokers [91]. High doses of nicotine produce dizziness, nausea, convulsions, vomiting, muscle paralysis, and cessation of breathing and may lead to circulatory collapse. Nicotine exerts these effects by enhancing the release of a variety of neurotransmitters, including dopamine, norepinephrine, acetylcholine, serotonin, γ-aminobutyric acid (GABA), glutamate, and endorphins [21]. Catecholamines (i.e., norepinephrine and epinephrine) play an important role in activating the sympathetic nervous system and subsequently cardiovascular reactivity. Nicotine binds to nicotinic receptors at sympathetic ganglia, stimulating adrenal medullary receptors and releasing catecholamines. Norepinephrine is also released from sympathetic nerve terminals in response to nicotine, and this leads to activation of the innervated organs. Chronic use of nicotine leads to tolerance. Tolerance is developed when effects obtained with the first dose are attenuated after repeated doses, or increased doses are needed to reach the effects attained by the original dose [17]. In a habitual smoker, the first cigarette of the day produces various pharmacological effects (e.g., pleasure, stimulation) but also contributes to tolerance. As the number of cigarettes increases, pharmacological effects will diminish because of the accumulation of nicotine in the

290

M. al’Absi et al.

body (i.e., tolerance). This leads to increased withdrawal symptoms between successive cigarettes. Withdrawal symptoms include depression, anger, anxiety, restlessness, difficulty concentrating, impatience, and insomnia [132].

Interactive Effects of Nicotine and Acute Stress It is well known that smokers often use nicotine as a means of coping with stressful circumstances [120]. Interestingly, hemodynamic and neuroendocrine responses induced by smoking are similar to those patterns observed during stress [3]. Examining the interactive effects of smoking and stress is therefore critical to address psychophysiological mechanisms of smoking behavior. Laboratory studies have found additive effects of smoking and stress on cardiovascular responses [44, 100], suggesting that a combination of smoking and acute stress impose myocardial work and oxygen demand. Other researchers have attempted to document individual differences (e.g., smokers vs. nonsmokers) to examine the extent to which chronic smoking alters physiological responses to acute stress [5]. Table 1 summarizes studies that have focused on examining differences in cardiovascular reactivity to stress between smokers and nonsmokers. Out of 16 studies, eight studies found no differences in resting blood pressure (BP) between smokers and nonsmokers. Four studies found elevated basal BP and other three found decreased BP in smokers compared with nonsmokers. While seven studies reported no differences in resting heart rate (HR) between smokers and nonsmokers, seven studies reported that smokers exhibited higher baseline HR than nonsmokers. These findings indicate that chronic smoking may be associated with altered cardiovascular activity during rest. With regard to cardiovascular responses to acute stress, seven studies found reduced responses in BP in smokers, and one found increased BP responses in smokers. Four studies found no differences in BP response between smokers and nonsmokers. Seven studies reported no differences in HR reactivity during acute stress. However, seven studies reported attenuated HR responses in smokers than in nonsmokers. These results generally suggest that BP and HR responses to stress may be diminished in smokers compared with nonsmokers. One possibility for the mixed findings is the different experimental protocol, such as smoking criteria, duration of abstinence, and the type of stress tasks used during the session. Since physiological responses due to withdrawal are similar to those found during stress, variability in the duration of abstinence is a confounding factor in assessing effects of smoking on cardiovascular reactivity.

Smoking and Cardiovascular Diseases It is now accepted that tobacco and cigarette smoking increase risk of various cardiac, cerebrovascular, and peripheral vascular diseases [143]. Smoking-related deaths from cardiovascular diseases are comparable to that of lung cancer

Back et al. (2008)

Straneva et al. (2000) al’Absi et al. [5]

Sheffield et al. (1997)

Girdler et al. (1997)

Tsuda et al. (1996)

Ward et al. (1994)

Stewart et al. (1994)

Kirschbaum et al. [84] Roy et al. (1994)

Perkins et al. (1992)

Study Mann et al. (1991)

23 S 23 NS 17 ad libitum S 21 abstinent S 32 NS

Sample size 59 S 118 NS 31 S 12 NS 10 S 10 NS 34 S 52 NS 13 S 13 NS 148 quitters 39 NS 19 recent S 14 abstinent S 16 NS 41 S 35 NS 177 S 596 NS 2h

13.8

n/a

21.0 (ad lib) 17.0 (abs.)

19.5

n/a

20.8

20.5 (rec.) 18.4 (abs.)

24.9

17.5

Mental arithmetic, public speaking, CPT Public speaking


18 h (abstinent)

TSST

Progressive matrices

>4 h: n ¼ 39 1–4 h: n ¼ 74 65 years of age with symptoms of depression but without evidence of myocardial ischemia. Am J Cardiol 89:419–424 73. Kudielka BM, Bellingrath S, von Känel R (2008) Circulating fibrinogen but not d-dimer level is associated with vital exhaustion in school teachers. Stress 11:250–258 74. Laghrissi-Thode F, Wagner WR, Pollock BG, Johnson PC, Finkel MS (1997) Elevated platelet factor 4 and [beta]-thromboglobulin plasma levels in depressed patients with ischemic heart disease. Biol Psychiatry 42:290–295 75. Lahlou-Laforet K, Alhenc-Gelas M, Pornin M, Bydlowski S, Seigneur E, Benetos A et al (2006) Relation of depressive mood to plasminogen activator inhibitor, tissue plasminogen activator, and fibrinogen levels in patients with versus without coronary heart disease. Am J Cardiol 97:1287–1291 76. Lavoie K, Pelletier R, Arsenault A, Dupuis J, Bacon S (2010) Association between clinical depression and endothelial function measured by forearm hyperemic reactivity. Psychosom Med 72:20–26 77. Lin TK, Weng CY, Wang WC, Chen CC, Lin IM, Lin CL (2008) Hostility trait and vascular dilatory functions in healthy Taiwanese. J Behav Med 31:517–524 78. Lippi G, Franchini M, Salvagno GL, Montagnana M, Guidi GC (2008) Higher morning serum cortisol level predicts increased fibrinogen but not shortened APTT. J Thromb Thrombolysis 26:103–105 79. Loucks EB, Berkman LF, Gruenewald TL, Seeman TE (2005) Social integration is associated with fibrinogen concentration in elderly men. Psychosom Med 67:353–358 80. Lowe GDO, Rumley A, Whincup PH, Danesh J (2002) Hemostatic and rheological variables and risk of cardiovascular disease. Semin Vasc Med 2:429–439 81. Lowe G, Danesh J, Lewington S, Walker M, Lennon L, Thomson A et al (2004) Tissue plasminogen activator antigen and coronary heart disease. Eur Heart J 25:252–259 82. Ludmer PL, Selwyn AP, Shook TL, Wayne RR, Mudge GH, Alexander RW, Ganz P (1986) Paradoxical vasoconstriction induced by acetylcholine in atherosclerotic coronary arteries. N Engl J Med 315:1046–1051 83. Maes M, Delange J, Ranjan R, Meltzer HY, Desnyder R, Cooremans W, Scharpé S (1997) Acute phase proteins in schizophrenia, mania and major depression: modulation by psychotropic drugs. Psychiatry Res 66:1–11 84. Malik I, Danesh J, Whincup P, Bhatia V, Papacosta O, Walker M et al (2001) Soluble adhesion molecules and prediction of coronary heart disease: a prospective study and meta-analysis. Lancet 358:971–976 85. Markovitz JH (1998) Hostility is associated with increased platelet activation in coronary heart disease. Psychosom Med 60:586–591

36

Hemostasis and Endothelial Function

885

86. Markovitz JH, Matthews KA, Kiss J, Smitherman TC (1996) Effects of hostility on platelet reactivity to psychological stress in coronary heart disease patients and in healthy controls. Psychosom Med 58:143–149 87. Matthews KA, Schott LL, Bromberger J, Cyranowski J, Everson-Rose SA, Sowers MF (2007) Associations between depressive symptoms and inflammatory/hemostatic markers in women during the menopausal transition. Psychosom Med 69:124–130 88. Mausbach BT, Ancoli-Israel S, von Känel R, Patterson TL, Aschbacher K, Mills PJ et al (2006) Sleep disturbance, norepinephrine, and D-dimer are all related in elderly caregivers of people with Alzheimer disease. Sleep 29:1347–1352 89. Mausbach BT, Aschbacher K, Patterson TL, von Känel R, Dimsdale JE, Mills PJ et al (2007) Effects of placement and bereavement on psychological well-being and cardiovascular risk in Alzheimer’s caregivers: a longitudinal analysis. J Psychosom Res 62:439–445 90. Mausbach BT, von Känel R, Aschbacher K, Roepke SK, Dimsdale JE, Ziegler MG et al (2007) Spousal caregivers of patients with Alzheimer’s disease show longitudinal increases in plasma level of tissue-type plasminogen activator antigen. Psychosom Med 69:816–822 91. Mausbach BT, von Känel R, Patterson TL, Dimsdale JE, Depp CA, Aschbacher K et al (2008) The moderating effect of personal mastery and the relations between stress and Plasminogen Activator Inhibitor-1 (PAI-1) antigen. Health Psychol 27:S179 92. McCaffery JM, Duan QL, Frasure Smith N, Barhdadi A, Lespérance F, Théroux P et al (2009) Genetic predictors of depressive symptoms in cardiac patients. Am J Med Genet B Neuropsychiatr Genet 150:381–388 93. Mercer DA, Lavoie KL, Ditto B, Pelletier R, Campbell T, Arsenault A, Bacon SL (2014) The interaction between anxiety and depressive symptoms on brachial artery reactivity in cardiac patients. Biol Psychol 102:44–50 94. Morel-Kopp MC, Mclean L, Chen Q, Tofler G, Tennant C, Maddison V, Ward C (2009) The association of depression with platelet activation: evidence for a treatment effect. J Thromb Haemost 7:573–581 95. Musselman DL, Tomer A, Manatunga AK, Knight BT, Porter MR, Kasey S et al (1996) Exaggerated platelet reactivity in major depression. Am J Psychiatry 153:1313–1317 96. Nadar S, Blann AD, Lip GYH (2004) Endothelial dysfunction: methods of assessment and application to hypertension. Curr Pharm Des 10:3591–3605 97. Narita K, Murata T, Hamada T, Takahashi T, Kosaka H, Yoshida H, Wada Y (2007) Association between trait anxiety and endothelial function observed in elderly males but not in young males. Int Psychogeriatr 19:947–954 98. Narita K, Murata T, Hamada T, Kosaka H, Sudo S, Mizukami K et al (2008) Associations between trait anxiety, insulin resistance, and atherosclerosis in the elderly: a pilot crosssectional study. Psychoneuroendocrinology 33:305–312 99. Neubauer H, Petrak F, Zahn D, Pepinghege F, Hägele AK, Pirkl PA et al (2013) Newly diagnosed depression is associated with increased beta-thromboglobulin levels and increased expression of platelet activation markers and platelet derived CD40-CD0L. J Psychiatr Res 47:865–871 100. Orth-Gomér K, Rosengren A, Wilhelmsen L (1993) Lack of social support and incidence of coronary heart disease in middle-aged Swedish men. Psychosom Med 55:37–43 101. Paine NJ, Ring C, Bosch JA, McIntyre D, Veldhuijzen van Zanten JJ (2013) The effect of acute mental stress on limb vasodilation is unrelated to total peripheral resistance. Psychophysiology 50:680–690 102. Palermo A, Bertalero P, Pizza N, Amelotti R, Libretti A (1989) Decreased fibrinolytic response to adrenergic stimulation in hypertensive patients. J Hypertens Suppl 7:S162–S163 103. Panagiotakos DB, Pitsavos C, Chrysohoou C, Tsetsekou E, Papageorgiou C, Christodoulou G, Stefanadis C (2004) Inflammation, coagulation, and depressive symptomatology in cardiovascular disease-free people: the ATTICA study. Eur Heart J 25:492–499 104. Parakh K, Sakhuja A, Bhat U, Ziegelstein RC (2008) Platelet function in patients with depression. South Med J 101:612–617

886

R. von Ka¨nel and S. L. Bacon

105. Pitsavos C, Panagiotakos DB, Papageorgiou C, Tsetsekou E, Soldatos C, Stefanadis C (2006) Anxiety in relation to inflammation and coagulation markers, among healthy adults: the ATTICA study. Atherosclerosis 185:320–326 106. Pollitt R, Kaufman J, Rose K, Diez-Roux AV, Zeng D, Heiss G (2008) Cumulative life course and adult socioeconomic status and markers of inflammation in adulthood. J Epidemiol Community Health 62:484–491 107. Pollock BG, Laghrissi-Thode F, Wagner WR (2000) Evaluation of platelet activation in depressed patients with ischemic heart disease after paroxetine or nortriptyline treatment. J Clin Psychopharmacol 20:137–140 108. Preckel D, von Känel R (2004) Regulation of hemostasis by the sympathetic nervous system: any contribution to coronary artery disease? Heart 4:123–130 109. Räikkönen K, Lassila R, Keltikangas-Järvinen L, Hautanen A (1996) Association of chronic stress with plasminogen activator inhibitor–1 in healthy middle-aged men. Arterioscler Thromb Vasc Biol 16:363–367 110. Rajagopalan S, Brook R, Rubenfire M, Pitt E, Young E, Pitt B (2001) Abnormal brachial artery flow-mediated vasodilation in young adults with major depression. Am J Cardiol 88:196–198 111. Reid GJ, Seidelin PH, Kop WJ, Irvine MJ, Strauss BH, Nolan RP et al (2009) Mental stress-induced platelet activation among patients with coronary artery disease. Psychosom Med 71:438–445 112. Ring C, Burns VE, Carroll D (2002) Shifting hemodynamics of blood pressure control during prolonged mental stress. Psychophysiology 39:585–590 113. Rosengren A, Wilhelmsen L, Welin L, Tsipogianni A, Teger-Nilsson AC, Wedel H (1990) Social influences and cardiovascular risk factors as determinants of plasma fibrinogen concentration in a general population sample of middle aged men. BMJ 300:634–638 114. Ruberg FL, Loscalzo J (2002) Prothrombotic determinants of coronary atherothrombosis. Vasc Med 7:289–299 115. Sarabi M, Lind L (2001) Mental stress opposes endothelium-dependent vasodilation in young healthy individuals. Vasc Med 6:3–7 116. Schins A, Honig A, Crijns H, Baur L, Hamulyak K (2003) Increased coronary events in depressed cardiovascular patients: 5-HT2A receptor as missing link? Psychosom Med 65: 729–737 117. Serebruany VL, Glassman AH, Malinin AI, Nemeroff CB, Musselman DL, van Zyl LT et al (2003) Platelet/endothelial biomarkers in depressed patients treated with the selective serotonin reuptake inhibitor sertraline after acute coronary events. Circulation 108:939–944 118. Sherwood A, Johnson K, Blumenthal JA, Hinderliter AL (1999) Endothelial function and hemodynamic responses during mental stress. Psychosom Med 61:365–370 119. Sherwood A, Hinderliter AL, Watkins LL, Waugh RA, Blumenthal JA (2005) Impaired endothelial function in coronary heart disease patients with depressive symptomatology. J Am Coll Cardiol 46:656–659 120. Siegrist J (1996) Adverse health effects of high-effort/low-reward conditions. J Occup Health Psychol 1:27–41 121. Smith EB, Keen GA, Grant A, Stirk C (1990) Fate of fibrinogen in human arterial intima. Arterioscler Thromb Vasc Biol 10:263–275 122. Spieker LE, Hurlimann D, Ruschitzka F, Corti R, Enseleit F, Shaw S et al (2002) Mental stress induces prolonged endothelial dysfunction via endothelin-A receptors. Circulation 105:2817–2820 123. Steptoe A, Kunz-Ebrecht S, Owen N, Feldman PJ, Rumley A, Lowe G, Marmot M (2003) Influence of socioeconomic status and job control on plasma fibrinogen responses to acute mental stress. Psychosom Med 65:137–144 124. Steptoe A, Magid K, Edwards S, Brydon L, Hong Y, Erusalimsky J (2003) The influence of psychological stress and socioeconomic status on platelet activation in men. Atherosclerosis 168:57–63 125. Steptoe A, Owen N, Kunz-Ebrecht SR, Brydon L (2004) Loneliness and neuroendocrine, cardiovascular, and inflammatory stress responses in middle-aged men and women. Psychoneuroendocrinology 29:593–611

36

Hemostasis and Endothelial Function

887

126. Steptoe A, Wardle J, Marmot M (2005) Positive affect and health-related neuroendocrine, cardiovascular, and inflammatory processes. Proc Natl Acad Sci U S A 102:6508–6512 127. Steptoe A, Gibson EL, Vounonvirta R, Williams ED, Hamer M, Rycroft JA et al (2007) The effects of tea on psychophysiological stress responsivity and post-stress recovery: a randomised double-blind trial. Psychopharmacology 190:81–89 128. Strike PC, Magid K, Brydon L, Edwards S, McEwan JR, Steptoe A (2004) Exaggerated platelet and hemodynamic reactivity to mental stress in men with coronary artery disease. Psychosom Med 66:492–500 129. Szijgyarto IC, King TJ, Ku J, Poitras VJ, Gurd BJ, Pyke KE (2013) The impact of acute mental stress on brachial artery flow mediated dilation differs when shear stress is elevated by reactive hyperemia vs. and grip exercise. Appl Physiol Nutr Metab 38:498–506 130. Tabassum F, Kumari M, Rumley A, Power C, Strachan DP, Lowe G (2014) Lifecourse social position and D-dimer: findings from the 1958 British birth cohort. PLoS One 9:e93277 131. Theorell T (2002) Job stress and fibrinogen. Eur Heart J 23:1799–1801 132. Thrall G, Lane D, Carroll D, Lip GYH (2007) A systematic review of the effects of acute psychological stress and physical activity on haemorheology, coagulation, fibrinolysis and platelet reactivity: implications for the pathogenesis of acute coronary syndromes. Thromb Res 120:819–847 133. Toda N, Nakanishi-Toda M (2011) How mental stress affects endothelial function. Pflugers Arch 462:779–794 134. Tomoda F, Takata M, Kagitani S, Kinuno H, Yasumoto K, Tomita S, Inoue H (1999) Different platelet aggregability during mental stress in two stages of essential hypertension. Am J Hypertens 12:1063–1070 135. Tsutsumi A, Theorell T, Hallqvist J, Reuterwall C, de Faire U (1999) Association between job characteristics and plasma fibrinogen in a normal working population: a cross sectional analysis in referents of the SHEEP Study. Stockholm Heart Epidemiology Program. J Epidemiol Community Health 53:348–354 136. Vaughan D (2005) PAI 1 and atherothrombosis. J Thromb Haemost 3:1879–1883 137. Veldhuijzen van Zanten JJ, Ring C, Burns VE, Edwards KM, Drayson M, Carroll D (2004) Mental stress-induced hemoconcentration: sex differences and mechanisms. Psychophysiology 41:541–551 138. Venugopal B, Sharon R, Abramovitz R, Khasin A, Miskin R (2001) Plasminogen activator inhibitor-1 in cardiovascular cells: rapid induction after injecting mice with kainate or adrenergic agents. Cardiovasc Res 49:476–483 139. von Känel R (2004) Platelet hyperactivity in clinical depression and the beneficial effect of antidepressant drug treatment: how strong is the evidence? Acta Psychiatr Scand 110:163–177 140. von Kanel R (2008) Psychological distress and cardiovascular risk: what are the links? J Am Coll Cardiol 52:2163–2165 141. von Känel R (2015) Acute mental stress and hemostasis: when physiology becomes vascular harm. Thromb Res 135:S52–S55 142. von Känel R, Dimsdale JE (2000) Effects of sympathetic activation by adrenergic infusions on hemostasis in vivo. Eur J Haematol 65:357–369 143. von Känel R, Orth-Gomér K (2008) Autonomic function and prothrombotic activity in women after an acute coronary event. J Women’s Health 17:1331–1337 144. von Känel R, Dimsdale JE, Ziegler MG, Mills PJ, Patterson TL, Lee SK, Grant I (2001) Effect of acute psychological stress on the hypercoagulable state in subjects (spousal caregivers of patients with Alzheimer’s disease) with coronary or cerebrovascular disease and/or systemic hypertension. Am J Cardiol 87:1405–1408 145. von Känel R, Mills PJ, Fainman C, Dimsdale JE (2001) Effects of psychological stress and psychiatric disorders on blood coagulation and fibrinolysis: a biobehavioral pathway to coronary artery disease? Psychosom Med 63:531–544 146. von Känel R, Mills PJ, Ziegler MG, Dimsdale JE (2002) Effect of beta2-adrenergic receptor functioning and increased norepinephrine on the hypercoagulable state with mental stress. Am Heart J 144:68–72

888

R. von Ka¨nel and S. L. Bacon

147. von Känel R, Dimsdale J, Adler K, Dillon E, Perez C, Mills P (2003) Effects of nonspecific beta-adrenergic stimulation and blockade on blood coagulation in hypertension. J Appl Physiol 94:1455–1459 148. von Känel R, Dimsdale JE, Patterson TL, Grant I (2003) Acute procoagulant stress response as a dynamic measure of allostatic load in Alzheimer caregivers. Ann Behav Med 26:42–48 149. von Känel R, Dimsdale JE, Patterson TL, Grant I (2003) Association of negative life event stress with coagulation activity in elderly Alzheimer caregivers. Psychosom Med 65:145–150 150. von Känel R, Dimsdale JE, Adler KA, Patterson TL, Mills PJ, Grant I (2004) Effects of depressive symptoms and anxiety on hemostatic responses to acute mental stress and recovery in the elderly. Psychiatry Res 126:253–264 151. von Känel R, Frey K, Fischer E (2004) Independent relation of vital exhaustion and inflammation to fibrinolysis in apparently healthy subjects. Scand Cardiovasc J 38:28–32 152. von Känel R, Kudielka BM, Abd-el-Razik A, Gander ML, Frey K, Fischer JE (2004) Relationship between overnight neuroendocrine activity and morning haemostasis in working men. Clin Sci 107:89–95 153. von Känel R, Kudielka BM, Schulze R, Gander ML, Fischer JE (2004) Hypercoagulability in working men and women with high levels of panic-like anxiety. Psychother Psychosom 73:353–360 154. von Känel R, Preckel D, Zgraggen L, Mischler K, Kudielka BM, Haeberli A, Fischer JE (2004) The effect of natural habituation on coagulation responses to acute mental stress and recovery in men. J Thromb Haemost 92:1327–1335 155. von Känel R, Kudielka BM, Preckel D, Hanebuth D, Herrmann-Lingen C, Frey K, Fischer JE (2005) Opposite effect of negative and positive affect on stress procoagulant reactivity. Physiol Behav 86:61–68 156. von Känel R, Dimsdale JE, Mills PJ, Ancoli-Israel S, Patterson TL, Mausbach BT, Grant I (2006) Effect of Alzheimer caregiving stress and age on frailty markers interleukin-6, C-reactive protein, and D-dimer. J Gerontol A Biol Sci Med Sci 61:963–969 157. von Känel R, Hepp U, Buddeberg C, Keel M, Mica L, Aschbacher K, Schnyder U (2006) Altered blood coagulation in patients with posttraumatic stress disorder. Psychosom Med 68:598–604 158. von Känel R, Nelesen RA, Ziegler MG, Mausbach BT, Mills PJ, Dimsdale JE (2007) Relation of autonomic activity to plasminogen activator inhibitor-1 plasma concentration and the role of body mass index. Blood Coagul Fibrinolysis 18:353–359 159. von Känel R, Hepp U, Traber R, Kraemer B, Mica L, Keel M et al (2008) Measures of endothelial dysfunction in plasma of patients with posttraumatic stress disorder. Psychiatry Res 158:363–373 160. von Känel R, Mausbach BT, Kudielka BM, Orth-Gomér K (2008) Relation of morning serum cortisol to prothrombotic activity in women with stable coronary artery disease. J Thromb Thrombolysis 25:165–172 161. von Känel R, Kudielka BM, Helfricht S, Metzenthin P, Preckel D, Haeberli A et al (2008) Effects of aspirin and propranolol on the acute psychological stress response in factor VIII coagulant activity: a randomized, double-blind, placebo-controlled experimental study. Blood Coagul Fibrinolysis 19:75–81 162. von Känel R, Kudielka BM, Helfricht S, Metzenthin P, Preckel D, Haeberli A et al (2008) The effects of aspirin and nonselective beta blockade on the acute prothrombotic response to psychosocial stress in apparently healthy subjects. J Cardiovasc Pharmacol 51:231–238 163. von Känel R, Kudielka BM, Haeberli A, Stutz M, Fischer JE, Patterson SM (2009) Prothrombotic changes with acute psychological stress: combined effect of hemoconcentration and genuine coagulation activation. Thromb Res 123:622–630 164. von Känel R, Thayer JF, Fischer JE (2009) Nighttime vagal cardiac control and plasma fibrinogen levels in a population of working men and women. Ann Noninvasive Electrocardiol 14:176–184 165. von Känel R, Bellingrath S, Kudielka BM (2009) Association of vital exhaustion and depressive symptoms with changes in fibrin D-dimer to acute psychosocial stress. J Psychosom Res 67:93–101

36

Hemostasis and Endothelial Function

889

166. von Känel R, Bellingrath S, Kudielka BM (2009) Overcommitment but not effort-reward imbalance relates to stress-induced coagulation changes in teachers. Ann Behav Med 37: 20–28 167. von Känel R, Bellingrath S, Kudielka BM (2009) Association between longitudinal changes in depressive symptoms and plasma fibrinogen levels in school teachers. Psychophysiology 46:473–480 168. von Känel R, Meister RE, Stutz M, Kummer P, Arpagaus A, Huber S et al (2014) Effects of dark chocolate consumption on the prothrombotic response to acute psychosocial stress in healthy men. Thromb Haemost 112:1151–1158 169. Wallen N, Held C, Rehnqvist N, Hjemdahl P (1997) Effects of mental and physical stress on platelet function in patients with stable angina pectoris and healthy controls. Eur Heart J 18:807–815 170. Walsh MT, Dinan TG, Condren RM, Ryan M, Kenny D (2002) Depression is associated with an increase in the expression of the platelet adhesion receptor glycoprotein Ib. Life Sci 70:3155–3165 171. Wamala SP, Murray MA, Horsten M, Eriksson M, Schenck-Gustafsson K, Hamsten A et al (1999) Socioeconomic status and determinants of hemostatic function in healthy women. Arterioscler Thromb Vasc Biol 19:485–492 172. Williams MRI, Westerman RA, Kingwell BA, Paige J, Blombery PA, Sudhir K, Komesaroff PA (2001) Variations in endothelial function and arterial compliance during the menstrual cycle. J Clin Endocrinol Metab 86:5389–5395 173. Wirtz PH, Ehlert U, Emini L, Rüdisüli K, Groessbauer S, Gaab J et al (2006) Anticipatory cognitive stress appraisal and the acute procoagulant stress response in men. Psychosom Med 68:851–858 174. Wirtz PH, Ehlert U, Emini L, Rüdisüli K, Groessbauer S, Mausbach BT, von Känel R (2006) The role of stress hormones in the relationship between resting blood pressure and coagulation activity. J Hypertens 24:2409–2416 175. Wirtz PH, Ehlert U, Emini L, Rüdisüli K, Groessbauer S, von Känel R (2007) Procoagulant stress reactivity and recovery in apparently healthy men with systolic and diastolic hypertension. J Psychosom Res 63:51–58 176. Wirtz PH, Bärtschi C, Spillmann M, Ehlert U, von Känel R (2008) Effect of oral melatonin on the procoagulant response to acute psychosocial stress in healthy men: a randomized placebo controlled study. J Pineal Res 44:358–365 177. Wirtz PH, Redwine LS, Baertschi C, Spillmann M, Ehlert U, von Känel R (2008) Coagulation activity before and after acute psychosocial stress increases with age. Psychosom Med 70: 476–481 178. Wirtz PH, Redwine LS, Ehlert U, von Känel R (2009) Independent association between lower level of social support and higher coagulation activity before and after acute psychosocial stress. Psychosom Med 71:30–37 179. Wium-Andersen MK, Ørsted DD, Nordestgaard BG (2013) Elevated plasma fibrinogen, psychological distress, antidepressant use, and hospitalization with depression: two large population-based studies. Psychoneuroendocrinology 38:638–647 180. Xu W, Zhao Y, Guo L, Guo Y, Gao W (2009) Job stress and coronary heart disease: A casecontrol study using a Chinese population. J Occup Health 51:107–113 181. Xu W, Hang J, Cao T, Shi R, Zeng W, Deng Y, Gao W, Zhao Y, Guo L (2010) Job stress and carotid intima-media thickness in Chinese workers. J Occup Health 52:257–262 182. Xu W, Hang J, Guo L, Zhao Y, Li Z, Gao W (2012) Plasma fibrinogen: a possible link between job stress and cardiovascular disease among Chinese workers. Am J Ind Med 55:167–175 183. Yamamoto K, Takeshita K, Shimokawa T, Yi H, Isobe K, Loskutoff DJ, Saito H (2002) Plasminogen activator inhibitor-1 is a major stress-regulated gene: implications for stressinduced thrombosis in aged individuals. Proc Natl Acad Sci 99:890–895

890

R. von Ka¨nel and S. L. Bacon

184. Yasunari K, Matsui T, Maeda K, Nakamura M, Watanabe T, Kiriike N (2006) Anxiety-induced plasma norepinephrine augmentation increases reactive oxygen species formation by monocytes in essential hypertension. Am J Hypertens 19:573–578 185. Yeung AC, Vekshtein VI, Krantz DS, Vita JA, Ryan TJ, Ganz P, Selwyn AP (1991) The effect of atherosclerosis on the vasomotor response of coronary arteries to mental stress. N Engl J Med 325:1551–1556 186. Zahn D, Petrak F, Franke L, Hägele AK, Juckel G, Lederbogen F, Neubauer H, Norra C, Uhl I, Herpertz S (2015) Cortisol, platelet serotonin content, and platelet activity in patients with major depression and type 2 diabetes: an exploratory investigation. Psychosom Med 7: 145–155 187. Zgraggen L, Fischer JE, Mischler K, Preckel D, Kudielka BM, von Känel R (2005) Relationship between hemoconcentration and blood coagulation responses to acute mental stress. Thromb Res 115:175–183

Catecholamines and Catecholamine Receptors in Cardiovascular Behavioral Medicine

37

Christine Tara Peterson, Michael G. Ziegler, and Paul J. Mills

Contents Cardiovascular Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stress Studies in Behavioral Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Issues Pertaining to Catecholamines in Plasma, Urine, and Gut Samples . . . . . . . . . . . . . . . . . . . . . Catecholamine Assay Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Catecholamine Kinetics and Adrenergic Receptor Assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acute Stress Studies and Catecholamine Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The SNS and Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

893 894 895 896 901 904 905 905 906

Abstract

Knowledge of catecholamine physiology and its integration into clinical research is of significant importance to behavioral medicine. Catecholamines have far ranging relevance to numerous areas of interest to behavioral medicine, including stress, mood, psychiatric disorders, immune regulation, cancer, microbiota, and cardiovascular diseases. As markers of sympathetic nervous system (SNS) activity, the catecholamines norepinephrine and epinephrine are commonly assessed in behavioral medicine research. Increased interest in the microbiota and microbiota-gut-brain axis has also prompted assessment of

C. T. Peterson · P. J. Mills (*) Department of Family Medicine and Public Health, Center of Excellence for Research and Training in Integrative Health, University of California San Diego, La Jolla, CA, USA e-mail: [email protected]; [email protected] M. G. Ziegler Department of Medicine, University of California, San Diego Medical Center, San Diego, CA, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_37

891

892

C. T. Peterson et al.

catecholamine levels in gut-derived samples. This chapter focuses on catecholamine-related methodologies that are important to successful behavioral medicine research endeavors. Reviewed are commonly used laboratory assay methodologies as well as several assessment approaches less commonly used but that provide conceptual relevance to studying catecholamines. The chapter also discusses methodology topics relevant to research including catecholamine stability and sample collection and processing. By way of background, we begin with a brief discussion of the SNS and catecholamines in the epidemiology of cardiovascular disease and in stress reactivity research and then proceed to issues relevant to methodology. Keywords

Catecholamines · Sympathetic nervous system · Cardiovascular · Assays · Microbiota Since its inception, the field of behavioral medicine has been keenly interested in the sympathetic nervous system (SNS). This is because the SNS has far ranging relevance to almost all areas that are of interest to behavioral medicine, including exposure to stressors, mood, psychiatric disorders such as depression and posttraumatic stress disorder (PTSD), neuroimmune regulation, cancer, microbiota, and cardiovascular diseases, to name a few. Assessing levels of the SNS agonists norepinephrine and epinephrine in blood and/or urine is a good choice for behavioral medicine research because it is relatively inexpensive and noninvasive compared to methods of assessing SNS activity directly, such as microneurography which provides a measure of the rate of sympathetic neural firing [13]. Fortunately, peripheral circulating levels of catecholamines provide a reasonably good assessment of SNS activity at large. For example, plasma norepinephrine levels exhibit a high correlation with direct electrical recordings of sympathetic nerve activity (correlation coefficients ¼ 0.76 to 0.87) [53]. Circulating catecholamine levels provide insight into basic hormonal mechanisms of day-to-day physiological regulation and provide an opportunity to understand mechanisms of many diseases and disorders. This chapter focuses on methodologies that are important to successful cardiovascular behavioral medicine research endeavors and includes a review of common approaches to assaying catecholamine levels in peripheral blood, stool, and urine, as well as assessing their end-organ effects. We also briefly discuss two other methodologies relevant to sympathetic activity that are conceptually important for those interested in pursuing catecholamine measurement, i.e., norepinephrine kinetics and adrenergic receptors. The chapter also includes relevant topics of catecholamine stability, sample collection and processing, and publishing guidelines for catecholamine data. In addition, listed in Table 1 are numerous factors that influence catecholamine levels that are of relevance behavioral medicine research. Prior to

37

Catecholamines and Catecholamine Receptors in Cardiovascular Behavioral. . .

893

Table 1 Factors influencing catecholamine levels that are of relevance to cardiovascular behavioral medicine research Age Caffeine consumption Congestive heart failure Depression Environment of subject (external stimuli) Hypertension Medications (including diuretics, vasodilators, ß-antagonists, α-agonists, antidepressants, amphetamines, neuroleptics) Meditation and relaxation Posture and exercise Physical fitness

Single-nucleotide polymorphisms Sleep disorders (e.g., sleep apnea) Spaceflight Sodium intake Stressors (acute and chronic) Temperature Thyroid disease

Tobacco smoking Time of day Venipuncture

delving into the methodology issues, we begin the chapter with a brief discussion of the SNS in the epidemiology of cardiovascular disease and in stress reactivity research.

Cardiovascular Epidemiology Catecholamines play a significant role in the epidemiology of cardiovascular diseases. At one extreme of the spectrum are cases of severely elevated catecholamines, which include diseases such as pheochromocytoma (a disease caused by a neuroendocrine tumor of the adrenal medulla) and stress-induced cardiomyopathy [22, 57]. The cardiomyopathy in both of these diseases results from direct catecholamine toxic effects on the cardiac myocytes. In addition to direct adverse effects on the myocardium, elevated catecholamines raise blood pressure and can precipitate cardiac arrhythmias. Catecholamines also play a role in endothelial dysfunction, the initial step in the long-term process of atherosclerosis [5, 6]. The inflammation associated with certain psychiatric illnesses, such as depression and PTSD and the associated SNS activation, can also contribute to the acceleration and adverse effects of existing vessel disease [30, 47, 54]. In addition to more chronic disease activity, catecholamines play a strong role in acute cardiovascular events. A surge in catecholamines can mediate stress-induced myocardial infarction [25]. Whether due to the experience of a strong stressor, waking up in the morning, or physical exertion, the accompanying acute increase in sympathetic activity and associated catecholamine release can cause vasoconstriction, thus limiting oxygen availability and simultaneously increasing oxygen consumption causing myocardial ischemia which leads to myocardial infarction.

894

C. T. Peterson et al.

Catecholamine surges also act indirectly to cause myocardial infarction through enhancing platelet aggregation [50].

