Dementia 3 volumes (Brain, Behavior, and Evolution)

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DEMENTIA

Dementia Volume 1: History and Incidence Volume 2: Science and Biology Volume 3: Treatments and Developments

DEMENTIA Volume 1: History and Incidence Patrick McNamara, Editor

Brain, Behavior, and Evolution Patrick McNamara, Series Editor

Copyright 2011 ABC-CLIO, LLC All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except for the inclusion of brief quotations in a review, without prior permission in writing from the publisher. Library of Congress Cataloging-in-Publication Data Dementia / Patrick McNamara, editor. p. cm.—(Brain, behavior, and evolution) Includes bibliographical references and index. ISBN 978-0-313-38434-9 (hard copy : alk. paper)—ISBN 978-0-313-38435-6 (ebook) 1. Dementia. 2. Alzheimer ’s disease. I. McNamara, Patrick, 1956– II. Series: Brain, behavior, and evolution [DNLM: 1. Dementia. WM 220] RC521.D4524 2011 616.8’3—dc22 2010041082 ISBN 978-0-313-38434-9 EISBN 978-0-313-38435-6 15

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This book is also available on the World Wide Web as an eBook. Visit www.abc-clio.com for details. Praeger An Imprint of ABC-CLIO, LLC ABC-CLIO, LLC 130 Cremona Drive, P.O. Box 1911 Santa Barbara, California 93116-1911 This book is printed on acid-free paper Manufactured in the United States of America

Contents

Series Foreword Preface: Hopeful Trends in Meeting the Challenge of the Dementias Patrick McNamara

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Volume 1: History and Incidence Chapter 1. Epidemiology of the Dementias Chengxuan Qiu and Laura Fratiglioni Chapter 2. Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia Wilm Quentin, Steffi G. Riedel-Heller, Melanie Luppa, Hanna Leicht, and Hans-Helmut König Chapter 3. A Stroke of Bad Luck: CADASIL and Friedrich Nietzsche’s “Dementia” or Madness Paul M. Butler Chapter 4. Promising Strategies for Preventing Dementia Laura E. Middleton Chapter 5. Cultivating a Cognitive Lifestyle: Implications for Healthy Brain Aging and Dementia Prevention Michael J. Valenzuela Chapter 6. Ethical Issues in the Care of Individuals with Dementia Art Walaszek

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Chapter 7. Cognitive Screening and Neuropsychological and Functional Assessment: Contributions to Early Detection of Dementia Mônica Sanches Yassuda, Mariana Kneese Flaks, and Fernanda Speggiorin Pereira Chapter 8. Does Poor Sleep Quality in Late Life Compromise Cognition and Accelerate Progression of the Degenerative Dementias? Peter Engel Chapter 9. Magnetic Resonance Spectroscopy: A Tool for Understanding Brain Chemical Changes in Dementias Jacquelynn N. Copeland and H. Randall Griffith

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About the Contributors

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About the Series Editor

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Index

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Series Foreword

Beginning in the 1990s, behavioral scientists—that is, people who study mind, brain, and behavior—began to take the theory of evolution seriously. They began to borrow techniques developed by the evolutionary biologists and apply them to problems in mind, brain, and behavior. Now, of course, virtually all behavioral scientists up to that time had claimed to endorse evolutionary theory, but few used it to study the problems they were interested in. All that changed in the 1990s. Since that pivotal decade, breakthroughs in the behavioral and brain sciences have been constant, rapid, and unremitting. The purpose of the Brain, Behavior, and Evolution series of titles published by ABC-CLIO is to bring these new breakthroughs in the behavioral sciences to the attention of the general public. In the past decade, some of these scientific breakthroughs have come to inform the clinical and biomedical disciplines. That means that people suffering from all kinds of diseases and disorders, particularly brain and behavioral disorders, will benefit from these new therapies. That is exciting news indeed, and the general public needs to learn about these breakthrough findings and treatments. A whole new field called evolutionary medicine has begun to transform the way medicine is practiced and has led to new treatments and new approaches to diseases, like the dementias, sleep disorders, psychiatric diseases, and developmental disorders that seemed intractable to previous efforts. The series of books in the Brain, Behavior, and Evolution series seeks both to contribute to this new evolutionary approach to brain and behavior and to bring the insights emerging from the new evolutionary approaches to psychology, medicine, and anthropology to the general public. The Brain, Behavior, and Evolution series was inspired by and brought to fruition with the help of Debora Carvalko at ABC-CLIO. The series editor,

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Dr. Patrick McNamara, is the director of the Evolutionary Neurobehavior Laboratory in the Department of Neurology at Boston University School of Medicine. He has devoted most of his scientific work to development of an evolutionary approach to problems of sleep medicine and to neurodegenerative diseases. Titles in the series will focus on applied and clinical implications of evolutionary approaches to the whole range of brain and behavioral disorders. Contributions are solicited from leading figures in the fields of interest to the series. Each volume will cover the basics, define the terms, and analyze the full range of issues and findings relevant to the clinical disorder or topic that is the focus of the volume. Each volume will demonstrate how the application of evolutionary modes of analysis leads to new insights on causes of disorder and functional breakdowns in brain and behavior relationships. Each volume, furthermore, will be aimed at both popular and professional audiences and will be written in a style appropriate for the general reader, the local and university libraries, and graduate and undergraduate students. The publications that become part of this series will therefore bring the gold discovered by scientists using evolutionary methods to understand brain and behavior to the attention of the general public, and ultimately, it is hoped, to those families and individuals currently suffering from those most intractable of disorders— the brain and behavioral disorders.

Preface: Hopeful Trends in Meeting the Challenge of the Dementias Patrick McNamara

It is estimated that 24.3 million people around the world have dementia and that, with an estimated 4.6 million new cases every year, we can expect about 43 million people and their families to face the challenge of dementia by 2020. There are several forms of dementia, with the most common being Alzheimer ’s disease (40% of cases), vascular dementia with or without Alzheimer features (25%), and dementia with Lewy bodies (25%), the latter being related to the increasingly important form of dementia associated with Parkinson’s disease. The annual healthcare costs for Alzheimer ’s disease alone is estimated at about $155 billion in the United States. A substantial portion of these costs is due to behavioral and neuropsychiatric disturbances associated with the dementing process— yet these neuropsychiatric and behavioral problems have only recently become the focus of study and treatment in the biomedical communities. The successes of neuropsychiatric approaches to the dementias is measured in reduced suffering for patients and their families and reduced healthcare costs for the system as a whole. The authors of the chapters in these three volumes, devoted to emerging trends in dementia studies, have virtually all emphasized identification, study, and treatment of behavioral and neuropsychiatric problems of patients and their families. The reason they have done so is the dawning realization in both the biomedical and caregiving communities that targeting behavioral and neuropsychiatric problems of dementia leads to some pretty effective scientific studies of mechanisms and very effective and low-cost treatment programs that act to alleviate both patients’ suffering and caregivers’ burdens.

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Although the standard, it has long been established that dementia most commonly occurs in older people, and that primary symptoms are memory impairment (both short- and long-term), deficits in executive functions, and impairments of abstract thinking and judgment. It has now become crystal clear that some of the best and earliest predictors of dementia risk are mood and personality changes, which all too often are misdiagnosed as depression or some other common mood disorder. Family members may express concern to a primary care physician, but these concerns too often get ignored or shunted aside as a standard mood disorder. It is vitally important to take reports of significant behavioral changes seriously as identification of cognitive components of a dementing process—may be a later-occurring symptom than the behavioral changes. Although the three-step diagnostic process (single question about memory, MMSE, neuropsychological testing) has high positive predictive value, it only detects 18% of future dementia cases. It is the behavioral and neuropsychiatric disturbances, along with incipient cognitive changes, that may yield better detection rates for dementia. Tremendous progress has been made in identification of biomarkers for dementia. The use of functional imaging, proteomic, genetic, biochemical and electrophysiological markers, including sleep polysomnographic techniques, has meant that our ability to detect dementia early on has vastly improved. In addition, the new appreciation of the importance of behavioral and psychiatric problems in dementia as well as validated assessment tools to measure these behavioral problems suggests that it is time to deploy all these new techniques to identify those at risk for dementia so as to prevent or to slow onset of the disorder in these individuals. What is needed are large-scale, multisite, comparative studies that can evaluate optimal use and validity of these various techniques for detecting and selecting asymptomatic people at risk for dementia. The recent Leon Thal Symposium 2009 in Las Vegas, Nevada, explored algorithms, biomarkers, and assessment tools for identifying asymptomatic individuals at elevated risk for dementia. The consensus recommendations of symposium participants included: 1. Establishment of a National Database for Longitudinal Studies as a shared research core resource; 2. Launch of a large collaborative study that will compare multiple screening approaches and biomarkers to determine the best method for identifying asymptomatic people at risk;

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3. Initiation of a Global Database that extends the concept of the National Database for Longitudinal Studies for longitudinal studies beyond the United States; and 4. Development of an educational campaign that will promote healthy brain aging. (Khachaturian et al. 2010) These are all laudable recommendations. But behavioral and neuropsychiatric assessment tools must be included in these large multisite studies of at-risk individuals. A perusal of the essays in these volumes (volume 1 focuses on epidemiologic, descriptive, historical, and diagnostic innovations in dementia; volume 2 focuses on biobehavioral mechanisms of dementia; and volume 3 focuses on emerging treatment strategies including treatments for behavioral problems of dementia) leaves one with a sense of hope and confidence that the daunting challenges of the dementias, both for patients and for families, are finally being effectively addressed. REFERENCE Khachaturian, Z. S., D. Barnes, R. Einstein, et al. 2010. Developing a national strategy to prevent dementia: Leon Thal Symposium 2009. Alzheimer’s and Dementia 6 (2): 89–97.

Chapter 1

Epidemiology of the Dementias Chengxuan Qiu and Laura Fratiglioni

Dementia is defined as a clinical syndrome characterized by progressive deterioration in multiple cognitive domains which is severe enough to interfere with daily functioning. Epidemiology deals with the distribution, determinants, and prevention of a disease in the population. Since the 1980s, numerous community-based prospective studies of aging and health have been implemented in the world; many of which have focused on dementia and its main subtypes of Alzheimer ’s disease (AD) and vascular dementia (VaD). These studies have significantly contributed to the understanding of epidemiology of the dementias, including occurrence, determinants, and prevention. In this chapter, we review the literature of epidemiological research in the dementias by focusing on the most recent studies. OCCURRENCE The occurrence of a disease can be measured as the proportion of people affected by the disease in a defined population at a specific time point (prevalence), or as the number of new cases that occur during a specific time period in a population at risk for developing that disease (incidence). The prevalence reflects the public health burden of the disease, whereas the incidence indicates the risk of developing that disease. The prevalence is determined by both incidence and duration of the disease, and in certain circumstances the prevalence may be estimated as incidence × average disease duration.

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Prevalence In a consensus report in 2005, it was estimated that more than 25 million people in the world were affected by dementia, most suffering from AD, with around 5 million new cases occurring every year (Ferri et al. 2005). As the population ages, the number of patients with dementia is anticipated to double every 20 years. In Europe, the number of dementia cases has reached more than 6 million in 2010; this number is projected to be 14 million in 2050 (Mura et al. 2010). In the United States, there were 4.5 million AD patients in the year 2000; the number is projected to reach 13.2 million by 2050 (Hebert et al. 2003). In the Asia Pacific region, the number of dementia cases will increase from 13.7 million in 2005 to 64.6 million by 2050 (Access Economics 2006). The global prevalence of dementia was estimated to be 3.9 percent in people aged 60+ years, with the regional prevalence being 1.6 percent in Africa, 4.0 percent in China and the western Pacific region, 4.6 percent in Latin America, 5.4 percent in Western Europe, and 6.4 percent in North America (Ferri et al. 2005). Figure 1.1 shows the age-specific prevalence of dementia across different regions. The prevalence of dementia is very low in persons under 60; after age 65, the rate doubles almost every five years until very old ages; nearly half of the oldest old (i.e., 90 years and older) become demented (von Strauss et al. 1999; Corrada et al. 2008). Thus, the overall prevalence and burden of the dementias depend largely on age structure of the population. The prevalence of dementia appears to vary by regions across the world, but this may be due to variation in age structure of the populations, diagnostic accuracy, and disease duration or survival. In Europe, the pooling data suggest that the age-standardized prevalence in people aged 65 years or older is 6.4 percent for dementia and 4.4 percent for AD (Lobo et al. 2000). A systematic review of studies from developing countries reported that the overall prevalence in people aged 65 years or over was 5.3 percent for dementia and 3.4 percent for AD (Kalaria et al. 2008). The 10/66 Dementia Research Group found that the prevalence of dementia (DSM-IV criteria) in people aged 65+ years in seven developing nations varied widely from less than 0.5 percent to more than 6 percent (Llibre Rodriguez et al. 2008). The prevalence of dementia in India and Sub-Saharan Africa was about half of other regions (Ferri et al. 2005). The proportion of subtype dementias also differs across continents. In Europe and North America, AD and VaD account for up to 70 percent and 20–30 percent, respectively, of all dementia cases (Lobo et al. 2000), whereas earlier studies from Asia showed a relatively high proportion for VaD (Chiu and Zhang 2000; Ikeda et al. 2001). The difference may be due

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Figure 1.1

Age-specific prevalence rates of dementia (per 100 population) across the world. (Lobo et al. 2000; Brayne 2006; Dong et al. 2007; Lopes et al. 2007; Nitrini et al. 2009).

to variations in diagnostic criteria, ascertainment procedure of the cases, selective survival, and geographical distribution of vascular diseases such as stroke (Matthews and Brayne 2005). Indeed, the large-scale communitybased surveys and meta-analysis in Asian countries have yielded the proportion of major dementia subtypes (e.g., AD and VaD) largely comparable with those in Western countries (Zhang et al. 2005; Kalaria et al. 2008). In addition, population-based neuropathological studies reveal that dementia often occurs with concomitant AD pathologies and cerebrovascular lesions (Schneider et al. 2007). Incidence Over the past decades, many incidence studies of dementia have become available; the majority of which are conducted among developed

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nations (Fratiglioni et al. 2008; Qiu, Kivipelto, et al. 2009). Pooling data in Europe suggested that the overall incidence of dementia in people aged 65 years or older was 19.4 per 1,000 person-years (Fratiglioni, Launer, et al. 2000), and the incidence was 13.6 per 1,000 person-years in Brazil (Kalaria et al. 2008). Figure 1.2 shows the age-specific incidence of dementia across the world. The incidence rates of dementia increase steeply with advancing age. In Europe, approximately two per 1,000 person-years become demented among people aged 65–69 years, and the incidence increases to 70 to 80 per 1,000 person-years for people 90 years or over (Fratiglioni, Launer, et al. 2000). The age-specific pattern of incidence for AD is similar to that of all-cause dementia, but the age-specific pattern for VaD is less stable. It remains debatable regarding whether the incidence of dementia continues to increase even in the oldest-old or reaches a plateau at a certain age. The Cache County Study found that the incidence of dementia increased with age, peaked, and then started to decline at extreme old ages for both men and women (Miech et al. 2002). But some meta-analyses and large-scale studies in Europe provided no evidence for the potential decline in the incidence of dementia among the oldest old (Fratiglioni et al. 2008; Matthews and Brayne 2005). The apparent decline seen in some studies may be an artifact due to poor response rate and survival effect in the very old. Several studies in Europe observed a higher incidence rate of dementia and AD in women than in men, especially among the oldest-old (Fratiglioni, Launer, et al. 2000), whereas studies in North America found no gender difference (Kawas et al. 2000; Kukull et al. 2002). Mortality and Case-Fatality Dementia is one of the leading causes of death in older people. However, death certificates grossly underreport its cause (Jin et al. 2004), even when multiple underlying causes of death are taken into account (Ganguli and Rodriguez 1999). The community-based follow-up studies could provide reliable data on mortality. In the Swedish Kungsholmen Project of people aged 75 years or over, the mortality rate of dementia was 2.4 per 100 person-years; 70 percent of incident dementia cases died within five years following the diagnosis (Agüero-Torres et al. 1999). A follow-up study of nursing home residents with advanced dementia suggested that 55 percent of the patients died over eighteen months; pneumonia, febrile episodes, and eating problems are the most frequent complications that significantly contribute to the six-month mortality (Mitchell et al. 2009). Several community-based studies have shown that dementia increases the risk of death by two to five times (Agüero-Torres et al. 1999; Jagger

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Figure 1.2

Age-specific incidence rates of dementia (per 1,000 person-years) across the world. (Jorm and Jolley 1998; Fratiglioni, Launer, et al. 2000; Canadian Study of Health and Aging Working Group 2000; Kukull et al. 2002; Matthews and Brayne 2005).

et al. 2000; Wolfson et al. 2001), supporting the malignancy of dementia. The median survival time of patients with dementia ranges from two to five years after the diagnosis depending on demographic features and comorbidity (Helzner et al. 2008; Xie et al. 2008). After the age of 85, although dementia still shortens life expectancy, the extent is somehow less than in younger-old people (Tschanz et al. 2004). Older age, male gender, low education, morbidities, and functional disability contribute to a shorter survival in patients with dementia (Helmer et al. 2001; Xie et al. 2008). DETERMINANTS The dementias are multifactorial disorders that are determined by genetic and environmental factors as well as their interactions.

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Population-based prospective study is the major epidemiological approach to identifying influential factors for chronic multifactorial diseases such as dementia, in which the life-course approach should be taken into consideration (Whalley et al. 2006). Age is the most powerful determinant of dementia, and gene mutations contribute to a small proportion of all cases. Two groups of modifiable factors for late-life dementias have been established, that is, the “vascular risk factors” that have been strongly associated with an increased risk of dementia, and the “psychosocial factors” that may contribute to the delay of dementia onset. Evidence supporting these etiological profiles has been fully summarized elsewhere (Qiu, Kivipelto, et al. 2009; Qiu, Xu, and Fratiglioni 2010), which also provide a complete list of references when not otherwise specified herein.

Gene Mutations and Genetic Risk Factors Mutations in amyloid precursor protein, presenilin-1, and presenilin-2 genes can cause early-onset familial AD that accounts for no more than 5 percent of all cases (Blennow et al. 2006). The majority of AD cases are sporadic, with considerable heterogeneity in their risk profiles and neuropathological features. First-degree relatives of AD patients have a higher lifetime risk for developing AD than relatives of nondemented people or the general population (Green et al. 2002; Seshadri and Wolf 2007). It is likely that shared genetic and environmental factors contribute to the familial aggregation; twin studies can address this issue. The Swedish Twin Study estimated that the heritability of AD ranged from 0.58 to 0.74, with other variance being attributable to environmental factors (Gatz et al. 2006). However, familial aggregation of AD can only be partially explained by known genetic factors such as APOE ε4 allele, indicating that other susceptibility genes may be involved in AD (Huang et al. 2004; Hayden et al. 2009). The APOE ε4 allele is the only established genetic risk factor for both early and late-onset AD; it is a susceptibility gene, being neither necessary nor sufficient for the development of AD. The risk of AD increases with increasing number of the ε4 alleles in a dose-dependent manner (Qiu, Kivipelto, et al. 2004), but the risk effect decreases with increasing age. Overall, approximately 15 to 20 percent of AD cases are attributable to the APOE ε4 allele (Slooter et al. 1998; Qiu et al. 2004). Other candidate genes for AD, such as angiotensin-I converting enzyme gene, cholesterol 24-hydroxylase gene, and insulin degrading enzyme gene, remains to be clearly identified (Bertram et al. 2007).

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Vascular Risk Factors A number of vascular risk factors and disorders have been linked to dementia and AD as well, but some factors have a differential association with the risk of dementia depending on the age when the exposure is assessed. The age-dependent association is likely due to the pathophysiological and metabolic changes with age in vascular factors such as blood pressure, body mass index (BMI), and total serum cholesterol. High Blood Pressure An association of elevated blood pressure in midlife with an increased risk of dementia and AD later in life has been reported in several population-based studies (Qiu et al. 2005; Alonso et al. 2009); such an association was particularly evident for blood pressure levels of 160/95 mm Hg or higher, and for people who had high blood pressure but who were not treated with blood pressure–lowering drugs. It is plausible that longterm hypertension can be linked to the dementias by causing cerebral atherosclerosis and microvascular lesions (e.g., white-matter lesions, silent infarcts, and microbleeds). Furthermore, the postmortem and neuroimaging studies have directly linked midlife high blood pressure to the brain pathologies and imaging markers of AD such as neuritic plaques, neurofibrillary tangles, and more severe atrophy of the hippocampus (Korf et al. 2004; Launer et al. 2008). Follow-up studies of late-life blood pressure and risk of dementia yield mixed results, largely depending on the length of follow-up. The shortterm follow-up studies (e.g., less than 3 years) often found no association or even an inverse association between blood pressure and risk of dementia and AD (Qiu et al. 2005). Because dementia has a long latent period and blood pressure may start to decline a few years before the onset of the dementia syndrome due to ongoing brain aging and degenerative process, the inverse or lack of association has been interpreted as a consequence of the disease. However, studies of very old people (e.g., 75 years or older) with a longer follow-up period (e.g., more than 6 years) also revealed an increased risk of dementia associated with low blood pressure (Qiu, Winblad, et al. 2009), suggesting that among very old people low blood pressure may also contribute to the development of dementia, possibly by influencing cerebral blood perfusion. Use of antihypertensive drugs has been associated with a decreased incidence of dementia and AD in several observational studies (Qiu et al. 2005); recent studies further suggest that the beneficial effect is

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more evident for young-old people, for angiotensin receptor blockers, and for long-term treatment (Peila et al. 2006; Haag et al. 2009; Li et al. 2010). Neuropathological data also showed fewer neuritic plaques and neurofibrillary tangles in medicated hypertensives than nonhypertensive groups, suggesting a possible effect of antihypertensive therapy against AD pathologies (Hoffman et al. 2009). However, systematic review and meta-analysis of major randomized controlled clinical trials conducted in hypertensive individuals have only shown a marginal beneficial effect of antihypertensive therapy against the dementias (Peters, Beckett, et al. 2008; McGuinness, Todd, et al. 2009). Diabetes Mellitus Diabetes has often been associated with VaD, but the association with AD is also reported in systematic reviews (Biessels et al. 2006; Lu et al. 2009; Kopf and Frölich 2009). Pooled analysis of eight follow-up studies has shown that diabetes is associated with a nearly 50 percent increased risk of dementia independent of cardiovascular factors and comorbidities (Lu et al. 2009). Neuropathological data from the Honolulu-Asia Aging Study indicated that diabetes, especially diabetes in combination with APOE ε4 allele, was associated with a substantially increased risk of dementia and a heavier burden of Alzheimer pathologies (Peila et al. 2002), although other studies remain uncertain whether diabetes is associated with AD pathologies (Arvanitakis et al. 2006; Sonnen et al. 2009). Furthermore, long-term follow-up studies show that midlife diabetes is more strongly associated with an elevated risk of dementia (Alonso et al. 2009; Xu et al. 2009), suggesting that long duration and more severe diabetes play a crucial role in determining the disease risk. In addition, a higher HbA1c is associated with lower cognitive function in individuals with diabetes (CukiermanYaffe et al. 2009), whereas a history of severe hypoglycemic episodes is associated with a greater risk of dementia (Whitmer et al. 2009). Finally, pre-diabetes, impaired glucose regulation, and impaired insulin secretion have also been associated with dementia (Xu et al. 2007). The association of diabetes with dementia and AD is likely due to the convergent effects of multiple pathological processes that include cerebral macrovascular and microvascular injury, chronic hyperglycemia, insulin resistance, advanced glycation end products, oxidative stress, and inflammation (Craft 2009). Cerebrovascular Lesions and Cardiovascular Disease Systematic reviews of population-based studies reveal an approximately two- to four-fold increased risk of incident dementia associated

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with clinical stroke (post-stroke dementia) (Pendlebury and Rothwell 2009; Savva and Stephan 2010). It is conceivable that an association of clinical stroke with AD is rarely reported due to the fact that a history of stroke is part of the current criteria for excluding the diagnosis of AD. However, asymptomatic cerebrovascular lesions such as silent brain infarcts and white-matter lesions have been associated with an increased risk of dementia and AD (Vermeer et al. 2003; Troncoso et al. 2008), although the association with AD is likely to be due to the inclusion of mixed dementia cases. In addition, a case-control study found that spontaneous cerebral emboli were associated with an increased odds ratio of AD and VaD (Purandare et al. 2006). The Cardiovascular Health Study (CHS) found that cardiovascular disease was associated with an increased incidence of dementia, with the highest risk seen among people with peripheral arterial disease, suggesting that extensive peripheral atherosclerosis is a risk factor for dementia (Newman et al. 2005). Atrial fibrillation, heart failure, and severe atherosclerosis measured with ankle-to-brachial index are also associated with the increased risk of dementia and AD (Ott et al. 1997; Qiu et al. 2006; van Oijen et al. 2007; Laurin et al. 2007). Neuropathological data show that cerebrovascular lesions and AD pathologies often coexist in patients with dementia, suggesting that these lesions may be the results of coinciding processes converging to cause additive brain damage and promote clinical manifestation of the dementia syndrome. Body Mass Index A lifespan-dependent relationship between BMI and risk of the dementias has emerged in which a higher BMI in midlife is related to an elevated risk of dementia and AD later in life, whereas an accelerated decline in BMI during late life may anticipate the onset of dementia (Gustafson 2006). The CHS showed that obesity at midlife was related to a higher risk of latelife dementia, whereas BMI measured after age 65 years was inversely related to dementia risk (Fitzpatrick et al. 2009). The long-term follow-up studies found a gradual decline in BMI over the years preceding dementia onset (Stewart et al. 2005; Hassing et al. 2009), which is supported by several follow-up studies of older people that show an association of low BMI and decline in BMI with subsequent development of dementia and AD (Atti et al. 2008; Beydoun, Lhotsky, et al. 2008). A meta-analysis of cohort studies suggested an increased risk of dementia for being underweight (pooled relative risk [RR], 1.36; 95 percent confidence interval [CI], 1.07–1.73) and obesity (RR, 1.42; 95 percent CI, 0.93–2.18); the association of increased dementia risk with obesity was stronger in studies with a longer follow-up period (e.g., more than 10 years) and younger age at BMI

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measurement (Rosengren et al. 2005; Beydoun, Beydoun, and Wang 2008). Thus, while obesity in midlife is a risk factor for late-life dementia, late-life low BMI and weight loss can be interpreted as markers for the predrome of dementia. Overweight or obesity in midlife may increase dementia risk through its close association with hypertension, hypercholesterolemia, diabetes, and other vascular diseases. High Serum Cholesterol As with blood pressure and BMI, an age-dependent association with the risk of dementia is also suggested for serum cholesterol, such that high total cholesterol at midlife is more consistently associated with an increased risk of dementia diagnosed more than 20–30 years later, whereas no or an inverse association between total cholesterol and the risk of dementia is often reported in cohort studies of older people. Interestingly, such a pattern of association was confirmed with all-cause dementia and AD, but not with VaD, in a meta-analysis and a follow-up study of middle aged cohort (Anstey et al. 2008; Solomon et al. 2009). Long-term follow-up studies have shown that total cholesterol levels begin to decline more than a decade before the onset of dementia (Stewart et al. 2007). This implies that decreasing total cholesterol after middle age and a lower cholesterol level in late life may reflect ongoing disease processes and thus could be a marker for future development of dementia (Solomon et al. 2007). Cross-sectional studies suggest a lower likelihood of dementia associated with the use of statins, but this could be due to different prescribing patterns by physicians for people with and without dementia, such that dementia patients were less likely to be prescribed with lipid-lowering drugs than nondemented people (Rodriguez et al. 2002). Several follow-up studies show no beneficial effect of statin therapy or only a modestly decreased risk of dementia (Qiu, Kivipelto, et al. 2009). Neuropathological studies also show inconsistent results whether use of statins is associated with a reduced burden of Alzheimer pathological markers and infarcts in the brain (Li et al. 2007; Arvanitakis, Schneider, et al. 2008). A systematic review of randomized controlled trials concludes that statins given in late life to individuals at risk of vascular disease have no effect in preventing dementia (McGuinness, Craig, et al. 2009). Nutrients and Dietary Factors Several follow-up studies have reported a decreased risk of AD and dementia associated with increasing dietary or supplementary intake

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of antioxidants (e.g., vitamins E and C) (Barberger-Gateau et al. 2007), although some negative results are also reported (Gray et al. 2008). Similarly, studies also showed mixed results on the association of serum vitamin B12 and folate with the risk of dementia and AD (Luchsinger and Mayeux 2004). The Cochrane review of eight randomized clinical trials concludes that supplementations of folic acid and vitamin B12 have no benefits on cognition in healthy or cognitively impaired older people, although they are effective in reducing serum homocysteine levels (Malouf and Evans 2008). A higher adherence to “Mediterranean diet” (i.e., a dietary pattern with higher intake of fish, fruits, and vegetables rich in antioxidants) has been associated with a reduced risk of dementia independent of vascular factors and physical activity in some studies (Scarmeas et al. 2006, 2009), but not in the French Three-City Study (Féart et al. 2009). A diet rich in high polyunsaturated and fish-related fats is known to be associated with a lower risk of vascular disease; thus, it is plausible to extend the beneficial effects to dementia. In support of this hypothesis, a systematic review suggested that a high dietary intake of fish and omega-3 polyunsaturated fatty acids (PUFAs) was associated with a decreased risk of cognitive decline (Fotuhi et al. 2009). However, this review also found that only four out of eight observational studies suggested a reduced risk of dementia and AD associated with consumption of fish and PUFAs independent of multiple potential confounders, and in two studies the protective effect disappeared after controlling for confounders such as demographics and income. Furthermore, two recent studies added no additional evidence for a possible role of high consumption of fish and PUFAs in reducing the risk of dementia and AD (Kröger et al. 2009; Devore et al. 2009). Finally, randomized clinical trials have failed to show any beneficial role for the use of PUFAs in the treatment and secondary prevention of dementia among elderly people (Fotuhi et al. 2009). High Serum Homocysteine Elevated total homocysteine (tHcy) is associated with an increased risk of cardiac and cerebrovascular disease and thus may increase dementia risk. The follow-up study of the Framingham cohort of older residents reported a nearly double-increased risk of AD and dementia associated with increase of one standard deviation in tHcy levels (Seshadri et al. 2002). A meta-analysis of prospective cohort studies revealed that hyperhomocysteine was associated with a pooled RR of 2.5 (95 percent CI, 1.4– 4.6) for AD (van Dam and van Gool 2009). Despite the association, the

