Medical Illness and
Schizophrenia Second Edition
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Medical Illness and
Schizophrenia Second Edition
Edited by
Jonathan M. Meyer, M.D. Henry A. Nasrallah, M.D.
Washington, DC London, England
Note: The authors have worked to ensure that all information in this book is accurate at the time of publication and consistent with general psychiatric and medical standards, and that information concerning drug dosages, schedules, and routes of administration is accurate at the time of publication and consistent with standards set by the U.S. Food and Drug Administration and the general medical community. As medical research and practice continue to advance, however, therapeutic standards may change. Moreover, specific situations may require a specific therapeutic response not included in this book. For these reasons and because human and mechanical errors sometimes occur, we recommend that readers follow the advice of physicians directly involved in their care or the care of a member of their family. Books published by American Psychiatric Publishing, Inc., represent the views and opinions of the individual authors and do not necessarily represent the policies and opinions of APPI or the American Psychiatric Association. If you would like to buy between 25 and 99 copies of this or any other APPI title, you are eligible for a 20% discount; please contact APPI Customer Service at
[email protected] or 800-368-5777. If you wish to buy 100 or more copies of the same title, please e-mail us at
[email protected] for a price quote. Copyright © 2009 American Psychiatric Publishing, Inc. ALL RIGHTS RESERVED Manufactured in the United States of America on acid-free paper 13 12 11 10 09 5 4 3 2 1 Second Edition Typeset in Adobe’s Book Antiqua and Albertus American Psychiatric Publishing, Inc. 1000 Wilson Boulevard Arlington, VA 22209-3901 www.appi.org Library of Congress Cataloging-in-Publication Data Medical illness and schizophrenia / edited by Jonathan M. Meyer, Henry A. Nasrallah. — 2nd ed. p. ; cm. Includes bibliographical references and index. ISBN 978-1-58562-346-4 (alk. paper) 1. Schizophrenics—Diseases. 2. Schizophrenics—Medical care. 3. Mentally ill—Diseases. 4. Mentally ill—Medical care. I. Meyer, Jonathan M., 1962– II. Nasrallah, Henry A. {DNLM: 1. Schizophrenia—complications. 2. Schizophrenia—epidemiology. 3. Comorbidity. 4. Patient Care. WM 203 M489 2009] RC514.M425 2009 616.89—dc22 2009005567 British Library Cataloguing in Publication Data A CIP record is available from the British Library.
Contents Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ix Disclosure of Interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
Part I Public Health Issues for Schizophrenia Patients
Chapter 1 Improving Physical Health Care for Patients With Serious Mental Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 David Folsom, M.D., M.P.H.
Chapter 2 Excessive Mortality and Morbidity Associated With Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Daniel E. Casey, M.D. Thomas E. Hansen, M.D.
Chapter 3 Medical Outcomes From the CATIE Schizophrenia Study . . . . . . . . . . . . . . . . . . . . . 37 Henry A. Nasrallah, M.D.
Part II Metabolic Disease, Heart Disease, and Related Conditions
Chapter 4 Obesity and Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . .61 Tony Cohn, M.B.Ch.B., M.Sc., F.R.C.P.C.
Chapter 5 Glucose Intolerance and Diabetes in Patients With Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91 David C. Henderson, M.D. Kathleen Miley, B.S.
Chapter 6 Effects of Antipsychotics on Serum Lipids . . . . . . . . . . . . . 117 Jonathan M. Meyer, M.D.
Chapter 7 The Spectrum of Cardiovascular Disease in Patients With Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . .169 Jimmi Nielsen, M.D. Egon Toft, M.D., F.E.S.C.
Chapter 8 Behavioral Treatments for Weight Management of Patients With Schizophrenia. . . . . . . . . . . . . . . . . . . . . . . . 203 Rohan Ganguli, M.D., F.R.C.P.C. Tony Cohn, M.B.Ch.B., M.Sc., F.R.C.P.C. Guy Faulkner, B.Ed., M.Sc., Ph.D.
Chapter 9 Nicotine and Tobacco Use in Patients With Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Andrea H. Weinberger, Ph.D. Tony P. George, M.D., F.R.C.P.C.
Part III Special Topics and Populations
Chapter 10 HIV and Hepatitis C in Patients With Schizophrenia . . . . . 247 Milton L. Wainberg, M.D. Francine Cournos, M.D. Karen McKinnon, M.A. Alan Berkman, M.D. Mark Drew Crosland Guimarães, M.D., D.Sc., M.P.H.
Chapter 11 Substance Abuse and Schizophrenia . . . . . . . . . . . . . . . . . 275 Peter F. Buckley, M.D. Jonathan M. Meyer, M.D.
Chapter 12 Sexual Dysfunction and Schizophrenia . . . . . . . . . . . . . . . . 303 Heidi J. Wehring, Pharm.D., B.C.P.P. Deanna L. Kelly, Pharm.D., B.C.P.P.
Chapter 13 Managing the Health Outcomes of Schizophrenia Treatment in Children and Adolescents . . . 343 Christoph U. Correll, M.D.
Chapter 14 Medical Health in Aging Persons With Schizophrenia . . . . 377 Samantha Brenner, M.P.H. Carl I. Cohen, M.D.
Chapter 15 Managing Health Outcomes of Women With Schizophrenia During Pregnancy and Breastfeeding . . . . . . .415 Adele C. Viguera, M.D., M.P.H. Mackenzie Varkula, D.O. Katherine Donovan, B.A. Ross J. Baldessarini, M.D.
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
Contributors Ross J. Baldessarini, M.D. Professor of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts; Director, International Consortium for Psychotic Disorders Research, McLean Hospital, Belmont, Massachusetts Alan Berkman, M.D. Associate Professor of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York Samantha Brenner, M.P.H. Medical Student, Department of Psychiatry, State University of New York Health Science Center at Brooklyn, Brooklyn, New York Peter F. Buckley, M.D. Professor and Chairman, Department of Psychiatry, Medical College of Georgia, Augusta, Georgia Daniel E. Casey, M.D. Professor of Psychiatry and Neurology, Oregon Health and Science University, Portland, Oregon Carl I. Cohen, M.D. Professor and Director, Division of Geriatric Psychiatry, Department of Geriatric Psychiatry, State University of New York Health Science Center at Brooklyn, Brooklyn, New York Tony Cohn, M.B.Ch.B., M.Sc., F.R.C.P.C. Director, Mental Health and Metabolism Clinic, Centre for Addiction and Mental Health; Assistant Professor, Departments of Psychiatry and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
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Christoph U. Correll, M.D. Assistant Professor of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine; Medical Director, Director, Adverse Events Assessment and Prevention Unit, The Zucker Hillside Hospital North Shore, Long Island Jewish Health System, Glen Oaks, New York Francine Cournos, M.D. Professor of Clinical Psychiatry, Columbia College of Physicians and Surgeons; Director, Washington Heights Community Service, New York State Psychiatric Institute, New York, New York Mark Drew Crosland Guimarães, M.D., D.Sc., M.P.H. Professor of Epidemiology, Department of Preventive and Social Medicine, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil Katherine Donovan, B.A. Clinical Research Coordinator, Massachusetts General Hospital Center for Women’s Health, Boston, Massachusetts Guy Faulkner, B.Ed., M.Sc., Ph.D. Professor, Faculty of Physical Education and Health, University of Toronto, Toronto, Ontario, Canada David Folsom, M.D., M.P.H. Assistant Professor of Psychiatry, University of California, San Diego, La Jolla, California Rohan Ganguli, M.D., F.R.C.P.C. Professor of Psychiatry and Executive Vice-President of Clinical Programs, Centre for Addiction and Mental Health, Toronto, Ontario, Canada Tony P. George, M.D., F.R.C.P.C. Head, Addiction Psychiatry Program, Department of Psychiatry, University of Toronto, and Schizophrenia Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada Thomas E. Hansen, M.D. Associate Professor of Psychiatry, Oregon Health and Science University; Staff Psychiatrist, Portland Oregon State Hospital, Portland, Oregon
Contributors
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David C. Henderson, M.D. Director, Schizophrenia, Diabetes, and Weight Reduction Research Program, Massachusetts General Hospital; Associate Professor of Psychiatry, Harvard Medical School, Boston, Massachusetts Deanna L. Kelly, Pharm.D., B.C.P.P. Associate Professor of Psychiatry and Acting Director, Treatment Research Program, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland Karen McKinnon, M.A. Research Scientist, New York State Psychiatric Institute; Project Director, Columbia University HIV Mental Health Training Project, Columbia University College of Physicians and Surgeons, New York, New York Jonathan M. Meyer, M.D. Assistant Professor of Psychiatry, University of California, San Diego; Staff Psychiatrist, San Diego VA Medical Center, La Jolla, California Kathleen Miley, B.S. Research Assistant, Massachusetts General Hospital, Boston, Massachusetts Henry A. Nasrallah, M.D. Professor of Psychiatry, Neurology and Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio Jimmi Nielsen, M.D. Research Psychiatrist, Unit for Psychiatric Research, Aarhus University Hospital and Aalborg Psychiatric Hospital, Aalborg, Denmark Egon Toft, M.D., F.E.S.C. Professor, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Mackenzie Varkula, D.O. Resident Physician, Department of Psychiatry and Psychology, Cleveland Clinic Foundation, Cleveland, Ohio Adele C. Viguera, M.D., M.P.H. Associate Professor of Psychiatry, Department of Psychiatry and Psychology, Cleveland Clinic Foundation, Cleveland, Ohio
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Milton L. Wainberg, M.D. Associate Clinical Professor of Psychiatry, Columbia University College of Physicians and Surgeons; Director of Medical Education, Columbia University HIV Mental Health Training Project, New York, New York Heidi J. Wehring, Pharm.D., B.C.P.P. Fellow, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland Andrea H. Weinberger, Ph.D. Assistant Professor, Program for Research in Smokers with Mental Illness, Division of Substance Abuse, Department of Psychiatry, Yale University School of Medicine, The Connecticut Mental Health Center, New Haven, Connecticut
Disclosure of Interests The following contributors to this book have indicated a financial interest in or other affiliation with a commercial supporter, a manufacturer of a commercial product, a provider of a commercial service, a nongovernmental organization, and/or a government agency, as listed below: Daniel E. Casey, M.D.—Consultant: Abbott Laboratories, Bristol-Myers Squibb, Dainippon Sumitomo Pharma, Janssen Pharmaceutica, NuPathe, Pfizer, Solvay Pharmaceuticals, and Wyeth Pharmaceuticals; Speaker’s bureau: Abbott Laboratories, Bristol-Myers Squibb, Janssen, and Pfizer. Tony Cohn, M.B.Ch.B., M.Sc., F.R.C.P.C.—Research grant funding: Pfizer Canada; Speaker’s fees: Pfizer Canada. Christoph U. Correll, M.D.—Grant support: Einstein Institute for Medical Research, National Institute of Mental Health (NIMH), and Stanley Foundation; Consultant: AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen, Otsuka, Pfizer, Supernus, and Vanda; Speaker's bureau: AstraZeneca, Bristol-Myers Squibb, and Otsuka. Rohan Ganguli, M.D., F.R.C.P.C.—Consultant: Janssen; Speaker’s honoraria: Bristol-Myers Squibb. Tony P. George, M.D., F.R.C.P.C.—Grant support: Donaghue Medical Research Foundation, National Alliance for Research on Schizophrenia and Depression (NARSAD), National Institute on Drug Abuse (NIDA), Sanofi-Aventis, and Sepracor; Advisory board/consultant: Eli Lilly, Evotec, and Pfizer. David C. Henderson, M.D.—Research grants: NIMH, Solvay, and Takeda; Honoraria: Bristol-Myers Squibb, Covance, Janssen, Pfizer, Primedia, Reed Medical Education, and Solvay Pharmaceuticals. Deanna L. Kelly, Pharm.D., B.C.P.P.—Advisory board: Bristol-Myers Squibb and Solvay. Jonathan M. Meyer, M.D.—Research support: Bristol-Myers Squibb, NIMH, and Pfizer; Speaking/advising fees: AstraZeneca, Bristol-Myers Squibb, Organon, Pfizer, Vanda Pharmaceuticals, and Wyeth. Henry A. Nasrallah, M.D.—Research support: AstraZeneca, Forest Pharmaceuticals, GlaxoSmithKline, Janssen, NIMH, Pfizer, Roche, and Sanofi; Consultant: AstraZeneca, Cephalon, Janssen, Pfizer, and Vanda; Speaker’s bureau: Abbott, AstraZeneca, Janssen, Pfizer, Solvay, and Vanda.
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Adele C. Viguera, M.D., M.P.H.—Grant/research support: AstraZeneca (Claffin Award), Berlex Laboratories, Eli Lilly, Epilepsy Foundation*, Forest, GlaxoSmithKline, Beecham Pharmaceuticals, Harvard Medical School's Scholars in Medicine Fellowship, Janssen, NARSAD, NIMH, Pfizer, Sepracor, Stanley Medical Research Institute, and Wyeth-Ayerst; Speaker's bureau/honoraria: AstraZeneca*, Eli Lilly, Forest*, GlaxoSmithKline, Novartis Pharmaceuticals, Wyeth-Ayerst; Advisory board: GlaxoSmithKline*, Novartis, and Wyeth-Ayerst*. *Funding in the last 12 months. Andrea H. Weinberger, Ph.D.—Grant support: NARSAD and Sepracor. The following authors affirmed that they have no competing interests: Alan Berkman, M.D. Samantha Brenner, M.P.H. Carl I. Cohen, M.D. Francine Cournos, M.D. Mark Drew Crosland Guimarães, M.D., D.Sc., M.P.H. Guy Faulkner, B.Ed., M.Sc., Ph.D. David Folsom, M.D., M.PH. Thomas E. Hansen, M.D. Karen McKinnon, M.A. Jimmi Nielsen, M.D. Egon Toft, M.D., F.E.S.C. Milton L. Wainberg, M.D. Heidi J. Wehring, Pharm.D., B.C.P.P.
American Psychiatric Publishing, Inc. (APPI)—Subsequent to peer review and acceptance for publication of this volume, APPI entered into an agreement for Dainippon Sumitomo Pharma America, Inc., to underwrite and distribute a separate printing of Medical Illness and Schizophrenia, 2nd Edition.
Preface Since the publication of the first edition of this volume in 2003, there has been a dramatic increase in the literature on optimizing medical outcomes for patients with schizophrenia. Sitting before me is the August 2008 issue of Psychiatric Services, which includes no less than eight research articles devoted to medical issues of relevance to those who care for individuals with severe mental illness. Despite the growing awareness of the unique medical needs in this patient population, natural causes of death remain the primary source of excess mortality, with recent data indicating a widening mortality gap between patients with schizophrenia and the general population (Saha et al. 2007). The recognition that antipsychotic treatment can be associated with deleterious metabolic and other health effects emphasizes the need to address medical aspects of schizophrenia treatment, yet the mental health community as a whole still has a great deal to accomplish toward achieving parity in physical health monitoring and treatment between the severely mentally ill and the rest of society. Barriers remain, but they are not insurmountable. We have expanded this second edition with the aim of providing a comprehensive resource for those who wish to be informed about the various medical issues that impinge on schizophrenia treatment. Our intent is to create a practical manual that can serve as a reference for the medical management of patients with severe mental illness across the age spectrum, with a focus on those areas of significant importance to schizophrenia patients, such as cardiometabolic disorders and their relationship to antipsychotic therapy, smoking, substance use, and infection with human immunodeficiency virus (HIV) and hepatitis C. We have also added a section on the important but neglected topic of sexual function in patients with schizophrenia and have devoted an entire chapter to the behavioral management of weight gain and obesity. The intent, as always, is to stimulate those who work in mental health settings to take a broader view of schizophrenia care, recognizing that this is a systemic disease with multiple manifestations that go beyond the obvious psychiatric symptoms. Jonathan M. Meyer, M.D. xv
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Reference Saha S, Chant D, McGrath J: A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time? Arch Gen Psychiatry 64:1123–1131, 2007
PART I Public Health Issues for Schizophrenia Patients
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CHAPTER 1 Improving Physical Health Care for Patients With Serious Mental Illness David Folsom, M.D., M.P.H.
Much has been written about the need to integrate mental health care into the primary care setting. Psychiatrists and other mental health care providers have often criticized primary care physicians for underrecognizing and undertreating depression, substance abuse, and other common mental health conditions (Wells et al. 2000). Meanwhile, the challenge of providing quality medical care to patients with serious mental illnesses represents a similar challenge for psychiatrists. Atypical antipsychotic medications are the mainstay of contemporary pharmacological treatment for psychotic disorders (Harrington et al. 2000; IMS Health 2002; Jin et al. 2004). However, in the past decade, concern and awareness have been growing that these medications may cause or exacerbate medical conditions including weight gain and obesity (Daumit et al. 2003; Lieberman et al. 2005), elevated blood sugar and diabetes (Henderson et al. 2005; Leslie and Rosenheck 2004; Lieberman et al. 2005), and cerebrovascular events (Schneider et al. 2005). The U.S. Food and Drug Administration currently mandates that the package inserts for all six atypical antipsychotics—aripiprazole (Abilify 3
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2008), clozapine (Clozaril 2008), olanzapine (Zyprexa 2008), quetiapine (Seroquel 2008), risperidone (Risperdal 2008), and ziprasidone (Geodon 2008)—include black-box warnings regarding the increased risk of mortality in elderly patients with dementia and a warning about the risk of hyperglycemia and diabetes. These warnings and the emerging data on the medical side effects of antipsychotics have raised psychiatrists’ concern and awareness about comorbid medical conditions in their patients and have led to guidelines for the medical monitoring of patients taking these medications (American Diabetes Association et al. 2004; Marder et al. 2002). However, evidence that predates the atypical antipsychotics suggested that patients with severe mental illness (SMI) are at higher risk for several medical conditions and often receive inferior medical care, both of which likely contribute to their elevated mortality. In this chapter, I briefly review the data on the prevalence of comorbid medical conditions and the lack of quality medical care for patients with SMI, examine prior studies that attempt to improve the health status of these patients, and consider policies that might improve the integration of medical and mental health care for patients with SMI.
Medical Comorbidity and Access to Quality Medical Care for Patients With Serious Mental Illness Patients with schizophrenia and other serious mental illnesses constitute a particularly vulnerable group, for whom access to quality medical care is problematic (Druss et al. 2001a; Harvey et al. 2005). Druss et al. (2001a) reported that following myocardial infarction, patients with schizophrenia had a 30% greater 1-year mortality than patients who were not mentally ill. Approximately half of this increased mortality was due to a lack of quality medical treatment after the myocardial infarction. Folsom et al. (2002) found that in homeless patients with mental illness, those with schizophrenia were less likely to receive primary and preventive care than were patients with major depression. Furthermore, investigators have reported elevated cardiovascular risk (McCreadie 2003), as well as inferior medical care and elevated mortality in persons with schizophrenia compared with the general population (Koran et al. 1989; Phelan et al. 2001). For example, the rates of reported hypertension were 40% lower in persons with schizophrenia than in the general population, but the rates of admission for end-stage complications of hypertension, including cardiomyopathy and pulmonary
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edema, were 1.8 and 1.5 times greater, respectively, in the patients with schizophrenia (Munk-Jørgensen et al. 2000). These findings suggest that patients with schizophrenia were less likely to receive treatment in the early phase of a disease and instead were receiving care later, when the disease became more severe and required hospitalization. Some encouraging data have also been published regarding health care for patients with schizophrenia. An investigation of patients treated in community-based settings found that, compared with the general population, a greater percentage of patients with schizophrenia had received primary care treatment in the prior year (Dickerson et al. 2003). At the same time, the growing concerns about the risk of diabetes, myocardial infarction, and stroke in patients taking atypical antipsychotics have increased the awareness of the importance of diagnosis and treatment of comorbid medical conditions in patients with schizophrenia (Folsom and Okereke 2005; Jin et al. 2004).
Models for Improving Medical Care for Patients With Serious Mental Illness Although the literature on interventions and service delivery models to improve the health outcomes of patients with SMI is not as extensive as that of treating depression in primary care, four basic approaches have been examined: 1) health care skills training for patients, 2) training of psychiatrists to directly provide primary care, 3) facilitated referral to primary care, and 4) co-location or integration of primary care with mental health care (Druss 2007).
Health Care Skills Training for Patients Several interventions that have been developed and tested focus on providing education and skills training for patients with chronic mental illnesses who also have a comorbid medical condition. Two skills training interventions specifically related to health care outcomes are particularly noteworthy. One intervention has been described by McKibbin et al. (2006), who enrolled 60 patients over age 40 with schizophrenia and comorbid diabetes into their diabetes awareness and rehabilitation training (DART) program. These authors tested whether patients who received an intervention based on social cognitive theory and incorporating standard diabetes self-management approaches experienced greater reductions in weight and hemoglobin A1c than did a comparison group that received only diabetes education. The intervention
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lasted 24 weeks, with patients randomly assigned to one of the two groups. At the end of the study, patients in the DART intervention lost an average of 2.3 kg, compared with a 2.7-kg weight gain in the comparison group; however, there were no significant differences in hemoglobin A1c between the two groups. The second skills training intervention is exemplified by Wu et al.’s (2008) study, which included patients who had experienced at least a 10% weight gain from atypical antipsychotics. A 2 ×2 factorial design was used to compare the effects of a lifestyle intervention, metformin (a diabetes medication that inhibits hepatic glucose production), both interventions, and placebo. This study, conducted in China, enrolled 128 patients and examined changes in weight after 12 weeks of treatment. Over the 12-week period, patients in the placebo group gained an average of 3.1 kg, whereas those receiving the lifestyle intervention lost 1.4 kg, those taking metformin lost 3.2 kg, and those in the combined lifestyle-metformin group lost 4.7 kg. Similar results were reported by Weber and Wyne (2006), who also tested a cognitive-behavioral intervention for overweight patients taking an atypical antipsychotic. The mean body mass index of patients enrolled in this study was 33, and at the end of the investigation, the mean weight loss for those in the intervention group was 2.4 kg, compared with 0.6 kg for those in the comparison group. These results demonstrate that lifestyle interventions modified for patients with schizophrenia and other serious mental illnesses can result in improved health outcomes. However, aside from weight gain and a single report on patients with diabetes, data are lacking on the effects of patient education interventions on other health outcomes for patients with SMI. Ongoing studies funded by the National Institute of Mental Health (NIMH) are examining health care outcomes, and in the coming years, information from these studies should help provide data on improving the health of patients with SMI and other health problems.
Training of Psychiatrists to Provide Primary Care A second approach to improving the health of patients with SMI is to have some aspects of primary health care provided by psychiatrists. McIntyre and Romano’s (1977) article “Is There a Stethoscope in the House (and Is It Used)?” highlighted the lack of practicing psychiatrists’ comfort with conducting physical examinations of their patients. Shore (1996) proposed that psychiatrists need to be able to provide
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basic primary care to their patients with SMI, arguing that psychiatrists are frequently the only physicians these patients see. Golomb et al. (2000) assembled a consensus panel of Veterans Affairs (VA) psychiatrists, internists, hospital administrators, and nurses to identify health conditions that would be appropriately treated by a psychiatrist who has primary care training and on-site supervision by an internist. All panel members agreed that a psychiatrist with primary care training could provide care for 17 of 38 preventive screening and counseling interventions but could perform only 10 of 157 evaluations of medical conditions and provide treatment for only 14 of 125 medical conditions. Examples of common medical conditions that the consensus panel thought primary care psychiatrists could treat included cough, headache, and nasal congestion. Examples of common conditions that the panel did not think that primary care psychiatrists could treat included chest pain and ear infection. A few psychiatry residencies have begun to train their residents to provide primary care to patients with serious mental illness. These programs include the University of California San Diego and the Oregon Health and Science University in Portland. In a series of reports, Dobscha and colleagues at the Portland VA describe the outcomes of providing this type of training in an integrated psychiatry primary medical care (PPMC) program (Dobscha and Ganzini 2001; Dobscha et al. 2005; Snyder et al. 2008). In one of these studies (Dobscha et al. 2005), nine graduates of the PPMC program and 34 other residency graduates were asked how comfortable they were with general medical conditions and how frequently they screened for or provided treatment for medical conditions. Responses indicated that, overall, the residents who worked in the PPMC program were more comfortable with general medical conditions than were their fellow residents who did not work in this program. However, few of the PPMC-trained residents provided direct medical care to their patients after graduation. In summary, despite some recommendations that training psychiatrists should provide a limited range of primary care to their patients with a serious mental illness, this model has been adopted in only a few residency training programs. In addition, although the data from these programs are limited, the psychiatrists whose training included providing primary care for some of their patients appeared to be more comfortable with the medical problems of their patients than were psychiatrists without such training, but the primary care–trained psychiatrists did not provide much direct medical care after leaving the training program.
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Facilitated Referral to Primary Care The third model for improving the health care of patients with SMI is to improve the ability of mental health centers to refer their patients to primary care providers and medical clinics. This can be done either by providing training, time, and resources to all of the mental health clinicians in a particular clinic or by having a specific care manager provide this linkage and care coordination. Some empirical evidence supports this model, and findings indicate that patients who receive specific help with accessing primary care treatment receive more primary and preventive health care than patients who do not receive such help. In a small pilot study, Bartels et al. (2004) compared the primary care received by 24 older patients with a serious mental illness who worked with a nurse care manager whose job was to help patients access primary and preventive health care. Using a chart review, the researchers found that, prior to the intervention, 17% of patients did not have a designated primary care provider and 29% had not received any preventive health care in the past year, whereas after working with the nurse case manager, all the patients had a designated primary care provider and had completed a preventive health care visit. In a larger study of 470 patients with a substance abuse disorder entering a detoxification program, Samet et al. (2003) randomly assigned half to a one-time comprehensive assessment plus working with a social worker to arrange for follow-up primary medical care at a clinic, and the other half of the patients received usual care. The patients in the clinic intervention group were more likely to have successfully seen a primary care physician at least once in the 12 months after completing the detoxification program than were patients who received usual care (69% vs. 53%). Currently, at least two ongoing NIMH-funded studies are examining whether this model would result in improved health outcomes for patients with serious mental illness (S. Bartels, personal communication, July 2008; Druss 2007).
Co-Location or Integration of Mental Health Treatment and Primary Care The fourth approach to improving the health care of patients with SMI is to co-locate primary care and mental health care within the same clinic, ideally in an integrated manner that has psychiatrists and primary care providers working together to care for patients with SMI and chronic medical conditions. These clinics would be staffed by a combination of psychiatry and primary care physicians, or by physicians du-
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ally trained in psychiatry and a primary care specialty (family medicine or internal medicine). Druss et al. (2001b) compared patient outcomes in a clinic that provided integrated medical and psychiatric care with those of a clinic providing a usual care model. In this VA-based study, 59 patients were randomly assigned to receive care in an integrated care clinic based in a VA mental health clinic that was also staffed by a full-time nurse practitioner, a part-time family physician, and a nurse case manager. The comparison group included 61 veterans who received their care in a typical VA outpatient medical clinic. Approximately three-fourths of the patients had a serious mental illness, with posttraumatic stress disorder, substance use disorders, and schizophrenia being the most common diagnoses. In addition, more than half of the patients had a previously unknown medical diagnosis. Compared to the patients receiving usual care, those in the integrated care clinic were more likely to use primary care services and less likely to use the emergency room. In addition, patients in the integrated clinic were more likely to receive preventive health care, including screening for colon cancer and diabetes, immunizations, and health education. Patients in the integrated clinic also rated their satisfaction with care more highly than did those in the comparison group, and demonstrated a small but significant improvement in their physical health-related quality of life. In an interesting qualitative assessment of this integrated clinic, Miller et al. (2003) used focus groups to identify three aspects of integrated care that were particularly important to people with a serious mental illness. First, patients reported that they had previously faced difficulty obtaining medical care in more traditional settings. For example, patients reported that in the past they had been turned away from medical clinics, were not treated with respect, or had their medical concerns dismissed. Second, the integrated clinic had greater flexibility and availability of resources, including a nurse case manager and a smaller patient panel size for the primary care providers. Finally, both patients and clinic staff noted that the communication between the patients’ psychiatrist and primary care provider was greater than what they had previously experienced, and primary care and mental health clinicians reported that they benefited from regular contact with each other. Another option for integrated care, training physicians in both psychiatry and a primary care specialty, has been less well studied. To date, two studies have been published describing the type of practice setting for dually trained physicians. A survey of physicians trained in both family medicine and psychiatry found that most (60%) practice both family medicine and psychiatry (McCahill and Palinkas 1997). In con-
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trast, psychiatrists also trained in internal medicine reported that only 15% practiced any type of medicine and 75% identified themselves as psychiatrists (Stiebel and Schwartz 2001). However, neither of these reports examined the patient outcomes of care provided by these dually trained physicians compared with usual care. Also, both of these studies are based on data that are more than a decade old, and more training programs are available now than when these data were published. Dual training programs in the United States are limited. Only seven training programs are offered in family medicine and psychiatry and 11 in internal medicine and psychiatry, and most of these programs take only two residents per year. In 2007, a total of 11 graduating medical students matched into family medicine and psychiatry residencies and 22 matched into internal medicine and psychiatry residencies (National Resident Matching Program 2008).Therefore, although this model of training physicians in both psychiatry and a primary care specialty offers the possibility of truly integrated medical and psychiatric care for patients with serious mental illness, the capacity of the current training programs is not large enough to provide this kind of care on a large scale. In summary, two models have been proposed for providing integrated medical and psychiatric care to people with serious mental illness: 1) co-locating primary care physicians in mental health clinics and 2) staffing these clinics with physicians trained in both psychiatry and a primary care specialty. Druss (2007) suggested that these types of clinics may be best suited for VA system and staff model health maintenance organizations, which use shared funding streams and medical records.