Stress Studies in Behavioral Medicine Dynamic changes in catecholamine concentrations have physiological effects. A small increase in an individual’s resting blood epinephrine levels from a low normal of 20 pg/ml to a high normal of 80 pg/ml is sufficient to alter glucose metabolism. Blood levels of norepinephrine in the normal range for a recumbent subject of 150 to 500 pg/ml have little physiologic effect, but blood levels of 1,000 pg/ml cause hemodynamic changes. There are numerous reasons why examining the catecholamine response to stressors is of interest to cardiovascular behavioral medicine. In terms of clinical relevance to blood pressure, examining catecholamine responses can provide insight into the epidemiology of high blood pressure. For example, levels of norepinephrine and epinephrine concentrations in response to mental stress predict future (e.g., 18 years later) systolic blood pressure levels indicating a potential causal factor in the development of essential hypertension [15]. The hypertensive response to the cold pressor test is mediated by catecholamines and also predicts future development of essential hypertension. The norepinephrine response to stress in hypertensives is related to the increase in cholesterol levels in response to stress, providing a potential mechanism by which stress might increase cardiovascular risk in hypertension [56]. In addition, the gut microbiome contributes to cardiovascular disease, including hypertension and atherosclerosis [21, 26]. Gut microbiota-derived signals, such as short-chain fatty acids (SCFAs) produced from the fermentation of dietary fiber, regulate renin secretion and blood pressure [41]. SCFAs activate cell receptors such as GPR41 (free fatty acid receptor 3), GPR43 (free fatty acid receptor 2), and Olfr78 (olfactory receptor 78) to regulate blood pressure and modulate SNS activity [20, 24, 38]. The activation of these G-protein-coupled receptors modulates peripheral synaptic transmission as well as norepinephrine synthesis and release [11, 58]. Furthermore, the gut microbiome affects the ability of gut enterochromaffin cells to produce norepinephrine, dopamine, and serotonin, which impacts the gutmicrobiota-brain axis and thus host responses to stress as well as the gastro-renal axis and thus the ability of the kidney to excrete sodium [40]. Other microbial metabolites such as hydrogen sulfide and trimethylamine N-oxide also have direct effects on blood pressure [49, 51]. Moreover, increased epinephrine and norepinephrine from stress responses promote the growth of some microbial pathogens and the increased production of bacterial virulence factors [2, 10, 16, 18, 28]. In addition, microbes also directly produce, transport, and modulate catecholamines. For example, Escherichia coli, Saccharomyces spp., and Bacillus spp. produce norepinephrine, while Bacillus and Serratia spp. produce dopamine [27, 29, 52]. The recognition of both microbially and host-produced neurotransmitters by receptors indicates an instance of interkingdom communication and regulation of the nervous and immune systems. The gut microbiota regulates the

37

Catecholamines and Catecholamine Receptors in Cardiovascular Behavioral. . .

895

biobehavioral stress response and the hypothalamic pituitary adrenocortical (HPA) axis set point of the host. Therefore, the composition and activity of the gut microbiota influence the immune system, inflammation, nervous system, and metabolism, which can affect blood pressure and the cardiovascular system generally. In addition to the interest in clinical considerations of blood pressure, examining catecholamine responses can also provide information on mechanisms responsible for dynamic cardiovascular regulation in healthy individuals. For example, the heart rate response and the lymphocytosis response (i.e., increase in number of lymphocytes in the peripheral circulation) to acute stressors are to a large extent driven by the increase in catecholamines in response to the acute stress [32, 34]. Studies examining effects of more chronic stress on catecholamine levels demonstrate downstream effects of elevated levels on immune cell adrenergic receptors [37] and immune cell functional chemotaxis [45] which are relevant to blood vessel health [4].

Issues Pertaining to Catecholamines in Plasma, Urine, and Gut Samples Norepinephrine is synthesized by the enzyme dopamine ß-hydroxylase (DBH), and while it is present in the adrenal medulla, most blood norepinephrine originates from sympathetic nerves of which a small fraction finds its way into the bloodstream. Epinephrine is synthesized from norepinephrine by phenylethanolamine N-methyltransferase (PNMT) and is present in the adrenal medulla. Blood levels of catecholamines vary with circadian and ultradian rhythms, but mean levels are generally consistent from week to week and month to month, thus providing a stable basis for longitudinal studies [33]. In addition to assessing circulating catecholamine levels, some research paradigms can benefit from their assessment in urine. Ranges for healthy individuals for norepinephrine in urine are 10 to 75 μg/24 h and for epinephrine 10 to 25 μg/24 h. Urinary excretion of catecholamines is considered to provide a more integrated measure of sympathetic activation than circulating levels. Catecholamines are much more concentrated in urine than in plasma and are derived from filtered plasma, renal nerves, and renal catecholamine synthesis. Other potential sources for urinary catecholamines are circulating catecholamine sulfates which might be deconjugated by a renal sulfatase and renal conversion of circulating dihydroxyphenylalanine (DOPA) to dopamine, the precursor to norepinephrine. The exact origin of urinary catecholamines is unknown, but it is apparent that urinary catecholamines do not simply reflect filtered plasma catecholamines. It is important to realize that norepinephrine has a circadian rhythm – with lowest levels at approximately 3:00 AM. Simply standing up from a recumbent posture doubles plasma norepinephrine levels, so peak levels can more than double shortly after awakening, and a sharp rise in norepinephrine is seen in the early morning hours, which has been implicated in the peak of cardiovascular mortality around 9: 00 AM. Plasma epinephrine levels are quite low so that it has been more difficult to

896

C. T. Peterson et al.

demonstrate the circadian rhythm for epinephrine. The ultradian fluctuation of plasma norepinephrine in humans is very large with the highest values typically twice as great as the lowest values obtained over a several hour period. The rhythm cycles over about 90 min and plasma norepinephrine and epinephrine levels generally correlate with one another. Since a circadian rhythm has only been shown for day versus night, a wide range of a given part of the day should be acceptable for catecholamine sampling. Urinary levels of norepinephrine and epinephrine are low during nighttime at rest and increase gradually during morning hours and reach a peak between noon and 4:00 PM. Urine epinephrine has a somewhat more pronounced diurnal pattern than does norepinephrine and is relatively independent of sleep-wakefulness patterns. Urine norepinephrine levels reflect variations in both posture and activity. As with plasma levels, urinary norepinephrine levels increase with age, although the effect may be due to the loss of physical fitness [48]. During rest and relaxation, there are generally no differences between urine catecholamines of men and women after adjusting for body surface area. Ideally, for obtaining an integrated measure of catecholamines, urine would be collected over a 24-h period. In our research studies, we typically have the participant collect their 24-h urine in two separate aliquots – one from bedtime to awakening and one from awakening to bedtime. These samples provide information on sympathetic activity during the night and during the day, respectively, or can be combined to provide a measure of 24-h excretion. Outpatient 24-h urine collections can be so variable that in some situations it is more reliable to express urinary catecholamines per gram of urinary creatinine rather than assuming that a subject has collected all of a 24-h urine sample. As the gut microbiota is becoming increasingly relevant in the context of many research paradigms, gut catecholamine levels may also be assessed. In terms of gastrointestinal regions, catecholamine concentrations are highest in the colon compared to more proximal sites. Most peripheral catecholamines in blood and urine exist in the conjugated, biologically inactive conformation [59]; however, most catecholamines in the lumen of the ileum and colon are present in the free form given that gut microbiota-generated beta-glucuronidase promotes deconjugation and thus the generation of luminal free catecholamine [1]. In animal studies, ileum, cecum, and colon luminal contents and tissues are harvested for catecholamine quantitation. In clinical trials, stool or biopsy samples are usually collected.

Catecholamine Assay Techniques Each of the current commonly used catecholamine assay techniques has idiosyncratic characteristics and sample storage considerations that can negatively affect their performance so that it is important to have a basic understanding about commonly employed assay techniques in order to adequately comprehend the catecholamine literature. As indicated in the following pages, the choice of an

37

Catecholamines and Catecholamine Receptors in Cardiovascular Behavioral. . .

897

assay system to measure catecholamines depends on individual needs. If, for example, only norepinephrine is to be measured, the PNMT radioenzymatic technique is relatively rapid and sensitive. High-performance liquid chromatography (HPLC) provides great dynamic range and sensitivity in measuring many catecholamines and metabolites. The sensitivity of the catechol-O-methyltransferase (COMT) radioenzymatic assay permits the use of smaller blood sample volumes compared to HPLC. Newer immunoassays are promising and also require small sample volumes. In the context of these assays, it is recommended to obtain the advice of a laboratory with experience in a particular technique before using it.

Radioenzymatic Assays In a radioenzymatic assay, the compound to be measured is incubated with a radioactive substrate and an enzyme that catalyzes a reaction between them. Over the past decade, with the advancement of the enzyme-linked immunosorbent assay (ELISA) methodologies (reviewed below) and the desire to move away from using radioactive compounds, fewer laboratories continue to use radioenzymatic assays. It is useful, however, to be familiar with these assays. The amount of radioactive metabolite formed from catecholamine levels is proportional to the level of the compound initially present. The most popular radioenzymatic assay for catecholamines utilizes COMT, a nonspecific enzyme which will 0-methylate most small catechols by transferring a methyl group from S-adenosyl methionine (SAM). In the assay’s simplest form, the catecholamines in un-extracted plasma are incubated in a buffer solution with radiolabeled SAM. COMT converts epinephrine to metanephrine, norepinephrine to normetanephrine, and dopamine to 3-methoxytyramine. A more specific form of the assay separates these metabolites by thin-layer chromatography to measure individual catecholamines. Many drugs such as isoproterenol, dobutamine, and methyldopa can interfere with the assay, but when the assay is combined with appropriate separation techniques, these other catechol drugs can also be measured. Plasma proteins, some compounds in urine, aluminum, and ascorbic acid interfere with the enzymatic activity of COMT, giving spuriously low catecholamine measurements. A technique for extraction and concentration of catecholamines prior to assay [23] removes interfering compounds and increases sensitivity tenfold. The COMT assay for catecholamines is quite complex and prone to technical error; however, it has advantages in several situations. The assay can be performed on low sample volumes of 50 μl of plasma and 5 μl of urine and is sensitive enough to measure basal levels of plasma epinephrine. Another radioenzymatic assay for catecholamines is the PNMT assay. PNMT converts norepinephrine to epinephrine in the adrenal medulla. It can be purified from cow adrenal glands, and [3H]-SAM can be used as a methyl donor to convert norepinephrine to [3H]-epinephrine. Unlike COMT, the enzyme PNMT is relatively specific for ß-hydroxylated phenylethanolamines so that it does not appreciably label dopamine or further label epinephrine. It too can be adapted to measure large numbers of samples. Since PNMT is not inhibited by aluminum, catecholamines can be concentrated on alumina prior to assay. This enables the assay to be very sensitive

898

C. T. Peterson et al.

when large volumes of plasma are concentrated onto alumina. The catecholamines are then eluted into 0.1 ml of acid solution. This pre-concentration step eliminates inhibiting substances that might interfere with the assay so that standardization of the PNMT assay is easier than that of the un-extracted COMT assay. However, the very specificity of the PNMT assay limits the number of compounds it can measure.

High-Performance Liquid Chromatography (HPLC) Experimental designs incorporating HPLC separate catecholamines, their metabolites, and an internal standard into highly resolved spectra. After separation, the catecholamines can be detected by native fluorescence, fluorescence of their chemical derivatives, or electrochemical detection. Reverse-phase HPLC (RP-HPLC) columns have been used directly to separate catecholamines, but most frequently they have been modified using “soap” chromatography with the addition of sodium heptylsulfonate or sodium octyl sulfate to the mobile phase. These hydrophobic, anionic detergents are strongly absorbed to the stationary phase and transform it into a cation exchange column. The column will separate neutral and anionic substances as well as catecholamines. Microparticulate cation exchange chromatography, a form of ion exchange chromatography (IEX), is also popular in the separation of catecholamines. Because catecholamines easily oxidize, they can be detected electrochemically when they are passed by a carbon electrode with an electrical potential in the range of +600 mV. The resulting electric current passing across the electrode is proportional to the amount of catecholamine present. This process provides detection limits in the range of 25 pg/ml. Catecholamines can also be detected by fluorescence with HPLC. Natural fluorescence of the catecholamines requires several nanograms for detection, but derivatized fluorescence techniques can greatly enhance sensitivity. Catecholamines may be derivatized by several methods, including trihydroxyindole, ethylenediamine, or fluorescamine methods to enhance their fluorescence. HPLC assays easily measure the high catecholamine concentrations found in urine, while the measurement of plasma and gut-derived catecholamines has proven more technically demanding. Luminal contents and gut tissue catecholamine levels are often determined by post-column HPLC with diphenylethylenediamine as a precolumn fluorogenic compound [1]. The modified method with fluorescence is preferred over standard HPLC assays commonly used to measure catecholamines in the central nervous system (CNS) given that gut samples contain problematic compounds and contaminants that lead to peak artifact issues. Glucuronide- and sulfate-conjugated catecholamine levels are measured using enzymatic deconjugation assay often used for plasma and urine samples [9]. The activity of beta-glucuronidase in gut samples is also determined with a simple enzymatic assay [44]. Metabolomics design paradigms such as untargeted metabolomics is also an option in this context. Gas chromatography coupled to mass spectrometry (GC-MS) can be employed to analyze volatile compounds in stool such as SCFAs and small molecule metabolites such as catecholamines [14]. Electrospray ionization coupled to liquid chromatography with

37

Catecholamines and Catecholamine Receptors in Cardiovascular Behavioral. . .

899

tandem mass spectrometry (ESI-LC-MS/MS) can also be used to detect a wide range of compounds in stool with high specificity and sensitivity [8].

Immunoassays ELISA and the radioimmunoassay (RIA) work on the same principles. They utilize enzyme-linked antibodies (ELISA) or radioisotope-labeled antibodies (RIA) that are specific for the analyte of interest (in this case, catecholamines). Immunoassays are easily the most widely used techniques in research laboratories. Their methodologies are highly standardized and easy to learn, provide a high degree of specificity and sensitivity, and are the most economical to use. ELISAs for the measurement of plasma, fecal, and urine catecholamines have been developed relatively recently. RIAs, on the other hand, have been available for several decades. Early RIAs showed poor performance in reliably, very high basal levels and minimal physiologic variation because of cross-reactivity by catecholamine sulfates. However, advances in the development of monoclonal antibodies with high specificity and acetylation of catecholamines improved the specificity, sensitivity, and reliability of these methods while reducing cross-reactivity with other compounds to 77 years) make significantly more medical errors than “young-old” adults (60– 77 years) [88]. Poorer cognitive function has been linked to poorer self-management of type 2 diabetes [104], poorer recall of medication instructions [83], and poorer medication management [8]. It is also related to lower adherence to lipid-lowering therapy [116, 117]. In addition to patients’ functional status following CABG, the beliefs they form about CABG and its effects on CHD may exert an important influence on their secondary prevention behaviors. For example, older CABG patients, who appear more likely to view old age as a cause of CAD than younger ones, may, as a consequence, be less motivated to engage in behavioral management of CHD risk [43]. In general, CABG patients who believed presurgically that behaviors caused their disease were more likely on 6-month follow-up to report favorable behavioral changes [43]. Older patients also appear more likely than younger ones to believe that surgery would be curative and tended to perceive less personal control over their illness, another set of beliefs that may be expected to reduce adherence to secondary prevention behaviors [43]. Failure to acknowledge illness severity is among the factors that predict nonattendance at cardiac rehabilitation [22, 125]. These and other beliefs, together with patients’ functional status across multiple domains, warrant further consideration as psychological factors contributing to long-term outcomes post-CABG.

Psychological Interventions for CABG Patients A large amount of intervention research has been conducted in an effort to improve CABG outcomes. Psychological treatments have been implemented both presurgically and postsurgically and have been conducted using a number of different modalities. The results have identified effective interventions, led to improvements in routine care, and informed theoretical frameworks for understanding the psychological impact of undergoing major surgery.

Presurgical Intervention Among the earliest evaluations of preparation interventions for major surgery were studies conducted by Egbert et al. [32], Layne and Yudofsky [66], and Schmitt and

52

Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical. . .

1259

Wooldridge [108]. Patients in the treatment groups received support, information about surgical procedures and their subjective impact, and instructions regarding exercises they could perform to facilitate their recovery (e.g., deep breathing). Positive effects were observed for outcomes such as emotional distress, amount of pain medication, confusion and delirium, and length of hospital stay. Over the next two decades, many presurgical preparation studies were conducted and overall indicated that the effects of psychological interventions were statistically reliable and clinically meaningful (for a review, see [18]). The findings led to enhancements in patient preparation procedures used in routine care of CABG patients such as those that were reviewed earlier in this chapter. In an early effort to provide a theoretical account of the effects of surgical preparation, Janis [49] argued that adaptation to surgery is optimized under conditions in which the patient experiences a moderate level of anticipatory fear. In this model, moderate fear stimulates productive, problem-focused thought (“the work of worry”), and the patient can comprehend, and later recall and utilize, information delivered by hospital staff. In comparison, too little fear fails to motivate productive worrying, whereas intense fear produces nonproductive worrying. Later theoretical analyses have focused on the role of information, training in specific coping strategies, and emotional support [2, 50, 110]. They also have emphasized social psychological processes whereby supportive contacts with hospital staff, family members, and fellow patients ameliorate stress and facilitate the utilization of information about surgical procedures, their subjective effects, and coping procedures for promoting recovery [18]. Presurgical intervention is also an issue for the families of CABG patients. Mahler and Kulik [75] reviewed evidence indicating that, as with other forms of major surgery, the families of CABG patients find the time periods beginning with the decision to undergo CABG through the postoperative phase to be highly stressful. Mahler and Kulik suggest that, as with patients, distress among their family members may reflect inadequate psychological preparation, especially regarding the delivery of information about surgical procedures and their effects. They further argue that better preparation of family members may improve patient outcomes. In a randomized trial, Mahler and Kulik found that female CABG patients whose spouses had viewed a videotape designed to promote a sense of mastery by depicting patients and their spouses as calm and confident experienced fewer post-discharge problems requiring a doctor visit by comparison with those receiving standard care.

In-Hospital Intervention In many cases, interventions delivered during the hospital stay following CABG have had positive effects with respect to in-hospital outcomes [18]. Much of this works resembles that reviewed above involving presurgical preparation interventions that focus on the delivery of information and instruction regarding exercises that help with recovery. In addition, alternative approaches have been pursued to take into account the post-CABG patient’s reduced functional capacity. This includes the use of more passive intervention modalities, such as soothing music

1260

T. M. Spruill et al.

[5, 84], music combined with massage and guided imagery [60], and other relaxation techniques [4]. Overall, the findings suggest that interventions delivered postsurgically may be worth further evaluation as a means of improving patients’ mental and physical state during the hospital stay. In-hospital interventions also have been aimed at facilitating recovery beyond the hospital stay. Phase I cardiac rehabilitation (CR) refers to a program of exercise, riskfactor education, and/or counseling that takes place in-hospital following a cardiac event. It usually consists of an assessment of the patient’s ability to perform activities of daily living and to engage in physical exercise and provision of education about secondary prevention behaviors. Phase I CR has been found to improve outcomes such as anxiety level at the time of hospital discharge [120] and physical functioning post-discharge [81]. One study evaluating an intervention with both pre- and postsurgical components found evidence of reduced anxiety and depression and improved subjective physical health [113]. However, effects of other in-hospital interventions have not always supported initial expectations (e.g., [41]). Researchers continue to investigate protocols for the management of postoperative care, early rehabilitation, and discharge planning to balance the effects of premature versus extended post-CABG hospital stays [33, 97].

Post-discharge Intervention The short-term goals of intervention studies implemented following hospital discharge have been to evaluate procedures for identifying and managing surgical complications and for maintaining a positive recovery trajectory. In the longer term, post-CABG patients and others with a documented history of CAD have undergone interventions designed to promote secondary prevention behaviors required for effective disease management. Other potential intervention targets for post-CABG patients vary widely and range from panic disorder [100] to undergoing inoculations to prevent pneumonia [9].

Home-Based Nursing The need for intervention during the period immediately following hospital discharge has increased as a consequence of the reduction that has occurred over the last few decades in the length of hospital stay following CABG. This has resulted in a reduction in the degree to which many patients are physically and psychologically recovered and prepared for the post-discharge period. Among the interventions that have been evaluated as a means of supplementing pre-discharge preparation and the dissemination of printed/video-recorded educational materials is home healthcare nursing. National nursing practice guidelines for the cardiac home care patient have been published [38] and have been shown to be effective in minimizing the number of required nurse visits [71]. A recent retrospective cohort study found that CABG patients who participated in a transitional care program delivered by nurse practitioners showed a significantly lower rate of readmission and death at 30 days compared to a matched control group [44].

52

Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical. . .

1261

Beyond physical recovery, home-based nursing interventions provide a potential means of reducing emotional distress in the post-discharge CABG patient. One randomized trial found that a brief, home-based nursing intervention for emotional distress did not reduce self-reported symptoms of depression or anxiety more than a standard control condition [69], though a subgroup analysis showed significant benefit among patients with high initial levels of anxiety or depression.

Telehealth A more recent intervention modality to be evaluated in the post-discharge CABG patient involves the promotion of recovery through the use of nurse telephone contacts. One study assessed the impact of a series of telephone six calls from nurses made during the 2 months following discharge [46]. Protocols for the phone contacts addressed a range of issues including pain, physical activity, medications, and diet. Preliminary indications are that telephone contact systems such as this allow nurses to deliver individually tailored psychoeducation to address physical, emotional, and lifestyle concerns as these issues arise during the post-discharge period. Other studies have shown that “telehealth” has a positive impact on both care quality and access to care [3] in heart patients, who become more effective advocates for their own care plan and experience improved QOL. A study of patients with heart failure, coronary heart disease, diabetes, or chronic obstructive pulmonary disease showed that home monitoring using an interactive audio/video system prevented hospitalization and emergency room visits [51]. There are indications that programs in which nurse telephone contacts are combined with nurse home visits may promote participation in CR and reduce home visits [15]. Cardiac Rehabilitation Phase II CR takes place post-discharge, usually at a hospital facility with close supervision. The goals include preparing the patient to return to work, where applicable, and to resume recreational activities, in addition to education and counseling regarding secondary prevention, stress, and depression. Phase III CR focuses on the long-term maintenance of secondary prevention behaviors at home with periodic communication with CR staff and physicians for reassessment. CR may be initiated from 2 to 6 weeks following CABG and can involve 36 or more sessions extending over about a 3-month time period. There is evidence to suggest that CR may be effective in promoting disease-management behaviors in post-CABG patients [65, 70, 119]. However, there are concerns about long-term adherence. A study by Squires, Montero-Gomez, Allison, and Thomas [114] found that more sustained effects on outcomes such as medication adherence could be attained by having coronary patients who had completed CR periodically return to the CR program over the following 3 years. Treatments for Depression Notwithstanding the high prevalence of depressive symptoms in post-CABG patients and the potential prognostic significance of depression for recurrent disease

1262

T. M. Spruill et al.

and mortality, there has been little research evaluating its treatment. A recent randomized controlled trial evaluated treatment consisting of 12 weeks of cognitive behavior therapy or supportive stress management versus usual care in a sample of 123 CABG patients who met DSM-IV criteria for major or minor depression within 1 year following surgery [39]. Both cognitive behavior therapy and supportive stress management were effective in reducing depression compared with usual care, though effects of cognitive behavior therapy were greater and more enduring. A randomized controlled trial of 8 weeks of cognitive behavior therapy versus usual care in CABG patients with major or minor depression 1 month post-discharge also found beneficial effects. Treatment initiated early (median time, 45 days postsurgery) was associated with greater reduction in depressive symptoms than later treatment (median time, 122 days postsurgery) [29]. In view of the prognostic significance of depression following CABG, a large study was undertaken to evaluate the impact of collaborative care for treating postdischarge depression [102]. The “Bypassing the Blues” study examined an 8-month program of nurse-delivered telephone-based care supervised by a psychiatrist and primary care expert (versus usual care) in 302 CABG patients with mood symptoms prior to and at 2 weeks following hospital discharge. Data also were obtained on a sample of 151 nondepressed controls. Compared with usual care, the intervention resulted in improved QOL, physical functioning, and mood at 8-month follow-up. An examination of insurance claims 12 months after randomization supported the cost-effectiveness of this collaborative care intervention [30].

Conclusions With the aging of the population, treatment advances that increase the probability of surviving an initial cardiac event, and technical improvements that allow major surgeries to be performed on older and more physically compromised patients, a large segment of the population will undergo CABG and live for decades thereafter. Research has responded to these developments by identifying a number of cognitive, social-contextual, and other psychological variables that operate as risk or vulnerability factors in CABG patients. This work has informed hospital procedures designed to facilitate presurgical preparation for CABG and subsequently promote more rapid postsurgical recovery. For the most part, this research treats CABG as a stressor and focuses on providing the patient with psychological resources for adapting to its short-term physical and psychological impact. Although these issues warrant further attention, there is also a need for efforts guided by a broader perspective that integrates both the shortterm goals of preparation for and recovery from surgery and the long-term goal of promoting secondary prevention behaviors. Such efforts should lead to further improvement in patients’ perioperative experiences and quality of life and, at the same time, bring about reductions in recurrent CHD and the need for repeat revascularization.

52

Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical. . .

1263

References 1. Allen JK (1990) Physical and psychosocial outcomes after coronary artery bypass graft surgery: review of the literature. Heart Lung 19(1):49–55 2. Anderson EA (1987) Preoperative preparation for cardiac surgery facilitates recovery, reduces psychological distress, and reduces the incidence of acute postoperative hypertension. J Consult Clin Psychol 55(4):513–520 3. Averwater NW, Burchfield DC (2005) No place like home: telemonitoring can improve home care. Healthc Financ Manage 59(4):46–48, 50, 52 4. Bafford DC (1977) Progressive relaxation as a nursing intervention: a method of controlling pain for open-heart surgery patients. Commun Nurs Res 8:284–290 5. Barnason S, Zimmerman L, Nieveen J (1995) The effects of music interventions on anxiety in the patient after coronary artery bypass grafting. Heart Lung 24(2):124–132 6. Barry LC, Kasl SV, Lichtman J, Vaccarino V, Krumholz HM (2006) Perceived control and change in physical functioning after coronary artery bypass grafting: a prospective study. Int J Behav Med 13(3):229–236 7. Bashour CA, Yared JP, Ryan TA, Rady MY, Mascha E, Leventhal MJ, Starr NJ (2000) Longterm survival and functional capacity in cardiac surgery patients after prolonged intensive care. Crit Care Med 28(12):3847–3853 8. Beckman AG, Parker MG, Thorslund M (2005) Can elderly people take their medicine? Patient Educ Couns 59(2):186–191 9. Bittner V, Sanderson BK (2007) Influenza vaccination in secondary prevention: an opportunity missed. J Cardiopulm Rehabil Prev 27(4):202–207 10. Blumenthal JA, Lett HS, Babyak MA, White W, Smith PK, Mark DB, Jones R, Mathew JP, Newman MF, Investigators N (2003) Depression as a risk factor for mortality after coronary artery bypass surgery. Lancet 362(9384):604–609 11. Brorsson B, Bernstein SJ, Brook RH, Werko L (2001) Quality of life of chronic stable angina patients 4 years after coronary angioplasty or coronary artery bypass surgery. J Intern Med 249 (1):47–57 12. Burg MM, Benedetto MC, Rosenberg R, Soufer R (2003) Presurgical depression predicts medical morbidity 6 months after coronary artery bypass graft surgery. Psychosom Med 65 (1):111–118 13. Burker EJ, Blumenthal JA, Feldman M, Burnett R, White W, Smith LR, Croughwell N, Schell R, Newman M, Reves JG (1995) Depression in male and female patients undergoing cardiac surgery. Br J Clin Psychol 34(Pt 1):119–128 14. Caine N, Sharples LD, Wallwork J (1999) Prospective study of health related quality of life before and after coronary artery bypass grafting: outcome at five years. Heart 81(4):347–351 15. Carroll DL, Rankin SH, Cooper BA (2007) The effects of a collaborative peer advisor/ advanced practice nurse intervention: cardiac rehabilitation participation and rehospitalization in older adults after a cardiac event. J Cardiovasc Nurs 22(4):313–319 16. Castellanos LR, Normand SL, Ayanian JZ (2009) Racial and ethnic disparities in access to higher and lower quality cardiac surgeons for coronary artery bypass grafting. Am J Cardiol 103(12):1682–1686 17. Chocron S, Etievent JP, Viel JF, Dussaucy A, Clement F, Alwan K, Neidhardt M, Schipman N (1996) Prospective study of quality of life before and after open heart operations. Ann Thorac Surg 61(1):153–157 18. Contrada R, Leventhal E, Anderson J (1994) Psychological preparation for surgery: marshalling individual and social resources to optimize self-regulation. In: Maes S, Leventhal H, Johnson M (eds) International review of health psychology, vol 3. Wiley, Chichester, pp 219–266 19. Contrada RJ, Goyal TM, Cather C, Rafalson L, Idler EL, Krause TJ (2004) Psychosocial factors in outcomes of heart surgery: the impact of religious involvement and depressive symptoms. Health Psychol 23(3):227–238

1264

T. M. Spruill et al.

20. Contrada RJ, Boulifard DA, Idler EL, Krause TJ, Labouvie EW (2006) Course of depressive symptoms in patients undergoing heart surgery: confirmatory analysis of the factor pattern and latent mean structure of the Center for Epidemiologic Studies Depression Scale. Psychosom Med 68(6):922–930 21. Contrada RJ, Boulifard DA, Hekler EB, Idler EL, Spruill TM, Labouvie EW, Krause TJ (2008) Psychosocial factors in heart surgery: presurgical vulnerability and postsurgical recovery. Health Psychol 27(3):309–319 22. Cooper A, Lloyd G, Weinman J, Jackson G (1999) Why patients do not attend cardiac rehabilitation: role of intentions and illness beliefs. Heart 82(2):234–236 23. Cserep Z, Losoncz E, Toth R, Toth A, Juhasz B, Balog P, Vargha P, Gal J, Contrada RJ, Falger PR, Szekely A (2014) Self-rated health is associated with the length of stay at the intensive care unit and hospital following cardiac surgery. BMC Cardiovasc Disord 14:171 24. Czajkowski SM, Terrin M, Lindquist R, Hoogwerf B, Dupuis G, Shumaker SA, Gray JR, Herd JA, Treat-Jacobson D, Zyzanski S, Knatterud GL (1997) Comparison of preoperative characteristics of men and women undergoing coronary artery bypass grafting (the Post Coronary Artery Bypass Graft [CABG] Biobehavioral Study). Am J Cardiol 79(8):1017–1024 25. Deaton C, Weintraub WS, Ramsay J, Przykucki R, Zellinger M, Causey K (1998) Patient perceived health status, hospital length of stay, and readmission after coronary artery bypass surgery. J Cardiovasc Nurs 12(4):62–71 26. Deb S, Wijeysundera HC, Ko DT, Tsubota H, Hill S, Fremes SE (2013) Coronary artery bypass graft surgery vs percutaneous interventions in coronary revascularization: a systematic review. J Am Med Assoc 310(19):2086–2095 27. DiMatteo MR (2004) Social support and patient adherence to medical treatment: a metaanalysis. Health Psychol 23(2):207–218 28. Doering LV, Moser DK, Lemankiewicz W, Luper C, Khan S (2005) Depression, healing, and recovery from coronary artery bypass surgery. Am J Crit Care 14(4):316–324 29. Doering LV, Chen B, Cross Bodan R, Magsarili MC, Nyamathi A, Irwin MR (2013) Early cognitive behavioral therapy for depression after cardiac surgery. J Cardiovasc Nurs 28 (4):370–379 30. Donohue JM, Belnap BH, Men A, He F, Roberts MS, Schulberg HC, Reynolds CF 3rd, Rollman BL (2014) Twelve-month cost-effectiveness of telephone-delivered collaborative care for treating depression following CABG surgery: a randomized controlled trial. Gen Hosp Psychiatry 36(5):453–459 31. Duits AA, Duivenvoorden HJ, Boeke S, Taams MA, Mochtar B, Krauss XH, Passchier J, Erdman RA (1999) A structural modeling analysis of anxiety and depression in patients undergoing coronary artery bypass graft surgery: a model generating approach. J Psychosom Res 46(2):187–200 32. Egbert LD, Battit GE, Welch CE, Bartlett MK (1964) Reduction of postoperative pain by encouragement and instruction of patients: a study of doctor-patient rapport. N Engl J Med 270:825–827 33. Ender J, Borger MA, Scholz M, Funkat AK, Anwar N, Sommer M, Mohr FW, Fassl J (2008) Cardiac surgery fast-track treatment in a postanesthetic care unit: six-month results of the Leipzig fast-track concept. Anesthesiology 109(1):61–66 34. Erikson E (1950) Childhood and society. WW Norton & Co, New York 35. Fitzgerald TE, Tennen H, Affleck G, Pransky GS (1993) The relative importance of dispositional optimism and control appraisals in quality of life after coronary artery bypass surgery. J Behav Med 16(1):25–43 36. Fogel J, Fauerbach JA, Ziegelstein RC, Bush DE (2004) Quality of life in physical health domains predicts adherence among myocardial infarction patients even after adjusting for depressive symptoms. J Psychosom Res 56(1):75–82 37. Foody JM, Ferdinand FD, Galusha D, Rathore SS, Masoudi FA, Havranek EP, Nilasena D, Radford MJ, Krumholz HM (2003) Patterns of secondary prevention in older patients undergoing coronary artery bypass grafting during hospitalization for acute myocardial infarction. Circulation 108(Suppl 1):II24–II28

52

Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical. . .