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beneficial effect of reducing serum homocysteine levels by supplementing vitamin B12 and folate on cognitive function remains to be established. Neuroimaging study suggested that higher plasma tHcy levels are associated with smaller brain volume and silent brain infarcts, even in healthy middle-aged adults, suggesting that both neurodegenerative and vascular mechanisms may underlie the association of tHcy with brain aging and dementia (Seshadri et al. 2008). Inflammation Inflammation is known to play a pivotal role in the pathogenesis of atherosclerosis. A higher level of serum C-reactive protein (CRP) in midlife was linked to an increased risk of AD and VaD, suggesting that inflammatory markers may reflect both peripheral and cerebral vascular mechanisms related to dementia, and the process can be a measurable long time before the dementia syndrome is manifested (Schmidt et al. 2002). Follow-up studies of older adults also showed an association between high levels of serum inflammatory markers (e.g., CRP and interleukin-1,6) and an increased incidence of dementia and AD (Engelhart et al. 2004; Tan et al. 2007). In addition, the systematic review of observational studies confirms that long-term use of nonsteroidal anti-inflammatory drugs (NSAIDs) (e.g., more than 2 years) is associated with a decreased risk of AD and dementia (Etminan et al. 2003), which provides additional evidence supporting the involvement of inflammation in AD and dementia. Thus, it seems plausible to hypothesize that inflammatory mechanisms play a part in the neurodegenerative process. However, neuropathological studies found no evidence for an association between use of NSAIDs and the reduced burden of AD pathologies (Arvanitakis, Grodstein, et al. 2008). Furthermore, the clinical trial of celecoxib or naproxen in AD prevention failed to show any beneficial effect of these drugs against AD; instead, an increased risk of AD related to drug therapy was observed (Martin et al. 2008). Smoking Follow-up studies have frequently shown an increased risk of dementia and AD associated with cigarette smoking, although the association may vary by APOE ε4 allele status (Qiu, Kivipelto, et al. 2009; Alonso et al. 2009). Meta-analyses of follow-up studies indicate that current smoking, compared to never smoking, is associated with an increased risk for dementia, especially for AD, but the increased risk for VaD seems less

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evident (Anstey et al. 2007; Peters, Poulter, et al. 2008). Neuropathological data show that the number of neuritic plaques is increased with increasing amount of cigarette smoking (Tyas et al. 2003). Thus, in contrast to the protective effect initially suggested in earlier cross-sectional and casecontrol studies, prospective cohort studies have actually provided convincing evidence that cigarette smoking, even long-term secondhand smoking (Barnes et al. 2010), is a risk factor for dementia and AD. Smoking is known to cause damage to the vascular system, but it remains unclear whether and how smoking can lead to Alzheimer pathologies. Alcohol Consumption Alcohol abuse may cause “alcoholic” dementia. A population-based study found that heavier alcohol drinkers at middle age had more than a three-fold increased risk of developing dementia later in life, especially among the carriers of APOE ε4 allele (Anttila et al. 2004). By contrast, epidemiological studies often reported a reduced incidence of dementia and AD associated with light-to-moderate alcohol intake (e.g., 1–3 drinks per day) (Qiu, Kivipelto, et al. 2009), leading to the hypothesis that light-tomoderate alcohol consumption may protect against dementia and cognitive decline. Two systematic reviews of prospective studies showed that light-to-moderate alcohol drinkers had an approximately 30–40 percent reduced risk of AD and dementia (Peters, Peters, et al. 2008; Anstey et al. 2009). However, a neuroimaging study did not support any protective effect of moderate alcohol consumption on brain aging (Paul et al. 2008). Moreover, the apparent cognitive benefits of light-to-moderate alcohol intake could be due to potential biases that result from methodological limitations of the observational studies such as information bias, confounding of socioeconomic status and healthy lifestyles, and inconsistent approaches of alcohol assessments. Clustering of Vascular Factors and Disorders Vascular risk factors and related disorders often coexist among elderly people. Several studies have consistently shown that the risk of dementia increases with an increasing burden of vascular factors (Whitmer et al. 2005; Qiu, Xu, Winblad, et al. 2010). In addition, the risk indices at both middle age and late life provide a reasonable estimation for the probability of future development of dementia, in which a cluster of multiple cardiovascular risk factors plays a relevant role (Kivipelto et al. 2006; Barnes et al. 2009). Clinical observations have suggested that treatment of

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Dementia

multiple vascular factors (e.g., high blood pressure, diabetes, and atherosclerotic disease) is associated with a slower cognitive decline in AD patients without cerebrovascular disease in a progressive gradient manner, that is, the more vascular factors that are treated, the smaller the decline in global cognitive function (Deschaintre et al. 2009). Finally, geneenvironment interaction, such as interactions of APOE ε4 allele severe atherosclerosis and high blood pressure, may be important in determining the risk of dementia (Hofman et al. 1997; Qiu, Winblad, et al. 2003). The metabolic syndrome is a constellation of obesity, dyslipidemia, high blood pressure, and hyperglycemia. Follow-up studies found little evidence for the association between the metabolic syndrome in late life and the risk of dementia and AD (Muller et al. 2007; Raffaitin et al. 2009), although some components of the syndrome (e.g., diabetes) have been linked to the dementias. It is likely that, due to age-related metabolic changes, a cluster of late-life specific factors in the metabolic syndrome may not be superior to some of its individual components in defining the risk of dementia. Psychosocial Factors Evidence from epidemiological research has been accumulating that some psychosocial factors and healthy lifestyle may postpone the onset of dementia, possibly by enhancing cognitive reserve. These factors include early-life high education, adult-life rich social network and social engagement, mentally stimulating activity, and regular physical exercise. High Educational Attainments Numerous longitudinal studies have consistently shown that a higher educational achievement in early life is associated with a decreased incidence of dementia, and of AD in particular (De Ronchi et al. 1998; Qiu et al. 2001; Ngandu et al. 2007). A meta-analysis of cohort studies reported that the lowest education, compared with the highest, was associated with an approximately 60 percent increased risk of dementia and AD (Caamano-Isorna et al. 2006). The reserve hypothesis has been proposed to interpret this association, such that education could enhance cognitive reserve, which provides compensatory mechanisms to cope with degenerative pathologies in the brain and therefore delay the onset of the dementia syndrome (Stern 2006; Fratiglioni and Wang 2007). In addition, high educational achievement can be a surrogate or an indicator of high intelligent quotient, high socioeconomic status, better living environment in early

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life, and less occupational toxic exposures experienced over adulthood; all these conditions favor a protective effect against dementia (De Ronchi et al. 1998; Qiu et al. 2001). Social Network and Social Engagement A systematic review of longitudinal studies suggested that a poor social network or social disengagement in late life was associated with an elevated risk of dementia (Fratiglioni et al. 2004). The dementia risk was also increased in older people with social isolation or with less frequent or unsatisfactory contacts with relatives and friends (Fratiglioni, Wang, et al. 2000). Furthermore, late-life low social engagement and a decline in social engagement from middle age to late life could double the risk of dementia (Wang et al. 2002; Saczynski et al. 2006). Finally, being widowed from midlife onwards was associated with a substantially increased risk of dementia, suggesting that living with a partner might imply cognitive and social challenges that have a potential protective effect against the development of dementia later in life (Håkansson et al. 2009). It is hypothesized that a rich social network and a high level of social engagement reflect better social support, which leads to better access to resources and material goods (Fratiglioni et al. 2004). In addition, large social networks can also provide intellectual stimulations that affect cognitive function and various health outcomes through behavioral, psychological, and physiological pathways. Finally, in line with the cognitive reserve hypothesis, neuropathological data have shown that the size of social networks could modify the association between Alzheimer pathologies and cognitive function, such that cognitive function remains higher in individuals with a heavier burden of global neuropathologies if they also have larger social networks (Bennett et al. 2006). Mentally Stimulating Activity Mentally stimulating activities at leisure time, such as reading, playing board games and musical instruments, knitting, gardening, and dancing, have been associated with a reduced risk of developing AD and dementia (Verghese et al. 2003; Akbaraly et al. 2009). A few studies have shown that a greater complexity of work, particularly the complex work with data or people, could reduce the risk of dementia (Andel et al. 2005; Karp et al. 2009), especially for VaD (Kröger et al. 2008), suggesting that greater mental requirements during the working life also play a relevant role. Complex mental activity could enhance cognitive reserve and delay the

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Dementia

onset of dementia. In addition, a neuroimaging study reported that a high level of complex mental activity across the lifespan was correlated with a reduced rate of hippocampal atrophy, which means that mental activity may also provide brain reserve (Valenzuela et al. 2008). Physical Activity Regular physical exercise was associated with a delayed onset of dementia among cognitively normal elderly (Fratiglioni et al. 2004). Even low intensity physical exercise such as walking may reduce the risk of dementia (Abbott et al. 2004; Larson et al. 2006). A study of Medicare beneficiaries in the United States showed that higher levels of physical activity were associated with a gradual reduction in dementia risk, suggesting a possible dose-response association (Scarmeas et al. 2009). A recent systematic review of prospective studies revealed that the highest physical activity, compared with the lowest, reduced the risk of dementia and AD by approximately 30–45 percent (Hamer and Chida 2009). Regular physical activity is likely to promote vascular and circulatory health by reducing blood pressure, serum lipids, BMI or obesity, and blood glucose. Because physical activity also contains components of social and cognitive activities, it may reduce the risk or postpone the onset of dementia also by providing cognitive reserve. Miscellaneous Hormone Replacement Therapy Hormone replacement therapy in postmenopausal women has been frequently reported to be associated with a lower risk of AD and dementia in numerous observational studies (Zandi et al. 2002; Qiu, Kivipelto, et al. 2009). However, the large-scale clinical trial of the Women’s Health Initiative Memory Study (WHI-MS) showed that estrogen therapy alone or in combination with progestin did not reduce the incidence of probable dementia and mild cognitive impairment (MCI); instead, the active treatment with estrogen or estrogen plus progestin was found to be associated with a two-fold increased risk for dementia and MCI (Shumaker et al. 2004). It has been argued that in the WHI-MS hormone replacement therapy was given 10 to 15 years after the menopause when the “window of critical time” for putative beneficial effects of estrogen therapy on cognition may have been missed; thus, use of hormone therapy at a younger age close to the time of menopause may reduce the risk of dementia later in life (Harman et al. 2005).

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Occupational Exposures Manual work involving goods production was associated with an increased risk of AD and dementia, suggesting the possible role of occupational exposure to toxics in the development of dementia (Qiu, Karp, et al. 2003). Occupational exposure to heavy metals such as aluminum and mercury is suggested to be a risk factor for AD; even high consumption of aluminum from drinking water is associated with an elevated risk of AD and dementia (Rondeau et al. 2009). However, this remains to be confirmed by further studies. In addition, occupational exposure to extremely low-frequency electromagnetic fields (ELF-EMFs) has been related to an increased risk of dementia and AD in a few follow-up studies (Feychting et al. 2003; Qiu, Fratiglioni, et al. 2004). The meta-analysis of epidemiological studies suggests an association of occupational ELF-EMF exposure with AD (Garcia et al. 2008). The biological plausibility linking high ELFEMF exposure to Alzheimer pathologies has been previously described (Sobel and Davanipour 1996). Other Factors Traumatic brain injury has been extensively investigated as a possible risk factor for AD. The meta-analysis of case-control studies supported an association between a history of head injury and the increased risk of AD (Fleminger et al. 2003). In contrast, some longitudinal studies found that AD was not associated with head trauma or only associated with severe traumatic head injury (Himanen et al. 2006). Several studies have reported an association of depression with subsequent development of dementia and AD. A meta-analysis of cohort studies yielded a pooled RR of 1.9 (95 percent CI, 1.6–2.3) for AD, and the sensitive analysis suggested that depression could be a risk factor, rather than a prodrome, for AD (Ownby et al. 2006). However, it remains debatable regarding whether depression is a preclinical symptom or a pure risk factor for dementia and AD (Amieva et al. 2008). PREVENTION OF DEMENTIA Identification of modifiable risk and protective factors for dementia provides potential for the primary prevention of the disease (Fratiglioni et al. 2008; Middleton and Yaffe 2009). Evidence from recent epidemiological research supports the notion that preventive strategies aiming at postponing the onset of dementia can be implemented in the general community.

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Epidemiological Evidence for Intervention Toward Dementia: A Summary Epidemiological evidence supporting the potential etiological role of modifiable risk and protective factors in dementia and AD is summarized in Table 1.1. Evidence is considered strong when several high-quality studies, especially with regard to randomized controlled trials, consistently report the same finding; moderately strong evidence is also from high-quality studies but with a limited number, or the quality of studies is moderately high (e.g., population-based prospective studies) but with numerous reports, and the finding is supported by systematic reviews and metaanalyses. Evidence from randomized placebo-controlled trials for primary intervention against dementia is currently limited for reasons such as: (1) most clinical trials have been conducted among older adults (e.g., 65 years or older) when traditional vascular risk factors are less important in dementia due to age-related pathophysiological changes (e.g., statin therapy); (2) dementia has been only considered a secondary endpoint in most clinical trials (e.g., antihypertensive therapy), in which clear benefits for primary endpoints (e.g., coronary heart disease and stroke) are shown usually in a short period of observation; and (3) intervention measures have been implemented in a period (e.g., 2–3 years) that is not sufficient long to show any efficacy. At the moment, we can conclude that moderately strong evidence, mostly from prospective observational studies, supports the hypotheses that vascular and psychosocial factors over the lifespan are involved in the development and clinical manifestation of AD and dementia. Intervention Strategies Against Dementia Intervention Toward Vascular Factors and Related Disorders Most vascular risk factors and related disorders are modifiable or treatable and can serve as targets in the development of primary preventative strategies against dementia. For example, antihypertensive therapy has been shown to reduce the risk of dementia in observational studies, and this finding was partly confirmed by clinical trials. Furthermore, studies have confirmed that obesity and diabetes can be prevented by changing dietary habits and lifestyles, and that health education may help someone quit smoking. Finally, preventing recurrent cerebrovascular disease and maintaining sufficient cerebral blood perfusion seems to be critical for postponing expression of the dementia syndrome in older people. Thus, controlling high blood pressure and obesity, especially from middle age,

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Table 1.1 Summary of Epidemiological Evidence Supporting the Modifiable Etiological Factors of Dementia and Alzheimer ’s Disease Profile

Risk, protective, or precipitating factors

Vascular factors Midlife risk factors: High blood pressure, diabetes, high body mass index (obesity or overweight), hyperlipidemia or high cholesterol, and smoking Late-life risk factors: Very high and very low blood pressure, diabetes, atherosclerosis, heart disease, cerebral microvascular disease (e.g., white matter lesions and infarcts), plasma hyperhomocysteine, and smoking Late-life protective factors: Use of antihypertensive medications, use of non-steroidal anti-inflammatory drugs, light-tomoderate alcohol consumption (note: the protective effect of alcohol intake may be due to information bias, residual confounding, etc.) Late-life precipitating factors or markers: Weight loss, low blood pressure in very old or decline in blood pressure, and low cholesterol or decline in serum cholesterol Psychosocial Protective factors (lifespan): High educafactors tion, rich social network, mentallystimulating activity, active social engagement, and regular physical activity

Epidemiological evidence Moderately strong

Moderately strong

Moderately strong

Limited

Moderately strong

and preventing diabetes and recurrent stroke could be the primary preventive measures against late-life dementia. Intervention Toward Psychosocial Factors and Lifestyles High educational achievements in early life can provide cognitive reserve that benefits the whole life in terms of cognitive health and delaying the onset of late-life dementia. Extensive social networks and active

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engagements in intellectually stimulating activities such as reading, doing crossword puzzles, and playing board games significantly lower the risk of dementia by providing cognitive reserve or by reducing psychosocial stress. Thus, it is likely that mentally and socially integrated lifestyles could postpone the onset of dementia. Regular physical exercise may reduce the risk of the dementias resulting from cerebral atherosclerosis. Leisure activities with all three components of physical, mental, and social activities may have the most beneficial effect on dementia prevention (Karp et al. 2006). Taken together, the most promising strategy for the primary prevention of dementia may be to encourage people implementing multiple preventative measures throughout the life course, including high educational attainment in childhood and early adulthood, an active control of vascular factors (e.g., smoking) and disorders (e.g., hypertension and diabetes) over adulthood, and maintenance of mentally, physically, and socially active lifestyles during middle age and later in life. CONCLUSIONS Dementia is a major cause of functional dependence, institutionalization, and mortality among elderly people. As the population ages in the decades to come, dementia will reach an epidemic level, a scenario that poses a serious threat not only to public health but also to the social and economic development of the modern society. Epidemiological studies have shown that vascular risk factors in middle age and later in life significantly contribute to the development and progression of the dementia syndrome, whereas extensive social network and active engagement in social, physical, and mental activities may delay the onset of the dementing disorders. Hence, one of the promising strategies to deal with the tremendous challenge from the epidemic of dementia is to implement appropriate intervention measures from the life-course perspective, such as achieving high education in early life and engaging in mentally stimulating activity over the course of adulthood to enhance cognitive reserve, and maintaining vascular health by adopting a healthy lifestyle and optimally controlling vascular diseases to reduce the burden of vascular lesions in the brain. These preventive measures will enable people to maintain cognitive ability in late life, even though they may have developed a high load of Alzheimer pathologies in their brain. REFERENCES Abbott, R. D., L. R. White, G. W. Ross, K. H. Masaki, J. D. Curb, and H. Petrovitch. 2004. Walking and dementia in physically capable elderly men. JAMA 292 (12): 1447–1453.

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Chapter 2

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia Wilm Quentin, Steffi G. Riedel-Heller, Melanie Luppa, Hanna Leicht, and Hans-Helmut König

Worldwide direct costs of dementia have been estimated to be US$156 billion (Wimo, Jonsson, and Winblad 2006), and annual costs of patients with neuropsychiatric symptoms of dementia have been found to amount to US$39,514 (Murman et al. 2002). Similar results of cost-of-illness (COI) studies of dementia are frequently reported in the scientific literature, policy discourses, and the media alike. They contribute to the impression that dementia is “very costly.” And they make people worry that high costs per case combined with the projected increase in dementia prevalence in countries around the world will place a heavy burden on societies. Of course, the main purpose of COI studies is not to make people worry. They are intended to provide estimates about the economic impact of diseases on different aspects of health systems and societies in order to assist policymakers in decisions of planning and financing (Bloom et al. 2001). However, unfortunately, methodological characteristics and estimated results of COI studies vary widely. Consequently, the usefulness of COI studies has come under debate because high variability puts into question the reliability of their results (Koopmanschap 1998). This chapter aims to provide an overview to theory and practice of COI studies of dementia and uses the results of existing studies to outline the impact of neuropsychiatric symptoms of dementia on resource

36

Dementia

use and costs. It first reviews the theoretical health economics background of COI studies of dementia before exploring in more detail methodological characteristics of studies and sources of variation of their results. The following section then presents findings of a recent systematic literature review of COI studies of dementia by Quentin et al. (2010). Their findings demonstrate that results of COI-studies show consistent patterns while the exact size of costs varies greatly among studies. Drawing on the literature review and on results of existing COI studies analyzing the impact of neuropsychiatric symptoms of dementia, the subsequent section explores the likely role of neuropsychiatric symptoms of dementia in determining resource use and costs. Finally, the chapter summarizes the argument and concludes that neuropsychiatric symptoms of dementia are important determinants of costs of care. However, the exact size of costs depends on many factors that should be controlled through greater standardization of COI studies of dementia. Results of existing studies always need to be interpreted by considering the specific design, the setting, and other methodological characteristics outlined in this chapter. BACKGROUND: HEALTH ECONOMICS AND DEMENTIA COI studies of dementia are part of the health economics literature, which makes use of economic concepts in order to assess issues related to the allocation of resources to and within the health sector (Folland, Goodman, and Stano 2007). One concept essential for understanding the results of COI studies is the opportunity costs or economic costs. In all societies resources are scarce and decisions have to be taken between alternative uses of resources. If resources are committed to one alternative, society gives up the opportunity to enjoy the benefits of the other. Therefore, the opportunity cost of one item is what is given up in order to obtain it. COI studies usually aim to estimate the opportunity costs of an illness, which are equal to the value of the forgone opportunity to use the resources used or lost due to the disease in another way. If there were no diseases at all, patients would be able to work, hospitals could be transformed into hotels, and doctors would have chosen to become bankers or painters. From a societal perspective, which is often adopted by economists in order to estimate the costs of dementia, the value of all resources that could have been employed in other ways to increase the welfare of society needs to be considered. As money is a medium to exchange and

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 37

store value, the monetary value of the resources used or lost due to illness indicates what is given up as a result of the illness. In order to estimate the opportunity costs of an illness, analysts usually proceed in two steps: First, they measure the quantity of resources consumed or lost due to illness (usually by following a sample of selected patients); and second, they attach monetary values (unit costs) to identified resources. In order to get an idea of the resources that are consumed, and of the determinants of resource consumption, it is useful to think about the normal course of the disease. In progressive diseases like dementia, resource consumption is highly dependent on the stage of the disease. In early stages, demented patients are usually cared for in the community. They receive informal care and support in activities of daily living (ADLs) from mostly female caregivers (Harrow et al. 2004). As the disease progresses and functional ability deteriorates, patients become increasingly dependent on others. Caregivers sometimes have to cut down working hours in order to take care of their parents or relatives and may even incur excess healthcare costs themselves (Moore, Zhu, and Clipp 2001). At the same time, increasing dementia severity leads to augmenting demand for formal community support services. Eventually, many patients require institutional nursinghome care once the subjective caregiver burden becomes overwhelming (Yaffe et al. 2002). In addition, demented patients have been found to be high users of healthcare services (Hill et al. 2006; Bynum et al. 2004), even though the evidence in this field is not unambiguous (Kane and Atherly 2000). Attaching monetary values to all of the resources used (e.g., formal medical and informal care) and lost (lost productivity of patients and caregivers) and adding them up yields the opportunity cost of dementia. In theory, the methodology appears relatively straightforward. However, in practice, things are more complicated. METHODOLOGICAL CHARACTERISTICS AND SOURCES OF VARIATION IN DEMENTIA COST-OF-ILLNESS STUDIES COI studies vary widely in their methodological characteristics. They differ in their methods of sample selection and data collection and in the employed cost-estimation techniques. Methodological characteristics of COI studies are important since they determine what is measured and what is reported in their results. This section provides an overview to methodological differences between COI studies of dementia and highlights the impact that these differences can have on estimated results.

38

Dementia

Differences in Sample Selection and Data Collection Table 2.1 presents different approaches of COI studies to solving the methodological problems of sample selection and data collection. Since different approaches influence estimated results, they become important sources of variation between studies. The last column in Table 2.1 presents examples of characteristics of existing studies. Sample Selection As mentioned above, in most COI studies resource consumption for a sample of patients is measured. Important sources of variation between

Table 2.1 General Design Features and Sources of Variation between COI Studies of Dementia Methodological Problem Source of Variation

Characteristics of Studies (examples)

Sample selection Identification of patients

Population screening Physician diagnosis Dementia (unspecified) Alzheimer ’s disease vs. vascular dementia, etc. Positive screening test (e.g., MMSEa) Proportion of patients living in institutional care settings Proportion of patients in different stages of disease Proportion of patients with neuropsychiatric symptoms of dementia Definition of severity (e.g., by MMSE, CDRb) Number of stages Questionnaire Analysis of medical claims data Retrospective Prospective 1 month 4 years

Definition of dementia

Sample composition

Classification of patients into disease stages Data collection

Source of information Study design Time period

a b

MMSE = Mini Mental State Examination CDR = Clinical Dementia Rating Scale

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 39

studies arise from different methods of identifying patients, different definitions of dementia, the resulting divergence in the composition of samples, and from differences in the methods used to classify patients into different stages of disease. The method for identification of patients, for example, whether an entire population is screened or whether patients are recruited from general practitioners or specialists, influences the sample composition: Studies using population screenings in order to identify patients are more likely to include a larger number of patients in earlier stages of the disease than studies recruiting patients from specialist practices. The resulting differences between samples (e.g., higher numbers of patients living in institutional-care settings) are bound to influence estimated costs. Similarly, the study definition of dementia can influence sample composition. In cases where an unspecified dementia diagnosis is sufficient for study inclusion, the sample may include varying proportions of patients with specific types of dementia. For example, the proportion of patients with vascular dementia (VaD) included in a study of “dementia patients” could be higher than one would expect in the general population. Consequently, resource consumption of “demented patients” would be overestimated as VaD patients have been found to have higher costs in earlier stages of dementia than patients with Alzheimer ’s disease (AD) (Wimo and Winblad 2003). Furthermore, studies analyzing stage dependency of costs can differ in their approach to the classification of patients into different stages of severity. On the one hand, they can apply different measures of severity. Some use the Mini Mental State Examination (MMSE) in order to assess the cognitive state of their sampled patients and classify patients accordingly. Other studies use measures of functional disability such as dependency in activities of daily living or instrumental activities of daily living in order to classify patients. On the other hand, researchers can classify patients into different numbers of stages of severity. Of course, depending on the way patients are classified into different stages of disease, reported cost estimates for mild, moderate, and severe dementia vary considerably. Data Collection Concerning data collection, studies can use various sources of information, can collect data retrospectively or prospectively, and may cover different periods of time. The method of data collection is likely to influence the accuracy of measurement. The most accurate estimates of resource consumption are likely to result from prospective studies relying on multiple sources of information and covering long periods of time.

40

Dementia

However, sources of information used in existing studies vary widely. They include interviews with patients, caregivers, and physicians either by phone or in person as well as administered or mailed questionnaires. Established questionnaires for data collection are the Resource Utilisation in Dementia (RUD) Instrument (Wimo et al. 2000) and the Client Service Receipt Inventory (Beecham and Knapp 1992). In addition, patient records or registries and medical claims databases of different payers are used for identifying costs of formal care. The study design determines whether data is collected retrospectively or prospectively, or whether both methods of data collection are used. Prospective studies have the advantage that they allow greater standardization of data collection, for example, by providing diaries to patients and caregivers in order to facilitate recording of resource consumption. In addition, they make it possible to follow patients over the course of the disease, which is an advantage as it allows estimating incidence costs of dementia (see below). Furthermore, studies differ in the time periods covered by data collection. Some studies collect data on resource use for only one month (e.g., Langa et al. 2001). Other studies track patients over a time period of up to four years (Zhu et al. 2008). Shorter time periods, are in general, more likely to produce biased estimates as the impact of extraordinary events or seasonal variation has a stronger influence than if data are collected over a longer time period. On the other hand, if patients, caregivers, or physicians have to recall resource consumption over a longer time period, it is more likely that important elements are omitted. Many studies, therefore, use several data-collection points over a long period of time in order to increase the robustness of their estimates. Cost-Estimation Technique The problem of high variation between COI studies of dementia is further compounded by differences in cost estimation techniques employed by different studies. Table 2.2 shows methodological problems in estimating costs of dementia that can be solved in different ways. COI studies adopt different approaches to the definition of costs, the measurement of resource use, and the valuation of resources. The last column in Table 2.2 presents examples of characteristics of existing studies. Defining Costs of Dementia The perspective of a study is important as it determines which cost categories have to be considered as a cost of a disease (Drummond et al.

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 41 Table 2.2 Cost-Estimation Technique and Sources of Variation between COI Studies of Dementia Methodological Problem

Source of Variation

Characteristics of Studies (examples)

Defining costs of dementia

Perspective

Society Specific payers Families Formal medical care Formal non-medical care Informal care Indirect costs Prevalence cost study Incidence cost study Estimation of total costs of demented patients Estimation of net costs of dementia Bottom-up approach Top-down approach Including all time spent with the patients Including only time spent providing assistance with ADLa and IADLb Use of national cost schedules Use of prices Use of cost study Replacement cost approach Opportunity cost approach

Inclusion of cost categories

Prevalence vs. incidence costs Objectives

Measurement of resource use

Costing approach Measurement of informal care

Valuation of used resource

Valuation of formal care

Valuation of informal care

a

Activities of daily living. Instrumental activities of daily living.

b

2005). For example, from a societal perspective, lost earnings of patients and informal caregiving time are important costs of dementia. However, from other perspectives, for example, from the perspective of Medicare, they are completely irrelevant. A study adopting a Medicare perspective would be concerned exclusively with estimating the financial costs of those resources that are covered under Medicare.