Challenges to Providing Medical Care for Patients With Serious Mental Illness Patient Level Researchers have offered several explanations as to why people with schizophrenia receive less health care than the general population. People with schizophrenia may have problems explaining their medical symptoms to primary care physicians. Physicians may be uncomfortable treating persons with schizophrenia (Lawrie et al. 1996), possibly reflecting a stigmatization of people with schizophrenia. Similarly, psychiatrists may not feel comfortable providing primary and preventive health care for their patients (McIntyre and Romano 1977). Fang and
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Rizzo (2007) used data from a national survey of physicians to examine whether psychiatrists had less access to medical services for their patients than did other medical specialists. This study compared psychiatrists’ reports of their ability to obtain medically necessary medical care for their patients with those of other specialist physicians. The study found that psychiatrists rated their ability to obtain most types of medical care “dramatically” lower than did other specialists, in part because many patients did not have insurance and those who did had barriers from their health plan.
Policy Level According to Horvitz-Lennon et al. (2006), challenges to integrating primary health care into specialty mental health treatment exist on three levels: clinical, organizational, and financial. Clinical challenges include the lack of training in physical health care by mental health clinicians. It is important to note that although most psychiatrists do not feel comfortable providing primary medical care, the knowledge of and comfort with medical problems is even lower among other mental health care providers, who rarely have formal medical training. Financial challenges include the high rates of uninsurance among persons with serious mental illness and the fact that financing for public mental health programs is typically separate from that of other public health programs. For example, in the San Diego County public mental health system, approximately 50% of patients with a serious mental illness are uninsured (Folsom et al. 2005). Furthermore, the application process for and financial restrictions of the public mental health system are separate and different from those of the safety-net health care program, resulting in a large number of patients receiving mental health treatment who do not have coverage for medical care.
Conclusion The growing recognition that people with schizophrenia and other serious mental illnesses have elevated rates of many medical conditions and often do not receive adequate medical care challenges psychiatrists and the mental health field to come up with practical approaches to improving their care. The four potential approaches examined in this chapter—health care skills training for patients, training psychiatrists to provide limited primary care, facilitated referral to primary care, and integration of primary care into mental health clinics—are all supported by some data. However, none of these approaches currently is
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widespread, and no single approach is likely to provide a solution. In an ideal world, psychiatrists would help ensure that their patients receive high-quality physical health care; staff at mental health clinics would help patients who need health care; mental health clinics would offer onsite primary care; and the financial barriers to providing integrated physical and mental health care would be removed.
Key Clinical Points ◗
Patients with schizophrenia and other serious mental illnesses receive less primary and preventive care than needed.
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Guidelines alone are unlikely to result in improved health care for patients with serious mental illness.
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Prior studies have examined four ways of improving health care for people with serious mental illness: health care skills training for patients, training psychiatrists to provide limited primary care, facilitated referral to primary care, and providing onsite primary care in mental health clinics.
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Integrating primary care into mental health clinics may require significant changes in how mental health care is funded and delivered.
References Abilify (package insert). Princeton, NJ, Bristol-Myers Squibb, 2008 American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, et al: Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes. Diabetes Care 27:596–601, 2004 Bartels SJ, Forester B, Mueser KT, et al: Enhanced skills training and health care management for older persons with severe mental illness. Community Ment Health J 40:75–90, 2004 Clozaril (package insert). East Hanover, NJ, Novartis Pharmaceuticals Corp., 2008 Daumit GL, Crum RM, Guallar E, et al: Outpatient prescriptions for atypical antipsychotics for African Americans, Hispanics, and whites in the United States. Arch Gen Psychiatry 60:121–128, 2003 Dickerson FB, McNary SW, Brown CH, et al: Somatic healthcare utilization among adults with serious mental illness who are receiving community psychiatric services. Med Care 41:560–570, 2003
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Dobscha SK, Ganzini L: A program for teaching psychiatric residents to provide integrated psychiatric and primary medical care. Psychiatr Serv 52:1651– 1653, 2001 Dobscha SK, Snyder KM, Corson K, et al: Psychiatry resident graduate comfort with general medical issues: impact of an integrated psychiatry-primary medical care training track. Acad Psychiatry 29:448–451, 2005 Druss BG: Improving medical care for persons with serious mental illness: challenges and solutions. J Clin Psychiatry 68:40–44, 2007 Druss BG, Bradford WB, Rosenheck RA, et al: Quality of medical care and excess mortality in older patients with mental disorders. Arch Gen Psychiatry 58:565–572, 2001a Druss BG, Rohrbaugh RM, Levinson CM, et al: Integrated medical care for patients with serious psychiatric illness. Arch Gen Psychiatry 58:861–868, 2001b Fang H, Rizzo JA: Do psychiatrists have less access to medical services for their patients? J Ment Health Policy Econ 10:63–71, 2007 Folsom DP, Okereke OI: Medical comorbidity in geriatric psychiatry. Am J Geriatr Psychiatry 13:1, 2005 Folsom D, McCahill M, Bartels S, et al: Medical comorbidity and receipt of medical care by older homeless people with schizophrenia or depression. Psychiatr Serv 53:1456–1460, 2002 Folsom DP, Hawthorne W, Lindamer L, et al: Prevalence and risk factors for homelessness and utilization of mental health services among 10,340 patients with serious mental illness in a large public mental health system. Am J Psychiatry 162:370–376, 2005 Geodon (package insert). New York, NY, Pfizer, Inc., 2008 Golomb BA, Pyne JM, Wright B, et al: The role of psychiatrists in primary care of patients with severe mental illness. Psychiatr Serv 51:766–773, 2000 Harrington C, Gregorian R, Gemmen E, et al; for The Lewin Group: Access and utilization of new antidepressant and antipsychotic medications. January 2000. Available at: http://aspe.hhs.gov/search/health/reports/Psychmedaccess/ index.htm#TOC. Accessed October 8, 2008. Harvey SB, Newton A, Moye GA: Physical health monitoring in schizophrenia: the use of an invitational letter in a primary care setting. Primary Care and Community Psychiatry 10:71–74, 2005 Henderson DC, Cagliero E, Copeland PM, et al: Glucose metabolism in patients with schizophrenia treated with atypical antipsychotic agents: a frequently sampled intravenous glucose tolerance test and minimal model analysis. Arch Gen Psychiatry 62:19–28, 2005 Horvitz-Lennon M, Kilbourne AM, Pincus HA: From silos to bridges: meeting the general health care needs of adults with severe mental illnesses. Health Aff (Millwood) 25:659–669, 2006 IMS Health: Atypical antipsychotics—generating evidence to inform policy and practice. 2002. Available at: http://research.imshealth.com/research/ research_schizophrenia.htm. Accessed May 15, 2006.
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Jin H, Meyer JM, Jeste DV: Atypical antipsychotics and glucose dysregulation: a systematic review. Schizophr Res 71:195–212, 2004 Koran LM, Sox HC, Marton KI, et al: Medical evaluation of psychiatric patients: results in a state mental health system. Arch Gen Psychiatry 46:733–740, 1989 Lawrie SM, Parsons C, Patrick J, et al: A controlled trial of general practitioners’ attitudes to patients with schizophrenia. Health Bull 54:201–203, 1996 Leslie DL, Rosenheck RA: Incidence of newly diagnosed diabetes attributable to atypical antipsychotic medications. Am J Psychiatry 161:1709–1711, 2004 Lieberman JA, Stroup TS, McEvoy JP, et al: Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med 353:1209–1223, 2005 Marder SR, Essock SM, Miller AL, et al: The Mount Sinai Conference on the Pharmacotherapy of Schizophrenia. Schizophr Bull 28:5–16, 2002 McCahill ME, Palinkas LA: Physicians who are certified in family practice and psychiatry: who are they and how do they use their combined skills? J Am Board Fam Pract 10:111–115, 1997 McCreadie RG; Scottish Schizophrenia Lifestyle Group: Diet, smoking and cardiovascular risk in people with schizophrenia: descriptive study. Br J Psychiatry 183:534–539, 2003 McIntyre JS, Romano J: Is there a stethoscope in the house (and is it used)? Arch Gen Psychiatry 34:1147–1151, 1977 McKibbin CL, Patterson TL, Norman G, et al: A lifestyle intervention for older schizophrenia patients with diabetes mellitus: a randomized controlled trial. Schizophr Res 86:36–44, 2006 Miller CL, Druss BG, Rohrbaugh RM: Using qualitative methods to distill the active ingredients of a multifaceted intervention. Psychiatr Serv 54:568– 571, 2003 Munk-Jørgensen P, Mors O, Mortensen PB, et al: The schizophrenic patient in the somatic hospital. Acta Psychiatr Scand Suppl 407:96–99, 2000 National Resident Matching Program. Available at: http://www.nrmp.org/ data/resultsanddata2007.pdf. Accessed August 12, 2008. Phelan M, Stradins L, Morrison S: Physical health of people with severe mental illnesses. BMJ 322:443–444, 2001 Risperdal (package insert). Titusville, NJ, Janssen Pharmaceutica, Inc., 2008 Samet JH, Larson MJ, Horton NJ, et al: Linking alcohol- and drug-dependent adults to primary medical care: a randomized controlled trial of a multidisciplinary health intervention in a detoxification unit. Addiction 98:509– 516, 2003 Schneider LS, Dagerman KS, Insel P: Risk of death with atypical antipsychotic drug treatment for dementia: meta-analysis of randomized placebocontrolled trials. JAMA 294:1934–1943, 2005 Seroquel (package insert). Wilmington, DE, AstraZeneca Pharmaceuticals, LP, 2008 Shore JH: Psychiatry at a crossroad: our role in primary care. Am J Psychiatry 153:1398–1403, 1996
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Snyder K, Dobscha SK, Ganzini L, et al: Clinical outcomes of integrated psychiatric and general medical care. Community Ment Health J 44:147–154, 2008 Stiebel V, Schwartz CE: Physicians at the medicine/psychiatric interface: what do internist/psychiatrists do? Psychosomatics 42:377–381, 2001 Weber M, Wyne K: A cognitive/behavioral group intervention for weight loss in patients treated with atypical antipsychotics. Schizophr Res 83:95–101, 2006 Wells KB, Sherbourne C, Schoenbaum M, et al: Impacts of disseminating quality improvement programs for depression in managed primary care: a randomized controlled trial. JAMA 283:3204, 2000 Wu RR, Zhao JP, Jin H, et al: Lifestyle intervention and metformin for treatment of antipsychotic-induced weight gain: a randomized controlled trial. JAMA 299:185–193, 2008 Zyprexa (package insert). Indianapolis, IN, Eli Lilly, Inc., 2008
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CHAPTER 2 Excessive Mortality and Morbidity Associated With Schizophrenia Daniel E. Casey, M.D. Thomas E. Hansen, M.D.
Psychiatric conditions have long been recognized to be associated with premature mortality. In his review, Brown (1997) noted that “lunatics” (perhaps 25% having schizophrenia) in a study from 1841 experienced mortality at three to 14 times the rate seen in the general population. According to Brown, Eugen Bleuler reported that mortality in patients with schizophrenia was 1.4 times the expected rate, caused by accidents, suicide, refusing food, infectious disease, and other diseases from poor sanitation. Brown stated that although Emil Kraepelin considered mortality to be only slightly increased in dementia praecox, he reported that suicide, negativism, diet, and poor cooperation with medical care contributed to increased mortality. Studies from the first half of the twentieth century found mortality rates in psychiatric patients to be two and four times higher than in age- and gender-matched populations (Brown 1997). Hazards of institutional care (such as tuberculosis and gastrointestinal disease) added to risks associated with the underlying psychiatric illness. Unnatural causes (suicide and accidental 17
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death) became an increasingly important cause of premature death with the shift from inpatient to outpatient care for patients with schizophrenia (Black et al. 1985). More recently, cardiovascular disease and diabetes may be increasing in importance as causes of death and morbidity in these patients (Jeste et al. 1996). This chapter updates our earlier review (Casey and Hansen 2003) of mortality and morbidity associated with schizophrenia. Data clearly demonstrate that the life span for patients with schizophrenia is shortened. Many conditions leading to premature death are evident during patients’ lifetimes and account for the majority of the excess morbidity seen in patients with schizophrenia. Both patient behaviors and societal aspects of care for schizophrenia can contribute to this increase in morbidity.
Life Span and Mortality in Patients With Schizophrenia Mortality Rates Reports on mortality rates commonly use record linkage methodology: databases of all deaths in a region are linked with population registers of psychiatric patients (community or hospital derived) so that the number of deaths in a group of patients with a specific diagnosis can be determined. This observed number is contrasted to the number expected based on life expectancy data for people of the same age and gender applied to the time period studied. The ratio of observed to expected cases is the standardized mortality ratio (SMR), sometimes reported as the ratio multiplied by 100. The 95% confidence interval (CI) values indicate statistical significance, with lower CI values above 1.0 (or 100, when the SMR is multiplied by 100) supporting significance for elevated SMR, and upper CI values below 1.0 (or 100) supporting significance for decreased SMR (Harris and Barraclough 1998). In this chapter, for ease of comparison, we report SMR and CI data without multiplying by 100, even if so reported in cited references. Reviews of mortality rates consistently find increased death rates in patients with schizophrenia. This consistency is remarkable when one considers variation in methods across individual studies and reviews. For instance, individual studies from earlier periods may be overly influenced by inclusion of hospitalized patients (lower risk of suicide, higher risk of diseases related to institutionalization), whereas later studies are likely to include outpatients (greater risk of suicide) and to be more precise in diagnosis.
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Three reviews that summarized results from studies conducted between 1942 and 1996 found the mortality rate in patients with schizophrenia to be between 1.5 and 2 times the expected rate. Allebeck (1989) reviewed nine studies published between 1942 and 1985 in which patients had been followed at least 5 years and found mortality about twice the expected rate. He noted difficulty in ascertaining whether rates had changed because diagnostic and study criteria had varied over time. Brown (1997) examined studies published between 1986 and 1996 with schizophrenia patient populations of at least 100 individuals recruited after 1952 that included observed and expected death rates and that had follow-up periods of at least 2 years, with losses to followup of less than 15%. He found an aggregate SMR of 1.51 (95% CI 1.48– 1.54). The SMR for suicide was elevated (8.38, 95% CI 7.84–8.94), as were the SMR for accidents (2.16, 95% CI 1.96–2.36) and the SMR for natural causes of death (1.1, 95% CI 1.05–1.15). Brown interpreted data about specific natural causes of death conservatively, stating that patients with schizophrenia die from the same diseases as the rest of the population. He noted that peptic ulcer (perhaps related to alcohol use) and pneumonia (probably associated with elderly institutionalized patients) caused deaths at higher than expected rates. Harris and Barraclough (1998) reviewed mortality rates in cohorts of patients with various mental disorders between 1966 and 1995 (20 studies, nine countries, almost 36,000 patients) and found a death risk in patients with schizophrenia that was 1.6 times expected. The death risk from unnatural causes (suicide and violence) was 4.3 times that expected, whereas death from natural causes was 1.4 times that expected, but natural causes accounted for 68% of the excess in expected deaths. In our earlier review (Casey and Hansen 2003), we described three studies published in 2000–2001, with data on patients studied between 1973 and 1995, in which mortality rates were reported to be somewhat higher than in the earlier reviews (significantly increased relative risk or SMR of 2.4–3.8, depending on patient gender). A meta-analysis by Saha et al. (2007) confirms that SMRs for schizophrenia are increasing; the analysis reviewed 37 articles published between 1980 and 2006 from 25 countries and found the overall SMR to be 2.58. When studies were grouped by midpoint of the follow-up period, SMR increased from 1.84 in the 1970s, to 2.98 in the 1980s, and to 3.2 in the 1990s. A study not included in the meta-analysis from rural China (Ran et al. 2007) followed 510 patients with schizophrenia for the 10-year period of 1994–2004 and further demonstrated higher mortality in more recent studies (overall SMR of 4.0, 95% CI 2.4–5.8).
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Life expectancy and survival curve data offer further confirmation of increased mortality associated with schizophrenia. In our earlier review (Casey and Hansen 2003), we noted that in three studies based on 5- to 10-year follow-up of patients, authors projected decreased life expectancy of 8–16 years in general and of 3–4 years for patients ages 60 years and older. Three more recent studies also utilize survival data. Patients with first admissions for psychosis in Suffolk County, New York, between 1989 and 1995 were evaluated for survival rates at 5 and 10 years using survival curves; survival rates were 96.9% at 5 years and 94.5% at 10 years for the 235 patients with schizophrenia or schizoaffective disorder, with 60% of deaths resulting from unnatural causes (Craig et al. 2006). In a 10-year (1981–1991) mortality study of 255 patients with schizophrenia from Uppsala, Sweden, 23% of patients died compared with 11% of the 1,275 controls (P=0.003) (Fors et al. 2007). Patients with schizophrenia (n =319) seen at the Mayo Clinic from 1950–1980 were followed up to 2005 (mean 23.5 years); 44% died during this time, a rate higher (P <0.001) than for a comparable U.S. population (the specific rate was not reported, but appears to be 34% from the survival curve comparison) (Capasso et al. 2008). Thus, data from the last 50 years demonstrate that the increase in mortality seen for patients with schizophrenia in earlier time periods persists. Furthermore, the disparity in death rates between patients with schizophrenia and the general population appears to be widening.
Suicide and Other Unnatural Causes of Mortality Reviews consistently find that patients with schizophrenia die from suicide at increased rates compared with the general population (Allebeck 1989; Harris and Barraclough 1998), although underreporting is a possibility because suicides can be incorrectly reported as resulting from accidents or undetermined causes (Allebeck 1989). In this section, we focus on suicide as an unnatural cause of death given the lower mortality rates from accidents. For instance, one review reported SMRs of 8.38 for suicide and 2.16 for accidents (Brown 1997). In a Danish register study of patients admitted between 1973 and 1993 who died by 1993, Hiroeh et al. (2001) reported for males an SMR of 10.7 for suicide compared with 2.1 for accidents, and for females an SMR of 10.8 for suicide compared with 2.9 for accidents. In our earlier review (Casey and Hansen 2003), we summarized data from multiple studies; SMR values generally ranged from 12 to 20, except SMR of 1.32 in one study (Mortensen and Juel 1990) that included mostly elderly institutionalized patients from 1957 to 1986. Several recent stud-
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ies have found suicide SMRs in the higher range. The rate of suicide in the general population and in patients with schizophrenia in Denmark declined between 1981 and 1997, but the incidence ratio remained 20 times higher for patients with schizophrenia (Nordentoft et al. 2004). Even correcting for social and demographic differences, the rate remained high at 12.26. A French 10-year follow-up study starting in 1993 (Limosin et al. 2007) found the suicide SMR for males to be 15.8 (95% CI 13.7–18.2) and for females to be 17.7 (95% CI 13.6–23.1). In both of these studies, suicide rates were highest within the first year of follow-up. The suicide mortality data support several observations. The increase in risk from suicide for patients with schizophrenia was much lower many years ago when patients were commonly kept in hospitals for long time periods. The risk is greatest early in the course of illness and close to the hospital discharge, so that mortality rates vary between studies depending in part on study inclusion criteria. Finally, the risk of suicide appears to be increasing, perhaps related to greater reliance on less intensive treatment emphasizing outpatient care. A national psychological autopsy study from Finland provides important details about the nature of suicide by patients with schizophrenia (Heila et al. 1997). All suicides occurring during a 1-year period during 1987 and 1988 were identified. Providers and next of kin were interviewed, and adequacy of medication was examined. This sample had a somewhat older age of suicide (mean 40 years old) than hospitaltreated or initially treated patient groups. The mean illness duration was 15.5 years, and 75% of patients had active symptoms. About 9% had current suicide-command hallucinations. Twenty-five (27%) were hospitalized at the time of suicide, and 29 (32%) had been discharged within 3 months. A depressive syndrome had been present during either a residual or an active phase of psychosis in 61 patients (66%), with depressive symptoms at the time of suicide in 59 (64%). Drug overdose was the most common single cause of death (34 patients, 37%), although various violent means were used in 37 cases (40%). Of 34 drug overdoses, 27 (79%) were with neuroleptic medication and 25 (74%) specifically involved low-potency neuroleptics. The authors suspected that with the shift toward outpatient management, the availability of drugs to use in overdoses has increased. Additional data on risk factors and adequacy of treatment were reviewed for the 88 patients who were in treatment at the time of suicide (Heila et al. 1999). Data on compliance and treatment responsiveness were available for about 72 patients. For the entire group of patients who committed suicide (either with active psychotic symptoms or during a residual phase of illness), only 34 (47%) appeared to be compliant and to be receiving an adequate dosage
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of antipsychotic medication. Data were similar when the patients in active phase of illness were examined separately. Only 26 (46%) of the 57 patients experiencing active symptoms at the time of suicide were receiving an adequate dosage (defined as 700 mg daily in chlorpromazine equivalents) and were compliant in taking medication. These two studies suggest the importance of effective medication management and provision of appropriate intensity of treatment (if not inpatient, then close outpatient observation) in this era of managed care. The French study cited above also notes the importance of substance abuse (Limosin et al. 2007). Estimates of the lifetime risk of suicide, defined as the proportion of all deaths to which suicide contributes, provide an additional perspective on the importance of suicide in schizophrenia-associated mortality. Data from a large meta-analysis (Harris and Barraclough 1998) were analyzed by plotting the percentage of patients who had died against the percentage of patients who had died by suicide. The curve that best modeled the data was extrapolated to the point at which all members of the cohort would be deceased; the percentage of deaths by suicide was estimated to be 4% of all deaths, which is lower than the frequently cited value of 10% lifetime risk for suicide in patients with schizophrenia (Inskip et al. 1998). This finding was confirmed in a more recent meta-analysis (Palmer et al. 2005). Using both proportionate mortality and case fatality statistics from 61 studies to maximize accuracy in estimating suicide risk, the authors concluded that 4.9% of patients with schizophrenia will commit suicide, usually near the onset of illness. An emphasis on suicide rates may be misleading in terms of absolute risk, because suicide is rare and natural causes of death are far more common in the general population. Roughly two-thirds of excess deaths in patients with schizophrenia are by causes other than suicide, illustrating that although many patients with schizophrenia die from suicide, far more die from other causes. For example, suicide accounted for only 38% of the excess mortality in one review (Harris and Barraclough 1998) and 35% of excess mortality in another (Brown 1997). In a Southampton, England, cohort treated in 1981–1982 with 13 years of follow-up, suicide and other unnatural causes accounted for 33% of the deaths (Brown et al. 2000). Another way to understand this issue is to note that although the relative risk for suicide in patients with schizophrenia compared with the general population is higher than the relative risk for natural causes of death, the absolute risk for natural causes of death is far higher than for suicide in patients with schizophrenia. Thus, excess mortality in schizophrenia derives primarily from natural causes rather than from suicide. (See Table 2–1.)
Excessive Mortality and Morbidity
TABLE 2–1. • • • • •
23
Evidence for increased mortality associated with schizophrenia
Increased overall mortality rates in patients with schizophrenia: 1.5–4 times expected Decrease in longevity from survival curves: 8–16 years Increased mortality rates for suicide: 12 to more than 20 times expected Lifetime death from suicide: about 5% Increased standardized mortality ratio from natural causes: at least 2 times expected (varies by disorder)
Natural Causes of Death in Patients With Schizophrenia An autopsy study from the time when phenothiazine medications were new, roughly 50 years ago, provided a view of death by natural causes (Hussar 1966). Autopsy data from 1,275 chronically hospitalized patients with schizophrenia who died at age 40 or older found that heart disease and cancer were the most common causes of death (similar to findings in the general population age 40+ years of that time). Pneumonia was somewhat overrepresented as a cause of death. Undetermined causes and aspiration of food were in the top eight causes of death in the patients with schizophrenia but not in the general population. The authors speculated that phenothiazines could be responsible, although they noted that other investigators at the time found no association between the new medications and death rates. More recent studies quantify increased mortality for various natural causes in patients with schizophrenia. Harris and Barraclough (1998) reviewed mortality rates in cohorts of patients with various mental disorders between 1966 and 1995. Cohorts of patients with schizophrenia had an SMR of 1.37 for natural causes, with significant increases in expected deaths from a variety of medical conditions (infectious, endocrine, circulatory, respiratory, digestive, and genitourinary disorders). In our previous review (Casey and Hansen 2003), we discussed several subsequent studies that reported somewhat higher values for diseasespecific SMR (1.9–2.3). Reviewing individual studies for increased risk of specific disorders presents challenges. Many studies report categories of diseases from the International Classification of Diseases (ICD) rather than specific disorders, not all studies report on the same categories of illness, and some illnesses could fall into more than one category. Also, findings vary between studies, perhaps because the SMR is greatly influenced by a
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small number of observed cases when the expected rate is low. Still, examining individual studies may be helpful in understanding risks for more recent periods, because reviews are likely to include older studies that reflect risks that are no longer as common (e.g., institutionalization and infectious diseases). Two recent Danish register studies of patients with schizophrenia covering overlapping time periods between 1973 and 2001 found significant increases in SMR (with some variation by gender) on the order of 1.44–2.98 for cardiovascular disease, cancer, respiratory diseases, endocrine conditions, gastrointestinal and genitourinary diseases, nervous system illnesses, and infectious diseases (Hiroeh et al. 2008; Laursen et al. 2007). (See Table 2–2.) Cancer, cardiovascular disease, and diabetes warrant additional discussion. The failure to demonstrate increased risk of cancer in patients with schizophrenia when all causes of cancer are aggregated has led to much speculation, including the possibility that having schizophrenia may convey protection from cancer. Consistent and increasing findings of both numbers of deaths from, and risk for, disorders such as cardiovascular disease and diabetes are of current interest. Cancer incidence and mortality rates reported for patients with schizophrenia range from decreased to increased, varying by type of cancer, gender, and country, as well as across studies. In a U.S. population report (Cohen et al. 2002) using a mortality follow-back database that sampled 1% of all deaths from 1986, cancer was the cause of 25 of the 130 deaths in patients with schizophrenia. The unadjusted odds ratio indicated a decreased risk for cancer (0.62, 95% CI 0.4–0.96). Overall, the patients with schizophrenia died at a younger age (55.6 vs. 63.7 years, P< 0.001); adjusting for age and other factors yielded an odds ratio of 0.59 (95% CI 0.38–0.93). A Danish register study (Dalton et al. 2005) of patients admitted with schizophrenia between 1969 and 1995 and followed through 1995 found no difference in overall cancer risk, but differences were seen based on gender and location of cancer. Men had decreased risks of prostate, rectal, and nonmelanoma skin cancer, and a nonsignificant decrease in lung cancer, whereas women had an increased risk of breast cancer. Other authors have noted offsets in mortality risk by location. A decreased rate of lung cancer in males was noted to offset an increased rate of breast cancer in females (Mortensen and Juel 1990) and digestive cancers (Newman and Bland 1991). In the latter study, the SMR for digestive disorders was 1.7 (P<0.05) and for lung cancer was 0.7 (NS). Limited access to cigarettes for institutionalized patients has been raised as a possible explanation for reduced rates of lung cancer (Harris and Barraclough 1998).