1265

38. Frantz A (1998) Summary of the nursing practice guidelines for the cardiac home care patient. Home Healthc Nurse 16(11):742–752 39. Freedland KE, Skala JA, Carney RM, Rubin EH, Lustman PJ, Davila-Roman VG, Steinmeyer BC, Hogue CW Jr (2009) Treatment of depression after coronary artery bypass surgery: a randomized controlled trial. Arch Gen Psychiatry 66(4):387–396 40. Gallagher R, McKinley S (2009) Anxiety, depression and perceived control in patients having coronary artery bypass grafts. J Adv Nurs 65(11):2386–2396 41. Gortner SR, Gilliss CL, Shinn JA, Sparacino PA, Rankin S, Leavitt M, Price M, Hudes M (1988) Improving recovery following cardiac surgery: a randomized clinical trial. J Adv Nurs 13(5):649–661 42. Goyal TM, Idler EL, Krause TJ, Contrada RJ (2005) Quality of life following cardiac surgery: impact of the severity and course of depressive symptoms. Psychosom Med 67(5):759–765 43. Gump BB, Matthews KA, Scheier MF, Schulz R, Bridges MW, Magovern GJ Sr (2001) Illness representations according to age and effects on health behaviors following coronary artery bypass graft surgery. J Am Geriatr Soc 49(3):284–289 44. Hall MH, Esposito RA, Pekmezaris R, Lesser M, Moravick D, Jahn L, Blenderman R, Akerman M, Nouryan CN, Hartman AR (2014) Cardiac surgery nurse practitioner home visits prevent coronary artery bypass graft readmissions. Ann Thorac Surg 97(5):1488–1493; discussion 1493–1485 45. Halpin LS, Barnett SD (2005) Preoperative state of mind among patients undergoing CABG: effect on length of stay and postoperative complications. J Nurs Care Qual 20(1):73–80 46. Hartford K (2005) Telenursing and patients’ recovery from bypass surgery. J Adv Nurs 50(5):459–468 47. Hermele S, Olivo EL, Namerow P, Oz MC (2007) Illness representations and psychological distress in patients undergoing coronary artery bypass graft surgery. Psychol Health Med 12(5):580–591 48. Hlatky MA, Boothroyd DB, Bravata DM, Boersma E, Booth J, Brooks MM, Carrie D, Clayton TC, Danchin N, Flather M, Hamm CW, Hueb WA, Kahler J, Kelsey SF, King SB, Kosinski AS, Lopes N, McDonald KM, Rodriguez A, Serruys P, Sigwart U, Stables RH, Owens DK, Pocock SJ (2009) Coronary artery bypass surgery compared with percutaneous coronary interventions for multivessel disease: a collaborative analysis of individual patient data from ten randomised trials. Lancet 373(9670):1190–1197 49. Janis I (1958) Psychological stress: psychoanalytic and behavioral studies of surgical patients. Wiley, Hoboken 50. Johnson JE (1999) Self-regulation theory and coping with physical illness. Res Nurs Health 22(6):435–448. https://doi.org/10.1002/(SICI)1098-240X(199912)22:63.0.CO;2-Q 51. Johnston B, Wheeler L, Deuser J, Sousa KH (2000) Outcomes of the Kaiser Permanente TeleHome Health Research Project. Arch Fam Med 9(1):40–45 52. Johnston G, Goss JR, Malmgren JA, Spertus JA (2004) Health status and social risk correlates of extended length of stay following coronary artery bypass surgery. Ann Thorac Surg 77(2):557–562 53. Juergens MC, Seekatz B, Moosdorf RG, Petrie KJ, Rief W (2010) Illness beliefs before cardiac surgery predict disability, quality of life, and depression 3 months later. J Psychosom Res 68(6):553–560 54. King KB, Reis HT (2012) Marriage and long-term survival after coronary artery bypass grafting. Health Psychol 31(1):55–62 55. Koch CG, Li L, Lauer M, Sabik J, Starr NJ, Blackstone EH (2007) Effect of functional healthrelated quality of life on long-term survival after cardiac surgery. Circulation 115(6):692–699 56. Koivula M, Tarkka MT, Tarkka M, Laippala P, Paunonen-Ilmonen M (2002) Fear and inhospital social support for coronary artery bypass grafting patients on the day before surgery. Int J Nurs Stud 39(4):415–427 57. Koivula M, Tarkka MT, Tarkka M, Laippala P, Paunonen-Ilmonen M (2002) Fear and anxiety in patients at different time-points in the coronary artery bypass process. Int J Nurs Stud 39 (8):811–822

1266

T. M. Spruill et al.

58. Krohne HW, Slangen KE (2005) Influence of social support on adaptation to surgery. Health Psychol 24(1):101–105 59. Kronish IM, Rieckmann N, Halm EA, Shimbo D, Vorchheimer D, Haas DC, Davidson KW (2006) Persistent depression affects adherence to secondary prevention behaviors after acute coronary syndromes. J Gen Intern Med 21(11):1178–1183 60. Kshettry VR, Carole LF, Henly SJ, Sendelbach S, Kummer B (2006) Complementary alternative medical therapies for heart surgery patients: feasibility, safety, and impact. Ann Thorac Surg 81(1):201–205 61. Kulik JA, Mahler HI (1987) Effects of preoperative roommate assignment on preoperative anxiety and recovery from coronary-bypass surgery. Health Psychol 6(6):525–543 62. Kulik JA, Mahler HI (1989) Social support and recovery from surgery. Health Psychol 8(2):221–238 63. Kulik JA, Mahler HI (1993) Emotional support as a moderator of adjustment and compliance after coronary artery bypass surgery: a longitudinal study. J Behav Med 16(1): 45–63 64. Kulik JA, Mahler HI (2006) Marital quality predicts hospital stay following coronary artery bypass surgery for women but not men. Soc Sci Med 63(8):2031–2040 65. Kummel M, Vahlberg T, Ojanlatva A, Karki R, Mattila T, Kivela SL (2008) Effects of an intervention on health behaviors of older coronary artery bypass (CAB) patients. Arch Gerontol Geriatr 46(2):227–244 66. Layne OL Jr, Yudofsky SC (1971) Postoperative psychosis in cardiotomy patients. The role of organic and psychiatric factors. N Engl J Med 284(10):518–520 67. Lazarus R, Folkman S (1984) Stress, appraisal, and coping. Springer, New York 68. Lewis MA, Butterfield RM (2005) Antecedents and reactions to health-related social control. Personal Soc Psychol Bull 31(3):416–427 69. Lie I, Arnesen H, Sandvik L, Hamilton G, Bunch EH (2007) Effects of a home-based intervention program on anxiety and depression 6 months after coronary artery bypass grafting: a randomized controlled trial. J Psychosom Res 62(4):411–418 70. Lin HH, Tsai YF, Lin PJ, Tsay PK (2010) Effects of a therapeutic lifestyle-change programme on cardiac risk factors after coronary artery bypass graft. J Clin Nurs 19(1–2):60–68 71. Loubani M, Mediratta N, Hickey MS, Galinanes M (2000) Early discharge following coronary bypass surgery: is it safe? Eur J Cardiothorac Surg 18(1):22–26 72. Lucas FL, Siewers AE, DeLorenzo MA, Wennberg DE (2007) Differences in cardiac stress testing by sex and race among Medicare beneficiaries. Am Heart J 154(3):502–509 73. Lyketsos CG, Toone L, Tschanz J, Corcoran C, Norton M, Zandi P, Munger R, Breitner JC, Welsh-Bohmer K, Cache County Study G (2006) A population-based study of the association between coronary artery bypass graft surgery (CABG) and cognitive decline: the Cache County study. Int J Geriatr Psychiatry 21(6):509–518 74. Mahler HI, Kulik JA (1998) Effects of preparatory videotapes on self-efficacy beliefs and recovery from coronary bypass surgery. Ann Behav Med 20(1):39–46 75. Mahler HI, Kulik JA (2002) Effects of a videotape information intervention for spouses on spouse distress and patient recovery from surgery. Health Psychol 21(5):427–437 76. Mallik S, Krumholz HM, Lin ZQ, Kasl SV, Mattera JA, Roumains SA, Vaccarino V (2005) Patients with depressive symptoms have lower health status benefits after coronary artery bypass surgery. Circulation 111(3):271–277 77. Martire L, Schulz R (2007) Involving family in psychosocial interventions for chronic illness. Curr Dir Psychol Sci 16(2):90–94 78. Mathew JP, Fontes ML, Tudor IC, Ramsay J, Duke P, Mazer CD, Barash PG, Hsu PH, Mangano DT, Investigators of the Ischemia R, Education F, Multicenter Study of Perioperative Ischemia Research G (2004) A multicenter risk index for atrial fibrillation after cardiac surgery. J Am Med Assoc 291(14):1720–1729 79. Mehta RH, Bhatt DL, Steg PG, Goto S, Hirsch AT, Liau CS, Rother J, Wilson PW, Richard AJ, Eagle KA, Ohman EM, Investigators RR (2008) Modifiable risk factors control and its

52

Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical. . .

1267

relationship with 1 year outcomes after coronary artery bypass surgery: insights from the REACH registry. Eur Heart J 29(24):3052–3060 80. Monahan D, Kohman L, Coleman M (1996) Open-heart surgery: consequences for caregivers. J Gerontol Soc Work 25(3–4):53–70 81. Moore SM (1996) The effects of a discharge information intervention on recovery outcomes following coronary artery bypass surgery. Int J Nurs Stud 33(2):181–189 82. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Judd SE, Kissela BM, Lackland DT, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Matchar DB, DK MG, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Willey JZ, Woo D, Yeh RW, Turner MB, American Heart Association Statistics C, Stroke Statistics S (2015) Heart disease and stroke statistics–2015 update: a report from the American Heart Association. Circulation 131(4):e29–e322 83. Neupert S, McDonald-Miszczak L (2004) Younger and older adults’ delayed recall of medication instructions: the role of cognitive and metacognitive predictors. Aging Neuropsychol Cogn 11(4):428–442 84. Nilsson U (2009) Soothing music can increase oxytocin levels during bed rest after open-heart surgery: a randomised control trial. J Clin Nurs 18(15):2153–2161 85. Oxlad M, Stubberfield J, Stuklis R, Edwards J, Wade TD (2006) Psychological risk factors for cardiac-related hospital readmission within 6 months of coronary artery bypass graft surgery. J Psychosom Res 61(6):775–781 86. Panagopoulou E, Maes S, Rime B, Montgomery A (2006) Social sharing of emotion in anticipation of cardiac surgery: effects on preoperative distress. J Health Psychol 11(5): 809–820 87. Parent N, Fortin F (2000) A randomized, controlled trial of vicarious experience through peer support for male first-time cardiac surgery patients: impact on anxiety, self-efficacy expectation, and self-reported activity. Heart Lung 29(6):389–400 88. Park DC, Morrell RW, Frieske D, Kincaid D (1992) Medication adherence behaviors in older adults: effects of external cognitive supports. Psychol Aging 7(2):252–256 89. Park DC, Hertzog C, Leventhal H, Morrell RW, Leventhal E, Birchmore D, Martin M, Bennett J (1999) Medication adherence in rheumatoid arthritis patients: older is wiser. J Am Geriatr Soc 47(2):172–183 90. Peterson ED, Coombs LP, Ferguson TB, Shroyer AL, DeLong ER, Grover FL, Edwards FH (2002) Hospital variability in length of stay after coronary artery bypass surgery: results from the Society of Thoracic Surgeon’s National Cardiac Database. Ann Thorac Surg 74(2): 464–473 91. Peterson JC, Charlson ME, Williams-Russo P, Krieger KH, Pirraglia PA, Meyers BS, Alexopoulos GS (2002) New postoperative depressive symptoms and long-term cardiac outcomes after coronary artery bypass surgery. Am J Geriatr Psychiatry 10(2):192–198 92. Phillips Bute B, Mathew J, Blumenthal JA, Welsh-Bohmer K, White WD, Mark D, Landolfo K, Newman MF (2003) Female gender is associated with impaired quality of life 1 year after coronary artery bypass surgery. Psychosom Med 65(6):944–951 93. Phillips-Bute B, Mathew JP, Blumenthal JA, Morris RW, Podgoreanu MV, Smith M, StaffordSmith M, Grocott HP, Schwinn DA, Newman MF, Perioperative G, Safety Outcomes Investigative T (2008) Relationship of genetic variability and depressive symptoms to adverse events after coronary artery bypass graft surgery. Psychosom Med 70(9):953–959 94. Pirraglia PA, Peterson JC, Williams-Russo P, Gorkin L, Charlson ME (1999) Depressive symptomatology in coronary artery bypass graft surgery patients. Int J Geriatr Psychiatry 14(8):668–680. https://doi.org/10.1002/(SICI)1099-1166(199908)14:83.0.CO;2-9 95. Poole L, Leigh E, Kidd T, Ronaldson A, Jahangiri M, Steptoe A (2014) The combined association of depression and socioeconomic status with length of post-operative hospital

1268

T. M. Spruill et al.

stay following coronary artery bypass graft surgery: data from a prospective cohort study. J Psychosom Res 76(1):34–40 96. Poole L, Kidd T, Leigh E, Ronaldson A, Jahangiri M, Steptoe A (2015) Psychological distress and intensive care unit stay after cardiac surgery: the role of illness concern. Health Psychol 34 (3):283–287 97. Probst S, Cech C, Haentschel D, Scholz M, Ender J (2014) A specialized post anaesthetic care unit improves fast-track management in cardiac surgery: a prospective randomized trial. Crit Care 18(4):468 98. Rankin S, Monahan P (1991) Great expectations: perceived social support in couples experiencing cardiac surgery. Fam Relat 40(3):297–302 99. Ravven S, Bader C, Azar A, Rudolph JL (2013) Depressive symptoms after CABG surgery: a meta-analysis. Harv Rev Psychiatry 21(2):59–69 100. Reid T, Denieffe S, Denny M, McKenna J (2005) Psychosocial interventions for panic disorder after coronary artery bypass graft: a case study. Dimens Crit Care Nurs 24(4):165–170 101. Roach GW, Kanchuger M, Mangano CM, Newman M, Nussmeier N, Wolman R, Aggarwal A, Marschall K, Graham SH, Ley C (1996) Adverse cerebral outcomes after coronary bypass surgery. Multicenter Study of Perioperative Ischemia Research Group and the Ischemia Research and Education Foundation Investigators. N Engl J Med 335(25):1857–1863 102. Rollman BL, Belnap BH, LeMenager MS, Mazumdar S, Houck PR, Counihan PJ, Kapoor WN, Schulberg HC, Reynolds CF 3rd (2009) Telephone-delivered collaborative care for treating post-CABG depression: a randomized controlled trial. JAMA 302(19):2095–2103 103. Ronaldson A, Poole L, Kidd T, Leigh E, Jahangiri M, Steptoe A (2014) Optimism measured pre-operatively is associated with reduced pain intensity and physical symptom reporting after coronary artery bypass graft surgery. J Psychosom Res 77(4):278–282 104. Rosen MI, Beauvais JE, Rigsby MO, Salahi JT, Ryan CE, Cramer JA (2003) Neuropsychological correlates of suboptimal adherence to metformin. J Behav Med 26(4):349–360 105. Sawatzky JA, Naimark BJ (2009) Coronary artery bypass graft surgery: exploring a broader perspective of risks and outcomes. J Cardiovasc Nurs 24(3):198–206 106. Scheier MF, Matthews KA, Owens JF, Magovern GJ Sr, Lefebvre RC, Abbott RA, Carver CS (1989) Dispositional optimism and recovery from coronary artery bypass surgery: the beneficial effects on physical and psychological well-being. J Pers Soc Psychol 57(6):1024–1040 107. Scheier MF, Matthews KA, Owens JF, Schulz R, Bridges MW, Magovern GJ, Carver CS (1999) Optimism and rehospitalization after coronary artery bypass graft surgery. Arch Intern Med 159(8):829–835 108. Schmitt FE, Wooldridge PJ (1973) Psychological preparation of surgical patients. Nurs Res 22 (2):108–116 109. Schroder KE, Schwarzer R, Endler NS (1997) Predicting cardiac patients’ quality of life from the characteristics of their spouses. J Health Psychol 2(2):231–244 110. Shahmansouri N, Janghorbani M, Salehi Omran A, Karimi AA, Noorbala AA, Arjmandi A, Nikfam S (2014) Effects of a psychoeducation intervention on fear and anxiety about surgery: randomized trial in patients undergoing coronary artery bypass grafting. Psychol Health Med 19(4):375–383 111. Shroyer AL, Coombs LP, Peterson ED, Eiken MC, ER DL, Chen A, Ferguson TB Jr, Grover FL, Edwards FH, Society of Thoracic S (2003) The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thorac Surg 75(6):1856–1864 112. Sjoland H, Caidahl K, Wiklund I, Haglid M, Hartford M, Karlson BW, Karlsson T, Herlitz J (1997) Impact of coronary artery bypass grafting on various aspects of quality of life. Eur J Cardiothorac Surg 12(4):612–619 113. Sorlie T, Busund R, Sexton J, Sexton H, Sorlie D (2007) Video information combined with individualized information sessions: Effects upon emotional well-being following coronary artery bypass surgery – a randomized trial. Patient Educ Couns 65(2):180–188 114. Squires RW, Montero-Gomez A, Allison TG, Thomas RJ (2008) Long-term disease management of patients with coronary disease by cardiac rehabilitation program staff. J Cardiopulm Rehabil Prev 28(3):180–186; quiz 187–188

52

Coronary Artery Bypass Grafting: Psychosocial Dimensions of a Surgical. . .

1269

115. Steine S, Laerum E, Eritsland J, Arnesen H (1996) Predictors of enhanced well-being after coronary artery bypass surgery. J Intern Med 239(1):69–73 116. Stilley CS, Sereika S, Muldoon MF, Ryan CM, Dunbar-Jacob J (2004) Psychological and cognitive function: predictors of adherence with cholesterol lowering treatment. Ann Behav Med 27(2):117–124 117. Stilley CS, Bender CM, Dunbar-Jacob J, Sereika S, Ryan CM (2010) The impact of cognitive function on medication management: three studies. Health Psychol 29(1):50–55 118. Sullivan MD, LaCroix AZ, Russo J, Katon WJ (1998) Self-efficacy and self-reported functional status in coronary heart disease: a six-month prospective study. Psychosom Med 60 (4):473–478 119. Sundin O, Lisspers J, Hofman-Bang C, Nygren A, Ryden L, Ohman A (2003) Comparing multifactorial lifestyle interventions and stress management in coronary risk reduction. Int J Behav Med 10(3):191–204 120. Thomas JJ (1995) Reducing anxiety during phase I cardiac rehabilitation. J Psychosom Res 39 (3):295–304 121. Timberlake N, Klinger L, Smith P, Venn G, Treasure T, Harrison M, Newman SP (1997) Incidence and patterns of depression following coronary artery bypass graft surgery. J Psychosom Res 43(2):197–207 122. Tully PJ, Baker RA, Turnbull D, Winefield H (2008) The role of depression and anxiety symptoms in hospital readmissions after cardiac surgery. J Behav Med 31(4):281–290 123. van der Poel A, Greeff AP (2003) The influence of coronary bypass graft surgery on the marital relationship and family functioning of the patient. J Sex Marital Ther 29(1):61–77 124. van Ryn M, Burgess D, Malat J, Griffin J (2006) Physicians’ perceptions of patients’ social and behavioral characteristics and race disparities in treatment recommendations for men with coronary artery disease. Am J Public Health 96(2):351–357 125. Whitmarsh A, Koutantji M, Sidell K (2003) Illness perceptions, mood and coping in predicting attendance at cardiac rehabilitation. Br J Health Psychol 8(Pt 2):209–221 126. Williams JB, Alexander KP, Morin JF, Langlois Y, Noiseux N, Perrault LP, Smolderen K, Arnold SV, Eisenberg MJ, Pilote L, Monette J, Bergman H, Smith PK, Afilalo J (2013) Preoperative anxiety as a predictor of mortality and major morbidity in patients aged >70 years undergoing cardiac surgery. Am J Cardiol 111(1):137–142

53

Heart Transplantation Sara S. Nash and Peter A. Shapiro

Contents Natural History of the Heart Transplant Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychiatric Issues in Transplant Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anxiety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anxiety Symptoms in Pre-Transplant Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anxiety in Post-Transplant Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Psychiatric Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ventricular Assist Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychiatric Effects of Post-Transplant Immunosuppressants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calcineurin Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Corticosteroids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychological Implications of Internalizing a New Organ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Psychiatric Assessment in Transplant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pre-Transplant: Psychiatric Assessment of Transplant Candidates . . . . . . . . . . . . . . . . . . . . . . . . Treatment Adherence and Non-adherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1273 1275 1275 1278 1278 1280 1281 1281 1282 1282 1284 1284 1285 1285 1286 1289

Abstract

Since the first successful orthotopic heart transplant in 1967, medical, surgical, and technological advances have transformed cardiac transplantation into a realistic endpoint for many patients with terminal cardiac disease. Currently, over 2000 transplants are performed annually in the United States, although a scarcity of organs, among other factors, results in 10–15% of candidates dying

S. S. Nash (*) Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA e-mail: [email protected] P. A. Shapiro Department of Psychiatry, Columbia University Medical Center, New York, NY, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_53

1271

1272

S. S. Nash and P. A. Shapiro

while on the waiting list. This chapter examines the behavioral and mental health issues associated with heart transplantation, including the opportunities for mental health interventions. Topics include a review of psychiatric comorbidities such as depression and anxiety, as well as management approaches related to congestive heart failure/end-stage heart disease, ventricular assist devices, and heart transplantation itself, including a discussion of the psychological implications related to internalization of a foreign organ. It also describes the pre-transplant psychosocial evaluation process and explores post-transplant areas of concern, such as drug-drug side effects and many of the challenges of post-transplant life, including adherence to medication and treatment. Keywords

Heart transplant · Heart failure · Left ventricular assist devices · Depression · Anxiety

Once a mere fantasy, human-to-human solid organ transplantation became a reality in 1954 when the first successful kidney transplant took place. Another 13 years would pass before Dr. Christiaan Barnard performed the first successful orthotopic heart transplant in Cape Town, South Africa, in 1967. Initial survival times were poor, reflecting high rates of graft rejection. Progress for all forms of solid organ transplantation occurred after the discovery of cyclosporine in the mid-1970s, and subsequent development and improvement of this and other immunosuppressive agents through the 1980s and 1990s. In addition, the advent of ventricular assist devices (VADs) has provided patients with end-stage heart failure an additional means of extended survival, either as a bridge to transplant or as a destination therapy. Currently, transplantation exists as a realistic endpoint for many patients with terminal cardiac disease. Between 1988 and 2009, nearly 48,000 heart transplant operations were performed in the United States. Importantly, however, many individuals do not survive until surgery. In the United States in the past decade, the number of patients on the transplant wait list has remained at about 3000; over 2000 transplants have been performed annually, and 10–15% of candidates die while waiting. Scarcity of donated organs remains one of the major impediments to transplantation. Blood type, body size, and serological mismatches further limit the suitability of donor hearts for any given patient. Even after a successful transplant procedure, patients and their care providers must be vigilant in their surveillance of signs or symptoms of graft rejection that in turn could threaten the viability of the new heart and lead to increased morbidity and mortality [49]. The transition from being a heart failure patient to a heart transplant candidate and ultimately a transplant recipient is a complex process that requires intensive evaluation and management by cardiologists, cardiac surgeons, and other members of a multidisciplinary team that may include various medical specialists, social workers, psychiatrists, psychologists, nurses, physical therapists, and others. The roles of the psychiatrist or psychologist as a member of the transplant team include assisting the team in the evaluation of patients’ candidacy for transplantation, including

53

Heart Transplantation

1273

assessment of their cognitive capacity and ability to consent; diagnosis and treatment of psychiatric and substance use-related disorders in the pre-transplant period; diagnosis and referral and/or management of these disorders in the post-transplant period; education and counseling about, and management of obstacles to, adherence; and expert consultation on interaction of psychotropic medications with the remainder of the medical regimen and the adverse effects of psychotropic medications [46]. An additional role may be to facilitate the working function of the transplant team as it encounters conflicting ethical demands and strong emotions in the course of its ongoing work [45]. In order to appreciate the behavioral and mental health issues associated with heart transplantation, we will first describe the natural history of heart transplantation, so as to provide context for recognition of situations deviating from the norm and opportunities for behavioral and mental health intervention. We will outline the psychiatric comorbidities and management issues related to congestive heart failure (CHF) and end-stage heart disease, ventricular assist devices, and heart transplantation itself, including the psychological implications related to internalization of a foreign organ. We will describe the psychosocial evaluation process. We will also review post-transplant issues including drug-drug side effects. Finally, this chapter will explore many challenges of post-transplant life, including adherence to medications and treatment.

Natural History of the Heart Transplant Experience For most potential recipients, the course to cardiac transplantation is a gradual process that follows a diagnosis of heart failure. The usual indication for heart transplantation is left ventricular dysfunction with decompensated heart failure; less common indications include cardiac tumors, treatment-refractory myocardial ischemia, or malignant arrhythmias. Coronary artery disease is the etiology for about half the cases. Other common diagnoses include idiopathic and toxic dilated cardiomyopathies, rheumatic and valvular heart disease, acute myocarditis, and congenital heart disease. Less common are restrictive and infiltrative diseases of the myocardium such as cardiac amyloidosis and sarcoidosis. Usually over a period of years, deterioration of cardiac function results in decompensated heart failure symptoms that become less and less responsive to medications. As treatment pushes the limits of medical management, patients are introduced to the idea of surgical interventions, including transplant and ventricular assist device placement. At this point, patients are referred by their primary cardiologists to heart failure/heart transplant experts at transplant centers. Because of the highly specialized nature of these centers, patients and their families may have to travel significant distances for care. Their commitment and ability to get to and from visits will factor into their ultimate evaluation for transplant. Some patients do not follow the typical course. Instead of having long-standing symptoms that worsen with time, these individuals become potential transplant candidates as a result of a sudden, catastrophic cardiac event. Often these patients, rendered unconscious following a massive myocardial infarction or cardiovascular collapse, wake up in an intensive care unit to discover that their previously normal lives have been irreparably changed. Sometimes these patients have already received

1274

S. S. Nash and P. A. Shapiro

surgical interventions, including intra-aortic balloon pumps or ventricular assist devices. For these patients, the transition in self-concept – from healthy individual to heart disease patient to transplant candidate – can be jolting and traumatic. Reactions of anger and denial must be carefully recognized and managed as patients go through an expedited transplant evaluation. Patients who are potential candidates for transplant undergo a process of evaluation that includes many medical tests, including cardiac catheterization, echocardiography, pulmonary function testing, abdominal sonography, and a battery of blood tests to examine the function of their hearts as well as other organs and to look closely at any existing comorbid conditions. Further tests, such as colonoscopy or organ biopsies, may be necessary depending on the initial medical results. In addition to meeting specialists from many medical disciplines and undergoing both noninvasive and invasive medical tests, patients also participate in interviews with many transplant team members: transplant coordinators help to provide education about the process; insurance experts assist patients in determining how existing or additional coverage will finance pre- and post-transplant care; and social workers meet with patients and families to understand patients’ individual support structures. Other team members, including nutritionists and physical therapists, work with patients on an as-needed basis. Pre-heart transplant psychosocial evaluations are required by federal regulations in the United States and, depending on the site, may include meeting with a team psychologist or psychiatrist, questionnaire measures of symptoms and social supports, and neurocognitive testing. Once a patient has completed the many parts of the transplant evaluation, the results are discussed in a multidisciplinary team meeting. It is during this meeting that the team as a whole decides whether a patient will be listed for transplant, temporarily deferred, or rejected for transplantation. During these meetings there is also discussion about whether patients may be candidates for ventricular assist devices, either as a bridge to transplant or as a destination treatment. Patients selected for listing then face the inevitable wait for a suitable organ. Waitlist times vary and depend on factors including the severity of a patient’s illness (patients in the cardiac intensive care unit on several intravenous inotrope and pressor medications to maintain cardiac output, for example, sit higher on the list than patients who are at home with less severe symptoms), blood type, and body size. During the waiting time, patients may need periodic hospitalizations to treat exacerbations of heart failure symptoms or other medical problems that might arise. Patients may be temporarily removed from or placed on inactive status on the waiting list if they develop infections or problems that require intervention before a transplant could safely occur. For many patients, the process of waiting for an organ is stressful. As they near the top of the list, patients and their families must be ready at any time to receive a call that an appropriate donor organ has been identified and to report quickly to the transplant center. Many patients describe initial anxiety about this process; as waitlist times often last several months to a year or longer, however, patients generally become accustomed to the uncertainty. Some patients experience symptoms of depression, especially if their wait time has become prolonged. Psychologists or

53

Heart Transplantation

1275

psychiatrists may work with these patients to help optimize their mood symptoms with psychotherapy and/or medications. At the time of transplantation, recipients are treated with large doses of immunosuppressant agents to prevent hyper-acute rejection of the transplanted organ. Varying combinations of antirejection medications are continued daily thereafter, for the rest of the patient’s life. A multitude of health issues from rejection and graft dysfunction to infections are faced by post-transplant patients. These serious and life-threatening problems can be challenging to manage, as prophylaxis against rejection diminishes the body’s ability to fight infection. Following cardiac transplant, recipients typically spend 2 to 4 weeks in the hospital or longer if complications arise. For the first several days after transplant, patients are in intensive care; subsequently they move to a cardiac surgery ward environment. In the days and weeks following surgery, patients begin their new lifelong requirement of taking immunosuppressant medications, as well as many other medicines. They undergo routine catheterizations with cardiac biopsies to evaluate for signs of rejection and may be treated with high-dose steroids if evidence of rejection occurs. After hospital discharge, patients return home and within a matter of months may regain much of their premorbid function. Many patients regain satisfying family life, employment, and sexual functioning. Patients must remain adherent to the medication regimen, diet, and regular check-ups in order to maintain a high quality of graft function. For some, the intensive regimen associated with post-transplant care becomes burdensome over time, and there is an increased risk of noncompliance. For all cardiac transplant recipients, ongoing interaction with the transplant team is critical for sustained success and survival.

Psychiatric Issues in Transplant Patients Depression Depression in Pre-Transplant Patients Depression is common in the general population and even more so in patients with heart failure. Approximately 10–15% of the more than five million people in the United States who suffer from CHF also experience depression; prevalence up to 36% has been reported in hospitalized patients [18, 39, 44]. This comorbidity, in turn, has been linked to impairment in functional status, decreased quality of life, worsened cardiac symptoms, and greater overall morbidity as well as mortality [39]. Depressive symptoms may increase as transplant candidates remain on the waiting list [18]. A long-standing prior history of depression has also been associated with an increased risk of suicidal or self-injurious ideation in heart failure patients [26]. Teasing out depressive symptoms in patients with heart failure requires disentangling medical and mood symptoms. Frequently, CHF patients who are volume- overloaded experience decreased energy (reduced exercise tolerance, dyspnea upon exertion), poor appetite, and disrupted sleep resulting both from difficulty

1276

S. S. Nash and P. A. Shapiro

Table 1 Overlapping features of depressive disorder and congestive heart failure Symptom Depressed mood Sleep disturbance Lack of interest in previously enjoyed activities Guilty thoughts Decreased energy Decreased concentration Disturbance of appetite Psychomotor agitation/retardation Recurrent thoughts of death or suicidal thoughts Shortness of breath

Diagnostic for depression + + + + + + + + +

Symptom of congestive heart failure +

+ +/ + +/ + (thoughts of death, but not suicidal ideas) +

Note: in order to meet criteria for a diagnosis of major depressive disorder, the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, fourth edition) requires the occurrence of five of the nine criteria listed above in the same 2-week period, including at least one of the criteria, “depressed mood” or “loss of interest in previously enjoyed activities”

breathing (orthopnea, paroxysmal nocturnal dyspnea) and recurrent nocturnal awakening from diuretic use. In addition, heart failure patients may report self-criticism and self-blame over past health behaviors such as smoking or dietary indiscretion and also may have very real thoughts and fears about death (see Table 1). Because somatic symptoms of heart failure resemble many of the somatic symptoms of depression, thorough evaluation should emphasize the degree and severity of mood symptoms, the extent to which the patient retains the ability to experience pleasure in life, as well as suicidal ideation and behavior. Past history of depressive episodes and family history of affective illness also provide important information. Patients’ psychosocial situations may also contribute to their mood state and should be explicitly discussed in a psychiatric interview. Decreased functional status and an increased illness burden may result in a heightened need for care that in turn may have a harmful impact upon self-esteem, marital relationships, and mental wellbeing. Treatment of depression in heart failure includes psychopharmacologic and psychotherapeutic modalities. Randomized, placebo-controlled, double-blind trials have provided some evidence for the effectiveness of sertraline, citalopram, and mirtazapine for treatment of depression in coronary heart disease patients, including specific evidence for the effectiveness of sertraline for depression treatment in patients with low left ventricular ejection fraction (LVEF) [19, 25, 50]. In addition, cognitive behavioral therapy has demonstrated effectiveness in post-MI patients [2, 39]. In addition to these treatments, electroconvulsive therapy (ECT) has demonstrated efficacy for treatment of depression in heart failure patients though potential cardiac complications of treatment require a highly individualized risk-benefit analysis for potential candidates [18].

53

Heart Transplantation

1277

Depression in Post-Transplant Patients Cardiac transplant, with its impressive potential for significant improvement in quality of life, may result in decreased depressive symptoms for some patients who become surgically “cured” of their terminal heart disease and attempt to regain their pre-illness functional status. For many others, however, the psychological stress of the transplant experience, compounded by the physiologic effects of posttransplant medications, can lead to depression. A high rate of major depressive disorder (MDD) has been observed especially in the first year after heart transplantation [8, 10, 40]. This is particularly true for female patients and for patients who have a history of pre-transplant MDD. For example, Dew and colleagues demonstrated that 25.5% of post-transplant patients experienced a major depressive episode in the first 3 years after transplant, with the majority of these episodes taking place during the first 12 months after surgery. In this group, more than half of the patients (60.5%) identified a transplant-related physical health problem as precipitating their depression; just under a third (30.2%) cited transplant-related psychosocial issues as a trigger. Specific risk factors for MDD in post-transplant patients included prolonged wait-list time prior to transplant, the need for mechanical circulatory support (intra-aortic balloon pump or ventricular assist device) in the period leading up to transplant, and increased physical limitations in the immediate post-transplant period [10]. Certain immunosuppressants, such as the corticosteroid prednisone, have been associated with specific mood disturbances including depression, irritability, and mania, as well as psychosis. Other immunosuppressant agents, including the calcineurin inhibitors cyclosporine and tacrolimus, frequently cause unpleasant side effects such as nausea, diarrhea, headache, and pain which, when persistent, can have deleterious effects on mood. These agents also carry the risk of neurotoxicity, which will be discussed later in this chapter, and nephrotoxicity, which can contribute to overall post-transplant illness burden. Other characteristic medical complications, including cytomegalovirus (CMV) infection, have also been associated with increased depressive symptoms. Psychosocial issues can also contribute to the development of depression in the post-transplant period. Prolonged hospital course and decreased support from family/primary caregivers constitute major forms of psychosocial stress. Caregivers may not anticipate the length of time required for recovery from transplant and may be unwilling to participate in the recovery process. This may become particularly pronounced if the recipient had a lengthy or complex pre-transplant course, especially if tensions preexisted in the relationship between the patient and the primary caregiver. A caregiver reluctant to abandon an ill pre-transplant patient may feel that he or she can detach once the patient has been made “well” by transplant. Patterns of psychological distress after heart transplant are similar to those seen in other solid organ transplants. In general, post-transplant patients experience their highest levels of emotional distress in the first few months following their surgery, with improvement over the course of the first year. Different temporal patterns of psychological distress have also been shown, including patients who have

1278

S. S. Nash and P. A. Shapiro

consistently low distress, consistently high distress, high distress with reduction over time, or fluctuating levels of distress [12]. Individual patients’ coping styles and past psychiatric histories, evident before transplant, can predict how well they will adapt to the initial challenge of transplant and how they will manage the potential for chronic stress during post-transplant life [41]. Regardless of its cause, post-transplant depression remains a critically important issue. Depressed patients tend to have greater difficulty with medication compliance and follow-up care, thus increasing the risk of graft rejection and other transplantassociated morbidity. In addition, elevated levels of depression have been found to correlate with an increased risk of death post-transplant [21].

Anxiety Anxiety Symptoms in Pre-Transplant Patients Much like depression, anxiety is prominent in patients waiting for heart transplants. Anxiety symptoms may represent a response to organ failure itself; may be the consequence of treatments, as is the case with certain medications or automatic implantable cardiac defibrillators (AICDs); and may also reflect the psychosocial stresses associated with the waiting period. Overall the prevalence of anxiety disorders is less well-defined but probably lower than that of depressive disorders in pre-transplant patients [5]. While the literature describing anxiety disorders in cardiac transplantation is less robust than data regarding mood disorders, reports have shown a median lifetime rate of anxiety disorders (other than adjustment disorders) in prospective transplant patients of about 6% [10]. Identifying and treating anxiety symptoms, however, remains critical to optimizing patients’ ability to cope with both pre- and post-transplant concerns. The evaluation of anxiety in patients with end-stage heart failure should first include an appropriate medical workup to rule out organically induced etiologies. Cardiac arrhythmias, including rapid atrial fibrillation and supraventricular tachycardias, pulmonary emboli, both hyper- and hypothyroidism, and hyperparathyroidism, can present with anxiety symptoms [6]. Appropriate treatment of these problems can mitigate anxiety symptoms. Similarly, shortness of breath related to pulmonary vascular congestion may result in anxiety, which may in turn be relieved as diuretics and inotropes, respectively, reduce congestion and facilitate improved pumping function of the heart. In addition to medical evaluation, a review of patients’ medication regimens is critical, as many agents (such as the bronchodilating agent albuterol or the fluoroquinolone class of antibiotics) can stimulate anxiety responses [6]. Primary anxiety disorders are also common in transplant patients, as in the general population, and most frequently include panic disorder, generalized anxiety disorder, and post-traumatic stress disorder [6]. Treatment of anxiety in transplant patients, as in the general population, depends on the etiology of the anxiety problem, the severity of the symptoms, and the level of functional impairment developed. It is not always possible to identify a medical

53

Heart Transplantation

1279

cause, or to remove offending medications, especially in patients whose heart failure has made them dependent on complex regimens. Adding anxiolytic medications to these regimens is sometimes helpful. Cognitive behavioral therapy has proven successful for helping some patients reframe maladaptive thought processes and reduce their overall anxiety burden [27]. For many patients, however, psychotherapy alone is insufficient, and the addition of antianxiety medications remains a central component of treatment. In patients without evidence of impending respiratory compromise, routine standing or intermittent (“as needed”) benzodiazepines can powerfully manage acute anxiety symptoms [6]. Shorter-acting benzodiazepines, including lorazepam, oxazepam, and temazepam, which are metabolized via conjugation-glucuronidation without prior demethylation or oxidation, are preferable to agents that require more extensive hepatic metabolism [6], especially when considering patients with hepatic congestion from heart failure. Patients with chronic underlying anxiety symptoms should also be treated with ongoing anxiolytic therapy, such as selective serotonin reuptake inhibitors (SSRIs) or buspirone [6]. Anxiety symptoms may become prominent during the transplant evaluation period. During this time, when already ill patients undergo a battery of new tests, they may also be flooded with information related to cardiac transplantation. Further anxiety may mount as the multidisciplinary team deliberates on whether or not particular patients will be listed for transplant, based on their degree of medical and psychosocial risk. Patients deferred or rejected from the transplant list may require assistance managing their disappointment or distress. These patients face an uncertain future with a terminal illness, perhaps with the option of seeking to restart the evaluation process at another center. For those patients accepted for transplant, initial relief may develop into worry as they begin their time on the waiting list. While transplant centers provide patients with guidance about average times from listing to transplant, the waiting phase may be viewed as an indeterminate period of time structured so as to induce psychological conflict within the patient. This is because, under current rules for organ allocation, more severely ill patients are given priority for heart transplantation, regardless of their time on the waiting list. Hence, there is an advantage to being sicker (earlier access to transplant) as well as the obvious disadvantage of increased risk of death or progression of illness and comorbidity to the point that transplant is precluded. Patients may also develop worsened anxiety symptoms if they experience or anticipate “false alarms,” in which they are summoned for transplant only to have the surgery cancelled at the last moment for one or another reason (usually related to final evaluation of the donor) [34]. During the waiting period, patients also may undergo additional life-sustaining procedures, such as the implantation of defibrillators (AICDs) or ventricular assist devices (VADs). AICDs provide an interesting case study in anxiety symptoms, including posttraumatic stress symptoms, in heart failure patients. Ventricular tachycardiaventricular fibrillation is one of the main causes of death in patients with heart failure, and randomized trials have proven a survival benefit of defibrillator implantation for primary prevention of sudden cardiac death in coronary artery disease and CHF [15, 17, 22, 29, 30, 37]. In consequence, defibrillators are implanted routinely

1280

S. S. Nash and P. A. Shapiro

not only in patients who have had an arrhythmic event but also in those without such a history but with significantly reduced left ventricular function. AICDs are programmed to provide lifesaving electrical shocks to the heart should otherwise fatal ventricular arrhythmias develop. Many patients view their AICDs as a life vest that protects them from death. For some, the belief that their AICD stands between them and sudden cardiac death helps to reduce anxiety in and of itself. For others, however, the fear of receiving a powerful, painful shock creates intense anxiety. This can occur whether or not the patient has ever experienced AICD firing. Recipients of prior shocks may develop anticipatory anxiety that becomes incapacitatingly severe. Patients may avoid activities or places where past shocks occurred and may experience hyper-arousal symptoms when internal or external factors trigger their memories. In these cases, the potential life-sustaining benefit of the defibrillator – which can be important in facilitating survival to transplant – must be weighed against the psychological risks of maintaining the device. Antianxiety medications, including benzodiazepines and SSRIs, as well as cognitive behavioral therapy focused at identifying false and overvalued thoughts can be useful and important treatments.