42

Dementia

Theoretically, the number of included cost categories should follow logically from the adopted perspective. A study adopting a societal perspective should include all of the following cost categories: inpatient care (IP), outpatient care (OP) (e.g., ambulatory care, hospital outpatient, emergency room, general practitioner), drugs (nootropics and others), nonmedical care (physiotherapy, nursing home care, domestic care, home help, day care, home health visits, and so on), informal care (e.g. support by family members), and indirect costs (lost productivity). However, the number of included cost categories varies considerably between studies and it is not always consistent with the stated perspective. The definition of the costs of dementia also determines whether prevalence or incidence costs are estimated. Most COI studies of dementia estimate prevalence costs, meaning that they are concerned with costs of demented patients during a specific year. Prevalence cost studies are less difficult to perform than incidence cost studies as they only look at the costs of patients during a specific year. Incidence cost studies follow patients over the course of the disease and usually aim to estimate costs from onset of the disease until death. They are more difficult since they require prospective analysis of patients over an extended period of time. However, the generated information is very useful as it can be more easily incorporated into economic evaluations of different interventions (Koopmanschap 1998). Furthermore, depending on the objectives of the study, COI studies of dementia may aim to estimate either total costs or net costs of the disease. Total-cost studies of dementia aim to estimate total costs of all care received by their sampled patients. Consequently, their results include all costs associated with the entire variety of pathologies that frequently occur in elderly populations (e.g., cardiovascular disease or cancer). Net-cost studies try to account for the fact that not all costs incurred by demented patients are actually caused by dementia (Langa et al. 2001). The ideal COI study estimating net costs of dementia would be able to appropriately identify all costs due to dementia and omit all costs attributable to other reasons (Akobundu et al. 2006). Different approaches to estimating net costs of dementia exist. However, it remains difficult to exactly identify the costs specifically due to a disease (Lee, Meyer, and Clouse 2001). Measurement of Resource Use Different costing approaches exist that allow estimating costs of a disease. Costs can be estimated either by using a bottom-up approach or a top-down approach. Most COI studies of dementia use a bottom-up approach. They

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 43

measure resource use by looking at individual services and aggregating resources until total resource use of patients has been estimated. Then monetary values are assigned. The bottom-up approach can proceed even further up and estimate national costs by multiplying estimated costs per patient with prevalence data for the number of patients in the country. Conversely, the top-down approach starts from highly aggregated data, for example, national registries and national accounts, and proceeds downwards. In the process, average resource use of patients is estimated by disaggregating total costs into smaller units and allocating them to specific diseases. The top-down approach has the advantage that it can be used to produce comparable cost estimates across different diseases (Koopmanschap 1998), facilitating comparative analyses of burden of disease. However, top-down studies are usually less detailed than bottom-up studies and do not generate information about informal care and indirect costs. Since informal care is an important cost category in COI studies of dementia, differences in measurement and valuation of informal care can have a particularly large impact on estimated results. When measuring the time requirements for informal care, some studies look at all time spent with the patient, others only include time spent providing ADL and instrumental activities of daily living (IADL) assistance, while still others include informal care only up to a maximum of 16 hours per day in order to allow for at least 8 hours of sleep of caregivers. Depending on the chosen method, total estimated hours of informal caregiving time differ between studies. Valuation of Resource Use The valuation of consumed resources can have a large impact on estimated costs. It ultimately determines the monetary value of the resources that are consumed and lost due to illness. Often studies use secondary sources of information like national cost schedules in order to attach monetary values to measured resources. In other cases, market prices of used resources are determined. However, only few studies explicitly describe the assigned values. Therefore, it is often difficult to assess the appropriateness of chosen values. The valuation of informal care is even more complicated as no market prices are available that could be used as unit costs. In theory, several solutions to this problem exist (McDaid 2001). Most dementia COI studies use either the opportunity cost approach or the replacement cost approach. The opportunity cost approach tries to estimate costs of forgone opportunities by asking caregivers about how they would make use of their time if they were not engaged in the provision of informal

44

Dementia

care. Depending on their answers, caregiving time is then valued either at the market wage rate, at a rate for the contribution to household production or the rate for leisure time. The alternative replacement cost approach assigns monetary values to caregiver time according to what it would cost to replace informal caregiving activities with formal care. If the alternative approaches result in different monetary values per hour of informal caregiving time, they can have a considerable impact on estimated costs of informal care. A SYSTEMATIC LITERATURE REVIEW OF COST-OF-ILLNESS STUDIES OF DEMENTIA The previous section has illustrated the high degree of variability among COI studies of dementia that results from differences in sample selection, data collection, and cost-estimation technique. A recent systematic literature review by Quentin et al. (2010) provides an overview to methods and results of COI studies of dementia focusing on stage dependency of costs. Their findings document the high degree of variability described in the previous section. Yet, by summarizing costs for different stages of disease, their results demonstrate that estimates of existing studies show consistent patterns of increasing costs from mild over moderate to severe dementia. This section presents methods, results, and limitations of their literature review. Review Methods Quentin et al. (2010) performed a systematic literature search in Medline (Pubmed), Cochrane Library, and NHS Economic Evaluations Database (NHSEED) between May and July 2008. They searched for COI studies of dementia from Europe and North America, restricting their search to studies that appeared after 1996 since earlier studies had been included in two prior literature reviews (Wimo, Ljunggren, and Winblad 1997; Ernst and Hay 1997). Twenty-eight studies were selected that fulfilled the following criteria: The primary objective of the study was the estimation of costs of dementia; the study had a sample size greater than 20; it included at least the cost categories of formal nonmedical care or informal care; and the main outcome of the study was reported as average costs per patient and time period presented separately for different stages of disease. Studies were divided into two groups: (1) total-cost studies assessing total costs of demented individuals, and (2) net-cost studies, which tried to discern net costs of dementia (costs specifically caused by dementia). Within these groups, subgroups were formed according to the care setting

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 45

of patients included in the study. Furthermore, reported costs of different cost categories (e.g., inpatient care, outpatient care, nursing home care, family caregiving) were aggregated to the categories of formal care and informal care. When the original studies presented cost estimates for more than three stages of disease (mild, moderate, severe), presented results were assigned to the appropriate stages of dementia on the basis of reported MMSE scores (mild: ≥18, moderate: 10–17, severe: ≤9) or the original studies’ definitions. When studies presented costs for only two stages of disease, the lower estimate was classified as mild dementia and the higher estimate as severe dementia unless specified otherwise by the original studies’ authors. All cost estimates were converted into 2006 USD purchasing power parities (PPP) (OECD 2008). Review Results Study Characteristics Table 2.3 summarizes characteristics of the 28 included studies. Their region of origin was evenly distributed between Europe (n = 14) and North America (n = 14). Among European studies, most studies were from Scandinavia and the United Kingdom, while eastern and southern Europe, with the exception of Spain, were not represented in the sample. Concerning the dementia type, 21 studies looked at costs of patients with AD, of which six studies also included patients with VaD or other dementias. Two studies aimed to identify differences between costs of AD patients and VaD patients (Andersen et al. 1999; Wimo and Winblad 2003). Three studies looked at patients with “cognitive impairment,” and four studies did not specify the type of dementia. Even within the same diagnosis, definitions of dementia varied. Seventeen studies required a formal dementia diagnosis based on established criteria (e.g., National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer ’s Disease and Related Disorders Association; NINCDS-ADRDA). Seven studies required a positive screening test (e.g., MMSE) or did not specify their definition of dementia. Most studies relied on interviews to collect information on resource use by patients during a certain time period prior to the interview. Concerning patient sample characteristics, the median sample size was 337 patients, reported mean age was mostly in the late seventies, and most samples were predominantly female. Concerning cost-estimation methods, studies differed widely. Sixteen studies analyzed costs from a societal perspective, four from the perspective of specific payers, and four from the perspective of families. Most studies included several cost categories of formal care. Nineteen

46

Dementia

Table 2.3 Characteristics of Included COI Studies of Dementia (N = 28) Characteristic Region: N (%) United States Canada Europe Sample Size Median (range) Mean Included Diagnoses: N (%) (Multiple diagnoses possible) Alzheimer’s disease Vascular dementia Other dementias Dementia (unspecified) Cognitive impairment Diagnostic Criteria: N (%) Formal diagnoses (e.g. according to NINCDS-ADRDAa) Physician diagnosis (unspecified) Other (e.g., MMSE) / not described Method for Data Collection / Data source: N (%) (Multiple methods possible) Interview Questionnaire Patient records Medical claims Not described Adopted Perspective: N (%) (Multiple perspectives possible) Society Payers Families Not described Included Cost Categories: N (%) (Multiple categories possible) Formal nonmedical care Ambulatory care Inpatient care Informal care Drugs Study Objectives Total costs Net costs Both

11 (39%) 3 (11%) 14 (50%) 337 (50 – 8.736) 880

21 (75%) 6 (21%) 4 (14%) 4 (14%) 3 (11%) 17 (61%) 4 (14%) 7 (25%)

21 (75%) 4 (14%) 3 (11%) 3 (11%) 1 (4%)

16 (57%) 5 (18%) 4 (14%) 4 (14%)

23 (82%) 20 (71%) 19 (68%) 19 (68%) 19 (68%) 4 (14%) 5 (18%)

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 47 Table 2.3 (Continued) Funding Source Pharmaceutical companies Public funds Other (e.g. Alzheimer’s Association)/not described

16 (57%) 6 (21%) 6 (21%)

Source: Adapted from König et al., Stadienspezifische Kosten der Demenz: Ergebnisse eines systematischen Literaturuberblicks, in Versonrgungsforschung fur demenziell erkrankte Menschen, ed. Dibelius and Maier, with permission. a National Institute of Neurological and Communicative Disorders and Stroke– Alzheimer’s Disease and Related Disorders Association.

studies estimated cost of informal care. In addition, studies pursued different objectives: 19 studies looked at total costs of demented patients while 9 others tried to estimate net costs of dementia or looked at both total and net costs of dementia. When looking at the sources of funding, most studies included financial contributions from pharmaceutical companies but several studies were financed exclusively through public funds. More information on the methods for valuation of informal care is provided in the original review article (Quentin et al. 2010). Estimated Costs Table 2.4 summarizes results of analyzed studies. It presents estimated costs from total-cost studies and net-cost studies separately for patients living in different care settings, and it further differentiates between costs for formal and informal care. The table shows the range of estimated average costs from analyzed studies and the median of their results. At first sight, the large range of estimated costs in each field might appear confusing. However, after close examination, consistent patterns of increasing costs from mild over moderate to severe dementia emerge. In most total-cost studies, costs more than double from mild to severe dementia. For example, Table 2.4 shows that the range of average total costs estimated by different studies for community-dwelling patients in the mild stage of dementia was between US$7,124 and $56,192, whereas average total costs reported for patients in the severe stage were between $8,544 and $115,450. Accordingly, the median of estimated results reported in different studies increased from about $18,000 to about $36,000. In net-cost studies, the increase of costs was even more pronounced: The median of estimated costs for community-dwelling patients increased from about $18,000 to about $63,000. However, in general, costs estimated in net-cost studies tended to be smaller than costs estimated in comparable total-cost studies.

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Dementia

Table 2.4 Results of Included COI Studies of Dementia by Study Objectives, Level of Severity, Care Setting, and Cost Category Estimated Costs per Year in USD-PPP (2006) Number of studies Range of costs [Median] Care Setting

Cost Category

Mild Dementia

Moderate Dementia

Severe Dementia

Total Cost Studies (24 studies) Communitydwelling patients

Formal care costs

n=9 1,268–14,388 [6,341]

n=6 5,234–32,283 [9,837]

n=9 4,255–36,428 [13,649]

n=8 783–41,677 [14,212]

n=6 615–54,544 [15,937]

n=8 1,225–78,504 [36,354]

Total costs

n=7 7,124–56,192 [18,041]

n=7 5,849–87,057 [25,331]

n=8 8,544–115,450 [36,354]

Formal care costs

n = 13 2,255–29,127 [10,261]

n = 11 7,592–34,897 [21,537]

n = 13 11,764–56,118 [29,549]

Informal care costs

n=6 3,932–13,039 [5,841]

n=5 4,050–9,001 [7,496]

n=6 2,392–14,261 [8,071]

Total costs

n=8 3,992–35,338 [12,340]

n=7 16,462–34,264 [25,492]

n=8 21,977–74,555 [38,204]

Formal care cost

n=2 n=2 21,990–42,319 28,665–44,941 [31,640] [36.803]

n=2 35,518–48,162 [41,840]

Informal care costs

Mixed group of patients

Institutionalized patients

Informal care costs Total costs

n=2 1,028–1,875 [1,452]

n=2 790–3,576 [2,183]

n=2 n=2 23,865–42,319 32,240–45,731 [33,092] [38,986]

n=2 834–2,306 [1,570] n=2 37,825–48,994 [43,410]

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 49 Table 2.4 (Continued) Net Cost Studies (9 Studies) Communitydwelling patients

Formal care costs

n=7 1,007–11,143 [3,330]

n=7 166–28,996 [7,208]

n=7 986–33,285 [9,816]

n=8 197–41,677 [5,647]

n=8 29–54,544 [12,042]

n=8 639–78,504 [33,103]

Total costs

n=7 1,470–52,947 [18,388]

n=7 195–83,770 [28,767]

n=7 2,890–112,307 [62,916]

Formal care costs

n=3 2,553–11,720 [8,373]

n=2 13,433–18,567 [16,000]

n=3 4,595–27,359 [26,052]

Informal care costs

Mixed group of patientsa

Source: Adapted from König et al., Stadienspezifische Kosten der Demenz: Ergebnisse eines systematischen Literaturuberblicks, in Versonrgungsforschung fur demenziell erkrankte Menschen, ed. Dibelius and Maier, with permission. Note: USD-PPP = US$–purchasing power parities. a Studies only report costs of formal care.

Furthermore, the patterns of increasing costs and the proportion of formal and informal care differ between care settings. In mild dementia, totalcost studies of patients living in institutional-care settings found higher costs (about US$33,000) than total-cost studies of community-dwelling patients (about $18,000). In contrast, in severe dementia, estimated costs of patients living in different care settings were similar. In studies analyzing samples of community-dwelling patients, costs increased mostly for informal care and accounted for about two-thirds of costs in the severe stage. In studies including community-dwelling and institutionalized patients (mixed group), costs for formal care increased more strongly from mild to severe dementia, which could be attributed to increasing proportions of institutionalized patients from mild to severe dementia in this group of studies. In studies of institutionalized patients, the proportion of informalcare costs remained relatively small across all stages of dementia. Limitations Reported results vary considerably even within the presented subgroups of studies and within the cost categories of formal and informal

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Dementia

care. On the one hand, these variations are related to limitations of the original studies. For example, some studies excluded certain cost categories or excluded patients with severe comorbidities; other studies used extremely high values for the valuation of informal care. On the other hand, variations are related to limitations of the review method, which had to ignore certain differences between studies in order to be able to compare their results. The review formed subgroups according to the care setting of patients and aggregated various cost categories into the categories of formal and informal care. However, studies differed considerably within subgroups. The samples of patients analyzed in the original studies varied greatly: Some studies included “dementia patients”; others recruited only AD patients. Studies used various methods to classify patients into different stages of disease; and informal-care costs were estimated through very different approaches. These limitations need to be considered when drawing on the results of the review by Quentin et al. (2010). However, despite all methodological differences between studies and variation of results, the patterns of increasing costs are clear enough to allow drawing conclusions for the effect of neuropsychiatric symptoms on costs of dementia. COST-OF-ILLNESS OF DEMENTIA: THE ROLE OF NEUROPSYCHIATRIC SYMPTOMS Two findings of the presented literature review are particularly important for a discussion about the role of neuropsychiatric symptoms (NPS) in determining resource use and costs of dementia: First, the review shows that informal care accounts for an important part of total resources used and constitutes the majority of costs of dementia care in communitydwelling patients; second, it indicates that costs of institutionalization are considerable already in early stages of dementia. The implications of these findings for costs of NPS can be explored by looking at existing studies of the importance of NPS in determining caregiver burden and nursing home placement. In addition, some studies exist that have specifically looked at the effect of NPS on cost-of-illness of dementia. Caregiver Burden and Determinants of Nursing Home Placement: The Role of Neuropsychiatric Symptoms Multiple studies have assessed the role of neuropsychiatric symptoms in determining caregiver burden and nursing home placement. Almost 30 years ago, Greene et al. (1982) found behavioral problems to be important

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 51

determinants of caregiver burden. More recently, these findings were confirmed by studies that looked at the specific type of neuropsychiatric symptom with the highest impact on caregiver burden as measured by standardized interviews of caregivers. For example, Craig et al. (2005) have found sleep disturbance, aggression/agitation, and depression/ dysphoria to be particularly distressing to caregivers. Another stream of research has looked at the time spent by caregivers on dealing with NPS of dementia. Beeri et al. (2002) found that caregivers spent about one-third of their time with managing behavioral and psychiatric symptoms. And Murman et al. (2002) found that patients with NPS required 3.5 hours more of caregiving per day than patients without these symptoms. Nursing home placement is the result of a complex set of interacting variables, including caregiver, patient, social, and cultural variables (Yaffe et al. 2002). However, high caregiver burden and presence of NPS of dementia are two particularly important factors influencing the likeliness of patients to be admitted to nursing homes. Even when controlling for other factors, Yaffe et al. (2002) found caregiver burden and age over 75 years to be the most important determinants of nursing home placement. Phillips and Diwan (2003) estimated that patients with NPS enter nursing homes nearly two years earlier than those without. These findings combined with the results of the literature review by Quentin et al. (2010) suggest that NPS are important determinants of costs of care for demented patients. Informal-care costs account for the majority of costs in community-dwelling patients and NPS are important determinants of caregiver burden. Consequently, total costs of care related to NPS of dementia can be expected to be considerable. In addition, neuropsychiatric symptoms of dementia contribute to increasing costs by leading to earlier nursing home placement. Since costs of nursing home placement are important even in the early stages of disease, the impact on total costs is likely to be important. COI Studies Focusing on Neuropsychiatric Symptoms of Dementia A small number of studies have looked specifically at the contribution of neuropsychiatric symptoms of dementia on costs of dementia care. Table 2.5 provides an overview to the characteristics of two of these studies, which compared groups of patients with different levels of neuropsychiatric symptoms of dementia. Both studies are from North America and estimated total costs of dementia care of a sample of patients which they divided into two groups: one group with a low score on the neuropsychiatric inventory (NPI) and one group with a high NPI score. Herrmann

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Dementia

Table 2.5 Characteristics of COI Studies Focusing on Neuropsychiatric Symptoms of Dementia Author (year) General study characteristics Country Dementia type Method for data collection Sample characteristics Size Mean age % receiving long-term care (patients with low/high NPI) Funding source Perspective Objectives Included cost categories Inpatient care Outpatient care Drugs Nonmedical Informal Indirect Valuation of informal care Unit cost value (in 2006 US$-PPP)

Herrmann et al. 2006

Murman et al. 2002

Canada Dementia (AD/VaD/other) Mailed questionnaire

USA Alzheimer ’s disease Interview

500 76.3 0%

128 76.2 (12%/27%)

Industry Society Total cost study

National Institute on Aging n/d (Society) Total cost study

+ + + + + +

+ + + + + -

98.01/day

5.82/hr, 9.02/hr, 10.68/hr (low, mid-range, high estimate)

Note: AD = Alzheimer ’s disease; n/d = not described; NPI = neuropsychiatric inventory; VaD = vascular dementia.

et al. (2006) studied patients living in the community, whereas Murman et al. (2002) included a certain number of patients receiving long term care. Both included a wide range of formal and informal care costs. In addition, Herrmann et al. (2006) included indirect costs by estimating the opportunity costs of the time that patients were prevented from performing their regular activities. Table 2.6 presents the results of the two COI studies focusing on neuropsychiatric symptoms of dementia. It is important to note that the studies estimated costs for disparate groups of patients. Herrmann et al. (2006)

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 53 Table 2.6 Results of COI Studies Focusing on Neuropsychiatric Symptoms of Dementia Mean annual costs per patient (in 2006 USD-PPP) Patients with low NPI Author (year) Herrmann et al. 2006 Murman et al. 2002

Patients with high NPI

DefiniDefinition of tion of Group Formal Informal Total Group Formal Informal Total NPI = 0 2,479

2,098

NPI <13 10,074 13,039

7,091

NPI >0

4,255

4,877

16,119

23,113 NPI ≥13 20,485 14,261 44,993

Note: NPI = neuropsychiatric inventory.

defined their “low NPI group” as patients with no neuropsychiatric symptoms, whereas the “low NPI group” of Murman et al. (2002) included all patients with an NPI score less than 13. Both studies found that costs of patients in the high NPI group were about twice as high as costs in the low NPI group. However, the size of costs varied considerably, which is not particularly surprising as the assessed groups of patients were very different. Both studies also used regression analyses in order to determine the effect of a one-point increase in NPI score on total costs of dementia care. Herrmann et al. (2006) estimated that costs would increase by US$346 for every one-point increase in NPI, while Murman et al. (2002) estimated the effect to lie somewhere between US$281 and $466 depending on the valuation of informal care (all in 2006 US$-PPP). The estimated effect of neuropsychiatric symptoms of dementia on costs, therefore, appears to be similar. However, it has to be considered that the estimate of Herrmann et al. (2006) includes indirect costs (the value of the time that patients were prevented from performing their regular activities), which they found to be considerable, whereas Murman et al. (2002) did not include this cost category. Presumably, further differences existed in the measurement and valuation of informal care, which are, however, difficult to evaluate. CONCLUSION This chapter has described the theoretical background and methodological characteristics of COI studies of dementia. It has highlighted the importance of considering methodological differences between studies when interpreting their results. Different approaches to sample selection, data collection, definition of costs, and measurement and valuation

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of resources determine the size of estimated costs in COI studies of dementia. Users of results of COI studies should be careful with generalizing results of COI studies since they are always specific to the study setting, the sample of patients, and the cost-estimation techniques. However, if these methodological characteristics are considered, estimated results can be useful for determining the impact of dementia on specific aspects of health systems and societies. The literature review by Quentin et al. (2010) demonstrated that costs increase from mild over moderate to severe dementia and that costs differ by care setting and depend on whether total or net costs of dementia are estimated. Informal-care costs were confirmed to account for the majority of costs in community-dwelling patients and costs of patients living in institutional-care settings were important already in the early stages of disease. Since neuropsychiatric symptoms of dementia are an important determinant of caregiver burden and nursing home placement, their impact on costs of dementia is considerable. Existing studies have found that costs of patients with neuropsychiatric symptoms of dementia are much higher than costs of patients without these symptoms. However, again, methodological differences between studies complicate comparisons of their results. An international consensus statement exists that defines how to measure benefits in dementia treatment trials (Katona et al. 2007), and reference cases have been developed for the presentation of cost-effectiveness results (NICE 2008). A similar international consensus on a dementia-specific reference case for conducting COI studies of dementia and presenting their results could improve quality of studies, facilitate comparisons, and increase certainty that results are reliable and transferable to other settings. ACKNOWLEDGMENT: This publication is part of the German Research Network on Dementia (KND) and the German Research Network on Degenerative Dementia (KNDD) and was funded by the German Federal Ministry of Education and Research (grants KND: 01GI0102, 01GI0420, 01GI0422, 01GI0423, 01GI0429, 01GI0431, 01GI0433, 01GI0434; grants KNDD: O1GI0710, 01GI0711, 01GI0712, 01GI0713, 01GI0714, 01GI0715, 01GI0716). REFERENCES Akobundu, E., J. Ju, L. Blatt, and C. D. Mullins. 2006. Cost-of-illness studies: A review of current methods. Pharmacoeconomics 24 (9): 869–890. Andersen, C. K., J. Sogaard, E. Hansen, A. Kragh-Sorensen, L. Hastrup, J. Andersen, K. Andersen, A. Lolk, H. Nielsen, and P. Kragh-Sorensen. 1999. The cost of dementia in Denmark: The Odense Study. Dement Geriatr Cogn Disord 10 (4): 295–304.

Cost of Illness Studies and Neuropsychiatric Symptoms of Dementia 55 Beecham, J., and M. Knapp. 1992. Costing psychiatric interventions. In Measuring Mental Health Needs, ed. G. Thornicroft, C. R. Brewin, and J. K. Wing, 179–190. London: Gaskell. Beeri, M. S., P. Werner, M. Davidson, and S. Noy. 2002. The cost of behavioral and psychological symptoms of dementia (BPSD) in community dwelling Alzheimer ’s disease patients. Int J Geriatr Psychiatry 17 (5): 403–408. Bloom, B. S., D. J. Bruno, D. Y. Maman, and R. Jayadevappa. 2001. Usefulness of US cost-of-illness studies in healthcare decision making. Pharmacoeconomics 19 (2): 207–213. Bynum, J. P., P. V. Rabins, W. Weller, M. Niefeld, G. F. Anderson, and A. W. Wu. 2004. The relationship between a dementia diagnosis, chronic illness, Medicare expenditures, and hospital use. J Am Geriatr Soc 52 (2): 187–194. Craig, D., A. Mirakhur, D. J. Hart, S. P. McIlroy, and A. P. Passmore. 2005. A cross-sectional study of neuropsychiatric symptoms in 435 patients with Alzheimer ’s disease. Am J Geriatr Psychiatry 13 (6): 460–468. Drummond, M. F., M. J, Sculpher, G, W. Torrance, B. J. O’Brien, and G. L. Stoddart. 2005. Cost analysis. In Methods for the Economic Evaluation of Health Care Programmes, 3rd ed., 49–102. New York: Oxford University Press. Ernst, R. L., and J. W. Hay. 1997. Economic research on Alzheimer disease: A review of the literature. Alzheimer Dis Assoc Disord 11 (Suppl 6): 135–145. Folland, S., A, C. Goodman, and M. Stano. 2007. Introduction. In The Economics of Health and Health Care, 5th ed., 1–19. Upper Saddle River, NJ: Pearson Prentice-Hall. Greene, J. G., R. Smith, M. Gardiner, and G. C. Timbury. 1982. Measuring behavioural disturbance of elderly demented patients in the community and its effects on relatives: A factor analytic study. Age Ageing 11 (2): 121–126. Harrow, B. S., D. F. Mahoney, A. B. Mendelsohn, M. G. Ory, D. W. Coon, S. H. Belle, and L. O. Nichols. 2004. Variation in cost of informal caregiving and formalservice use for people with Alzheimer ’s disease. Am J Alzheimers Dis Other Demen 19 (5): 299–308. Herrmann, N., K. L. Lanctot, R. Sambrook, N. Lesnikova, R. Hebert, P. McCracken, A. Robillard, and E. Nguyen. 2006. The contribution of neuropsychiatric symptoms to the cost of dementia care. Int J Geriatr Psychiatry 21 (10): 972–976. Hill, J., H. Fillit, S. K. Thomas, and S. Chang. 2006. Functional impairment, healthcare costs and the prevalence of institutionalisation in patients with Alzheimer ’s disease and other dementias. Pharmacoeconomics 24 (3): 265–280. Kane, R. L., and A. Atherly. 2000. Medicare expenditures associated with Alzheimer disease. Alzheimer Dis Assoc Disord 14 (4): 187–195. Katona, C., G. Livingston, C. Cooper, D. Ames, H. Brodaty, and E. Chiu. 2007. International Psychogeriatric Association consensus statement on defining and measuring treatment benefits in dementia. Int Psychogeriatr 19 (3): 345–354. Koopmanschap, M. A. 1998. Cost-of-illness studies. Useful for health policy? Pharmacoeconomics 14 (2): 143–148.

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Langa, K. M., M. E. Chernew, M. U. Kabeto, A. R. Herzog, M. B. Ofstedal, R. J. Willis, R. B. Wallace, L. M. Mucha, W. L. Straus, and A. M. Fendrick. 2001. National estimates of the quantity and cost of informal caregiving for the elderly with dementia. J Gen Intern Med 16 (11): 770–778. Lee, D. W., J. W. Meyer, and J. Clouse. 2001. Implications of controlling for comorbid conditions in cost-of-illness estimates: A case study of osteoarthritis from a managed care system perspective. Value Health 4 (4): 329–334. McDaid, D. 2001. Estimating the costs of informal care for people with Alzheimer ’s disease: Methodological and practical challenges. Int J Geriatr Psychiatry 16 (4): 400–405. Moore, M. J., C. W. Zhu, and E. C. Clipp. 2001. Informal costs of dementia care: Estimates from the National Longitudinal Caregiver Study. J Gerontol B Psychol Sci Soc Sci 56 (4): S219–228. Murman, D. L., Q. Chen, M. C. Powell, S. B. Kuo, C. J. Bradley, and C. C. Colenda. 2002. The incremental direct costs associated with behavioral symptoms in AD. Neurology 59 (11): 1721–1729. NICE. 2008. Guide to the methods of technology appraisal. London: National Institute for Health and Clinical Excellence (NICE). OECD. 2008. Health Data 2008: Statistics and Indicators for 30 Countries. Organisation for Economic Co-operation and Development (OECD). Phillips, V. L., and S. Diwan. 2003. The incremental effect of dementia-related problem behaviors on the time to nursing home placement in poor, frail, demented older people. J Am Geriatr Soc 51 (2): 188–193. Quentin, W., S. G. Riedel-Heller, M. Luppa, A. Rudolph, and H. H. Konig. 2010. Cost-of-illness studies of dementia: A systematic review focusing on stage dependency of costs. Acta Psychiatr Scand 121 (4): 243–259. Wimo, A., L. Jonsson, and B. Winblad. 2006. An estimate of the worldwide prevalence and direct costs of dementia in 2003. Dement Geriatr Cogn Disord 21 (3): 175–181. Wimo, A., G. Ljunggren, and B. Winblad. 1997. Costs of dementia and dementia care: A review. Int J Geriatr Psychiatry 12 (8): 841–856. Wimo, A., G. Nordberg, W. Jansson, and M. Grafstrom. 2000. Assessment of informal services to demented people with the RUD instrument. Int J Geriatr Psychiatry 15 (10): 969–971. Wimo, A., and B. Winblad. 2003. Societal burden and economics of vascular dementia: Preliminary results from a Swedish-population-based study. Int Psychogeriatr 15 (Suppl 1): 251–256. Yaffe, K., P. Fox, R. Newcomer, L. Sands, K. Lindquist, K. Dane, and K. E. Covinsky. 2002. Patient and caregiver characteristics and nursing home placement in patients with dementia. JAMA 287 (16): 2090–2097. Zhu, C. W., R. Torgan, N. Scarmeas, M. Albert, J. Brandt, D. Blacker, M. Sano, and Y. Stern. 2008. Home health and informal care utilization and costs over time in Alzheimer ’s disease. Home Health Care Serv Q 27 (1): 1–20.