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TABLE 2–2. Conditions associated with increased mortality from schizophrenia • • • • • • • •
Cardiovascular disease Diabetes Respiratory disorders Gastrointestinal diseases Central nervous system disorders Genitourinary diseases Infectious diseases Cancer (controversial; breast cancer may be increased and lung cancer decreased)
Two incidence studies on cancer in patients with schizophrenia illustrate varying results between geographic locations for different types of cancer. The authors of the World Health Organization’s three-cohort study (Gulbinat et al. 1992) concluded that patients with schizophrenia do not demonstrate any consistent increase or decrease in risk for cancer. In contrast, the authors of a records linkage study using the Finnish cancer and psychiatric illness registries found a modest but significantly elevated standardized incidence ratio (SIR) for cancer (1.17, 95% CI 1.09–1.25) in patients with schizophrenia (Lichtermann et al. 2001), with the incidence of lung cancer twice as high in patients with schizophrenia (SIR 2.17, 95% CI 1.78–2.60). They also noted that the incidence of gallbladder cancer was elevated (SIR 2.07, 95% CI 1.03–3.70) and wondered about the role of obesity and poor diet in patients with schizophrenia, given that these are risk factors for gallstones, and hence possibly for gallbladder cancer. An investigation of possible lower rates of lung cancer death in patients with schizophrenia (Masterson and O’Shea 1984) led to speculation that phenothiazine medication might have antitumor activity. However, an alternative explanation may be that patients with schizophrenia die from other causes, such as cardiovascular disease, before they reach the expected age of death from lung cancer. Arguing against the hypothesis that phenothiazines have antitumor activity, the authors found that breast cancer caused proportionately more deaths in the patients with schizophrenia (8.3% vs. 3.4% in the general population, P<0.04). The authors also speculated that being nulliparous and having elevated prolactin, both associated with schizophrenia illness and treatment, while practicing inadequate breast care (insufficient self-examinations and incomplete reporting of lumps) could have caused the increase in breast cancer.
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Whether patients with schizophrenia have an increased risk of breast cancer remains unresolved. Two reports using similar Danish register data analysis methods and time periods came to different conclusions about breast cancer risk (Dalton et al. 2003, 2005). As part of a larger study of all types of cancer in men and women, female patients (N= 9,743) admitted for schizophrenia between 1969 and 1993 had a breast cancer SIR of 1.2 (95% CI 1.05–1.38) (Dalton et al. 2005). In another study of women who developed breast cancer between 1970 and 1997, patients with schizophrenia were identified (N=7,541) and found to have decreased risk (relative risk 0.97, 95% CI 0.76–1.20). When controlled for fertility issues, the relative risk fell to 0.90 (95% CI 0.71–1.12). The authors noted that this finding is not surprising because women with schizophrenia are more likely to be nulliparous, a risk factor for breast cancer. They also speculated that alcohol use, another risk factor for breast cancer (not controlled for in their study), may contribute to the apparent increase in risk for breast cancer reported in other studies (Dalton et al. 2003). Researchers who study disease-related mortality rates often report on endocrine and metabolic conditions without specifically mentioning diabetes, although one might expect it to be the most common endocrine condition. For instance, in a British study of patients followed from 1981 to 1994 (Brown et al. 2000), the SMR for all endocrine disorders was elevated at 11.66 (95% CI 3.79–27.21) and the diabetes SMR was 9.96 (95% CI 2.05–29.11), indicating that most of the endocrine mortality was accounted for by diabetes. Two recent studies found lower but significant elevations in mortality from all endocrine disorders. In a Danish register study of patients between 1973 and 1993 (Hiroeh et al. 2008), the endocrine SMR was 1.99 (95% CI 1.37–2.91) for females and 2.29 (95% CI 1.58–3.32) for males. In another Danish study (Laursen et al. 2007) covering 1973–2001, the rate was higher for males (SMR 3.56, 95% CI 2.68–4.73) and lower for females (SMR 1.69, 95% CI 1.14–2.5). Given the accepted increased risk of diabetes associated with some atypical antipsychotic medications (American Diabetes Association et al. 2004), one should expect increasing mortality from diabetes in the future. However, some of the increase may instead appear as increased cardiovascular mortality. Cardiovascular disease causes a large number of deaths in both the general population and patients with schizophrenia. In the review by Harris and Barraclough (1998), circulatory diseases caused 2,313 deaths out of the 5,591 deaths from natural causes, leading to an SMR in males of 1.1 (95% CI 1.04–1.16) and in females of 1.02 (NS, 95% CI 0.96–1.08). Although the increase in risk is relatively small, the number of excess
Excessive Mortality and Morbidity
27
deaths is substantial (230 more than expected compared with a total of excess deaths from natural causes of 1,250). Subsequent studies found higher cardiovascular mortality rates. In one study (Brown et al. 2000), circulatory disease had an SMR of 2.49 (95% CI 1.64–3.63); more specifically, cardiovascular disease had an SMR of 1.87 (95% CI 1.02–2.98). The Danish register studies noted comparable rates of 1.6–2.1. In their study involving patients from 1973 to 1993, Hiroeh et al. (2008) found the circulatory disorder SMR in females to be 1.61 (95% CI 1.47–1.76) and in males to be 1.91 (95% CI 1.75–2.08), whereas Laursen et al. (2007), in their 1973–2001 study, found the cardiovascular disease SMR in females to be 1.72 (95% CI 1.53–1.93) and in males to be 2.07 (95% CI 1.85–2.32). With rising mortality rates related to natural causes, especially diabetes and cardiovascular disease, increased attention to morbidity and health behaviors in patients with schizophrenia is warranted. In this chapter, we touch on these topics only briefly because they are covered in detail in subsequent chapters of this volume (Chapter 4, “Obesity and Schizophrenia”; Chapter 5, “Glucose Intolerance and Diabetes in Patients With Schizophrenia”; Chapter 7, “The Spectrum of Cardiovascular Disease in Patients With Schizophrenia”; and Chapter 9, “Nicotine and Tobacco Use in Patients With Schizophrenia”).
Morbidity in Schizophrenia Medical Conditions The increased rate of mortality from natural causes associated with schizophrenia should be reflected in higher rates of morbidity. In our chapter (Casey and Hansen 2003) in the previous edition of this book, we discussed reviews and studies that found medical conditions in 35%–70% of patients with schizophrenia. The disorders included diabetes, hyponatremia, thyroid disorder, urinary tract infection, bladder dysfunction, hypertension, liver disease, seizures, and visual problems, among others. Results from the interview survey study of patients with schizophrenia from the Patient Outcomes Research Team program (Dixon et al. 1999) illustrate increased medical morbidity. Of 719 patients, 469 (65%) reported having at least one lifetime medical condition, 256 (36%) had more than one condition, and 343 (48%) said the medical condition was active. The authors did not have a comparison control group but noted that the rates for diabetes, hypertension, and cardiac diseases were all higher than rates reported for these conditions in the National Health
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Medical Illness and Schizophrenia
Interview Survey. Not surprisingly, the number of medical comorbidities correlated significantly with patient self-rating of physical health. Jeste et al. (1996) reviewed reports on medical comorbidity in schizophrenia; for unclear reasons, rheumatoid arthritis seems to occur only rarely in patients with schizophrenia, whereas rates of cardiovascular disease and diabetes mellitus appear to be increased. The authors reported that studies vary in finding increased rates of other specific illnesses and that the impression of decreased risk of cancer does not appear to be valid. In the first of two morbidity studies of middle-aged to elderly patients, the authors compared patients with schizophrenia with a group of patients with depression and another group with Alzheimer’s disease; lower rates of physical illnesses were found in the patients with schizophrenia. In the second study, 45 patients with schizophrenia were compared with 38 normal control subjects; patients in both groups were older than age 45 years. Indications of morbidity were comparable, but the control group was about 10 years older than the patients with schizophrenia, suggesting that the schizophrenia group’s rate of morbidity was comparable to that of an older population. The authors noted that patients with schizophrenia endorsed more medical concerns when questioned in a structured manner. The expected increase in morbidity in patients with schizophrenia was also seen in a case-control study of hospitalizations in Stockholm, Sweden (Dalmau et al. 1997). The authors gathered data regarding treatment of somatic conditions in 775 schizophrenia patients and 775 case-matched control subjects regardless of whether the treatment occurred in a psychiatric or a general medical ward. The schizophrenia group had 523 (67%) admissions compared with 373 (48%) for the control group over the 15-year study period (McNemar test, P=0.000). An increased odds ratio was found for almost all medical disorders occurring in patients with schizophrenia, with the exception of genitourinary, musculoskeletal, and sensory organ diseases. Tumors (combined malignant and benign) were found twice as often in patients with schizophrenia. In summary, data demonstrate increased morbidity from medical conditions in patients with schizophrenia. Although almost all disorders can occur at higher than expected rates, cardiovascular disease and diabetes are particular concerns.
Substance Abuse Patients with schizophrenia have increased rates of all types of substance abuse (Dixon 1999), as is covered in detail in Chapter 11,
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“Substance Abuse and Schizophrenia.” The adverse psychiatric effects (Dixon 1999) combined with medical problems caused by substance abuse lead to the expectation of increased morbidity and mortality in patients with schizophrenia who abuse drugs or alcohol. Not surprisingly, Dalmau et al. (1997) found that inclusion of schizophrenia patients with substance abuse in their study cohort increased the number of somatic conditions that occurred more frequently in schizophrenia patients than in a nonpsychiatric control population. Also, results from a study of diabetes mortality in patients with co-occurring psychotic and substance use disorders suggested that diabetes may be increased in this group and be associated with a high mortality rate (Jackson et al. 2007).
Health Maintenance in Patients With Schizophrenia Treatment Concerns Treatment during the first half of the twentieth century undoubtedly contributed to morbidity and mortality in schizophrenia. Physical interventions such as leucotomy, insulin coma therapy, and cardiazolinduced convulsive therapy all carried substantial risk (Brown 1997). In the past 50 years, morbidity and mortality concerns related to treatment have shifted to medications, with recent attention focused on extrapyramidal side effects, cardiac conduction, and metabolic side effects (American Diabetes Association et al. 2004; Fontaine et al. 2001). In a 10-year follow-up study of mortality and medication use in patients with schizophrenia, Waddington et al. (1998) found that antipsychotic polypharmacy and absence of cotreatment with anticholinergic medication significantly increased mortality. No clear explanation related to other factors that might covary with polypharmacy seemed plausible, leaving the authors to wonder whether concurrent use of multiple antipsychotic medications causes some adverse biological consequence, or whether unrecognized subclinical drug-induced parkinsonism could contribute to mortality. A 1978–1980 Finnish study also found that increased mortality correlated with the number of prescribed typical antipsychotic medications (Joukamaa et al. 2006). Certainly, extrapyramidal side effects, including tardive dyskinesia, cause substantial morbidity in patients with schizophrenia (Casey 1993; Hansen et al. 1997). For further reading on the contribution of atypical antipsychotic medications to various metabolic problems, please see other chapters in this
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Medical Illness and Schizophrenia
volume: Chapter 4, “Obesity and Schizophrenia”; Chapter 5, “Glucose Intolerance and Diabetes in Patients With Schizophrenia”; Chapter 7, “The Spectrum of Cardiovascular Disease in Patients With Schizophrenia”; and Chapter 9, “Nicotine and Tobacco Use in Patients With Schizophrenia.”
Patient Health Habits and Related Concerns Behaviors such as abuse of drugs and alcohol, smoking, lack of exercise, and dietary indiscretion appear to contribute to mortality in the general population, and thus are likely to do so in patients with schizophrenia (Brown et al. 1999). Brown et al. (2000) noted that the SMR related to natural causes of death was significantly elevated in smokers (3.6, 95% CI 2.7–4.71) but not in nonsmokers (1.78, 95% CI 0.85–3.28). The authors reviewed apparently avoidable natural causes of death in these patients; causes included failure to recognize medical disease by the patient or care provider (3–8 cases), missed diagnoses (3 cases), poor treatment compliance (unable to quantify), treatment refusal (2 cases), and inadequate social support (1 case). Brown et al. (1999) directly investigated lifestyle concerns in 102 patients with schizophrenia. Patients with schizophrenia ate a diet significantly higher in fat and lower in fiber (significant for males, trend for females) than the reference population. Roughly one-third of the patients with schizophrenia reported doing no exercise, and no patient reported doing strenuous exercise (comparison rates not provided). In their sample, female patients demonstrated a trend toward obesity (P= 0.09). The rate of smoking was higher in patients with schizophrenia than in the comparison sample (68% vs. 28% in males, 57% vs. 25% in females); alcohol consumption was decreased in males and unexceptional in females. A study of 22 outpatients with schizophrenia conducted in 1992– 1993 found similar results (Holmberg and Kane 1999): patients with schizophrenia were less likely to practice health-promoting behaviors than were nonpsychiatric populations. In a study of smoking in Irish inpatients with schizophrenia (Masterson and O’Shea 1984), the rate of smoking was higher than for the general population (males 84% vs. 41%, females 82% vs. 36%), as were daily consumption of cigarettes, preference for medium- to high-tar cigarettes (59% vs. 1%), and duration of smoking (longer than 16 years, 80% vs. 56%). The sum of the evidence indicates clearly that patients with schizophrenia engage in behaviors that can be expected to increase morbidity and mortality.
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Access to Health Care One can easily imagine that decreased access to either medical or psychiatric care could exacerbate morbidity and contribute to increased mortality in patients with schizophrenia, and a number of reports suggest that access to medical care may be limited for patients with schizophrenia. Multiple factors could contribute to decreased access, including limitations in the communication of symptoms by patients, poor cooperation by psychotic patients, stigma toward patients with schizophrenia, and insufficient attention to medical problems by mental health providers (Druss and Rosenheck 1997; Goldman 1999). Dixon et al. (1999) found that less than 70% of the patients with medical problems were receiving treatment for their medical conditions in a study of 719 patients with schizophrenia from the Patient Outcomes Research Team project. Masterson and O’Shea (1984) speculated that inadequate breast examinations and incomplete reporting of breast lumps could have contributed to the increased rate of death from breast cancer in their study. The diagnosis of schizophrenia was significantly negatively associated with timeliness, access, and intensity of postdischarge medical care in a study of U.S. veterans (Druss and Rosenheck 1997). Finally, two related studies have demonstrated disparity in care for patients with schizophrenia following myocardial infarction. Patients with schizophrenia were less likely to have cardiac catheterization, more likely to die in the year after discharge, and less likely to receive interventions that represent quality of care following acute myocardial infarction (i.e., reperfusion therapy, or at discharge, aspirin, beta-blockers, and angiotensinconverting enzyme inhibitors) (Druss et al. 2000, 2001). Among the homeless, having a diagnosis of schizophrenia may be associated with better care and reduced mortality compared with the general homeless population. The age-adjusted mortality rate for a New York City homeless sample (follow-up period 1987–1994) was about four times that in a comparable reference group (Barrow et al. 1999). Having a mental illness had a significant protective effect, possibly explained by provision of relatively greater services and better housing for homeless people who were mentally ill than for other homeless people. Likewise, having schizophrenia appeared to convey protection from mortality in a Boston homeless population (Hwang et al. 1998). The benefit of additional care most likely creates only an impression of protection, however, considering that the comparison groups are highly ill and impoverished. For example, mortality was studied in homeless individuals who had been referred to a clinic in Sydney, Australia, between 1988 and 1991 (Babidge et al. 2001). The
Medical Illness and Schizophrenia
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SMR for the nonschizophrenia group was 4.41 (95% CI 2.02–6.19) compared with 2.52 (95% CI 1.8–3.43) for the schizophrenia group. Thus, although the SMR was much lower in the schizophrenia group, it still represented a mortality rate more than twice that expected in the general population.
Conclusion The literature conclusively establishes that patients with schizophrenia die at higher rates and earlier than other people. The causes of this excess mortality have varied over time, reflecting issues related to changes in the care of patients with schizophrenia and the unhealthy lifestyles that result from a combination of their illness and the type of care available. At this time, interventions must address risks from suicide and from medical illnesses. Effective medications with side-effect profiles that promote adherence to treatment plans must be provided. Avoiding adverse metabolic side effects, or at least monitoring and attempting to correct them as they occur, is also critical in reversing the trend toward higher mortality in patients with schizophrenia. Finally, the mental health system must work to ensure that adequate social services, medical treatment, and psychiatric care are provided despite society’s drive to reduce health care costs.
Key Clinical Points ◗
Schizophrenia carries increased mortality, as demonstrated by increased mortality rates and shortened life span.
◗
Differences in mortality rates between patients with schizophrenia and the general population are increasing.
◗
Suicide rates are very high for patients with schizophrenia, especially early in the course of illness.
◗
Increased rates of death from natural causes account for many of the excess deaths seen in patients with schizophrenia, although the increases in mortality rates associated with natural causes are not as great as those associated with suicide.
◗
The natural causes of death for patients with schizophrenia are similar to those that occur in the general population, with cardiovascular disease and diabetes being notably problematic.
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33
References Allebeck P: Schizophrenia: a life-shortening disease. Schizophr Bull 15:81–89, 1989 American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, et al: Consensus Development Conference on Antipsychotic Drugs and Obesity in Diabetes. Diabetes Care 27:596–601, 2004 Babidge NC, Buhrich N, Butler T: Mortality among homeless people with schizophrenia in Sydney, Australia: a 10-year follow-up. Acta Psychiatr Scand 103:105–110, 2001 Barrow SM, Herman DB, Cordova P, et al: Mortality among homeless shelter residents in New York City. Am J Public Health 89:529–534, 1999 Black DW, Warrack G, Winokur G: The Iowa record-linkage study, I: suicides and accidental deaths among psychiatric patients. Arch Gen Psychiatry 42:71–75, 1985 Brown S: Excess mortality of schizophrenia: a meta-analysis. Br J Psychiatry 171:502–508, 1997 Brown S, Birtwistle J, Roe L, et al: The unhealthy lifestyle of people with schizophrenia. Psychol Med 29:697–701, 1999 Brown S, Inskip H, Barraclough B: Causes of the excess mortality of schizophrenia. Br J Psychiatry 177:212–217, 2000 Capasso RM, Lineberry TW, Bostwick JM, et al: Mortality in schizophrenia and schizoaffective disorder: an Olmsted County, Minnesota cohort: 1950– 2005. Schizophr Res 98:287–294, 2008 Casey DE: Neuroleptic-induced acute extrapyramidal syndromes and tardive dyskinesia. Psychiatr Clin North Am 16:589–610, 1993 Casey DE, Hansen TE: Excessive morbidity and mortality in schizophrenia, in Medical Illness and Schizophrenia. Edited by Meyer JM, Nasrallah HA. Washington, DC, American Psychiatric Publishing, 2003, pp 13–34 Cohen ME, Dembling B, Schorling JB: The association between schizophrenia and cancer: a population-based mortality study. Schizophr Res 57:139–146, 2002 Craig TJ, Ye Q, Bromet EJ: Mortality among first-admission patients with psychosis. Compr Psychiatry 47:246–251, 2006 Dalmau A, Bergman B, Brismar B: Somatic morbidity in schizophrenia: a case control study. Public Health 111:393–397, 1997 Dalton SO, Laursen TM, Mellemkjaer L, et al: Schizophrenia and the risk for breast cancer. Schizophr Res 62:89–92, 2003 Dalton SO, Mellemkjaer L, Thomassen L, et al: Risk for cancer in a cohort of patients hospitalized for schizophrenia in Denmark, 1969–1993. Schizophr Res 75:315–324, 2005 Dixon L: Dual diagnosis of substance abuse in schizophrenia: prevalence and impact on outcomes. Schizophr Res 35(suppl):S93–S100, 1999 Dixon L, Postrado L, Delahanty J, et al: The association of medical comorbidity in schizophrenia with poor physical and mental health. J Nerv Ment Dis 187:496–502, 1999
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Druss BG, Rosenheck RA: Use of medical services by veterans with mental disorders. Psychosomatics 38:451–458, 1997 Druss BG, Bradford DW, Rosenheck RA, et al: Mental disorders and use of cardiovascular procedures after myocardial infarction. JAMA 283:506–511, 2000 Druss BG, Bradford WD, Rosenheck RA, et al: Quality of medical care and excess mortality in older patients with mental disorders. Arch Gen Psychiatry 58:565–572, 2001 Fontaine KR, Heo M, Harrigan EP, et al: Estimating the consequences of antipsychotic induced weight gain on health and mortality rate. Psychiatry Res 101:277–288, 2001 Fors BM, Isacson D, Bingefors K, et al: Mortality among persons with schizophrenia in Sweden: an epidemiological study. Nord J Psychiatry 61:252– 259, 2007 Goldman LS: Medical illness in patients with schizophrenia. J Clin Psychiatry 60 (suppl 21):10–15, 1999 Gulbinat W, Dupont A, Jablensky A, et al: Cancer incidence of schizophrenic patients: results of record linkage studies in three countries. Br J Psychiatry Suppl 75–83, 1992 Hansen TE, Casey DE, Hoffman WF: Neuroleptic intolerance. Schizophr Bull 23:567–582, 1997 Harris EC, Barraclough B: Excess mortality of mental disorder. Br J Psychiatry 173:11–53, 1998 Heila H, Isometsa ET, Henriksson MM, et al: Suicide and schizophrenia: a nationwide psychological autopsy study on age- and sex-specific clinical characteristics of 92 suicide victims with schizophrenia. Am J Psychiatry 154:1235–1242, 1997 Heila H, Isometsa ET, Henriksson MM, et al: Suicide victims with schizophrenia in different treatment phases and adequacy of antipsychotic medication. J Clin Psychiatry 60:200–208, 1999 Hiroeh U, Appleby L, Mortensen PB, et al: Death by homicide, suicide, and other unnatural causes in people with mental illness: a population-based study. Lancet 358:2110–2112, 2001 Hiroeh U, Kapur N, Webb R, et al: Deaths from natural causes in people with mental illness: a cohort study. J Psychosom Res 64:275–283, 2008 Holmberg SK, Kane C: Health and self-care practices of persons with schizophrenia. Psychiatr Serv 50:827–829, 1999 Hussar AE: Leading causes of death in institutionalized chronic schizophrenic patients: a study of 1,275 autopsy protocols. J Nerv Ment Dis 142:45–57, 1966 Hwang SW, Lebow JM, Bierer MF, et al: Risk factors for death in homeless adults in Boston. Arch Intern Med 158:1454–1460, 1998 Inskip HM, Harris EC, Barraclough B: Lifetime risk of suicide for affective disorder, alcoholism and schizophrenia. Br J Psychiatry 172:35–37, 1998
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Jackson CT, Covell NH, Drake RE, et al: Relationship between diabetes and mortality among persons with co-occurring psychotic and substance use disorders. Psychiatr Serv 58:270–272, 2007 Jeste DV, Gladsjo JA, Lindamer LA, et al: Medical comorbidity in schizophrenia. Schizophr Bull 22:413–430, 1996 Joukamaa M, Heliövaara M, Knekt P, et al: Schizophrenia, neuroleptic medication and mortality. Br J Psychiatry 188:122–127, 2006 Laursen TM, Munk-Olsen, T, Nordentoft M, et al: Increased mortality among patients admitted with major psychiatric disorders: a register-based study comparing mortality in unipolar depressive disorder, bipolar affective disorder, schizoaffective disorder, and schizophrenia. J Clin Psychiatry 68:899–907, 2007 Lichtermann D, Ekelund J, Pukkala E, et al: Incidence of cancer among persons with schizophrenia and their relatives. Arch Gen Psychiatry 58:573–578, 2001 Limosin F, Loze J-Y, Philippe A: Ten-year prospective follow-up study of the mortality by suicide in schizophrenic patients. Schizophr Res 94:23–28, 2007 Masterson E, O’Shea B: Smoking and malignancy in schizophrenia. Br J Psychiatry 145:429–432, 1984 Mortensen PB, Juel K: Mortality and causes of death in schizophrenic patients in Denmark. Acta Psychiatr Scand 81:372–377, 1990 Newman SC, Bland RC: Mortality in a cohort of patients with schizophrenia: a record linkage study. Can J Psychiatry 36:239–245, 1991 Nordentoft M, Laursen TM, Agerbo E, et al: Change in suicide rates for patients with schizophrenia in Denmark, 1981–97: nested case-control study. BMJ 329:261, 2004 Palmer BA, Pankratz VS, Bostwick JM: The lifetime risk of suicide in schizophrenia: a reexamination. Arch Gen Psychiatry 62:247–253, 2005 Ran MS, Chen E, Conwell Y, et al: Mortality in people with schizophrenia in rural China: 10-year cohort study. Br J Psychiatry 190:237–242, 2007 Saha S, Chant D, McGrath J: A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time? Arch Gen Psychiatry 64:1123–1131, 2007 Waddington JL, Youssef HA, Kinsella A: Mortality in schizophrenia: antipsychotic polypharmacy and absence of adjunctive anticholinergics over the course of a 10-year prospective study. Br J Psychiatry 173:325–329, 1998
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CHAPTER 3 Medical Outcomes From the CATIE Schizophrenia Study Henry A. Nasrallah, M.D.
When the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) schizophrenia study was designed and submitted to the National Institute of Mental Health (NIMH) for funding in 1999, the main purpose of the study was to establish whether second-generation antipsychotics (SGAs) were more effective than first-generation antipsychotics (FGAs). Specifically, effectiveness was defined as discontinuation due to any of four reasons: lack of efficacy, lack of tolerability, emergence of safety problems (i.e., potential medical health threat), and patient decision (i.e., nonadherence and dropping out of the study). Measures related to physical health were considered a secondary outcome of this large study. By the time the initial findings of the CATIE trial were published in 2005 (Lieberman et al. 2005), the predominant focus in the field of schizophrenia pharmacotherapy had shifted from claims over relative efficacy of various SGAs to the medical complications of the SGA class, specifically metabolic disorders such as obesity, diabetes, hyperlipidemia, and hypertension (Nasrallah et al. 2006; Newcomer 2005). Fortunately, the CATIE study included several laboratory measures of metabolic function, including all five metabolic syndrome criteria: 37
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waist circumference, fasting triglycerides, fasting high-density lipoprotein, blood pressure, and fasting serum glucose (Grundy et al. 2004). A total of 1,460 persons with schizophrenia consented to participate in the CATIE study, and several analyses of metabolic parameters at CATIE baseline and again after administration of the SGA and FGA medications used in phases 1 and 2 have been published and are discussed in this review. Interestingly, although the effectiveness results of CATIE (i.e., the differential discontinuation of the four SGA agents and one FGA agent during phase 1) were subjected to intense critiques due to methodological issues, the metabolic data were widely accepted and were regarded as objectively valid and uninfluenced by design stipulations (Kasper and Winkler 2006; Meyer 2007). In addition to analyzing the CATIE metabolic data (with pre hoc hypotheses based on the totality of the literature and evidence that emerged while CATIE was being conducted 1999–2004), the researchers involved in the CATIE study also accumulated data on other healthrelated issues such as substance abuse and neurological movement disorders, both of which are discussed in this overview of the medical findings of the CATIE study.