Anxiety in Post-Transplant Patients When heart transplant recipients and their caregivers were evaluated for evidence of mild to moderate anxiety in a 2005 study, only 4% of patients but 23% of partners were found to have anxiety symptoms [3]. Another study that followed 191 heart transplant patients for 3 years following transplantation found a cumulative risk of 17.7% for developing an adjustment disorder with anxious features (which was significantly greater than the risk of developing adjustment disorder with depressive features) and a cumulative risk as high as 17% for developing post-traumatic stress disorder related to the transplant (PTSD-T). Several patients also met symptom criteria for generalized anxiety disorder (GAD), but did not experience the symptoms long enough to warrant the diagnosis of GAD [10]. While not as prevalent as post-transplant depressive disorders, anxiety symptoms and disorders in the post-cardiac transplant setting are nevertheless important to identify and treat as they can lead to significant morbidity in heart transplant recipients. In the early months following transplantation, anxiety symptoms are often triggered by transplant-related events, including acute graft rejection or other complications that might increase overall length of hospitalization. In addition, poor emotional support by caregivers is associated with a faster rate of onset of posttransplant anxiety disorders, and women transplant recipients appear to be at higher risk of developing anxiety symptoms than men. Limitations in physical functional status are also associated with post-transplant anxiety. As patients progress farther from the transplant period, however, general life stressors tend to generate anxiety symptoms as much or more than transplant-specific stressors. Unlike the more gradual onset of major depression, the majority of anxiety disorders in the posttransplant period tend to occur in the first year after surgery [10].

53

Heart Transplantation

1281

Some investigators have highlighted the importance and prevalence of posttraumatic stress disorder related specifically to stressors experienced in the course of transplant-related illness and care (PTSD-T), which appears to occur most significantly in the first year following transplant. Dew and colleagues described PTSD-T as “a failure of the patient to come to terms with the transplant experience” [10]. Risk factors for PTSD-T include a history of prior psychiatric illness (especially GAD or MDD), female gender, and poorer psychosocial support [43]. As Stukas et al. point out, cardiac transplantation by its very nature is a process beyond the realm of normal human experience, during which patients face the constant, very real possibility of death. These are key features of post-traumatic stress and may also apply to caregivers of transplant patients as well as to the patients themselves. Indeed, a 1999 study of PTSD-T in heart transplant recipients and their caregivers found that 7.7% of caregivers met full criteria for PTSD-T, with an additional 11% who met many (but not full) criteria resulting in a designation of “probable cases” [43].

Other Psychiatric Issues Ventricular Assist Devices Ventricular assist devices (VADs) are implantable pumps that provide mechanical circulatory support for patients with severe heart failure. Over the past quarter of a century, device technology has evolved from external to internal pump placement, pneumatic to electrical drivers, and pulsatile to continuous flow. Devices have become smaller, more durable, and easier for patients to tolerate. By 2015, more than 20,000 VADs have been placed, with a current rate of over 2500 VAD implantations per year. There are three main indications for VADs. First, VADs serve as a bridge to transplant, inserted to help stabilize patients and extend life expectancy for weeks to months until the time of transplant. At transplant, the device is explanted. Second, VADs are used as a bridge to recovery. Some evidence points to the role of VADs in reversing the deleterious ventricular structural-anatomical, functional, and molecular changes associated with heart failure, referred to as cardiac remodeling. If VAD therapy results in restoration of adequate ventricular structure and function, the VAD is explanted. Finally, for some patients who are not suitable candidates for heart transplantation, VADs may serve as a “destination” therapy. It is important to note that for this group, the rates of complications and mortality remain high [47]. The present discussion will focus on the use of left ventricular assist devices as bridge to transplant only. For patients awaiting cardiac transplantation, left ventricular assist devices (LVADs) may be not only life-sustaining but also helpful in improving cardiac output and hemodynamics and reversing many of the problems associated with heart failure, including hepatic and renal failure as well as encephalopathy. But LVADs are not without complications of their own, both medically and psychiatrically. The REMATCH (Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure) trial found LVADs to be associated

1282

S. S. Nash and P. A. Shapiro

with significant unique morbidity and mortality when compared to medical therapies [24, 35]. Because of associated risk of clotting and thromboembolic events, anticoagulation and antiplatelet therapy are necessary components of therapy for some VADs and have been associated with increased rates of cerebral hemorrhage and stroke. Other important VAD complications are infection and delirium. Shapiro and colleagues, examining 30 early LVAD recipients in the mid-1990s, found that all patients with preoperative cognitive impairment experienced organic mental syndromes in the post-LVAD implantation period [42]. Dew and colleagues studied quality of life in patients who had successfully been bridged to transplant via LVAD. With follow-up at 2, 7, and 12 months posttransplant, the authors evaluated dimensions of quality of life including physical functional status (sleep, body care and movement, mobility, ambulation), emotional and cognitive well-being (depression, anxiety, anger-hostility, transplant-related post-traumatic stress disorder), and social functioning. They found that LVADs improved pre-transplant quality of life in patients and that overall VAD patients had lower rates of anxiety and PTSD-T, as well as fewer somatic complaints and faster overall functional improvement. They also uncovered, however, that patients who had been bridged to transplant with a VAD demonstrated worse post-transplant cognitive function, with more interpersonal withdrawal and decreased likelihood of returning to post-transplant employment, regardless of the time they had been on VAD. In this study the increased risk of neurologic events during VAD time was hypothesized to explain these findings [11].

Psychiatric Effects of Post-Transplant Immunosuppressants Following cardiac transplantation, recipients require an intensive drug regimen that includes several immunosuppressive antirejection agents; antibiotic, antiviral, and antifungal medications; diuretics; antihypertensive medications; and treatments for postoperative complications as well as other general medical problems. Immunosuppressive medications such as the calcineurin inhibitors cyclosporine (Neoral, Sandimmune) and tacrolimus (FK506, Prograf) and corticosteroids (prednisone, prednisolone, methylprednisolone) are critical to post-transplant survival but often have important neuropsychiatric side effects. These range from mild mood lability to delirium and overt psychosis. Symptom severity ranges from inconvenient to extremely disturbing. Patients must be closely monitored and treated to prevent the development of severe or nonreversible neuropsychiatric sequelae (Table 2).

Calcineurin Inhibitors The exact mechanism of action of calcineurin inhibitors is uncertain, though these medications are known to inhibit T-lymphocyte activity and affect sympathetic activity. Common adverse effects of calcineurin inhibitors include nephrotoxicity and hypertension, and neurotoxicity has been shown to occur in 10 to 28% of

53

Heart Transplantation

1283

Table 2 Common neuropsychiatric and other side effects of immunosuppressant drugs. (Adapted from: [1, 51]) Corticosteroids (prednisone, prednisolone, methylprednisolone)

Cyclosporine

Tacrolimus

Mycophenolate mofetil

Sirolimus Monoclonal antibodies used in induction (e.g., daclizumab) Antithymocyte globulin

Mild to moderate: Mood changes (not to level of mood disorder), anxiety, fear, agitation, insomnia, irritability, lability, hypomania, tearfulness, distractibility, reversible cognitive impairments Severe: Symptoms consistent with mood disorder (depression, mania, mixed states; can include suicidal ideation), psychotic disorder (hallucinations, paranoia, can include suicidal ideation), delirium Mild (more common): Mental status changes, somnolence, tremor, paresthesias, neuralgia, peripheral neuropathy Severe (less common): Altered level of consciousness, psychosis (including auditory and visual hallucinations), cortical blindness, seizures, leukoencephalopathy, cerebellar ataxia, confusion, coma Other: Parkinsonian symptoms Mild: Mood disturbances, headache, vertigo, tremor, insomnia, nightmares, vertigo, photophobia Severe: Psychosis, seizures, focal neurologic deficits, encephalopathy, akinetic mutism Mild to moderate: Headache, insomnia, asthenia, tremor Severe: Progressive multifocal leukoencephalopathy Headache Headache, insomnia, tremor, dizziness Unclear

patients receiving cyclosporine and in 3.6 to 32% of those treated with tacrolimus following transplantation of any organ [1]. Deficits in sensory and motor functioning resulting from cyclosporine toxicity may manifest as tremor, paresthesias, peripheral neuropathy, and parkinsonism. In addition, patients may experience mental status alterations, visual disturbances (including visual hallucinations and cortical blindness), cerebellar symptoms, and seizures. Occasionally, patients may experience a severe syndrome “characterized by an altered level of consciousness, confusion, psychosis, visual and auditory hallucinations, blindness, seizures, cerebellar ataxia, motoric weakness, or leukoencephalopathy” [1]. Interestingly, neuropsychiatric toxicity symptoms of cyclosporine may not be immediately apparent and can take months to years to develop. Cumulative cyclosporine neurotoxicity has also been shown to result in relative long-term decline in cognitive function, which in turn has been associated with a reduced quality of life [1, 20]. Symptoms typically are worse at higher doses and with intravenous administration. Unfortunately, dose reduction and/or discontinuation of calcineurin inhibitors does not always reverse all symptoms of neurotoxicity. Nevertheless, one consideration in post-transplant patients is to use a calcineurin inhibitor in the immediate post-transplant period, when the effects are thought to be most beneficial, and ultimately move to a cyclosporine or

1284

S. S. Nash and P. A. Shapiro

tacrolimus-sparing regimen that includes instead the alternative immunosuppressant mycophenolate mofetil (MMF, CellCept). A typical management strategy is use of combination therapy to minimize calcineurin inhibitor dosing.

Corticosteroids Corticosteroids typically comprise part of the post-cardiac transplant immunosuppressive regimen. Both in combination with other immunosuppressants and on their own, corticosteroids can contribute to the development of psychiatric symptoms, even in patients with no past psychiatric history. For example, high-dose methylprednisolone impairs hepatic calcineurin inhibitor metabolism, resulting in elevated levels of cyclosporine or tacrolimus that may subsequently result in neuropsychiatric side effects. When used alone, corticosteroids are associated with severe reactions in 6% of patients and mild to moderate reactions in 28% of patients; other studies have demonstrated wide-ranging incidence for corticosteroid-induced psychiatric symptoms [51]. Common neuropsychiatric symptoms include mood lability (hypomania, mania, or depression), anxiety symptoms, delirium, cognitive impairment, and psychosis. Hypomania and mixed states are more frequently found with acute corticosteroid use, whereas chronic use tends to result more in depressive symptoms. It is critical to assess for suicidality in patients with steroid-induced psychotic or mood symptoms [51]. Psychiatric adverse effects can occur at any time during the course of treatment with corticosteroids, though symptoms are more likely to occur early in the process. Medication dose correlates directly with risk of developing psychiatric symptoms. The Boston Collaborative Drug Surveillance Program demonstrated the following rates of psychiatric disturbances in hospitalized patients (not necessarily transplant recipients) taking prednisone: 1.3% of patients taking 80 mg/d [31, 51]. Psychiatric symptoms of corticosteroids can be uncomfortable and disconcerting to patients, as well as potentially dangerous, and must be managed thoughtfully. Often symptoms will remit spontaneously when corticosteroid dose is decreased. In situations such as acute graft rejection, when elevated doses of corticosteroids are essential to post-transplant patients and cannot be reduced, additional medications including antipsychotics and mood stabilizers are useful in treating corticosteroidinduced psychiatric symptoms. In heart transplant patients, high-dose corticosteroids are used predominantly in the immediate postoperative period, as well as in “pulsed” dosages in the setting of acute rejection episodes.

Psychological Implications of Internalizing a New Organ A unique aspect of psychological adaptation to heart transplantation is the recipient’s reaction to taking into his or her body the heart of another person. The heart holds special cultural meaning, across many if not all human cultures, not only as a

53

Heart Transplantation

1285

muscular circulatory pump but also as the seat of emotion and especially of love and attachment to others. For many people, the heart symbolizes one’s personal identity. As a consequence, heart transplantation involves both the physiological task of placing the graft organ in position to function as a pump, with adequate immunotolerance, and also the psychological task of incorporating the donor’s heart into one’s sense of self. Patients consciously fantasize about changes in their personhood as a result of the incorporation of the transplant into their bodies. More psychologically mature or healthy patients may be able to repress these fantasies or turn them into matters of joking, but some patients become quite anxious. This may be especially true for children and more psychologically unsophisticated individuals, who may express the notion that they will not awaken from surgery speaking the same language, adhering to the same religious faith, enjoying the same kind of food, or feeling attachment to the same people that they did before surgery – that they will take on the personal-historical characteristics of the donor in place of their own. Issues such as the sex of the donor, his or her interests, and his or her social role may trouble the recipient. The recipient may manifest inability to get past this stage of adaptation by referring to him- or herself as two people, calling his heart by another person’s name, or reporting fantastical changes in self-perception and attributing them to traces of the donor’s memory and personality somehow embedded in the tissues of the graft heart. A healthier level of adaptation seems to be characterized by acknowledgment of the gift of a new opportunity to live a healthy life, gratitude toward the family of the donor, and a restored sense of ability to move on as one’s own self, facing the new challenges of life after a complex medical procedure [4, 38].

Psychiatric Assessment in Transplant Pre-Transplant: Psychiatric Assessment of Transplant Candidates Organ transplantation is a complicated process in which terminally ill patients must work closely with a multidisciplinary team of physicians and surgeons to procure a severely limited resource during a narrow time interval and subsequently undergo lifelong, high-cost treatment in order to survive. Given the ever-increasing demand for scarce organs, only a fraction of the population in need of transplantation will receive a new organ. Consequently, transplant programs exist in a social and political milieu that demands that they ensure that patients who undergo organ transplantation are medically and psychologically optimized in order to have the most successful post-transplant survival. This determination is based largely on an extensive pre-transplantation workup that involves a battery of medical tests as well as a psychiatric and psychosocial assessment. Psychiatric assessment for organ transplantation serves several purposes. It allows an opportunity to assess a patient’s capacity to understand and make decisions about transplantation and related medical care; identifies knowledge gaps and misconceptions about transplant and provides education that may help correct these problems; uncovers potentially treatable emotional distress and psychiatric

1286

S. S. Nash and P. A. Shapiro

disorders; provides information to the transplant team about patients’ readiness to undergo transplant and the presence of any psychosocial barriers to successful outcome; and develops intervention plans for psychosocial problems that are a barrier to successful transplant outcome, allowing patients who might otherwise be excluded to become candidates. In a global way, then, the psychiatric/psychosocial evaluation identifies levels of risk important for the entire transplant team to consider as it determines an individual candidate’s eligibility for transplantation. The goal of the psychiatric assessment is not to exclude candidates because of their psychiatric or behavioral problem history; the presence of particular risk factors, including active substance abuse, personality disorders, and limited psychosocial supports however, has been shown to predict poorer outcomes and is critical to address prior to moving forward with transplantation. While not all transplant programs use the same criteria in their assessments, most psychosocial evaluations include ten basic goals: (1) assessment of coping skills, (2) diagnosis of comorbid psychiatric illness, (3) determination of the patient’s ability to understand the transplant process, (4) assessment of the patient’s ability to work with the transplant team and comply with treatment, (5) assessment of current and past substance abuse (including alcohol and tobacco, as well as other substances) and ability to comply with long-term abstinence, (6) identification of modifiable heath behaviors (such as smoking or poor eating habits) that can affect post-transplant morbidity, (7) assisting the transplant team in understanding the patient as an individual, (8) identification and evaluation of a patient’s social support system, (9) determination of patient and family psychosocial needs, and (10) assessment of baseline mental functioning [16]. Identification of suboptimal functioning should prompt assessment regarding possible intervention and treatment. Cardiac transplant programs vary with respect to how patients are psychiatrically evaluated. Some programs require that all potential candidates undergo an evaluation conducted by a psychiatrist; in others, social workers meet with candidates and their family members, and psychiatrists become involved only if problem areas are identified. Screening instruments can offer ratings of patients’ affective, cognitive, and psychosocial functioning; additional evaluation and interventions can be made based on these results [16]. Several standardized instruments exist to facilitate the psychosocial evaluation of patients in the pre-transplant period [28, 32, 48]. Other scales and questionnaires, evaluating quality of life or issues related to specific disease states, may also be used. Substance abuse scales, including the HighRisk Alcohol Relapse Scale, can be important in the assessment and monitoring of patients during the pre- and post-transplant periods [52].

Treatment Adherence and Non-adherence “Compliance” is a term often used to refer, roughly, to following medical instructions, and “noncompliance” to “behavior that fails to coincide with medical recommendation” [9]. The term implies bending to external force and has been criticized as

53

Heart Transplantation

1287

implicitly endorsing a simplistic model of the doctor-patient relationship that puts all power in the hands of the physician, in which the doctor gives orders and the patient’s behavior is to be shaped by these orders. The patient’s behavior is seen as the passive object of the doctor’s prescriptive activity [23]. “Adherence,” as an alternative term, may be used to refer, roughly, to “sticking with” a treatment program. The term implies an active application of the patient’s effort and can be appreciated to better reflect the patient’s autonomous function as a collaborator with the physician and others in the process of medical care. The World Health Organization defines adherence as “the extent to which a person’s behavior corresponds with the agreed recommendations from a healthcare provider” [36]. While sticking with treatment may be both important and challenging across a broad spectrum of medical diagnoses, its role in transplantation management carries heightened importance. Even small deviations from prescribed regimens can elevate risk of poor outcomes, including late acute rejection and graft losses (association with non-adherence estimated at 50% and 15%, respectively) [36], as well as death. The financial costs of non-adherence and its related morbidity and mortality are significant. In cardiac transplantation, adherence includes taking medication, monitoring vital signs, abstinence from substance abuse, attendance at medical clinic visits, completion of blood work, and maintaining a healthy diet and exercise program [9]. Currently there is no universally applied standard of adherence in cardiac transplantation, as transplant programs vary in their particular requirements [13].

Assessment of Adherence While multiple methods are commonly used to assess adherence in transplant patients, it is important to recognize that each method carries with it unique biases that can affect the rate of non-adherence being determined. For example, patients may self-report non-adherence with immunosuppressant and exercise regimens, while reports from others, such as primary care doctors or family members, are more likely to disclose information about substance use that patients are reluctant to provide. Still other measures, such as examining serum levels of medications, using pill counts, or investigating pharmacy records, can also uncover important information. Because each measure of assessment runs the risk of being uniquely manipulated, reliance on a composite of assessment methods is likely to yield the most accurate assessment of adherence (Laederach-Hofmann [13, 23]). Evidence of good adherence – and the support systems to promote continued adherence – is an essential component of the psychiatric evaluation of transplant candidates. Yet even patients who have demonstrated the appropriate motivation and behavior to receive a heart transplant have a surprising level of non-adherence. Dew and colleagues showed that there is up to 20% non-adherence to immunosuppressant medication in heart transplant patients (which overall was less than levels of non-adherence in other solid organ transplant patients). In addition, 5 to 19% of patients resume smoking after orthotopic heart transplantation (OHT), 3 to 15% do not keep clinic appointments, 18 to 24% do not follow their prescribed diet, and up to 48% of patients after heart transplants fail to exercise appropriately [9, 13].

1288

S. S. Nash and P. A. Shapiro

When assessing non-adherence, it is important to consider and address a number of possible practical indications that may affect patients’ behavior and choices. Transplant regimens are elaborate and complex, often involving taking a great number of pills up to several times daily. The rate of non-adherence rises with regard to both the number of pills per dose and doses per day (70% compliance with oncedaily regimen; 50% with twice-daily dosing, 40% with three times/day dosing, and 20% with four times/day dosing) [23]. Without good planning and discipline, confusing or missing doses seems inevitable. The cost of these complicated, multidrug regimes must also be considered [13, 23]. Additionally, the occurrence of unpleasant, lifelong side effects, especially in patients who have otherwise attained an improved level of post-transplant cardiac health, can tempt non-adherence. Finally, it is important to consider that some patients may discontinue life-sustaining immunosuppressant medications as a method of attempting suicide [23].

Predictors of Non-adherence Major factors that predict non-adherence in heart transplant recipients are, not surprisingly, similar to predictors of non-adherence in patients with other chronic illnesses [9]. Psychiatric illness – including depression, personality disorders, and active substance abuse – and factors such as cognitive impairment, adolescence, and psychosocial stressors such as financial instability contribute importantly to non-adherent behavior and its consequences [33, 41, 46]. Where possible, psychiatric intervention can address and attempt to treat these problems, resulting in an important impact on adherence and post-transplant survival [33]. The use of antidepressant agents and psychotherapy, including cognitive behavioral therapy (CBT), may mitigate symptoms of depression and anxiety including negativistic beliefs that often result in poor judgment and decision-making [46]. Shapiro and colleagues found substance abuse and an overall estimate of psychosocial risk based on multi-domain assessment of psychosocial function to be primary predictors of noncompliance (odds ratios of 3.69 and OR 3.76, respectively); once substance abuse was taken into account, the effect of other variables was largely lost [41]. Importantly, however, Dew and colleagues demonstrated that most transplant patients who have a history of substance abuse neither relapse posttransplant nor are non-adherent [14]. These authors propose that substance abuse relapse post-transplant involves an accumulation of risk factors, contributing additive effects, with a high risk of relapse above some threshold of cumulative risk. Interestingly, the drug with greatest risk of post-transplant relapse found in this study was tobacco, which in turn is associated with risk of coronary artery disease and mortality. Interventions Despite wide acknowledgment of the scope of non-adherence, there is a limited literature describing interventions designed to improve medication compliance in solid organ transplantation. A recent systematic review found only 12 studies, with only 5 demonstrating statistically significant improvement in a medicationadherence outcome as the result of the proposed intervention [7]. These authors

53

Heart Transplantation

1289

concluded that a combination of strategies involving interventions at the patient, provider, and healthcare system level would be essential for successful behavioral change. Areas of future study may include reassessment of pre-transplant evaluations. Special focus on issues related to post-transplant problems, including pre-transplant history of substance abuse and other psychiatric illness, such as depression, will be essential. Effective interventions may involve not only better identification and treatment of these comorbidities, but aggressive follow-up as well. Pre-transplant patients are by nature a captive audience; transplant recipients less so. The burden of improved adherence includes not only the patients, but involves healthcare providers and recipients’ social supports as well. The relevance of improvement in non-adherence is obvious, given its direct links to increased morbidity and mortality and the potential waste of limited resources and enormous costs associated with transplantation.

References 1. Bechstein W (2000) Neurotoxicity of calcineurin inhibitors: impact and clinical management. Transpl Int 13:313–326 2. Berkman LFBJ, Burg M, Carney RM, Catellier D, Cowan MJ, Czajkowski SM, DeBusk R, Hosking J et al (2003) Effects of treating depression and low perceived social support on clinical events after myocardial infarction: the enhancing recovery in coronary heart disease patients (ENRICHD) randomized trial. JAMA 289:3106–3116 3. Bunzel BL-HK, Wieselthaler GM, Roethy W, Drees G (2005) Posttraumatic stress disorder after implantation of a mechanical assist device followed by heart transplantation: evaluation of patients and partners. Transplant Proc 37:1365–1368 4. Castelnuovo-Tedesco P (1978) Ego vicissitudes in response to replacement or loss of body parts. Certain analogies to events during psychoanalytic treatment. Psychoanal Q 47:381–397 5. Chacko RCHR, Kunik M, Young J (1996) Relationship of psychiatric morbidity and psychosocial factors in organ transplant candidates. Psychosomatics 37:100–107 6. Crone CCGG (2004) Treatment of anxiety and depression in transplant patients: pharmacokinetic considerations. Clin Pharmacokinet 43:361–394 7. De Bleser LMM, Dobbels F, Russell C, De Geest S (2009) Interventions to improve medicationadherence after transplantation: a systematic review. Transpl Int 22:780–797 8. Dew MARL, Schulberg HC, Simmons RG, Kormos RL, Trzepacz PT, Griffith BP (1996) Prevalence and predictors of depression and anxiety-related disorders during the year after heart transplantation. Gen Hosp Psychiatry 18:48S–61S 9. Dew MAKR, Roth LH et al (1999) Early post-transplant medical compliance and mental health predict physical morbidity and mortality one to three years after heart transplantation. J Heart Lung Transplant 18:549–562 10. Dew MAKR, DiMartini AF, Switzer GE, Schulberg HC, Roth LH, Griffith BP (2001) Prevalence and risk of depression and anxiety -related disorders during the first three years after heart transplantation. Psychosomatics 42:300–313 11. Dew MAKR, Winowich S, Harris RC, Stanford EA, Carozza L, Griffith BP (2001) Quality of life outcomes after heart transplantation in individuals bridged to transplant with ventricular assist devices. J Heart Lung Transplant 20:1199–1212 12. Dew MAML, Switzer GE, DiMartini AF, Schulberg HC, Kormos RL (2005) Profiles and predictors of the course of psychological distress across four years after heart transplant. Psychol Med 35:1215–1227

1290

S. S. Nash and P. A. Shapiro

13. Dew DA, De Vito Dabbs A et al (2007) Rates and risk factors for nonadherence to the medical regimen after adult solid organ transplantation. Transplantation 83:858–873 14. Dew MA, Steel J et al (2008) Meta-analysis of risk for relapse to substance use after transplantation of the liver or other solid organs. Liver Transpl 14:159–172 15. DiMarco JP (2003) Implantable cardioverter-defibrillators. N Engl J Med 349:1836–1847 16. DiMartini AFDM, Trzepacz PT (2005) Organ transplantation. In: JL L (ed) Textbook of psychosomatic medicine. American Psychiatric Publishing Inc, Washington, DC 17. Epstein AEDJ, Ellenbogen KA, Estes NA 3rd, Freedman RA, Gettes LS, Gillinov AM et al (2008) ACC/AHA/HRS 2008 guidelines for device-based therapy of cardiac rhythm abnormalities: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to revise the ACC/AHA/NASPE 2002 guideline update for implantation of cardiac pacemakers and antiarrhythmia devices) developed in collaboration with the American Association for Thoracic Surgery and Society of Thoracic Surgeons. J Am Coll Cardiol 51:1–62 18. Fusar-Poli PPM, Martinelli V, Bhattacharyya S, Cortesi M, Barale F, Polit P (2006) Antidepressive therapies after heart transplantation. J Heart Lung Transplant 25:785–792 19. Glassman AHOCC, Califf RM, Swedberg K, Schwartz P et al (2002) Sertraline treatment of major depression in patients with acute MI or unstable angina: sertraline antidepressant heart attack randomized trial. JAMA 288:701–709 20. Grimm MYW, Laufer G, Madl C, Kramer L, Eisenhuber E, Simon P, Kupilik N et al (1996) Cyclosporine may affect improvement of cognitive brain function after successful cardiac transplantation. Circulation 94:1339–1345 21. Havik OESB, Relbo A, Hellesvik M, Grov I, Geiran O, Andreassen AK, Simonsen S, Gullestad L (2007) Depressive symptoms and all-cause mortality after heart transplantation. Transplantation 84:97–103 22. Irvine JDP, Baker B et al (2002) Quality of life in the Canadian Implantable Defibrillator Study (CIDS). Am Heart J 144:282–289 23. Laederach-Hofmann KBB (2000) Noncompliance in organ transplant recipients: a literature review. Gen Hosp Psychiatry 22:412–424 24. Lazar RM, Shapiro PA, Jaski BE, Parides MK, Bourge RC et al (2004) Neurological events during long-term mechanical circulatory support for heart failure: the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) experience. Circulation 109:2423–2427 25. Lesperance FF-SN, Koszycki D et al (2007) Effects of citalopram and interpersonal psychotherapy on depression in patients with coronary artery disease: the Canadian cardiac randomized evaluation of antidepressant and psychotherapy efficacy (CREATE) trial. JAMA 297:367–379 26. Lossnitzer NM-TT, Lowe B, Zugck C, Nelles M, Remppis A, Haass M, Rauch B, Junger J, Herzog W, Wild B (2009) Exploring potential associates of suicidal ideation and ideas of selfharm in patients with congestive heart failure. Depress Anxiety 26:764–768 27. Mago RGJ, Gupta N, Kunkel EJS (2006) Anxiety in medically ill patients. Curr Psychiatry Rep 8:228–233 28. Maldonado JR, Dubois HC, David EE, Sher Y, Lolak S, Dyal J, Witten D (2012) The Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT): a new tool for the psychosocial evaluation of pre-transplant candidates. Psychosomatics 53:123–132 29. Moss AJHW, Cannom DS, Daubert JP, Higgins SL, Klein H et al (1996) Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia: multi-center automatic defibrillator implantation trial investigators. N Engl J Med 335:1933–1940 30. Moss AJZW, Hall WJ, Klein H, Wilber DJ, Cannom DS et al (2002) Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med 346:877–883 31. Muzyk AJHS, Gagliardi JP (2010) Corticosteroid psychosis: stop therapy or add psychotropics? Curr Psychiatry 9:61–69

53

Heart Transplantation

1291

32. Olbrisch MELJ, Hamer R (1989) The PACT: a rating scale for the study of clinical decisionmaking in psychosocial screening of organ transplant candidates. Clin Transpl 3:164–169 33. Owen JEBC, Wellisch DK (2006) Psychiatric evaluations of heart transplant candidates: predicting post-transplant hospitalizations, rejection episodes, and survival. Psychosomatics 47:213–222 34. Pudlo RPM, Zakliczynski M, Zembala M (2009) The occurrence of mood and anxiety disorders in heart transplant recipients. Transplant Proc 41:3214–3218 35. Rose EAGA, Moskowitz AJ et al (2001) Long-term mechanical circulatory support for end stage heart failure: the REMATCH trial. N Engl J Med 345:1435–1443 36. Sabate E (2003) World Health Organization report: adherence to long-term therapies. Evidence for action. World Health Organization 37. Schron EBED, Yao Q et al (2002) Quality of life in the antiarrhythmics versus implantable defibrillators trial: impact of therapy and influence of adverse symptoms and defibrillator shocks. Circulation 105:589–594 38. Shapiro PA (1990) Life after heart transplantation. Prog Cardiovasc Dis 32:405–418 39. Shapiro P (2007) Treatment of depression in patients with congestive heart failure. Heart Fail Rev 40. Shapiro PA, Kornfeld DS (1989) Psychiatric outcome of heart transplantation. Gen Hosp Psychiatry 11:352–357 41. Shapiro PA, Williams DL, Foray AT, Gelman IS, Wukich N et al (1995) Psychosocial evaluation and prediction of compliance problems and morbidity after heart transplantation. Transplantation 60:1462–1466 42. Shapiro PA, Levin HR, Oz MC (1996) Left ventricular assist devices. Psychosocial burden and implications for heart transplant programs. Gen Hosp Psychiatry 18:30S–35S 43. Stukas AADM, Switzer GE, DiMartini A, Kormos RL, Griffith BP (1999) PTSD in heart transplant recipients and their primary family caregiver. Psychosomatics 40:212–221 44. Sullivan MDLW, Crane BA, Russo JE, Spertus JA (2004) Usefulness of depression to predict time to combined end point of transplant or death for outpatients with advanced heart failure. Am J Cardiol 94:1577–1580 45. Surman OSPR (1992) Reevaluation of organ transplantation criteria: allocation of scarce resources to borderline candidates. Psychosomatics 33:202–212 46. Surman OSCA, Di Martini A (2009) Psychiatric care of patients undergoing organ transplantation. Transplantation 87:1753–1761 47. Terracciano CMML, Yacoub MH (2010) Contemporary use of ventricular assist devices. Annu Rev Med 61:255–270 48. Twillman RKMC, Wellisch DK, Wolcott DL (1993) The transplant evaluation rating scale: a revision of the psychosocial levels system for evaluating organ transplant candidates. Psychosomatics 34:144–153 49. UNOS. http://optn.transplant.hrsa.gov/latestData/rptData.asp 50. van den Brink RH, Honig A, Schene AH, Crijns HJ, Lambert FP, Ormel J (2002) Treatment of depression after myocardial infarction and the effects on cardiac prognosis and quality of life: rationale and outline of the myocardial INfarction and depression-intervention trial (MIND-IT). Am Heart J 144:219–225 51. Warrington TPBJ (2006) Psychiatric adverse effects of corticosteroids. Mayo Clin Proc 81:1361–1367 52. Yates WRBB, Reed DA et al (1993) Descriptive and predictive validity of a high-risk alcoholism relapse model. J Stud Alcohol 54:645–651

Measuring Behavioral Outcomes in Cardiac Rehabilitation

54

David E. Verrill

Contents AACVPR Outcomes Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Medication Adherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplemental Oxygen Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tobacco Use and Smoking Cessation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutritional Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual Participant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group of Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attendance Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual Participant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group of Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cardiac Disease Knowledge Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual Participant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group of Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exercise Compliance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual Patient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group of Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1295 1300 1302 1303 1305 1306 1306 1307 1308 1309 1309 1310 1311 1311 1313 1313 1314 1314

Abstract

Outcome measurement in cardiac rehabilitation (CR) is required for optimal assessment of program performance, quality, and effectiveness of treatments and evaluation of patient progress. The process of measuring, collecting, and analyzing patient outcomes has now become an integral component for CR D. E. Verrill (*) Department of Kinesiology, University of North Carolina at Charlotte, Charlotte, NC, USA Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, and University of Zurich, Zurich, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_54

1293

1294

D. E. Verrill

program operation and implementation. To meet current standards of practice, the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) developed an Outcomes Matrix that includes four primary domains: health, clinical, behavioral, and service. While the clinical and health domains have been the most commonly used in outcome reporting, behavioral measures have received less attention, primarily because they have been perceived as being more difficult to measure and quantify over time. This chapter describes five common behavioral outcome measures that may be utilized in CPR programs: smoking cessation, medication use, supplemental oxygen use, nutritional behaviors, and exercise habits. Sample questions and calculations for each of these behavioral measures are also presented and discussed. Through utilization of these measures at CR program entry and at each follow-up evaluation, CR practitioners may more effectively track and document behavioral changes over time for physicians, third-party insurance providers, and hospital administrators. These behavioral patient “markers” can provide evidence of the effectiveness of exercise and educational interventions for patient overall health and well-being. Keywords

Behavioral assessment · Cardiac rehabilitation · Compliance · AACVPR Outcomes Matrix · Patient outcomes

Cardiac rehabilitation (CR) is a comprehensive, multidisciplinary exercise, education, and behavioral modification program designed to improve the physical and emotional condition of those with cardiovascular diseases. The focus of CR is to decrease morbidity and mortality and improve a variety of clinical and behavioral outcomes, functional capacity, quality of life (QOL), and the ability to perform activities of daily living (ADL) more effectively [2, 5, 39, 42]. With current advances in medicine and a greater focus on positive lifestyle changes, survival of patients suffering an acute myocardial infarction (MI) has improved, resulting in more patients with coronary artery disease (CAD). Unfortunately, only a small percentage of these patients currently receive the optimal care and treatment throughout their continuum of care [2]. Thus, participation in a CR program is now more important than ever for the individual with CAD who has had or is at risk of having an untoward cardiovascular event. In 2004, the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) published a consensus statement on CR patient outcomes, with a specific focus on the behavioral, clinical, health, and service domain measures and ways to track these measures within the components of care of the AACVPR Outcomes Matrix [37]. In 2010, the American Heart Association (AHA), American College of Cardiology (ACC), and AACVPR presented an updated evidence-based scientific statement stressing the importance of documenting patient outcomes and program performance measures within the core components of care reflecting progress toward patient goals [42]. As a result of multiple clinical trials over many years of peer-reviewed research, a number of practice guidelines for CR and secondary

54

Measuring Behavioral Outcomes in Cardiac Rehabilitation

1295

Table 1 AACVPR Outcomes Committee definitions of the Outcomes Matrix domains* Health domain

Clinical domain

Behavioral domain

Service domain

Outcomes related to the global health of the patient, including occurrence, recurrence, or exacerbations of the primary disease or comorbid conditions, either fatal or nonfatal, and utilization of healthcare resources in response to these events. This domain also includes the patient’s perceived health-related quality of life, measured either through a generic or disease-specific instrument, which is an indication of the patient’s perception of global health and its impact on their day-to-day living Outcomes related to the clinical detection and evaluation of disease status and the effects of therapy. This domain includes tests for the diagnostic and prognostic evaluation of disease and its components, vital measurements, and laboratory values. It also includes the results of surveys, questionnaires, or other instruments to assess psychological, emotional, and cognitive function Outcomes related to the patient’s self-reported behaviors, self-efficacy (selfconfidence), knowledge, and compliance/adherence to medical and behavioral therapies and lifestyle modifications. These outcomes are typically measured through the use of standardized questionnaires or surveys, logs, and diaries and by observations of the cardiopulmonary rehabilitation (CPR) staff Outcomes related to overall program management and service delivery, including program usage statistics such as referral, enrollment, and completion rates. These are not typically patient-specific outcomes, but more properly reflect program processes and performance in patient management. Patient satisfaction is included in this domain as a measure of the program’s ability to meet patient’s needs and goals

*Adapted from references [2, 39, 45]

prevention programs with intervention recommendations have now been written and are currently used in daily practice [5, 17, 37, 39, 42, 45]. Each of these documents stresses the importance of monitoring patient outcomes throughout CR participation. Outcome measurement in each of the four domains (Table 1) [2, 37] is essential for optimal monitoring of individual patient improvement (or decline), quality assurance, accountability, and overall program effectiveness in CR. Behavioral outcomes have traditionally been difficult to measure in CR due to widespread variability in the definition of a behavioral outcome, interpretation of performance measures, and calculation of outcome parameters. In 2009, the AACVPR published a position statement on assessing behavioral outcomes in CR with a specific focus on five commonly used outcome measures [45]. The purpose of this chapter is to discuss how to assess and accurately measure behavioral outcome measures as presented in this American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) position statement. Other important and commonly assessed behavioral measures used in CR programs will also be discussed in this chapter.