Chapter 3

A Stroke of Bad Luck: CADASIL and Friedrich Nietzsche’s “Dementia” or Madness Paul M. Butler

Philosophy is its own time raised to the level of thought. G. W. F. Hegel (1821/1991) The early eighteenth-century Prussian philosopher Georg Wilhelm Friedrich Hegel envisaged history as a dynamic, dialectic system—an ineluctable process of unfolding epochs. Reason, seen as the highest form of human cognition, is forever ensconced within these historical movements. Regardless of the absolute veridicality of Hegel’s thought, he proposes an interesting way to think about reason and history (Hegel 1837/1997). Logical analysis is restricted to comprehending its object using the tools available within a given stage of history. Likewise, science and the art of medicine are bound by the limits of historically constrained reason. This is well illustrated by pondering the vastly different ways human reason has apprehended natural reality across time (i.e., from the Ptolemaic epicyles to Steven Hawking’s arrow of time or Aelius Galen’s circulatory model to William Harvey’s heart-pump model). Medical diagnoses will always only be as accurate as the categories that contain them. Take, for instance, the history of stroke and the diagnosis of “softening of the brain” in the early 1800s. In 1814 Jean Andre Rochoux maintained that apoplexy or ramollissement (softening) of the brain was exclusively the result of hemorrhagic bleeding. No other categories or

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ways of thinking about brain lesions existed at this time in medical history. It was not until 1823, when Leon Rostan proposed that ossification of the arteries was associated with parenchymous brain lesions that the concept of brain softening was divided into hemorrhagic and occlusive-based lesions (Paciaroni and Bogousslavsky 2009). And so it is today in clinical neurology; our reason is trapped in history. Of course, multitudinous diagnostic categories now exist as tools for the practicing physician compared to just 100 years ago, but pathologic etiologies yet to be discovered still hover beyond our conceptual grasp. What was seen as “softening of the brain” in 1810 and diagnosed as an embolic stroke due to atrial fibrillation in 2010 will be phrased in a molecular cardiac basis (an undiscovered channelopathy?) in the future. With this line of thought in mind, I introduce here a potential diagnosis unknown to physicians of the late nineteenth century to explain the dementia of Friedrich Nietzsche. When Nietzsche was brought to Dr. Otto Binswanger ’s clinic in Jena, Switzerland, in January 1889, the diagnostic category most fitting to explain his sudden onset of bizarre ideas, grandiosity, dementia, and apparent Argyll-Robertson pupils was paresis paralytica (tertiary syphilis) (Volz 1990). This diagnosis was very reasonable at that time in medical history because dementia presenting in a middle-aged adult male was nearly always due to syphilitic infection. So what physicians saw in 1889 as syphilitic paresis is ascertained, at least in my estimation, as genetically testable CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) with dementia and perhaps, in the indeterminate future, reason will diagnostically supplant our present-day diagnosis with a new category, such as Type II CADASIL, endophenotype 10b with features of epigenetic modulation (for want of a better imaginary example). OVERVIEW OF NIETZSCHE’S CASE PRESENTATION For my life’s terrible and almost unremitting martyrdom makes me thirst for the end, and there have been some signs which allow me to hope that the stroke which will liberate me is not too distant. Nietzsche (1880/1996) I conducted a comprehensive review of Nietzsche’s medical records and over 500 letters written by Nietzsche (see Nietzsche 1971, 1985, 1996; Frenzel 1967; Volz 1990). Translations from primary sources reveal that he suffered from shifting headaches beginning in adolescence. Further, this evidence clearly demonstrates that Nietzsche was specifically tormented

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by migraine with aura and with motor symptoms giving him an IHS classification of (familial, vide infra) hemiplegic migraine: IHS1.2.4 (International Headache Society 2004). Nietzsche’s headaches emerged during his mid-twenties, peaked in his thirties, and attenuated in his forties. They were trigger-sensitive to weather, travel, and dietary habits and exhibited prolonged duration, photosensitivity, emesis, gastrointestinal pain, and fatigue. Medical records and correspondence demonstrate these headache attacks occurred for many hours, often causing tonic eye cramps and reversible paralysis of the right oculomotor muscles. His right eyelid would droop and his gaze would move into the right lower temporal quadrant. Dizziness, disturbance of speech, temporary loss of consciousness, facial hemi-paralysis, and feelings of numbness accompanied the headaches. Nietzsche described a “flickering in front of the eye” and “numerous danger signals” prior to the onset of these attacks (Volz 1990). Nietzsche’s correspondence also suggests mood disorder. He entertained thoughts of death, experiencing profound depression during the 1870s and early 1880s with an emergent hypomania in the early to mid1880s. There is a sudden progression to fulminant mania by the time of his mental collapse at the end of 1888. Nietzsche’s intense depression alternated with euphoric moods. He wrote letters, within the same month to the same individual, stating that he “hungered for the end” believing that “the cerebral coup de grace is close enough at hand.” Just days later he wrote, “My joyous thirst for knowledge brings me to heights where I can triumph over all torment and despair. On the whole I’m happier than ever before in my life.” In an explosion of 10 days of racing thoughts and flights of ideas during the winter of 1882, Nietzsche produced book 1 of Thus Spake Zarathustra. Just months later, he wrote book 2 over another 10-day stretch of prolonged creativity. For a relatively isolated individual, Nietzsche exhibited pressured speech and loose concatenations of thought in a written form known clinically as hypergraphia. A progressive and predominately right-sided retinal inflammation was noted throughout his life in medical records, in addition to a rapidly developing myopia and eventual blindness. Several physicians commented on this “mysterious inflammatory mechanism” that caused “light granulations” and “retinitis pigmentosa.” In 1884 Nietzsche reported a sudden change in vision not connected with headache that likely reflected a stroke-induced Charles Bonnet syndrome. He described “blots, veils, and darkening” in his visual fields as an “opacification.” With his eyes closed Nietzsche experienced hallucinations filled with fantastic colorful flowers that flowed and changed in brilliant displays of movement. A sudden change in Nietzsche’s mental status occurred during the final week of 1888.

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He sent off cryptic letters signed “The Anti-Christ” and “The Man on the Cross.” This was followed by multiple nonresolving ischemic-like attacks ultimately causing subcortical dementia initially presenting as a pseudobulbar palsy, gait disturbance, and emotional lability. Nietzsche developed a labile affect, tearful outbursts and inappropriate laughter, dysfunction in cranial nerves IX-XII—Binswanger noted tongue deviation to the right and spasmodic left shoulder jerking—in addition to left body upper motor neuron dysfunction evidenced by left ankle clonus and an exaggerated patellar reflex. Under full-time familial care, Nietzsche slowly became bedridden and demented, and died in 1900 after suffering several strokes.

NIETZSCHE’S CASE DETAILS Family History Because of Nietzsche’s fame, his family medical history is partly known. Nietzsche was born in 1844 to an already sick father, Karl Ludwig Nietzsche. In brief, Nietzsche’s melancholic father also experienced intense headaches from adolescence until death at age 35. Beginning in his thirties, he dealt with increasingly labile mood, epileptic-like fits, and multiple strokes leading to facial hemi-paralysis, blindness, and eventual dementia and death. Autopsy revealed a “softening of the brain” affecting 25% of brain tissue. Additionally, records state that Karl’s father, Friedrich August Ludwig Nietzsche, suffered from similar symptoms. So three successive paternal generations, Friedrich August, Karl Ludwig, and Friedrich Nietzsche, suffered from similar symptoms and untimely deaths.

Nietzsche’s Health from 1844 to 1888 Ocular Disturbances Nietzsche struggled with his vision throughout life and saw many eye doctors. An ophthalmologist, Dr. Vater, diagnosed Nietzsche with myopia in early childhood. Dr. Schelbach examined Nietzsche’s vision at age five and noted congenital differences in Nietzsche’s pupils (anisocoria). The right pupil was abnormally shaped, significantly larger than the left, and reacted slowly to light. Dr. Schelbach’s records note the young Nietzsche’s mother, Franziska, also had an uneven right pupil slow to react to light. Even though the right eye was congenitally worse, Nietzsche still needed eye correction for both eyes. His vision deteriorated from early

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childhood at varying rates until eventual blindness occurred before death. In 1873 Dr. Schiesse again noted the right pupillary shape disparity in addition to small pigment granulations on the retina of both sides. He diagnosed “retinitis pigmentosa” in both eyes and “strabismus convergens” in the right eye. In 1877 Dr. Kruger also noticed an inflammatory process present in both retinas that seemed radically changed from previous years. He diagnosed this inflammation as “chorio-retinitis centralis.” He further observed retinal exudates that were spreading toward the macula of both eyes. In January 1884 Nietzsche reported a sudden change in vision not connected with headache. He experienced “blots, veils, and darkening” in his visual fields, which he described in one word as “opacification.” Resa Schirnhofer, a friend of Nietzsche, visited him in 1884 during this abrupt change in vision, and later wrote that he saw fantastic colors after closing his eyes—colorful flowers that flowed and changed in brilliant displays of movement. Hallucinations result from many potential causes, such as schizophrenia, temporary psychosis, and Parkinson’s disease, but the relationship between vision loss and hallucinatory experiences in Nietzsche suggests Charles Bonnet syndrome (CBS). CBS is characterized by vivid, elaborate, and recurrent visual hallucinations in the absence of external stimuli in individuals with preserved intellectual functioning. Associated with anomalies at any juncture of the visual system, the condition can be episodic, periodic, or chronic. Macular degeneration and stroke-induced lesion in the visual system accounts for approximately 75–85% of CBS cases. Headaches As early as 11 years old, Nietzsche missed school due to headaches and eye pain. His letters suggest that he began to suffer from increasingly frequent and intense headache attacks throughout his 20s and 30s. Sometime between 1870 and 1871 he began to suffer from migrainous attacks. The situation became progressively worse over the next decade. He complains in a letter to his colleague and friend Carl von Gersdorff, who Nietzsche had befriended at Schulpforta, “I’ve been through a very bad time, and there may be an even worse one to come. My stomach could no longer be tamed, even with an absurdly strict diet. Chronic headaches of the fiercest sort, which lasted for days. Vomiting on an empty stomach, for hours on end. In short the machine seemed to want to disintegrate, and I won’t deny having wished several times that it would do just that. Great fatigue, difficulty getting about, hypersensitivity to light . . .” (Nietzsche 1971). Writing to Dr. Otto Eiser in January of 1880 he

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asserts, “And yet!—constant pain, a feeling much like seasickness several hours each day, a semi-paralysis which makes speaking difficult and, for a change of pace, furious seizures (the last involved three days and nights of vomiting; I lusted for death)” (Nietzsche 1971). Nietzsche registered several important somatic complaints that are consistent with a diagnosis of migraine with aura (MA). His MA attacks involved fully reversible homonymous visual symptoms, unilateral sensory loss, and dysphasic speech. His headaches were prolonged and accompanied by photosensitivity, emesis, gastrointestinal pain, and fatigue. Medical evidence suggests that individuals more susceptible to triggers are likely to have headaches with a longer history of duration, more premonitory symptoms, throbbing, pressure, stabbing, nausea, photophobia, rhinorrhea, tearing of eyes, and higher headache frequency. These individuals are likely to choose rest during headaches and suffer from anxiety, depression, mood swings, and general pain. Nietzsche’s MA attacks match well with this picture. His MAs lessened in the 1880s but returned suddenly in the summer 1888. Mood Disorders Nietzsche’s correspondence also supports a diagnosis of major depression during the 1870s and early 1880s with an emergent hypomania in the early to mid-1880s. Just prior to the expression of subcortical dementia at the end of 1888 and beginning of 1889, Nietzsche’s behavior shifted from hypomanic to manic and was accompanied by a psychotic break from reality. In the early 1880s, Nietzsche reached the nadir of his depression. His writings were not being acknowledged for their brilliance, plans of love and marriage with the young Russian Lou Salomé ended abruptly, and he distanced himself from his mother and sister. In August of 1883 he wrote, “I am now working like a man who is ‘putting his house in order before departing.’” Nietzsche’s intense depression began to alternate with a mood of euphoria. Beginning in the 1870s, Nietzsche’s struggle with melancholic moods and suicidal ideation became apparent to his closest friends and colleagues. In 1877 Nietzsche writes to Malwida von Meysenbug, a close friend he met through his prior friendship with Richard Wagner, “on the ship I had only the blackest thoughts, my only doubts about suicide concerned where the sea might be deepest, so that one would not be immediately fished out again and have to pay a debt of gratitude to one’s rescuers in a terrible mass of gold—sometimes such a feeling of emptiness comes over me that I want to scream” (Nietzsche 1996).

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In March 1883 he writes, “My dear friend—I’ve lost interest in everything. Deep down, an unyielding black melancholy. And weariness too. Most of the time I’m in bed. I’ve suffered too much and sacrificed too much; I feel so incomplete, so inexpressibly conscious of having bungled and botched my whole creative life. It’s all hopeless. I won’t do anything worthwhile again. Why do anything anymore!” (Nietzsche 1971). Just a year later he writes an ecstatic letter to Peter Gast asserting, Thoughts have emerged on my horizon the likes of which I’ve never seen—I won’t even hint at what they are, but shall maintain my own unshakeable calm. I suppose now I’ll have to live a few years longer! Ah, my friend, I sometimes think that I lead a highly dangerous life, since I’m one of those machines that can burst apart. The intensity of my feelings makes me shudder and laugh. Several times I have been unable to leave my room, for the ridiculous reason that my eyes were inflamed. Why? Because I’d cried too much on my wanderings the day before. Not sentimental tears, mind you, but tears of joy, to the accompaniment of which I sang and talked nonsense, filled with a new vision far superior to that of other men. (Nietzsche, 1971) In an explosion of ten days of racing thoughts and flight of ideas during the winter of 1882, Nietzsche produced Book One of Thus Spake Zarathustra. Just months later, he wrote Book Two over another ten-day stretch of prolonged creativity (Nietzsche 1971). While the evidence from his letters first suggests a diagnosis of major depression, as time elapsed his depressive episodes gave rise to cycling moods consistent with bipolar. For a relatively isolated individual, the pressured speech and loose concatenations of thought were expressed in written form, known as hypergraphia. Nietzsche’s Health from 1888 to 1900 In late December 1888 Nietzsche experienced a profound change in mental status. In early January 1889 he allegedly collapsed while in public. Nietzsche was brought to Dr. Ludwig Wille’s psychiatric clinic near the Alsatian border for three days. He was an unruly patient, bursting into song or scream at any moment and demonstrating constant motor agitation. His gait was stumbling and not steady, and he seemed stiff at times. Next, Nietzsche was moved to Basel, where the patient records state, “Pupillary disparity, right larger than left, reaction sluggish. Convergent strabismus-acute myopia. Tongue heavily furred, no deviation, no tremor! Facial nerve almost normal; right nasolabial fold slightly contracted. Exaggerated patellar reflex; plantar reflexes normal” (Volz 1990).

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Within a few days Nietzsche was moved to a clinic in Jena that was managed by a well-known physician, Dr. Otto Binswanger (who first described Binswanger ’s disease, coined by Alois Alzheimer). Both Wille and Binswanger diagnosed Nietzsche with paralytica progressiva due to syphilis. With this assessment, both doctors assumed Nietzsche only had two years to live. Dr. Binswanger ’s medical notes on Nietzsche give, Pupils right wide, left rather narrower, left contracted with slight irregularity, all reactions normal on left, on right only reaction to convergence, consensual reactions only on left . . . symmetrical smile, tongue non-tremulous with deviations to right . . . Romberg negative . . . screws left shoulder up spasmodically when walking . . . slight ankle clonus on left . . . head percussion not sensitive, facial nerves sensitive. After several weeks, Nietzsche seemed to improve with no further deterioration. His mother surreptitiously removed him from the clinic to bring him home to care for him full-time. By 1893 he was completely bedridden and retained no memories of his life as a writer. During 1898 and 1899, Nietzsche suffered at least two more strokes that left him unable to speak or walk. On August 24, 1900, Nietzsche died either from another stroke or a pneumonia-like infection. Nietzsche was buried alongside his father ’s grave next to the parsonage in Röcken, Germany. DIAGNOSIS AND DISCUSSION Table 3.1 displays a historical list of diagnoses in the literature posited to explain Nietzsche’s condition. Following the emergence of new diagnostic categories since 1889, novel diagnostic possibilities now exist to explain Nietzsche’s constellation of symptoms. Nietzsche’s persistent medical issues are explainable with one unifying, genetically testable diagnosis: cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). The diagnosis of CADASIL explains Friedrich’s, Karl Ludwig’s, and Friedrich August Nietzsche’s condition. In this section I present our diagnosis and consider competing ideas. CADASIL is likely the cause of Nietzsche’s illness. It takes all of his relevant findings into account: retinal abnormalities, migraine with aura, mood disorders, early onset history of stroke-like episodes, pseudobulbar palsy, dementia, and three generations of paternal family history. A positive genetic test of Nietzsche’s DNA for a NOTCH3 gene mutation would be diagnostic.

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Table 3.1 List of Previous Diagnostic Attempts to Explain Nietzsche’s Illness Diagnosis

Source

Paresis paralytica (neurosyphilis)

Binswanger 1889; Mobius 1902; Lange-Eichbaum 1930 Hildebrandt 1926 Cybulska 2000 Schain 2001 Sax 2003 Orth and Trimble 2006 Owen, Schaller, and Binder 2007

Slow-growing benign brain tumor Bipolar disease and multi-infarct dementia Schizophrenia Meningioma of right optic nerve Frontotemporal dementia (FTD) Meningioma of right medial sphenoid wing Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL)

Butler (this chapter), Hemelsoet, Hemelsoet, and Devreese 2008

CADASIL is a genetic mutation in the NOTCH3 protein on chromosome 19p13.2-p13.1. This autosomal dominant condition leads to microangiopathy of the brain. The presentation of CADASIL is variable even among family members. CADASIL is suspected when stroke-like episodes occur before the age of 60, accompanied by MA, executive dysfunction, or behavioral abnormalities. TIAs (transient ischemic attacks) occur in 85% of symptomatic individuals with the average age of onset at 46 (range 19–67 years). Ischemic episodes are recurrent, leading to severe disability usually including gait disturbance, urinary incontinence, and pseudobulbar palsy. Eighty-five percent of patients develop cognitive dysfunction and eventual dementia. These symptoms fit Nietzsche’s disease progression. He presented with gait disturbance and pseudobulbar palsy in 1889, slowly progressing over the course of years to a demented, bedridden, and paralyzed state. Migraine occurs in about 40% of CADASIL patients with the first attack occurring at a mean age of 26 years. Of the CADASIL patients with migraine 90% have MA. In some families with CADASIL, MA is the most prominent symptom. Again, Nietzsche’s presentation clinically matches. His correspondences evince the development of MA by his mid- to late 20s. Approximately 30% of individuals with CADASIL develop psychiatric disturbances, with depression, bipolar, and personality changes being most common. Undoubtedly Nietzsche struggled with major depression and mood swings that suggest bipolar disorder. Full-blown mania with delusions of grandeur afflicted him by the end of 1888.

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Epilepsy is less common (~10% of CADASIL patients) and tends to develop in middle age. Although Nietzsche mentioned having a seizure in a letter cited earlier from January 1880, it is hard to interpret exactly what Nietzsche meant by his limited use of that term. However, seizures clearly afflicted Nietzsche’s father. Most likely, Karl’s episodes of loss of consciousness were either seizure-induced or TIA-induced syncope. Both seizure and TIAs are common in CADASIL, so differentiating between them is unimportant for diagnosis in this case. Nietzsche’s family history supports the finding that CADASIL’s clinical manifestations are variable even among relatives. Most recently, retinal abnormalities have been linked to CADASIL. Retinal vascular abnormalities, inflammation, and vision loss have all been implicated. Nietzsche’s right-sided retinal exudates, vision loss, and alleged eye movement abnormalities were likely expressions of CADASIL. Recent research findings suggest that CADASIL can lead to diminished optic nerve fiber layers, retinal vasculopathies, retinal inflammation, visual field loss, eye movement abnormalities, and visual-spatial defects. By reasonable assumption, Nietzsche has a positive family history. His father suffered from numerous stroke-like episodes before the age of 60, struggled with depression, and developed pseudobulbar affect and potential palsy. There is evidence of seizures, hemi-facial paralysis, dysarthria, cognitive decline, dementia, and eventual death from stroke. CADASIL explains this array of seemingly disparate symptoms. Although less is known about him, Karl’s father was afflicted by a similar array of symptoms. This pattern fits the autosomal dominance inheritance pattern of CADASIL. Genetics studies have traced original mutations back to the 1600s in some Northern European communities. It is therefore reasonable to assume the existence of CADASIL mutations in Nietzsche’s patrilineage based on historical, demographic, and founder effect studies. CADASIL as cause for Nietzsche’s illness is testable. Because the common NOTCH3 mutations are well established and currently tested for genetically, it is possible to obtain nuclear DNA from Nietzsche, amplify the gene of interest via polymerase chain reaction techniques, and test for CADASIL with 90% sensitivity. The author is actively pursuing this research goal. DNA can be extracted from minute salivary samples sealed in time between envelope folds or stamps adhered by Nietzsche during his lifetime. Theoretically, preserved DNA samples from envelopes sealed by Nietzsche’s saliva could be amplified in the region of the NOTCH3 gene and tested for CADASIL. This would provide incontrovertible evidence for this diagnosis. Concurrent to the preparation of this publication, an independent research effort by Hemelsoet, Hemelsoet, and Devreese (2008) also

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suggests CADASIL as a potential diagnosis for Nietzsche. Herein, we extend the confidence in the diagnosis of CADASIL by including evidence for three successive generations of disease, Nietzsche’s history of migraine with aura, psychiatric disturbance, fits, and retinal abnormalities. Without evidence to extend the paternal history of disease to three generations, CADASIL becomes less likely. With a two-generation model, the recessive form of CADASIL, known as CARASIL (Maeda’s syndrome), must remain on the differential diagnosis in addition to other more common nongenetic sources of multi-infarct dementia, such as Binswanger ’s disease (ironic to Nietzsche’s case) or cerebrovascular disease. Our evidence of three successive paternal generations with similar symptomology greatly increases confidence in the diagnosis of CADASIL (Volz 1990). Further, I suggest clear evidence that Nietzsche suffered from migraine with aura and not migraine without aura as argued by Hemelsoet et al. Migraine with aura and not migraine without aura is clinical evidence of CADASIL (Oberstein, Boon, and Dichgins 2006.) Further, we explain Nietzsche’s retinal abnormalities as a manifestation of CADASIL pathology (Parisi et al. 2007; Robinson 2001; Warner 2004). His blindness and subsequent Charles Bonnet syndrome also fit with our diagnosis. The neurosyphilis hypothesis has repeatedly been questioned even at the time of Nietzsche’s diagnosis. Sax (2003) summarized the key weaknesses in the syphilis hypothesis: the lack of documentation of syphilitic infection, Nietzsche’s prolonged life after his 1889 collapse, the laterality of his symptoms, lack of tremulous tongue, and the extended history of his headaches. Also, the alleged Argyll-Robertson pupils noted in Nietzsche’s medical notes from Binswanger were due to congenital anisocoria, which was a condition unknown to the physicians at the Jena clinic. Owen, Schaller, and Binder (2007) suggest that Nietzsche had a slowgrowing medial sphenoid meningioma. This is plausible because intracranial mass lesions can cause lateral visual symptoms, headaches, cranial nerve dysfunction, psychiatric disturbances, and dementia. Several ideas argue against a brain mass as cause for all of Nietzsche’s illnesses. There is a significant female-to-male predominance in medial sphenoid meningiomas, headaches are rare and tend to be dull and brief, tumors typically emerge in the sixth and seventh decades of life, and common predisposing factors include family history, focal trauma, and radiation exposure—none of which apply to Nietzsche (Demchuk and Forsyth 1997; Zachariah 2008). Cybulska (2000) suggests Nietzsche suffered from bipolar disorder followed by multi-infarct dementia. Cybulska’s diagnosis lacks definitive testability and cannot explain the retinal findings, family history,

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and cranial nerve findings. Likewise, several other diagnostic possibilities, such as schizophrenia, frontotemporal dementia (FTD), and mitochondrial myopathy–encephalopathy–lactic acidosis–stroke syndrome (MELAS) fail to make sense of the paternal family history and Nietzsche’s seemingly disparate illnesses. MELAS is worth consideration because its heterogeneous manifestations potentially explain Nietzsche’s retinal disturbances, headaches, vomiting, psychiatric disturbance, and stroke-like episodes. However, Nietzsche did not have a maternal history indicative of MELAS. With the exception of congenital anisocoria, records suggest Franziska was healthy throughout her life; she died from cancer at the age of 71, her mother lived to 82, and her daughter (Elisabeth, Nietzsche’s sister) lived to 89.

CONCLUSION Nietzsche has a positive three-generation family history—his father suffered from numerous stroke-like episodes before the age of 60, struggled with depression, and developed pseudobulbar affect and potential palsy. There is evidence of seizures, hemi-facial paralysis, dysarthria, cognitive decline, dementia, and eventual death from stroke (“softening of the brain”). CADASIL explains this array of seemingly disparate symptoms. Although less is known, Karl’s father was afflicted by a similar array of symptoms. This pattern fits the autosomal dominant inheritance pattern of CADASIL (Rufa et al. 2007). Genetics studies have traced original mutations back to the 1600s in some Northern European communities. It is therefore reasonable to assume the existence of CADASIL mutations in Nietzsche’s patrilineage based on historical, demographic, and founder effect studies (Mykkanen et al. 2004). CADASIL as cause for Nietzsche’s illness is testable. Because the common NOTCH3 mutations are well established, it is possible to obtain Nietzsche’s nuclear DNA from historical samples, amplify the region of interest (19p13.1–19p13.2) via PCR techniques, and test for CADASIL. The author is actively pursuing this research goal: DNA can be extracted from minute salivary samples sealed in time between envelope folds or stamps adhered by Nietzsche during his lifetime. The best analytical tools at the time of Binswanger ’s diagnosis pointed to syphilitic paresis of the insane. Present-day reason leads us to believe Nietzsche (and his paternal lineage) suffered from a rare genetic mutation in the NOTCH3 gene. Neurologists of the future will likely comprehend the diagnostic complexities with conceptual categories yet-to-be revealed.

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POSTSCRIPT: THE INFLUENCE OF CADASIL ON NIETZSCHE’S LIFE AND THOUGHT This research is important for understanding Nietzsche’s biography, and potentially for the interpretation of his life’s work. If Nietzsche was indeed afflicted by CADASIL, understanding the course and pathophysiology of this condition gives reason to Nietzsche’s extreme suffering with one unifying diagnosis. Only Nietzsche fully comprehended the depth of his suffering, how powerfully it shaped his life and thought. To illustrate the effect CADASIL had on Nietzsche’s life and thought, I will first consider Nietzsche’s supposed stylistic preference for pithy and profound statements in the form of aphorisms. Due to CADASIL-induced retinopathy, migraines with aura, and epileptic fits, Nietzsche’s vision continually degraded with temporary blindness occurring during some of his worst attacks. These attacks occurred from several hours to days. His struggle intensified throughout the 1870s until he was forced to resign his professorship in 1879. During these fits, friends or family would care for him, reading aloud to him or recording his dictations as Nietzsche lay still in a dimly lit room. Elisabeth, Nietzsche’s sister, recalled him saying he would have been a bookworm if it were not for his fits, migrainous attacks, and ill health (Volz 1990). Nietzsche was often allowed brief windows of time to collect his own thoughts without the invasion of ill health. This likely shaped his stylistic preference in part—driving him to master the form of aphorism. Nietzsche filled countless notebooks with aphoristic insights from early adulthood until the full development of subcortical dementia in 1889. These aphoristic notes were the basis for many of his publications, such as Beyond Good and Evil, The Antichrist, and Genealogy of Morals. In The Antichrist, Nietzsche writes, “it is my ambition to say in ten sentences what everyone else says in a book.” Nietzsche became a dedicated master of the aphorism, in part because of the impact of CADASIL during his productive life. After resigning from professorship at the University of Basel in 1879, Nietzsche spent the 10 years prior to his collapse moving seasonally from Northern Italy, Austria, France, Germany, and Switzerland. Nietzsche’s personal library contained books on weather patterns, which he likely consulted, as he sought the perfect climate to attenuate the various triggers to his intense migraine attacks. Nietzsche accompanied this itinerant lifestyle with an ascetic diet—he refrained from consuming tea, alcohol, and tobacco, for instance. These behaviors were built around Nietzsche’s trialand-error approach to control all of the factors that triggered his CADASIL-associated attacks, and led to a largely isolated life, which Nietzsche

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intermittently filled with periods of brilliant literary productivity. One of Nietzsche’s colleagues, Paul Deussen, commented on Nietzsche’s eccentric behavior and lifestyle (due to negotiating his illnesses) after visiting the demented Nietzsche in the April 1889, writing, No one can say to what extent the seeds of insanity were already present as a disposition in this highly talented mind. But if Nietzsche had not diligently separated himself from human society, in which he occupied such an honorable position, if he had kept his position, established a family, and allowed the fruits of his mind to mature slowly, instead of pursuing his thoughts in solitude with ascetic over-exertion of his energies on tiring walks during the day and at night compelling elusive sleep by stronger and stronger narcotics— who knows whether he might not still be living with us in full health and be able to offer us, instead of the torso of his posthumous works, the perfected divine image of an eccentric but highly noteworthy worldview. (Deussen 1890/1922) The pain Nietzsche endured due to CADASIL strongly shaped him as a thinker, an artist, and a philosopher. From a young age Nietzsche demonstrated musical talent. He composed pieces and played improvisational piano with considerable skill. One of the titles of his compositions from 1861 arranged when he was only 17 years old was entitled Schmerz ist der Grundton der Natur or translated “Pain is the elemental tone of Nature.” Lou Salomé, an alleged romantic and intellectual interest of Nietzsche’s, later wrote that he was “a sadomasochist toward himself” attempting to find some contorted version of pleasure in the psychic and physiologic pain he was forced to endure (Volz 1990). In 1879 following the completion of The Wanderer and His Shadow, Nietzsche wrote in a letter, “The completed Wanderer is to me something almost unbelievable . . . the entire “humanity” with the 2 supplements is from a time of the most bitter and continual pains—and yet seems to me to be a thing full of health. This is my triumph.” Nietzsche firmly believed that “everything deep loves a mask.” Nietzsche continually layered meanings into his writings that interleaved his own personal experiences within depth psychological insight, aesthetics, and philosophical conjecture. Nietzsche’s CADASIL-induced Charles Bonnet Syndrome experience echoes in his section of the Logic of the Dream. In Human, All Too Human, he wrote, “If we close our eyes, the brain produces a host of light-impressions and colours” and likely in reference to his life’s pain he later wrote in Twilight of the Idols, “increscunt

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animi, virescit volnere virtus” (“the spirit grows, strength is restored by wounding”) (Nietzsche 2003, 2006). Summarizing the profound physical suffering and mental anguish he endured, Nietzsche proffered, in combating my sick conditions I always instinctively chose the right means. . . . I took myself in hand, I myself made myself healthy again. . . . I made out of my will to health, to life, my philosophy. . . . For pay heed to this: it was in the years of my lowest vitality that I ceased to be a pessimist: the instinct for self-recovery forbade me to a philosophy of indigence and discouragement. (Nietzsche 1989) Nietzsche wrote those words just months before the onset of his subcortical dementia. Four years earlier he asserted in Beyond Good and Evil (1955): “I have come to realize what every great philosophy up to now has been: the personal confession of its originator, a type of involuntary and unaware memoir.” Still earlier, in an unpublished notebook from 1873, he wrote, “For what purpose humanity is there should not even concern us: why you are there, that you should ask yourself: and if you have no ready answer, then set for yourself goals, high and noble goals, and perish in pursuit of them! I know of no better life purpose than to perish in attempting the great and the impossible” (Nietzsche 2009). As many opinions exist as the number of profound thinkers who have attempted to interpret Nietzsche’s philosophical teaching (e.g., Heidegger 1961/1984; Lampert 1993; Rosen 1995). If Nietzsche did in fact suffer from CADASIL, this does not adjudicate among the many laudable attempts to delineate Nietzsche’s philosophical thought. I merely suggest that the pathophysiology of CADASIL profoundly shaped the person of Nietzsche, his life events, thought, and philosophy. Nietzsche built many of his philosophical teachings, such as will to power, eternal return of the same, and the idea of the übermensch, around his personal experience with physical and mental pain. Nietzsche proffered, A philosopher who has traversed many kinds of health, and keeps traversing them, has passed through an equal number of philosophies; he simply cannot keep from transposing his states every time into the most spiritual form and distance: this art of transfiguration is philosophy. (Nietzsche 1974) ACKNOWLEDGMENT: Special thanks go to Dorothe Poggel, PhD for help with translation of original German medical records. Also, I thank Peter

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Bergethon, MD for help contributing commentary and constructive criticism for the manuscript. Special thanks also go to Patrick McNamara, PhD and Benjamin Wolozin, PhD, MD for early support of this project. REFERENCES Cybulska, E. M. 2000. The madness of Nietzsche: A misdiagnosis of the millennium? Journal of Hospital Medicine 61 (8): 571–575. Demchuk, A., and P. Forsyth. 1997. Headache in the cancer patient. In Handbook of Clinical Neurology, ed. P. K. Vinken, G. W. Bruyn, and C. J. Vecht, 25 (69): Neuro-Oncology, pt. 3, 241–266. Deussen, Paul. 1890/1922. Erinnerungen an Friedrich Nietzsche. Leipzig: Brockhaus. As cited in Conversations with Nietzsche: A Life in the Words of His Contemporaries, ed. Sander L. Gilman, trans. David J. Parent. New York: Oxford University Press. Frenzel, Ivan. 1967. Friedrich Nietzsche: An Illustrated Biography. New York: Pegasus. Hegel, Georg Wilhelm Friedrich. 1821/1991. Elements of the Philosophy of Right. Edited by Allen W. Wood, translated by H. B. Nisbet. Cambridge: Cambridge University Press. Hegel, Georg Wilhelm Friedrich. 1837/1997. Reason in History. Translated by Robert S. Hartman. Upper Saddle River, NJ: Prentice-Hall. Heidegger, Martin. 1961/1984. Nietzsche: Volumes 1 and 2, The Will to Power as Art and The Eternal Recurrence of the Same. Translated by David Farrell Krell. San Francisco: HarperCollins. Hemelsoet, D., K. Hemelsoet, and D. Devreese. 2008. The neurological illness of Friedrich Nietzsche. Acta Neurologica Belgica 108: 9–16. Hildebrandt, K. 1926. Gesundheit und krankheit in Nietzsches leben und werk. Berlin: Karger. International Headache Society, Headache Classification Subcommittee. 2004. The international classification of headache disorders. Cephalalgia, 24. Koszka, C. 2009. Friedrich Nietzsche (1844–1900): A classical case of mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome? Journal of Medical Biography 17: 161–164. Lampert, L. 1993. Nietzsche and Modern Times: A Study of Bacon, Descartes, and Nietzsche. New Haven, CT: Yale University Press. Lange-Eichbaum, W. 1930. Nietzsche als psychiatrisches problem. Deutsche Medizinische Wochenschrift, 1538. Mobius, P. J. 1902. Ueber das pathologische bei Nietzsche. Wiesbaden: J. F. Bergmann. Mykkanen, K., M. L. Savontaus, V. Juvonen, et al. 2004. Detection of the founder affect in Finnish CADASIL families. Journal of European Human Genetics 12: 813–819. Nietzsche, Friedrich. 1955. Beyond Good and Evil. Translated by Marianne Cowan. Chicago: Henry Regnary.