The Buildup to the CATIE Awareness of the adverse metabolic effects from certain SGAs accelerated during the 5 years that it took to conduct the CATIE study (Nasrallah 2003; Newcomer et al. 2004). A key review by Allison et al. (1999) raised awareness of the seriousness of weight gain secondary to antipsychotic treatment, whether from FGAs or SGAs. Allison et al.’s literature review of comparative weight gain across old and new antipsychotics was a turning point in that it reframed the discussion about weight gain and its serious metabolic consequences as an important threat to the health of patients receiving antipsychotics. Awareness of the serious (and occasionally fatal) metabolic effects posed by some SGAs essentially replaced concerns over tardive dyskinesia, which had reigned supreme as the greatest health issue related to FGA exposure. Interestingly, Allison et al.’s review also implied that weight gain was a major problem with certain low-potency FGA agents (thioridazine and chlorpromazine), a fact that was often overlooked due to the preoccupation with tardive dyskinesia and the increasing use of highpotency FGAs in the years prior to widespread SGA use. Allison et al.’s analysis influenced the CATIE study design to the extent that on the basis of Allison et al.’s data, a weight-neutral SGA, ziprasidone, was planned as a treatment arm more than 2 years before final U.S. Food
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39
and Drug Administration (FDA) approval in 2001. Because of its benign metabolic profile, ziprasidone was included not only as one of the SGA options in phase 1 but also as a key element of the tolerability pathway in phase 2 of the CATIE study (Stroup et al. 2006). Aripiprazole, another metabolically benign SGA, came too late to be included in phase 1 or 2 but was included in phase 3, the open-label phase of CATIE in which investigators could choose any approved antipsychotic for subjects who had dropped out of phase 2 (Stroup et al., in press). Although weight gain secondary to clozapine was widely recognized in the 1990s, prior to the CATIE study (Umbricht et al. 1994), it was regarded as the unavoidable cost for treating refractory schizophrenia patients for whom there were no options; however, after several first-line SGA drugs were introduced (risperidone in 1993, olanzapine in 1996, quetiapine in 1997), the incidence of weight gain and diabetes increased markedly due to the drugs’ widespread use. By the turn of the millennium, while patients were being actively recruited for the CATIE study, metabolic disorders related to SGA use moved into the spotlight for both clinicians and researchers. From 2002 through 2004, the FDA MedWatch data from cases of diabetes and diabetic ketoacidosis (including deaths) were published in a series of papers (Koller and Doraiswamy 2002; Koller et al. 2003, 2004). The European Union and Japan subsequently added a diabetes warning to olanzapine’s product label in 2002, and in August 2003, the FDA imposed a class warning for diabetes on all SGA drugs, despite the absence of data implicating ziprasidone and aripiprazole. Three months after the FDA class warning, the American Diabetes Association, along with the American Psychiatric Association, American Association of Clinical Endocrinologists, and North American Association for the Study of Obesity, held a meeting in Philadelphia on November 19, 2003. The purpose of this joint conference was to generate a consensus statement (American Diabetes Association et al. 2004) on the association between SGA exposure and weight gain, lipid changes, and diabetes (predominantly level 2, 3, and 4 evidence) after reviewing the pre-CATIE world literature. The consensus statement, which was published jointly in the February 2004 issues of Diabetes Care and the Journal of Clinical Psychiatry, concluded that the members of the SGA class were associated with differential risks of weight gain, diabetes, and hyperlipidemia. Clozapine and olanzapine were found to have the highest risk, risperidone and quetiapine to have intermediate risk, and aripiprazole and ziprasidone to have the lowest risk. As will be discussed in the next section, the prospective, randomized, double-blind CATIE trial generated level 1 evidence about metabolic complications
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of SGAs that professionals were waiting for, and confirmed the conclusions of the consensus statement regarding obesity and diabetes. Moreover, the CATIE data refined the knowledge of the relative metabolic effects of quetiapine and indicated that risperidone might be somewhat less of a metabolic offender than quetiapine, particularly with regard to effects on serum triglyceride levels (Meyer et al. 2008a, 2008b).
CATIE Baseline Data on Medical Illness in Schizophrenia Demographics Several health-related findings were reported in the CATIE schizophrenia sample, which comprised 1,460 outpatients from 57 public, academic, and private settings around the United States. The sample was 75% male, with a mean age of 41 years and a mean duration of illness of over 14 years. About 60% of the sample was white, 35% black, and 5% other races. Twelve percent of the sample was of Hispanic or Latino ethnicity. The mean educational level was 12.2 years, about 60% had never married, and 85% were unemployed. CATIE employed a broad enrollment strategy in an attempt to replicate the patient population seen in real-world clinical settings, as opposed to the type of patient often recruited for pharmaceutical trials, which excluded patients with medical comorbidities or substance use. As a result, the data provide a representative, cross-sectional view of the health of chronic schizophrenia patients in the United States.
Diabetes and Metabolic Syndrome At CATIE study baseline, 11% of enrolled subjects had diabetes type 1 or 2, 14% had hyperlipidemia, and 20% had hypertension. Table 3–1 shows the mean number of metabolic syndrome criteria met at CATIE study baseline in subjects with fasting blood samples available, along with overall metabolic syndrome prevalence (McEvoy et al. 2005). Metabolic syndrome prevalence in the CATIE sample, in both males and females, was twice the rate seen in a sample from the general population that was matched for age, gender, race, and ethnicity (using the National Health and Nutrition Examination Survey [NHANES] III study population as the source for comparator matching) (Figure 3–1). These were the first detailed data documenting an unusually high prevalence of metabolic syndrome in the schizophrenia outpatient population in the United States during the period when subjects were being recruited into the CATIE study.
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41
Cardiovascular Risk A comparison of 10-year cardiac risk estimates in the CATIE schizophrenia sample compared to matched controls from the NHANES III was also conducted using the baseline CATIE data (Goff et al. 2005). This analysis showed that the 10-year coronary heart disease (CHD) risk (using the Framingham CHD risk algorithm) was significantly elevated in the CATIE sample versus the matched general population (P= 0.0001) in males (9.4% vs. 7.0%) and females (6.3% vs. 4.2%). The patients with schizophrenia also had significantly higher rates of smoking (68% vs. 35%), diabetes (13% vs. 3%), and hypertension (27% vs. 17%), and lower HDL cholesterol (43.7 vs. 49.3 mg/dL) compared to the NHANES III control group. Goff et al. concluded that the CATIE data showing greater 10-year CHD risk are consistent with the published studies of increased cardiac mortality in schizophrenia.
Lack of Medical Treatment
Percentage of patients with metabolic syndrome
In light of the high prevalence of metabolic syndrome in the CATIE schizophrenia sample, one would expect that the subjects with metabolic disorders such as diabetes, hyperlipidemia, and hypertension would be receiving the appropriate standard medical treatments for
60
CATIE (N = 689) 50 40
*
NHANES (N = 689)
*
30 20 10 0
Males
Females
FIGURE 3–1. Prevalence of metabolic syndrome in CATIE schizophrenia study participants at baseline versus the general adult population (National Health and Nutrition Examination Survey [NHANES] III data). *P= 0.0001 CATIE versus NHANES.
42
TABLE 3–1.
Metabolic syndrome and criteria prevalences among CATIE fasting subjects Subject cohort
Syndrome/criteria Mean age (years) 2
Criteria met
0 1 2 3 4 5 Mean number of criteria met
MS prevalence, AHA criteria Criteria met
0 1 2
Black male
Hispanic male
White female
Black female
Hispanic female
39.8±11.2
38.5±11.6
38.0 ±12.3
44.2 ±10.7
44.0± 9.8
44.5±10.2
28.9±6.3
27.7±5.8
29.0 ±6.6
32.5 ±8.2
33.8± 8.1
31.7±6.5
40.9% (n=342)
22.7% (n=141)
31.8% (n=66)
56.5% (n=92)
43.1% (n=72)
73.3% (n=15)
(n =343) 12.2% 23.9% 23.0% 24.2% 14.3% 2.3% 2.11± 1.32
(n =141) 23.4% 31.9% 22.0% 10.6% 9.2% 2.8% 1.59± 1.35
(n =67) 13.4% 19.4% 35.8% 14.9% 13.4% 3.0% 2.05± 1.31
(n =93) 8.6% 12.6% 22.6% 26.9% 24.7% 4.3% 2.59±1.34
(n= 73) 5.5% 23.3% 28.8% 27.4% 12.3% 2.7% 2.26 ±1.19
(n= 15) 6.7% 6.7% 13.3% 46.7% 20.0% 6.7% 2.87 ±1.25
44.4% (n=342)
23.4% (n=141)
40.9% (n=66)
58.1% (n=93)
47.2% (n=72)
73.3% (n=15)
(n =343) 12.0% 21.0% 22.7%
(n =141) 22.7% 31.2% 22.7%
(n =67) 13.4% 16.4% 29.9%
(n =93) 8.6% 11.8% 21.5%
(n= 73) 5.5% 20.6% 27.4%
(n= 15) 6.7% 6.7% 13.3%
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Mean BMI (kg/m ) MS prevalence, ATPIII criteria
White male
TABLE 3–1.
Metabolic syndrome and criteria prevalences among CATIE fasting subjects (continued) Subject cohort
Syndrome/criteria
Mean number of criteria met
Black male
Hispanic male
White female
Black female
Hispanic female
22.3% 16.9% 4.1% 2.25± 1.38
11.4% 9.2% 2.8% 1.62± 1.35
20.9% 14.9% 4.5% 2.21± 1.38
26.9% 25.8% 5.4% 2.66±1.36
28.8% 13.7% 4.1% 2.37 ±1.23
40.0% 26.7% 6.7% 2.93 ±1.28
Criteria prevalence: all subjects Waist circumference 41.2% (n= 663) Blood pressure 46.9% (n= 674) HDL 54.3% (n= 671)
28.5% (n= 351) 55.3% (n= 358) 35.6% (n= 357)
37.8% (n =127) 38.8% (n =129) 55.0% (n =129)
70.8% (n =195) 38.4% (n =198) 69.1% (n =194)
76.5% (n =153) 48.1% (n =154) 52.6% (n =154)
77.0% (n= 40) 47.5% (n= 40) 64.1% (n= 39)
Criteria prevalence: fasting subjects Waist circumference 39.0% (n= 341) Blood pressure 45.9% (n= 342) Triglycerides 58.3% (n= 343) HDL 55.1% (n= 343) Glucose≥ 100 26.5% (n= 343) Glucose≥ 110 13.4% (n =343)
26.2% (n= 141) 57.1% (n= 141) 31.9% (n= 141) 34.8% (n= 141) 17.7% (n= 141) 14.9% (n= 141)
38.5% (n =65) 40.9% (n =66) 61.2% (n =67) 53.7% (n =66) 28.4% (n =67) 11.9% (n =67)
76.1% (n =92) 41.9% (n =93) 53.8% (n =93) 72.0% (n =93) 22.6% (n =93) 16.1% (n =93)
74.7% (n= 71) 50.0% (n= 72) 28.8% (n= 73) 52.1% (n= 73) 34.3% (n= 73) 23.3% (n= 73)
86.7% (n= 15) 60.0% (n= 15) 46.7% (n= 15) 66.7% (n= 15) 33.3% (n= 15) 26.7% (n= 15)
43
Note. AHA=American Heart Association; ATPIII= National Cholesterol Education Program Adult Treatment Panel III; BMI= body mass index; HDL=high-density lipoprotein; MS =metabolic syndrome.
Medical Outcomes From the CATIE Study
3 4 5
White male
44
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these serious medical disorders. However, Nasrallah et al. (2006) found that a high proportion of subjects were not receiving treatment for their serious medical diseases at the time of their enrollment into the CATIE trial. This serious lack of care means that persons with schizophrenia face a double jeopardy for early cardiovascular mortality: high rate of metabolic disorders and diminished quality of medical treatment. This combination of high medical risk and poor medical care may be one of the reasons that persons with schizophrenia lose an average of more than 25 potential years of life, according to research from several U.S. states (Colton and Manderscheid 2006; B.J. Miller et al. 2006). Metabolic disorders are also highly associated with depression; when CATIE subjects with metabolic syndrome were compared to CATIE subjects without metabolic syndrome, the only significant difference found was a higher rate of depression and somatic concern among the group with metabolic syndrome (Meyer et al. 2005).
Other Medical Risk Factors Of the CATIE subjects, 37% met criteria for substance abuse or dependence, 23% met criteria specifically for substance use, and 40% were abstinent (Swartz et al. 2008). Given the health risks of oral and intravenous substance use, abuse, and dependence, this is an additional medical risk factor for individuals with schizophrenia, a fact discussed at length in Chapter 11, “Substance Abuse and Schizophrenia.” Another medical risk factor in the CATIE sample was tardive dyskinesia, a serious form of neurotoxicity. Of the 1,460 subjects, 231 (15.8%) had tardive dyskinesia at the time of enrollment in CATIE (D.D. Miller et al. 2005). Of interest are the findings that the tardive dyskinesia subgroup had 1.5 times higher prevalence of substance abuse and 4.5 times higher odds of more severe psychopathology compared to those without tardive dyskinesia.
CATIE Phase 1 Medical Findings Several metabolic parameters were differentially influenced by the five antipsychotic agents—perphenazine (an FGA), risperidone, quetiapine, olanzapine, and ziprasidone (added after 40% of the sample had been recruited)—selected for phase 1 of the CATIE trial (Lieberman et al. 2005). Figure 3–2 shows the mean monthly weight change, with the olanzapine group showing over fourfold greater weight gain compared with the other antipsychotics during phase 1, and subjects randomized
Medical Outcomes From the CATIE Study
45
to ziprasidone showing a mean decrease in weight. Figure 3–3 shows the percentage of subjects who gained a significant amount of weight (> 7%) with each of the five antipsychotics. Again, the olanzapine cohort had the greatest problems with weight gain, with 30% gaining more than 7% of their baseline weight, and ziprasidone was the least offending medication, causing weight gain in only 6% of subjects. No statistically significant between-group changes in mean fasting glucose levels occurred across the various antipsychotics, although olanzapine had the highest numeric increase and ziprasidone the lowest. However, results indicated significant between-drug differences for change in glycosylated hemoglobin, a long-term measure of glycemic control that reflects values over the prior 2 months. As seen in Figure 3–4, the greatest increase in glycosylated hemoglobin was in the olanzapine group, and the ziprasidone group had an overall decrease. Similar divergences were seen with cholesterol and triglycerides (Figures 3–5 and 3–6) and are discussed in detail in Chapter 6, “Effects of Antipsychotics on Serum Lipids” (see Tables 6–2 and 6–3). Each patient’s corrected QT interval on the electrocardiogram (QTc) was measured every 3 months while participating in the CATIE study. Figure 3–7 shows that the QTc was minimally increased in all antipsychotic groups during CATIE phase 1. These negligible increases suggest that, contrary to the perception at the launch of the CATIE study, antipsychotic-related QTc changes are not a major medical threat to persons receiving atypical antipsychotics. The effects of antipsychotics on QTc are discussed at length in Chapter 7, “The Spectrum of Cardiovascular Disease in Patients With Schizophrenia,” but data from CATIE phase 1 revealed that in routine clinical practice, there are not worrisome QT changes with any SGA, including ziprasidone, which had been a source of concern. The past several years of clinical experience with millions of patients confirms the CATIE data in that unlike metabolic complications (e.g., metabolic syndrome and diabetes), which have increased markedly in populations receiving SGAs, morbidity or mortality from QTc changes is relatively minuscule. The development of cataracts was a concern only for patients taking quetiapine, based on preclinical findings from animal models. In phase 1, no difference was found among the antipsychotics, with quetiapine in particular showing minimal risk for cataracts, confirming that cataract development is a phenomenon encountered for the preclinical species studied (beagle dogs) but not humans.
46
2.5
2.0
Ziprasidone
Risperidone
Quetiapine
Olanzapine
Perphenazine
Clozapine
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Mean change from baseline (lbs)
1.5
1.0 0.5
0 –0.5
–1.0 –1.5
–2.0
Phase 1
Phase 2T
Phase 2E
FIGURE 3–2. Monthly weight gain (pounds/month) by subjects during CATIE phase 1, phase 2 tolerability arm (2T), and phase 2 efficacy arm (2E).
35
Ziprasidone
Risperidone
Quetiapine
Olanzapine
Perphenazine
Clozapine
30
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Percentage
25
20
15
10
5
0 Phase 1
Phase 2 E
Percentage of subjects with weight gain >7% in CATIE phase 1, phase 2 tolerability arm (2T), and phase 2 efficacy 47
FIGURE 3–3. arm (2E).
Phase 2 T
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48
50
Ziprasidone
Risperidone
Olanzapine
Perphenazine
Quetiapine
Mean change from baseline (%)
40
30
20
10
0
–10
–20
FIGURE 3–4.
Phase 1 changes in glycosylated hemoglobin.
Change in Metabolic Syndrome Status With Treatment Detailed analysis of changes in metabolic syndrome criteria in CATIE phase 1 were published in 2008 (Meyer et al. 2008a) and showed that after 3 months of exposure, the prevalence of metabolic syndrome increased in the olanzapine group from 34.8% to 43.9%, but decreased in the ziprasidone group from 37.7% to 29.9% (olanzapine vs. ziprasidone P=0.001) (Table 3–2). Subjects in the olanzapine and quetiapine groups also had an average waist circumference increase of 1.8 cm, compared with a 1-cm increase in the risperidone group and no change in the ziprasidone group, whereas subjects in the perphenazine group experienced a loss of 1 cm. The greatest increase in triglycerides occurred with olanzapine (+21.5 mg/dL). Although these findings are consistent with metabolic studies published before release of the CATIE results, they indicated greater effects of quetiapine on adiposity than previously indicated by the American Diabetes Association et al. (2004) consensus paper.
Nonfasting Triglyceride Levels Although the metabolic syndrome criterion for triglycerides is a fasting level of 150 mg/dL or greater, recent studies indicate that nonfasting
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TABLE 3–2. Proportion of subjects at baseline and 3 months meeting the criteria for metabolic syndrome in phase 1 of the CATIE schizophrenia trial—all classifiable subjects Metabolic syndrome Agent
n
Olanzapine 164 Perphenazine 129 Quetiapine 143 Risperidone 147 Ziprasidone 77 Overall treatment difference
Baseline
3 months
34.8% 37.2% 37.8% 30.6% 37.7%
43.9%a 38.0% 37.1% 30.6% 29.9%a 0.015
a
Change from baseline to 3 months in proportion of subjects meeting criteria for metabolic syndrome is greater for olanzapine than for ziprasidone (P=0.001) among all classifiable subjects. triglyceride levels may have a stronger association with cardiovascular risk than do fasting triglycerides. An analysis of the phase 1 CATIE data after 3 months of exposure (Meyer et al. 2008b) showed that the highest mean and median increases were in subjects randomly assigned to quetiapine (mean +54.7 mg/dL, median +26 mg/dL) and olanzapine (mean +54.7 mg/dL, median +26.5 mg/dL), whereas ziprasidone was neutral (mean +0.0 mg/dL, median +8 mg/dL) and decreases were seen in both risperidone (mean −18.4 mg/dL, median −6.5 mg/dL) and perphenazine (mean −1.3 mg/dL, median −22 mg/dL). Pairwise comparison indicated a significant between-group difference for perphenazine versus olanzapine and a trend for perphenazine versus quetiapine.
Inflammatory Markers Several biomarkers of cardiometabolic risk were collected in phase 1 of the CATIE trial and are still being analyzed at the time of writing of this chapter. However, data for one important inflammatory marker, C-reactive protein (CRP) have been analyzed (J.M. Meyer et al., in preparation) to examine the comparative effects of various antipsychotics after 3 months of exposure. Olanzapine and quetiapine had the greatest numerical increases, and significant treatment differences were observed between olanzapine versus perphenazine (P< 0.001) and risperidone (P = 0.001) in subjects with lower baseline cardiovascular risk (defined as CRP <1 mg/L). After 12 months of exposure, the difference
50
80
Ziprasidone
Risperidone
Quetiapine
Olanzapine
Perphenazine
Clozapine
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Change from baseline (mg/dL)
60
40
20
0
–20
–40 Ph ase 1
FIGURE 3–5.
Ph ase 2 T
Ph ase 2 E
Changes in serum triglycerides across CATIE phase 1, phase 2 tolerability arm (2T), and phase 2 efficacy arm (2E).
20
Ziprasidone
Risperidone
Quetiapine
Olanzapine
Perphenazine
Clozapine
Medical Outcomes From the CATIE Study
Change from baseline (mg/dL)
15
10
5
0
–5
–10
–15 Ph ase 1
FIGURE 3–6.
Ph ase 2T
Ph ase 2E
Changes in total cholesterol across CATIE phase 1, phase 2 tolerability arm (2T), and phase 2 efficacy arm (2E). 51
52
8
Ziprasidone
Risperidone
Olanzapine
Perphenazine
Quetiapine
4
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Mean change from baseline (msec)
6
2
0
–2
–4
–6 Phase 1
FIGURE 3–7.
Phase 2T
Mean changes in QT interval in CATIE phase 1 and phase 2 tolerability arm (2T).
Medical Outcomes From the CATIE Study
53
between olanzapine and perphenazine groups remained significant, and a significant difference (P= 0.003) was observed between olanzapine and ziprasidone. These data confirm that agents associated with greater cardiometabolic risk are associated with adverse changes in other measures that correlate with deleterious cardiovascular outcomes.
Phase 2 Findings Subjects who discontinued their antipsychotics in phase 1 of the CATIE study due to lack of efficacy were randomized either to clozapine or to one of three atypical antipsychotics: olanzapine, quetiapine, or risperidone. Those who discontinued due to tolerability or safety reasons (or who refused participation in the efficacy arm) were randomized to either ziprasidone or to one of the three atypical antipsychotics. In both arms of phase 2, the tolerability arm and the efficacy arm, all subjects were randomly assigned to receive agents that they did not take in phase 1. Metabolic parameters (weight, waist circumference, glucose, triglycerides, and cholesterol) were measured as in phase 1. (See Figures 3–5 and 3–6.) Figure 3–2 shows the mean weight gain per month in both pathways of phase 2 compared to phase 1, and Figure 3–3 shows the percentage of subjects with weight gain >7% for both phase 2 pathways. Figure 3–8 also shows the weight changes in phase 2 in subjects who had a significant amount of weight gain (> 7%) in phase 1. Only those randomized to ziprasidone lost much of the weight they had gained from the phase 1 antipsychotic. Differences did emerge between the phase 1 results and those seen in the phase 2 efficacy and tolerability pathways, but a consistent pattern remains: olanzapine is associated with the greatest deleterious changes in metabolic parameters, whereas ziprasidone is associated with the most benign changes.
Phase 3 Findings The open-label phase 3 included subjects who had discontinued phases 1 and 2. They were given a wide range of FGAs and/or SGAs. Aripiprazole, the only atypical antipsychotic that was not included in the first two phases of the CATIE because the drug was launched too late, was used in some patients in phase 3. The only surprising data regarding the metabolic changes in phase 3 were that glucose levels increased by 13 mg/dL in patients taking aripiprazole, which was a larger increase than with any other antipsychotic in this phase (Stroup et al., in press). This finding was unexpected, because aripiprazole has demonstrated a benign metabolic profile in FDA trials and in previous published studies. All other metabolic findings were consistent with phase 1 and phase 2 profiles.
54
4
Ziprasidone
Risperidone
Quetiapine
Olanzapine
0 –2 –4 –6 –8 –10 –12
FIGURE 3–8.
Phase 2 tolerability arm weight change in CATIE subjects with weight gain >7% in phase 1.
Medical Illness and Schizophrenia
Mean change from baseline (lbs)
2
Medical Outcomes From the CATIE Study
55
Conclusion The 5-year, double-blind, NIMH-sponsored effectiveness study known as the CATIE schizophrenia trial provided highly informative data about the comparative metabolic profiles of various antipsychotics and revealed the high risk for metabolic syndrome and CHD in a large sample (N= 1,460) of patients with chronic schizophrenia in the United States. These metabolic findings are likely to influence clinical practice research studies, prevention initiatives, and public policy in the foreseeable future. Although the primary measure outcome of the CATIE schizophrenia trial was effectiveness (i.e., all-cause discontinuation), a valuable body of metabolic data was obtained from the study sample, one that points to significant health problems seen in patients with schizophrenia.
Key Clinical Points ◗
The prevalence of metabolic syndrome in the CATIE schizophrenia subjects at the time of enrollment was 43%, about twice the prevalence in the general population.
◗
Olanzapine was associated with a fourfold higher monthly weight gain in phase 1 compared to the other antipsychotics.
◗
Fasting blood glucose, cholesterol, and triglyceride levels were most elevated with olanzapine and were not changed or declined with ziprasidone.
◗
Quetiapine resulted in higher lipid increases than risperidone, which had no deleterious effect on lipids.
◗
The overall metabolic profile of second-generation antipsychotics in phase 2 (both the efficacy and tolerability pathways) were the same as seen in phase 1 for olanzapine (highest increases) and ziprasidone (lowest increases or actual decreases), but were somewhat variable for quetiapine and risperidone.
◗
Subjects with >7% body weight increase in phase 1 lost weight only if they were assigned to ziprasidone in phase 2.
◗
QTc was minimally affected by all second-generation antipsychotics used in the CATIE study.
◗
Levels of the inflammatory marker C-reactive protein were increased with olanzapine or quetiapine but not with other antipsychotics.
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56 ◗
Aripiprazole was associated with the highest mean increase in fasting glucose among first-generation or second-generation antipsychotics in phase 3, the opposite of what was expected.
References Allison DB, Mentore JL, Heo M, et al: Antipsychotic-induced weight gain: a comprehensive research synthesis. Am J Psychiatry 156:1686–1696, 1999 American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, et al: Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes. Diabetes Care 27:596–601, 2004 Colton CW, Manderscheid RW: Congruencies in increased mortality rates, years of potential life lost, and causes of death among public mental health clients in eight states. Prev Chronic Dis 3:1–14, 2006 Goff DC, Sullivan L, McEvoy JP, et al: A comparison of ten-year cardiac risk estimates in schizophrenia patients from the CATIE study and matched controls. Schizophr Res 80:45–53, 2005 Grundy SM, Brewer B, Cleeman JI, et al: Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 109:433–438, 2004 Kasper S, Winkler D: Addressing the limitations of the CATIE study. World J Biol Psychiatry 7:126–127, 2006 Koller EA, Doraiswamy PM: Olanzapine-associated diabetes mellitus. Pharmacotherapy 22:841–852, 2002 Koller EA, Cross JT, Doraiswamy PM, et al: Risperidone-associated diabetes mellitus: a pharmacovigilance study. Pharmacotherapy 23:735–744, 2003 Koller EA, Weber J, Doraiswamy PM, et al: A survey of reports of quetiapineassociated hyperglycemia and diabetes mellitus. J Clin Psychiatry 65:857– 863, 2004 Lieberman JA, Stroup TS, McEvoy JP, et al: Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med 353:1209–1223, 2005 McEvoy JP, Meyer JM, Goff DC, et al: Prevalence of the metabolic syndrome in patients with schizophrenia: baseline results from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial and comparison with national estimates from NHANES III. Schizophr Res 80:19–32, 2005 Meyer JM: Strategies for the long-term treatment of schizophrenia: real-world lessons from the CATIE trial. J Clin Psychiatry 68 (suppl 1):28–33, 2007
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Meyer JM, Nasrallah HA, McEvoy JP, et al: The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial: clinical comparison of subgroups with and without the metabolic syndrome. Schizophr Res 80:9–18, 2005 Meyer JM, Davis VG, Goff DC, et al: Change in metabolic syndrome parameters with antipsychotic treatment in the CATIE schizophrenia trial: prospective data from phase 1. Schizophr Res 101:273–286, 2008a Meyer JM, Davis VG, McEvoy JP, et al: Impact of antipsychotic treatment on nonfasting triglycerides in the CATIE schizophrenia trial. Schizophr Res 103:104–109, 2008b Miller BJ, Paschall CB, Svendsen DP: Mortality and medical comorbidity among patients with serious mental illness. Psychiatr Serv 57:1482–1487, 2006 Miller DD, McEvoy JP, Davis SM, et al: Clinical correlates of tardive dyskinesia in schizophrenia: baseline data from the CATIE schizophrenia trial. Schizophr Res 80:33–43, 2005 Nasrallah H: A review of the effect of atypical antipsychotics on weight. Psychopharmacology 28:83–96, 2003 Nasrallah HA, Meyer JM, Goff DC, et al: Low rates of treatment for hypertension, dyslipidemia, and diabetes in schizophrenia: data from the CATIE schizophrenia trial sample at baseline. Schizophr Res 86:15–22, 2006 Newcomer JW: Second-generation (atypical) antipsychotics and metabolic effects: a comprehensive literature review. CNS Drugs 19 (suppl 1):1–93, 2005 Newcomer JW, Nasrallah HA, Loebel AD: The Atypical Antipsychotic Therapy and Metabolic Issues National Survey: practice patterns and knowledge of psychiatrists. J Clin Psychopharmacol 5 (suppl 1):S1–S6, 2004 Stroup TS, Lieberman JA, McEvoy JP, et al: Effectiveness of olanzapine, quetiapine, risperidone, and ziprasidone in patients with chronic schizophrenia following discontinuation of a previous atypical antipsychotic. Am J Psychiatry 163:611–622, 2006 Stroup TS, Lieberman JA, McEvoy JP, et al: Results of phase 3 of the CATIE schizophrenia trial. Schizophr Res (in press) Swartz MS, Wagner HR, Swanson JW, et al: The effectiveness of antipsychotic medications in patients who use or avoid illicit substances: results from the CATIE study. Schizophr Res 100:39–52, 2008 Umbricht DS, Pollack S, Kane JM: Clozapine and weight gain. J Clin Psychiatry 55 (suppl B):157–160, 1994
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PART II Metabolic Disease, Heart Disease, and Related Conditions
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CHAPTER 4 Obesity and Schizophrenia Tony Cohn, M.B.Ch.B., M.Sc., F.R.C.P.C.