AACVPR Outcomes Matrix Outcomes within the behavioral domain are individualized and reflect the cardiac patient’s self-reported behaviors, self-efficacy, knowledge, lifestyle modifications, and adherence to many medical and behavioral therapies. Table 2 presents the current

Exercise testing and training

Core components of care Overall management

Exercise testing 1. Maximal exercise test 2. Submaximal exercise test or functional assessment (e.g., 6-minute walk test) Resting, exercise, and recovery responses 1. Heart rate and rhythm 2. Blood pressure 3. Rating of perceived effort 4. Peak metabolic equivalents (METS) 5. Rating of perceived dyspnea 6. Oxygen saturation level

Clinical Risk factor profile Evaluation of symptoms Hemodynamic regulation Activity of daily living assessment

Table 2 The AACVPR Outcomes Matrix*

Exercise compliance 1. Supervised sessions 2. Home or outside sessions 3. Adherence to exercise prescription Energy expenditure 1. Minutes of physical activity per week 2. Calorie expenditure daily, weekly Physical activity stage of change

Behavioral Self-efficacy 1. Improved knowledge and application of self-care actions 2. Return to desired physical activity level 3. Desire to return to work Cardiac disease knowledge score Appropriate response to symptoms and complications Medication adherence, compliance Accessibility to needed resources Session attendance rate

Health Morbidity and mortality 1. Healthcare utilization: a. Hospitalizations, readmissions b. Emergency room visits c. Physician sick visits 2. Untoward events during supervised sessions 3. Health-related quality of life 4. Return to work, loss of work days

Service Patient satisfaction 1. Satisfaction with the care received 2. Progress toward goals Performance measures 1. Cost per patient 2. Program cost 3. Enrollment rate 4. Dropout rate 5. Completion rate 6. Admission rate

1296 D. E. Verrill

Psychosocial management

Nutrition and weight management

Diabetes management

Hypertension management

Lipid management

Strength and flexibility training

Strength measures (e.g., 1–5 RM, grip strength) Flexibility measures (e.g., sit-and-reach test, goniometer) Lipid levels Initiation of or adjustment in medication dosage Resting blood pressure Exercise and recovery blood pressures Initiation of or adjustment in antihypertension medication dosage Blood glucose levels HbA1c Initiation of or adjustment in hypoglycemic medication dosage Anthropometric measures 1. Height, weight, BMI 2. Body fat, lean body weight measures 3. Abdominal circumference 4. Sum of skinfolds, girths Nutritional biochemical markers, bone mineral density test Measurements of mood Depression, anxiety, hostility, emotional distress Coping mechanisms Stress management and relaxation skills

Adherence to diet, exercise, and medications Diet and exercise stage of change Adherence to diet, exercise, and medications Diet and exercise stage of change Self-monitoring behaviors Adherence to diet, exercise, and medications Diet and exercise stage of change Self-monitoring behaviors Adherence to diet and exercise Diet and exercise stage of change Diet recording logs Physical activity recording logs Diet habit scores

(continued)

54 Measuring Behavioral Outcomes in Cardiac Rehabilitation 1297

Behavioral Social support network Sexual dysfunction Smoking stage of change

Clinical

Measurements of cognitive function Memory, orientation, judgment Serum cotinine levels Exhaled carbon monoxide Number of cigarettes or cigars smoked per day Duration of smoking habit (pack/years)

Health

*Adapted from references1,4,6 Abbreviations: MET metabolic equivalent, RM repetitions maximum, HbA1c glycosylated hemoglobin, BMI body mass index

Smoking cessation

Core components of care

Table 2 (continued) Service

1298 D. E. Verrill

54

Measuring Behavioral Outcomes in Cardiac Rehabilitation

1299

AACVPR CR Outcomes Matrix [2]. The AACVPR Outcomes Committee first developed this matrix in 2004 [37], and it has since been modified to reflect current standards, position statements, guidelines, and policies [2, 5, 17, 37, 39, 42, 45]. This matrix conveniently and accurately divides most of the commonly measured outcomes into behavioral, clinical, health, and service domains. The clinical, behavioral, and health domains are based on Green’s Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation (PRECEDE) model [33]; http://www. enotes.com/precede-proceed-model-reference/precede-proceed-model). The service domain was created by the AACVPR as a category to measure patient satisfaction and service utilization [2]. Within each domain are outcome measures related to the core components of care in CR and secondary prevention programs [5, 17, 42]. Table 3 presents commonly measured behavioral domains in cardiopulmonary rehabilitation. Outcomes such as these are typically measured through the use of standardized and validated surveys, questionnaires, patient logs, or diaries and by staff observations. It is important that the behavioral outcome measure meets at least three criteria: (1) it must be significant to health and well-being, (2) it must be universally addressed in CR, and (3) it must be objectively measured [2, 33, 37]. This chapter will focus on seven commonly measured behavioral outcomes in CR: (1) medication adherence, (2) supplemental oxygen use, (3) smoking cessation, (4) nutritional habits, (5) CR session attendance rate, (6) knowledge of CR educational objectives, and (7) exercise habits. Measurement techniques for each of these measures will be described with simple formulae and calculations, most often performed upon entry into the CR program and again at regular follow-up intervals throughout CR participation (e.g., 3 months, 6 months, 1 year, 18 months, 2 years). Each assessment technique will be described and followed by specific examples of how to assess the given behavioral measure. It should be noted that much more research is needed to validate these various behavioral assessment techniques and that these are all presented in the context of measuring specific patient outcomes in CR programs based upon recommendations from the AACVPR and associated position stand papers. Table 3 Outcome measures in the behavioral domain* Adherence to the exercise prescription in the CPR facility and at home Adherence to proper supplemental oxygen use Adherence with diet instructions and healthy eating habits Adherence with post-CPR exercise instructions Appropriate response to symptoms and complications (during supervised exercise or at home) CPR session attendance rate Knowledge of CPR educational objectives Medication adherence and compliance Minutes of daily exercise and/or calories burned daily or weekly Success with smoking cessation efforts Success with relaxation, stress management, and coping techniques Technical effectiveness of energy conservation techniques Technical effectiveness of breathing retraining techniques Technical effectiveness of pacing techniques Adapted from reference6 CPR ¼ cardiopulmonary rehabilitation

1300

D. E. Verrill

Medication Adherence Management of chronic disease depends in large part on the patient’s adherence to their prescribed therapies, particularly their medications. Ideally, the patient should take the correct medication and dosage at the proper time. Nonadherence to medical therapy affects the interpretation of responses, impacts clinical decision-making, and negates the benefits on mortality and morbidity that the medications may confer [20]. The CR practitioner, as a member of the patient’s healthcare team, should regularly assess the patient’s medication adherence and knowledge. Assessment of compliance to medication therapy is a common component of the evaluation processes described in the core components and performance measures of CR programs [5, 42]. There are a number of objective methods for evaluating adherence to medical therapy. Electronic pharmacy monitoring, pill counts, and measurement of serum levels of drugs can be used but are impractical in the CR setting. Various standardized questionnaires are available, such as the Medication Adherence Report Scale (MARS) [25, 43] and the Drug Attitude Inventory (DAI) [21]. However, these are valid only in specific clinical populations and unfortunately have not been validated in CR participants. Information such as missed scheduled appointments and lack of responsiveness to an adequate dose of medication have been shown to be weak predictors of nonadherence to taking medications as prescribed [40]. The most useful method for assessing medication adherence may be simply by asking the patient directly to list the medications they are currently taking, the dose, and the purpose of each medication. To accomplish this, they may refer to a written copy of their medications, as many patients take multiple medications for a plethora of disease states. Questions about adherence should always be framed in a nonthreatening and nonjudgmental way. For example, the CR practitioner may say: “People often have difficulty taking their pills for one reason or another.” A conversation about adherence can then be initiated by asking the question, “Have you missed any pills in the past week” [19]? The outcome measure is the number of pills missed during the past week at the time of the assessment, with the ultimate goal of 100% compliance. One or more missed doses signal decreased adherence, which may warrant interventions at improving the patient’s medication compliance. One technique of assessing medication compliance is using a simple scale that denotes the percentage of time one’s medications are taken as prescribed, ranging from 0% (“Not taking as prescribed at any time”) to 100% (“Taking as prescribed 100% of the time”). Another example would be a 5-point Likert scale, where “0” ¼ never compliant, “1” ¼ rarely compliant, “2” ¼ sometimes compliant, “3” ¼ often compliant, and “4” ¼ very compliant [45]. To track changes in compliance over time, the practitioner divides the difference in scores from one assessment to the next by the initial score and then multiplies by 100 to obtain the percent change. This is illustrated in the example below:

54

Measuring Behavioral Outcomes in Cardiac Rehabilitation

1301

Formula: Medication Compliance ¼

Follow  up Score  Initial ðEntryÞ Score  100 Initial ðEntryÞ Score

Example: When asked about her medication compliance at CR entry, Sheena reported that she was “often compliant” with her daily medication usage (“3” on the aforementioned Likert scale). She was then asked the same question after 12 weeks of CR participation at her follow-up evaluation and stated that she was “very compliant” with her medication usage (“4” on the Likert scale). Calculation: 43  100 3 ¼ 33% improvement in her medication compliance score

Medication compliance ¼

Medication compliance can also be determined for a group of patients in a given time period. This can be assessed with the formula presented below: Formula: Medication Compliance ¼

Follow  up Group Score  Initial Group Score Initial ðEntryÞ Group Score  100

Example: In the Presbyterian Hospital CR program, 40 (of 60) patients reported that they were “often compliant” (“3” on the Likert scale) in taking their medications at program entry. These same 40 patients (the same cohort of patients must be used in this calculation) were then asked the above medication compliance question at their 16-week follow-up evaluation. Of these 40 patients, 29 rated their medication compliance as “very compliant” (“4” on the Likert scale). Calculation: Medication Compliance ¼

43  100 ¼ 25% groupimprovement in 29 patients 4

Thus, after 16 weeks of CPR intervention, 73% (29 of 40 patients) showed an improvement of 25% in medication compliance.

1302

D. E. Verrill

Nonadherence is generally defined as the patient adhering to their prescriptions less than 80% of the time. It should be noted that these types of subjective questions are open to the “halo effect” and recall, as patients tend to overestimate their actual adherence to medical or behavioral therapies [8]. Other ways of monitoring medication compliance likely exist, and the best method should be determined by the CR medical director and staff.

Supplemental Oxygen Usage More patients than ever before are now joining CR programs with comorbidities such as underlying chronic lung or neuromuscular diseases that require the use of supplemental oxygen during exercise. Common reasons for this include entering a CR program with chronic lung disease, having chronic lung disease and then having a cardiac event, or being diagnosed with lung disease while participating in a CR program. Supplemental oxygen utilization is a behavioral outcome measure characterized by the degree of compliance with proper oxygen use in patients who use oxygen during CR exercise sessions. Patients on oxygen should be frequently assessed for knowledge of its use, appropriate setup and delivery via mask or cannula, as well as the appropriate flow rates at rest and during exercise. It is not practical to track absolute oxygen usage or compliance across patients as a group in CR programs, as there are often only a few patients who use supplemental oxygen and oxygen utilization varies over time depending upon the physical condition of the patient. However, there are methods to track the patient’s compliance with supplemental oxygen usage to report to their referring physician and/or the CR program medical director. One example is by asking about the patient’s oxygen compliance at CR entry and again at program discharge or follow-up with questions such as: “On a scale of 0–4, how compliant are you with using your oxygen as prescribed by your physician?” The patient then states a number, which may be converted to a Likert scale such as 0 ¼ 0%, 1 ¼ 25%, 2 ¼ 50%, 3 ¼ 75%, and 4 ¼ nearly always. As this is a subjective estimate based upon the CR participant’s perception, observations by the CR staff may be more prudent in tracking this outcome properly. Examples of staff observations of oxygen usage are listed below [45]: 1. The Patient Uses Supplemental Oxygen Properly at All Times in the CR Setting. (Yes/No) If the answer is “No” to the above, the following two observations may be reported. 2. The patient uses supplemental oxygen properly upon arrival to the CR setting. (Yes/No). 3. The patient uses supplemental oxygen properly during or following exercise in the CR setting. (Yes/No).

54

Measuring Behavioral Outcomes in Cardiac Rehabilitation

1303

Examples of improper oxygen usage include not turning their oxygen on, adjusting to an inappropriate flow rate, improperly hooking up their nasal cannula, or improperly filling their liquid oxygen dispensers from reservoirs. Proper oxygen usage and compliance can be calculated as the number of times a patient uses supplemental oxygen properly as prescribed by their physician divided by the number of attended exercise sessions (illustrated in the example below) [45]: Formula: Percentage of time a patient was compliant with proper oxygen usage ¼

# of exercise visits patient used oxygen properly  100 total # of exercise visits by the patient

Example: Walter attended 32 CR sessions during the recording period of September 30 through December 31. During this time, he used his supplemental oxygen properly upon arrival into the CR program for 26 exercise sessions. Calculation: 26  100 32 ¼ 81% ðfor the 12-week periodÞ

Patient Oxygen Use Compliance Rate ¼

Tobacco Use and Smoking Cessation For CR participants, tobacco use often falls within a continuum of “stages.” Participants who enter a CR program are often in a “tobacco use transitional phase,” meaning that they have recently stopped using tobacco products (e.g., cigarettes, cigars, pipes, chewing tobacco, and/or dip) following hospitalization and are actively trying to quit. In order to track successful strategies in tobacco avoidance, it is recommended that CR practitioners use the following classification scheme adapted from recent definitions presented by the AHA, AACVPR, and ACC [2, 5, 39, 42]: 1. Never: The patient has never used tobacco products. 2. Former: The patient has not used tobacco products within the past 12 months. 3. Current: The patient is currently using tobacco products or has used tobacco products within the past 12 months. Those who are categorized as “Current” should be targeted for treatment with the goal of complete tobacco cessation by program discharge or by the next follow-up evaluation. Smoking recidivism is particularly high within the first year of quitting, hence the classification of those who have recently quit as “current” by national standards. A very useful source of validated tools and

1304

D. E. Verrill

techniques currently used for smoking cessation in clinical settings is the Clinical Practice Guideline: Treating Tobacco Use and Dependence [16]. Cigarettes smoked per day (packs/day) and the number of years of smoking (pack/years) have been the traditional clinical domain measures of tobacco use. Tobacco use can also be assessed through other clinical domain measures such as assessment of tobacco metabolites in the blood or urine (e.g., cotinine) or in exhaled breath. Presence of tobacco metabolites denotes current usage. From a behavioral perspective, tobacco use can be determined by asking the patient if they are currently using tobacco products. If so, specific questions can be asked with regard to the frequency and amount of tobacco use, and various calculations can be used to monitor smoking cessation success (presented below) [45]: 1. How many days each week are you currently smoking or using smokeless tobacco? 2. How many cigarettes, pipes of tobacco, cigars, or dips of smokeless tobacco do you currently smoke or use each day? 3. How many times each week are you exposed to second-hand tobacco smoke at home, at work, or in your social settings? Example: Questions 1–3 asked (pre-CR score) 1. 7 days 2. 40 cigarettes 3. 21 times

12-week follow-up answer (post- CR score) 1. 4 days 2. 20 cigarettes 3. 7 times

% change - 43% - 50% - 67%

24-week follow-up evaluation answer to questions 1–3 1. 1 day 2. 2 cigarettes 3. 2 times

% change (from entry) - 86% - 95% - 90%

Sample Calculation: #cigarettes=day at CR entry  #cigarettes=day at 12 weeks # of cigarettes=day at CR entry 100 40  20 ¼  100 40 ¼ 50% reduction in#of cigarettes smoked

Cigarette reduction ¼

Monitoring second-hand or environmental tobacco smoke exposure is also very important given the proven health risks of passive exposure to tobacco smoke. Environmental exposure to smoke should be assessed for every patient, and efforts should be made to reduce exposure and risk. Environmental smoke exposure includes unintentional exposure to smoke generated from cigarettes, cigars, or pipes and is defined below [45]:

54

Measuring Behavioral Outcomes in Cardiac Rehabilitation

1305

1. Current: The patient is currently exposed to environmental tobacco smoke at home, at work, or in recreational settings or has been exposed within the past 12 months. 2. Former: The patient has had former exposure to environmental tobacco smoke at home, at work, or in recreational settings. Those who have used tobacco should be targeted for additional support and intervention throughout CR participation. Ideally, tobacco use should be assessed at every CR or office visit, advising the patient to quit if he/she continues to use tobacco products, particularly during the first 2 weeks of cessation. It is important to remember the increased likelihood of relapse and all efforts to prevent relapse are warranted. The patient should also be assessed for his or her willingness to quit. A plan for quitting that incorporates relapse prevention should be developed with the CR therapist or healthcare provider to include follow-up, referral to special programs (e.g., acupuncture, group therapy, 1:1 intervention), or pharmacotherapy [16]. The patient should also be strongly advised to avoid situations with exposure to environmental tobacco smoke. Long-term goals include complete abstinence from use of tobacco products for at least 12 months from the quit date and no exposure to environmental tobacco smoke at work or at home [5].

Nutritional Assessment Nutritional assessment, including individualized anthropometric evaluation, is now of vital importance for CR practitioners given the current obesity and diabetes epidemics in America today and the multiple medical problems associated with obesity [1, 24, 27, 30, 32, 35, 46] and metabolic syndrome [6, 9, 26, 28]. Thus, it is prudent for CR staff to assess both nutritional habits in the behavioral domain and various anthropometric parameters in the clinical domain associated with increased cardiovascular and metabolic risk for participants upon entry into the program and again at program discharge or at each follow-up evaluation [44]. At a minimum, CR staff should (1) measure height and weight to calculate body mass index (BMI) and monitor weight changes over time, (2) measure waist circumference at the iliac crest, and (3) perform a comprehensive nutrition evaluation with counseling [2, 44]. As well-established benchmarks, patients in CR or secondary prevention programs should strive for a BMI of 18.5–24.9 kg/m2 and a waist circumference of 20% vs. 20%. Similar results have been found in a variety of other studies in different cardiac settings.

Effects of Cardiac Interventions on Quality of Life Prospective cardiac intervention trials using different medications, percutaneous interventions, or surgical procedures have repeatedly shown significant beneficial effects on quality of life. For example, Weintraub et al. [69] showed that in Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE), a randomized trial of percutaneous coronary interventions (PCI) plus optimal medication versus optimal medication only for coronary artery disease, there was a significant benefit on the occurrence of angina in the PCI group 1 month to 24 months after inclusion but no effect on survival. Quality of life improved significantly in both groups, but significantly larger improvements were observed in the PCI group 3 to 24 months after randomization. During this time interval, all scores on the SAQ and several dimensions

56

Quality of Life and Subjective Health: Strengthening the Subjective. . .

1355

of the SF-36 showed better quality of life in the PCI group. However, neither rates of freedom from angina nor quality of life scores were significantly different between groups 3 years after study inception. In the medication group, quality of life improved not only in the whole group but also in patients remaining on medication only, i.e., those who did not cross over to revascularization later on. However, those patients who crossed over to revascularization had worse SAQ scores at baseline. Further subgroup analyses showed that PCI effects were largest in the third with most frequent angina and not significant in the third with least frequent angina. In those with frequent angina, half of the effect was accounted for by regression to the mean. In the discussion the authors admit that due to the unblinded nature of the trial, a placebo effect could not be ruled out. Given the high power of placebo to affect anginal symptoms [5], this mechanism is quite likely to explain at least part of the results. In the first study addressing quality of life after coronary angioplasty, percutaneous transluminal coronary angioplasty (PTCA) vs. medication only, the Angioplasty Compared to Medicine (ACME) trial, Strauss et al. [63] also found significantly larger improvement in quality of life in a highly selected sample with single-vessel CHD receiving PTCA vs. medication only. Although the authors state that quality of life improved only in those PTCA patients with the highest improvement in postPTCA exercise test performance, this statement is misleading. Largest effects on overall quality of life and psychological well-being were actually observed in PTCA patients showing an intermediate increase in exercise endurance, while both those with smaller and larger improvements in exercise duration had worse quality of life. In medically treated patients, no associations were found between improved exercise test endurance and any of the quality of life scores. Quality of life improved significantly in patients with significantly reduced coronary stenoses but not in those with unchanged stenoses. However, the small group of patients with an increase in stenosis showed improvements of the same magnitude as patients with reduced stenosis (result nonsignificant due to small sample size). As in COURAGE, the proportional effect of improved coronary perfusion vs. placebo effects on quality of life remains unclear. Furthermore, no significant differences in the effects of PTCA vs. medication were observed in patients with double-vessel disease treated in the same study [22]. Data on quality of life after coronary bypass surgery are inconclusive [31]. Usually one finds a marked improvement in quality of life in surgical samples from before to some months to years after the procedure. However, it has been argued that anxious preoccupation with the impending operation leads to psychological distress and artificially low quality of life scores before the procedure and the postsurgery improvement in large parts mainly reflects a regression to levels experienced before the decision for surgery has been made [34]. Several studies have addressed the issue of quality of life in patients with implanted defibrillators (ICDs; [9, 60]). In the randomized Canadian Implantable Defibrillator Study (CIDS; [39]), quality of life clearly improved in patients receiving an ICD for secondary prevention of ventricular tachyarrhythmia, as compared to patients receiving amiodarone only. Effects were sustained up to 12 months after implantation. In contrast, the Multicenter Automatic Defibrillator Implantation Trial II (MADIT II; [50]), comparing ICD implantation with medication for primary

1356

C. Herrmann-Lingen

prevention of sudden cardiac death, found no beneficial effect of ICD implantation vs. medical treatment only on QALYs over a 3-year follow-up. While mean survival time was longer for ICD patients than controls, health utilities were lower in the ICD group, resulting in comparable QALY numbers. Impaired quality of life had also been found in the ICD group of the primary prevention coronary artery bypass grafting (CABG)-Patch trial [49]. In Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), a more recent primary prevention trial, ICD treatment vs. medication only was associated with transient improvement in some quality of life domains, while there was no difference between groups at the 30-month follow-up. There was no evidence of reduced quality of life in the ICD group, except in patients who had received a shock in the month preceding the assessment [45]. In patients with chronic heart failure, several treatments have been studied for their effects on quality of life. In a review of 11 randomized controlled trials of cardiac resynchronization therapy (CRT), McAlister et al. [46] reported a mean relative improvement in MLHFQ scores of 8 points (p < 0.0001; effect size 130 mm Hg systolic and 80 to 89 mm Hg diastolic and Stage 2 hypertension as >140/ 90 mm Hg. Normal BP is defined as 102 cm in men and > 88 cm in women) is used as a measure of central obesity and is included in the definition of the metabolic syndrome [61]. Some also advocate use of the waist-hip ratio of >0.95 and > 0.88 for men and women, respectively, as index of abdominal obesity. Results of multiple case-control or cross-sectional investigations have shown relations of obesity (and sometimes overweight) to lower levels of cognitive performance in non-demented, stroke-free cohorts ranging from children to older adults following adjustment for correlated risk factors such as hypertension and diabetes (for review see [121, 151]). Affected measures typically include executive function, in addition to learning and memory and speed of processing. A recent study

57

Cardiovascular Disease and Cognitive Function

1369

examining the moderating effects of age and education on the relation between BMI and cognition found age differences in cognitive performance increased in individuals with higher BMI [85]. Interactions with sex also have been examined. Examining participants from the Framingham Study [47], Elias and colleagues reported associations of obesity to executive function and memory in men only. These investigators further noted a significant cumulative effect of obesity and hypertension on several memory measures. Our group has reported significant interactions of BMI (or waist circumference) with BP level [172]. Those with higher BMI and BP showed diminished performance on tests of motor speed and manual dexterity and executive function (i.e., response inhibition). Prospective data indicated that midlife central obesity, in conjunction with hypertension, was associated with decreased executive function and visual memory 12 years later [191]. Yet, the relation between central obesity and cognitive function is diminished after adjustment for physical activity [38]. In contrast, Kuo et al. [88] found that overweight persons performed better than normal-weight persons on tests of reasoning and visuospatial speed of processing. Obese persons were also better than normal-weight individuals on the latter measure. Similarly, another study found an association between being overweight and better executive function in elderly adults [147]. Leanness has also been related to lower Mini-Mental State Examination (MMSE) scores in the elderly [133]. Sturman et al. [153] reported nonlinear associations of BMI to cognitive function. It has been posited that relations of lower BMI to lesser cognitive performance, particularly among older adults, may in part reflect weight loss that is apparent prior to the diagnosis of Alzheimer’s dementia. More generally, lower BMI may be associated with poorer cognitive outcomes among older adults (see [61]). Potential mechanisms linking obesity to the brain and cognition include metabolic, inflammatory, vascular, degenerative, and lifestyle (e.g., exercise) factors (see [61, 153]). A recent study examined potential mediators of the obesity and cognition relation in older adults and found that inflammation and elevated fasting plasma glucose, but not hypertriglyceridemia, partially mediated the relation [62]. Additionally, increased BMI or waist-hip ratio has been associated with temporal lobe, hippocampal, and subcortical atrophy, greater overall brain atrophy, greater white matter disease, and lesser white matter integrity [76, 122, 150, 181]. There is some suggestion that the frontal lobes may be particularly affected (see [61]), as well as recent work utilizing functional anisotropy showing lower structural integrity in a brain region connecting the frontal and temporal lobes [18]. Additionally, studies have also found a greater reduction of gray matter volumes in frontal and limbic structures in obese children [3]. Central obesity may also negatively affect the brain via neuroendocrine disturbances such as hypercortisolemia, low levels of sex steroid and growth hormones [15], as well as impacting the ghrelin system [201]. Both central and total obesity have been associated with other hormonal abnormalities such as hyperleptinemia (i.e., high serum levels of leptin – a hormone that plays a major role in fat metabolism), which has known central effects [123]. These hormonal abnormalities have been related to enhanced sympathetic nervous system activity [15, 123] that

1370

S. R. Waldstein et al.

may promote silent cerebrovascular disease [189, 190]. Both central and total obesity have also been associated with enhanced proinflammatory factors [11, 190]. Sweat [156] found that C-reactive protein was associated with decreased frontal lobe function among overweight or obese women (but not men). Obesity may also operate, in part, via correlated CV risk factors such as diabetes and the metabolic syndrome, a pattern characterized by glucose intolerance, insulin resistance, central adiposity, dyslipidemia (increased TG and decreased HDL-C), and hypertension.

Glucose Parameters and Diabetes CV diseases are highly comorbid with diabetes mellitus. Further, CV risk factors commonly aggregate into the metabolic syndrome. There is a strong association between the metabolic syndrome and atherosclerosis. The National Cholesterol Education Program Adult Treatment Panel III (ATP III) definition has been most commonly used [158]; metabolic syndrome is diagnosed when 3 of the following five risk factors are present: (1) fasting plasma glucose 100 mg/dL, (2) HDLC < 40 mg/dL in men or < 50 mg/dL in women, (3) triglycerides 150 mg/dL, (4) waist circumference  102 cm in men or  88 cm in women, and (5) BP  130 mm Hg systolic or 85 mm Hg diastolic or drug therapy for hypertension. Those with the metabolic syndrome are at increased risk of developing diabetes mellitus. Criteria for diagnosing diabetes mellitus include either a fasting glucose level higher than 126 mg/dL on two occasions, random (non-fasting) blood glucose level >200 mg/dL, or glucose level >200 mg/dl at 2 h during an oral glucose tolerance test [5]. Levels between 100 and 126 mg/dl are referred to as impaired fasting glucose or prediabetes. There are two main forms of diabetes mellitus: Type 1 is characterized by loss of the insulin-producing beta cells of the islets of Langerhans of the pancreas leading to a deficiency in insulin secretion. Type 2 is the most common type, accounting for >90% of all cases of diabetes mellitus, and is characterized by variable degrees of insulin deficiency and resistance. Relations of both Type 1 and Type 2 diabetes mellitus to lower levels of cognitive function are well documented (for reviews see [22, 132]). Type 1 diabetes has been associated with difficulties in attention, learning and memory, visuospatial abilities, executive function, and perceptuo-motor and motor speed. Problems with learning and memory are more prevalent in those with Type 2 versus Type 1 diabetes, whereas both disorders are associated with mental and motor slowing, as well as worse attention and executive function [132]. Age interactions suggest a greater impact of Type 2 diabetes on cognitive function in older- than middle-aged adults [132], and higher HbA1c has been negatively related with cognitive function in people with Type 2 diabetes [56]. Type 2 diabetes has also been associated with cognitive decline, with duration of disease being an important predictive factor [42]. The fluctuation of glucose levels in diabetics may also be linked to cognitive impairment. Interestingly, depression has been shown to accelerate cognitive decline in patients with Type 2 diabetes [154].

57

Cardiovascular Disease and Cognitive Function

1371

Outside the context of frank diabetes, investigations have shown relations of the metabolic syndrome to cognitive function, often using cognitive screening measures [157]. A review demonstrated that in adolescents, the majority of findings relating metabolic syndrome and cognition implicate executive functions, whereas in adults they are more widespread [195]. Impaired or increased fasting glucose have been associated with decreased memory performance [128]. Higher levels of insulin has been related to lower levels of cognitive function [44, 128, 152], and insulin resistance has been associated with cognitive decline [196]. Biological mechanisms linking diabetes to cognitive difficulties are thought to be largely independent of comorbid CV risk factors and diseases. As reviewed previously (see [22, 132]), chronic hyperglycemia may be associated with the development of advanced glycosylated end products – oxidation products that are found in senile plaques and neurofibrillary tangles characteristic of Alzheimer’s disease (AD) pathology. Hyperglycemia may increase aldose reductase activity and protein kinase C activity, each of which may negatively impact basic cellular and neuronal functions. Hyperinsulinemia is also thought to impact brain function perhaps via modulation of synaptic activity. Diabetes may alter blood-brain barrier structure and function [73] and has been associated with cortical brain atrophy, white matter disease, and silent brain infarction [22, 127, 132].

Inflammation Circulating biomarkers of systemic inflammation including C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor- α (TNF-α) have been extensively studied as correlates of CV disease. Among stroke- and dementia-free persons, higher levels of inflammatory biomarkers have been associated with lower levels of performance, particularly on tests of executive functions, working memory, and episodic memory (for review see [99]). Studies in various community-based samples of older adults have generally found that higher levels of inflammatory biomarkers predict greater decline in cognition (e.g., memory, psychomotor speed) [78, 116, 136, 145, 182, 193, 194]. Significant interactions between inflammation and other variables have been reported with regard to cognitive and brain outcomes, including moderating effects of the metabolic syndrome [194], APOE ε4 genotype [136], and age. In that regard, relative to younger individuals, older adults with higher IL-6 displayed worse white matter integrity [14]. primarily in those with the metabolic syndrome [194]. Further, relative to younger individuals, older adults with higher IL-6 displayed worse white matter integrity [14]. Lastly, higher levels of IL-6 and CRP were associated with greater cognitive decline among those with the APOE ε4 genotype [136]. Mechanistic underpinnings of inflammation-cognition. Mechanistic underpinnings of inflammation-cognition associations may include structural atrophic changes in the hippocampus and prefrontal cortex (see [99]). Higher IL-6 levels have further been associated with a greater likelihood of having MRI-defined brain infarcts or white matter hyperintensities [54]. Among dementia-

1372

S. R. Waldstein et al.

free elderly in the Rotterdam Scan Study, CRP was associated with both prevalent white matter lesions and progression of these lesions over 4 years, but not with prevalent or incident lacunar infarction [162].

Behavioral, Psychosocial, and Psychophysiological Risk Factors As discussed elsewhere in this volume, enhanced CV risk is conferred by a host of behavioral, psychosocial, and psychophysiological risk factors. Each of these factors may exert direct and indirect effects on neurocognitive and brain health through the promotion (or amelioration) of the above CV risk factors (and diseases). In that regard, select behavioral or lifestyle factors are detrimental to brain and cognitive health including smoking, excessive alcohol consumption, multiple elements of poor diet (e.g., high fat, high sugar, low dietary antioxidants), and inadequate physical activity for physical fitness. In contrast, moderate alcohol consumption, healthy diet, physical activity (even at moderate levels), and physical fitness may confer benefit to brain and cognitive health. For reviews see [32, 59, 94, 124, 162, 167, 169]. Psychosocial factors and psychiatric disorders, such as perceived stress, depression, and anxiety, have been associated with poorer brain health and cognitive function (see [52, 84, 97, 103, 104]). In addition, various dimensions of stress physiology, including autonomic nervous system indicators such as increased BP variability on ambulatory monitoring [164], greater stress-induced BP responses [119, 171], decreased heart rate variability [55], and higher levels of resting and reactive cortisol [102, 115], have been associated with poorer brain health and cognitive performance.

Summary The preceding sections cite evidence linking a variety of CV risk factors with lower levels of cognitive function and cognitive decline among largely stroke- and dementia-free samples. It is critical to remember the potent interrelations among these variables and that they may exert a cumulative negative impact on cognitive outcomes. For example, the cumulative negative impact of CV risk factors has been demonstrated in several studies using compilations based on the Framingham Stroke Risk Factor Profile (e.g., [102]). Variability is noted in terms of the domains of neurocognitive function most affected by different risk factors, and there remains limited information on effect modifiers that may confer vulnerability or resilience. We and others have discussed that, among persons without stroke or dementia, the effect sizes noted in studies of CV risk and cognitive function range from small to large, thus indicating heterogeneity of effects and the likelihood of effect modification. The clinical significance of the reduced levels of cognitive performance in relation to the CV risk factors has yet to be determined. However, we have suggested that even small or subtle differences that fall within the range of “normal” performance, such as the difference between above-average and average test scores, may

57

Cardiovascular Disease and Cognitive Function

1373

be perceived as significant at an individual level and could impact role or daily functioning [173, 174, 179]. This is an area in great need of investigation. We have further suggested that these subtle associations present the first manifestations of the impact of CV risk on the brain and cognitive function. Because these correlates are seen in children and young adults and because midlife risk factors predict late life cognition, it is critical to intervene aggressively with risk factor profiles early in the life course. Otherwise, CV risk factors tend to develop into CV diseases, which appear to have an even greater negative impact on cognitive function.

Cardiovascular Diseases and Neurocognitive Function A rapidly growing body of literature has examined relations of CV diseases to neurocognition. Here we briefly discuss select cardiac conduction disturbances, subclinical and clinical manifestations of CV disease, and heart failure. For discussion of associated lifestyle (e.g., dietary, exercise), medical (e.g., medications), and surgical (e.g., coronary artery bypass surgery, heart transplantation) interventions, see [169]).