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Nietzsche, Friedrich. 1971. Nietzsche: A Self-portrait from His Letters. Edited and translated by Peter Fuss and Henry Shapiro. Cambridge: Harvard University Press. Nietzsche, Friedrich. 1974. The Gay Science: With a Prelude in Rhymes and an Appendix of Songs. Translated by Walter Kaufmann. New York: Vintage Books. Nietzsche, Friedrich. 1985. Selected Letters: Nietzsche. Translated by A. N. Ludovici and edited by O. Levy. London: Soho Book Company. Nietzsche, Friedrich. 1989. On the Genealogy of Morals and Ecce Homo. Translated by Walter Kaufmann and R. J. Hollingdale. New York: Vintage Books. Nietzsche, Friedrich. 1996. Selected Letters of Friedrich Nietzsche. Edited and translated by Christopher Middleton. Chicago: Hackett Publishing. Nietzsche, Friedrich. 2003. Twilight of the Idols and the Antichrist. Translated by R. J. Hollingdale. New York: Penguin Books. Nietzsche, Friedrich. 2006. Human, All Too Human: A Book for Free Spirits. Translated by R. J. Hollingdale. New York: Cambridge University Press. Nietzsche, Friedrich. 2009. Writings from the Early Notebooks. Edited by Raymond Geuss and Alexander Nehamas. New York: Cambridge University Press. Oberstein, S. L., E. Boon, and M. Dichgans. 2006. CADASIL: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. http://www.nih.gov/genetics/CADASIL (accessed November 1, 2009). Orth, M., and M. R. Trimble. 2006. Friedrich Nietzsche’s mental illness—general paralysis of the insane vs. frontotemporal dementia. Acta Psychiatrica Scandanavia, 439–445. Owen, C., C. Schaller, and D. K. Binder. 2007. The madness of Dionysus: A neurosurgical perspective on Friedrich Nietzsche. Journal of Neurosurgery Online 61 (3): 626–632. Paciaroni, M., and J. Bogousslavsky. 2009. How did stroke become of interest to neurologists? Neurology 73: 724–728. Parisi, V., F. Pierelli, G. Coppola, et al. 2007. Reduction of optic nerve fiber layer thickness in CADASIL. European Journal of Neurology 14 (6): 627–631. Robinson, W. 2001. Retinal findings in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Survey of Ophtalmology 45 (5): 445–448. Rosen, S. 2004. The Mask of Enlightenment: Nietzsche’s Zarathustra. New Haven, CT: Yale University Press. Rufa, A., F. Guideri, M. Acampa, et al. 2007. Cardiac autonomic nervous system and risk of arrhythmias in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Stroke 38: 276–280. Sax, L. 2003. What was the cause of Nietzsche’s dementia? Journal of Medical Biography 11: 47–54. Schain, R. 2001. The Legend of Nietzsche’s Syphilis. Westport, CT: Greenwood. Volz, P. 1990. Nietzsche im labyrinth seiner krankeit: Eine medizininische-biographische untersuchung. Würburg: Königshausen and Neumann.

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Vukicevic, M., and K. Fitzmaurice. 2008. Butterflies and black lacy patterns: The prevalence and characteristics of Charles Bonnet hallucinations in an Australian population. Journal of Clinical and Experimental Ophthalmology 36: 659–665. Warner, J. 2004. Vasculopathies affecting the eye. Journal of Neuro-Ophthalmology 24: 164–169. Zachariah, S. 2008. Meningioma, sphenoid wing. http://www.emedicine.com (accessed March 1, 2008).

Chapter 4

Promising Strategies for Preventing Dementia Laura E. Middleton

Age is the greatest risk factor for dementia, with the prevalence of dementia nearly doubling with every five years of age. The oldest-old, which generally refers to people 85 years of age and older, are the fastest growing demographic in the United States. Increasing longevity over the coming decades is expected to cause a dramatic increase in the prevalence of dementia. The resources required to care for people with dementia will rise along with the prevalence. Healthcare systems are largely unprepared for the expected rise in prevalence and for the complex care many people with dementia require. People with severe dementia depend on caregivers or medical staff to complete basic activities of daily living such as eating, bathing, and toileting. Co-morbid illnesses are common in people with dementia and require concurrent treatment; however, pharmaceutical treatments can exacerbate cognitive impairment, especially if multiple medications are taken concurrently. Outside of health care, significant demands are placed on the caregivers of people with dementia, often spouses. Caregivers frequently have lost productivity and increased absenteeism (Alzheimer ’s Association 2008). Furthermore, caregivers are at increased risk for adverse health outcomes such as depressive and anxiety disorders (Schulz and Martire 2004). Dementia does not appear to be an inevitable part of aging. Some people do not develop dementia despite extreme old age and even in the presence of neuropathic features normally associated with dementia. As a

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result, increasing attention is been paid to identifying successful prevention strategies. It is important to note that prevention may not be “all or none.” Prevention may translate into less severe symptoms or delayed onset of disease. However, if the onset of Alzheimer ’s disease (the most common form of dementia) can be delayed by five years, the expected prevalence would decrease by 1 million cases after 10 years and more than 4 million cases after 50 years in the United States (Brookmeyer, Gray, and Kawas 1998). Current pharmaceutical treatment for dementia can only modestly improve symptoms and cannot cure or prevent dementia. As a result, prevention of dementia through identification and modification of risk factors is critical. Researchers have identified many risk and protective factors through observational studies. Clinical trials confirming the relationship are often still preliminary. In this chapter, we will discuss some of the most promising strategies for the prevention of dementia, including cognitive activity, physical activity, social engagement, diet, and vascular risk-factor control.

PREVENTION STRATEGIES Cognitive Activity People who engage in higher levels of cognitive activity appear to have lower risk of dementia than those who participate in less. Aside from age, education is arguably the most established risk factor for dementia. People who are more educated have lower rates of Alzheimer ’s disease and allcause dementia than those with less education (Stern 2009). High levels of education are also associated with slower cognitive decline during normal aging (Albert et al. 1995; Colsher and Wallace 1991; Snowdon, Ostwald, and Kane 1989). Interestingly, people who are more highly educated may have faster cognitive decline after the onset of Alzheimer ’s disease than those who are less educated, though not all studies agree (Fritsch et al. 2002; Stern 2009). Some researchers suggest that this occurs because people with more education can withstand greater neuropathic load before they show symptoms of Alzheimer ’s disease. There is some evidence to support this theory. One study reported that people who were more educated had greater neuropathic loads before presenting symptoms of dementia compared to those who were less educated (Bennett et al. 2003). This ability to withstand neuropathic load is referred to as “cognitive reserve”; occupational attainment and leisure activity are also thought to contribute to cognitive reserve (Stern 2009).

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Research has emerged to suggest that cognitive activity, more generally, is also associated with reduced risk of cognitive decline and dementia (Stern 2009). Several prospective observational studies indicate that people who engage in mentally stimulating activities—such as learning, reading, or playing games—at younger ages (Carlson, Helms, et al. 2008) or older ages (Fratiglioni and Wang 2007) are less likely to develop dementia compared to those people who do not engage in these activities. Moreover, interventional trials have demonstrated that cognitive training can improve cognitive performance in older adults regardless of baseline cognitive status (normal cognition; mild cognitive impairment, MCI; or dementia). In the ACTIVE trial, a large clinical trial of 2802 elderly people, training in memory, reasoning, and speed of processing were associated with improvements in cognitive performance equivalent to a 7- to 14-year reduction of normal aging effects (Ball et al. 2002). However, the benefits of cognitive training in this study and others appeared to be specific to the domain trained. Cognitive training does not appear to generalize across domains or improve daily functioning (Acevedo and Loewenstein 2007; Ball et al. 2002). Furthermore, there is some evidence to suggest that older people with memory impairment may be less able to make gains from memory training than those without impairment (Unverzagt et al. 2007). However, people with memory impairment appear to be equally able to make gains in reasoning and reaction time with training. Although the role of cognitive training in people with dementia is unclear, cognitive activity appears to be a promising strategy to improve cognition in old age—and may thereby prevent or reduce the risk of dementia. However, because interventions to date show little benefit to daily function, future trials should investigate whether adapted multidomain interventions, designed to mimic daily life, might be effective in improving global cognition and daily functioning. Simple interventions that include mental activities such as playing games or learning a new skill, which are associated with reduced rates of dementia and cognitive decline in observational studies (Wilson et al. 2002), might be effective in interventions. Trials should also investigate whether cognitive interventions might prevent the onset of dementia by including a long follow-up period. Physical Activity The evidence for physical activity as a potentially protective factor against the risk of dementia has expanded greatly over the last decade. Studies using a variety of ages, definitions of exercise, and countries have concluded that people who are more physically active have a lower risk

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of dementia. Specifically, physically active people may have a lower incidence of Alzheimer ’s disease and vascular dementia, though the association is more consistent for the former (Ravaglia et al. 2008; Rockwood and Middleton 2007). The positive relationship between physical activity and the risk of dementia seems to hold true for physical activity both at older and younger ages. Most observational studies have examined physical activity in older populations (at least 65 years) and have had only a short follow-up time (approximately 5 years). Nearly all conclude that people who are physically active at older ages have 10–45% less risk of dementia at follow up than those who are inactive (Rockwood and Middleton 2007). A number of studies have investigated the association between midlife physical activity and late life cognitive impairment. People who are more physically active at mid-life seem to have a lower incidence of both Alzheimer ’s disease and all-cause dementia in late life, especially if the physical activity is performed during leisure time (Rockwood and Middleton 2007; Rovio et al. 2005, 2007). People who are active at mid-life also have lower risk of MCI in late life than those who are inactive (Geda et al. 2010). Few studies have examined the relationship between physical activity in early life and cognition in old age. However, it appears that people who are physically active in early life also have better cognition in old age. Two studies indicated that people who were active in early life (teens to 30s) had better information processing speed and slower memory decline in later life (Dik et al. 2003; Richards, Hardy, and Wadsworth 2003). In another study, people who were physically active at teen age had lower risk of cognitive impairment in late life. Interestingly, physical activity status at teen age was more strongly related to reduced likelihood of cognitive impairment in late life than physical activity status at age 30, age 50, or in late life in this study (Middleton et al. 2010). It is reasonable to suggest that a longer duration of exercise is better than shorter, even though the benefits of exercise can be realized at any point in the life span. Age should not be a contra-indication to taking up an exercise program, other things being equal. Significantly, physical activity is associated with augmented rates of stable or improved cognition and reduced rates of cognitive decline in people of all cognitive abilities (Lytle et al. 2004; Middleton et al. 2009; Weuve et al. 2004). Regardless of cognitive status, those people who are physically active seem to have better cognitive function and slower cognitive decline than those who are sedentary. However, it is unclear from observational studies whether physical activity improves cognition, delays impairment, or whether, in some cases, it prevents cognitive impairment entirely.

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Interventional studies have confirmed that even short periods of exercise training can improve cognitive performance. A meta-analysis concluded that people who were not previously physically active showed improvements in cognitive functioning after exercising for as little as four months (Angevaren et al. 2008). Exercise interventions may also reduce the rate of cognitive decline in people with cognitive impairment (Lautenschlager et al. 2008). Executive function appears to be the cognitive domain most benefited by exercise (Colcombe and Kramer 2003). The mechanisms by which physical activity affects cognition are likely complex and multifactorial. People who exercise have higher levels of brain-derived neurotrophic factors, which are implicated in neuroplasticity and neurological repair. Physical activity also reduces vascular risk; vascular risk factors are, as discussed later, associated not only with increased risk of vascular dementia but also of Alzheimer ’s disease. In addition, rats with high levels of voluntary physical activity also have less ␤-amyloid plaque formation, a hallmark of Alzheimer ’s disease (Dishman et al. 2006; Ott et al. 1999). Despite the promising results from controlled trials to date, the trials of exercise interventions in relation to cognition have generally been low to moderate in both size and quality (Angevaren et al. 2008). Larger trials are needed to definitively determine the role of physical activity in the maintenance of cognitive performance and the prevention of dementia in old age. Such trials are underway. For example, the Lifestyle Interventions and Independence for Elders (LIFE) Study will begin in 2010 and will randomize 1600 people to either exercise or control groups and will follow them for an average of 2.7 years. The LIFE Study includes cognitive function as a secondary outcome. While we wait for results from ongoing trials, however, physical activity can be carefully recommended—if not for cognitive impairment, then for other health outcomes strongly linked to physical activity such as cardiovascular disease and some types of cancer (Warburton, Nicol, and Bredin 2006). Social Engagement Higher social engagement, measured in a variety of manners, appears to be associated with reduced risk of dementia. People who have an extensive social network have lower likelihood of dementia than those with few social connections (Seidler et al. 2003). Participation in socially engaging leisure activities—such as visits with friends and relatives, going to movies, clubs, centers, and church/synagogues, and volunteering—is also associated with reduced risk of dementia (Fratiglioni and Wang 2007).

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Some suggest that social activity may increase cognitive reserve, similar to cognitive activity, so that people who are socially active can maintain cognitive performance even with neuropathic features normally associated with dementia (Fratiglioni and Wang 2007). Indeed, one study indicated that people with broader social networks had better cognitive performance, especially for memory, at a given neuropathic load (Bennett et al. 2006). However, the direction of this relationship is less clear. It may be that people who are able to maintain cognitive performance despite neuropathic feature normally associated with dementia are also more able to maintain social networks. The results of one study suggest as much. In this study, the relationship between low social engagement and high risk of dementia was restricted to those subjects who experienced a decline in social engagement from mid-life to late life (Saczynski et al. 2006). This suggests that low social engagement may be an early symptom of cognitive impairment rather than a risk factor. There are no controlled trials that examined social engagement on its own in relation to dementia risk or cognitive outcomes. As a result, the importance of social engagement in a successful prevention strategy is still unclear. However, a volunteering intervention that was designed to include social, cognitive, and physical components showed a trend towards improved cognition in the intervention group compared to a control group. The volunteering intervention appeared to be most beneficial to those with baseline cognitive impairment (Carlson, Saczynski, et al. 2008). Further studies are needed to determine whether social interventions might curb cognitive decline. However, the interactions between social activity, cognitive activity, and physical activity are difficult to disengage (Figure 4.1). One study concluded that each component is equally important in the protection against dementia (Karp et al. 2006). As a result, interventions that include cognitive, social, and physical components might be the best strategy to reduce the risk of cognitive impairment; research should further investigate this possibility. A larger, controlled trial should be instigated to evaluate whether multidomain interventions (cognitive, physical, and social) might be able to improve cognitive outcomes in those at risk for dementia. Diet Many risk factors for dementia (hypertension, diabetes, and obesity) and pathologic features (inflammation) associated with dementia can be modified by diet. Thus, it is reasonable to suggest that the risk of dementia

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Figure 4.1

The cognitive, social, and physical components of leisure activity may evenly contribute to the prevention of dementia. Many leisure activities have all three types (cognitive, social, and physical) of stimulation.

itself could be modified by diet. Results from several observational studies support this hypothesis. In most studies, adherence to a Mediterranean diet is associated with lower likelihood of Alzheimer ’s disease and all-cause dementia, as is greater consumption of fruit and vegetables, which is characteristic of a Mediterranean diet (Barberger-Gateau et al. 2007; Feart et al. 2009; Scarmeas et al. 2006). Adherence to a Mediterranean diet may also slow cognitive decline (Feart et al. 2009). Other studies found that people who consume high amounts of fish have lower dementia risk and slower cognitive decline (Barberger-Gateau et al. 2002; Kalmijn et al. 1997; Morris et al. 2003; van Gelder et al. 2007).

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The reason for the association between fish, fruit, and vegetable intake and dementia risk has not been definitely identified but may be related to anti-oxidative, anti-inflammatory, or metabolic effects. On a component level, some attribute the relationship between Mediterranean diet and cognition to antioxidants and/or polyunsaturated fatty acids—consumption of each is associated with reduced risk of dementia and improved cognition in observational studies (Gillette Guyonnet et al. 2007). However, it may be that overall diet is more important than any one component. The interest in antioxidants in relation to dementia risk stemmed from the observation that oxidative stress may contribute to neuropathic features associated with Alzheimer ’s disease. This observation led to the hypothesis that a high dietary intake of antioxidants might slow cognitive decline and lower the risk of dementia. Indeed, in some studies, people with higher intake of vitamin E and C (both antioxidants) through diet or supplements have slower cognitive decline and a lower risk of Alzheimer ’s disease. However, the relationship has not been consistent. Other large, prospective observational studies found no association between vitamin intake and dementia risk (Gillette Guyonnet et al. 2007). Furthermore, randomized controlled trial evidence has, at best, been inconsistent, with most studies finding no relationship between vitamin E supplementation and cognitive performance (Gillette Guyonnet et al. 2007; Isaac, Quinn, and Tabet 2008; Kang et al. 2006; Yaffe, Clemons, et al. 2004). This suggests that the association between antioxidants and cognitive impairment in observational studies may be due to uncontrolled confounding or other biases, rather than causation. Alternatively, vitamin supplementation may only be beneficial for those who are vitamin deficient. Studies regarding consumption of long-chain omega-3 fatty acids, one type of essential polyunsaturated fatty acid common in many types of fish, have been similarly inconclusive (Fotuhi, Mohassel, and Yaffe 2009; van de Rest et al. 2008). Despite associations in observational studies, randomized controlled trials have not found a consistent association between omega-3 fatty acid supplementation and cognitive outcomes (Fotuhi, Mohassel, and Yaffe 2009). Omega-3 fatty acid supplementation also had no effect on memory and attention in cognitively healthy elderly people (van de Rest et al. 2008). However, most studies have been limited by a short follow up period. The relationship between diet and dementia is likely confounded by numerous variables such as education, physical activity, vascular disease, and socioeconomic status. This may explain the inconsistent results of observational and controlled trials. Alternatively, it may be that one supplement is not sufficient to improve cognition or prevent dementia

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but that overall diet and lack of deficiencies is more important in optimizing cognitive outcomes. Interventions should examine the effect of supplementation on cognition in people who are deficient versus sufficient. Furthermore, an intervention focused on comprehensive dietary education and modification may have more of an effect than individual supplements. However, given that adherence to a Mediterranean diet is associated with reduced risk of mortality and cardiovascular disease (Sofi et al. 2008), people who adopt healthy diets are likely to have positive health outcomes regardless of the effect on cognitive functioning. Increasing attention has recently been paid to vitamin D as a means to prevent dementia. Although evidence is very preliminary and generally cross-sectional, some studies suggest that higher serum 25-hydroxyvitamin D may be associated with better global cognition (Annweiler et al. 2009). Vitamin D is also associated with a number of risk factors for dementia including diabetes, cerebrovascular disease, and depression (Grant 2009). Future prospective observational and controlled trials should examine vitamin D intake in relation to cognition, particularly in people deficient in Vitamin D as is common in institutionalized elderly people. Vascular Risk-Factor Reduction Although Alzheimer ’s disease and vascular dementia have traditionally been viewed as distinct disorders, it is now generally agreed that the two rarely occur in isolation. Both types of dementia share many risk factors and pathologic features with atherosclerosis (Launer 2002). Even mild cerebrovascular disease appears to increase the risk of cognitive impairment for any level of Alzheimer ’s disease pathology (Snowdon et al. 1997). Thus, control of vascular risk factors might reduce the likelihood or severity of dementia, regardless of type. Traditional cardiovascular risk factors such as hypertension, dyslipidemia, and diabetes appear to increase the risk of developing dementia in old age (Table 4.1). Hypertension is arguably the most studied vascular risk factors in relation to cognition, with inconsistent results. People with hypertension in mid-life had increased likelihood of dementia in late life in a number of observational studies (Launer et al. 2000; Qiu, Winblad, and Fratiglioni 2005). However, the relationship between late life hypertension and cognitive impairment is less clear. Both high systolic blood pressure and low systolic blood pressure in late life have been associated with augmented risk for dementia (Qiu, Winblad, and Fratiglioni 2005; Wu et al. 2003). Another study found no association between late life blood pressure and the incidence of dementia (Johnson et al. 2008). The reason for

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Table 4.1 Summary of Evidence Regarding Vascular Risk Factors for Dementia Risk Factor

Observational Studies

Hypertension

• Antihypertensives have • Mid-life hypertennot consistently reduced sion is associated the risk of dementia/ with increased risk of cognitive impairment dementia in late life. The association between among people with hypertension. However, late-life hypertension cognitive outcomes have and dementia is less generally been secondconsistent. • People with hypertenary and studies may be sion who take antihyunderpowered pertensive medications have reduced risk of dementia compared to those who do not in most studies. • In most studies, people • In a trial with no with diabetes in mid- or control group, diabetlate life have higher risk ics showed improved cognition with glycemic of MCI and dementia. • Diabetics have faster management. cognitive decline in normal aging. • Diabetics may have slower cognitive decline after dementia onset, possibly due to onset with less severe neuropathic features. • People with high levels • Two large, randomizedcontrolled trials sugof low-density lipoproteins have increased risk gested that statins do of cognitive impairment not improve cognitive outcomes. and vascular dementia in late life. • Statin therapy does not appear to reduce the risk of cognitive impairment among those with dyslipidemia.

Diabetes

Dyslipidemia

Trials

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Table 4.1 (Continued) Obesity

Metabolic Syndrome

• Obesity at mid-life is associated with higher risk of dementia in late life. • In late life, very high BMI or very low BMI may be associated with increased likelihood of dementia, possibly because obesity is a risk factor for dementia but weight loss may be an early symptom of the disease. • People with metabolic syndrome have increased risk of cognitive impairment and cognitive decline in late life.

—

—

• The effects of each vascular risk factor may be additive.

these inconsistent results is unknown but it may reflect that hypertension is a risk factor for dementia but that hypotension is an early symptom of the disease. If hypertension in mid-life is a risk factor for dementia, then it follows that antihypertensives have the potential to reduce the risk of dementia in people with hypertension. In observational studies, this appears to be the case. In a number of studies, people with hypertension who took antihypertensives had a lower risk of dementia than those who do not (Korf et al. 2004; Skoog 2009; Skoog et al. 2005). However, the results from controlled trials have been less consistent (Fillit et al. 2008; Peters et al. 2008; Prince et al. 1996). The largest study, the Hypertension in the Very Elderly Trial (HYVET), was not entirely conclusive but favored treatment of hypertension to improve cognitive outcomes when the results were combined into a meta-analysis with previous studies. However, most trials—including HYVET—were designed to examine other primary outcomes (Peters et al. 2008; Prince et al. 1996). The trials did not include detailed cognitive

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measures and may not have been sufficiently powered to detect a relationship between antihypertensives and cognitive outcomes. There is general accord that people with diabetes in late life have higher risk of dementia and MCI compared to those who do not (Luchsinger, Reitz, et al. 2007; Ott et al. 1999; Yaffe, Blackwell, et al. 2004), though some studies show no association between the two (Luchsinger and Gustafson 2009). Similarly, mid-life diabetes is associated with augmented risk of dementia in some but not all studies. The relationship with diabetes seems to be stronger and more consistent for vascular dementia and vascular cognitive impairment than for Alzheimer ’s disease and amnestic MCI (Hassing et al. 2002; Luchsinger and Gustafson 2009; MacKnight et al. 2002; Whitmer 2007). Counterintuitively, studies report that people who are treated for diabetes have a greater likelihood of dementia than those who are not (Luchsinger et al. 2001; Ott et al. 1999); however, this relationship is likely confounded by severity, where people who receive treatment have more severe diabetes than those who are not treated. Diabetics with hypoglycemic episodes, a complication of uncontrolled diabetes, also have a higher risk of dementia (Whitmer et al. 2009). Diabetes is also associated with faster cognitive decline in normal aging (Gregg et al. 2000). In contrast, diabetics have slower cognitive decline after Alzheimer ’s disease onset (Sanz et al. 2009). The slower decline after Alzheimer ’s disease onset may occur because diabetics have less severe Alzheimer ’s disease neuropathic features at onset. Indeed, there is evidence to suggest that people with dementia who have type 2 diabetes have fewer plaques and neurofibrillary tangles than people with dementia only, indicating that people with diabetes may have more severe symptoms of cognitive impairment for a given level of neuropathology (Beeri et al. 2005). Results from one controlled trial suggested that glycemic control by medical management improved cognitive outcomes in type 2 diabetics; however the trial was not placebo controlled (Ryan et al. 2006). Consequently, the results are very preliminary. The Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes Study (ACCORD-MIND) examined whether intense glycemic control improved cognitive outcomes relative to standard care in type 2 diabetics who are 60 years or older (Williamson et al. 2007). At baseline, those with better insulin control as measured by A1C had better cognitive performance on a number of tests (Cukierman-Yaffe et al. 2009). Although the longitudinal cognitive results have not yet been released, the main ACCORD study was terminated early due to an excess of deaths in the intense glycemic control group

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relative to the standard control group (Hoogwerf 2008). It is unlikely that any potential cognitive benefits could justify the excess deaths associated with intense glycemic control. Other vascular risk factors are also associated with increased likelihood of cognitive impairment, though evidence is more limited. Dyslipidemia is associated with enhanced risk of cognitive impairment in old age. People with high levels of low-density lipoproteins in late life have increased risk of cognitive impairment and dementia with stroke (Moroney et al. 1999; Yaffe et al. 2002). A number of observational trials have examined whether statin therapy is associated with reduced risk of cognitive impairment in people with dyslipidemia but the results are not convincing. Most crosssectional studies found a link between statin use and lower likelihood of dementia but longitudinal studies did not (Rockwood 2006). Furthermore, two large, randomized controlled trials concluded that statin therapy did not improve cognitive outcomes (Heart Protection Study Collaborative Group 2002; Trompet et al. 2009). Consequently, current evidence suggests that statin therapy may not have a role in dementia prevention. Obesity at mid-life, as measured using body mass index (BMI) or waist circumference, is also associated with higher likelihood of dementia in late life (Gustafson et al. 2003; Whitmer et al. 2005). However, the relationship is less consistent in later-life. In people 76 years and older, the relationship between BMI and dementia may resemble a U-shaped curve, where those with very high or low BMI have increased risk of dementia. (Luchsinger, Patel, et al. 2007) It is possible that high body fat is a risk factor for dementia while weight loss is a symptom of dementia pathology prior to diagnosis. A cluster of three or more vascular risk factors (hypertension, hyperglycemia, abdominal obesity, and/or low high-density lipoprotein) is referred to as the metabolic syndrome. Not surprisingly, people with the metabolic syndrome also have augmented risk of cognitive impairment and cognitive decline in a number of studies (Komulainen et al. 2007; Vanhanen et al. 2006; Yaffe et al. 2007). One study suggested that the effects of each vascular risk factor were approximately additive (Yaffe 2007). Interestingly, another study found that metabolic syndrome was associated with slower cognitive decline in the oldest old (van den Berg et al. 2007). Why this is so is unclear but may reflect differential survival in this age group. The mechanisms linking vascular risk factors to cognitive impairment are likely multifactorial. Since it is more common to have multiple vascular risk factors than just one, it is difficult to establish mechanistic links between individual vascular risk factors and dementia. The direct relationship between hypertension, cerebrovascular disease (in its most

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severe case, stroke), and subsequent dementia is well established. Cerebrovascular disease may also be the mechanistic pathway linking obesity, diabetes, and dyslipidemia with cognitive impairment. The degenerative changes in the cerebrovascular vessels may also cause dysfunction of both the endothelium and blood-brain barrier, causing the endothelial cells to produce an excess of free radicals and subsequent oxidative stress. This may result in increased blood-brain barrier permeability to proteins, leading to ␤-amyloid accumulation (Duron and Hanon 2008). There is also a growing body of work that suggests a direct link between insulin and Alzheimer ’s disease pathology. Specifically, in vitro studies indicate that insulin causes a significant increase in extracellular ␤-amyloid levels (Luchsinger and Gustafson 2009). Consequently, people with insulin resistance, such as type II diabetics, or those with precursor hyperinsulemia, may have increases in ␤-amyloid levels caused by elevated insulin levels. In addition, adipose tissue secretes both metabolic and inflammatory factors (Launer et al. 1995). Specifically, the secretion of inflammatory adipocytokines may be involved in neurodegenerative pathways. It is unclear, however, whether adipose tissue is directly linked to cognitive impairment or whether the adipose tissue is a marker of insulin resistance and hyperinsulinemia (Launer et al. 1995). Another mechanistic pathway may be cholesterol, which is a key regulator of neuronal function thought to contribute to regulation of ␤-amyloid plaque deposition in the brain (Fillit et al. 2008). There are several areas that still need to be studied with regards to vascular risk management and cognition. Lifestyle management of vascular risk factors should be examined in relation to cognition in people with high vascular risk. In addition, a large, randomized controlled trial of medical management for glycemic control in diabetics is needed to definitely determine whether standard glycemic control improves cognitive outcomes; however, given the cardiovascular benefits of standard glycemic control, a randomized controlled trial to examine the cognitive benefits is unlikely to occur. Despite the inconsistent or missing results regarding vascular risk management (Table 4.1), it is relatively agreed that people with vascular risk factors are also at augmented risk for dementia compared to those without vascular risk factors. As a result, clinicians treating people with vascular risk factors should be aware of and screen for symptoms of cognitive impairment. Preventative strategies—which may include lifestyle management and medications targeting dementia pathologic features—may be efficacious in reducing the likelihood of dementia in this high-risk group.