Obesity is a common and burgeoning health problem that has an impact on both life expectancy and quality of life. Epidemic proportions of individuals are overweight and obese worldwide (James et al. 2001; Ogden et al. 2007). The prevalence of obesity is even greater among individuals with schizophrenia, who are known to have increased morbidity and mortality from obesity-related diseases (McEvoy et al. 2005; Osby et al. 2000), as well as poor access to primary and preventive health care (Druss et al. 2001; Meyer 2007). Individuals with schizophrenia may be predisposed to obesity because of biological and lifestyle factors but also require long-term treatment with antipsychotic medications that can promote weight gain (Green et al. 2000; Newcomer 2005). With the growth in prescription of second-generation antipsychotic (SGA) medications over the past 10 years, antipsychoticassociated weight gain has emerged as an iatrogenic issue of major public health concern. The problem is compounded further by the fact that commonly coprescribed psychiatric medications, antidepressants, and mood stabilizers also contribute to weight gain (Aronne and Segal 2003; Fava 2000). In addition, patients with schizophrenia are challenged by cognitive and functional deficits that need to be considered in clinical evaluation and in the delivery of obesity interventions. Although obesity medicine is now being recognized as a subspecialty in its own right, 61
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its application for individuals with serious mental illness, such as schizophrenia, is in its infancy. The onus for management is falling to mental health professionals. Clinicians of today who treat patients with schizophrenia are challenged to become knowledgeable in the assessment, prevention, and treatment of obesity and its complications. In this chapter, I review the clinical definition of obesity and its medical consequences; the epidemiology and determinants of obesity in the general population and in individuals with schizophrenia; and the assessment, monitoring, and management of obesity for patients with schizophrenia within a mental health context.
Definition and Measurement Obesity is considered a disease by the World Health Organization (2004), which defines obesity as a condition of excess body fat to the extent that health is impaired. However, measuring body fat accurately is difficult and requires highly specialized procedures (Pi-Sunyer 2000). For clinical purposes, obesity is usually defined by the body mass index (BMI), a measure of weight that takes height into account. In addition to BMI, a measure of the distribution of body fat is important, because the central accumulation of fat within the abdominal region (central, visceral, or abdominal adiposity) is a major factor that influences the effect of increasing weight on health. Central adipose tissue is now seen as an endocrine organ that releases substances (adipokines) that have systemic effects. Adipokines include leptin, tumor necrosis factor-alpha, adiponectin, C-reactive protein, interleukin-6, and plasminogen activator inhibitor type 1. Central adiposity is most accurately measured by computer-assisted tomography or magnetic resonance imaging, which enable a distinction to be made between subcutaneous and intra-abdominal fat. However, for practical purposes, the simple waist circumference is the common clinical proxy for central adiposity (Lemieux et al. 1996). Waist-to-hip ratio has also been used for this purpose, but this measure is less favored because racial and ethnic differences in hip size complicate interpretation (Ogden et al. 2007). Both BMI and waist circumference are independently correlated with health outcomes, but waist circumference has been shown to be an even better predictor of obesity-related disease. BMI and waist circumference measures have limitations. Although body fat is a continuous variable with an incremental health risk, BMI and waist circumference are often used as categorical variables with cutoff values that reflect risk. Also, these measures do not adequately take
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into account individual, gender, and racial or ethnic differences in body type and body composition. BMI is calculated by dividing weight (measured in kilograms) by height (measured in meters) squared (i.e., BMI=kg/m2). Consensus exists among international health agencies, including the National Heart, Lung, and Blood Institute and the World Heath Organization, regarding BMI cutoffs. A BMI of 25–29.9 kg/m2 is considered overweight, and a BMI of ≥30 kg/m 2 is deemed obese. Obesity is further classified as class 1 (BMI 30–34.9 kg/m2), class 2 (BMI 35–39.9 kg/m2), and class 3 or severe obesity (BMI ≥40 kg/m2). BMI is a less accurate correlate for body fat when used for muscular individuals, who may have a high BMI and normal or low body fat, and for very short individuals (Pi-Sunyer 2000). In addition, because of differences in body composition, for a given BMI, women tend to have a higher percentage of body fat than men, and older individuals have a higher percentage of body fat than younger individuals (Ogden et al. 2007). Waist circumference is measured in the standing position with a simple tape measure placed around the waist. The waist is defined as midway between the top of the pelvis and the lowest rib. Measurement is taken after a modest expiration. For individuals with a pendulous abdomen, the waist circumference can be measured using the maximum girth, with the patient lying on his or her side on an examination table. Cutoff points are 88 cm for women and 102 cm for men. Racial or ethnic differences in body habitus can also influence the interpretation of waist circumference; therefore, the International Diabetes Federation has defined ethnic-specific cutoffs for waist circumference for the metabolic syndrome (Alberti et al. 2006). Metabolic syndrome, also known as syndrome X and insulin-resistance syndrome, is a clustering of risk markers reflecting a state of insulin resistance and compensatory hyperinsulinemia that has wide-ranging medical consequences. According to the revised Adult Treatment Panel III/American Heart Association definition, three out of the five following component criteria are required to diagnose metabolic syndrome (Grundy et al. 2005): 1. Abdominal obesity (waist circumference) ≥88 cm in women and ≥ 102 cm in men 2. Blood pressure ≥130/85 mmHg or antihypertensive treatment 3. Fasting glucose ≥5.6 mmol/L (100 mg/dL) or antidiabetic treatment 4. Fasting triglycerides ≥1.8 mmol/L (150 mg/dL) or treatment 5. Fasting high-density lipoprotein (HDL) <1.03 mmol/L (40 mg/dL) in men and <1.29 mmol/L (50 mg/dL) in women
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By the International Diabetes Federation definition of metabolic syndrome (Alberti et al. 2006), abdominal obesity is a necessary criterion, and two additional criteria (as listed above) are required for the diagnosis. Waist circumference cutoffs vary by racial or ethnic group as follows: • Europids or Middle Eastern: men >94 cm; women >80 cm • South Asian, Chinese: men > 90 cm; women >80 cm • Japanese: men >85 cm; women > 90 cm
Health Consequences of Obesity Central adiposity is specifically associated with the development of insulin resistance and, consequently, type 2 diabetes, hyperlipidemia (especially raised triglycerides and low HDL), coronary heart disease, hypertension, and polycystic ovarian disease. With obesity in general, rates of gallbladder disease; respiratory disease, including obstructive sleep apnea; certain cancers (colorectal, prostate, endometrial, gallbladder, cervical, ovarian, and breast); gout; and arthritis are also increased (Pi-Sunyer 1993). Obesity reduces life expectancy. In examining deaths attributable to being overweight and obese, investigators found that more than 80% of these deaths occurred in individuals with BMIs of at least 30 kg/m2 (Allison et al. 1999b). Years of life are lost primarily because of coronary heart disease (Dorn et al. 1997). Shortened life span has been specifically noted in patients with serious mental illness, such as schizophrenia (Osby et al. 2000), with a recent report indicating as much as 25–30 years of potential life lost (Colton and Manderscheid 2006). Obesity also has a negative impact on the quality of life, and this has also been explored in samples of schizophrenia patients. Based on results from the Psychological Well-Being Index, Allison et al. (2003) reported that weight gain with antipsychotic treatment was related to poorer quality of life as well as reduced well-being and vitality. Strassnig et al. (2003a) found that the physical component score of the Medical Outcomes Study 36-Item Short-Form Health Status Survey, a measure of quality of life that assesses both physical and mental health, was inversely correlated with the physical component score of patients with schizophrenia, suggesting that the burden from obesity is primarily experienced as a physical problem, a finding that was confirmed by Faulkner et al. (2007a) who found that, in addition, waist circumference was a better predictor of reduced quality of life than BMI. Those with
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obesity describe also being stigmatized. In the general population it has been demonstrated that this phenomenon also occurs with health services, causing obese individuals to avoid visiting health care providers (Drury and Louis 2002). Patients with schizophrenia face the additional stigma of having a mental illness. The economic burden of obesity is immense (Thompson and Wolf 2001). Direct health care costs are estimated to account for 5.5%–7.0% of national health expenditure in the United States and 2.0%–3.5% in other countries. When considering comorbidities, the three largest contributors to cost are hypertension, type 2 diabetes, and coronary artery disease. The cost of lost productivity due to obesity is almost as great as the direct medical costs. In the treatment of patients with schizophrenia, lipid lowering, antihypertensive, and antidiabetic medications are now commonly coprescribed with antipsychotic medications, adding considerable costs to health care budgets.
Epidemiology of Obesity In the general population, rates of overweight and obesity are rising at an alarming rate, with no plateau in sight. In the United States, almost two-thirds of adults (66%) were overweight or obese in 2004; 32% were obese and 5% had class 3 obesity (Wang and Beydoun 2007). The prevalence of obesity has more than doubled since 1980 (Ogden et al. 2007). In Canada in 2004, 65% of the adult population was overweight or obese, but the overall rate of obesity (23%) was lower than in the United States (Tjepkema 2006). Using linear regression, Wang and Beydoun (2007) predicted that 75% of the U.S. adult population will be overweight or obese and 41% will be obese by 2015. Studies have demonstrated that the distribution of obesity varies by gender, racial and ethnic group, and socioeconomic status, with minority and low-socioeconomic-status groups having higher prevalence. In 2004, more men than women were overweight and obese, but women had higher rates of obesity, especially in non-Hispanic black and Mexican American communities, where rates of obesity in women were as high as 49% and 38%, respectively (Wang and Beydoun 2007). Weight appears to vary more by socioeconomic status, race and ethnicity, and nationality for women than for men, and some authors have suggested that weight may be associated more closely with social and cultural roles for women than for men (Ogden et al. 2007). In patients with schizophrenia, rates of obesity are even higher than in the general population and are estimated to fall between 40% and
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60% (Catapano and Castle 2004). The average BMI in recent schizophrenia trials has often approximated or been in the obese range, as illustrated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study, where the recruitment strategy was designed to be broadly representative of patients with schizophrenia. In the sample of patients (n =1,460) recruited between January 2001 and December 2004 (Lieberman et al. 2005), the mean BMI at baseline was 29.7 kg/m2 (men 28.5 kg/m 2; women 33.0 kg/m2) (McEvoy et al. 2005). In a Canadian study of 240 adult patients with schizophrenia recruited in 2003 and 2004 and treated with antipsychotic medication, Cohn et al. (2004b) found that the mean BMI was 28.7 kg/m2 (men 28.0 kg/m 2; women 30.1 kg/m2) and the rate of obesity was 31% in men and 43% in women. In the same study, the rate of metabolic syndrome was two times higher in patients with schizophrenia than in age- and gender-matched general population controls. Similarly, baseline CATIE data revealed that men and women with schizophrenia were 138% and 251% more likely, respectively, to have metabolic syndrome than a National Health and Nutrition Examination Survey matched sample (McEvoy et al. 2005). To compare rates of obesity in schizophrenia patients with the general population prior to the widespread use of atypical antipsychotics, Allison et al. (1999a) investigated BMI in patients with and without schizophrenia using data from the mental health supplement of the 1989 National Health Interview Schedule. Men with schizophrenia had a BMI similar to that of men without schizophrenia (26.14 vs. 25.63), but women with schizophrenia had a higher BMI than women without schizophrenia (27.36 vs. 24.50, P< 0.001). A consistent finding in these various studies has been that women with schizophrenia have a differentially higher rate of obesity and metabolic syndrome relative to the general population than do men with schizophrenia. Although this finding is similar to what has been found in various minority and lowincome groups, the reasons have yet to be fully explored.
Determinants and Mechanisms of Obesity General and Evolutionary Factors Weight maintenance is governed by a formula that is deceptively simple: energy input must equal energy output. Energy input is essentially food consumed, whereas energy output comprises a number of elements of which physical activity is the main modifiable factor and accounts for about 30% of energy output. Other energy output factors include basal metabolism (60% of energy output), diet-induced thermo-
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genesis (10% of energy output), and adaptive thermogenesis or alteration in metabolism related to environmental, psychological, or other influences, such as habitual body movement (Groff and Gropper 1999). (Subtle akathisia, which was a common side effect with classic antipsychotics, probably accounted for significant energy expenditure.) Energy balance is tightly controlled but tilted toward weight gain because of physiological adaptations that defend against weight loss, so that a very small positive energy balance of 100 calories a day (the equivalent of one slice of bread) results in a gain of 4.5 kg in 1 year. Evolutionary concepts best explain the evolving epidemic of obesity in developed and developing countries (Ogden et al. 2007; Pi-Sunyer 2003). The human body has evolved to survive the threat of famine from a time when food was scarce and a great deal of effort and energy was required to procure food. In contrast, many people now have an abundant supply of cheap, easily obtainable, and palatable food, and at the same time less requirement and opportunity for exercise. What was an advantage in times of food scarcity becomes a liability when the food supply is abundant or when there is change from a traditional diet to a more westernized diet. When food is scarce and weight is lost, the body has a powerful biological drive to regain weight: basal metabolic rate drops, and hunger signals increase. In contrast, when excessive weight is gained, biological signals are muted; there is no great increase in basal metabolism, and individuals tend to become less rather than more active. The thrifty phenotype hypothesis, as proposed by Hales and Barker (2001), is an extension of this view: With poor intrauterine nutritional conditions, the child efficiently conserves energy and is prepared for survival in an environment where food resources will be scarce. Then, when exposed to an environment of food excess, such an individual who is efficient at conserving energy would be predisposed to obesity. This hypothesis has gained considerable favor in explaining susceptibility to metabolic disturbance, particularly type 2 diabetes. The thrifty phenotype hypothesis also suggests a mechanism whereby patients with schizophrenia may be predisposed to obesity and associated metabolic disturbance. Epidemiological research has shown an association between intrauterine and childhood undernutrition and the lifetime risk of schizophrenia. For example, there was a doubling of the rate of schizophrenia following both the Dutch winter famine (1945) and the Chinese famine (1959–1961) (Hoek et al. 1998; St Clair et al. 2005). In addition, studies have demonstrated a relationship between schizophrenia and small size at birth, thinness in childhood, and short stature (Gunnell et al. 2003; Wahlbeck et al. 2001).
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Genetics and Neurophysiology Estimates of the heritability of obesity range from 30% to 70% (Martínez-Hernández et al. 2007). Different sets of monozygotic twins that are overfed differ in the degree to which excess calories are stored as fat, but within each set of twins the susceptibility to gain weight in a given environment is similar. Although a number of rare syndromes associated with obesity have been found to be caused by discrete genetic defects or chromosomal abnormalities (e.g., Prader-Willi syndrome), the more common forms of obesity likely involve more complex interactions between the environment and a multitude of polymorphisms located in genes and candidate regions throughout the genome. Genetic investigation has focused on genes that encode and regulate food intake, energy expenditure, and adipogenesis. Food intake is regulated by peptides such as leptin and neuropeptide Y that bind with receptors in the hypothalamus, as well as by a range of peptides synthesized along the gastrointestinal tract, such as ghrelin and cholecystokinin (Schwartz et al. 2000). Research on energy expenditure has focused on the sympathetic nervous system, in particular the β-adrenergic receptor gene family, and on uncoupling proteins at the mitochondrial level (Martínez-Hernández et al. 2007). In adipogenesis, peroxisome proliferator–activated receptor-γ (PPAR-γ) has become a focus of investigation (Marti et al. 2002).
Lifestyle Habitual diet and level of physical activity are important factors that contribute to energy balance and the development of obesity. Dietary intake has been studied in schizophrenia samples, but results have been conflicting. In a cohort of 102 middle-aged patients with schizophrenia living in the community, English researchers reported that patients had a diet higher in fat and lower in fiber than the general population (Brown et al. 1999). Using a 24-hour diet recall in 146 outpatients with schizophrenia living in Pittsburgh, Strassnig et al. (2003b) reported that patients consumed more food than matched control subjects, but the macronutrient composition (percentage of calories from fat, protein, and carbohydrate) was similar in both groups. In contrast, Henderson et al. (2006) used 4-day food diaries—a more rigorous sampling strategy—to evaluate the diet of 88 patients with schizophrenia attending a community mental health clinic in Boston and made comparisons with general population controls matched by age, gender, and ethnicity. The patients consumed significantly fewer calories and less carbohydrate, protein, total fat, and saturated fat than the general population controls.
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Habitual diet is notoriously difficult to measure and is prone to various degrees of underreporting (O’Neil 2001), which may help explain these discrepant findings. Patients with schizophrenia are often financially disadvantaged, and diet can vary based on the local availability of low-cost foods. Patients are often housed in group homes and boarding houses, where food, if it is provided, is cost-based and tends to be high in starch and sugar. In a Toronto study, Cohn et al. (2004a) found that the diet of 162 outpatients with schizophrenia was similar in calorie count to that of individuals in the general population but was high in simple carbohydrates, particularly easily available chips and soda—a diet that has been implicated in development of metabolic syndrome (McKeown et al. 2004). Findings regarding physical inactivity in samples of patients with schizophrenia have been more consistent. Multiple investigators have reported low levels of physical activity in patients compared with the general population (Brown et al. 1999; Cohn et al. 2004a; Daumit et al. 2005; Ussher et al. 2007). Daumit et al. (2005) pointed out that walking tended to be the only form of physical activity and that women and those without regular social contact were more inactive. Ussher et al. (2007) indicated that patients reported low levels of social support for exercising and that fatigue and illness were the most common barriers to activity.
Obesogenic Environment An important consideration is how the environment influences lifestyle and the development of obesity. The “obesogenicity” of an environment is the sum of influences that the surroundings, opportunities, or conditions of life have on promoting obesity in individuals or populations (Swinburn et al. 1999). The concept of the obesogenic environment has been applied to the built environment, particularly in cities and suburbs (Papas et al. 2007). Attempts have been made to analyze obesogenic factors in school systems and to promote healthier environments with access to nutritious foods and the promotion of physical activity (Nollen et al. 2007). However, little attention has been paid to the obesogenic environment in mental health settings. How environments are structured in psychiatric group homes, inpatient units, and community clinics, with provision of opportunities for physical exercise and healthy eating, is an important focus for future research.
Antipsychotic-Induced Weight Gain Antipsychotic medications are a mainstay in the treatment of schizophrenia, and in most cases long-term maintenance treatment is advised.
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Significant weight gain has been recognized as a common side effect, particularly with low-potency classic antipsychotics and certain second-generation antipsychotics. This issue, which is now recognized as a public health concern (Green et al. 2000), has garnered much attention, particularly over the past 10 years. In a comprehensive review of the published literature, including randomized clinical trials, Newcomer (2005) identified clozapine and olanzapine as being associated with the greatest risk of clinically significant weight gain; risperidone, quetiapine, amisulpride, and zotepine as having intermediate risk; and ziprasidone and aripiprazole as having the lowest risk. This view is consistent with the findings of a consensus development conference among the American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, and the North American Association for the Study of Obesity. The report, published simultaneously in two journals in 2004, similarly assigned antipsychotics to high, intermediate, and low risk for weight gain and attributed the associated risk for type 2 diabetes and dyslipidemia in the same rank order (American Diabetes Association et al. 2004). Antipsychotics are associated with both short-term (10–12 weeks) and long-term weight gain. Allison et al. (1999c) conducted a metaanalysis to compare the weight-gain liability of antipsychotics over the short term (10 weeks). Using a random-effects model, average weight gain was 4.5 kg with clozapine, 4.1 kg with olanzapine, 2.5 kg with chlorpromazine, 2.1 kg with risperidone, 1.1 kg with haloperidol, and 0.05 kg with ziprasidone (Allison et al. 1999c). Other investigators have reported the short-term weight gain to be 2.3 kg with quetiapine (Jones et al. 1999) and 0.6 kg with aripiprazole (Marder et al. 2003). Weight gain tends to be rapid, particularly during the first 3 months of treatment, with significant weight increases often noted within a few weeks of treatment (Newcomer 2005). Weight gain generally plateaus within 9 months or 1 year, although clozapine tends to have a longer weight-gain trajectory, with patients continuing to gain weight for up to 5 years (Henderson et al. 2000). Considerable interindividual variability in weight gain occurs, and average weight gain belies the fact that individual patients can gain a very large amount of weight (sometimes in excess of 45 kg) on a particular medication. Susceptibility to weight gain may be genetically determined, and at a future time genetic testing might be used for predicting which patients are prone to excessive weight gain (Müller and Kennedy 2006). Clinical experience has shown that the following variables predict weight gain:
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1. Type of antipsychotic: Antipsychotics differ considerably in weightgain liability. 2. Previous antipsychotic exposure: Patients who are antipsychotic naive or switched from a classic antipsychotic or an SGA with low weight-gain risk to an antipsychotic with high weight-gain risk are more susceptible to weight gain. In contrast, those switched from an antipsychotic with high to one with low weight-gain liability tend to lose weight. 3. Age: Children, adolescents, or young adults are more likely to gain weight; older patients are less susceptible to weight gain. 4. Baseline weight: Patients with a low baseline weight tend to gain more weight. 5. Race and ethnicity: Certain groups, including those of African, South Asian, Hispanic, and Native American descent, have higher baseline rates of metabolic disturbance and may also be more susceptible to weight gain. 6. Clinical response: Although controversial, a number of investigators have found an association between weight gain and antipsychotic response. 7. Antipsychotic dose and plasma level: These are generally not correlated with weight gain when prescribed within the recommended dose range, although clozapine may be an exception. Longer-term weight gain is difficult to compare across antipsychotics because of patient attrition and the difficulties adjusting for variables such as prior antipsychotic exposure. In a randomized, controlled trial of 263 first-episode patients with no or limited prior exposure to antipsychotics that compared olanzapine (mean modal dose 9.1 mg/day) and haloperidol (mean modal dose 4.4 mg/day), the weight gain after 2 years of treatment for olanzapine was 10.2 kg using a last-observationcarried-forward analysis and 14 kg for observed cases. For haloperidol, weight gain was 4 kg using a last-observation-carried-forward analysis and 7.5 kg for observed cases (Zipursky et al. 2005). The neuroendocrine mechanisms that underlie antipsychoticinduced weight gain are not fully understood. However, the relationship between neurotransmitter-binding profiles of different antipsychotics and their propensity to induce weight gain has been explored (Nasrallah 2008). Wirshing et al. (1999) astutely observed a correlation between weight gain and antipsychotic binding affinity for the histamine-1 (H1) receptor. Kroeze et al. (2003) more recently concluded that H 1 receptor affinity is positively correlated with weight gain among both typical and atypical antipsychotics. Others have confirmed this
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(S.F. Kim et al. 2007; Matsui-Sakata et al. 2005). S.F. Kim et al. (2007), using a mouse model, found that antipsychotics that induce weight gain selectively activate hypothalamic AMP-kinase via the H1 receptors. H1 activity is involved in appetite regulation and sedation, both relevant to weight gain. These findings may guide antipsychotic drug development toward drugs less implicit in weight gain. Other targets for investigation include the 5-HT2C gene (De Luca et al. 2007; Reynolds et al. 2002) and the SNAP-25, α-2a, leptin, and GNB3 genes (Müller and Kennedy 2006).
Managing Obesity in Schizophrenia The management of obesity includes an understanding and evaluation of risk factors, prevention of weight gain, metabolic assessment, and ongoing monitoring of the patient, as well as the application of pharmacological and nonpharmacological strategies of prevention and treatment. Clearly, psychiatrists cannot do this work alone. What is required is to develop a system of assessment and management in which roles and tasks are clearly defined and often delegated.
Tooling the Office or Clinic Clinicians should think through how to set up the environment, equipment, systems, and personnel within the office or clinic to facilitate care of patients who are obese (Kushner 2007). The clinic or office should be accessible for these patients. Hallways and doors should not be too narrow, washrooms need to be large enough, and the waiting area and examination or interview room should have a number of sturdy, armless chairs. Posters, artwork, and magazines should not overly promote “thinness.” However, posters promoting balanced healthy eating and physical activity are appropriate, as is information about weight-loss programs and services. The examination area should be equipped with appropriate equipment for assessing obese patients. Most office or hospital scales do not measure above 300–350 lb (135–160 kg). Clinics should obtain a scale with greater capacity and position it so the patient can be weighed privately and away from public view. A wall-mounted measure or an extendable height meter attached to the scale should be available for measuring the patient’s height. Also, a variety of cuff sizes should be provided for measuring blood pressure. “Miscuffing” is the most common error in measuring blood pressure, and undercuffing accounts for the majority of errors (Manning et al. 1983). Tape measures should be
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available for measuring waist circumference, and a calculator or chart is needed for determining BMI. Each office or clinic needs to establish a procedure for metabolic monitoring. The paper or electronic health record should allow for the recording and tracking of screening medical history questions, clinical measures, and laboratory tests used in metabolic monitoring. Commercial tracking sheets, usually sponsored by pharmaceutical companies, are available. The clinic where I work has developed an electronic tool that is integrated into the medical record and the patient care plan that allows for the tracking of metabolic parameters and performs calculations such as whether the patient has metabolic syndrome, Framingham risk score, target lipid values, and so forth (Khoury et al. 2008). Forms or computer systems should be readily available to order needed laboratory tests. Systems need to be in place for referring patients for primary care or specialist consultation and for referring patients for nutrition counseling and to professionals who promote physical activity. Larger clinics or programs should develop group-based interventions to promote healthy lifestyle changes. The roles and tasks of personnel should be clearly defined. Registered nurses can obtain the vital measurements, including weight, height, waist circumference, and blood pressure, and provide health teaching. Nurse practitioners can provide most components of primary health care. Co-location of primary health care (primary care physician or nurse practitioner) within psychiatric services is an excellent model for integrating health care (Druss et al. 2001). Office secretaries and assistants can facilitate referrals, remind patients of appointments, and distribute written material. Although the exact role and function of each team member varies from one setting to another, what is most important is that care is coordinated and that individuals work together as a team.