Cardiac Arrhythmias and Cardiac Arrest The cardiac arrhythmias comprise disorders of heart rhythm. Two of the most common and clinically important cardiac arrhythmias – atrial fibrillation and ventricular fibrillation – have been studied in relation to cognitive function. During atrial fibrillation, the heart’s two upper chambers (the atria) beat chaotically and irregularly, and their contractions are not coordinated with the contractions of the ventricles. The irregular and often rapid heart rate compromises cardiac output and reduces systemic blood flow. Atrial fibrillation increases risk of developing blood clots that may lead to stroke. Ventricular fibrillation is the uncoordinated, often very rapid ineffective contractions of the ventricles caused by chaotic electrical impulses. In ventricular fibrillation, no blood is pumped from the heart, so it is a form of cardiac arrest that may be fatal unless treated immediately. Indeed, the overwhelming majority of sudden cardiac deaths are thought to result from ventricular fibrillation. Cross-sectional comparisons of patients with atrial fibrillation and those with normal sinus rhythm suggest decreased cognitive performance, particularly on tests of executive functions, learning and memory, and speed of processing (see [141]). Reviews further suggest both cognitive decline and cognitive impairment [74, 80]. Atrial fibrillation is thought to be related to cognitive dysfunction via correlated CV risk factors, cardiogenic brain embolism, cerebral hypoperfusion, cerebral microbleeds, cerebral small vessel disease, and inflammation [140]. The presence of silent brain infarction, mainly in cortical regions, is twice as likely among those with atrial fibrillation as those without [105]. Ventricular fibrillation has been studied in the context of resuscitated cardiac arrest. Early case studies suggested a potent negative impact of cardiac arrest on the

1374

S. R. Waldstein et al.

brain and neurocognition, with reports of isolated amnesia and extensive damage to the hippocampal regions presumably due to abrupt hypoxia and ischemia (see [105]). Further investigations confirm that cognitive deficits may be severe, but suggest that these deficits are not isolated to memory, but rather extend to motor and executive functions [95]. Important predictors of subsequent cognitive difficulties include delay in the start of CPR and the need for advanced cardiopulmonary life support [160]. Some recovery of function has been noted in the 3 months following cardiac arrest, but pronounced residual deficits may remain [12, 96]. The cognitive consequences of cardiac arrest have been attributed to diffuse and sudden ischemichypoxic injury [25]. Because these are typically persons with preexisting cardiac disease, the mechanisms discussed in prior and future CV risk factor and disease sections are also likely operative.

Subclinical Cardiovascular Disease Multiple subclinical disease states, including atherosclerosis, arterial stiffness, endothelial dysfunction, and left ventricular hypertrophy, have been linked with decrements in concurrent cognitive function and/or prospective cognitive decline. Here we examine relations of these factors to cognitive function above and beyond standard CV risk factors.

Atherosclerosis The most frequently studied indices of subclinical atherosclerosis are carotid intimal medial thickness (IMT) and plaque, a more advanced form of atherosclerotic disease. IMT, a measure of arterial wall thickness, has been used as a surrogate measure for generalized atherosclerotic disease [60, 134]. Overall, current evidence supports a cross-sectional association between carotid atherosclerosis and cognitive function across multiple population-based samples [9, 23, 26, 83, 101, 109, 144, 183, 200] and CV disease samples [30, 66]. Significant associations between increased carotid IMT and diminished cognitive function have been found across a number of cognitive domains, including global cognitive function, attention, psychomotor speed, verbal memory, nonverbal memory, language, verbal fluency, inductive reasoning, and mental flexibility. Recently, it was reported that these associations vary across race and socioeconomic status (SES) such that the disadvantage seems to be most pronounced among those with higher SES and white participants [187]. However, conclusions regarding the most affected cognitive domains are premature, given that each domain has not been examined with sufficient frequency. Longitudinal research has also generally linked carotid atherosclerosis with prospective cognitive decline. Several studies have identified longitudinal relations in population-based samples [63, 77, 87, 130, 146], but these associations were largely restricted to performance on brief cognitive screening measures such as the Modified Mini-Mental State Examination (3MS) and the Digit Symbol Substitution Test. Our group noted decline on several memory tests as a function of greater

57

Cardiovascular Disease and Cognitive Function

1375

carotid IMT [184], and among middle-aged adults in the Coronary Artery Risk Development in Young Adults Study, greater IMT has been associated with slower processing speed 5 years later [197]. The bulk of research seems to corroborate these inverse associations in cognitively normal, cognitively impaired, and clinical cardiovascular disease samples [7, 53, 135, 183], although domain specificity remains unclear.

Arterial Stiffness Two common markers of arterial stiffness, pulse pressure (PP) and pulse wave velocity (PWV), are considered indicators of subclinical CV disease [35, 93]. Pulse pressure, computed as the difference between systolic and diastolic BP values, is viewed as a surrogate marker of arterial disease, whereas PWV is regarded as a direct measure of arterial stiffness [93]. PWV is measured between two locations in the arterial tree; carotid and femoral peripheral sites are typically utilized to provide a measure of aortic stiffness. In an examination of participants in the Maine-Syracuse Study [125], greater PP was associated with lower levels of performance on a global composite of cognitive function and specific measures of verbal concept formation, attention, perceptuomotor speed, and visuo-constructional ability. In another more recent study, high PP was associated with diminished performance on a cognitive screening measure among older adult participants in the Third National Health and Nutrition Examination Survey [113]. However, in a group of very elderly (75+ years) Italians, higher PP was associated with better MMSE performance, suggesting possible age modification [114]. Cross-sectional evidence largely links high PWV with diminished cognitive function. PWV has been found to correlate inversely with MMSE performance among individuals referred for memory deficit [137], even among those without overt vascular disease [111]. Growing evidence also suggests an association between arterial stiffness and cognitive decline that is independent of BP level. Scuteri and colleagues [138] found an association between higher baseline PWV and MMSE decline among participants with memory complaints. Expanding upon these findings, Waldstein and colleagues [178] examined longitudinal relations of PP and PWV to multiple domains of cognitive function among non-demented, stroke-free participants in the Baltimore Longitudinal Study of Aging. Increasing levels of PP were significantly related to prospective decline on tests of verbal learning, nonverbal memory, working memory, and a cognitive screening measure over up to 11 years of follow-up. Similarly, persons with higher baseline PWV exhibited prospective decline on tests of verbal learning and delayed recall, nonverbal memory, and a cognitive screening measure. In contrast, an examination of Rotterdam Study participants failed to identify a significant association between PWV and cognitive decline over two time points [120]. A systematic review of arterial stiffness, brain characteristics, and cognitive function concluded that markers of arterial stiffness are indeed associated with concurrent and future cognitive dysfunction but that the literature is substantially limited by variable methodological approaches and heavy reliance on the MMSE [142]. A recent review demonstrated a relation between arterial stiffness

1376

S. R. Waldstein et al.

and cerebral small vessel disease and concurrently showed that this microvascular brain disease contributes to cognitive impairment [163].

Endothelial Dysfunction Endothelial function represents an important component of vascular health and contributes to the maintenance of vascular homeostasis [91]. Disruptions in vascular homeostasis, mediated by endothelial dysfunction, can precipitate atherogenesis and other harmful vascular events such as transient ischemia, plaque rupture, thrombosis, and infarction. Brachial artery flow-mediated dilatation (FMD), measured as the magnitude of arterial dilatation after an induction of forearm ischemia, is a commonly used marker of endothelial function [31]. Specifically, the temporarily high blood flow following forearm ischemia triggers the release of nitric oxide (NO), a powerful vasodilator. NO release is reduced in the presence of endothelial dysfunction, thereby resulting in a reduced brachial artery FMD. Lower values of brachial artery FMD thus indicate poorer endothelial function. Relatively little research has directly examined the relation between brachial artery FMD and cognitive function. A recent review concluded that lower FMD is associated with worse cognitive functioning, particularly executive functioning and working memory; however, relations were not found in other memory subdomains, such as visual spatial tasks, information processing, and language [112]. In a study of geriatric outpatients with CV disease, Cohen and colleagues [30] demonstrated consistent associations between decreased brachial artery FMD and lower levels of performance on measures of attention, executive function, and psychomotor speed. Consistent with the pattern of impairment typically observed in vascular disease, significant associations were not identified among other domains tested, including language ability, memory, and visual-spatial function. In the aforementioned study, brachial artery FMD was significantly associated with reduced whole brain volume, but not white matter disease. In contrast, in another sample of older adults with CV disease [72], brachial artery FMD was significantly associated with the latter, but not the former, index. Left Ventricular Hypertrophy Increased left ventricular mass, or left ventricular hypertrophy (LVH), can be assessed noninvasively via echocardiography. The extent of LVH is often, but not always, a reflection of the cumulative impact of another symptomatic or asymptomatic CV disease, such as hypertension, on the myocardium over time. Despite its recognition as a measure of subclinical CV disease among otherwise healthy individuals, limited research has examined LVH in relation to cognitive function. Across several studies, greater LV mass was related to lower information processing speed, lower scores on a cognitive screening measure, and poorer performance on tests assessing verbal concept formation, verbal memory, and visual-spatial memory and organization [48, 72, 139, 161]. For the latter study, these relations significantly attenuated following statistical adjustment for BP, treatment for hypertension, other vascular risk factors, and prevalent CV disease. Among elderly participants in the

57

Cardiovascular Disease and Cognitive Function

1377

Helsinki Aging Study, LVH was present more often in individuals with cognitive impairment or dementia than cognitively intact participants [79]. Furthermore, baseline LVH predicted decline in MMSE performance over 5-year follow-up. Lastly, a recent review noted prospective relations between LVH and risk for cognitive impairment [57].

Subclinical Vascular Disease Mechanisms Researchers have proposed a number of mechanisms through which subclinical CV disease may directly or indirectly affect cognitive function. The subclinical measures described above have been associated with various CV risk factors, including demographic, metabolic, immunologic, and lifestyle factors, which in turn have been associated with lower levels of cognitive function. However, relations of subclinical CV disease to cognition are unlikely to be due solely to these shared risk factors. Other potential mechanisms include a common genetic vulnerability, chronic cerebral hypoperfusion, micro- and macro-cerebrovascular disease, and other associated structural brain changes, such as cortical atrophy (see [134]). It should be noted that subclinical disease states often co-occur and may act additively or synergistically in the prediction of diminished cognitive function. The possibility thus exists that one subclinical disease may mediate another subclinical disease’s effects on the brain and cognition, potentially in combination with other mediating variables.

Coronary Heart Disease (CHD) Select manifestations of CHD include angina, acute coronary syndromes, and myocardial infarction (MI). Non-demented cardiac patients have been described as exhibiting dysfunction on tests of executive function, memory, fine motor control, and orientation, as well as executive function, and risk for cognitive impairment [10, 36, 39]. Interestingly, one study found that CHD was related to non-amnestic, but not amnestic, mild cognitive impairment [126]. Cardiac patients assessed prior to CABG surgery have displayed decreased word fluency, manual dexterity, verbal learning, and psychomotor speed, with performance similar to persons with carotid stenosis [165]. Others have similarly found cognitive impairment in pre-surgical coronary patients [71]. Prospective investigations have reported relations between several diagnoses of vascular disease such as CHD and MI with lower levels of future performance on cognitive screening measures (Elwood et al.; [143]). Mechanisms linking MI with cognitive dysfunction may include the presence of systemic vascular disease that leads to cardiac and cerebrovascular insufficiency, brain infarction due to cardiogenic embolism, acute or chronic hypoxia due to impaired myocardial function that leads to decreased cerebral perfusion, and postMI depression (see [165]). CHD has been associated with brain atrophy [1] and white matter lesions on MRI [24]. However, history of coronary artery disease has been associated with declines in global cognition, verbal memory, and executive

1378

S. R. Waldstein et al.

function, even after controlling for changes in white matter hyperintensities, silent brain infarcts, and hippocampal and cortical gray matter volume suggesting additional underlying mechanistic processes [199].

Peripheral Arterial Disease Peripheral arterial occlusive disease (PAD), a subtype of peripheral vascular disease (PVD), results from atherosclerosis of the arteries (i.e., abdominal aorta, iliac, femoral, popliteal, tibial) that supply the lower extremities. PAD is associated with comorbid atherosclerosis of the coronary and carotid arteries, and risk for atherosclerotic events such as MI, PAD, and stroke clusters among individuals [34, 82]. An early review by Phillips [118] concluded that patients with PVD displayed mild neuropsychological dysfunction or showed similar cognitive function as patients with carotid disease. In a comparison of PVD amputees and non-amputees with mild to moderate claudication to healthy control subjects and atherothrombotic stroke patients, Phillips et al. [175] found that PVD patients performed more poorly than healthy controls on tests of attention, psychomotor speed, executive function, visual memory, and visuospatial ability. Furthermore, the performance of the PVD patients was typically quite similar to that of the stroke patients. Our research group also demonstrated that PAD patients performed significantly more poorly than hypertensives and normotensives, but better than stroke patients, on seven tests of nonverbal memory, concentration, executive function, perceptuomotor speed, and manual dexterity [175], concluding that the findings suggested a continuum of cognitive impairment associated with increasingly severe manifestations of cardiovascular disease. In the population-based Rotterdam Study, Breteler, Claus, et al. [23] found that individuals having an ankle-brachial index (ABI) 16 AgeCen/Grade CES-Dcut>16/Grade

Coefficient 0.72 0.42 0.35 0.02 0.14 0.01 0.04

OR 0.49 0.66 1.42 0.99 0.87 0.99 0.96

*** *** **

*

1462

A. B. Zonderman et al.

Fig. 8 Change in probability of hypertension by age, educational attainment, and symptoms of depression.

Table 10 Data organization for mixed-effects regression analysis ID 901 901 901 902 902 903 903 903 903 903 904 904

Age 83.5 83.0 87.0 79.0 79.5 68.0 69.0 79.0 76.0 73.0 74.0 74.5

Sex Women Women Women Women Women Women Women Women Women Women Men Men

SBP 133 109 96 126 140 131 144 137 NA 124 127 127

DBP 72 78 71 69 64 78 77 78 NA 73 79 84

HTN 0 0 0 0 1 0 1 0 NA 0 0 0

MAP 92.33 88.33 79.33 88.00 89.33 95.67 99.33 97.67 NA 90.00 95.00 98.33

CES-D 15 31 NA 14 16 24 16 14 NA 23 22 14

Depress 16 >16 NA 16 16 >16 16 16 NA >16 >16 16

start of a clinical trial). We note that this manner of organization differs from historical methods in which all data for each participant occupied a single row and repeated measures were denoted in various ways such as ending each variable name with number (e.g., t1, t2, t3). Using the structure of the H-EPESE data from our examples (but not the actual data), an illustration might illuminate how we organized these data (Table 10). In this example, ID is the participant identification code; Age is age at time of measurement; Sex is a time-invariant measure; and systolic blood pressure (SBP), diastolic blood

60

Measuring Change

1463

pressure (DBP), Hyperten, MAP, CES-D, and Depress are time-varying measures of systolic and diastolic blood pressure, MAP, normotensive versus hypertensive status, CES-D score, and CES-D status below or above the cutoff score of 16. Note that the exact way these data are coded may depend on the program you use to analyze these data. However, the data organization is suitable for all programs about which we are aware.

Full Disclosure In this introductory chapter, we avoided discussions of some aspects of mixedeffects regression because they would have distracted from our central aims to provide a descriptive guide for measuring change. There are a variety of topics we omitted. Although we mentioned logistic mixed-effects regression briefly, we did not mention that it is one of a family of methods called generalized linear mixed models (GLMM). These methods include link functions for examining count data such as changes in numbers of symptoms over time, multinomial data such as various unordered categories such as changes in occupation, and ordinal data such as severity ratings. We refer the reader to the following material [30, 32]. We also omitted any discussion about modeling the pattern of correlations within individuals [22]. In the mixed-effects regression literature, this is usually referred to as the covariance structure or the correlations among repeated measures. We can make a variety of assumptions about these associations, or we can make no assumptions. The latter is called an unstructured covariance matrix. It assumes no particular pattern among the correlations but at the cost of fitting the most parameters. We used an unstructured structure in all of our examples. Other alternative covariance structures include diagonal, autoregressive, and compound symmetry [5]. A diagonal pattern assumes identical variances for measures regardless of when they are assessed and no association among measures. An autoregressive pattern assumes homogenous variances among repeated measures regardless of assessment time and correlations among measures that decline as a function of the time separating them. Compound symmetry assumes homogeneous variances and identical correlations regardless of how much time separates repeated measures. Most investigators assume an unstructured covariance matrix because it has no constraints on variances or covariances; it is the default structure for most software. We also omitted information about missing data, particularly the concept of missing at random. However, we noted a strength of mixed-effects regression is that it doesn’t require every participant to have data on every visit and that visits may be spaced irregularly. However, only measurements of occasions with data on all modelled variables are included in analyses. We did not present any of the pros or cons concerning imputing missing data. We also omitted any discussion about methods for calculating our results, particularly the distinction between maximum likelihood (ML) and restricted maximum likelihood (REML) estimation. We used REML in our examples; it is the default estimation method for most software.

1464

A. B. Zonderman et al.

We also did not recommend any particular software package for performing mixed-effects regression analyses. Although we can hardly resist recommending R [23] from which we produced all our results and graphs, other packages will likely yield satisfactory results. We used the lme4 [2] package to perform our analyses.

Final Word There is a large and expanding literature on mixed-effects regression. We are loathe to recommend particular volumes, but Singer and Willett [26] are one we’ve found that well balances descriptive exposition with technical detail. In addition, all of the examples in their book have been worked out in several statistical packages. They are available on a website supported by the University of California at Los Angeles [27].

References 1. Angel JL, Angel RJ, McClellan JL, Markides KS (1996) Nativity, declining health, and preferences in living arrangements among elderly Mexican Americans: implications for longterm care. Gerontologist 36(4):464–473 2. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48 3. Chakraborty H, Gu H (2009) A mixed model approach for intent-to- treat analysis in longitudinal clinical trials with missing values. RTI International, Research Triangle Park 4. Cnaan A, Laird NM, Slasor P (1997) Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med 16(20):2349–2380 5. Detry MA, Ma Y (2016) Analyzing repeated measurements using mixed models. JAMA 315(4):407–408 6. Fisher RA (1919) XV. – The Correlation between relatives on the supposition of Mendelian inheritance. Trans Roy Soc Edinb 52(2):399–433 7. Fitzmaurice GM, Davidian M, Verbeke G, Molenberghs G (2009) Longitudinal data analysis. Chapman & Hall/CRC handbooks of modern statistical methods. CRC Press, Boca Raton 8. Gibbons RD, Hedeker D, DuToit S (2010) Advances in analysis of longitudinal data. Annu Rev Clin Psychol 6:79–107 9. Gueorguieva R, Krystal JH (2004) Move over ANOVA: progress in analyzing repeatedmeasures data and its reflection in papers published in the Archives of General Psychiatry. Arch Gen Psychiatry 61(3):310–317 10. Gupta SK (2011) Intention-to-treat concept: a review. Perspect Clin Res 2(3):109–112. https:// doi.org/10.4103/2229-3485.83221 11. Hernan MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, Manson JE, Robins JM (2008) Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 19(6):766–779 12. Kleinbaum DG, Klein M, Pryor ER (2010) Logistic regression: a self-learning text. In: Statistics in the health sciences, 3rd edn. Springer, New York 13. Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38(4): 963–974 14. Lewinsohn PM, Seeley JR, Roberts RE, Allen NB (1997) Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging 12(2):277–287

60

Measuring Change

1465

15. Mallinckrodt CH, Clark WS, David SR (2001a) Type I error rates from mixed effects model repeated measures versus fixed effects Anova with missing values imputed via last observation carried forward. Drug Inf J 35(4):1215–1225 16. Mallinckrodt CH, Clark WS, David SR (2001b) Accounting for dropout bias using mixedeffects models. J Biopharm Stat 11(1–2):9–21 17. Manson JE, Hsia J, Johnson KC, Rossouw JE, Assaf AR, Lasser NL, Trevisan M, Black HR, Heckbert SR, Detrano R, Strickland OL, Wong ND, Crouse JR, Stein E, Cushman M, Women’s Health Initiative I (2003) Estrogen plus progestin and the risk of coronary heart disease. N Engl J Med 349(6):523–534 18. Markides KS (2009) Hispanic established populations for the epidemiologic studies of the elderly, 1993–1994: [Arizona, California, Colorado, New Mexico, and Texas]. Inter-university Consortium for Political and Social Research (ICPSR) [distributor]. https://doi.org/10.3886/ ICPSR02851.v2 19. Moher D, Schulz KF, Altman DG, Consort G (2001) The CONSORT statement: Revised recommendations for improving the quality of reports of parallel-group randomized trials. Ann Intern Med 134(8):657–662 20. Montori VM, Guyatt GH (2001) Intention-to-treat principle. Can Med Assoc J 165(10): 1339–1341 21. Newell DJ (1992) Intention-to-treat analysis: Implications for quantitative and qualitative research. Int J Epidemiol 21(5):837–841 22. Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-PLUS. Statistics and computing. Springer, New York 23. R Core Team (2017) R: a language and environment for statistical computing, 3.3.2 edn. R Foundation for Statistical Computing, Vienna 24. Radloff LS (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1(3):385–401 25. Raudenbush SW, Bryk AS (2002) Hierarchical linear models: applications and data analysis methods. Advanced quantitative techniques in the social sciences, vol 1, 2nd edn. Sage, Thousand Oaks 26. Singer JD, Willett JB (2003a) Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press, Oxford/New York 27. Singer JD, Willett JB (2003b) Textbook examples from applied longitudinal data analysis: modeling change and event occurrence. http://www.ats.ucla.edu/stat/examples/alda.htm. Accessed 05 Oct 2016 28. Sutin AR, Terracciano A, Milaneschi Y, An Y, Ferrucci L, Zonderman AB (2013) The trajectory of depressive symptoms across the adult life span. JAMA Psychiatr 70(8):803–811 29. West BT, Welch KB, Galecki AT (2007) Linear mixed models: a practical guide using statistical software. Chapman & Hall/CRC, Boca Raton 30. Wilson JR, Lorenz KA (2015) Modeling binary correlated responses using SAS, SPSS and R. ICSA Book series in statistics. Springer, New York 31. Woltman H, Feldstain A, MacKay JC, Rocchi M (2012) An introduction to hierarchical linear modeling. Tutor Quant Methods Psychol 8(1):52–69 32. Zuur AF (2009) Mixed effects models and extensions in ecology with R. Statistics for biology and health. Springer, New York

Confounding, Mediation, Moderation, and General Considerations in Regression Modeling

61

Michael A. Babyak and Laust Hvas Mortenson

Contents What is a Model and Why Use One? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Models and Causation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Confounding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Causal Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Including Variables to Increase Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mediation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total, Direct, and Indirect Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Why Mediation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Causal Knowledge as a Prerequisite for Mediation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How to Analyze Mediation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adjustment for Path-Specific Confounding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No Interaction Between Exposure and Mediator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decomposition of Total Effects and Indirect Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mediation and Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interactions and Moderation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Assumption of Homogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling Heterogeneity: Test Interactions, Not Within Groups . . . . . . . . . . . . . . . . . . . . . . . . . . An Important Aside: Preserve Measurement Information Wherever Possible . . . . . . . . . . . .

1469 1470 1471 1472 1474 1474 1474 1475 1477 1478 1479 1480 1480 1481 1482 1483 1483 1483 1484

M. A. Babyak (*) Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA e-mail: [email protected] L. H. Mortenson Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark e-mail: [email protected] © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_61

1467

1468

M. A. Babyak and L. H. Mortenson

Some Additional Considerations on Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample Size in Multivariable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing the Degrees of Freedom in a Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1485 1485 1487 1489 1489

Abstract

Although causation cannot be established solely by statistical methods, multivariable modeling can be a useful tool in illuminating hypotheses about causal processes. In the present chapter, we discuss several concepts that are vital to exploiting multivariable models for this purpose. After a brief overview of modeling in general, we present the concept of confounding, including a brief introduction to the graphic representation of causal hypotheses. From graphic models, we move on to a relatively extensive section on mediation, including some specific recommendations on conducting mediation tests, and further discussion of graphs. We then present a short section on testing interactions and subgroup analysis, followed by some final comments on sample size, variable selection, and the preservation of measurement forms.

Keywords

Multivariable modeling · Statistical confounding · Causal hypotheses · Statistical mediation · Statistical moderation

Multivariable regression and its variations are currently the most frequently used type of statistical technique in behavioral medicine research. A typical example of a multivariable model in our field might be a regression model that attempts to evaluate a set of psychosocial predictors of disease, such as heart failure. The terms “multivariable” and “multivariate” are often confused. “Multivariable” indicates that the model contains a single response (dependent) variable, in this case the marker of heart failure, and at least two predictor variables 50 in the model. “Multivariate,” in contrast, indicates the presence of at least two response variables. The measure indicating disease could be operationalized in a number of measurement forms: as a continuous variable, such as left ventricular ejection fraction; as two or more categories, such as the ordinal values of a symptom severity score; or even as the time elapsed between some defined occasion and the diagnosis of heart failure. The ostensible aim is to understand the “independent” association of each predictor variable with the response variable. In the most commonly used type of model, the regression coefficient or parameter estimate for a given predictor represents the association between that predictor and the response, adjusting for all other predictors in the model. We also might have more than one response variable, perhaps two separate indicators of heart failure, such as LVEF and a symptom severity score. We might simply conduct a separate regression analysis for each outcome, but also we might elect to use a model that contains the two response variables, referred to as a

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1469

multivariate model. Regardless of which model we choose, a number of important decisions must be made in developing the model. Foremost is the selection of the form of probability model that best suits the response variable(s) under study. Next, and often the most difficult part of the process, is to decide which predictors should be included in the model. For the vast majority of work we do in behavioral medicine, an important part of the variable selection process involves our presumed causal model. If we conduct a regression model to examine the association between, say, tobacco use and heart failure, we are more often than not proposing that tobacco use is a cause of heart failure. Upon proposing this model, we must immediately set about identifying potential confounders, that is, other variables that may threaten our causal conclusion. We also may be interested in variables that carry information about mechanisms that occur in between the act of smoking and the outcome of heart failure. Finally, we also may be concerned, or even believe a priori, that the association between a given predictor and the response may differ depending on the level of another variable. For example, tobacco use may be related to heart failure only for persons with a certain genotype. Of course, there are many additional considerations in conducting a multivariable regression analysis, including testing assumptions, proper scaling, or standardization of the predictors, perhaps centering, rescaling, or orthogonalizing predictors, to name a few. In the present chapter, our focus will be relatively narrow. After a few preliminaries, we will discuss (1) considerations in selecting predictor variables for a model; (2) modern approaches to mediation; (3) testing for moderation; and, finally, (4) the role of sample size in estimating regression models.

What is a Model and Why Use One? The statistical models we use in behavioral medicine typically take the general form of one or more “predictor” variables and one outcome or response variable, such as y ¼ b1x1 þ b2x2 þ b3x3 +. . . where y is the response variable, the xs are the predictor variables, and the bs are parameter estimates, also referred to as regression weights or coefficients, associated with their respective predictor variables. In the vast majority of modern modeling algorithms, the predictor variables can be of any measurement form, including continuous, categorical, and ordinal (there is no normality requirement for variables on the predictor side of an equation). A few words about nomenclature are appropriate here. Techniques such as analysis of variance (ANOVA) and analysis of covariance (ANCOVA) and multivariable (often referred to as “multiple”) regression have been almost entirely displaced by more general models (e.g., the general linear model and the generalized linear model). This transition has created a somewhat confusing amalgam of terminology from these older techniques. Variables on the x side of the equation are referred to interchangeably as independent variables; predictors; covariates; covariables; or, for variables measured as categories, factors. Variables on the y side are referred to as the response, outcome, or dependent variable.

1470

M. A. Babyak and L. H. Mortenson

Models per se tend to be preferred over traditional tests (e.g., t-tests, chi-square tests) nowadays for several reasons. First, models provide not only the same information as more conventional testing approaches, that is, whether the effect of interest is “statistically significant,” but also yield information about the size of the effect of interest, along with information about the uncertainty of the effect estimate, usually in the form of a confidence interval. For example, in a clinical trial, comparing a new blood pressure–lowering drug to a standard drug could legitimately be evaluated using a simple t-test that compares the treatment groups on mean blood pressure at the end of the trial. However, the t-test would provide no information on how big the difference was. A key advantage of multivariable models is that we can include so-called adjustment variables in addition to the primary variable or variable of interest. These adjustment variables can serve a variety of purposes in a multivariable model, and these purposes are at the heart of the remainder of this chapter. In modern practice, most of the earlier techniques, such as t-tests, chi-square tests, ANOVA, ANCOVA, and multiple regression, have been subsumed under the a few more general algorithms. The generalized linear model [35], for example, can contain one or more variables of virtually any measurement form on the predictor side, and the probability distribution of the dependent variable can take a variety of forms beyond normal. These include the binomial, negative binomial, and gamma distributions. Hence, multiple regression, logistic regression, Poisson regression, and many other conventional models can be estimated using the generalized linear model. For time-to-event data, the Cox regression model [13] is probably the most commonly used approach today, although parametric techniques also appear with relative frequency. In addition to general linear model, structural equation models (SEM) [38] have now been extended sufficiently such that they also can perform virtually all of the above functions. SEM also has the advantage of allowing so-called indirect relations to be estimated and tested, which we will discuss further in the section on mediation below. We note here that the above models represent only a portion of the types of models available at the time of this writing – the past decade or so has witnessed a virtual explosion of new statistical models.

Models and Causation Except for the relatively rare case where a regression model is used for completely blind empirical prediction, researchers typically use regression models to help understand something substantive about the phenomena under study. Regardless of whether we care to admit it or not, researchers are largely interested in using regression models to understand the cause. Why would we measure and model, for example, risk factors as predictors of cardiac disease if we were not interested in those risk factors as causes? If understanding causation underlies our models, most would agree that a useful model will include as many of the casually relevant variables in the system as possible. What makes a variable “relevant”? This question

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1471

has been of great interest and debate from many decades in the statistics literature and is often cast in terms of the problem of “variable selection.” We argue that relevance depends on the causal model underlying the analysis.

Confounding In the context of causal hypotheses, confounders represent a highly relevant type of variable. For a variety of reasons, we know that we should never be fooled into believing that an association between two variables is sufficient evidence for causation. It may be the case, for example, that the putative cause is confounded with another variable. The term confounding derives from the Latin confundere, to pour together or to mix [18]. At root, confounding is the mixing of the role of two predictor variables. Imagine you are at the bottom of a steep ravine looking up at a train trestle. Suddenly a very small boy goes running across the train trestle, followed shortly by a much larger boy who is shouting at the small boy. You conclude that the large boy is chasing the small boy; that is, the large boy is causing the small boy to run. However, shortly after the boys cross the trestle, a train comes barreling across the trestle behind them. In fact, the larger boy was not chasing the small boy at all; the train was causing both of them to run across the trestle quickly. The causal role of the large boy and the train was mixed up or confounded. The presence of the large boy was really just a red herring; he just happened to be running from the train too. In conducting research, we study one or just a few variables that are of particular interest in order to understand something about the causal relation between that variable and some outcome variable. A simple example in a research context is presented in a didactic paper by Rubin [47]. Rubin presents several large epidemiological studies that all seem to show that smoking tobacco in a pipe or cigarette is associated with a higher rate of cancer deaths than smoking tobacco in cigarette form. It is often helpful to draw a diagram of our causal hypothesis. This result is, of course, contrary to our understanding of the relative dangers of the types of tobacco delivery. So, we need to ask whether tobacco type (pipe/cigar vs. cigarette) might be confounded with some other variable. More formally, we consider the general criteria for confounding, which are as follows: (1) the confounding variable is presumed to be causally related to the predictor under study; (2) the confounding variable is presumed to be causally related to the outcome; (3) the confounding variable is either common cause or a proxy for a common cause of the predictor and the outcome. In our tobacco example, tobacco type is the predictor of interest, and cancer death is the outcome. What variable might be associated with the tobacco type and cancer death but is not in the causal chain between the two? One obvious candidate is age. Older people are more likely than younger people to smoke cigars or pipes and are also more likely to die of cancer. Although chronological age is clearly causally related to cancer death, age cannot be caused by the type of tobacco we smoke. In Rubin’s examples, in each of the samples, it was clear that pipe/cigar smokers were much older on average than cigarette smokers and that the death rate also was higher

1472

M. A. Babyak and L. H. Mortenson

among older individuals. When age was properly accounted for in the analysis, the death rate among pipe/cigar smokers was no longer higher than among cigarette smokers – in fact, it became lower. Thus the “effect” of tobacco type was confounded with age. In actual practice, confounding can be accounted for or tested using a number of techniques. The most typical approach is to add the putative confounding variable as a predictor in the regression equation that contains the hypothesized causal predictor. In the tobacco example, we would simply add the age variable to the equation in which tobacco type predicts the cancer outcome. If the regression weight for tobacco type becomes trivial or the confidence intervals become unacceptably wide (and if the above criteria for confounding are met), then we typically conclude that tobacco type was confounded with age. Of course, there are instances where the regression weight is reduced but still substantial, or where the confidence intervals are still relatively narrow, we might conclude that there is only partial confounding. Rubin also describes several alternative methods for addressing confounding, including an increasingly popular approach called propensity scoring. Very briefly, propensity scoring extends the concept of matching to minimize the influence of confounders. In the tobacco and cancer example, we would stratify the sample into age groups, say young and old; calculate the association between tobacco type and cancer within each group; and then pool the estimate. The within-group estimates are not distorted by the age differences between the tobacco types and produce a pooled estimate that has reduced confounding with age. The real power of propensity scoring is seen in more complex situations, where propensity scores are developed for a large number of potential confounders. We will not delve further into this area but suggest the Rubin paper [47] as an excellent starting point for studying propensity scoring. We should also add here that our discussion above has considered only classical confounding, sometimes called “positive” confounding. Other types of confounding exist, such as negative confounding, a type of suppressor effect, where adding the putative confounder actually increases the magnitude of the regression weight for the hypothesized causal predictor. More information about these other types of confounding can be found in Lynn [30].

Causal Graphs Causal models can be easier to comprehend if presented in graphic form. Often the graphs are used as an informal heuristic tool, and sometimes they are employed in a more formal way as (causal) directed acyclic graphs (DAGs) or to represent a structural equation model (SEM). Providing an introduction to graph theory is beyond the scope of this text, but a nontechnical introduction to causal DAGs can be found in Glymour et al. [19], and an overview of causal analysis in the context of mediation is provided by VanderWeele and Vansteelandt [54]. We will say much more about DAGs in the section on mediation below, but for now, we’ll introduce

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1473

just one basic element of DAG notation. Causal DAGs use single-headed arrows to represent the hypothesized causal direction between variables. Double-headed arrows, in contrast, represent associations with no specified causal direction. In the first DAG below, tobacco type and cancer mortality are associated but with no causal direction. In the second DAG, tobacco type is posited as the cause of cancer mortality.

The arrows in each of the figures are, at this point black boxes, representing a potential host of processes, not all of which are necessarily causal. Put differently, a raw or zero-order correlation or an unadjusted regression coefficient between two variables can be a function of a variety of different processes, some possibly causal, but many others that have nothing to do with the cause. In this case, the association between tobacco type and cancer was actually generated by the presence of a “third variable,” age, which was a common cause of both tobacco type (in that age captures the cultural cohort) and cancer mortality. This confounding is depicted below. When we use regression models to study the effect of one or a few putative causes of an outcome, we strive to identify and include other variables in the model that might confound the relations under study. A critical step in planning a study of virtually any design is considering carefully what variables might confound the relations under study and then being sure to measure those variables. This is particularly important when the design is observational where there is no randomization to control for confounding. By including confounding variables in the analysis of observational data, we may be at least a bit closer to being able to understand the cause. Considering potential confounders is also important in randomized experiments. Except in extremely large studies, perfect baseline balance is rarely achieved across randomized arms. When there is baseline imbalance in a randomized experiment, the treatment effect under study may be confounded with the variable that is not balanced. Unless the arms are substantially unbalanced, including potential confounding variables as adjustment variables in a model will effectively reduce the threat of confounding when interpreting the treatment effect.

1474

M. A. Babyak and L. H. Mortenson

Including Variables to Increase Precision Variables other than confounders may be relevant to the regression model. We also want our model to include predictors that are associated with the outcome, even if they are not associated with other predictors. In a linear model, such as multiple regression, including additional predictors in the model (within the limits of sample size, which we will discuss below), the precision of the parameter estimates is improved, and the power of the tests of the regression weights is improved. Intuitively, power is improved because additional predictors explain variance in the response and therefore reduce the magnitude of the error term by which the individual regression estimates are evaluated. For nonlinear models, such as logistic regression and Cox survival models, the picture is a bit more complicated. Adding additional variables will increase the standard errors for the parameter estimates, resulting in less power. However, the estimates will also always be larger. Simulation studies have shown that the benefit of the increased magnitude of the estimates outweighs the problem of larger standard errors [51]. Thus, when the sample size is large enough, including additional predictors is generally desirable.

Mediation In addition to addressing confounding and increasing precision, we also might include additional predictors in a model to study the possibility of mediation. Since the early paper on mediation by Baron and Kenny [5], analyses of mediation have become increasingly prevalent in the literature. Its importance has grown so much, in fact, that we have elected to devote a substantial section of this chapter to it. The notion of mediation is used to describe a scenario where a variable affects another variable through one or more intermediary variables. In the following sections, we will review some conceptual issues involved in mediation and discuss methods that can be used to statistically model mediation.

Total, Direct, and Indirect Effects We begin with a little orientation to the nomenclature of modern mediation analysis. Recall our graphic representation of a proposed causal association between two variables:

In this graph, the arrow pointing from X to Y indicates that the variable X affects the variable Y. We will refer to this as the total effect of X on Y. The total effect of X on Y depicted in Figure 1 may come about through any number of intermediary variables, but these can be left out when the objective is to describe the total effect. If there are intermediary variables between X and Y, as we noted earlier, the arrow from

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1475

X to Y in the above graph is a black box: We know the input (X) and the output (Y), but not the mechanisms responsible for creating the association.