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Figure 4.2

Interaction between promising strategies for the prevention of dementia.

CONCLUSION Understanding how behavioral and biological factors might alter the risk of dementia is crucial to the prevention of the disease. Observational studies have identified factors including cognitive activity, physical activity, social activity, vascular risk factors, and diet that could be important both in identifying people at risk for dementia and for interventional strategies to reduce the risk. Though preliminary interventional studies have been less conclusive, future trials should continue to examine the impact of risk-factor modification on cognitive outcomes. Given that the risk factors are largely correlated—people who are more active generally have a

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better diet and lower vascular risk, it may be that living a healthy, engaged life is the best way to prevent dementia and that any one single factor is insufficient to prevent the disease (Figure 4.2). Future trials should continue to examine the implication of risk factor modification and, in particular, how we might combine interventions for optimal results. In the most optimistic view, dementia could be delayed or even prevented by these interventions. At worst, people will improve their overall health and enjoy a more cognitively and socially engaging life. ACKNOWLEDGMENT: Dr. Middleton is supported in part by a Canadian Institutes of Health Research Fellowship. REFERENCES Acevedo, A., and D. A. Loewenstein. 2007. Nonpharmacological cognitive interventions in aging and dementia. J Geriatr Psychiatry Neurol 20 (4): 239–249. Albert, M. S., K. Jones, C. R. Savage, L. Berkman, T. Seeman, D. Blazer, and J. W. Rowe. 1995. Predictors of cognitive change in older persons: MacArthur studies of successful aging. Psychol Aging 10 (4): 578–589. Alzheimer ’s Association. 2008. Alzheimer ’s disease facts and figures 2008. http:// www.alz.org/national/documents/report_alzfactsfigures2008.pdf (accessed March 24, 2008). Angevaren, M., G. Aufdemkampe, H. J. Verhaar, A. Aleman, and L. Vanhees. 2008. Physical activity and enhanced fitness to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev (3): CD005381. Annweiler, C., G. Allali, P. Allain, S. Bridenbaugh, A. M. Schott, R. W. Kressig, and O. Beauchet. 2009. Vitamin D and cognitive performance in adults: A systematic review. Eur J Neurol 16 (10): 1083–1089. Ball, K., D. B. Berch, K. F. Helmers, J. B. Jobe, M. D. Leveck, M. Marsiske, J. N. Morris, et al. 2002. Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA 288 (18): 2271–2281. Barberger-Gateau, P., L. Letenneur, V. Deschamps, K. Peres, J. F. Dartigues, and S. Renaud. 2002. Fish, meat, and risk of dementia: Cohort study. BMJ 325 (7370): 932–933. Barberger-Gateau, P., C. Raffaitin, L. Letenneur, C. Berr, C. Tzourio, J. F. Dartigues, and A. Alperovitch. 2007. Dietary patterns and risk of dementia: The ThreeCity cohort study. Neurology 69 (20): 1921–1930. Beeri, M. S., J. M. Silverman, K. L. Davis, D. Marin, H. Z. Grossman, J. Schmeidler, D. P. Purohit, et al. 2005. Type 2 diabetes is negatively associated with Alzheimer ’s disease neuropathology. J Gerontol A Biol Sci Med Sci 60 (4): 471–475. Bennett, D. A., J. A. Schneider, Z. Arvanitakis, J. F. Kelly, N. T. Aggarwal, R. C. Shah, and R. S. Wilson. 2006. Neuropathology of older persons without

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Chapter 5

Cultivating a Cognitive Lifestyle: Implications for Healthy Brain Aging and Dementia Prevention Michael J. Valenzuela

The concept of brain reserve capacity is most often invoked when explaining individual differences in clinical outcomes from brain injury or disease (Satz 1993). When used as a “black box” in this fashion it is intuitively appealing. Ever since formal investigation of head-injured patients began in times of war, clinicians and researchers have been amazed at the possible diversity of personal outcomes. Sometimes relatively small contusions can lead to devastating consequences; in others massive brain injury does not in the end produce a discernable difference in day-to-day function. Indeed, the volume of disrupted brain tissue is but a poor predictor of clinical symptoms (Grafman et al. 1986). Similar neuroclinical discordance occurs in stroke injury as well (Desmond et al. 2000). So while most neuroscientists and clinicians would agree that the brain has some form of reserve capacity that differs significantly between individuals, the nature of this capacity has remained frustratingly difficult to define. A large part of this difficulty is because the notion of “reserve” can be analyzed at many levels. This is most apparent in the field of dementia, where arguably “reserve” has undergone the most research (Valenzuela 2008; Stern 2002). In the following section, different interpretations of reserve in dementia-related research will be contrasted, with the caveat that a clinical effect should not be confused with the action of several potential mediating

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mechanisms. “Reserve” is a singular term, but it is highly unlikely that only a single mode of action underlies the brain’s remarkable variability in clinical response to insult. Multiple, as yet undefined, interacting “reserve capacities” are therefore at play, and so the term risks losing explanatory power. Anchoring the phenomena at the behavioral level is suggested as perhaps the most tractable strategy. Cognitive lifestyle is introduced as one objective approach. This has already been shown to predict longitudinal changes in cognitive function and brain morphology. Next a brief review of the possible neuronal mediators of cognitive lifestyle will follow and more specifically address how these could lead to the well-established finding that those with a more active cognitive lifestyle benefit from a significant reduction in dementia risk. The main theme here is that all the ingredients of a rich cognitive lifestyle stimulate neuroplasticity in the brain, ranging from activity-dependent gene expression to the adaptation of large-scale cortical networks. Given dementia is, in the end, a failure of neuroplasticity, this has significant consequences for potential preventative strategies. In the third and final section of this chapter, whether neuroplasticity can be co-opted for preventative purposes against dementia is critically assessed. Trials of structured complex mental activity training in the form of cognitive brain training are assessed in the areas of normal healthy aging and mild cognitive impairment (MCI), and shown to have great promise. These themes are brought together with clinical recommendations as well as views on how the field can continue to grow and shed light on this most interesting of brain-behavior interactions.

DEFINITION AND IMPLICATIONS OF COGNITIVE LIFESTYLE Neurocentric Perspectives on Brain Reserve Since the time of Tomlinson, Blessed, and Roth’s (1970) pioneering dementia studies, there has existed a central paradox for the field: why do some individuals who died with significant levels of Alzheimer disease (AD) pathology have intact cognition immediately prior to death? While initially considered to be clinical rarities (Roth 1986), more recent population-based studies have shown that 33% of individuals with nontrivial AD at death were not demented in life (Neuropathology Group 2001). Obviously there is something unique about these individuals, but what could this be? Katzman et al. (1988) were the first to propose a possible explanation for these cases. These individuals were observed to manifest three main differences compared to individuals who had clinically succumbed to their

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disease. First, they contained a greater number of large pyramidal neurons throughout the neocortex. Second, they had heavier brain weights, and third, they had performed at the highest levels on antemortem cognitive tests. Overall, these individuals “had incipient Alzheimer ’s disease but did not show it clinically because of this greater reserve” (144). Given these observations and since neurons cannot be counted in life, early brain reserve research focused primarily on gross brain parameters. Intracranial volume (ICV), head circumference (Borenstein et al. 2001), and even head width (Jorm et al. 1997) have been used as basic proxies for maximal brain weight, which in turn was suggested to provide an estimate of neuronal numbers. This position has a number of problems. First, whether maximal brain volume or weight is highly correlated with neuronal number is debatable. Second, since a more sophisticated model has been missing for specifying which neurons and where in the brain numerical differences may be most important, aggregate numbers and therefore volume has been overemphasized. There is a long and ignoble history of attempts to link gross brain volume or weight to general cognitive features (Gould 1991). But most important, the key test for a putative neuronal corollary of reserve is that variations in this quantity can account for variance in a clinical outcome, in this case incident dementia. Studies have in general failed to show an inverse linear association between dementia incidence and the full range of ICV; an increased risk appears to be restricted to the low to very low ICV ranges (Schofield et al. 1997), or when in the presence of an additional risk factor such as APOE ε4 (Borenstein et al. 2001). Another straightforward problem for a “hard” neurocentric interest in maximal brain volume is that it restricts the explanatory variable to a nonmodifiable property. Maximum ICV and head circumference are generally achieved by puberty (Mortimer 1997) and reflect genetic variance in neuronal quantum as well as developmental, nutritional and environmental factors in early life (Altman et al. 1968). More important, since these measures do not generally change after the onset of adulthood, does this mean that our underlying brain reserve is fixed? As will be reviewed in the next section, the weight of evidence from the epidemiological, clinical, and experimental literature suggests quite the opposite. Cognitive Perspective Katzman and colleagues had of course noted both cognitive and neurological differences in their sample. So another perspective of reserve has been to focus on “how well we use what has been left behind” rather than

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how much of it we had in the first place (Stern 2002; Mortimer 1988). Two distinctions are possible. If we substitute “neuronal numbers” with “neuropsychological competence” or “IQ,” a threshold model can be applied whereby high cognitive reserve individuals simply perform better on cognitive tests to start with and therefore require a larger decrement before crossing a diagnostic threshold (Satz 1993). This model suggests no interaction with the underlying disease process and predicts no differential rates in cognitive decline. Only neuropsychological starting points differ. It has therefore been termed a passive version of reserve (Stern 2002), and identified as a potential source of systematic error in longitudinal studies (Tuokko et al. 2003). In the context of aging and dementia, any systematic definition of reserve must account for this passive effect, which in practical terms means demonstrating differential rates of neurological or cognitive change over time. A more active form of cognitive reserve contends that individuals who have developed a range of deliberate cognitive strategies for solving complex problems are more likely to remain within normal functional limits for longer. This dynamic account predicts that two individuals may begin at the same cognitive starting point, suffer the same progressive burden of disease, but due to increased use of strategic coping mechanisms one may perform better at follow-up testing or experience less day-to-day functional limitations. While certainly an important clinical phenomena that captures part of the ecological nature of how individuals differ, this notion of reserve is surprisingly difficult to measure. Simply asking subjects about their use of deliberate strategies while performing cognitive tasks can produce more questions than answers (Naveh-Benjamin, Brav, and Levi 2007). This active and deliberative form of cognitive reserve therefore suffers from a lack of feasible operationalization. Computational Perspective More recent incarnations of reserve capacity have focused on computational processes such as network redundancy and flexibility (Valenzuela, Breakspear, and Sachdev 2007). In this case, individuals may not only vary on their range of deliberative strategies but also possess differences in the diversity of neural pathways available for execution of these cognitive processes. Having multiple neural pathways for instantiation of the same computational problem (redundancy), or an enhanced ability to reorganize pathways after “network attack” (in computational terms “degeneracy”; see Tononi, Sporns, and Edelman 1999), is theorized to facilitate maintenance of function after neurological insult. While this approach

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benefits from a unification of brain and cognitive reserve via an explicit mechanism, outside of computational simulations operationalization is again problematic (Rubinov et al. 2009). A Behavioral Perspective: Defining Cognitive Lifestyle The alternative pursued in our group has been to simply ask how mentally active and engaged has a person been over his or her lifespan in comparison to the average? Relevant information here includes level and duration of formal education from young adulthood to the present day, the nature and complexity of occupations throughout his or her working life, and the diversity, frequency and cognitive challenge of past and present leisure activities. This has been combined into a validated assessment tool, the Lifetime of Experience Questionnaire (LEQ) (Valenzuela and Sachdev 2007). Higher LEQ scores independently predict not only attenuated cognitive decline over time, but also a reduced rate of hippocampal atrophy (Valenzuela et al. 2008) (see Figure 5.1). This straightforward approach has the main advantage of providing a working operational definition that is clinically relevant. The behavioral perspective inherent in the LEQ does not identify itself with a specific neurological quantity, computational property, or cognitive process. Participation in complex mental activities throughout the lifespan is assumed to lead to changes in a number of interacting mechanisms at different temporal and spatial scales (Valenzuela, Breakspear, and Sachdev 2007). Together these alter an individual’s risk for dementia and cognitive dysfunction. Indeed, we have suggested elsewhere that perhaps there is no one brain or cognitive reserve, but a number of reserves (Valenzuela 2008). For too long researchers have seemed to confuse a single potential reserve mechanism, of which there are certainly a plurality, with the apparent unity of the reserve effect (i.e., clinical protection). The approach of using behavioral anchor points for the assessment of cognitive lifestyle is reliable and clinically predictive, and so it is hoped that more powerful and meaningful mechanistic studies will follow. Cognitive Lifestyle and Dementia Risk Cognitive lifestyle and dementia are linked. Highly consistent connections between complex mental activity and reduced dementia risk have been found across large-scale prospective studies of dementia incidence. Our meta-analysis of the area combined data from 22 international cohort studies and showed that overall individuals with more active cognitive

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Figure 5.1

Scatterplot showing a positive relationship between the Lifetime of Experiences Questionnaire on the x-axis (a validated measure of cognitive lifestyle) and hippocampal volume on the y-axis. Insets show examples from individuals with high (left) and low (right) LEQ scores along with volumes of their hippocampus.

lifestyles were at 46% reduced risk for incident dementia (CI: 0.49–0.59) (Valenzuela and Sachdev 2006). In this systematic review, the effects of education (OR = 0.53), occupational complexity (OR = 0.56), and late-life leisure activities (OR = 0.50) were each individually highly consistent. Similar protective effects of 40–50% risk reduction are also found when specifically isolated to cognitive lifestyle in late life (i.e., after 60 years of

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age), independent of earlier exposures to education or occupational complexity (Scarmeas et al. 2001). This has now been replicated internationally (Wang et al. 2002; Fratiglioni et al. 2000; Wilson et al. 2002). There is furthermore evidence for a dosage effect (Valenzuela et al. 2006). Verghese et al. (2003), for example, found a 50 percent risk reduction for incident dementia over five years in those with a moderate number of cognitive lifestyle activities compared to those with low numbers, while those with the highest degree of participation had a 67 percent reduction in incident dementia. Protective effects in individuals with a more active cognitive lifestyle in later life even after controlling for earlier life experiences gives great hope that interventions implemented at this time can still be effective for helping prevent dementia. Yet despite such convergent epidemiological data, the underlying reasons for the relationship remain unclear. A brief review of possible mediating mechanisms is therefore presented next. MECHANISMS UNDERLYING BENEFITS OF COGNITIVE LIFESTYLE Both the structure and function of the brain can change in response to environmental complexity. Thirty years of research has now been amassed on the effects of environmental enrichment in rodents, a relatively simple intervention that involves moving animals from standard housing to a home environment with additional toys, mazes, wheels, and littermates (for a review see Nithianantharajah and Hannan 2006). Enrichment is therefore a multiplex intervention that increases animals’ cognitive, physical, and social activity. This of course makes precise isolation of the mechanisms’ underlying solely mental activity, in contradistinction to physical exercise or socialization, quite challenging. Yet rodent studies suggest more similarities than differences when comparing mechanisms involved in voluntary running (Cotman, Berchtold, and Christie 2007) to cognitive stimulation (Nithianantharajah and Hannan 2006). Similar principles may also apply in humans, but as yet we lack the tools to probe the brain in vivo at sufficient spatial and temporal resolution. Despite these limitations, neuroimaging studies are beginning to chart the nature of activitydependent brain changes. Overall, human and animal research indicates that mental stimulation induces a complex web of biological mechanisms at different spatial and temporal scales. An active cognitive lifestyle therefore more than likely contributes to a stronger defense against dementia by a number of different means.

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Molecular Mechanisms Long-term potentiation (LTP) and depression (LTD) are important cellular and molecular processes implicated in memory (Malenka and Bear 2004). Both of these depend on activity-dependent changes to excitatory AMPA and NMDA receptors, which in effect change the probability that a postsynaptic neuron will fire in response to presynaptic stimulation. It is therefore significant that as little as five days of enrichment can upregulate AMPA receptors (Naka et al. 2005), and thereafter alter LTP and LTD (Artola et al. 2006). Upstream to these effects in both space and time are molecular changes to gene expression. Remarkably, microarray analysis has shown dozens of gene expression changes, including those implicated in regulating synaptic plasticity, following as little as three hours of environmental stimulation (Rampon et al. 2000). However, arguably the most important molecular changes occur in relation to brain derived neurotrophic factor (BDNF). BDNF is a “master molecule” of sorts, implicated in a wide range of neuroplastic processes including, neural stem cell survival, synaptogenesis, neurogenesis, dendritic arborisation, and synaptic plasticity (Fumagalli, Racagni, and Riva 2006). Enrichment causes profound increases in BDNF production throughout the brain, particularly in the hippocampus (Mohammed et al. 2002; Ickes et al. 2000). There is therefore increasing interest in BDNF as an “enviromimetic” (McOmish and Hannan 2007), although much further research is needed to understand the pathways involved in its regulation and differential effects.

Disease Modification A number of studies of transgenic Alzheimer mice have now investigated the effects of environmental enrichment, with mixed findings. One study found a 50% reduction in amyloid burden subsequent to five months of enrichment, with a suggestion this was due to increased plaque breakdown (Lazarov et al. 2005). Another study found increased plaque load, but paradoxically, improved behavioral outcomes (Janowsky et al. 2005). A third group also noted cognitive improvements, along with evidence for both amyloid-dependent and -ndependent mechanisms (Arendash et al. 2004; Costa et al. 2006). Interestingly, for the optimal triple-pronged effect of decreased AD burden, increased synaptic density, and improved memory performance, all three aspects of enrichment were needed, that is, cognitive, social, and physical activity (Cracchiolo et al. 2007). Whether

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this disease modification is relevant to humans is of course difficult to determine given the wide gulf between transgenic models and humans. Clinical studies using amyloid imaging are therefore eagerly anticipated. Cellular Mechanisms Synaptogenesis is arguably the most robust neuroplastic change, with enrichment leading to increases in synaptic density in the order of 150– 300% (Levi et al. 2003). Changes in synaptic density of this sort are highly correlated to memory function in the rat (Frick and Fernandez 2003). Moreover, this finding is relevant to human brain aging and dementia, since post mortem synaptophysin levels are strongly correlated to cognitive and clinical status before death: Two independent laboratories have found correlations between 0.7–0.8 (Terry et al. 1991; Scheff and Price 2003). One of the key mechanisms by which an active cognitive lifestyle leads to reduced dementia risk may be through upregulation of synaptogenesis in important memory-dependent areas of the brain. Experience-dependent changes in neurogenesis (Kempermann 2006) and angiogenesis (Black et al. 1990) also occur, which in combination may explain why enrichment seems to lead to increased gross brain volume (Altman et al. 1968). However, the functional significance of neurogenesis remains highly controversial—correlations between neurogenesis and spatial memory performance in older animals have for example been contradictory (Kempermann 2006; Drapeau et al. 2003; Bizon and Gallagher 2005). Whether cognitive lifestyle modulates dementia risk through neurogenesis is not clear. Cortical Network Mechanisms Glucose-labeled PET studies can estimate the brain’s overall rate of metabolic consumption as well as its regional variation. Using this approach, repeated cognitive exercise was found to lead to increased efficiency in the shape of a 25–30% reduction in global resting metabolism (Haier et al. 1988). On the other hand, cognitive exercise also results in selective and temporary increases in hemodynamic responsivity in those same brain areas engaged by the tasks (Olesen, Westerberg, and Klingberg 2004; Moore, Cohen, and Ranganath 2006). Cortical compensatory processes are also important and refer to an enhanced ability to adapt against progressive disease in one part of the brain, through functional reorganization in another part of the brain. Studies have, for example, shown that elders with preserved memory

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ability—behaviorally equivalent to that of younger individuals—engage bilateral prefrontal brain areas in comparison to older memory-deficient peers, who continue to only activate a unilateral brain network like younger individuals (Cabeza 1997; Grady et al. 2003; Rosen et al. 2002; Scarmeas et al. 2003). Successful brain aging may therefore not only involve the continued deployment of the same neural processing pathways, but also the recruitment of new brain networks better suited to the aged brain. More recent neuroimaging studies have focused on characterizing changes in direct response to mental and physical training. In general these have found evidence for expansion of cortical grey matter after several weeks of training (Draganski et al. 2004; Boyke et al. 2008; Colcombe et al. 2006), as well as improvements in cerebral blood flow (Mozolic, Hayaska, and Laurienti 2010; Colcombe et al. 2004). Training may therefore at least partially counteract age-related and disease-related atrophy in different brain regions; however, more research is required since these findings appear to be highly dependent on analytical approach (Thomas et al. 2009). Memory training can also lead to specific increments in phosphocreatine concentration in the hippocampus as revealed by magnetic resonance spectroscopy (Valenzuela et al. 2003), of interest since AD leads to phosphocreatine depletion (Valenzuela and Sachdev 2001) and dietary supplementation is neuroprotective in animal models (Brustovetsky, Brustovetsky, and Dubinsky 2001). While more research is required, initial neuroimaging reports suggest that mental and physical training can have positive effects on the brain. How Does This Delay or Prevention Dementia? Complex mental activity is evidently a strong stimulator of the brain’s myriad neuroplastic mechanisms. By contrast, the degenerative conditions that underlie dementia—including age-related degeneration, AD, and cerebrovascular disease—all combine to severely reduce neuroplasticity (Mesulam 1999). In a simplistic sense, an active cognitive lifestyle may therefore help protect against dementia by counteracting negative disease-related effects on neuroplasticity. More precise models and detailed human data are required to better understand these important therapeutic and preventative mechanisms. Theoretical considerations also reveal a more fundamental link between cognitive lifestyle and dementia. For several decades we have considered dementia to simply represent the endpoint of a gradual buildup of pathology and associated neuronal loss. This view assumes a unidirectional relationship, implies a high level of clinicopathologic determinism, and is

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the basis for the search for better disease “biomarkers” based on amyloid imaging, hippocampal structural modeling, and so forth. However, as reviewed above, clinical symptoms do no arise in almost a third of cases with supra-threshold pathological AD at post mortem. These individuals have benefited in life from some as-yet-undefined countervailing factors and, for reasons outlined above, individual differences in neuroplasticity may be implicated. For example, the great variability witnessed in cortical compensation and reorganization suggests that dementia may be better conceptualized as a “compensatory failure” (Valenzuela, Breakspear, and Sachdev 2007). This view suggests that disease burden is not the only salient factor when attempting to make sensible predictions and judgments about dementia, but that an assessment of an individual’s neuroplastic capacities is also needed (see Figure 5.2). Dementia onset therefore becomes a dynamic tension between a progressive disease and the brain’s limited but ever-surprising ability to adapt, react, and regenerate. An important consequence of this paradigm is that interventions and practice that can enrich our neuroplastic capacities also help to subvert the clinical effects of neurodegenerative disease. In the next section we will therefore move from theory to practice and review the evidence base for interventions designed to augment cognitive lifestyle. CAN ENRICHMENT OF COGNITIVE LIFESTYLE HELP PREVENT DEMENTIA? Despite the strong epidemiological links between cognitive lifestyle and reduced dementia risk, and a wealth of potential explanatory mechanisms, important questions remain over the arrow of causality: does preceding mental activity reduce or delay expression of future dementia, or is preclinical dementia causing a reduction in participation in activities prior to formal diagnosis? In order to disentangle this complex “chicken or egg” problem, data from clinical trials of cognitive training are most important. When reviewing a potentially vast literature, a specific type of cognitive lifestyle activity has been chosen for pragmatic reasons: cognitive training. Given the sometimes unclear usage of the term and possible overlap with other cognitive interventions such as cognitive remediation, cognitive rehabilitation, and cognitive stimulation, we have striven to use an explicit definition. Cognitive training is any intervention aimed at improving, maintaining, or restoring mental function through the repeated and structured practice of tasks that pose an inherent problem or mental challenge and that target specific cognitive domains (Gates and Valenzuela 2010). This definition does not include training in strategies to compensate

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Figure 5.2

A framework for conceptualizing dementia as a dynamic balance between disease burden and neuroplastic capacities. Above, an individual with a given disease burden expresses cognitive symptoms in the context of relatively low neuroplastic capacities. Below, the same disease burden leads to no clinical symptoms due to greater neuroplastic capacities. The aim of interventions based on boosting cognitive lifestyle is to shift individuals at risk for dementia to the lower scenario.

for deficits, traditionally a rehabilitative or remedial approach (Sitzer, Twamley, and Jeste 2006). Theoretical Issues Repetitive cognitive training undoubtedly improves performance on the trained task— there is indeed more than 20 years of cognitive psychology

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research on this topic (Rebok, Carlson, and Langbaum 2007). To determine whether cognitive training could potentially help reduce or delay the incidence of dementia, two major issues need to be addressed. Generalization or Transfer of Effect Does the cognitive training intervention only lead to improvement in the trained task, or does it also transfer to nontrained tasks? We have proposed a hierarchy of generalization of increasing clinical relevance (Gates and Valenzuela 2010) (see Figure 5.3): 1. Transfer to nontrained tasks in same cognitive domain 2. Transfer to nontrained tasks in other cognitive domains 3. Transfer to global measures of general cognitive ability (e.g, Alzheimer ’s Disease Assessment Scale–Cognitive, tests for general intellectual ability, etc.) 4. Transfer to measures of general function (e.g., Instrumental Activities of Daily Living, Quality of Life, Mood, etc.) Improvement on the same tasks as covered in training are therefore clinically trivial, while generalization of training to include better overall cognitive function, day-to-day abilities, and quality of life should be considered benchmark tests when evaluating clinical efficacy. Persistence or Durability of Effect Does the effect of cognitive training intervention last beyond the immediate posttraining period, or is continual cognitive training required? Longitudinal follow-up of cognitive training efficacy is required to answer this question. In the following brief review, a summary of randomized controlled trials (RCTs) in the healthy aging and MCI areas is provided. RCTs in Healthy Aging We have recently published a systematic review of RCTs of cognitive training in healthy older individuals in which longitudinal follow-up was a critical design feature (Valenzuela and Sachdev 2009). A total of seven trial outcomes suggested that a discrete program of cognitive training in the order of 2–3 months can have long-lasting and persistent protective effects on cognition. The overall weighted mean difference was strong in magnitude, estimated at 1.07 (CI: 0.32–1.83) and the nonweighted average relative effect size was Cohen’s d = 0.5.

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Figure 5.3

Hierarchy of generalization whereby change on the trained task is clinically trivial, while improvements in day-to-day function and subjective measures of well-being are the most clinically valuable.