Assessment and Metabolic Monitoring of the Patient Patients can be assessed at various points—as a new antipsychotic medication is prescribed, after substantial weight has been gained, upon admission to hospital, at annual examinations, at each visit, and so on. Ideally, to inform care, these various assessments should be recorded and tracked, along with prescribed medications and intervention efforts. Electronic record systems are particularly helpful in this regard and can also provide reminders when assessments are due. We (Cohn and Sernyak 2006) have reviewed recommendations regarding metabolic monitoring published in various countries. Table 4–1
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provides a structure informed by these guidelines and used in our clinic for assessment of patients who are overweight or obese and are being treated with psychiatric medications associated with weight gain (antipsychotics, mood stabilizers, and antidepressants). Monitoring, which consists of medical history, measurements, and laboratory tests, is conducted at the first opportunity—when the patient first presents at the office of clinic or at the time of hospital admission. Monitoring is then repeated annually. A new baseline is established when a new antipsychotic (or mood medication) is started, with a follow-up evaluation 3 months later. Weight is measured at every visit and graphed over time across encounters (inpatient and outpatient). Diet, activity, motivation, and environmental factors (see “General and Lifestyle” column in Table 4–1) are evaluated during clinic visits. To evaluate diet and physical activity, we have found it useful in our clinic to have the patient, with the support of family or caregivers, complete 3 days of food records, a standard food frequency questionnaire, and a physical activity questionnaire prior to the initial appointment. We have been able to validate the reliability of the International Physical Activity Questionnaire (Craig et al. 2003) in a sample of patients with schizophrenia (Faulkner et al. 2006). These assessments are mailed to the patient before the appointment, reviewed during the initial consultation, and used to inform the development of a treatment plan. We have also found it useful to get a baseline measure of fitness by using the 6-minute walking test (ATS Committee on Proficiency Standards 2002), a simple structured assessment of the distance an individual walks over 6 minutes.
Strategies for the Prevention and Management of Obesity Behavioral lifestyle interventions focused on diet and physical activity should be combined with biological (mostly pharmacological) strategies to benefit patients who are obese (Faulkner and Cohn 2006). Figure 4–1 provides a recommended outline for the sequencing of interventions. Behavioral interventions are discussed in detail in Chapter 8, “Behavioral Treatments for Weight Management of Patients With Schizophrenia.” This section focuses on biological strategies. Medication Choice. Weight-gain liability should be factored in when choosing psychotropic medications. The choice of psychotropic medication may have the greatest influence on weight gain and associated metabolic disturbance. Good evidence exists for a range of weight-gain liability for antipsychotic medications (Allison et al. 1999c; Newcomer
TABLE 4–1.
Metabolic assessment of patients treated using antipsychotic medications Medical history
•
Medications • Current antipsychotic medication(s) • Previous antipsychotic trials • Newly prescribed antipsychotic (date) • Coprescribed mood stabilizers • Coprescribed antidepressants • Coprescribed lipid medication(s) • Coprescribed diabetes medication(s) • Coprescribed antihypertensive medication(s)
• •
•
Current psychiatric and medical contacts Income and source Living situation: Are meals provided? Who does the shopping and cooking? Recreational opportunities? Self-report of weight change over time
Physical activity • Premailed International Physical Activity Questionnaire 1 • Interest in and barriers to increasing physical activity • 6-Minute walking test 2 Diet • Premailed food frequency questionnaire and 3-day food diary • Interest in and feasibility of making dietary changes
Examination (measurement) Laboratory tests • • • •
Height Weight Waist circumference Blood pressure
For nondiabetic patients (No known diagnosis of diabetes) • Fasting glucosea Measuring guide • Fasting insulin Height: No shoes • Fasting lipid Weight: No shoes; single layer profile of clothing (Total, HDL, and Waist circumference: Standing LDL cholesterol position and triglycerides) Blood pressure: Average of two measures. Patient seated For diabetic patients Risk factors and resting for at least 5 • Race/ethnicity • Fasting glucose minutes, >30 minutes from • Family history of diabetes • Fasting lipid last cigarette or coffee. Arm • History of gestational diabetes profile relaxed and supported at • History of coronary vascular disease • HBA1C heart level. Choose (angina, myocardial infarction, or • Random urine appropriate cuff size as stroke) microalbuminb indicated by cuff markers. • Cigarette smoking
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a Patients with impaired fasting glucose (5.6–6.9 mmol or 100–125 mg/dL) have follow-up glucose tolerance test (fasting glucose and 2-hour glucose after 75-g oral glucose drink). bIf elevated, urine albumin/creatinine ratio. 1 Craig et al. 2003; Faulkner et al. 2006. 2ATS Committee on Proficiency Standards 2002. Source. Adapted from Cohn and Sernyak 2006.
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2005). Antidepressant and mood-stabilizing medications also contribute to weight gain (Aronne and Segal 2003; Fava 2000) because these medications are commonly coprescribed with antipsychotics. Lithium carbonate, valproic acid, selective serotonin reuptake inhibitors, and mirtazapine have all been implicated in weight gain. Young, first-episode patients are more susceptible to antipsychoticinduced weight gain (Zipursky et al. 2005) and also tend to have a robust response to antipsychotic medications (Perkins et al. 2004). Initial treatment with antipsychotic medications known to induce significant weight gain should be avoided. Care should also be taken in the choice of concomitant psychiatric medications. In patients who have gained significant weight or developed metabolic complications, all medications should be reviewed from both metabolic and psychiatric perspectives (Faulkner and Cohn 2006). Medication Switching. Switching from one antipsychotic medication to another can have significant effects on body weight and metabolic parameters. Reviews (Weiden 2007; Weiden and Buckley 2007) suggest that switching to low-liability antipsychotics (ziprasidone or aripiprazole) can have a more substantial impact on reversing weight gain and lipid disturbance than other intervention strategies, particularly if the weight gain and metabolic derangement clearly occurred during the course of prior treatment with an antipsychotic medication with greater metabolic liability. The metabolic benefit from switching is therefore dependent on the prior treatment. Outpatients, predominantly with schizophrenia, were switched from a previous antipsychotic—olanzapine (n = 104), risperidone (n = 58), or conventional antipsychotic (n =108)—to ziprasidone (mean dose 91 mg/day) and reevaluated after 6 weeks (Weiden et al. 2003a). Weight and metabolic benefit depended on which drug the patients were originally taking. Those switched from olanzapine experienced the greatest metabolic benefit, with significant reductions in body weight (1.76 kg), nonfasting total cholesterol, and triglycerides. Those switched from risperidone experienced less weight loss (0.86 kg) but similar reductions in nonfasting total cholesterol and triglycerides. However, those switched from conventional antipsychotics had no change in weight or lipid measures. A companion paper from the same data set (Weiden et al. 2003b) supported the view that stable but symptomatic patients can safely be switched from a previous antipsychotic using a variety of switching strategies (e.g., abrupt discontinuation of previous antipsychotic or gradual cross-titration), often with benefit to psychiatric symptomatology. An extension of this study provided long-
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Consider metabolic risk in choice of antipsychotic
Provide patient, family, and caregiver education about potential metabolic side effects
Conduct baseline screening and regular metabolic monitoring
Provide lifestyle counseling
With significant weight gain/ metabolic disturbance: Review all medications from a metabolic and psychiatric perspective; consider risk/benefit of switch
Provide referral to structured lifestyle intervention focused on diet, physical activity, and behavior change
If lifestyle intervention is unsuccessful, consider adjunctive pharmacotherapy; for severe obesity and significant disability, consider evaluation and workup for obesity surgery
FIGURE 4–1. Prevention and management of antipsychotic-related metabolic disturbance. Source.
Adapted from Faulkner and Cohn 2006.
term data for 153 patients followed for 58 weeks (Weiden et al. 2008). Average weight loss from baseline was 10.3% (9.8 kg) and 7.8% (6.9 kg) if the previous antipsychotic was olanzapine or risperidone, respectively. Those patients switched from olanzapine and risperidone also sustained significant reductions in nonfasting cholesterol and triglycerides, with maximum reductions occurring rapidly at 6 weeks after the switch. Those switched from conventional antipsychotics to ziprasidone did not change in body weight or lipid measures. Data from the CATIE study (Lieberman et al. 2005) also provide evidence in a randomized trial of differential effects on body weight
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when antipsychotics are switched. Patients switched to olanzapine gained 0.9 kg per month. Patients switched to quetiapine and risperidone also gained weight, but less (0.23 kg per month on quetiapine and 0.18 kg per month on risperidone). Those switched to ziprasidone and perphenazine lost 0.13 and 0.09 kg per month, respectively. Patients switched to aripiprazole (Casey et al. 2003) experience weight changes very similar to patients who are switched to ziprasidone. Again, weight change depended on the previous antipsychotic. Taken together, data from these studies indicate that weight loss and metabolic benefit can be achieved by switching medication, particularly if these problems clearly developed while a patient was taking the prior medication. However, switching is always hazardous because patients can decompensate. The biggest challenge is the patient who has been doing well psychiatrically while taking an antipsychotic but has gained weight or developed metabolic complications. In such a case, the risks of switching may be greater, and the patient and family need to be informed of the potential risks and benefits and then carefully consider whether to switch medication. Discontinuing or switching antipsychotics becomes a critical issue when diabetes or diabetic ketoacidosis can be clearly linked to an antipsychotic prescription, because resolution of diabetes can occur (Jin et al. 2004). Medication Addition. One strategy to prevent weight gain or to promote weight loss is to add a medication associated with weight loss to the antipsychotic medication regimen. In the general population, controlled clinical trials have established modest efficacy for obesity drugs in combination with lifestyle therapy (Li et al. 2005). Orlistat (lipase inhibitor) and sibutramine (serotonin/dopamine/norepinephrine reuptake inhibitor) are the only drugs currently approved for long-term weight loss. Average weight loss in the general population (psychiatric patients were excluded) at 12 months was 2.7 kg (95% CI 2.3–3.1) for individuals taking orlistat and 4.3 kg (95% CI 3.6–4.9) for those taking sibutramine (Padwal et al. 2003). In the context of antipsychoticinduced weight gain, brief (6–16 weeks) randomized controlled trials have been conducted in patients with schizophrenia using a number of compounds, as summarized in Table 4–2. In a Cochrane review, Faulkner et al. (2007b) focused on weight change to compare efficacy reported in studies identified using a systematic search and retrieval protocol. Unfortunately, no long-term studies have been published. In these studies, the weight loss medication is either added when the antipsychotic is first prescribed to prevent weight gain (e.g., reboxetine in first-episode psychosis being treated with olanzapine) (Poyurovsky
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et al. 2003, 2007) or added later when patients have already gained substantial weight (e.g., the use of metformin in children and adolescents whose weight had increased by more than 10% in less than 1 year while taking atypical antipsychotics) (Klein et al. 2006). Many of these studies have been conducted in patients taking clozapine and olanzapine. The studies have been heterogeneous, and results have been mixed. When there is a weight benefit, it tends to be modest (approximately 2–4 kg over 3 months). Negative results have been seen with fluoxetine and reboxetine addition. Sibutramine was found effective with patients on olanzapine (Henderson et al. 2005) (but the benefits were reversed when patients stopped taking sibutramine). Sibutramine was ineffective in clozapine-treated patients (Henderson et al. 2007). Topiramate has been more consistently effective for weight loss in open-label as well as double-blind studies (J.H. Kim et al. 2006; Ko et al. 2005), and doses of 200 mg/day have been more effective than 100 mg/day (Ko et al. 2005). However, topiramate has many side effects, both common (paresthesia) and potentially serious (cognitive impairment, narrowangle glaucoma, and metabolic acidosis). Metformin may be more helpful in reducing or preventing weight gain during the early stages of treatment for psychosis or in younger individuals. Metformin was found to be effective in reducing weight gain when coprescribed with olanzapine in patients treated for a first psychotic episode (Wu et al. 2008) and in reversing weight gain and improving insulin sensitivity in children and adolescents who had recently gained weight during treatment with atypical antipsychotics (Klein et al. 2006). However, metformin addition was not able to prevent weight gain in an adult sample of schizophrenia patients with a more chronic course who were switched to olanzapine (Baptista et al. 2006). The release of rimonabant, a selective cannabinoid CB1 receptor antagonist, was highly anticipated because of promising phase 3 results in weight loss, smoking cessation, and improvement of metabolic profiles in patients with metabolic syndrome, all of which would benefit patients with schizophrenia (Cox 2005). Rimonabant is currently approved in Europe but not in the United States or Canada. Because recent data indicate significant psychiatric adverse effects, primarily depression and anxiety, there was a recommendation against rimonabant approval in 2007 by the FDA (Rumsfeld and Nallamothu 2008). Given that H1 receptor affinity appears to be implicated in antipsychotic-associated weight gain, interest has been shown in the use of betahistine, an H1 receptor agonist and H3 antagonist, to prevent weight gain. Promising results were reported in a small open-label study (Poyurovsky et al. 2005). Placebo-controlled studies are still needed.
Antipsychotic × trial duration
Amantadine (antiparkinsonian) Famotidine (H2 receptor antagonist) D-Fenfluramine (removed from market) Fluoxetine (SSRI)
Olanzapine × 16 weeks (Deberdt et al. 2005) Olanzapine × 6 weeks (Poyurovsky et al. 2004) Typical depots × 12 weeks (Goodall et al. 1988) Olanzapine × 8 weeks (Poyurovsky et al. 2002) Olanzapine × 16 weeks (Bustillo et al. 2003) Olanzapine × 12 weeks (Wu et al. 2008) Atypical AP × 16 weeks (Klein et al. 2006) Olanzapine × 14 weeks (Baptista et al. 2006) Olanzapine × 8 weeks (Atmaca et al. 2003) Olanzapine × 16 weeks (Cavazzoni et al. 2003)
Metformin (antidiabetic)
Nizatidine (H2 receptor antagonist)
Agent dose: n for intervention group: outcome, expressed as weight change relative to placebo [95% CI] 100–300 mg: n= 35: −1.7 [−3.9, 0.5] kg 40 mg OD: n= 7: no effect 30 mg OD: n= 9: −2.6 [−5.5, −0.1] kg 20 mg OD: n= 15: no effect 60 mg OD: n= 15: no effect 250 mg tid: n= 18: −5.0 [−7.3, −2.7] kg 500 mg OD: n= 9 (ages 10–17): −4.1 [−7.3, −1.0] kg 850–1,750 mg: n= 20: no effect 150 mg bid: n =18: −6.8 [−7.9, −5.7] kg 300 mg bid: n =58: less wt gain, 3 and 4 wk; effect lost, 16 wk 150 mg bid: n =57: no effect
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TABLE 4–2. Randomized, controlled trials of medication addition for weight gain in schizophrenia
TABLE 4–2. Randomized, controlled trials of medication addition for weight gain in schizophrenia (continued) Antipsychotic × trial duration
Phenylpropanolamine (removed from market) Reboxetine (SNRI)
Clozapine × 12 weeks (Borovicka et al. 2002) Olanzapine × 6 weeks (Poyurovsky et al. 2003) Olanzapine × 6 weeks (Poyurovsky et al. 2007) Clozapine × 12 weeks (Henderson et al. 2007) Mixed × 16 weeks (Weiden et al. 2003c) Olanzapine × 12 weeks (Henderson et al. 2005) Atypical AP × 12 weeks (Ko et al. 2005)
Sibutramine (serotonin/ dopamine RI)
Topiramate (anticonvulsant)
Agent dose: n for intervention group: outcome, expressed as weight change relative to placebo [95% CI] 75 mg: n =8: no effect 4 mg/d: n =13: −3.0 [−5.6, −0.5] kg 4 mg/d: n =31: −1.5 [−2.9, −0.1] kg 15 mg: n =11: no effect 15 mg: n =29: no effect
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15 mg: n =19: −4.6 [−5.2, −4.0] kg 200 mg/d: n =17: −5.1 [−7.4, −2.7] kg 100 mg/d: n =16: −1.4 [−4.2, 1.5] kg
Note. RI= reuptake inhibitor; SNRI=serotonin-norepinephrine reuptake inhibitor; SSRI= selective serotonin reuptake inhibitor. Source.
Adapted from Faulkner and Cohn 2006.
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Insufficient data are available to suggest the routine use of medication additions (Faulkner and Cohn 2006). Each medication brings the potential of additional side effects, effect sizes are small, and accessing and covering the costs of these drugs are often problematic. Medication additions should, however, be contemplated when other approaches such as behavioral interventions have been exhausted and switching to a different antipsychotic has been ruled out. Obesity Surgery. Surgery is an effective intervention for treating severe obesity (Schneider and Mun 2005; Sharma and Iacobellis 2006), resulting in more significant and longer-lasting loss of weight than other treatments. Criteria for selecting patients for bariatric surgery include the following: 1) BMI > 40 kg/m2 or BMI > 35 kg/m 2 with significant obesity-related comorbidities; 2) repeated failure of other nonsurgical interventions; 3) psychologically stable patient with realistic expectations; and 4) absence of alcoholism, other addictions, or major psychopathology (NIH Conference 1991). These criteria make it difficult for patients with schizophrenia and other forms of severe mental illness to access surgical intervention. Surgical procedures can involve gastric restriction using vertical or adjustable bands to create a small neogastric pouch, or shortening the functional length of the intestinal surface for nutrient absorption, as in gastric bypass or biliopancreatic diversion. The current gold standard for bariatric surgery is the Roux-en-Y gastric bypass, which is a combination of the restrictive and bypass procedures (Sharma and Iacobellis 2006). Patients with schizophrenia require advocacy and very close medical follow-up and support to access and derive maximum benefit from any of these effective interventions.
Conclusion Although obesity is endemic in the general population, its prevalence and consequences are amplified for patients with schizophrenia. Responsibility for organizing health promotion and obesity interventions lies with the providers of psychiatric care, because these services are often not otherwise accessible and because medications used to treat schizophrenia contribute to the development of obesity. Genetic, medication, lifestyle, and environmental factors are determinants of obesity in patients with schizophrenia, but genetic prediction of an individual’s susceptibility to gain weight taking a particular medication is not yet a reality, and practitioners have to rely on clinical evaluation and
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ongoing monitoring. Clinicians and mental health services are challenged to set up systems for the assessment, monitoring, prevention, and treatment of obesity.
Key Clinical Points ◗
Obesity affects life expectancy and quality of life and is more common in patients with schizophrenia than in the general population.
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Genetics, psychotropic medications, lifestyle, and the environment are contributing factors.
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Clinical assessment of the patient includes a review of medical history, diet, and level of physical activity; physical measurements (weight, height, waist circumference, and blood pressure); and laboratory tests. These parameters should be tracked in an organized manner along with prescribed psychotropic medication (metabolic monitoring).
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The clinic or office should be set up to facilitate care of obese patients. Considerations include the environment, equipment, systems, and use of personnel.
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Prevention and management of obesity in schizophrenia require consideration of choice of psychotropic medication, health counseling and behavioral interventions, medication switching, medication addition, and obesity surgery.
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CHAPTER 5 Glucose Intolerance and Diabetes in Patients With Schizophrenia David C. Henderson, M.D. Kathleen Miley, B.S.
In recent years, tremendous progress has been made in the treatment of schizophrenia and other psychotic disorders. In particular, the introduction of atypical antipsychotic agents has helped numerous patients gain control of both positive and negative symptoms of schizophrenia. Studies also suggest that the newer antipsychotic agents increase compliance, prevent relapse (Csernansky et al. 2002), and may offer improvement in the treatment of cognitive dysfunction (Meltzer 2001). These treatment successes, combined with a lower risk of extrapyramidal symptoms (e.g., parkinsonism) and improved control of positive and negative symptoms, have led many psychiatrists to favor the newer, atypical antipsychotic medications over the older, typical agents. Furthermore, the atypical antipsychotic agent clozapine has been found to be a highly effective agent in improving symptoms for those patients who have had treatment resistance to other antipsychotic medications (Lewis et al. 2006). 91
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The use of atypical antipsychotic medications, however, has not proven to be trouble free. Numerous reports in the psychiatric and medical literature have suggested an association between atypical antipsychotic agents and impaired glucose metabolism, diabetes mellitus, and diabetic ketoacidosis (Elias and Hofflich 2008; Henderson 2002; Henderson et al. 2007; Perez-Iglesias et al. 2007; Saddichha et al. 2008). After a brief review of diabetes pathophysiology, especially type 2 diabetes mellitus, and its short- and long-term medical consequences, we examine glucose intolerance and diabetes in individuals with schizophrenia before and following the introduction of typical and atypical antipsychotic medications. Finally, we make recommendations for monitoring and screening for glucose intolerance and diabetes in patients with schizophrenia.
Diagnostic Criteria and Pathophysiology of Diabetes Mellitus Both diabetes and glucose intolerance are characterized by problems in glucose-insulin regulation. According to the American Diabetes Association (ADA), if an individual has the typical symptoms of diabetes, such as polyuria, polydipsia, and unexplained weight loss, plus a casual plasma glucose level ≥200 mg/dL (11.1 mmol/L), a fasting plasma glucose level ≥126 mg/dL (7.0 mmol/L), or a 2-hour plasma glucose level ≥200 mg/dL (11.1 mmol/L) post 75-g oral glucose load, then he or she has diabetes mellitus (American Diabetes Association 2008). Similarly, if an individual has a 2-hour plasma glucose level ≥140 mg/dL and <200 mg/dL post 75-g oral glucose load, he or she is said to have impaired glucose tolerance. Impaired fasting glucose is defined as a level from 100 to 125 mg/dL, whereas fasting glucose values <100 mg/dL are considered to be in the normal range (American Diabetes Association 2008). Two pathophysiological processes can lead to the development of glucose intolerance and diabetes mellitus, the first being a problem with insulin secretion, as in type 1 and the later stages of type 2 diabetes mellitus, and the second being a problem with insulin action, otherwise known as insulin resistance, as seen in type 2 diabetes mellitus (Lebovitz 2001). In the first case, the beta cells of the pancreas have been destroyed by an autoimmune process, and hyperglycemia occurs because not enough insulin is secreted to facilitate glucose uptake in skeletal muscle tissue, inhibit glucose production in hepatic tissue, and suppress lipolysis in adipose tissue. In type 2 diabetes mellitus, although
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enough insulin may be secreted, insulin resistance prevents the insulin from working at the sites of skeletal muscle, hepatic, and adipose tissues. Indeed, a reactive hyperinsulinemia may occur, which may help control plasma glucose levels initially, but eventually the beta cells in the pancreas begin to deteriorate at a genetically determined rate, and the compensatory hyperinsulinemia decreases (Lebovitz 2001). As this occurs, the postprandial plasma glucose levels increase progressively, and the individual progresses from normal glucose tolerance to impaired glucose tolerance to type 2 diabetes mellitus. Thus, patients with type 2 diabetes mellitus have both insulin resistance and an insulin secretory deficit due to decreased beta cell function. Both type 1 and type 2 diabetes mellitus are diseases with multifactorial inheritance. Researchers are confident that no single gene causes type 1 or type 2 diabetes mellitus, and that development of this heterogeneous group of disorders is likely the result of multiple genes and environmental factors. For example, the interaction of genetic factors such as beta cell abnormalities and a predisposition for central obesity with excess caloric intake, high fat ingestion, and decreased physical activity can lead to type 2 diabetes mellitus in some individuals (Lebovitz 2001). Drugs are another environmental factor that can cause diabetes mellitus by either destroying the beta cells in the pancreas or by causing insulin resistance by one of the following mechanisms: 1) increasing appetite and caloric intake, leading to obesity; 2) altering fat distribution (central obesity is a risk factor for diabetes mellitus); 3) decreasing physical activity because of increased sedation; 4) decreasing oxidative metabolism in tissues; 5) interfering with the insulin action cascade; 6) increasing counterregulatory hormones; or 7) increasing free fatty acid release from adipose tissue (Lebovitz 2001). Later in this chapter, we discuss the possible mechanisms by which atypical antipsychotic medications may cause glucose intolerance or diabetes mellitus, exacerbate existing diabetes, or induce diabetic ketoacidosis in patients with schizophrenia.
Medical Complications of Glucose Intolerance and Diabetes Mellitus Hyperglycemia and diabetes mellitus are associated with acute and chronic complications associated with significant morbidity and mortality. Diabetic ketoacidosis, an acute complication of diabetes mellitus, is seen more often in patients with type 1 than in patients with type 2 diabetes mellitus and is a serious and potentially fatal complication.
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Ketoacidosis is defined by low serum pH (≤7.35), low serum bicarbonate levels (≤15), and an anion gap in the presence of ketonemia (Westphal 1996). The diabetic patient is also susceptible to a variety of chronic complications that affect the cardiovascular system, nervous system, eyes, kidneys, and wound-healing capabilities. Most of these complications are a result of microvascular and macrovascular disease that is more extensive and appears much earlier in the diabetic patient than in the general population (Newcomer 2001). Macrovascular disease in the form of atherosclerosis increases the risk of cardiovascular and cerebrovascular events such as myocardial infarction and stroke, thus accounting for much of the disability and death among diabetic patients (Haupt and Newcomer 2001; Henderson 2001). Results from studies conducted with large samples of patients without diabetes show that even modest increases in fasting plasma glucose levels that do not meet the diagnostic criteria for diabetes mellitus put patients at increased risk for cardiovascular disease and complications, including coronary artery disease, myocardial infarction, and other vascular problems, as well as increased risk for cardiovascular death (Gerstein et al. 1999). In peripheral sites, atherosclerosis can cause claudication and “diabetic foot,” a condition in which patients develop nonhealing ulcers that are prone to infection on their lower extremities and feet as a result of vascular insufficiency and sensory deficits from impairments in the peripheral nervous system. Diabetic neuropathy is a complication that contributes significantly to morbidity in diabetic patients because it not only contributes to diabetic foot but also can affect any part of the nervous system, resulting in sensory deficits, paresthesias, motor abnormalities, or autonomic dysfunction (Henderson 2001). A large percentage of diabetic patients also experience ophthalmic complications such as diabetic retinopathy and diseases of the anterior chamber that affect vision (e.g., cataracts), leading to blindness and significant disability. Finally, many diabetic patients suffer a great deal from microvascular- and macrovascular-induced nephropathy, which can cause hypertension, proteinuria, and a decrease in the glomerular filtration rate, leading to renal failure. Indeed, diabetic nephropathy is the most prevalent cause of end-stage renal failure (Schernthaner 2008) and is a leading cause of morbidity and mortality in diabetic patients.
Metabolic Syndrome Obesity (especially visceral), insulin resistance, and dyslipidemia, along with hypertension (Henderson et al. 2004), are major, modifiable
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cardiovascular risk factors associated with macrovascular complications. These metabolic abnormalities and hypertension are key components of metabolic syndrome, which is highly predictive of overt type 2 diabetes mellitus and cardiovascular disease (Wannamethee et al. 2005). The prevalence of metabolic syndrome and cardiovascular disease in the schizophrenia population taking atypical antipsychotics is much higher than in the general population (Bobes et al. 2007; Casey et al. 2004; Newcomer 2004, 2007a, 2007b; Newcomer and Haupt 2006; Osby et al. 2000). McEvoy et al. (2005) found that the age-adjusted prevalence rates of metabolic syndrome in patients in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial were 40.9% based on criteria from the third report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults and 42.7% based on criteria from the American Heart Association.