In this graph, the variable X affects the variable Yand the variable M. Also, we can see that the variable M affects the variable Y. As a consequence, we can distinguish between two different kinds of effects of X on Y: a direct effect (X ! Y) and an indirect effect through the variable M (X ! M ! Y). The second graph suggests that there is both a direct and an indirect effect of X on Y. In other words, Figure 2 suggests that the variable M mediates some of the total effect of X on Y, but it also suggests that there is an effect of X on Y that does not involve M. It is important to note that the direct effect may in fact involve intermediary variable, just not the intermediary variable M, so the direct effect might more appropriately be termed the non-Mmediated effect as the direct effect can be thought of as the sum of all pathways from X to Y that does not involve the mediator M. Establishing the relative importance of the direct and indirect effect is often a primary concern in mediation analysis. Figure 2 also illustrates the difference between confounding and mediation: M is a mediator between X and Y because it lies on the pathway from X to Y. X is a confounder of the association between M and Y because X affects M and Y.

Why Mediation? Before elaborating further on the technique of mediation, it may prove fruitful to examine the motivation for looking at mediation in the first place: Why is mediation important to begin with? A recent paper by Hafeman and Schwartz [22] listed three reasons: to support the evidence of the main effect hypothesis, to examine the importance of path-specific mechanisms, and to provide targets for intervention. In 2005, a paper reported that women with a high level of perceived stress had a decreased risk of breast cancer [39]. This finding was quite surprising to many as high levels of stress had previously been shown to have detrimental effects on various health outcomes, so could it be that the findings were due to bias and confounding rather than a causal effect of perceived stress on the risk of breast cancer? In the discussion, the authors argue that the effect of stress was due to the fact that stress hormones suppress estrogen secretion, which lowers the risk of developing breast cancer. This pathway acts as a mediator between perceived stress and breast cancer. No information on estrogen levels where available in this study, but an analysis of the mediating role of estrogen would have improved the argument for a causal role of perceived stress in the development of breast cancer because it would have served to open the black box of how the exposure and outcome were connected. In fact, another research group had previously used this strategy to show that the association between BMI and breast cancer was mediated by serum estrogen levels [27].

1476

M. A. Babyak and L. H. Mortenson

Another use for mediation is to examine path-specific hypotheses. An association between low parental socioeconomic position and low offspring birth weight has been observed in many different populations and across different measures of socioeconomic position. A study by Mortensen et al. examined the role of two possible mediators of the relationship between maternal educational attainment and offspring birth weight in a cohort of women followed throughout pregnancy [37]. The two mediators were prepregnant body mass index (BMI) and smoking in the third trimester. Smoking in pregnancy and high BMI are more prevalent among mothers with short education, but these two factors have different effects on birth weight: A high BMI increases birth weight, while smoking decreases it. This means that these two pathways have opposite contributions to the total effect: if all mothers had the BMI of the highest-educated mothers, the educational differences would be larger because the higher prevalence of obesity among women with short educations increases their children’s birth weights. If all mothers smoked like the highesteducated mothers, mothers with a shorter education would in fact give birth to the heaviest babies because of the high prevalence of overweight and obesity among this group. The total effect of education (short education is associated with a lower birth weight) reflects that the birth-weight-reducing influence of the smoking pathway is stronger that the birth-weight-increasing BMI pathway. The example of Mortensen et al. shows that the examination of different pathways can increase our understanding of the total effects. For example, it suggests that the educational gradient in birth weight that has been observed in numerous studies might reverse once smoking among pregnant women is eliminated. It also underscores that mediation might be worth looking at, even in the absence of a total effect. This is because a lack of association between the exposure and the outcome might occur when different pathways that pull the total effect in opposite directions balance each other out. This is sometimes referred to as a suppressor effect. In this case, an analysis of the relevant mediators would help the investigator retrieve the pathway-specific effects of the exposure on the outcome. A third use of mediation is to improve and evaluate interventions. Mediation is in a certain sense an integrated part of the setup in all randomized controlled trials: The effect of randomization to treatment on the outcome is mediated by the treatment received.

The intention-to-treat analysis is a measure of the effect of randomization to intervention, regardless of the intervention actually received. In mediation terms, this corresponds to the total effect of randomization. The motivation for the intention-totreat analysis is that the results, because of the random assignment to intervention or control, are unconfounded by factors that affect the intervention received and the outcome, e.g., compliance to assigned treatment. However, the effect of the

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1477

intervention on the outcome is often the quantity of substantive interest, not the effect of randomization to intervention. If this is indeed the case, the intention-totreat analysis can be supplemented with analyses of mediation [28]. A similar use of mediation can be found in studies that use naturally occurring experiments rather than experiments under the investigator’s control. Mendelian randomization is a strategy for causal inference that uses genetic variants as proxies for potentially modifiable factors, obesity, for example [49]. In mendelian randomization, the effect of the gene on the outcome is mediated by the modifiable factors. There are special statistical methods (instrumental variable methods) that can be used to recover the effect of the modifiable factors in a way that potentially avoids many of the biases in observational studies. Another use of the concept of mediation in intervention studies is that of surrogate endpoint in randomized controlled trials, where the aim typically is to examine if an intervention has an effect on one or more clinical disease endpoints such as cancer or cardiovascular disease. In order to detect effects, clinical endpoint trials often require that a large number of participants are followed for a long time. Because of this, surrogate endpoints are often used [11]. Surrogate endpoints are biomarkers for disease progression and are as such mediators between the intervention and the clinical endpoints. For example, CD4 cell count can be used as a surrogate endpoint in HIV treatment trials and serum cholesterol levels as a surrogate of coronary heart disease. Because mediation allows the investigator to peak into the black box, it can also provide insight into why interventions might work or fail and thus guide future interventions. The paper by Mortensen et al. suggests that inventions that target smoking will likely reduce the educational gradient in birth weight, particularly if the intervention is successful among mothers with a short education. Such analyses of randomized trials might also provide clues as to what the “active ingredient” in a given intervention might be. Analyses of mediation are, however, not a free lunch: they come at the cost of a number of added assumptions.

Causal Knowledge as a Prerequisite for Mediation The attentive reader will have noticed that we used the term “affect” to describe the relationship between variables. This is because, as was the case for confounding, the notion of mediation makes little sense unless we have a causal model in mind. In the case of mediation, the variables involved must be known or at least proposed to be causally related in a way that is at least partly known to the investigator. For example, Boyle et al. reported that the association between hostility and mortality was partly mediated by a pattern of episodic excessive alcohol use (binge drinking) among hostile men [7]. If high hostility is the cause of binge-drinking use (i.e., hostility ! binge drinking), then the investigators’ conclusion is correct. Let us assume that (unknown to the investigators) binge drinking over time increases hostility. If binge drinking is the cause of hostility (i.e., binge drinking ! hostility), then alcohol use is not a mediator between hostility and mortality, but rather a

1478

M. A. Babyak and L. H. Mortenson

common cause of these two variables. If this was the case, binge drinking would act as a confounder of the association between hostility and mortality, not as a mediator. In order for analyses of mediation to make sense, assumptions about the nature of the relationships between variables are needed. This may at first seem like a rather strong requirement because it appears to force the investigator to make conclusions in advance about the relationships that are under investigation. However, causational direction of relationship cannot be extracted from data alone [12]. Investigators will usually get around this by relying to existing knowledge. In the example of Boyle et al., the prospective design will ensure that the outcome (mortality) occurs after the exposures are recorded. But the relationship between hostility and binge drinking is cross-sectional, so there is nothing in the design of the study to help the investigator decide about the direction of the relationship. Most studies carefully consider whether the exposure in fact causes the outcome. It is probably fair to say that in general less caution is exercised when it comes to making assumptions about the causal relationship between exposure and mediator. Nevertheless the analysis is conducted, and the findings will most often be interpreted as if the mediator is caused by the exposure. To this end, graphs are a helpful tool because they encode the investigator’s assumptions about the possible causal relationships between variables. Bearing this in mind, it may be fruitful to think of mediation in terms of (hypothetical) interventions: If we could somehow intervene and change the subjects’ hostility levels in a certain way, would we expect their alcohol use to decline? Would the association between hostility and mortality change if the investigators had forced everyone not to drink alcohol or forced everyone to binge drink once a week? Thinking of mediation in terms of possible interventions has the added advantage of providing a nontechnical interpretation of the outcome of the analysis (given that the analysis is conducted accordingly). Starting off with a vague question (“does alcohol mediate the association between hostility and mortality?”) may make it difficult to interpret the results. Just as important, it will also serve to make the, in many cases, highly hypothetical nature of the mediation analysis apparent [12, 14].

How to Analyze Mediation There are numerous ways to statistically model mediation (for a review, see [32]). In a much cited 1986 paper, Baron and Kenny stated that the objective of such an analysis was to “test for mediation” [5]. This led them to devise a method that was based on a significance test. However, it can be argued that the question of interest is not to determine if a given mediator is a statistically significant mediator, but rather to quantify how important the mediator is. This follows the general arguments against relying only on test of statistical significance in medical research [43, 50]. In the applied literature, one of two somewhat different modeling approaches to mediation is often used. One of these approaches is to use a structural equation model (SEM), and the other is to run a series of regressions to obtain and compare the total and direct (non-mediated) effect of the exposure on the outcome. This latter

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1479

approach, which is a simplified version of the method of Baron and Kenny, involves controlling for the mediator to estimate the direct effect [26]. In some cases, these two approaches will yield similar results; in other cases, the results will be different. The SEM approach has the advantage that the statistical model corresponds to the graphs typically used to conceptualize mediation so that every arrow in the graph is estimated as a parameter from one single model. SEMs are primarily used in the social sciences, whereas in the health sciences, SEMs appear to be the less popular choice. This is perhaps because SEMs are somewhat limited in the sense that they are an extension of linear regression, which is not always well suited for the kinds of data encountered in medicine. However, modern SEM theory (and modern SEM software) is relatively flexible with regard to finding models that fit most problems that involve mediation. A perhaps more important reason of the lack of popularity of SEMs for mediation analyses in the medical sciences is that most investigators and scientific journals in the health sciences will be familiar with multiple regression, but may not have experience with SEMs. In the following, we will concentrate on the mediator adjustment approach. For an example of an applied paper that uses both approaches, see Batty et al. [6] The mediator adjustment approach involves estimating the total effect and direct effect in two separate regressions. To estimate the total effect, we need to take account of confounders of the exposure outcome association. In the simple situation where there is no confounding, the total effect is simply the outcome regressed on the exposure. The direct effect is typically estimated as the association between exposure and outcome when conditioning on the mediator. Once we condition on the mediator, we get the controlled direct effect, the association between exposure and outcome. This is called a controlled effect because it corresponds to evaluating the association between exposure and outcome in a population where the mediator is forced by intervention at a certain level. In order for this to make sense, some conditions need to be met. We will discuss these conditions using the example of Boyle et al.

Adjustment for Path-Specific Confounding In addition to adjustment for confounders of the association between exposure and outcome, all confounders of the association between the mediator and the outcome have to be controlled for. Consider again the example of Boyle et al.

1480

M. A. Babyak and L. H. Mortenson

In this graph, early-life socioeconomic position (SEP) confounds the relationship between hostility and mortality because it affects both. It also confounds the association between binge drinking and mortality. The graphs thus suggest that we should adjust for early-life SEP when estimating the total effect and when estimating the direct effect. Suppose that unemployment affects binge drinking and mortality (loss of a job ! increased binge drinking, mortality) but that this variable is not affected by hostility (the dotted arrow does not exist). Then unemployment is not a confounder of the total effect, but acts as a confounder of the association between binge drinking and mortality. In this situation, the investigator needs to adjust for unemployment even though unemployment does not confound the total effect. If we fail to adjust for unemployment when estimating the direct effect of hostility on mortality, the results will generally be biased [12]. This is because we need to condition on binge drinking to estimate the non-binge-drinking-mediated effect of hostility on mortality: Among those who binge drink, unemployment will be more frequent, and mortality will be increased as a consequence. Suppose that highly hostile men tend to fight with colleagues and management and that they consequently are more likely to become unemployed (indicated by the dotted arrow). In this case, unemployment confounds the association between binge drinking and mortality. This suggests that we should condition on it when estimating the direct effect. However, if we control for unemployment, we eliminate the contribution of the hostility ! unemployment ! mortality pathway to the indirect effect. The problem arises because the dotted arrow contributes both to the direct effect (non-binge-drinking-mediated) and the indirect (binge-drinking-mediated) effect of hostility on mortality. This problem can be solved by resorting to a SEM or by applying special methods [42].

Measurement Error The mediator has to be measured without error. While mismeasurement is generally something that should be avoided, studies that aim to examine mediation should pay particular attention to measurement error. This is because even random error in the measurement of the mediator will bias both the direct effect and the indirect effect but in different directions. The actual direction and strength of this bias depend on the pattern of mismeasurement. For example, suppose that instead of measuring binge drinking in the study by Boyle et al., the investigators tossed a coin for each participant to determine whether he was a binge drinker. In this case, the direct effect would most likely be overestimated to the point that it would equal the total effect. SEM software usually has built-in features for handling measurement error, whereas some work is needed to take account of this in multiple regression [21].

No Interaction Between Exposure and Mediator The direct effect of the exposure on the outcome must not depend on at which particular value of the mediator variable it is assessed. In statistical terms, this can be

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1481

viewed as an assumption of no statistical interaction between the exposure and the mediator. For example, if the effect of hostility on mortality is stronger among those who binge drink than among those who do not, we can estimate two different controlled direct effects: one for binge drinkers and one of non-binge-drinkers. Unless there is a strong argument for at what value of the mediator the association between exposure and outcome should be evaluated, the controlled direct effect does not make sense in the presence of statistical interactions. This also relates to the difference between mediators and moderators. As discussed above, a mediator is a variable that lies in a causal pathway (e.g., hostility ! binge drinking ! mortality). Moderation has to do with how two (or more) variables alone and in combination affect a third variable. In statistical terms, this is an interaction. It is important to note that statistical interaction depends on the choice of scale: Two variables that do not interact on multiplicative scale (e.g., in a logistic regression) will interact on an additive scale (linear regression) and vice versa. Because interaction depends on the choice of effect measure, statistical interaction is often denoted effect measure modification in epidemiology [44]. The concepts of mediation and moderation are fundamentally different and not mutually exclusive so that a given variable can act as a mediator or as a moderator or as both. A discussion of interaction and moderation is given by Cole and Hernan [12].

Decomposition of Total Effects and Indirect Effects We have now examined how total effects and the controlled direct effects can be estimated given, but what about the indirect (mediated) effect? In an SEM context, the total effect can readily be decomposed into a direct and indirect effect, but this is more difficult when using the mediator adjustment approach. Intuitively it seems reasonable to assume that the total is the sum of the parts so that the indirect effect can the calculated by subtracting the direct effect from the total effect. This is the case in some situations, but in many situations, it is not. If linear regression is used to estimate the total and direct effect, this strategy works well, although the standard error of the indirect effect is not directly estimated. But often various kinds of nonlinear regression models are used. Studies that use logistic regression will often report the percent reduction in the odds ratio after adjustment for the mediator(s). Unfortunately, this strategy will generally not work [15, 26, 33]. The problem is that this approach assumes that the change in odds ratio from one logistic regression to another has a very specific interpretation. This trick works in linear models because a mixture of two linear regressions is a linear regression, but this is not generally the case in logistic regression, for example. It is worth noting that total and direct effects can be estimated from nonlinear regression, but the indirect effect cannot consistently be calculated by contrasting the total and direct effects. There are also other (nontechnical) reasons for resorting to a linear model. For example, it was long believed that traditional risk factors did not explain the social gradient in cardiovascular disease. This finding was predominantly supported by

1482

M. A. Babyak and L. H. Mortenson

studies that used the mediator adjustment approach in multiplicative, nonlinear models. This was something of a paradox insofar that research on the traditional risk factors suggested that these explained 90% of the cases. A landmark paper by Lynch et al. from 2003 showed that this apparent paradox was explained by the choice of relative measures of association. After adjustment for traditional cardiovascular risk factors (the mediators), the relative differences decreased by about 25%. The absolute risk differences, however, were reduced by about 75% [29]. This highlights an inherent problem in calculating a relative change in a relative measure.

Mediation and Interaction As noted above, the problem of assessing mediation in the presence of statistical interactions is exacerbated by the fact that statistical interactions are dependent on the choice of scale. This means that the choices of statistical model and measure of association will in part determine whether mediation is a tractable problem, which is both impractical and conceptually unsatisfying. One solution to this problem is to take a close look at the relationship between the exposure and the mediator. Recall that the controlled direct effect is estimated by fixing the mediator at some value, for example, eradicating all binge drinking. For many real-life problems, it is difficult to imagine scenarios where forcing the mediator to attain a particular value is possible. But is this is quite different from what we would expect the exposure to do to the mediator? If we could somehow manipulate the exposure by intervention, a reasonable expectation would be that the distribution of the mediator would shift from the distribution it had under no exposure to the distribution it has when the exposure is present. Consider the pathway perceived stress ! estrogen ! breast cancer from Nielsen et al. If we somehow intervened and eliminated all perceived stress, we would expect the subjects’ levels of serum estrogen to increase. This would result in a shift in the distribution of estrogen. So, instead of fixing the mediator at certain level, we can then calculate the direct effect when the mediator has a certain distribution. If we wanted to estimate the direct (non-estrogen-mediated) effect of perceived stress on breast cancer, we could use this information. For example, we could evaluate the association between perceived stress under the distribution that estrogen has under no exposure to perceived stress instead of evaluating it in an analysis where we fixed everyone’s estrogen levels to attain the exact same value. This leads to the estimation of a natural direct effect. This is done using simple standardization techniques such as those used to calculate standardized rates. It is important to note that using natural direct effects will yield results that are identical to controlled direct effect unless there is statistical interaction between exposure and outcome, but even in this case, the concept does add value: Not only does the concept of natural effects provide a definition of direct effects in the presence of interaction, they also lead to a definition of indirect effects. A natural indirect effect can be defined as the change in outcome when the exposure is fixed and the distribution of the mediator is changed. The reader is referred elsewhere for a comprehensive review of natural effects [40, 42, 54].

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1483

Interactions and Moderation The Assumption of Homogeneity We already have discussed the importance of interaction in the process of carrying mediational tests. We now consider the idea of interaction in more general application. An assumption that all regression model makes is that the effect of a given variable is homogeneous across all levels of all other variables, irrespective of whether those variables are measured and included in the model or not. For example, if we estimate a model in which depression is a predictor of cardiac disease, we implicitly make the assumption that the association between depression and disease is the same (within sampling error) for men and women, the old and the young, across ethnicities, genotypes, etc. Even if these other variables were measured and included in the model as adjustment covariables, such a model would not yield any information about this possible heterogeneity. There are several ways we might tackle this question. One intuitively appealing method would be to divide the sample into subgroups and evaluate the regression coefficient within each of the groups. For example, we might divide our sample based on gender and estimate the relation between depression and cardiac disease separately for me and women. Subgroup tests, however, are highly controversial and generally discouraged by statisticians for a number of reasons [3]. Among the objections to subgroup testing, the most important are the inflated error rate, the differential power of the tests, and the increased imprecision of the parameter estimates due to the smaller sample sizes. Conducting many tests of any kind inflates the type I error rate. In the case of subgroup tests, of course, all the parameters in a given model are re-estimated within each subgroup, creating a whole host of new opportunities for capitalizing on the idiosyncrasies of sample, with the added disadvantage of conducting those tests on fewer data points! Correction for multiple testing in these cases can be of some help, but unless the study was designed specifically for the subgroup test, the power can and usually will be quite different for different subgroups. Hence, some subgroup tests will have more power than others, making it virtually impossible to manage the error rate coherently. If subgroup tests are of interest, the sampling plan must take them into account before the study is carried out to ensure adequate and consistent power across them. The inferences from pre-planned subgroup analyses are, of course, more robust than those which arose from post hoc analyses. If the design did not take these tests into account, subgroup analyses should either not be conducted at all or be interpreted as highly preliminary.

Modeling Heterogeneity: Test Interactions, Not Within Groups Finally, if we are interested in studying heterogeneity of associations, the preferred approach is to test the corresponding interaction term rather to examine subgroups separately [1, 2]. There are also Bayesian methods, which may overcome some of the problems with conventional subgroup analyses (see [16, 48]). For example, if

1484

M. A. Babyak and L. H. Mortenson

one is interested in whether a treatment is more effective in one ethnic group than another, the proper test is a treatment group by ethnicity interaction term. In a multivariable model setting, when more than one interaction term is of interest, the error rate can be minimized by entering all the interaction terms of interest in the model as a block simultaneously and testing the change in model fit associated with the block [10, 23]. If the test of the entire block is not significant, then the individual interaction terms are interpreted as inconclusive or noise. If the null hypothesis for the interaction is rejected (or, as some would argue, if the effect size for the interaction looks potentially important), estimates of within-group effects should be generated using the predicted values from the full sample model; separating the sample into subgroups and performing the analysis on the separate samples can be useful for a quick, intuitive look at the data but has undesirable properties with regard to standard errors and precision. I add a reminder here that in most statistical models nowadays, all lower-order component terms must be included in the model with a higher order terms such as interaction. For example, if we are testing a treatment group by ethnicity interaction, we also must include the treatment group and ethnicity main effects – otherwise, the interaction term is not really interpretable as an interaction in the conventional sense of the concept.

An Important Aside: Preserve Measurement Information Wherever Possible On a final note, when testing interactions, one might be tempted to create dichotomies or groups out of continuously measured variables. Researchers also make artificial categories for other reasons, such as ease of interpretation, evaluating nonlinearity, to parallel clinical cut points, or even in the belief that the grouping somehow improves measurement precision. Indeed, creating groups out of continuous variables has a long history in psychology, medicine, and epidemiology. What many modern researchers fail to realize, however, is that this tradition arose strictly out of necessity. In the early days of modern statistical practice, it was apparently well understood that the practice of grouping was less than ideal, but there was little choice given the lack of computational power. With the availability of ample computational power, modern authorities in methodology have repeatedly discouraged researchers from adopting this practice [9, 24, 31, 46]. Compared to the categorized version of a variable, using the continuous form yields substantially greater statistical power [9]; is less likely to produce spurious significance [34]; and, from a measurement perspective, is a more reliable instantiation of the variable under study [24]. The much-preferred alternative to categorizing is to model the continuous variable as measured. If nonlinearity is a concern, techniques such as restricted cubic splines [23] or fractional polynomials [45] will allow for a nonlinear association without discarding information or making arbitrary cut points. Despite the overwhelming evidence of the inadequacy of the categorization approach, a quick glance at many scientific journals suggests that the force of tradition is apparently quite strong. We once again appeal to readers to avoid this fundamental error in data analysis.

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1485

Some Additional Considerations on Regression Models Sample Size in Multivariable Models We now turn to the last few concepts that bear directly on the above material in terms of producing replicable models. Earlier, we alluded to the idea that although it is a good idea to include potential confounders and additional predictors of the response in a model, the number we can include in a model and still obtain reproducible results is determined by the sample size we have to work with. Before the advent of simulation studies, statisticians often offered rules of thumb based on their experience. One well-known rule of thumb for linear regression models is that there should be at least ten, preferably 15, cases for every degree of freedom used in estimating the equation. Typically, each predictor uses one degree of freedom. For example, if we want to study ten predictors with no interactions or curvilinear terms, we should have at least 100 observations in our sample. Perhaps it is not a surprise, but modern simulation studies have tended to support this rule of thumb, demonstrating empirically that following this guideline will result in a regression model that is more likely to replicate in new samples. There are also rules of thumb that have been empirically tested for logistic regression models and also survival models such as Cox regression. The rules of thumb for logistic and time-to-event models are similar to that for linear regression, about 10–15 observations per predictor. However, there is an important difference in how the number of observations is counted in the logistic and time-to-event models. In these models, the number of observations is based on something called the effective sample size. The effective sample size for a time-to-event regression model is simply the number of events. So, if there are 1000 participants in a study and only ten of them sustain the event being studied, the effective sample size is ten. For logistic regression models, in which the outcome is a binary variable, the effective sample size is the count of events or nonevents, whichever is the smaller number of the two. For example, if there are 200 individuals in the sample and 20 had an event, the effective sample size is 20, not 200, and at best two variables can be studied with reasonable confidence. If there were 180 events rather than 20, the effective sample size would still be 20. In more technical parlance, the number of cases in a logistic regression model with a binary response is min (q, nq), where min represents “the minimum of the following quantities,” q is the number of events, and n is the total sample size. Finally, for ordinal logistic regression models, that is, models with more than two ordered category as the response, the effective sample size is given by n

k 1 X 3 n n2 i¼1 i

where n is the sample size and k is the number of response categories [23]. What are the consequences of studying more variables than the guidelines suggest? Perhaps the most serious consequence of trying to squeeze too many variables in a model is overfitting. Overfitting is a condition in which the

1486

M. A. Babyak and L. H. Mortenson

Fig. 1 Results of a simulation using automated stepwise regression with 15-candidate predictor variables. In the true model, predictors were randomly generated and therefore unrelated to the response variable, meaning that the true R-squared was zero. The ratio of predictors to sample size was then manipulated by altering the sample size. The frequency of falsely high R-squared increases as the sample size to predictors ratio decreases

idiosyncrasies of the sample lead to an overly optimistic overall fit of the model. Intuitively, we might say that there is simply not enough information (in terms of observations) to distinguish noise from true signal. The fewer observations per degree of freedom in a model, the more likely the model will be overfit. Overfitting is discussed in greater detail in Babyak [4] and Steyerberg [51]. Figure 1 displays the results of a series of simulations carried out by Babyak [4]. The plot shows the distribution of model R-squared values for various levels of predictors/observations for a model with ten predictors whose values are merely randomly generated, i.e., are pure noise. Because the predictor values are randomly generated, the “true” model should have an R-squared value of zero, with any nonzero R-squared arising simply due to random sampling fluctuation. The plot demonstrates that when there are relatively many observations per predictor, the vast majority of R-squared values are zero or very close to zero. However, as the predictor/observation ratio becomes smaller, the typical R-squared values become larger and more varied, with some even reflecting a fairly large amount of variance explained. In addition to generating overly optimistic model fit, having too few observations per predictor also results in bias in the estimates for the individual parameters. Peduzzi et al. [41] showed in a series of simulations that an inadequate predictors/observations ratio also leads to serious bias in the estimates of the regression coefficients in logistic regression and time-to-event models. Some have argued that in the case of models in which we are interested in a single predictor and are merely concerned about ruling out confounding, fewer variables per predictor may be required. Vittinghoff and McCulloch [55] have argued that in this circumstance, perhaps as few as five events/cases per predictor may be sufficient, but the authors also show that under some circumstances, even more than 15 per predictor

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1487

may not be enough. Perhaps the most prudent advice is that more is always better when it comes to sample size and that when there are relatively fewer cases than the guidelines suggest, interpret such results with great caution.

Reducing the Degrees of Freedom in a Model If you are confronted with a situation in which you wish to study more variables than the sample size allows, what are the alternatives? A popular approach in the past has been to use automated “stepwise” methods. There are actually a variety of these techniques, but they are typically characterized by sequentially entering and removing variables based on the correlations and partial correlations between the predictors and response variable until some arbitrary criterion is met. For example, in forward stepwise selection, the algorithm scans the correlations between the predictors and response variable and selects the predictor with the largest correlation with the response. In the next step, the correlations between the remaining candidate predictors and the response are partialled for the effect of the first variable that was chosen, and the algorithm selects the largest of these partialled correlations. The process continues until some predetermined measure of fit is achieved. Unfortunately, these algorithms have been subsequently shown to be significantly flawed in terms of inference. They do generate models that will fit the sample data well, but they are almost certain to not produce a replicable model. That is, when we compare the fit of the model and the parameter estimates from the stepwise model to a model based on a new sample, not much will be the same. Intuitively, the overly optimistic fit can be understood as a function of the fact that we have tested many variables and that by chance alone (i.e., random sampling fluctuation), we are bound to find at least a few, and sometimes even many, predictor variables that display a nontrivial association with the response variable. On the other side of the same coin, the automated algorithm will also miss potentially important variables, again due to sampling error, yielding a model with parameter estimates that may not be appropriately adjusted, i.e., a misspecified model. Further problems arise with automated algorithms when there are correlations among the candidate predictor variables. In these instances, the choice to select one or the other by the algorithm can be quite arbitrary. Not surprisingly, in recent years, the use of automated stepwise methods has been almost uniformly discouraged by statisticians. Several journals, in fact, will not accept papers that are based on conventional stepwise analyses [17, 52]. A commonly used alternative to stepwise selection is univariate prescreening of variables. In this approach, the researcher evaluates the univariate relation between each predictor and the response variable and selects those which are statistically significant for entry in a final regression model. Unfortunately, this technique suffers from essentially the same and at times worse shortcomings than seen in the automated stepwise algorithms. The fit is again biased toward being too good, because we are selecting predictors whose parameters are of the largest magnitude without accounting for the possibility that the magnitude of the predictor is also influenced by random sampling error. Steyerberg [51] calls selection based on

1488

M. A. Babyak and L. H. Mortenson

p-values “testimation bias.” As a more general principle, using the sample data to determine what to include in a model will produce fit that may be too good and parameters that are too large. A further difficulty with univariate prescreening is that variables behave differently in univariate setting compared to a multivariate model. It is entirely possible, for example, for a potential predictor to look quite uninteresting in a univariate setting and then come to life when partialled for other variables. Arguably the best alternative to automated techniques and prescreening is to specify the model in its entirety before even collecting the data. A prespecified model is preferable for a number of reasons. First and foremost, it requires a thoughtful consideration of the phenomenon under study before collecting the data. Second, it is transparent. There is no doubt as to whether other variables were considered but just not reported. Finally, the p-values for the fit of the model and for the parameters will be “honest.” In other words, once predictors are tested either during pretesting or some other selection process and discarded, the tests of the model with the remaining variables, as well as the test of model fit, will be too optimistic (for a simulation study demonstrating this principle, see [8]. Sometimes, of course, it is not possible or even desirable to have a single prespecified model. We simply may not know quite enough about the entire system of variables we are studying, or perhaps collecting some of the data is expensive, and we want to cull as many of the non-important variables out of the equation. There are a variety of approaches that will either allow us to include more variables that the rules of thumb suggest or that will remove extraneous variables with the correct adjustment. The simplest technique for reducing degrees of freedom is to combine predictors in some rational way. Combining is useful when there are variables that are acting solely as nuisance or adjustment variables for which we are not particularly interested in their individual regression coefficients, but still want the information they provide to be included in the model. We can simply create a composite score from two or more variables, by summing their ranks or converting the variables to standardized scores and summing them. Alternatively, we can use a clustering technique such as principal components or common factor analysis to develop a composite that captures the information in the variables. The resulting composite that we create is then used instead of the individual variables in the model. More details on these approaches are available in Harrell [23]. More sophisticated methods for automated model selection have been developed recently and are now becoming more widely available in popular software packages. The techniques include the lasso and least-angle regression approaches developed by Tibshirani [53], Bayesian model averaging [25], and the use of penalization [36] or random effects [20]. The details of these techniques are beyond the scope of this chapter, but they do show some promise in terms of allowing an algorithm to make reasonable selections of variables while accounting for uncertainty. Because these approaches properly correct for capitalizing on the idiosyncrasies of the sample, however, many researchers may be quite displeased with the failure to find “significant” results. Nevertheless, these approaches generate far more realistic appraisals of the extent to which our results will replicate in a new sample.

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1489

Summary This paper has reviewed some of the issues involved in the estimation of regression models in terms of variable selection and underlying causal models. Specifically, regression models that attempt to illuminate causal understanding are most useful when we try to account for potential confounders, including additional variables that enhance precision and test for mediators. For mediation, SEMs are currently the best choice for the applied researcher because they are linear, provide consistent decomposition of the total effect into direct and indirect contributions, and allow the investigator to take measurement error into account. If interactions among two or more variables are suspected, care must be taken to design the study in such a way that these potential interactions can be adequately studied. When testing mediation, if there are strong interactions between the exposure and the outcome, methods beyond simple SEMs are needed. Finally, in order to increase the likelihood that our models will replicate and hence be generalizable, attention should be paid to the number of parameters we seek to estimate in the context of sample size.

References 1. Altman DG, Bland JM (2003) Statistics notes: interaction revisited: the difference between two estimates. Br Med J 326(7382):219. https://doi.org/10.1136/bmj.326.7382.219 2. Altman DG, Matthews JNS (1996) Statistics notes: interaction 1: heterogeneity of effects. Br Med J 313(7055):486 3. Assmann SF, Pocock SJ, Enos LE, Kasten LE (2000) Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 355(9209):1064–1069 4. Babyak MA (2004) What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 66:411–421 5. Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51 (6):1173–1182 6. Batty GD, Gale CR, Mortensen LH, Langenberg C, Shipley MJ, Deary IJ (2008) Pre-morbid intelligence, the metabolic syndrome and mortality: the Vietnam Experience Study. Diabetologia 51(3):436–443 7. Boyle SH, Mortensen L, Gronbaek M, Barefoot JC (2008) Hostility, drinking pattern and mortality. Addiction 103(1):54–59 8. Budtz-Jørgensen E, Keiding N, Grandjean P, Weihe P (2007) Confounder selection in environmental epidemiology: assessment of health effects of prenatal mercury exposure. Ann Epidemiol 17(1):27–35 9. Cohen J (1983) The cost of dichotomization. Appl Psychol Meas 7(3):249–253 10. Cohen J, West SG, Aiken L, Cohen P (2002) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Taylor and Francis, London 11. Cohn JN (2004) Introduction to surrogate markers. Circulation 109(25 Suppl 1):IV20–IV21 12. Cole SR, Hernan MA (2002) Fallibility in estimating direct effects. Int J Epidemiol 31(1):163– 165 13. Cox DR, Oakes D (1984) Analysis of survival data. Chapman & Hall, London 14. Dawid AP (2000) Causal inference without counterfactuals. J Am Stat Assoc 95(450):407–424 15. Ditlevsen S, Christensen U, Lynch J, Damsgaard MT, Keiding N (2005) The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable. Epidemiology 16(1):114–120

1490

M. A. Babyak and L. H. Mortenson

16. Dixon DO, Simon R (1992) Bayesian subset analysis in a colorectal cancer clinical trial. Stat Med 11(1):13–22 17. Freedland KE, Babyak MA, McMahon RJ, Jennings JR, Golden RN, Sheps DS (2005) Statistical guidelines for psychosomatic medicine. Psychosom Med 67:167 18. Glare PGW (1982) Oxford Latin dictionary. Oxford University Press 19. Glymour MM, Greenland S, Rothman KJ, Lash TL (2008) Causal diagrams. In: Modern epidemiology, vol 3rd. Lippincott Williams & Wilkins, Philadelphia, pp 183–212 20. Greenland S (2000) When should epidemiologic regressions use random coefficients? Biometrics 56(3):915–921 21. Gustafson P (2003) Measurement error and misclassification in statistics and epidemiology: impacts and Bayesian adjustments. CRC Press, Boca Raton 22. Hafeman DM, Schwartz S (2009) Opening the Black Box: a motivation for the assessment of mediation. Int J Epidemiol 38(3):838–845 23. Harrell FE (2001) Regression modeling strategies: with applications to linear modeling, logistic regression, and survival analysis. Springer, New York 24. Harrell FE (2008) Problems caused by categorizing continuous variables. http://biostat.mc. vanderbilt.edu/twiki/bin/view/Main/CatContinuous 25. Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14:382–417 26. Kaufman JS, MacLehose RF, Kaufman S (2004) A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiol Perspect Innov 1(1):4 27. Key TJ et al (2003) Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. J Natl Cancer Inst 95(16):1218–1226 28. Kraemer HC, Wilson GT, Fairburn CG, Agras WS (2002) Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry 59:877–883 29. Lynch J, Davey SG, Harper S, Bainbridge K (2006) Explaining the social gradient in coronary heart disease: comparing relative and absolute risk approaches. J Epidemiol Community Health 60(5):436–441 30. Lynn HS (2003) Suppression and confounding in action. Am Stat 57(1):58–61 31. MacCallum RC, Zhang S, Preacher K, Rucker D (2002) On the practice of dichotomization of quantitative variables. Psychol Methods 7(1):19–40 32. Mackinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V (2002) A comparison of methods to test mediation and other intervening variable effects. Psychol Methods 7(1):83–104 33. Mackinnon DP, Lockwood CM, Brown CH, Wang W, Hoffman JM (2007) The intermediate endpoint effect in logistic and probit regression. Clin Trials 4(5):499–513 34. Maxwell SE, Delaney HD (1993) Bivariate median splits and spurious statistical significance. Psychol Bull 113:20 35. McCullagh P, Nelder J (1989) Generalized linear models. Chapman and Hall, London 36. Moons KGM, Donders ART, Steyerberg EW, Harrell FE (2004) Penalized maximum likelihood estimation to directly adjust diagnostic and prognostic prediction models for overoptimism: a clinical example. J Clin Epidemiol 57(12):1262–1270 37. Mortensen LH, Diderichsen F, Smith GD, Andersen AM (2009) The social gradient in birthweight at term: quantification of the mediating role of maternal smoking and body mass index. Hum Reprod 24(10):2629–2635 38. Muthen LK, Muthen B (2004) Mplus user’s guide, 3rd edn. Muthen and Muthen, Los Angeles 39. Nielsen NR, Zhang ZF, Kristensen TS, Netterstrom B, Schnohr P, Gronbaek M (2005) Self reported stress and risk of breast cancer: prospective cohort study. Br Med J 331(7516):548 40. Pearl J (2005) Direct and indirect effects: technical report R-273. In: Proceedings of the American Statistical Association, Minneapolis, MN, pp 1572–1581 41. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49(12):1373– 1379

61

Confounding, Mediation, Moderation, and General Considerations in. . .