The ACTIVE study is the largest trial in the area (Ball et al. 2002) and examined the effects of 10 sessions of cognitive training on 2832 healthy older individuals. Participants completed three different intervention groups: memory training, reasoning training, and processing speed training. Two years later, each intervention improved cognitive ability only in the targeted area, an effect of limited clinical value. Follow up at five years, however, found that reasoning training protected against functional decline compared to any of the other interventions or the control wait-andsee condition, albeit with very a modest effect size (Willis et al. 2006). This is therefore the first large clinical trial to demonstrate potentially clinically relevant transfer effects. There is also a high degree of community and commercial interest in computer-based cognitive training. One group has conducted a RCT with such a product (Mahncke et al. 2006). The attraction of computerized cognitive training is that training can be standardized and allows a gradient in task difficulty to be automatically incorporated as individuals’ skill

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levels progress. Neuropsychological tests immediately after the end of the training period found verbal memory performance improved by up to 25% of a standard deviation, and testing three months later showed that short term memory performance remained enhanced. The combination of both cognitive and physical exercise is also of great interest. This has yet to be tested in a rigorous RCT. The Sim-A study investigated the effects of cognitive, physical, and combined training in healthy older individuals over a five-year period (Oswald et al. 2006). Thirty paper-and-pencil cognitive training sessions produced a significant effect over both the 12-month and five-year follow-up periods. Moreover, this effect seemed to transfer to a measure of general cognition. The group that did both cognitive and physical training experienced a larger effect size than the simple addition of those who completed just one type of training, suggestive of a potential synergistic action. Other smaller studies with samples of less than 100 individuals have found positive trends but have lacked power (Derwinger, Stigsdotter Neely, and Backman 2005; Scogin and Bienias 1988). The overall effect size and consistency across longitudinal trials of cognitive training in the healthy elderly is therefore promising, yet many questions remain. There has been a wide variety of primary outcome measures across the trials, and details of the applied cognitive exercises also varied. Quality of trial design and reporting has in general been low. However, it is encouraging that those studies with longer-term follow-up showed no evidence of less potent effects. A durable long-term effect from cognitive training may therefore be realistic. Significantly, two of the more recent clinical studies have also shown that their training protocols generalize to domains beyond the narrow focus of the trained tasks (Willis et al. 2006; Oswald et al. 2006). RCTs in Mild Cognitive Impairment Mild cognitive impairment (MCI) may be an optimal stage at which to intervene for the purpose of prevention and delay of progression to bona fide dementia. A nonsystematic review of cognitive training in MCI has suggested potential efficacy for cognitive outcomes (Belleville 2008). At least three RCTs have been reported which adhere to our definition of cognitive training and are summarized below. A small study (n=8) of community-based individuals with MCI tested a multifaceted memory enhancement training with a no-treatment control (Rapp, Brenes, and Marsh 2002). Training comprised of six two-hour meetings held weekly and also involved education, relaxation skills, and homework

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practice. Both cognitive training and rehabilitative strategies were therefore combined. Initially, there were no group differences; however, at the six month follow-up the treatment group had superior delayed list recall than the controls, suggestive of durable effects. No relative improvements in other cognitive domains or global cognitive function were evident. Olazaran et al. (2004) studied a mixed sample of 12 individuals with MCI plus 72 people with AD, randomly assigned to a psychosocial control group or a cognitive motor intervention that included cognitive training. Results across diagnostic groups were combined, so it is impossible to isolate the effect size in the MCI group. Overall, Alzheimer ’s Disease Assessment Scale–Cognitive (ADAS-Cog) scores remained stable in the treatment group while it declined in controls. There was also a significantly positive effect of depression scores. The most rigorous study to date has been by Rozzini et al. (2007) who conducted a RCT with individuals diagnosed solely with MCI (n=59). Participants were allocated into one of three groups: treatment with cholinesterase inhibitors (ChEI), ChEI plus cognitive training, or no treatment control. Cognitive training was based on a computerized software package and targeted multiple cognitive functions with increasing complexity. Participants completed 60 one-hour sessions of training over a period of nine months. Three months after the end of training, episodic memory and abstract reasoning were significantly increased in the combined ChEI+CT group, with a moderate relative effect size of 0.7 (in comparison to ChEI alone). This study therefore suggests an enduring effect of training in two areas of cognitive function above and beyond medical treatment. Transfer to general functioning was assessed with a mood scale and both treatment groups demonstrated reduced levels of depressive symptoms. The combined treatment group was also notable for a significant reduction in behavioral disturbance. This trial therefore provides clinically relevant evidence that cognitive training may be useful in MCI, an effect that is durable for at least three months and which seems to transfer to general daily function. Research Challenges for the Field The last 10 years has seen intense medical, community, and commercial interest in trying to harness the power of neuroplasticity for the prevention of age-related cognitive dysfunction. Perhaps the greatest influence has been a sociological trend away from pharmacological agents and toward behavioral and lifestyle modification and positivistic health attitudes. Yet this enthusiasm should not obscure our demand for rigorous scientific

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evidence when attempting to translate preclinical findings to individual and community interventions. The greatest challenge for the field of cognitive training and dementia prevention is in the domain of clinical trials. No trial has, for example, definitively shown that cognitive training reduces the incidence of dementia, as opposed to the rate of cognitive decline. Higher-caliber RCTs are therefore required, with close attention to implementation of active control groups, longitudinal evaluation, choice of cognitive training protocol and outcome measures, and recruitment of relevant samples. While the optimal dose, nature, and frequency of cognitive training in MCI is unclear, a trend that does emerge is that training across multiple cognitive domains leads to better long-term cognitive outcomes (Gunther et al. 2003; Rozzini et al. 2007). A basic recommendation for maximum efficacy is therefore to trial cognitive training protocols that exercise a broad range of cognitive domains using the drill-and-practice approach. This information will then need to inform wider community programs, and many more questions arise. For example, is starting a new cognitively demanding hobby as good as so many hours of computer-based cognitive training (Carlson et al. 2009)? If so, are all activities equally effective or only some? How often and what intensity of engagement is required? Is group participation better than individual practice at home? Generic issues of scalability, accessibility, economy and accountability will also need to be addressed. Clinical Recommendations In the meantime, the general public needs to be well informed about the links between cognitive lifestyle and reduced dementia risk. Given the negligible potential for harm, it is sensible to encourage all individuals to increase their levels of complex, enjoyable, and engaging cognitive activity for optimal brain health, particularly after retirement. Activities that combine cognitive, social, and physical exercise are likely to be the most powerful, and popular examples include learning to dance (Verghese et al. 2003), tai chi, learning a language and then traveling with it, among many others. Individuals should be encouraged to use their own preferences for participating in a new activity that combines these three key ingredients. There is of course a clinical obligation to also realistically manage expectations, for no intervention can guarantee the absolute prevention of dementia. An active cognitive lifestyle may help prevent dementia and minimize risk, but as yet there is no strategy that can fully eliminate this risk. An active cognitive lifestyle should therefore be part of a holistic

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risk-reduction strategy that includes blood pressure control, minimization of other cardiac risk factors, and a healthy diet. CONCLUSIONS An active cognitive lifestyle involves a lifespan interest and engagement in cognitively complex pursuits, including formal and informal education, a preference for cognitively demanding work, and cognitively loaded leisure activities. There is now consistent epidemiological evidence showing that an active cognitive lifestyle is a protective factor against dementia. The neurobiological basis for this is complex and potentially involves stimulation of several interacting neuroplastic processes. Yet most important, these mechanisms can be exploited even well into later life, such that interventions like cognitive training that boost cognitive lifestyle, appear to slow the rate of cognitive decline. There are therefore good grounds to expect that interventions based around an enhanced cognitive lifestyle may contribute to the primary prevention of dementia, but much more research is required. In the meantime, we should encourage individuals to stay mentally active, particularly after retirement, for the promotion of brain health.

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Chapter 6

Ethical Issues in the Care of Individuals with Dementia Art Walaszek

THE PRINCIPLES OF MEDICAL ETHICS Three ethical principles are fundamental to medical care in general and the care of individuals with dementia in particular: autonomy, welfare, and social justice (ABIM Foundation 2002). The principle of autonomy dictates that individuals must be allowed to make decisions about their own medical care and about their overall welfare, and to act independently and without coercion; in turn, clinicians must respect these decisions and thereby support their patients’ autonomy. Maintaining autonomy is a challenge for individuals with dementia: cognitive, emotional, behavioral, and functional impairments interfere with one’s ability to comprehend, to reason, to recall, and to have a coherent sense of self. As will be discussed in detail below, clinicians are often called upon to assess the capacity of older adults to make medical decisions, to live independently, and to manage their affairs. Clinicians are obligated to provide treatment that serves the best interests of their patients—thus the principle of patient welfare (or beneficence). In dementia care, a number of issues arise in this area: diagnosing dementia early and using biomarkers appropriately; ensuring that individuals who do not have the capacity to make decisions receive appropriate care; prescribing antipsychotic medications for behavioral and psychological symptoms of dementia; protecting vulnerable older adults from physical, emotional, and financial exploitation; and ensuring comfort at the end of life.

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The principle of social justice calls for a fair distribution of healthcare resources and an elimination of discrimination in health care (ABIM Foundation 2002). The aging population and ever-escalating costs of health care will test this principle as society debates the equitable use of scarce resources. The debatable cost-effectiveness of cognitive enhancers and the appropriate role of palliative approaches in advanced dementia are also issues of social justice. ETHICAL CHALLENGES IN THE DIAGNOSIS OF DEMENTIA The diagnosis of dementia remains essentially a clinical one, using established criteria that are reasonably accurate when compared with the gold standard, pathological examination at autopsy. Early concerns about the utility of diagnosing an irreversible, terminal condition such as Alzheimer ’s disease (AD) have waned as effective treatments have emerged (Walaszek 2009). In fact, the ethical principle of truth-telling dictates that patients must be informed of their diagnosis. Early diagnosis allows an individual to prepare advance directives and designate a power of attorney, to consider participating in research, to participate in support groups, and to decide whether or not to take a cognitive enhancer (Post 2000). Great interest now exists in biological markers that would either result in more definitive diagnosis and appropriate treatment, or that could identify individuals at risk of dementia so that preventive measures can be developed. Nevertheless, there has been understandable concern that testing for such markers in young, asymptomatic individuals, in the absence of effective preventive measures, may result in psychological distress, stigma, discrimination, and difficulty with employability. This is in particular true for genetic polymorphisms associated with AD: the ε4 allele of apolipoprotein E and mutations in the PS1, PS2, and APP genes. A 1997 position statement argued that apolipoprotein E testing should have a limited role in diagnosis and no place in screening asymptomatic individuals; PS1, PS2 and APP testing may be useful in evaluating individuals with early-onset dementia (Post et al. 1997). Several recent studies have further refined this issue. The REVEAL (Risk Evaluation and Education for Alzheimer ’s Disease) study examined the effect of genetic disclosure on adult children of patients who had autopsy-confirmed AD. The subjects randomly assigned to learn their apolipoprotein E status, including those who carried the ε4 allele, did not become more depressed or anxious than those who did not learn their status; also, subjects who learned their status became more engaged in

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activities (such as physical exercise) that may lower the risk of developing AD (Roberts et al. 2005). All subjects had extensive genetic counseling, suggesting that genetic testing in asymptomatic relatives of patients who have AD may be ethical if proper supports are in place. Consistent with this finding, the Alzheimer ’s Association argues that genetic testing should only be done when coupled with comprehensive pre- and posttest genetic counseling (Alzheimer ’s Association 2008). Because genetic risk varies by ethnicity, genetic counseling must be tailored to each individual’s ethnic background (Christensen et al. 2008). An extension of the REVEAL study to subjects tested for the PS1 or PS2 mutation (which confers a vastly greater risk of developing AD than the ε4 allele) also found that subjects who learned their genetic status experienced no greater psychological distress than those who did not (Cassidy et al. 2008). Longitudinal follow-up of the REVEAL study (six weeks, six months and one year after disclosure or nondisclosure of genetic status) confirmed that disclosure did not result in excessive distress, though a baseline high level of emotional distress was predictive after greater distress after disclosure (Green et al. 2009). Concern remains about disclosure of genetic test results. The Alzheimer ’s Association asserts that anonymous testing should be available, that is, the test result should not enter a patient’s medical record (Alzheimer ’s Association 2008). In the United States, the Genetic Information Nondiscrimination Act (GINA) of 2008 prohibits discrimination in health coverage and employment based on genetic information, which may help allay some of these concerns (National Human Genome Research Institute 2008). In summary, asymptomatic individuals receiving a positive result of genetic testing for AD may not be subject to as many psychological and other consequences as initially thought and may in fact alter their behavior in ways that could lower their risk of developing AD. Nevertheless, until effective preventive measures are developed, caution should be exercised when testing AD biomarkers in asymptomatic individuals, in particular as new biomarkers (e.g., functional neuroimaging with positron emission tomography) become available. THE ASSESSMENT OF CAPACITY IN INDIVIDUALS WITH DEMENTIA By definition, dementia results in a decline in cognition and functioning, which in turn diminishes one’s capacity to make personal choices and to care for oneself. Clinicians are often called upon to assess the capacity

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of an individual with dementia to make medical decisions. Legal systems become involved to determine if an individual is incompetent, that is, unable to make decisions about one’s welfare. Such situations involve balancing the ethical principles of autonomy and patient welfare. An individual’s ability to provide informed consent requires that information relevant to the decision is available, that the individual is free to make a choice without coercion or manipulation (voluntarism), and that the individual has the ability to make a decision (decisional capacity). Dementia can impair both voluntarism and decisional capacity. Voluntarism may be affected by illness-related considerations (e.g., executive dysfunction, apathy, inattention, memory loss, poor impulse control), psychological issues (e.g., depression, loneliness, impulsivity, anxiety, paranoia), cultural values (e.g., discomfort questioning authority), and external pressures (e.g., pressure from or coercion by caregivers) (Roberts 2002). An individual with apathy due to dementia may appear to consent to an intervention without actually having the capacity to do so (Grimes et al. 2000), thus it behooves a clinician to consider the possibility of incapacity not only when an individual refuses but also when one assents. Concerns about voluntarism are especially relevant when an individual with dementia must rely on a caregiver for assistance and when one is a resident of a long-term care facility. Having decisional capacity requires an individual to be able to express a preference, understand relevant information about a situation and the choice to be made, manipulate the relevant information and thereby reason about the situation, and appreciate how the situation is personally relevant (Roberts and Dyer 2004, 54–56). A “sliding scale” approach is widely accepted; that is, the higher the risk of accepting or refusing an intervention, the higher the threshold required for consent or refusal (Drane 1984). Although a diagnosis of dementia does not in and of itself mean that an individual has lost decisional capacity, dementia is a powerful contributor to incapacity. For example, a prospective study of subjects with mild AD found them to have, when compared to healthy older adults, impairments in the capacity domains of understanding, reasoning and appreciation that worsened over the course of two years. At baseline, 70% of AD subjects were deemed capable of reasoning, but at two-year follow-up, only 30% were; interestingly, no AD at baseline were thought to be fully capable of understanding, presumably due to deficits in short-term memory and executive function (Huthwaite et al. 2006). Another prospective study comparing 53 subjects with dementia to 53 older adult controls without dementia showed that 9.4% of subjects had incapacity at baseline, but only

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nine months later, 26.4% had incapacity, primarily due to declining ability to reason (Moye et al. 2006). Performance on neuropsychological testing has been shown to account for a significant amount of the variance in the decisional capacity among subjects with dementia, including 77.8% of the variance in understanding (Gurrera et al. 2006). Though the specifics of assessing capacity and addressing incapacity vary among jurisdictions (and clinicians should be aware of local laws and regulations), a general framework has been proposed. A clinician asks an individual with dementia a series of questions to assess her or his understanding, appreciation, reasoning and ability to express a choice (Karlawish 2008): 1. After disclosing relevant information (e.g., the risks and benefits of a treatment), the clinician asks the individual to repeat the information in her or his own words. 2. The clinician asks about the individual’s beliefs about the diagnosis of dementia (i.e, whether s/he believes s/he has dementia) and about possible benefits to her or him of treatment. 3. The clinician ascertains if the individual can compare options and can infer how a choice will affect her or him, testing for logic and consistency. 4. The individual must be able to communicate a consistent decision. Multiple instruments are available to standardize and/or augment the assessment of decisional capacity. In a review of 15 capacity assessment instruments, Dunn et al. (2006) argued that the MacArthur Competence Assessment Tool for Treatment (MacCAT-T) is the best studied and has the broadest application to various clinical situations. The MacCAT-T is based on the gold standard MacArthur capacity model. Training is required to ensure inter-rater reliability. The assessment begins with a discussion of the clinical situation, including the diagnosis, the proposed treatment, and its risks, benefits and alternatives. The interviewer asks multiple questions to assess the components of decisional capacity: understanding, reasoning and appreciation. The patient makes a choice and explains her or his reasoning. Finally, the interviewer comes to a conclusion about whether or not the patient has capacity to make the decision (Grisso, Appelbaum, and Hill-Fotouhi 1997). It has been suggested that the MacCAT-T and similar competency assessment tools are best used as adjuncts to the clinical assessment described above. Studies assessing the utility of bedside cognitive tests have yielded mixed results. Kim, Karlawish, and Caine (2002), in their review of this

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literature, argue that a Mini-Mental State Exam (MMSE) (Folstein et al. 1975) score of greater than 24 is predictive of capacity, less than 18 predictive of incapacity, with intermediate scores requiring further assessment to determine capacity. A subsequent study of 37 subjects with mild-tomoderate AD found the specificity for incapacity to be 92.9% for an MMSE cutoff of 19, and the sensitivity for incapacity to be 91.3% for a cutoff of 26 (Kim and Caine 2002). However, no bedside cognitive test alone can accurately predict capacity; rather, a clinician may incorporate such testing as part of a comprehensive capacity assessment. Whereas the preceding discussion has focused on the assessment of capacity to make medical decisions, there are a number of other capacities that may be affected by dementia.

Everyday Decisionmaking and the Ability to Live Independently At some point in the course of their illness, individuals with dementia will require assistance with their basic and instrumental activities of daily living, and they may eventually lose the ability to live independently. Individuals with AD perform poorly on standardized measures of everyday problem-solving (Willis et al. 1998). Executive dysfunction in particular has been associated with worsening functional status. Structured assessments of executive function such as the Executive Interview and of functional abilities such as the Kohlman Evaluation of Living Skills (KELS) may help identify individuals needing higher levels of care. The KELS may in particular be useful in suspected cases of self-neglect (Royall, Chiodo, and Polk 2005; Pickens et al. 2007).

Finances Difficulty managing finances is an early disruption in AD in the instrumental activities of daily living. Patients with dementia and their caregivers frequently misestimate the patients’ financial abilities, suggesting that an objective evaluation tool may be useful (Okonkwo et al. 2008). For example, the Financial Capacity Instrument has been used to determine that individuals with mild AD had high rates of impaired financial capacity (47–87%), and those with moderate AD were almost uniformly incapacitated (90–100%) (Marson et al. 2000). Concerns that arise in this setting include the risk of financial exploitation of the elder, and the need to identify a proxy to manage finances (either by invoking a durable power of attorney or by appointing a guardian).

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Driving Another early functional impairment in AD is the ability to drive, which raises the possibility of harm of self and others. The American Academy of Neurology recommends that individuals with mild cognitive impairment or mild AD should be monitored closely and should be considered for a formal evaluation of driving skills. Those with moderate or severe AD should not drive (Dubinsky, Stein, and Lyons 2000). Clinicians should familiarize themselves with local laws regarding their obligation to report potentially dangerous driving. Sexual Relations Many older adults with cognitive impairment remain sexually active. Of particular ethical interest are situations wherein one partner in a relationship has developed dementia and the other is cognitively intact. Unfortunately, the capacity of individuals with dementia to consent to sexual relations has been poorly studied. One model suggests that having any deficits in awareness of the relationship, in the capacity to avoid exploitation, or in the awareness of potential risks of sex indicates that the individual does not have the capacity to consent to a sexual relationship (Lichtenberg and Strzepek 1990). Voting A standard for assessing the capacity to vote has only recently been developed (Karlawish et al. 2004). Though most individuals with mild dementia retain the capacity to vote, those with severe dementia typically do not have that capacity (Appelbaum, Bonnie, and Karlawish 2005). In the United States, surrogate decision-makers are not allowed to cast votes for those who do not have capacity (Karlawish et al. 2004). On the other hand, individuals with capacity who reside in long-term care facilities may be at risk of being disenfranchised because of procedural problems (Karlawish, Bonnie, et al. 2008). Testamentary Capacity One of the rationales for early diagnosis in dementia is that the individual is more likely to have the capacity to make decisions about her or his future, including creating an advance directive, identifying powers of attorney, and executing a will. The International Psychogeriatric

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Association has developed guidelines for the assessment of testamentary capacity (Shulman et al. 2009). ADDRESSING INCAPACITY A finding of incapacity should lead to (a) the identification and addressing of any reversible causes of incapacity; (b) attempts to improve capacity by means of cognitive and educational strategies; and/or (c) surrogate decision-making. Although most causes of dementia are irreversible, there may be other contributions to incapacity, including depression, anxiety, delirium, and polypharmacy (Walaszek 2009). Given that cognitive enhancers may improve cognition and functioning in a subset of individuals with dementia, it is possible that treatment with a cognitive enhancer may improve capacity—although this hypothesis has yet to be tested, and it presupposes that an individual has capacity to consent to treatment with a cognitive enhancer in the first place. A variety of strategies have been developed to improve capacity in older adults to make medical decisions, including simplifying information, providing it in the form of a story book or video, disclosing information in parts, using health educators, and quizzing subjects (Sugarman, McCrory, and Hubal 1998). As described below, an “enhanced written consent procedure” has been developed to improve the capacity of individuals with dementia to participate in research (Mittal et al. 2007). Roberts and Dyer (2004, 70) recommend a stepwise approach for consent in individuals with deficits in decisional capacity: seek consent to simply begin treatment; then, as the individual’s symptoms (and presumably capacity) improve, discuss more substantive or challenging choices. When capacity cannot be restored, a surrogate or proxy decisionmaker must step in. The identity of the decisionmaker depends on whether or not the incapacitated individual had previously designated a surrogate, namely, a power of attorney. Often individuals have not designated a power of attorney; in such cases, most jurisdictions have laws that describe the sequence of which family members (e.g., spouse, adult children, siblings, and so on) become surrogate decisionmakers. The “best interest” and “substituted judgment” standards govern the behavior of surrogate decisionmakers. In the former, the surrogate makes a decision based on what the surrogate perceives is in the best interest of the individual with dementia. In the latter, the surrogate makes a decision based on the choice the individual would have made, if the individual had been able to do so; in some cases, the individual may have created

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an advance directive to express future preferences and thereby guide this decision-making. A combined approach has been recommended, wherein the substituted judgment standard is applied when it is clear what the individual would have decided; otherwise, the best interest standard is used (Gutheil and Appelbaum 2000, 232–233). A capacity assessment yields a dichotomous outcome: an individual is deemed able or not able to make a medical decision. In reality, however, individuals with dementia often collaborate with family members and other caregivers in decisionmaking. This has been referred to as a “shared decisionmaking” model, wherein the individual can make a decision with assistance or the responsibility for decisionmaking is shared among the parties; unfortunately, legal procedures are generally not available to support this pragmatic approach (Kapp 2002). When an individual has been found incapable of making everyday decisions, managing her or his affairs, and living independently, a court will declare her or him incompetent and appoint a guardian. Clinicians should be aware of local laws governing guardianship proceedings, especially because they may be called upon to testify regarding the individual’s mental state and decisional capacity. Moye et al. (2007) have reviewed the process of evaluating an individual for guardianship. ETHICAL CHALLENGES IN THE USE OF COGNITIVE ENHANCERS Diagnosing dementia, especially early in the course of the illness, offers individuals the opportunity to decide on treatment with cognitive enhancers. Although currently available cognitive enhancers (donepezil, rivastigmine, galantamine, and memantine) do not reverse cognitive losses or modify the course of dementia, a subset of patients may benefit from a clinically significant delay in cognitive decline (Qaseem et al. 2008). However, a number of ethical concerns have been raised about the use of these agents. First, an individual with dementia may have diminished capacity to consent to treatment with a cognitive enhancer. A study of 48 subjects with mild or moderate AD found that only 40% had the capacity to consent to cognitive enhancing treatment (Karlawish et al. 2005). Anosognosia, or lack of recognition that one has dementia, interferes with capacity as well. Even if an individual initially consents, it is possible that, due to cognitive decline, s/he will no longer be able to consent to continued treatment. Thus, clinicians may need to assess to capacity of an individual with dementia to provide informed consent, as described above.

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Post et al. (2001), citing qualitative data from focus groups of patients, caregivers, and clinicians, raised the possibility of instilling false hope among patients and caregivers regarding the outcome of treatment with cognitive enhancers. However, a more recent study employing semistructured interviews of 12 caregivers of patients who had used cognitive enhancers failed to replicate this finding. Interestingly, the authors noted that “some hope is necessary to continue to live through the situation” (Huizing et al. 2006). If cognitive enhancers prolong morbidity or extend life, then treatment will increase suffering rather than decrease suffering (Post and Whitehouse 1998). However, there is no evidence that the use of cholinesterase inhibitors alone or in combination with memantine is associated with a change in time to death. This study also replicated earlier findings that the use of cognitive enhancers is associated with delayed time to nursing home placement (Lopez et al. 2009). The cost-effectiveness of cognitive enhancers is uncertain, raising concerns about social justice and the appropriate use of limited resources. The cost of donepezil may be over US$120,000 per quality-adjusted year of life, and the savings resulting from delayed institutionalization may not offset the cost of medication (Loveman et al. 2006). The National Institute for Health and Clinic Excellence (NICE) in the United Kingdom has responded to these concerns by limiting the use of cholinesterase inhibitors to patients with moderately severe AD (MMSE score between 10 and 20) and recommending against the use of memantine (NICE 2007). A review of the accessibility of cognitive enhancers in 23 of 26 European Union nations revealed widely varying criteria for use, with upper limits of MMSE ranging between 20 and 30 and lower limits of MMSE between 10 and 13 for cholinesterase inhibitors; for memantine, the respective ranges were 11–26 and 0–13. There were also marked differences in policies regarding who could prescribe cognitive enhancers (general practitioners versus specialists) and reimbursement for cognitive enhancers (Oude Voshaar, Burns, and Olde Rikkert 2006). It is unlikely that such variability is solely due to different interpretations of the research literature regarding cost-effectiveness; rather, local political and financial considerations probably contribute to the variability. The combined use of a cholinesterase inhibitor and memantine may further increase costs, though a clinical trial is currently underway to determine the cost-effectiveness of combination therapy (Jones et al. 2009). Finally, it is not clear when to discontinue treatment with cognitive enhancers. In addition to evidence of modest benefit in severe dementia for cholinesterase inhibitors (Winblad et al. 2009) and memantine (Thomas and Grossberg 2009), there are reports of declines in cognition,

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functioning, or behavior with discontinuation of cognitive enhancers (e.g., Daiello et al. 2009). Blass et al. (2008) found that 30% of nursing home residents with advanced dementia were prescribed cognitive enhancers, though the prevalence decreased to 10% by the time of death. A consensus panel of geriatricians at the University of Chicago concluded that the use of cholinesterase inhibitors and memantine was “never appropriate” in the palliative care of advanced dementia (Holmes et al. 2008), yet a study of 10,065 individuals with advanced dementia admitted to U.S. hospices found that 21% were prescribed a cognitive enhancer (Weschules, Maxwell, and Shega 2008). Clinicians, patients, and proxy decisionmakers should closely collaborate in the on-going monitoring of the risks and benefits of continued treatment with cognitive enhancers. Further research is required to determine at what point to discontinue treatment with cognitive enhancers. ETHICAL CHALLENGES IN THE TREATMENT OF BEHAVIORAL AND PSYCHOLOGICAL SYMPTOMS OF DEMENTIA Although dementia is defined in terms of cognitive impairment and functional decline, emotional and behavioral symptoms are also very common. In fact, at least 80% of individuals with dementia will experience behavioral and psychological symptoms of dementia (BPSD) such as depression, anxiety, psychosis, and agitation (Lyketsos et al. 2002). These symptoms may lead to poor patient quality of life, increased caregiver burden, concerns about patient safety, and higher risk of institutionalization. Atypical antipsychotics, the pharmacological agents most likely to be effective in addressing these symptoms, are associated with significant morbidity and mortality (Schneider, Dagerman, and Insel 2006). A meta-analysis of trials of atypical antipsychotics in dementia found a mortality rate of 3.5% versus 2.3% for placebo, with an odds ratio of 1.54 (95% confidence interval, 1.06–2.23) (Schneider, Dagerman, and Insel 2005). In April 2005 the U.S. FDA added a black-box warning advising against the use of antipsychotics in older adults with dementia. Clinical treatment guidelines (e.g., Lyketsos et al. 2006) recommend a sequential strategy to addressing BPSD that includes: • first identifying and addressing possible medical causes (delirium, pain, dehydration, urinary tract infection, and so on); • then using evidence-based nonpharmacologic interventions (behavioral management, cognitive stimulation, socialization); • then considering pharmacological interventions if BPSD cause significant distress or are potentially dangerous.

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Additional safeguards exist in nursing homes by means of U.S. federal regulations limiting the initiation of psychotropic medications to specific indications, requiring regular review of medications by pharmacists, and requiring trials of drug discontinuation (Kapp 2009, 478); see below for details. Nevertheless, pending the development of more effective treatments, antipsychotic medications will likely remain the mainstay of managing severe BPSD. Clinicians thus face the ethical dilemma of recommending a treatment that may hasten death. A further complication is that the patients themselves rarely have the capacity to consent to treatment with antipsychotics, and so a surrogate decisionmaker must be involved. Thus, a clinician considering prescribing an antipsychotic medication for BPSD must very carefully review the risks, benefits, and alternatives of such treatment with patients and their surrogates, emphasizing that these medications are not appropriate long-term solutions (Lyketsos et al. 2006). As a result of behavioral disturbances that are imminently dangerous to self or others, an individual with dementia may require a higher level of supervision and care, for example, a psychiatric hospitalization. Local regulations regarding involuntary psychiatric treatment vary tremendously, and the clinician must be familiar with the standards and laws in her or his own jurisdiction. In general, criteria for involuntary commitment include that the patient must, as a result of being mentally ill, pose an imminent danger to self, pose an imminent danger to others, or be unable to care for self; some jurisdictions allow for involuntary hospitalization if, in the absence of treatment, severe deterioration in the patient’s condition is likely (Gutheil and Appelbaum 2000, 52–53). Voluntary psychiatric hospitalization is complicated by the high rates of incapacity to make medical decisions among individuals with dementia. For example, a study of 379 older adults admitted to general medical units and then assessed for capacity found that 59% lacked medical decisionmaking capacity, with dementia and delirium being major contributors to incapacity (Mujic et al. 2009). Maxmin et al. (2009) interviewed 99 older adult psychiatric inpatients, only 52.5% of whom had capacity for admission and 38.4% for treatment decisions; again, dementia was associated with incapacity. It has been suggested that a lower threshold for capacity for admission is acceptable as long as patients agree to “enter the hospital and some in-hospital review process [is] available to pass [judgment] on the appropriateness of their decision” (Gutheil and Appelbaum 2000, 48). Because medical causes of BSPD, including delirium, are common, it is critical that a patient admitted to the hospital receive an appropriate

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medical evaluation, either by admission to a medical unit initially or by medical consultation on a psychiatric unit. ELDER ABUSE AND SELF-NEGLECT Individuals with dementia are vulnerable to victimization by others or, more commonly, to self-neglect. Types of elder abuse include physical abuse, emotional or psychological abuse, sexual abuse, material abuse (i.e., financial exploitation), neglect, abandonment, and self-neglect. Rates of elder abuse range from 4.5 to 14.6 per 1,000, with an estimated 1–2 million cases each year in the United States (Jogerst et al. 2003). A recent survey of American older adults indicated that 1 in 10 had experienced elder abuse in the last year (Acierno et al. 2010). Risk factors for elder abuse include a diagnosis of dementia, presence of BPSD, low social supports, need for help with activities of daily living, female gender, low socioeconomic status, African American ethnic background, declining physical function, caregiver burden and caregiver depression/anxiety (Cooper et al. 2010; Acierno et al. 2010). Family members are implicated in 90% of elder abuse cases. After self-neglect, financial exploitation is the most common type of elder abuse (see Tueth 2000 for a review of this topic). All 50 states have laws to protect older adults from abuse and neglect, with four states (Colorado, New York, North Dakota, South Dakota) allowing for voluntary reporting and all other states requiring mandatory reporting when healthcare professionals suspect elder abuse (Walaszek 2009). Surveys of healthcare professionals indicate that 33.7–39.9% had detected elder abuse in the last year, but only half had reported suspected abuse (Cooper et al. 2009). Face-to-face training of health care professionals about the management of suspected abuse appears to increase knowledge and increase reporting rates (Cooper, Selwood, and Livingston 2009). Clinicians should familiarize themselves with local laws regarding the reporting of elder abuse. Those working with patients with dementia should maintain a high index of suspicion of self-neglect. Self-neglect is the most common form of elder abuse and can be difficult to detect because individuals with dementia may not be able to provide accurate reports of their functioning; caregivers and family members are typically not available to report the self-neglect. Self-neglect has been associated with a marked increase in risk of mortality (Dong et al. 2009). Concern about self-neglect should lead to a clinician formally assessing the individual’s capacity to live independently, as described above. Relevant local authorities tasked with addressing elder abuse may need to be contacted.