Lifestyle Factors Allison et al. (2003) reported that patients treated with antipsychotic medications who have experienced weight gain have a reduced quality of life, poorer self-reported general health, and decreased vitality. By increasing a patient’s risk of obesity, antipsychotic agents may be placing patients at risk for associated morbidity and mortality (Pi-Sunyer 2002). Patients who gain greater than 7% of their total body weight are at risk for developing hypertension and type 2 diabetes mellitus. The risk for diabetes mellitus is increased approximately twofold in mildly obese, fivefold in moderately obese, and 10-fold in severely obese persons (Pi-Sunyer 1993). Although patients with schizophrenia tend to consume fewer calories, their food choices differ from those of the general population and they are less likely to make healthy dietary choices (Brown et al. 1999; Henderson et al. 2006a; McCreadie et al. 1998; Pereira et al. 1997; Strassnig et al. 2003). Studies suggest that high dietary saturated fat and low omega-3 polyunsaturated fatty acid play a key role in the development of type 2 diabetes mellitus (Peet 2004a) and contribute to a poor clinical outcome for people with schizophrenia (Christensen and Christensen 1988; Peet 2003, 2004b). No studies have reported on whether atypical antipsychotic-induced insulin resistance has any relationship to poor dietary profile in this population. However, one study showed that clozapine-treated patients with schizophrenia consumed almost twice as much sugar as those taking other antipsychotic agents (Stokes and Peet 2004). Coccurello et al. (2006) and Fell et al. (2007) observed that olanza-
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pine treatment in mice altered dietary macronutrient selection such that they preferred a diet high in fat and sugar. Identification of any link between poor dietary profile and glucose metabolism abnormalities induced by atypical antipsychotic drugs in the schizophrenia population would be helpful to emphasize preventive measures through dietary modification prior to taking aggressive therapeutic approaches. Central obesity is a significant risk factor for the development of type 2 diabetes mellitus in patients with schizophrenia as well as in the general population. The prevalence of obesity and overweight in people with type 2 diabetes mellitus is as high as 80%–90% (Carolino et al. 2008). Apparently, however, the duration of obesity is a greater determinant of risk than that conferred by simply being obese. That is, if a patient gains a considerable amount of weight and maintains this weight, his or her risk of developing hyperglycemia or type 2 diabetes mellitus appears to be increased (Henderson 2001).
Glucose Intolerance and Diabetes Mellitus in Patients With Schizophrenia Findings From the Pre-Antipsychotic Era Early reports dating back to the 1920s, before the use of antipsychotic agents, suggest that individuals with schizophrenia and other psychotic disorders exhibited an elevated risk for developing glucose intolerance or diabetes mellitus (Braceland et al. 1945; Haupt and Newcomer 2001; Marinow 1971; Saddichha et al. 2008; Waitzkin 1966). Specifically, the reports indicate a pattern of insulin resistance in patients with schizophrenia independent of adverse medication effects. These studies, however, suffered from several methodological problems: flaws in the diagnostic criteria for schizophrenia, and lack of control for age, weight, fat distribution, ethnicity, diet, or exercise, all of which are variables now known to play a role in an individual’s risk for developing glucoregulatory disturbances (Haupt and Newcomer 2001). Thus, because no well-controlled studies exist, the debate continues as to whether individuals with schizophrenia, when unmedicated, are at increased risk for developing diabetes compared with the general population.
Conventional Antipsychotic Agents, Diabetes, and Glucose Intolerance Conventional antipsychotic agents, which have primarily antidopaminergic activity, may alter glucose-insulin homeostasis (Hägg et al. 1998).
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In particular, the low-potency phenothiazines may induce diabetes mellitus or aggravate existing diabetes mellitus (Hägg et al. 1998; Haupt and Newcomer 2001). Because of this finding, chlorpromazine has been used to prevent hypoglycemia in patients with malignant insulinoma. Furthermore, chlorpromazine has been shown to induce hyperglycemia in healthy volunteers as well as in patients with latent diabetes (Hägg et al. 1998). Other conventional antipsychotic agents, such as the high-potency agent haloperidol, are associated with a decrease in the prevalence rate of diabetes in the schizophrenia population. For example, Mukherjee et al. (1996) found an overall diabetes prevalence rate of 15% in 95 patients with schizophrenia. In patients younger than age 50 years, there were no cases of diabetes mellitus. For patients ages 50–59 years, however, the prevalence rate was 12.9%, and for patients ages 60–69 years, the prevalence rate was 18.9%. Finally, for those ages 70–74 years, the prevalence rate was 16.7%. After controlling for age, gender, and cumulative duration of antipsychotic treatment, Mukherjee et al. (1996) found that medication-free patients were more likely to develop diabetes mellitus than were those receiving treatment with conventional agents. Of note, the prevalence rates quoted in this study exceeded those expected for type 2 diabetes mellitus in the general population, lending further evidence to the argument that schizophrenia may indeed be an independent risk factor for the development of diabetes mellitus (Henderson 2002; Henderson et al. 2005a). Several other reports have suggested higher rates of type 2 diabetes mellitus in patients with schizophrenia than in the general population, even before widespread use of atypical antipsychotics (Dixon et al. 2000). In a sample of 26 medication-naive first-episode schizophrenia patients, Ryan et al. (2003) found evidence of insulin resistance and impaired glucose tolerance, with 15% of subjects exhibiting abnormal glucose metabolism on an oral glucose tolerance test. Several more recent studies of neuroleptic-naive first-episode schizophrenia patients have also confirmed the presence of glucose-insulin homeostasis dysfunction in this patient population (Cohn et al. 2006; Spelman et al. 2007; van Nimwegen et al. 2008; Venkatasubramanian et al. 2007). In summary, examination of reports discussing the prevalence rates of diabetes mellitus or impaired glucose tolerance in patients with schizophrenia shows that among the conventional antipsychotic agents, the effects of glucose regulation may vary in magnitude across individual agents. Specifically, the phenothiazines may increase a patient’s susceptibility for developing diabetes mellitus, but no significant association has been found between diabetes mellitus and the use of other conventional antipsychotic medications.
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Atypical Antipsychotic Medications, Diabetes, and Glucose Intolerance The greatest benefits of the introduction of the atypical antipsychotic medications as a group—clozapine, olanzapine, quetiapine, risperidone, and ziprasidone—have been improvements in the treatment of negative symptoms and concomitant improvements in cognitive function (Lewis et al. 2006; Meltzer 2001). The improvements in cognitive function, which include the aspects of executive function, verbal fluency, attention, and memory and learning, can lead to improved functioning in both home and work environments (Meltzer 2001). However, these benefits have not come without a cost, because many atypical antipsychotic agents are associated with significant weight gain, which has an adverse effect on health and medication compliance. In the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) schizophrenia study, funded by the National Institute of Mental Health, 74% of patients discontinued their study medication (either perphenazine, olanzapine, quetiapine, risperidone, or ziprasidone) before 18 months in phase 1 (Lieberman et al. 2005). The time to discontinuation of treatment for any cause was significantly longer in the group taking olanzapine. Although symptom outcomes were similar in phase 1 for all groups, as indicated by total scores on the Positive and Negative Syndrome Scale for Schizophrenia, the groups demonstrated significant differences in weight gain and significant glucose metabolism changes (with olanzapine resulting in the greatest negative effect). In the phase 2 tolerability arm, the time to treatment discontinuation was longer for both olanzapine (6.3 months) and risperidone (7 months) than for quetiapine (4 months). In subjects who had discontinued their previous phase 1 drug because of side effects and were switched to another drug, olanzapine was more effective than quetiapine and ziprasidone, and risperidone was more effective than quetiapine (Stroup et al. 2006). However, olanzapine, compared with the other agents, was again associated with greater and more significant weight gain and increases in lipids and glucose (Nasrallah 2006). The CATIE trial also found that at baseline, the subjects with schizophrenia had elevated rates of metabolic syndrome and its individual components compared with matched controls from the general population (McEvoy et al. 2005). Additionally, elevated risk for cardiovascular disease was observed at baseline compared with this same pool of matched controls (Goff et al. 2005). (For more discussion of the CATIE trial findings, see Chapter 3, “Medical Outcomes From the CATIE Schizophrenia Study.”)
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Although CATIE has been the largest randomized, double-blind, non-industry sponsored trial to report metabolic data, numerous earlier reports in the medical and psychiatric literature have linked the use of atypical antipsychotic agents to the development of glucose intolerance, new-onset diabetes mellitus, diabetic ketoacidosis, and exacerbation of existing type 1 or type 2 diabetes mellitus, even in patients who are not obese (Henderson 2002; Henderson et al. 2007). Koller and colleagues from the U.S. Food and Drug Administration research program conducted a MedWatch surveillance program analysis and reported 384 cases of clozapine-related diabetes (Koller et al. 2001), of which 242 were new-onset diabetes cases and 54 were cases of exacerbation in patients with preexisting diabetes. Their report also identified 80 cases of clozapine-induced probable diabetic ketoacidosis and 25 deaths. Of note, the majority of diabetic ketoacidosis episodes occurred within the first 6 months of clozapine treatment, and one patient developed diabetes mellitus immediately after he accidentally took a 500-mg dose of clozapine. In another study, Koller and Doraiswamy (2002) found a total of 289 cases of olanzapine-related diabetes, of which 225 were newonset diabetes, with 100 cases of diabetic ketoacidosis and 25 deaths. Given that the MedWatch surveillance program is able to identify only a small percentage of cases, the incidence of clozapine- and olanzapineinduced diabetes mellitus and diabetic ketoacidosis is likely much greater than reported. Hägg et al. (1998), using an oral glucose tolerance test, found that 12% of patients treated with clozapine developed type 2 diabetes and 10% developed impaired glucose tolerance compared to 6% and 3%, respectively, of patients treated with conventional depot antipsychotic medications. In an analysis of 45 published cases of new-onset diabetes mellitus or diabetic ketoacidosis following treatment with atypical antipsychotic agents, Jin et al. (2002) reported that 20 patients had received clozapine, 19 olanzapine, 3 quetiapine, and 3 risperidone. In a study of the association between the atypical antipsychotic agents and diabetes mellitus in patients with schizophrenia or related psychoses, Melkersson et al. (1999) found elevated fasting insulin levels and reduced growth hormone–dependent insulin-like growth factor I in 28 patients taking conventional antipsychotic agents compared with 13 patients taking clozapine, thus suggesting insulin resistance with secondary increased hyperinsulinemia related to clozapine therapy. Our group conducted a 5-year naturalistic study examining, in 96 patients with schizophrenia treated with clozapine, the incidence of treatment-emergent diabetes in relation to other factors such as weight gain, lipid level abnormalities, age, clozapine dose, and concomitant treat-
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ment with valproic acid (Henderson et al. 2000). Patients with diabetes prior to clozapine initiation experienced a twofold increase in insulin requirements or required a switch to insulin from an oral hypoglycemic agent. At baseline, the patients’ mean weight was 79.6 kg and mean body mass index was 26.9 kg/m2. Their mean age at the time of clozapine initiation was 36.4 years. The sample was 26.8% female and 91% Caucasian. During the 5-year follow-up, 30 patients (36%) were diagnosed with diabetes. Patients experienced significant weight gain, which continued until approximately month 46 from the beginning of clozapine treatment, but weight gain was not a significant risk factor for the development of diabetes in this study. After the conclusion of the 5-year study, this cohort was followed for an additional 5 years, resulting in a 10-year naturalistic study that examined the metabolic and cardiovascular side effects of clozapine treatment. At the end of the 10-year study, Henderson et al. (2005c) reported a mean weight gain of approximately 13.6 kg and an estimated 10-year cardiovascular disease mortality rate of 9%. Over the same period, 33 (34%) of the 96 patients developed diabetes, to generate a 43% estimated 10-year incidence rate of new-onset diabetes. In another study, we examined data for patients with schizophrenia treated at our hospital during a 7-year period and found that 18.4% of patients with schizophrenia were diagnosed with diabetes mellitus compared to 6.6% in the general hospital population (P< 0.001) (Henderson et al. 2007). Twenty-three patients with schizophrenia were identified with diabetic ketoacidosis; of these, 11 had new-onset diabetes presenting as diabetic ketoacidosis, 8 had diabetic ketoacidosis with known diabetes mellitus, 2 had new-onset diabetes mellitus– hyperosmolar hyperglycemic syndrome, and 2 had hyperosmolar hyperglycemic syndrome with known diabetes mellitus. The incidence of diabetes presenting as diabetic ketoacidosis in patients with schizophrenia was more than 10-fold higher than that reported in the general population: 14.93 per 10,000 patient years in patients with schizophrenia versus 1.4 per 10,000 patient years in the general population (P< 0.000001) and versus 1.98 per 10,000 patient years in the general hospital population (P<0.000001). The incidence of diabetic ketoacidosis in patients taking each of the atypical antipsychotic drugs over the 7-year period was as follows: clozapine, 2.2%; olanzapine, 0.8%; and risperidone, 0.2% (no incidence with ziprasidone or quetiapine). Of the 11 patients with diabetes presenting as diabetic ketoacidosis, the mean hemoglobin A1c level at admission was 13.3% ± 1.9% (10.4%–16.9%). The elevated hemoglobin A1c levels observed suggest that patients had undiagnosed diabetes mellitus for at least several weeks before their diabetic ketoacidosis episode.
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A few reports have linked the atypical antipsychotic agents risperidone and quetiapine to new-onset diabetes mellitus and glucose intolerance, but not nearly as many as those described for clozapine and olanzapine (Henderson 2001; Henderson et al. 2005a; Perez-Iglesias et al. 2007). Griffiths and Springuel (2001), using the Canadian Adverse Drug Reaction Monitoring Program, reported 37 cases of suspected glucose metabolism disorders associated with atypical antipsychotic agents. With clozapine there were 8 cases of diabetes mellitus, 5 cases of diabetic ketoacidosis, and 4 cases of hyperglycemia. For olanzapine 2 cases of diabetes mellitus were reported, along with 3 cases of diabetic ketoacidosis, 2 cases of diabetic coma, and 3 cases of hyperglycemia. For quetiapine, 1 case of diabetes mellitus and 2 cases of diabetic ketoacidosis were reported. Finally, for risperidone 1 case of diabetes mellitus, 1 case of labile blood glucose levels, 3 cases of hyperglycemia, and 2 cases of hypoglycemia in patients with a previous history of diabetes mellitus were reported. Of note, there were 3 deaths from the 10 cases of diabetic ketoacidosis. Wirshing et al. (2001) reported two cases of new-onset diabetes in risperidone-treated patients. Both subjects were African American, were overweight or obese prior to risperidone initiation, and gained significant weight while receiving treatment with risperidone. These cases highlight the importance of understanding risk factors for diabetes in patients receiving treatment with atypical antipsychotic agents. Finally, Fryburg et al. (2001) reported a double-blind controlled trial comparing olanzapine and ziprasidone therapy on the basis of metabolic indices. In a comparison of baseline to 6-week follow-up, olanzapine subjects showed a significant increase in body weight, fasting serum insulin, cholesterol, triglycerides and a parameter for insulin resistance, suggesting increased insulin resistance in the olanzapine group but not the ziprasidone group. Similarly, Perez-Iglesias et al. (2007) conducted a 12-week study in which 128 patients taking either haloperidol, olanzapine, or risperidone were assessed. Although all treatment groups showed a worsening lipid profile, increases in triglyceride levels were observed only in the olanzapine group. All of the reports indicate that the strength of the association between the atypical antipsychotic medications and disturbances in glucose regulation can vary across the different medications, with many more cases of medication-induced hyperglycemia, diabetes mellitus, and diabetic ketoacidosis occurring with clozapine and olanzapine treatment than with treatment with the atypical agents risperidone, quetiapine, and ziprasidone (Haupt and Newcomer 2001). Also, it is important to recognize that the reports of diabetic ketoacidosis associ-
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ated with clozapine and olanzapine treatments and the increased incidence of new-onset type 2 diabetes may represent two distinct populations with varying risks for developing the diseases (Henderson 2002; Henderson et al. 2007).
Causative Mechanisms With Atypical Antipsychotic Agents The atypical antipsychotic medications could lead to hyperglycemia and diabetes mellitus in a number of ways. As discussed earlier in this chapter, decreased sensitivity (increased resistance) to insulin and decreased insulin secretion as a result of decreased beta cell function are involved in the development of type 2 diabetes. A few controlled studies suggest that the atypical antipsychotic medications affect insulin resistance rather than causing a primary defect in insulin secretion (Henderson 2007; Henderson et al. 2005a). The insulin resistance seen during atypical antipsychotic treatment may be a result of increased central adiposity or may arise from the direct effect of the medication’s action on the glucose transporter function (Haupt and Newcomer 2001). Dwyer et al. (1999) studied the effects of atypical antipsychotic agents on glucose transporter function. They suggested that a structurefunction relationship exists in which similar drugs, such as clozapine and olanzapine, achieve relatively higher intracellular concentrations and bind to, and thus interfere with, the function of the glucose transporter proteins. Another mechanism by which the atypical antipsychotic agents may lead to hyperglycemia and diabetes mellitus is by antagonism of the serotonin 5-HT1A receptors. Antagonism of these receptors may decrease pancreatic beta cell responsiveness to blood sugar levels, thus resulting in disturbances in glucose metabolism secondary to decreased insulin secretion (Gilles et al. 2005). Melkersson et al. (2000), however, found that patients treated with olanzapine had higher fasting insulin levels on comparison of baseline to 5-month follow-up, suggesting that impaired beta cell function is an unlikely cause for olanzapine-associated diabetes mellitus. Their group found that although 11 of 14 (79%) olanzapine-treated patients were normoglycemic and only three showed increased blood glucose values, the majority of patients (10 of 14, or 71%) had insulin levels above the normal limit. The report also noted increased rates of hyperleptinemia, hypertriglyceridemia, and hypercholesterolemia. In short, Melkersson et al. found that olanzapine treatment was associated with weight gain and elevated levels of insulin, leptin, and blood levels as well as insulin resistance, with three patients
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diagnosed with diabetes mellitus. The elevated insulin values argue against the theory that antagonism of the serotonin receptors by some atypical antipsychotic agents, a property that could theoretically lead to decreased beta cell insulin production, causes hyperglycemia and diabetes mellitus. It is also worth noting that olanzapine has little affinity for 5-HT1A receptors. Notably, in several of the case studies and research studies mentioned in this chapter, weight gain was not associated with the development of diabetes mellitus or diabetic ketoacidosis during atypical antipsychotic therapy. Indeed, many of the individuals who developed diabetes mellitus or diabetic ketoacidosis were of normal weight. Thus, although all obese individuals are at increased risk for the development of type 2 diabetes, the added obesity caused by the atypical antipsychotic medications does not appear to be the sole reason for the development of diabetes mellitus or diabetic ketoacidosis in patients with schizophrenia. Nevertheless, if patients with schizophrenia gain considerable amounts of weight and maintain that weight, their risk of developing type 2 diabetes is significantly increased.
Interventions The potential benefit of improving health behavior was demonstrated in a longitudinal study of 85,000 nurses. Results indicated that 83% of coronary heart disease events were avoided by 3% of individuals who maintained a desirable weight, ate a healthy diet, exercised regularly, drank alcoholic beverages moderately, and did not smoke (Stampfer et al. 2000). In nurses who achieved three of the above components, 51% of coronary events were avoided. Although achieving a healthy lifestyle may be more difficult for patients with schizophrenia, the potential benefits warrant intensive efforts toward this goal on the part of patients and caregivers. Switching to a more weight-neutral atypical antipsychotic agent offers promise in halting or reversing weight gain and improving glucose metabolism, but many patients and their clinicians are reluctant to risk a worsening or return of psychotic symptoms and risk relapse (Casey et al. 2003; Luebbe et al. 2006; Spurling et al. 2007; Weiden et al. 2003). Menza et al. (2004) reported on a 52-week multimodal weight control program using nutrition, exercise, and behavioral interventions in 31 patients with schizophrenia or schizoaffective disorder and found, using a last observation carried forward (LOCF) analysis, a significant reduction in weight, body mass index (BMI), hemoglobin A1c, and systolic and diastolic blood pressure, with 20 of 31 completing the program.
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A number of pharmacological interventions have been evaluated to improve glucose metabolism. One study randomized 26 first-episode schizophrenia patients to either olanzapine 10 mg or olanzapine 10 mg plus reboxetine 4 mg (Poyurovsky et al. 2003). Patients receiving the combination had less weight gain (2.5 ±2.7 kg) than patients receiving olanzapine alone (5.5± 3.1 kg). In another study, fluoxetine was ineffective in diminishing olanzapine-induced weight gain in first-episode schizophrenia patients (Poyurovsky et al. 2002). In a 16-week doubleblind trial, Radulovic et al. (2002) evaluated the addition of sibutramine with behavioral weight counseling to an ongoing antipsychotic regimen in stable, overweight, or obese outpatients with schizophrenia. Twentyone patients were assigned to either sibutramine (up to 15 mg/day) or placebo in a 2:1 ratio. Nineteen completed at least 4 weeks of doubleblind treatment (sibutramine: 14; placebo: 5), with 11 completing the full 16 weeks. There were no significant differences between groups on weight loss (placebo group −4.2 kg; sibutramine group −3.7 kg) or BMI (−1.46 kg/m 2 in the placebo group; −1.32 kg/m 2 in the sibutramine group) in an LOCF analysis. The small sample size and high dropout rate may have limited the opportunity to detect differences between groups. In a placebo-controlled trial of sibutramine with a behavioral nutrition program in olanzapine-treated subjects (Henderson et al. 2005b), the mean weight loss was 3.8± 1.1 kg in the sibutramine group and 0.8 ±0.7 kg in the placebo group (P< 0.05). However, no improvements in lipids were detected in this study. Another study examined the effect of amantadine for weight loss in olanzapine-treated patients (Deberdt et al. 2005). An LOCF analysis indicated a modest but significant difference in change in weight comparing amantadine to placebo group at weeks 8, 12, and 16. At week 16 the result was −0.19±4.58 kg for amantadine versus 1.28± 4.26 kg for placebo (P= 0.045). In a study of 24 patients with treatment-resistant schizophrenia who began taking aripiprazole with decreased doses of clozapine, Karunakaren et al. (2006) reported an average weight loss of 5.1 kg over 34 weeks. Mitsonis et al. (2007) reported on 27 patients for whom aripiprazole was added to stable doses of clozapine. Although no significant weight loss was noted, patients’ total scores on the Positive and Negative Syndrome Scale for Schizophrenia improved significantly. In a 6-week open-label trial to examine the effects of adjunctive aripiprazole in 10 clozapine-treated subjects, patients decreased in weight (from 99.5± 18.1 kg to 96.9± 17.4 kg) (P=0.003) from baseline to study endpoint (Henderson et al. 2006b). Significant decreases were also seen in fasting total serum cholesterol (from 211 ±27 mg/dL to 184±27 mg/dL) (P=0.002) and triglycerides (from 274±229 to 176±106 mg/dL) (P=0.04).
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Morrison et al. (2002) openly treated 19 adolescents taking olanzapine, risperidone, quetiapine, or valproate, with metformin 500 mg three times a day. The mean weight loss at 12 weeks was 2.93 ±3.13 kg, with 15 of 19 patients losing some weight. In a 12-week study comparing the addition of fluvoxamine to low-dose clozapine (≤250 mg/day) with high-dose clozapine monotherapy (≤600 mg/day), Lu et al. (2004) found that subjects on monotherapy had significantly greater increases in weight, BMI, serum glucose, and triglyceride levels. Wu et al. (2008) randomized 40 first-episode schizophrenia patients to treatment with either olanzapine 15 mg/day plus metformin 750 mg/day or olanzapine plus placebo for 12 weeks. They found that weight, BMI, waist circumference, insulin, and insulin resistance increased less with the combination. Baptista et al. (2008) reported on a 12-week study with metformin (850–1,700 mg) plus sibutramine (10–20 mg, n = 13) or placebo (n =15) in olanzapine-treated patients with chronic schizophrenia. Weight loss was similar in both groups, although the combination did prevent a triglyceride increase.
Monitoring and Screening Recommendations The development of glucose intolerance and diabetes mellitus may be a very serious comorbid complication of treatment with antipsychotic agents, thus contributing to morbidity and mortality in patients with schizophrenia. The development of hyperglycemia in patients with schizophrenia often goes unrecognized, as it does in approximately 50% of the general population. Because patients taking antipsychotic medications may be at increased risk for developing diabetes, close monitoring and screening for obesity and hyperglycemia are imperative for individualizing treatment decisions and reducing the risks of morbidity and mortality. Before starting treatment with an atypical antipsychotic, the clinician should perform a risk factor assessment for diabetes mellitus and other metabolic disorders. This risk assessment should include baseline serum lipid and glucose values (preferably fasting), weight, BMI, age, ethnicity, family history of diabetes in a firstdegree relative, level of physical activity, and diet (American Diabetes Association et al. 2004). The American Diabetes Association (2008) recommends that routine screening for diabetes begin at age 45 in whites with no other risk factors for diabetes, and further recommends repeat testing for this group every 3 years thereafter if the results are within normal range.
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The association recommends beginning surveillance at an earlier age and more frequent monitoring for individuals with any of the following risk factors for diabetes and cardiovascular disease: obesity, a firstdegree relative with diabetes, membership in a high-risk ethnic population (African Americans; Hispanic Americans; Asian Americans, including Indian Asians; Pacific Islanders; and Native Americans), previous diagnosis of gestational diabetes or delivery of a baby larger than 4.4 kg, hypertension (>130/85 mmHg), a high-density lipoprotein level ≤40 mg/dL in men and ≤50 mg/dL in women, a triglyceride level ≥150 mg/dL, or previous diagnosis of impaired fasting glucose (fasting plasma glucose level ≥ 100 mg/dL but <126 mg/dL) or impaired glucose tolerance (oral glucose tolerance test revealing 2-hour postload glucose level of ≥ 140 mg/dL but <200 mg/dL). Because schizophrenia is associated with increased rates of diabetes mellitus or impaired glucose regulation, and because the newer, atypical antipsychotic agents appear to increase this risk even further, it seems reasonable that patients with schizophrenia, and especially patients taking atypical antipsychotic medications, should be screened for hyperglycemia before age 45 and monitored more frequently than every 3 years. This is especially appropriate in light of case reports of newonset diabetes mellitus associated with atypical antipsychotic therapy in patients under age 20. Additionally, patients with higher risk factors should be examined more closely. African American, Asian, and Latino patients should be monitored closely, particularly if they have other risk factors for diabetes independent of ethnicity. If factors such as weight gain, elevated lipids, or hypertension develop, early interventions are necessary to prevent the onset of diabetes. Although very little is known about the underlying reasons that some patients treated with atypical antipsychotic agents experience diabetic ketoacidosis, undertaking a risk assessment with each patient may help in choosing safer agents for each individual. We recommend monitoring weight changes and blood pressure at each office visit, as well as fasting glucose and lipids at baseline and every 3 months. Also, patients must be instructed to notify the treatment team if they experience any physical symptoms or medical disorders, because a number of diabetic ketoacidosis episodes have occurred in conjunction with other acute medical disorders such as infections and pancreatitis. A recent study found that hemoglobin A1c levels were significantly elevated in patients with schizophrenia with new-onset diabetes mellitus presenting as diabetic ketoacidosis (Henderson et al. 2007). Therefore, more frequent monitoring of hemoglobin A1c levels may aid in forewarning physicians of a patient’s risk for developing diabetes mellitus or diabetic ketoacidosis. Finally, pa-
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tients treated with atypical antipsychotic agents, particularly clozapine and olanzapine, should be screened more frequently. In contrast to use of some of the atypical antipsychotic agents, treatment with ziprasidone and aripiprazole are associated with minimal impact on metabolic parameters. Ziprasidone may offer significant advantages because it appears to be weight neutral (Allison et al. 1999; Gardner et al. 2005; Henderson 2007); however, additional clinical experience is necessary with this agent before definitive conclusions can be reached. Aripiprazole also appears to be associated with minimal weight gain and little or no negative impact on serum lipids or glucose metabolism (Melkersson and Dahl 2004). Therefore, a reasonable approach would be to take baseline measures (fasting blood glucose, lipids, weight, waist measurements, blood pressure) for all patients and to repeat fasting glucose and lipids every 6 months if possible. Weight and blood pressure can be monitored more frequently in the office. Monitoring of glycohemoglobin may be useful in this population if fasting glucose is difficult to obtain. However, glycohemoglobin may not be sensitive enough to detect lower levels of hyperglycemia, glycohemoglobin changes will lag 2–3 months behind changes in glucose homeostasis, and criteria for diagnosing diabetes with glycohemoglobin do not exist. Education and referral to weight reduction and exercise programs may play a significant preventive role. In addition, weight gain should initiate meaningful dietary intervention. Finally, the clinician should be alert to the possibility of diabetic ketoacidosis. If a patient develops diabetes while receiving treatment with an atypical antipsychotic agent, consideration should be given to switching to another antipsychotic agent. Although diabetic ketoacidosis appears to be uncommon, it is of great concern because it increases the risk of death. Patients who experience an episode of diabetic ketoacidosis should be switched to a different antipsychotic agent that has a lower impact on glucose metabolism. Reports and clinical experience suggest that in a case of atypical antipsychotic agent–associated diabetes or diabetic ketoacidosis, discontinuation of the antipsychotic agent may result in a complete resolution of the hyperglycemia and diabetes. Clozapine-treated patients have few options. Clozapine remains the most effective agent for the treatment-resistant schizophrenia population, and patients often have failed to benefit from several other antipsychotic agents. Interventions to reduce the factors that contribute to impaired glucose tolerance, such as weight loss programs, encouraging exercise, and lowering the clozapine dose, may lead to improvement. Other approaches, including augmentation with a more neutral anti-
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psychotic agent such as aripiprazole, may result in improvements in patients’ weight and metabolic parameters (Henderson et al. 2006b). For patients treated with other atypical agents, switching to another agent should be considered in addition to the preceding interventions. Finally, diabetic patients who are placed on these agents must be monitored more closely to prevent a worsening of glycemic control. The widely distributed guidelines published by the American Diabetes Association et al. (2004) recommend monitoring of all patients who are taking atypical antipsychotic agents by performing a baseline assessment, including fasting glucose and lipids, waist and weight measurements, and blood pressure. They recommend repeating the measures 3 months later and then annually. Regardless of whether these specific guidelines are followed, individual clinicians and programs should develop and adhere to a monitoring system to improve the safety of patients taking these medications.