1491

42. Petersen ML, Sinisi SE, van der Laan MJ (2006) Estimation of direct causal effects. Epidemiology 17(3):276–284 43. Rothman KJ (1986) Significance questing. Ann Intern Med 105(3):445–447 44. Rothman KJ (2002) Measuring interactions. In: Epidemiology: an introduction, vol 1st. Oxford University Press, New York, pp 168–180 45. Royston P, Altman DG (1994) Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. J R Stat Soc: Ser C: Appl Stat 43(3):429–467 46. Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25(1):127–141 47. Rubin DB (1997) Estimating causal effects from large data sets using propensity scores. Ann Intern Med 127(8_Part_2):757–763 48. Simon R (2002) Bayesian subset analysis: application to studying treatment-by-gender interactions. Stat Med 21(19):2909–2916 49. Smith GD, Ebrahim S (2004) Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol 33(1):30–42 50. Sterne JA, Davey SG (2001) Sifting the evidence-what’s wrong with significance tests? Br Med J 322(7280):226–231 51. Steyerberg EW (2009) Clinical prediction models. Springer, New York 52. Thompson B (1995) Stepwise regression and stepwise discriminant analysis need not apply here: a guidelines editorial. Educ Psychol Meas 55(4):525–534 53. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16 (4):385–395 54. VanderWeele TJ, Vansteelandt S (2009) Conceptual issues concerning mediation, interventions and composition. Stat Interface 2:457–468 55. Vittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 165:710–718

62

Systematic Reviews and Meta-analysis in Behavioral Medicine Seth M. Noar and Noel T. Brewer

Contents Narrative Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Systematic Reviews and Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correcting for Deficiencies in Narrative Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conducting Systematic Reviews and Meta-analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ensuring Quality of Systematic Reviews and Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meta-analysis Versus Systematic Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitations to Systematic Reviews and Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitations of Systematic Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitations of Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1494 1496 1497 1498 1502 1504 1505 1505 1506 1507 1507

Abstract

As best practices to cumulate research findings, systematic reviews and metaanalyses are critically important. They can clarify biological and behavioral mechanisms as well as inform clinical practice in cardiovascular behavioral medicine. Systematic reviews and meta-analyses have the potential to avoid the established shortcomings of narrative reviews by reducing article selection bias, increasing the rigor of data extraction, and providing methods to synthesize results across studies. Typical steps in systematic reviews are defining the specific research question, defining inclusion criteria such that comparable studies are S. M. Noar (*) School of Media and Journalism, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA e-mail: [email protected] N. T. Brewer Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 S. R. Waldstein et al. (eds.), Handbook of Cardiovascular Behavioral Medicine, https://doi.org/10.1007/978-0-387-85960-6_62

1493

1494

S. M. Noar and N. T. Brewer

reviewed, conducting a comprehensive search for relevant literature, coding study characteristics, and specifying the nature of the effects. Meta-analyses also involve these steps and in addition provide a statistical synthesis of the magnitude of effect sizes across the reviewed studies. Available software and reporting guidelines (e.g., Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) and Meta-Analysis Reporting Standards (MARS)) for conducting systematic reviews and meta-analyses are briefly reviewed. This chapter concludes with current challenges in conducting and interpreting systematic reviews and meta-analyses. Keywords

Meta-analysis · Systematic review · Research synthesis · Publication bias · Causal inference Cumulative science in behavioral medicine and other fields is a two-part proposition [15]. It requires theoretical cumulativeness, research to establish “empirical laws and theoretical structures [that] build on one another so that later developments extend and unify earlier work” ([15], p. 443). It also necessitates empirical cumulativeness by way of reviews that assess “the degree of agreement among replicated experiments or the degree to which related experimental results fit into a simple pattern that makes conceptual sense” ([15], p. 443). The tool that behavioral scientists use to assess such cumulative knowledge is literature review. Review articles integrate existing studies in a line of inquiry typically with the aim to (1) create generalizations from the literature, (2) analyze findings critically using a focal theory or theories, (3) resolve conflicts in the literature, and (4) identify gaps and directions for future research [9, 10]. Our chapter discusses best practices in summaries of empirical work that can inform clinical practice as well as advance our understanding of cumulative research knowledge in cardiovascular behavioral medicine.

Narrative Reviews Traditionally, scientists have assessed cumulative knowledge through research synthesis articles known as narrative reviews of the literature [10, 20]. Narrative reviews are commonly used in psychology to develop theories of cognitive processes and in medicine to summarize knowledge about a particular disease process. In this use, narrative reviews are often an extended argument, supported by selective references to the literature. While appropriate in some circumstances, such reviews can be misleading when applied to focused research questions where a synthesis of all available empirical literature is desired. When this is the case, narrative reviews can fall short of offering a comprehensive and accurate summary of a body of work. Specifically, narrative reviews have three key deficiencies that we will now be discussing along with an example. The example used is a narrative review by

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1495

James that concluded that caffeine consumption and associated elevations in blood pressure may cause cardiovascular disease [18]. While we use this article to illustrate some of the shortcomings of narrative reviews, we wish to emphasize that all narrative reviews are subject to the same limitations. Narrative reviews’ first key deficiency, the selection problem, concerns the way that a reviewer identifies studies of interest. Rather than conducting a systematic and comprehensive search to identify all studies in that literature, many narrative reviews instead use a “convenience sample” of studies. That is, narrative reviewers often will include only those studies with which they are familiar and perhaps those that a cursory literature review is identified. As investigators often have different training, different theoretical orientations, and different research experiences, a “convenience sample” of studies gathered by one narrative reviewer may look very different from a sample gathered by another reviewer. The result is that the selection of studies is biased in terms of representing the breadth and frequency of research findings, and thus any conclusions drawn regarding the body of work are likely to be similarly biased [10, 12, 40]. This unsystematic selection strategy makes narrative reviews prone to bias and results in suboptimal reliability and replication potential that we typically demand of primary research studies. For example, the James review [18] reported no methods for identifying studies of the caffeine-cardiovascular disease link, such as the databases reviewed or the search terms, making the representativeness of the studies reviewed unknown and the replication of such a review virtually impossible. A second key deficiency is the rigor problem. Transparency and complete reporting of methods in primary studies allow other researchers to replicate a study. While reviews and primary studies should have the same level of scientific rigor, unfortunately, researchers do not consistently apply the same level of rigor to reviews that they apply to primary studies [10, 29]. While one narrative reviewer may give only a cursory look to each study and its key findings, another may delve more carefully into the details of each study included in the review. Similarly, while one reviewer may try to devise a note-taking system that attempts to characterize what individual studies did and found, another may simply rely on his or her “general impressions” of the literature. In any of these cases, it is difficult to assess the accuracy of the review when researchers do not use and report a clear and replicable method for data extraction. For example, the James narrative review [18] did not report methods for reviewing studies of the caffeine-cardiovascular disease link, such as how studies were coded or effect sizes derived. Indeed, this problem points to the larger problem that no standard or systematic method exists for conducting narrative reviews. It is thus no surprise that narrative reviews sometimes come to different conclusions regarding the same or a similar set of studies [12, 20, 31]. A third and final key deficiency is the synthesis problem. Narrative reviews lack a robust method for synthesizing research findings from individual studies. One result is the overreliance on statistical significance as the primary criterion for judging the results of empirical studies [20, 38, 39]. For instance, many narrative reviews use informal “vote counting” procedures in which they compare the number of

1496

S. M. Noar and N. T. Brewer

statistically significant ( p < 0.05) to nonsignificant studies in a given area and make conclusions based on this comparison. Such an approach has many problems. Studies that were statistically underpowered, for instance, because of small sample sizes, may be counted as having no effects when effects actually existed, while studies with very large sample sizes may be counted as having meaningful effects when such effects were of minimal practical or clinical significance. The result is a method that uses dichotomous decisions that ignore the sample sizes of individual studies and the limitations of null hypothesis significance testing as it is typically applied in the literature [7, 25, 39]. In the James review [18], no methods for synthesizing effect sizes for the caffeine-cardiovascular disease link were reported. In the absence of such reports, it is typically the case that statistical significance is used as the sole criterion for judging study outcomes. Thus, as many narrative reviews suffer from selection, rigor, and synthesis problems, such reviews at best yield little new information and contribute little to progress in the field and at worst produce conclusions that are biased and that may impede cumulative knowledge [38, 39]. As mentioned above, studies have demonstrated that it is not unlikely for narrative reviews to come to differing conclusions regarding the same set of studies [8, 12, 14], and further newer, more sophisticated review methods are superior to the traditional method of narrative reviewing [3, 6, 12]. Indeed, it is worth noting that because studies in the same research domain often use different theories, measures, populations, and statistical analyses, the natural course of many literatures is to produce studies that have findings that are at odds with one another [36, 37]. The goal of the reviewer of that literature is thus to (1) carefully and systematically integrate the existing literature and (2) attempt to explain the variability across extant studies in the literature. A review which contains problems with selection, rigor, and synthesis is highly unlikely to fulfill such a challenging purpose.

Systematic Reviews and Meta-analysis To correct the deficiencies described above, a newer set of systematic methods has emerged. While this newer set of methods has been referred to by different names, (e.g., systematic review, research synthesis, meta-analysis), there appears to be emerging consensus as to precise definitions for these terms. A systematic review is “a review of a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant research, and to collect and analyze data from the studies that are included in the review” ([28], p. 1). Metaanalysis refers to “the use of statistical techniques in a systematic review to integrate the results of included studies” [28]. There is little reason to conduct a meta-analysis of a non-systematically identified set of studies because the mean (aggregate) effect size would have limited generalizable meaning. Thus, as we will use the term, metaanalyses are systematic reviews with the addition of the statistical synthesis or “meta-analysis.”

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1497

In contrast, however, not all systematic reviews are meta-analyses, as they may or may not contain the meta-analytic portion. To reduce the possibility of confusion in the current chapter, we will use the term systematic review to refer to any review that meets the above definition, but does not contain a meta-analysis of the effect sizes within it. We will use the term meta-analysis to refer to any review that meets the above definitions, in that it is a systematic review that uses statistical techniques to integrate the results of included studies. Our operationalization of these terms here is consistent with writings on systematic reviews and meta-analysis in medicine and public health that view meta-analyses as a subset of systematic reviews which carry out a statistical synthesis of study effects [29].

Correcting for Deficiencies in Narrative Reviews How do these newer methods correct for the deficiencies inherent to narrative reviews? Both systematic reviews and meta-analyses do this in several ways (see Table 1). By their very nature, systematic reviews and meta-analyses correct for the selection problem that is so common in narrative reviews, by demanding a comprehensive and systematic review of the literature. While narrative reviews often do not include comprehensive literature searches and may also have “fuzzy” criteria in terms of what the boundaries of the literature are, systematic reviews and metaanalysis by contrast are very clear on these points. Specifically, systematic reviews clearly delineate boundaries of the literature and develop and apply explicit inclusion criteria. Reporting guidelines mandate that systematic reviews and meta-analyses report inclusion criteria as well as the “flow” of how many studies were excluded and for what reasons [1, 28]. Furthermore, systematic reviews and meta-analyses correct for the rigor problem by implementing strict, objectively implemented methods. Systematic reviews and meta-analyses typically report the specific search terms used as these are part of their methodology. They also use standardized coding procedures. Typically, systematic reviews develop a coding sheet and pretest it for accuracy and comprehensiveness. Multiple coders independently rate study characteristics, and inter-coder reliability is tracked and reported in the article. This provides a “checks and balances” element to the review, giving readers more confidence that the literature is accurately described in the review. Specifying coding of research articles also provides transparency to what components of the studies were coded and how these codes were operationalized, which are not found in most narrative reviews. Finally, by their very nature, meta-analyses correct for the synthesis problem of narrative reviews, as they convert study findings into a common effect size metric and analyze those data using sophisticated statistical methods. The researcher calculates aggregate (mean) effect sizes, assesses heterogeneity, and uses moderator analyses to explain variability across studies. While the procedures provide the statistical significance of effect sizes, the focus is on the magnitude of effects. Systematic reviews may or may not focus on effect sizes; when they do, the focus (where possible) is on the magnitude and direction of study effects rather than simple

1498

S. M. Noar and N. T. Brewer

Table 1 Solutions to problems in narrative reviews provided by systematic reviews and metaanalysis Problem with narrative reviews Selection problem – Lack of a comprehensive and systematic review of studies. Instead, use of a convenience sample of studies that are known and easily accessible to the researcher Rigor problem – Lack of transparent and replicable methods for retrieving study characteristics from study reports. Instead, often unclear what method was used to assess the literature

Synthesis problem – Lack of a robust method for cumulating research findings from individual studies. Instead, vote counting often used

Solution (systematic reviews) Clear formulation of literature boundaries and inclusion criteria. Comprehensive, systematic search for studies using several search methods. Unpublished work often sought out and included Development of coding sheet for coding study characteristics. Coders are trained, and the coding sheet is pretested and revised. Multiple coders are used to ensure reliability in the information extracted from study reports. Inter-coder reliability is reported May or may not synthesize study outcomes (may instead focus on describing the body of literature in terms of what has been done, what gaps remain, etc.). If study outcomes are synthesized, a systematic approach is taken. Increasingly (and where possible), focus is on magnitude of effects rather than solely statistical significance.

Solution (meta-analysis) Same as systematic review

Same as systematic review

Common effect size metric is chosen, and data from study reports is converted into that metric. Effect sizes are then weighted and aggregated across studies, yielding a weighted mean effect size. Analysis of heterogeneity of study effect sizes is conducted, as are analyses of potential moderating variables

“vote counts” of statistical significance [29]. Systematic reviews (as defined here) lack the formal statistical integration of study findings into an aggregate effect size across studies, along with other analyses of the meta-analytic dataset (more on this below).

Conducting Systematic Reviews and Meta-analyses Conducting a systematic review or meta-analysis is a step-by-step process. Several authors have offered lists of such steps [10, 20, 29], which appear in Table 2. The first step in a systematic review or meta-analysis is to define the research question to be examined. For example, van Melle and colleagues asked the question “What is the association of depression with the cardiovascular outcome of patients following myocardial infarction in terms of mortality and cardiovascular events?”

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1499

Table 2 Steps in conducting systematic reviews and meta-analysis Step 1: Define the research question Step 2: Set boundaries for the literature (i.e., inclusion criteria) Step 3: Locate all relevant literature Step 4: Develop a coding sheet and code study characteristics Step 5: Estimate the magnitude of effect in each study Step 6: Analyze the meta-analytic database Step 7: Present results and draw conclusions

Systematic review X X

Meta-analysis X X

X X

X X

Sometimes

X X X

X

([42], p. 814). As in primary research, reviewers give a justification for the systematic review or meta-analysis. For example, van Melle et al. [42] note in their literature review that conflicting findings have existed in the literature on this topic and that a meta-analysis was needed to resolve the debate on this issue. The next step (step 2) involves setting boundaries for the literature to be reviewed as well as developing explicit inclusion criteria for the review. What is critical (particularly in meta-analysis but also in systematic reviews) is that the studies chosen have conceptual comparability, meaning that the primary studies are similar enough that synthesizing them will result in a meaningful outcome [23]. A variety of factors are typically considered and ultimately used in inclusion criteria, including type of publication (and years published), study population, type of research design used, treatment/intervention applied, and outcomes. For example, van Melle et al. [18] included all published and unpublished studies available by January 2004, if they met the following criteria: (1) They included patients who were hospitalized for myocardial infarction (MI) and for whom depression was measured; (2) depression was determined within 3 months after MI using standardized depression measures, such as self-report questionnaires; (3) cardiovascular prognosis was assessed in a depressed group compared with a control group in a prospective study; and (4) the study measured all-cause mortality, cardiac mortality, or cardiovascular events within 24 months of the MI. These inclusion criteria make it clear how the boundaries of this particular literature were defined in this review and specifically which studies were to be included and excluded. The next step (step 3) in the process is to locate all relevant literature for the metaanalysis or systematic review. This can be undertaken using a variety of literature search methods. Widely used search methods include electronic database searches (e.g., Medline, PsycINFO), citation searches (i.e., examining reference lists in review articles or key primary studies, examining all articles that have cited a particular seminal article in the field), and journal searches (i.e., searching contents of relevant journals over a defined time period). Other methods (particularly to find unpublished works) include personal communications via email and relevant listservs and searching conference proceedings. It is also possible to find research documents through various registries (e.g., the trial registration from the Food and

1500

S. M. Noar and N. T. Brewer

Drug Administration) and Google for publicly available reports. Since each search method can lead the reviewer to somewhat different studies (with much overlap of course), it is critical that systematic reviews and meta-analyses apply multiple search strategies [23]. For example, in a meta-analysis, van Melle et al. [42] searched the Medline, EMBASE, and PsycINFO electronic databases using relevant keyword combinations. They also examined reference lists of review articles and books in this area. Finally, they asked researchers working in the area to provide relevant published and unpublished work (i.e., personal communications). They included in the metaanalysis all published and unpublished work that they identified through these search methods. A figure in their article shows how they went from initial searches that yielded thousands of studies, to just over 500 studies on depression in this area, to 38 studies that met inclusion criteria. After discarding duplicate reports on the same dataset, the final number of studies included in the meta-analysis was 22. Step 4 involves developing a coding sheet and coding studies’ key characteristics. In a systematic review, the key reason to code is to then be able to describe the studies, both individually and as a collective whole. In a meta-analysis, coding also allows the analyst to use these study characteristics in statistical analyses. For example, variables such as age and sex may be coded both to describe these characteristics of the study samples (individually and collectively) and to conduct analyses testing whether age or sex moderates the effect of a treatment or intervention. Other factors on which studies can be coded are design (e.g., experimental vs correlational), study quality (e.g., the rigor of the research methods), use of theory, and measurement characteristics. Coding in systematic reviews and especially in meta-analysis is a challenging process. This is the case because (1) authors of studies often report demographic and other characteristics in different forms; (2) reliability is critical and this can only be achieved with a clear and well-thought-out coding sheet and trained coders; and (3), since we cannot know everything that will be encountered at the start of the coding process, it is sometimes an iterative process where a coding form is revised at different points in the process. The literature makes several important suggestions for successful coding in systematic reviews and meta-analysis [23]. In van Melle et al.’s [42] meta-analysis, two coders independently coded study characteristics such as percent of study participants who were men, participants’ mean age, year of study and type of depression assessment and recruitment characteristics such as sample size, representativeness of the study population, and percentage lost to follow-up. Step 5 focuses on estimating the magnitude of effect for each study. This step may not be taken in systematic reviews where the primary goal is to describe the characteristics of a set of studies without examining study outcomes (e.g., what types of theories, methods, and components are being used in studies of Internetbased tailored interventions?) [24]. In systematic reviews that are focused on the outcomes of a set of studies, the more common state of affairs, this step is taken, but the method differs from that applied in meta-analysis. In such reviews, a systematic approach is applied to take stock of what kinds of effects a set of

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1501

studies have demonstrated. Where possible, the focus is on the magnitude of study effects and other key statistics (e.g., confidence intervals) rather than solely on the statistical significance of study findings [29]. As an example of this, a systematic review of the impact of psychosocial factors on coronary heart disease categorized studies (based on the magnitude of relative risk statistics) into one of three groups: no association, moderate association, or strong association [16]. In this article, the authors produced a table with each study’s effect size listed as well as a judgment as to the direction and size of the effect (i.e., none, moderate, strong). Another example of this comes from a systematic review of human papillomavirus (HPV)related beliefs and vaccine acceptability [5]. These researchers created a summary table of knowledge about HPV, averaging percentages across studies to provide simple weighted average statistics on this issue. Meta-analysts undertake a more complex set of procedures. Specifically, step 5 begins by deciding on a common effect size statistic (such as an odds ratio) that is appropriate for association of interest and then converting all study findings into that metric. Many treatments of meta-analysis describe how to make such effect size conversions [13, 23, 38]. These writings point out that the three most common effect size indicators used in meta-analysis are (1) the correlation coefficient (Pearson’s r), used to characterize associations between continuous variables; (2) the standardized mean difference, which is applied in cases with a categorical independent variable and a continuous dependent variable; and (3) the odds ratio, which is applied in cases with dichotomous independent and dependent variables. The goal of the metaanalyst is to choose the effect size that is most appropriate to the type of data reported in the set of studies being reviewed [20]. As an example, van Melle et al. [42] converted study findings into odds ratios. They chose this statistic because studies reported their findings in odds ratios or other similar statistics, such as hazard ratios. In their meta-analysis, three outcomes were of interest, and data on each available outcome from each study was extracted and converted (where necessary) to an odds ratio. This resulted in three metaanalytic databases, one for each outcome, including all-cause mortality (k ¼ 14 studies), cardiovascular mortality (k ¼ 11), and cardiovascular events (k ¼ 12). Step 6, analyzing the meta-analytic database, is only applied in meta-analysis, as systematic reviews do not undertake a formal analysis of study findings. As discussed above, steps 5 and 6 are the key elements that distinguish a meta-analysis from a systematic review. Effect sizes are weighted such that larger studies, which contain more precise effect size estimates, are weighted more heavily in the aggregation of the overall effect size than are smaller studies. Some approaches to metaanalysis also advocate making additional statistical corrections (for methodological factors) to each individual effect size before aggregation [17]. The researcher calculates the weighted mean effect size along with its 95% confidence interval and examines the statistical significance of the mean effect size. The researcher also calculates whether the mean effect size is homogeneous or heterogeneous (e.g., by looking at the Q statistic), to see whether the study effect sizes vary more than would be expected based upon sampling error alone. Especially in cases where significant heterogeneity among study effect sizes exists, moderator analyses may be able to

1502

S. M. Noar and N. T. Brewer

explain the variability in effect sizes according to moderator variables specified a priori. While meta-analysts tend to agree on this general analysis approach, the details of their statistical approaches to meta-analysis may differ [13, 19]. In addition, analysts often use software designed specifically for meta-analysis, including Comprehensive Meta-Analysis, DSTAT, and RevMan. Some treatments of metaanalysis also discuss how to use more standard software packages, such as Statistical Analysis Software (SAS) and the Statistical Package for the Social Sciences (SPSS), for meta-analysis [23]. In the van Melle et al. [42] meta-analysis, statistically significant and relatively large effect sizes existed for each outcome examined. While two of the three outcomes were statistically homogeneous, one outcome (cardiovascular events) was heterogeneous. Examination of moderator analyses suggested that studies published before 1992 had larger effects than those published in 1992 or later. However, associations did not vary by two other potential moderators: assessment method for depression and length of follow-up. Finally, in step 7, both systematic reviews and meta-analyses present their results and draw their conclusions. Given that meta-analysis converts each study’s findings into a common metric, allowing for head-to-head comparisons of studies, it is common to present these effect sizes not only in tabular form but also in visual form. A variety of figure types are available for this purpose, including forest plots, box plots, and stem and leaf plots [13, 38]. For example, the van Melle et al. [42] meta-analysis chose to use forest plots that displayed the effect sizes for each of the three outcomes. Figures such as forest plots give the reader a useful visual means with which to interpret each effect size, its corresponding confidence interval, and heterogeneity of effects across studies. Based on these seven steps, researchers draw their final conclusions from a systematic review or meta-analysis and discuss limitations of the project or potential alternative explanations for findings. van Melle et al. [42] drew the conclusion that the relationship between post-MI depression and impaired cardiovascular prognosis is consistent. They also noted, however, that limitations of the meta-analysis included the varied design of the different studies included as well as the possibility of bias in this literature toward publishing only findings that reached statistically significance.

Ensuring Quality of Systematic Reviews and Meta-analysis Similar to the randomized controlled trial (RCT), a systematic review or metaanalysis requires numerous decisions and careful implementation, as well as a very detailed writing up of the report for reasons of transparency and replicability. The Consolidated Standards of Reporting Trials (CONSORT) statement for RCTs was developed to improve both the conduct and reporting of RCTs [27], and evidence to date suggests that it has achieved just that [35]. Similar efforts to improve both the conduct and reporting of systematic reviews and meta-analyses have focused

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1503

primarily on meta-analysis, including the Quality of Reporting of Meta-Analysis of Randomized Controlled Trials (QUOROM) statement [26] as well as the Handbook of Research Synthesis [11]. More recent efforts include an updated set of standards (to replace QUOROM) that applies to both systematic reviews and meta-analyses, referred to as the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) statement [28]; an updated Handbook of Research Synthesis and Meta-Analysis [13]; and new guidelines from the American Psychological Association on the reporting of meta-analysis, referred to as Meta-Analysis Reporting Standards or MARS [1]. While the Handbook is a lengthy and in-depth treatment of meta-analysis, covering all aspects of the technique, the PRISMA and APA efforts emphasize the quality of systematic review and meta-analytic reports. These reporting guidelines and checklists may also ultimately affect the conduct of systematic reviews and meta-analyses, as more complete and transparent reporting will likely encourage researchers to perform more methodologically rigorous reviews. Both the American Psychological Association (APA) and PRISMA statements include tables that describe recommended sections of reports, systematic reviews, and meta-analyses as well as the information that should be reported in each of those sections. For example, perhaps the simplest recommendation is to include the term systematic review or meta-analysis in the title of the report, depending on which is most appropriate. Such a simple action can have an important pragmatic benefit, however, in that it instantly clarifies the type of review conducted and makes it easier to locate the review in a database search. The PRISMA statement also includes a flow diagram illustrating the flow of studies through the search process, including how many studies were excluded during different phases of the search process and for what reasons. Both the PRISMA table and figure template are available for download at www.prisma-statement.org. Systematic reviews and meta-analyses are empirical studies with the same rules for transparency in reporting methods. Completeness of reporting is particularly important in systematic reviews and meta-analyses, because they are large and complex projects and because they have a unique potential to greatly influence a particular field of study. Researchers make numerous decisions in the course of a systematic review or meta-analysis that will be unknown to the reader of that review unless the information is reported. Thus, many systematic reviews and meta-analyses have lengthy methods sections with the finest of details regarding search procedures, screening of studies, the coding process, and effect size conversion and analysis (for meta-analysis). Indeed, one should be suspicious of any systematic review or meta-analysis with a short methods section that lacks sufficient detail. Finally, a newer tool for potentially increasing quality and reducing duplication is registration. Similar to registration for randomized controlled trials, registration of a systematic review or meta-analysis allows other researchers to see what is planned in advance compared to the final product. It may also avoid duplication of efforts by allowing researchers thinking about a systematic review topic to examine whether another team already has such a project underway. Systematic reviews and meta-

1504

S. M. Noar and N. T. Brewer

analyses can be registered online at PROSPERO (https://www.crd.york.ac.uk/ PROSPERO/#index.php).

Meta-analysis Versus Systematic Reviews Given that a meta-analysis provides everything that a systematic review provides, as well as a more sophisticated quantitative synthesis of study findings, a question arises: why would one choose to conduct a systematic review over a meta-analysis? While a meta-analysis may be a researcher’s first choice, it may not always be possible, feasible, or even desirable within a particular literature. For example, some systematic reviews seek to survey and describe a literature without providing a synthesis of study effects. This type of review would be one in which a “lay of the land” is desired without necessarily getting into the issue of precisely what type of effects studies have had. For example, in the area of Internet-based tailored health communication research, a systematic review was conducted to examine types of programs that have been developed and elements included in those programs [24]. Because this type of review does not synthesize study findings together, the conceptual comparability of studies is somewhat less important, and the inclusion criteria can be broader. In other cases, a synthesis of study effects may be desired, but meta-analysis cannot be meaningfully employed. For example, in the area of health communication campaigns, many interesting field studies have been conducted but with relatively weak outcome evaluation designs [32]. These weak designs do not lend themselves to strong casual attribution; in addition, the varied outcome evaluation designs are different enough from one another that a synthesis of study outcomes may lack real meaning. Thus, reviews in this area tend to be systematic reviews which detail the types of campaigns that have taken place and the elements used in those campaigns [30, 34], without a synthesis of the magnitude of study effects. This illustrates an important principle of meta-analyses: they are only as strong as the weakest designed studies that are included. Thus, if the research design of some or all studies is either weak or extremely varied, a systematic review may be a better choice than a meta-analysis. Other reasons can drive the decision to conduct a systematic review rather than a meta-analysis. For example, in their systematic review of psychosocial factors affecting coronary heart disease, Hemingway and Marmot [16] describe their decision not to employ meta-analysis, citing concerns regarding the comparability of study outcomes and the meaning of a mean effect size given the difficulty in locating all of the literature in this particular area. If real concerns exist with regard to issues such as the comparability of study designs or outcomes, this presents a strong case against a meta-analysis and for a systematic review in its place. One possible alternative, however, would be to employ meta-analysis but test the varying study design or other factors as possible moderators [23]. For example, a meta-analysis of health communication campaigns examined how study design impacted effect size

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1505

[41]. Perhaps not surprisingly, results indicated that weaker study designs produced larger, and potentially inflated, effect sizes.

Limitations to Systematic Reviews and Meta-analysis A review is essentially an observational study of a population of primary scientific studies. Thus, like all research, reviews can have strengths, but reviews are also subject to a long list of potential pitfalls. Here, we briefly summarize key limitations of systematic reviews and meta-analyses using the Campbell framework for validity [40] that focuses on internal, external, construct, and statistical conclusion validity.

Limitations of Systematic Reviews The primary strength of systematic reviews is their comprehensiveness, which supports the external validity of inferences based on their findings. That is, as reviews increase in comprehensiveness, so does our confidence in making generalizations about a population of studies. No criteria exist as to how many original studies are needed to warrant a systematic review, though it is somewhat rare to see a review with fewer than ten studies. The main limitations arise when the search is incomplete (e.g., the review searched too few sources for articles, ignored the unpublished or “gray” literature, or used poor search terms). Other issues related to representativeness are that one cannot generalize the findings of a review beyond the observed range of predictor variables (e.g., outside of the range of intensity or duration of the reviewed interventions), outcomes (e.g., to constructs or durations other than those studied), or populations (e.g., to men if only women were studied). The issues of external validity also apply to conclusions of primary studies. Thus, it is important to know response rates for the primary studies reviewed, how they sampled participants (was it a probability sample?), and the nature of the samples included (only college students? Racially or ethnically diverse?). Conclusions from systematic reviews also have limitations in terms of what can be said about internal validity or causality. The plural of anecdote is not evidence: cumulating many correlational studies does not suddenly permit us to claim a causal association, and cumulating weak study designs (correlational study designs) with strong ones (experiments) may cloud inferences about causality. The construct validity of predictor and outcome merits careful consideration. Behavioral medicine interventions are often highly diverse in nature and scope; combining highly dissimilar interventions may not be defensible. Similarly, it may not be reasonable to quantitatively combine highly disparate outcome measures. As discussed above, in these cases, a systematic review may be more appropriate than a meta-analysis. Finally, a major limitation of systematic reviews is their statistical conclusion validity. Systematic reviews synthesize study findings using only qualitative narratives (or, at best, a quasi-quantitative counting procedure). These approaches do not allow us to say whether several nonsignificant studies, when quantitatively

1506

S. M. Noar and N. T. Brewer

combined, would yield a statistically significant finding, nor do they permit us to give more weight to larger studies. They also do not allow us to use statistical techniques to examine moderators that would speak to causality (do experiments yield different findings than correlational studies?) and generalizability (do studies of healthy populations yield different findings than patient populations?). Finally, publication biases such as the file-drawer effect (i.e., the non-publication of nonsignificant or “unwanted” findings) may remain undetected in systematic reviews, though they can be examined in meta-analysis [23].

Limitations of Meta-analysis Strengths and limitations discussed for systematic reviews also apply to metaanalyses, with one important exception. Because meta-analyses permit truly quantitative synthesis, they overcome the limitations related to combining effects for various studies and weighting. However, this new freedom gives rise to other important questions. Data from studies can be combined using powerful fixed-effects analyses, but this approach may not be appropriate when studies have dissimilar findings (i.e., heterogeneity). In this case, it may be more appropriate to conduct randomeffects analyses [4, 23]. In extreme cases, the best course of action may be to not conduct a meta-analysis at all. Furthermore, when studies are heterogeneous, it is also important to look for moderator variables. This is essentially stratifying the effect sizes for one group of studies on a particular variable and comparing them to another group of studies. In many ways, these types of analyses have the potential to greatly advance a field, as they often attempt to get at study mechanisms and test a priori hypotheses using meta-analytic data. The key limitations of these moderational analyses, however, are that moderator variables are not randomly dispersed across primary studies (i.e., they are not independent of one another). Thus, it is possible for us to make conclusions based upon moderational analyses that are the result of spurious associations [22]. For example, the van Melle et al. [42] meta-analysis discussed earlier reported that studies published before 1992 had larger effects than those published 1992 or later. But why? Studies published before 1992 may vary in several ways from those published in 1992 and later, and we may falsely attribute this effect to the wrong variable. Another more direct example comes from a meta-analysis of tailored print health behavior change interventions [33]. A moderator analysis found that particular types of print materials, such as pamphlets and newsletters, were more successful in sparking behavioral change than letters and manuals. However, it is entirely possible that studies that used pamphlets and newsletters differed in other ways from studies that used letters and manuals, and one of these other variables may in fact account for this effect. While meta-analysts are increasingly using meta-regression to attempt to root out which moderators matter most [4], the technique has its limitations. Ultimately, results of moderator analyses may best be viewed as preliminary findings that warrant additional study using experimental methods.

62

Systematic Reviews and Meta-analysis in Behavioral Medicine

1507

Conclusion Systematic reviews and meta-analysis have greatly advanced the science of conducting reviews and building cumulative knowledge in a myriad of ways. Perhaps most notably, they give behavioral medicine researchers and others a method to accurately summarize research literatures where previously no real method existed. Thus, while poor cumulation was in the past a serious impediment to scientific progress [38], we now have tools that allow us to summarize what the literature shows more accurately as well as to test what mechanisms may account for particular effects. Indeed, the strengths of systematic reviews and meta-analysis rest on the basic tenets of science: no one study is perfect, and replication is necessary. Only when we see consistent effects across a body of research can we have great confidence in a particular effect. It perhaps comes as no great surprise that studies have documented remarkable growth in the use of meta-analysis. For example, Bausell and colleagues [21] examined meta-analyses published in the social and health sciences between 1980 and 1993 using a number of electronic databases. They documented an average yearly increase of 15% in published meta-analyses until 1992, when there was a 52% increase, primarily due to new meta-analyses appearing in the health sciences literature [2]. In another such analysis, Lee, Bausell, and Berman [21] examined the growth of health-related meta-analyses published between 1980 and 2000 and indexed in Medline. They found an increasing linear trend from 1980 to 2000, such that in 1980, there were only a handful of published meta-analyses; in 1990, there were over 200; and, in the year 2000, approximately 400 new meta-analyses were published [21]. More recent analyses continue to point to the growing application of meta-analysis year after year [13, 31]. Thus, the best evidence of the value of these new review tools comes perhaps from the fact that scientists are finding them increasingly useful in answering critical scientific questions. By applying these tools, we are advancing our understanding of cumulative knowledge in a variety of areas of inquiry and are increasing the precision with which we understand the effects of our treatments and interventions.

References 1. APA Publications and Communications Board Working Group on Journal Article Reporting Standards (2008) Reporting standards for research in psychology: why do we need them? What might they be? Am Psychol 63(9):839–851 2. Bausell RB, Yu-Fang L (1995) The growth of meta-analytic literature from 1980 to 1993. Eval Health Professions 18(3):238–251 3. Beaman AL (1991) An empirical comparison of meta-analytic and traditional reviews. Personal Soc Psychol Bull 17(3):252–257 4. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009) Introduction to meta-analysis. Wiley, West Sussex 5. Brewer NT, Fazekas KI (2007) Predictors of HPV vaccine acceptability: a theory-informed, systematic review. Prev Med 45(2):107–114

1508

S. M. Noar and N. T. Brewer

6. Bushman BJ, Wells GL (2001) Narrative impressions of literature: the availability bias and the corrective properties of meta-analytic approaches. Personal Soc Psychol Bull 27(9): 1123–1130 7. Cohen J (1994) The earth is round (p