Conclusion Atypical antipsychotic medications have helped to improve the lives of patients with schizophrenia by alleviating positive and negative symptoms and bringing some improvement in cognitive function. This improvement is helping patients to function in the home and in the workplace, thus improving the patients’ quality of life. At the same time, however, the atypical antipsychotics appear to be associated with the development of glucose intolerance, new-onset diabetes mellitus, diabetic ketoacidosis, and exacerbation of existing diabetes mellitus. These disturbances in glucose metabolism have their own medical consequences, including cardiovascular disease, cerebrovascular disease, diabetic retinopathy, neuropathy, and diabetic nephropathy, all of which can lead to considerable morbidity and mortality. Thus, to minimize morbidity and mortality associated with the use of atypical antipsychotic medications, close screening and monitoring for diabetes mellitus should become a priority for all clinicians treating patients with schizophrenia who are receiving atypical antipsychotic therapy.
Key Clinical Points ◗
Many clinicians choose to prescribe atypical antipsychotic medications, such as clozapine, olanzapine, quetiapine, risperidone, ziprasidone, and aripiprazole, instead of the older agents, because the newer agents have fewer extrapyramidal symptoms and may increase adherence and
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prevent relapse. However, these medications may also increase the risk of metabolic disorders, such as impaired glucose metabolism, diabetes mellitus, and diabetic ketoacidosis. ◗
The choice of antipsychotic medications should be based on perceived efficacy and knowledge of short- and long-term side effects. A generally reasonable approach is to choose the safest medications available first and, if the patient has no response, work the way up the risk ladder. For metabolic problems, prevention is preferable over intervention.
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Diabetes mellitus and glucose intolerance are characterized by problems in glucose-insulin regulation. The symptoms that warrant a diabetes diagnosis include polyuria, polydipsia, unexplained weight loss, a casual plasma glucose level ≥200 mg/dL, a fasting plasma glucose ≥126 mg/dL, or a 2-hour plasma glucose level ≥ 200 mg/dL post 75-g oral glucose load. Glucose intolerance is characterized by a 2-hour plasma glucose level ≥140 mg/dL and <200 mg/dL post 75-g oral glucose load.
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Glucose metabolism abnormalities, including onset of diabetes mellitus, diabetic ketoacidosis, cerebrovascular disease, cardiovascular disease, myocardial infarction and stroke, diabetic retinopathy, and neuropathy, are associated with considerable medical morbidity and mortality.
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Atypical antipsychotic agents have been associated with significant weight gain, particularly the development of central obesity, which can lead to several comorbid cardiovascular conditions as well as type 2 diabetes.
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The incidence of disturbances in glucose regulation varies across antipsychotics; many more cases of medication-induced hyperglycemia, diabetes mellitus, or diabetic ketoacidosis occur with clozapine and olanzapine than with risperidone, quetiapine, ziprasidone, and aripiprazole.
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Mechanisms by which atypical antipsychotic agents could lead to glucose abnormalities and diabetes mellitus include causing insulin resistance by either increasing central or visceral adiposity or affecting the glucose transporter function antagonism of serotonin 5-HT1A, which may decrease pancreatic beta cell responsiveness to blood sugar levels and treatment-induced weight gain.
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Close and frequent monitoring and screening for obesity and hyperglycemia is imperative for individualizing treatment decisions and reducing the risks for morbidity and mortality for patients with schizophrenia taking antipsychotic mediations.
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Lifestyle interventions and recommendations should be given at the start of the medications and reinforced at every visit.
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CHAPTER 6 Effects of Antipsychotics on Serum Lipids Jonathan M. Meyer, M.D.
Patients with severe mental illnesses, such as schizophrenia or bipolar disorder, are a medically vulnerable population at high risk for cardiovascular mortality (Newcomer and Hennekens 2007), with standardized mortality ratios from cardiovascular disease that are two times greater than in the general population (Osby et al. 2000, 2001). Although much of psychiatric care for individuals with schizophrenia is focused on suicide prevention, cardiovascular disease remains the single largest cause of death among males and females with schizophrenia. Given this sobering data, it is imperative that those who care for patients with severe mental illness have a working knowledge of the risks associated with cardiovascular disease and the patterns of risk factors seen in this patient population. Recognition and treatment of diabetes mellitus is covered in Chapter 5, “Glucose Intolerance and Diabetes in Patients With Schizophrenia,” but the importance of diabetes relates not only to the adverse effects of hyperglycemia but also to its impact on cardiovascular risk. Diabetes mellitus is considered equivalent to having established coronary heart disease (CHD) for future risk of major cardiovascular events (e.g., myocardial infarction, sudden death) 117
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(Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults 2001). This view of diabetes-related CHD risk is based on indications that patients with established diabetes have the same future myocardial infarction incidence as nondiabetics who have already experienced a myocardial infarction (Haffner et al. 1998). For nondiabetics, the focus for preventing cardiovascular disease remains on modification of the traditional risk factors of hypertension, hyperlipidemia, and smoking. Baseline data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE), sponsored by the National Institute of Mental Health, provide the most timely and complete picture of the risk patterns in patients with chronic schizophrenia residing in the United States (Goff et al. 2005). Among the 1,460 schizophrenia patients assessed at study entry, 10-year CHD risk, calculated using Framingham scores, was significantly elevated compared with age-, gender-, and race- and ethnicity-matched controls from a general population database in both males (9.4% vs. 7.0%) and females (6.3% vs. 4.2%) (P= 0.0001). In particular, schizophrenia patients compared with controls had significantly higher rates of smoking (68% vs. 35%), diabetes (13% vs. 3%), and hypertension (27% vs. 17%) and lower highdensity lipoprotein (HDL) cholesterol levels (43.7 vs. 49.3 mg/dL) (P< 0.001). Moreover, CATIE subjects also had greater prevalence of central adiposity and elevated serum triglycerides, both of which are components of the metabolic syndrome and are associated with insulin resistance and future diabetes risk (McEvoy et al. 2005). The importance of monitoring serum triglyceride values during antipsychotic treatment will become readily apparent, because this is the lipid parameter most greatly affected by offending medications. Another concerning finding from CATIE (covered in Chapter 3, “Medical Outcomes From the CATIE Schizophrenia Study”) was the significant undertreatment of hypertension and dyslipidemia, reinforcing the idea that clinicians must redouble efforts to both monitor and treat the basic cardiovascular risks in patients with schizophrenia (Nasrallah et al. 2006). The induction of hyperlipidemia during antipsychotic therapy thus represents a serious condition, not only because of its inherent impact on cardiovascular risk but also because of its occurrence in a group that already possesses considerable risk (Saari et al. 2004). What has become evident in recent years is that the atypical antipsychotics have a decreased liability for neurological side effects, but certain agents in this class have a marked propensity for adverse metabolic outcomes, especially hyperlipidemia (Meyer and Koro 2004). One is tempted to hypothesize that the widespread use of atypical antipsychotics with metabolic liabilities may be responsible for the widening mortality gap
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between schizophrenia patients and the general population (Saha et al. 2007), but the fact remains that more must be done to prevent morbidity and early mortality from CHD. This chapter is intended to guide the clinician in selecting medications and appropriate monitoring strategies for hyperlipidemia based on the best available data. This discussion is bolstered by the publication of the double-blind, controlled data from phases 1 and 2 of the CATIE schizophrenia trial (Daumit et al. 2008; Lieberman et al. 2005; Meyer et al. 2008a, 2008b; Stroup et al. 2006). The large population under study in CATIE is one of the best sources of prospective information regarding certain compounds, especially perphenazine and quetiapine, for which prospective data were previously sorely lacking. Due to increased interest in improving medical outcomes for patients with severe mental illness (Marder et al. 2004), minimization of iatrogenically induced lipid problems and appropriate monitoring of those at risk are increasingly becoming the standards of care for this patient population. Recognizing which antipsychotics impose the greatest risk for hyperlipidemia and understanding the common dyslipidemia patterns seen during use of these antipsychotics are necessary for providing high-quality care to antipsychotic-treated patients.
Hyperlipidemia and Coronary Heart Disease A complete review of lipid physiology is beyond the scope of this chapter; however, some information is worth noting herein. The major classes of lipoproteins are divided on the basis of their weight under centrifugation (Kwiterovich 2000). The lightest are chylomicrons derived from dietary triglycerides and very-low-density lipoproteins (VLDL) that are endogenously produced triglyceride-rich particles. The next heavier particles are low-density lipoproteins (LDL) and highdensity lipoproteins (HDL). Total cholesterol, as obtained on a lipid panel, reflects the combination of serum concentrations of VLDL (calculated as serum triglyceride levels divided by 5), LDL, and HDL. Normal serum total cholesterol concentration is defined as a fasting level under 200 mg/dL, normal HDL is ≥40 mg/dL, and the ideal serum LDL concentration is determined by cardiovascular risk factors as noted below. The rate-limiting step in cholesterol synthesis is a reaction catalyzed by the enzyme 3-hydroxy-3-methylglutaryl coenzyme A reductase, the target for a class of lipid-lowering agents called statins. The Multiple Risk Factor Intervention Trial clearly established the linear association
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between CHD and serum cholesterol, but further research clarified that risk is more properly associated with abnormalities involving the LDL and HDL cholesterol fractions and separately by serum triglycerides (Stamler et al. 1986). A series of landmark studies published in the mid-1990s demonstrated the significant association between reduction in LDL levels and reduced cardiovascular events and mortality. The Scandinavian Simvastatin Survival Study was a randomized, double-blind, placebocontrolled trial of cholesterol lowering in 4,444 patients with coronary heart disease who were already following a lipid-lowering diet and were followed for a median of 5.4 years (“Randomised Trial of Cholesterol Lowering” 1994). This secondary prevention study found that statin therapy reduced total cholesterol by 28% and LDL by 38%, and led to a 42% reduction in coronary deaths and a 30% decrease in all-cause mortality compared to the placebo cohort. A randomized, double-blind, placebo-controlled secondary prevention trial of pravastatin in 9,014 patients (ages 31–75) with ischemic heart disease followed for 6.1 years on average showed a 24% reduction in nonfatal myocardial infarction or death due to CHD (“Prevention of Cardiovascular Events and Death” 1998). A similar 24% reduction in nonfatal myocardial infarction or death due to CHD was seen in a randomized, double-blind, placebo-controlled secondary prevention trial of pravastatin after myocardial infarction in 4,159 patients who had normal cholesterol levels (Sacks et al. 1996). Moreover, two primary prevention studies of patients without CHD but with either hypercholesterolemia or low HDL levels and average total cholesterol identified profound benefits from statin therapy. The West of Scotland Coronary Prevention Study was a randomized, double-blind, placebo-controlled trial of pravastatin in 6,595 men, ages 45–64 years, with mean total cholesterol of 272 mg/dL and no history of myocardial infarction, followed for a mean of 4.9 years (Shepherd et al. 1995). A 32% reduction in the development of CHD and a 22% reduction in total mortality were found in this trial (Shepherd et al. 1995). The Air Force/Texas Coronary Atherosclerosis Prevention Study was a primary prevention trial of lovastatin versus placebo in men and women with no clinical evidence of cardiovascular disease and average total cholesterol and LDL levels, but low HDL (Downs et al. 1998). Over the 5.2 years of this study, a 37% reduction in risk of major coronary events was seen in the lovastatin group compared to the placebo group. To establish the 2001 National Cholesterol Education Program guidelines, the Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (2001) used the results of these
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and other studies to establish LDL goals based on the number of underlying major cardiovascular risk factors: smoking, hypertension (having blood pressure ≥140/90 mmHg or taking antihypertensive medication), low HDL cholesterol (<40 mg/dL), family history of premature CHD (CHD in male first-degree relative <55 years or female first-degree relative <65 years), and age (men ≥45 years or women ≥55 years). The fasting LDL goal is <160 mg/dL for those with zero or one risk factor, <130 mg/dL for those with two or more risk factors, and <100 mg/dL for those with CHD or CHD-equivalent disorders (e.g., diabetes mellitus). Recent updates to the 2001 guidelines suggest even lower LDL goals (<70 mg/dL) are appropriate for those who are at very high risk by virtue of having diabetes mellitus, multiple metabolic syndrome risk factors, and persistent risk factors such as smoking in conjunction with established CHD or acute coronary syndrome (Stone et al. 2005). Although LDL continues to be the main focus of lipid-lowering therapy due to evidence from multiple large studies, other lipid parameters must be addressed and are secondary targets for therapy. Significant data from the Framingham Heart Study show a 2%–4% increase in risk of CHD-related death for each 1-mg/dL decrease in HDL, with over 30% of deaths occurring in those with normal total cholesterol values (Wilson et al. 1988). Because low HDL is one of the metabolic syndrome criteria, the 2005 National Cholesterol Education Program update suggests targeting low HDL in those who have already met LDL goals but continue to manifest the characteristic dyslipidemia associated with metabolic syndrome (i.e., low HDL, elevated fasting triglycerides) (Stone et al. 2005). Elevated fasting triglycerides occur as a direct result of insulin resistance, because insulin-dependent lipases in fat cells are normally inhibited by insulin. As insulin resistance worsens, inappropriately high levels of lipolysis lead to the release of excess amounts of free fatty acids that are hepatically transformed into triglycerides (Smith 2007). Elevated fasting triglyceride levels thus become a sensitive marker of insulin resistance, and therefore are included as a metabolic syndrome criterion. Moreover, a fasting triglyceride:HDL cholesterol ratio ≥ 3.0 performs better than fasting glucose in predicting insulin resistance among prediabetic individuals (McLaughlin et al. 2003). The sensitivity of the triglyceride:HDL ratio to insulin resistance derives from the fact that increased triglyceride levels interfere with an important regulatory function governing the production of apolipoprotein B100, a core lipoprotein in very-low-density, intermediate-density, and low-density lipoprotein particles (Smith 2007). The overproduction of apolipoprotein B100 results in more of these triglyceride-rich particles,
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with resultant hypertriglyceridemia. In addition, the greater presence of these light triglyceride-rich lipoproteins causes the transfer of triglycerides to HDL at the expense of HDL cholesterol content. After passage through the liver, where triglyceride is cleaved by enzymatic processes, the remaining HDL particle is smaller than normal and more readily cleared in the kidney, resulting in the characteristic low serum HDL levels seen with insulin-resistant states (Smith 2007). The triglyceride:HDL ratio thus reflects the combined effects of low HDL and elevated triglycerides seen in insulin-resistant patients. Although serum triglyceride concentrations are considered a secondary focus of lipid-lowering treatment, evidence from several studies link elevated triglycerides (i.e., ≥200 mg/dL) to increased CHD risk (Cullen 2000; Jeppesen et al. 1998; Rubins 2000), an increased risk that is independent of HDL levels (Jeppesen et al. 1998). Evidence also has been reported that supports monitoring of nonfasting triglycerides, because atherosclerosis may be a postprandial phenomenon in which atherogenic remnant lipoproteins (chylomicrons and VLDL) play a critical role (Eberly et al. 2003). These triglyceride-rich particles are smaller than other lipid components and more readily penetrate arterial intimal cells. Individuals are in a nonfasting state most of the day with respect to serum triglycerides, with triglyceride levels peaking 4 hours after an oral fat load and returning to baseline values only after 8 hours (Nordestgaard et al. 2007). Large population studies indicate a significant linear correlation between nonfasting triglyceride values and directly measured remnant lipoproteins (Nordestgaard et al. 2007). Data from a prospective Copenhagen, Denmark, study (N =13,981, mean follow-up 26 years) solidified the link between nonfasting triglycerides and cardiovascular events by identifying the significant relationship between nonfasting triglyceride levels in men and women and risk of major cardiovascularrelated events, including CHD, myocardial infarction, and mortality (Nordestgaard et al. 2007). Compared with individuals with nonfasting triglyceride levels <88.5 mg/dL, women and men with levels of 177.0– 264.6 mg/dL had adjusted hazard ratios for myocardial infarction of 2.5 and 1.6, respectively. The superiority of nonfasting triglycerides over fasting triglycerides is also seen in prospective data from the Women’s Health Study (N = 26,509, median follow-up 11.4 years) (Bansal et al. 2007). Although no relationship was found between increasing tertiles of fasting triglyceride values and risk of cardiovascular events in fully adjusted models, nonfasting triglyceride tertiles were significantly associated with cardiovascular risk, with triglyceride levels measured 2–4 hours postprandially showing the strongest association. The impor-
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tance of fasting and nonfasting hypertriglyceridemia is of further concern for those who care for schizophrenia patients, given the association between elevated triglycerides and therapy with low-potency phenothiazines and the dibenzodiazepine-derived compounds clozapine, olanzapine, and quetiapine (a dibenzothiazepine).
Hyperlipidemia and Typical Antipsychotics Although typical antipsychotics are used in less than 10% of patients with schizophrenia in the United States, they are commonly available throughout the world and represent an important class of psychotropic medications. Moreover, a review of the lipid effects of typical antipsychotics illustrates an important concept seen in the atypical antipsychotic data: medications with similar modes of therapeutic action can have disparate metabolic profiles. Within a decade after the widespread use of chlorpromazine and other low-potency phenothiazines, several studies emerged examining the metabolic profiles of this class of antipsychotics (Clark and Johnson 1960; Clark et al. 1967; Mefferd et al. 1958). In general, these compounds were found to elevate serum triglycerides and total cholesterol but with greater effects on triglyceride concentrations. Subsequent studies of the phenothiazine chlorpromazine and related compounds (Clark et al. 1970, 1972) confirmed these findings that high serum triglycerides seemed to be the primary significant lipid abnormality, but elevated total cholesterol could also be found. What also emerged from this early literature was the fact that certain D2 antagonists did not exert the same changes on serum lipids as seen with the lower-potency phenothiazines. The lipid neutrality of high-potency typical antipsychotics was seen in the early uncontrolled studies of butyrophenone derivatives published in the mid-1960s (Braun and Paulonis 1967; Clark et al. 1968; Simpson and Cooper 1966; Simpson et al. 1967) and a placebo-controlled trial in 1971 (Serafetinides et al. 1971). Comparative trials of low-potency phenothiazines and butyrophenones (primarily haloperidol) published in the 1970s confirmed the lipid neutrality of high-potency agents, whereas low-potency phenothiazine treatment was associated with hyperlipidemia, primarily in the form of hypertriglyceridemia (Serafetinides et al. 1972; Vaisanen et al. 1979). Surprisingly, only a limited amount of subsequent work has been published in the past two decades covering lipid changes during typical antipsychotic therapy. Table 6–1, which is a summary of studies,
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case reports, and case series related to lipids and antipsychotics that have been published since 1980, demonstrates the paucity of controlled data on typical antipsychotics after 1980. Two papers were published by a Japanese group in 1984 and 1985 that analyzed serum total cholesterol and triglycerides in male chronic schizophrenia inpatients taking phenothiazines or butyrophenones compared with age- and sexmatched controls (Sasaki et al. 1984, 1985). The cohort exposed to phenothiazines had mean serum triglycerides of 163 mg/dL, compared with 104 mg/dL for the butyrophenone group and 127 mg/dL for the control group, with no significant differences in total cholesterol across the three groups. The phenothiazine cohort also had higher serum LDL cholesterol and decreased HDL cholesterol concentrations compared with the other study arms. Cross-sectional studies from Pakistan (Shafique et al. 1988) and a psychiatric hospital in Spain (Martínez et al. 1994) substantiated earlier findings, although specific analysis on the basis of type of neuroleptic prescribed was not performed in the Spanish study. Lastly, the CATIE schizophrenia trial employed a medium-potency phenothiazine, perphenazine, as one of its treatment arms in phase 1, thus providing controlled comparative data versus atypical antipsychotics in a randomized, double-blind study (Lieberman et al. 2005). As shown in Table 6–2, use of perphenazine was associated with modest increases in serum triglycerides and total cholesterol, with a greater effect on triglycerides. The extent of lipid changes related to perphenazine use in neuroleptic-naive patients might actually be greater than seen in CATIE phase 1, because 22% of the CATIE sample at baseline was taking olanzapine. Given olanzapine’s known effects on serum lipids, olanzapine-exposed patients switched to perphenazine in phase 1 would likely have minimal further increases in serum triglycerides. Further analysis of CATIE data at the end of phase 1 (Table 6–3), obtained after approximately 9 months of exposure, revealed that perphenazine exposure was associated with favorable effects on serum HDL in whites (+2.7 mg/dL) in a manner that was significantly different from olanzapine (−1.7 mg/dL); however, perphenazine induced a mean decrease of 1.3 mg/dL in nonwhites, and this was significantly different from that seen for ziprasidone (+4.3 mg/dL) (Meyer et al. 2008a). Whether this represents a specific moderating effect of race and ethnicity on perphenazine’s metabolic effects or merely the results of an unusual cohort remains to be seen. The effect of perphenazine on fasting triglycerides depended on the subject’s baseline triglyceride values—those with low fasting triglycerides experienced a mean increase of 28.7 mg/dL, comparable to most of the other agents studied,
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whereas those with high baseline triglycerides (>148 mg/dL) experienced a mean decrease of 27.5 mg/dL. With the increasing use of atypical antipsychotics throughout the world, these may be the last randomized, controlled data published on the lipid effects of a typical antipsychotic.
Hyperlipidemia and Atypical Antipsychotics Atypical antipsychotics have been designed primarily to mimic the identifiable features of clozapine’s pharmacology: weaker D 2 antagonism than typical antipsychotics, combined with potent serotonin 5-HT2A antagonism (Meyer and Simpson 1997). A recurring theme, related to the metabolic effects of this antipsychotic class, echoes the earlier findings for typical agents, namely that drugs with similar mechanisms of action may have disparate metabolic adverse effects. This analogy is more than superficial, because the structurally related dibenzodiazepine-derived atypical antipsychotics—olanzapine, clozapine, and quetiapine—all appear to have significantly greater effects on serum triglycerides than total cholesterol, much like the low-potency phenothiazines, whereas other atypical antipsychotics—ziprasidone, risperidone, and aripiprazole—have minimal lipid effects, as did the high-potency typical antipsychotics.
Clozapine Through 2002 virtually no randomized, prospective, controlled data had been published on the lipid effects of the atypical antipsychotics, but the early literature is replete with case reports and retrospective studies, primarily focused on clozapine, the only atypical antipsychotic available until 1994. The first reports of hyperlipidemia with atypical antipsychotics were small studies of fluperlapine, a dibenzodiazepinederived compound never marketed. Two trial reports published in the mid-1980s documented elevated triglycerides, in one case as high as 900 mg/dL (Fleischhacker et al. 1986; Müller-Oerlinghausen 1984). Despite clozapine’s use from the 1960s onward, the first case report of hyperlipidemia (presenting as hypertriglyceridemia) occurred in 1994 (Vampini et al. 1994) and was followed in 1995 by a report of four clozapine-treated patients with hypertriglyceridemia whose triglycerides normalized upon switching to risperidone (Ghaeli and Dufresne 1995). The following year (1996), the authors of that case series published
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a chart review comparing serum lipids in patients exposed to clozapine or typical antipsychotics (primarily butyrophenones) for at least 1 year with no prior history of hyperlipidemia or use of lipid-lowering agents (Ghaeli and Dufresne 1996). This retrospective study found serum triglycerides 114 mg/dL higher in the clozapine cohort (P < 0.001) but nearly identical total cholesterol levels (clozapine 217.0± 52.9 mg/dL vs. typical antipsychotics 215.0± 43.2 mg/dL). A 1998 study comparing 30 patients taking clozapine to 30 patients taking typical antipsychotics for at least 1 year also found higher triglycerides in the clozapine group (202.9 ± 131.1 mg/dL) compared with the typical group (134.4 ± 51.9 mg/dL), but again without significant differences in total cholesterol (clozapine 197.1±46.4 mg/dL vs. typical antipsychotics 194.9±51.5 mg/dL) (Spivak et al. 1998). A small prospective study (Dursun et al. 1999) and two subsequent chart reviews of long-term clozapine-treated patients, one with 70 patients (Spivak et al. 1999) and the other with 222 patients (Gaulin et al. 1999), demonstrated that the mean increase in serum triglycerides ranged from 41% to 45%. More recent cross-sectional and short- and long-term prospective clozapine studies have noted the same pattern of marked elevations of serum triglycerides with modest increases in total cholesterol (Atmaca et al. 2003b; Baymiller et al. 2003; Henderson et al. 2000; Leonard et al. 2002; Wirshing et al. 2002) compared with baseline and with typical antipsychotics (Lund et al. 2001). A 1-year prospective study of 50 clozapine-treated patients (Baymiller et al. 2003) found mean increases of 41.7% in serum triglycerides, exactly in the range predicted by prior retrospective data, and only a 7.5% increase in total cholesterol. The authors also noted that no significant changes occurred in HDL and LDL and that triglyceride elevations peaked between days 41 and 120 and then declined, but still remained elevated at the 1-year interval. As part of a 5-year naturalistic study of 82 clozapine-treated patients with schizophrenia, Henderson et al. (2000) found ongoing increases in serum triglycerides (linear coefficient 2.75 mg/dL per month, P=0.04). A subsequent 10-year follow-up study of clozapine-treated patients found that triglycerides had plateaued significantly, with a linear coefficient over that time frame of 0.5 mg/dL/month (P=0.04) (Henderson et al. 2005). In neither study were total cholesterol changes significant. Other cross-sectional, retrospective and other nonprospective clozapine studies are noted in Table 6–1, but the “efficacy” arm of CATIE phase 2 (hereafter referred to as phase 2b) generated prospective randomized data on a sample of 49 schizophrenia patients compared with 50 subjects assigned to other agents (McEvoy et al. 2006). As seen in Table 6–2, the exposure-adjusted mean increases in serum lipids for
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clozapine-treated subjects were greatest for triglycerides and substantially less for total cholesterol. The samples for each of the other phase 2b drug arms are extremely small (<15), so the findings for the other atypical antipsychotics in phase 2b are of limited reliability.
Olanzapine The first published studies of olanzapine-associated hypertriglyceridemia appeared in 1999 and revealed patterns of dyslipidemia similar to those reported for clozapine. A study of nine patients followed for an average of 16 months while taking olanzapine (mean age 41 years, mean olanzapine dose 19 mg/day) showed a mean increase in serum triglycerides of 41%, with no significant changes in total cholesterol levels and a mean weight gain of 10 kg (Sheitman et al. 1999). A subsequent report on 25 inpatients followed prospectively for 12 weeks found a mean fasting triglyceride increase of 37%. Both weight and triglyceride increases were significant (P <0.05), and a significant association was found between weight gain and triglyceride change (P<0.02); however, after controlling for