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Clinicians face an ethical quandary when they suspect elder abuse, but the elder does not wish to have the abuse reported; this represents a tension between the ethical principles of beneficence and autonomy. In such situations, clinicians will need to carefully evaluate the reasons why the patient does not want abuse reported (e.g., fear of retaliation), determine if the patient believes s/he is abused, and assess the patient’s capacity to refuse further intervention. Of course, if there is evidence that an individual is in immediate danger, local authorities must be contacted. ETHICAL ISSUES RELEVANT TO LONG-TERM CARE FACILITIES Among the most challenging transitions for an individual with dementia is the move from one’s home into a long-term care facility. Ethically, institutionalization is a response to the changing balance between the principles of autonomy and beneficence. That is, as an individual with dementia becomes progressively less able to care for herself or himself, some autonomy may be lost in order to support her or his well-being. The individual with dementia may not have the capacity to make this decision or may actively refuse admission to a long-term care facility, thereby necessitating the involvement of surrogate decisionmakers or legal processes such as guardianship. The assessment of capacity to live independently is discussed above. Clinicians and their patients appear to disagree frequently on the appropriateness of institutionalization (Bartels et al. 2003) and there are significant cross-cultural differences in attitudes toward long-term care (McCormick et al. 2002). Individuals in long-term care face substantial limitations on making personal choices, affecting their autonomy and voluntarism (Kapp 1998). Thus, when an individual with dementia is no longer able to live independently, careful consideration should be made of her or his beliefs and values when determining an appropriate next step. For example, alternatives to institutionalization may include increased services within one’s home and increased support for family members serving as caregivers. Though only 4% of individuals with dementia live in a long-term care facility at any one time (Matthews and Dening 2002), approximately 90% will eventually move to a long-term care facility, compared to 50% of nondemented older adults (Smith, Kokmen, and O’Brien 2000). The majority of nursing home residents (56% in one estimate) have dementia (Matthews and Dening 2002), and the rates of incapacity to make medical decisions are high (Moye and Marson 2007).

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Extensive U.S. federal regulations (referred to collectively as OBRA 1987) govern the behavior of nursing homes (Kapp 2009, 466). Nursing homes must comply with Medicaid regulations (CMS, or Centers for Medicare and Medicaid Services), with the Americans with Disabilities Act and the Rehabilitation Act, with standards of accrediting bodies (e.g., the Joint Commission), and with state regulations. These measures are intended to insure the appropriate treatment of nursing home residents with neuropsychiatric issues, including dementia and BPSD. Screening must take place prior to admission to a nursing home to determine if a patient has mental-health needs and if that facility can offer appropriate services for those needs (Kapp 2009, 468, 469). Residential care and assisted living facilities have emerged as common alternatives to nursing homes; rates of dementia and BPSD are similar in these settings to nursing homes, but with less oversight and with regulation that varies from state to state (Gruber-Baldini et al. 2004). The use of physical restraints has increasingly been recognized as often “unnecessary, improper, and even abusive.” The CMS have encouraged the aggressive enforcement of federal regulations limiting the use of “any physical restraints imposed or psychoactive drug administered for purposes of discipline or convenience, and not required to treat the resident’s medical symptoms” (Kapp 2009, 476, 477). In order to respect the autonomy and preserve the dignity of nursing home residents, facilities should address BPSD by employing nonpharmacologic interventions (as described above), improving the environment (e.g., more activities for residents to prevent boredom), and/or making administrative or staff changes. A randomized controlled trial in four Norwegian nursing homes of a two-day staff training followed by six monthly meetings was associated with decreased use of restraints at 6-month follow-up (though not at 12 months), and decreased BPSD among nursing home residents at both 6 months and 12 months (Testad et al. 2010). This suggests that training, regular monitoring and on-going guidance are necessary to promote and maintain changes in the use of restraints. A goal of OBRA 1987 regulations has been to prevent the use of psychotropic medications as “chemical restraints” or for environmental control, though it is recognized that there are appropriate uses of psychotropic medications. OBRA 1987 has resulted in decreased use of antipsychotic medications and increased use of antidepressant medications, the former through strict guidelines about the use of antipsychotics and the latter through increased detection of depression (Lantz, Giambanco, and Buchalter 1996). As described above, antipsychotic medications may

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have a role in the treatment of severe BPSD, but this decision should be weighed carefully with proxy decisionmakers. OBRA 1987 requires that antipsychotic medications be given only if they are “necessary to treat a specific condition as diagnosed and documented in the clinical record.” CMS considers a “dementing illness with associated behavioral symptoms” a valid indication for antipsychotic medications, but the symptoms must be dangerous to self or others or must result in severe distress or decline in functioning. The resident’s record must include documentation of the decisionmaking process, including diagnostic evaluation of BPSD, alternative nonpharmacologic interventions, and risk-benefit analysis. Furthermore, efforts must be made to discontinue antipsychotic medications (unless clinically contraindicated) by means of gradual dose reductions and behavioral interventions (CMS 2009). Clinicians prescribing psychotropic medications in long-term care facilities should familiarize themselves with relevant regulations. RESEARCH INVOLVING INDIVIDUALS WITH DEMENTIA Clinical research is essential for developing effective preventive, diagnostic, and treatment strategies in dementia care. However, by virtue of their cognitive impairment, individuals with dementia may have diminished capacity to consent to participate in research. In this section, we discuss assessing the capacity of a potential research subject to provide informed consent, methods of increasing capacity to participate in research, the use of surrogate decisionmakers to provide informed consent, and advance directives for participation in research. Dunn and Misra (2009) have reviewed the literature on ethical issues in research involving older adults with neuropsychiatric disorders, including dementia. They note that a research setting is inherently different from a clinical setting, as “participation is voluntary and the risk-to-benefit ratio is typically skewed toward more direct risk with less direct benefit.” Individuals with AD have diminished capacity to consent to clinical research compared with older adults with schizophrenia or diabetes mellitus (Palmer et al. 2005). Capacity varies significantly among those with AD, though severity of cognitive impairment is a strong predictor of capacity and individuals with AD eventually lose capacity (Dunn and Misra 2009). The MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR) (Appelbaum and Grisso 2001) is a widely accepted and well-validated instrument to measure capacity to participate in research (Grisso and Appelbaum 1995). This tool is analogous to the MacCAT-T

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described above. The MacCAT-CR is adapted for discussion of the specific research study; over the course of 15–20 minutes, information is gathered about the potential subject’s understanding, appreciation, reasoning, and ability to express a choice. Though some element of subjectivity remains in the assessment of capacity using the MacCAT-CR, this systematic and structured approach may allow for specific impairments in decisional capacity to be identified and perhaps addressed (Dunn and Misra 2009). Methods have been developed to enhance an individual’s capacity to participate in research. Mittal and colleagues (2007) studied the effectiveness of a multimedia presentation describing a hypothetical clinical trial versus that of an “enhanced written consent procedure” in 35 subjects with AD or mild cognitive impairment. As assessed by the MacCAT-CR, subjects in both study groups demonstrated an improvement in their understanding of the trial. Further research is required in larger samples, in more diverse populations, and involving different types of hypothetical studies. Surrogate decisionmakers may, under certain circumstances, provide consent for participation in research. According to an Alzheimer ’s Association position statement (1997), a proxy may provide consent in the following circumstances: • if the research entails minimal risk to the individual with dementia; • if the research entails greater than minimal risk, but there is also “a reasonable potential for benefit to the individual”; • if the research entails greater than minimal risk and there is no reasonable potential for benefit, but the individual had executed an advance directive for research (see below)—in which case, the role of the surrogate is to “monitor the individual’s involvement in the research.” It should be noted, however, that standards vary significantly among organizations, regulatory bodies, and legal statutes, so individual institutional review boards have reached different conclusions regarding the appropriateness of surrogate consent. Surveys of older Americans seem to indicate strong public support for surrogate consent for dementia research. Kim et al. (2009) surveyed a subset (N=1,515) of subjects from the Health and Retirement Study, a nationally representative study of persons 51 and older. Subjects were randomized to review one of four plausible AD research scenarios: a study involving lumbar puncture (LP), a randomized controlled trial (RCT) of a new drug, a vaccine study, or a gene transfer neurosurgical study. When

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subjects were asked to consider if they would allow a close family member to provide consent for them to participate in these hypothetical studies, 70.8% responded affirmatively to the LP study, 79.7% to the RCT, 57.4% to the vaccine study, and 68.7% to the gene transfer study. When subjects were asked if our society should allow families to consent for individuals who cannot consent themselves, the affirmative responses were, respectively, 72.0%, 82.5%, 70.5% and 67.5%. Predictors of assenting to proxy consent included a personal willingness to participate in research, female gender, being married (versus not married), and excellent self-related health; ethnic background was not a predictor, and support for proxy consent was strong among African Americans and Hispanics. Other studies (e.g., Karlawish et al. 2009) have similarly demonstrated strong public support for surrogate consent for dementia research. The use of a surrogate begs the question of what method the surrogate should use when deciding whether or not to enroll an individual with dementia in a study. As noted above, there are two standards governing the behavior of surrogate decisionmakers: best interest and substituted judgment (Gutheil and Appelbaum 2000, 232). Studies investigating which standard surrogates follow in dementia research have generally yielded support for the best interest model. Karlawish, Kim, et al. (2008) surveyed the views of research subjects and their “study partners” enrolled in a randomized control trial of simvastatin for the treatment of AD; the results indicated that study partners (some of whom were also surrogate decisionmakers) followed the best-interest standard. Interestingly, study partners were highly involved in decisionmaking whether or not they were formally identified as surrogates, suggesting that a shared decisionmaking model, as described above, may apply in dementia research. It is possible that an individual with intact decisionmaking capacity could execute an advance directive for participation in dementia research. However, a controlled trial of a research advanced directive in 149 subjects with dementia and their proxies did not demonstrate altered study enrollment rates, decision ease or proxy comfort and certainty compared to a control group (Stocking et al. 2007). Furthermore, concerns have been raised about the inability of a research advance directive to address the specific information required to consent to a particular study (Dunn and Misra 2009). END-OF-LIFE CARE IN INDIVIDUALS WITH DEMENTIA The terminal stage of dementia is marked by severe cognitive impairment and a high rate of morbidity and mortality. For example, a prospective

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18-month study of nursing home residents with advanced dementia (mean MMSE score of 5) revealed a mortality rate of 54.8%; medical morbidities included poor eating in 85.5% of residents, a febrile episode in 52.6%, and pneumonia in 41.1%, each of which in turn increased the risk of death. Distressing symptoms were common: 46.0% of residents experienced dyspnea and 39.1% had pain. Advanced dementia has a life expectancy similar to metastatic breast cancer and stage IV congestive heart failure (Mitchell et al. 2009). It is therefore clinically and ethically appropriate to employ a palliative care approach for individuals who have severe dementia, including a focus on promoting patient comfort, avoiding hospitalization and surgery, respecting advance directives, and employing do-not-resuscitate orders (American Academy of Neurology Ethics and Humanities Subcommittee 1996). Post (2000) also presents this issue in terms of social justice: The cost to society of interventions to extend the life of patients who have advanced dementia may not be just or justifiable. Whereas a terminally ill but cognitively intact individual may personally decide to receive care that is focused on comfort, a proxy decisionmaker must be involved in palliative-care decisions for individuals with dementia. Mitchell et al. (2009) found that only 18.0% of proxies had received prognostic information from a physician. Patients whose proxies believed that the patient had less than 6 months to live and understood the clinical complications of dementia were markedly less likely to undergo burdensome interventions (hospitalization, tube-feeding, parenteral therapy) (Mitchell at al. 2009). Thus, surrogate decisionmakers must receive adequate information and support in order to ensure that individuals with advanced dementia receive appropriate palliative care (Hertogh 2006). Interestingly, clinicians themselves may need further education in this regard: In a study of patients with advanced dementia admitted to nursing homes, only 1.1% were perceived to have a life expectancy of less than six months, whereas 71.0% died during that period (Mitchell, Kiely, and Hamel 2004). Among the most challenging steps is the withholding of artificial hydration and nutrition. As noted above, stopping eating is often a precursor of death in individuals with dementia. Feeding tubes probably do not reduce a patient’s suffering and may, in fact, cause suffering (Gillick 2000). An Alzheimer ’s Association position statement argues that it is ethically permissible to withhold nutrition and artificial hydration from individuals who refuse to eat or drink (Alzheimer ’s Association 1988). Roberts and Dyer (2004, 186–191) provide a useful framework for ethical issues at the end of life, including addressing the patient’s physical and psychological pain; ensuring that all decisions are consistent with

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the patient’s values, preferences, and spiritual beliefs; addressing difficult issues with the patient and caregivers; and consulting all relevant parties, including family members, other healthcare providers, attorneys, and spiritual figures. CONCLUSIONS Many clinical decisions in the care of individuals with dementia are inherently ethical decisions. The very nature of dementia is to progressively rob an individual of her or his capacity to remember, to make personal decisions, and to care for oneself—thereby striking at the core of autonomy. Yet, each individual is unique in terms of her or his own beliefs, cultural background, personality, life experiences, relationships, and progression of illness. Thus, clinicians, patients, and their caregivers must work together to carefully review the risks and benefits of options related to the diagnosis and treatment of dementia, management of distressing symptoms, participation in research trials, institutionalization, and endof-life issues. Because individuals with dementia are vulnerable to abuse and exploitation, clinicians must be especially vigilant for any evidence of elder abuse. Given aging world populations, discussions must take place at a societal level regarding the fair and just distribution of medical resources—while not forgetting that it is our moral obligation to address the suffering of individuals with dementia. REFERENCES ABIM Foundation. 2002. Medical professionalism in the new millennium: A physician charter. Annals of Internal Medicine 136: 243–246. Acierno, R., M. A. Hernandez, A. B. Amstadter, H. S. Resnick, K. Steve, W. Muzzy, and D. G. Kilpatrick. 2010. Prevalence and correlates of emotional, physical, sexual, and financial abuse and potential neglect in the United States: The National Elder Mistreatment Study. American Journal of Public Health 100: 292–297. Alzheimer ’s Association. 1988. Treatment of patients with advanced dementia. http://www.alz.org/national/documents/statements_advancedementia. pdf (accessed April 26, 2010). Alzheimer ’s Association. 1997. Ethical issues in dementia research (with special emphasis on “informed consent”). http://www.alz.org/national/documents/ statements_ethicalissues.pdf (accessed April 24, 2010). Alzheimer ’s Association. 2008. Genetic testing. http://www.alz.org/national/ documents/statements_genetictesting.pdf (accessed April 24, 2010).

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Sugarman, J., D. C. McCrory, and R. C. Hubal. 1998. Getting meaningful informed consent from older adults: A structured literature review of empirical research. Journal of the American Geriatrics Society 46: 517–524. Testad, I., C. Ballard, K. Brønnick, and D. Aarsland. 2010. The effect of staff training on agitation and use of restraint in nursing home residents with dementia: A single-blind, randomized controlled trial. Journal of Clinical Psychiatry 71: 80–86. Thomas, S. J., and G. T. Grossberg. 2009. Memantine: A review of studies into its safety and efficacy in treating Alzheimer ’s disease and other dementias. Clinical Interventions in Aging 4: 367–377. Tueth, M. J. 2000. Exposing financial exploitation of impaired elderly person. American Journal of Geriatric Psychiatry 8: 104–111. Walaszek, A. 2009. Clinical ethics issues in geriatric psychiatry. Psychiatric Clinics of North America 32: 343–359. Weschules, D. J., T. L. Maxwell, and J. W. Shega. 2008. Acetylcholinesterase inhibitor and N-methyl-D-aspartic acid receptor antagonist use among hospice enrollees with a primary diagnosis of dementia. Journal of Palliative Medicine 11: 738–745. Willis, S. L., R. Allen-Burge, M. M. Dolan, R. M. Bertrand, J. Yesavage, and J. L. Taylor. 1998. Everyday problem solving among individuals with Alzheimer ’s disease. Gerontologist 38: 569–577. Winblad, B., S. E. Black, A. Homma, E. M. Schwam, M. Moline, Y. Xu, C. A. Perdomo, J. Swartz, and K. Albert. 2009. Donepezil treatment in severe Alzheimer ’s disease: A pooled analysis of three clinical trials. Current Medical Research and Opinion 25: 2577–2587.

Chapter 7

Cognitive Screening and Neuropsychological and Functional Assessment: Contributions to Early Detection of Dementia Mônica Sanches Yassuda, Mariana Kneese Flaks, and Fernanda Speggiorin Pereira

As the elderly population increases, dementia and depression have become the most prevalent neuropsychiatric disorders among aged individuals (Ferri et al. 2005). Considering the fact that dementia is a neurodegenerative disease with progressive neuronal loss, it becomes a medical and social problem in a large and growing scale. The final diagnosis of most dementia syndromes depends on neuropathologic examination. However, a thorough clinical examination including anamnesis, psychiatric evaluation, neuropsychological assessment, and physical and neurological examination, combined with biochemical tests and neuroimaging, could enhance the accuracy of early and differential diagnosis. Technological innovations that use structural and functional neuroimaging techniques as well as molecular biology and molecular genetics procedures have provided new tools that can facilitate the early diagnosis of dementias, especially of Alzheimer ’s disease (Ho et al. 2010; Hampel et al. 2008; Shaw et al. 2007). The expected advances in pharmacological treatment, seeking to modify pathogenic processes, increase the need to identify the disease in its early stages, before dysfunctional and severe cognitive deficits are established

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(Bischkopf, Busse, and Angermeyer 2002). As a result, differential diagnosis carries therapeutic and prognostic implications. Despite the important scientific progress related to imaging and biological markers, the diagnosis of dementia syndromes remains a clinical process, supplemented by relevant investigations such as cognitive testing (Portet et al. 2006). Due to the difficulties in identifying the early signs of dementia only by means of clinical assessment and routine medical examination, cognitive assessment is a key tool that can improve diagnostic accuracy. In this context, cognitive screening tools, which are used at the beginning of the diagnostic process, and the neuropsychological instruments, which are used in more extensive investigations, are required. Neuropsychological assessment can often point toward patterns of cognitive alteration that are typical of dementia. The tests are sensitive to brain dysfunction and identify damaged areas that are not yet evident in imaging and in an electroencephalogram. Neuropsychological testing may define the location and lateralization of brain dysfunction related to behavioral impairment by quantifying changes in sensory or motor functions and by examining more complex brain functions such as language, visual-spatial awareness, verbal and nonverbal memory (Lezak, Howieson, and Loring 2004). Neuropsychological assessment is important when the clinical pattern is ambiguous or complex, making it possible to reliably identify different types of dementia early in the course of the disease, and to distinguish dementia from normal aging or from other illnesses. Additionally, it provides guidance to physicians and to family members with regards to deficit compensation strategies for a particular patient. It can also provide information concerning conduct and therapeutic options during the disease (Lezak, Howieson, and Loring 2004). Furthermore, consecutive cognitive testing presents objective data as to whether changes in the clinical profile are occurring as expected for a given diagnosis. In what follows, we review a range of validated neuropsychological assessment tools that can facilitate accurate diagnosis of an early dementing process in aged individuals. We provide guidance on interpretive strengths and weakness of each of these tools. We emphasize our experience within the Brazilian context in order to provide a more global picture of the contributions of neuropsychological markers for the dementias, in addition to the standard North American account of this emerging area of study and practice. According to criteria from the National Institute for Communicative Disorders and Stroke–Alzheimer ’s Disease and Related Disorders

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Association (NINCDS-ADRDA Work Group—Dubois et al. 2007), before recommending extensive and often costly neuropsychological assessment, screening tools should be used to verify the need for this procedure in case of possible dementia. The purpose of the screening tool is different from that of the neuropsychological instrument. The screening process indicates the likelihood of dementia. If results from the screening are positive, the assessment may ratify or reject the diagnostic hypothesis. COGNITIVE SCREENING TOOLS Shulman and Feinstein (2003) state that the ideal screening test should be: 1. Brief 2. Well accepted by patients without giving rise to discomfort or defensive reactions 3. Easy to apply and to review 4. Relatively free of confounding elements such as schooling, culture and language 5. Have good psychometric properties such as reliability and validity regardless of the tester and in test-retest situations, sensitivity, specificity, and high positive and negative predictability 6. Cover a representative range of intellectual functions On the other hand, screening tests should be used and interpreted with caution, considering that there is no flawless cognitive assessment tool. Special attention should be paid to the likelihood of a high percentage of false-negatives, when the screening is conducted in early stage dementia, or in individuals with high intellectual levels or with many years of schooling (Katzman 1993; Stern et al. 1994; Cummings et al. 1998). Falsepositive results may occur among healthy individuals with low schooling. Few years of schooling and illiteracy are associated with a greater prevalence of dementia (De Ronchi et al. 1998; Herrera et al. 2002; Bottino et al. 2008), yet many individuals who cannot read score within ranges typical of dementia even though their functions are completely preserved. This emphasizes the importance of tests that are less vulnerable to educational experience or for normative values corrected for education. Special attention should be paid to developing countries (Ferri et al. 2005; Yassuda et al. 2009), where underschooling is frequently observed among elders. Some cognitive screening tools specifically designed for suspected dementia are described below. Diagnostic accuracy data is also reviewed.

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Mini Mental State Examination The Mini Mental State Examination (MMSE) (Folstein, Folstein, and McHugh 1975) is a widely used and studied screening tool to detect changes in cognition in geriatric patients in many countries. It is part of an array of neuropsychological batteries such as the CAMDEX-R (Cambridge Examination for Mental Disorders of the Elderly) (Roth et al. 1986) and the CERAD battery (The Consortium to Establish a Registry for Alzheimer ’s Disease) (Morris et al. 1989). The MMSE provides objective assessment of cognition through questions clustered into seven categories: 1. 2. 3. 4.

Orientation to time Orientation to place Memorization of three words (immediate memory) Attention and calculation (subtract the number 7 from 100 five consecutive times) 5. Recall of the previous three words (delayed memory) 6. Language (objects naming, repetition of a sentence, execute a verbal and a written command, write a sentence) 7. Visual construction (copy of a drawing)

The scores range from 0 to 30, and the overall score decreases as cognitive impairment increases. It takes little more than five minutes, a pencil, and a sheet of paper to administer. In developing countries, where a wide range of educational profiles can be found, schooling must be taken into account before administering the test. In addition, when testing minorities in developed countries who may not have received more than a high school education, caution must be used when interpreting MMSE performance. Therefore, the cutoff scores for dementia syndromes may need to be adjusted according to the educational level. Studies from different countries have been seeking to establish differential cutoff points for specific educational strata, for high and low educational levels, to improve accuracy in identifying possible cases of dementia (Ostrosky-Solis, Lopez-Arango, and Ardila 2000; Rosselli et al. 2000; Espino et al. 2001; de Silva and Gunatilake 2002; Brucki et al. 2003; Xu et al. 2003; Reyes-Beaman et al. 2004; Simpao et al. 2005; Crane et al. 2006; Laks et al. 2007; O’Bryant et al. 2008, Kohn et al. 2008). In addition, cross-cultural comparisons of the MMSE in different countries provide information regarding the cut-off scores in each population (Gibbons et al. 2002; Jones 2006; Dodge et al. 2009).

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Clock Drawing Test Spreen and Strauss (1998) wrote a thorough review on use of the Clock Drawing Test (CDT), indicating it has been widely used as part of short mental tests in neurological investigations. Moreover, its use is often recommended as a screening test in suspected dementia cases. The first systematic use of the CDT was reported by Goodglass and Wingfield (1993), and the test was included as part of the Boston Aphasia Battery. Since then, different application and correction protocols for the CDT have been developed (Shulman 2000). Due to this fact, no specific CDT norms will be cited in this chapter. Unlike most assessment tools for dementia, which emphasize memory and attention in verbal domains, the CDT is based on visuo-spatial skills, dependent upon motor execution skills (Sunderland et al. 1989). The test assesses, in addition to visuo-spatial functions, the executive ability needed to recreate the memory of a clock face from a verbal command and translate it into a graphic image (Spreen and Strauss 1998). This constructive praxis involves not only visuo-perceptual analysis but also motor execution, attention, language comprehension and understanding of numbers (Mendez, Ala, and Underwood 1992). The CDT can be carried out in a variety of ways regarding directions and scoring, based on the concepts devised by different authors. The needed tools to administer the CDT are just a sheet of paper and a pencil. The CDT is considered a suitable tool to identify individuals with possible dementia, yet its accuracy may be reduced among individuals with limited education (Shulman 2000; Storey et al. 2002; Nishiwaki et al. 2004; Parker and Philp 2004; Fuzikawa et al. 2007; Atalaia-Silva and Lourenço 2008; Aprahamian et al. 2010). Syndrom Kurztest—Short Cognitive Performance Test The Syndrom Kurztest (SKT) (Erzigkeit 1992) is a screening tool to asses the magnitude of attentional deficits, taking into account information processing speed and memory deficits. It comprises nine subtests; six are attention subtests and three are memory subtests. The attention tests measure simple attention, processing speed, concentration, inhibitory control, and working memory. Memory is assessed in its visual aspect involving immediate and delayed recall and recognition. The required time to administer this test is estimated at 10 minutes, and scoring at three minutes. SKT overall score ranges from 0 to 27 points, and the higher the score the more severe the cognitive deficit. The total score can be subdivided into attention and memory subscores,

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describing the contribution of each function to the final score. Special material is required, such as a board with numbers, a stimulus booklet, and a chronometer. The test presents five parallel versions that were developed to avoid learning effect in case of retesting. Its normative values take into consideration age and intelligence level (Erzigkeit 2001). The SKT is most frequently used to detect mild cognitive impairment and mild to moderate stages of dementia (Erzigkeit 1992; Ihl et al. 1992; Overall and Schaltenbrand 1992; Kim, Nibbelink, and Overall 1993; Lehfeld and Erzigkeit 1997; Weyer et al. 1997; Flaks et al. 2006, 2009). The instrument loses its capacity for precise staging in cases of severe cognitive deficits when the understanding of instructions is markedly impaired (Erzigkeit 2001). Cross-cultural stability was found between several research centers (Lehfeld et al. 1997). However, the test is influenced by educational bias when applied in subjects with no or low educational level (Ostrosky-Solís et al. 1999; Flaks et al. 2009). Frontal Assessment Battery The Frontal Assessment Battery (FAB) (Dubois et al. 2000) is a more recent instrument designed to assess frontal lobe functions. It is a brief bedside cognitive and behavioral battery to screen for executive dysfunctions, more specifically, to assess functions related to the dorsolateral and medial frontal cortex (Guedj et al. 2008). The FAB consists of six subtests that explore: 1. Conceptualization (abstraction taking into account similarities between two concepts) 2. Verbal fluency (mental flexibility) 3. Motor programming 4. Sensitivity to interference (tendency to distraction based on conflicting instructions) 5. Inhibitory control 6. Prehension behavior (environmental autonomy) Scores range from 0 to 18, with higher scores indicating better test performance. It takes about 10 minutes to administer and only a chronometer is requested for test application. Some research centers indicate that the FAB is a test capable of distinguishing frontotemporal dementia from Alzheimer ’s disease, as a measure of executive function (Slachevsky and Dubois 2004; Lipton et al. 2005; Castiglioni et al. 2006; Oguro et al. 2006; Kugo et al. 2007; Nakaaki et al.

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2007). A recent research study regarding the performance of healthy aged individuals suggested the FAB may be influenced by schooling (Beato et al. 2007). Verbal Fluency The purpose of verbal fluency tests (VFT) is to assess the spontaneous production of words beginning with a given letter (phonemic association) or within one category (semantic association) for a limited 60-second time period. These tests assess executive and language functions, and could also evaluate semantic memory (Spreen and Strauss 1998). A study conducted in healthy elderly indicated that a good performance in verbal fluency is related to the ability to quickly organize information and formulate effective recall strategies (Bolla et al. 2006). The needed tools to administer the verbal fluency tests are just a sheet of paper, a pencil, and a chronometer. Lam et al. (2006), in a study of VFT, with semantic restriction, described the test as capable of discriminating different stages of cognitive impairment, and Libon et al. (2009) emphasized this characteristic in frontotemporal lobar degeneration patients. Investigators found that special attention must be paid when the test is administered to subjects with low educational level, because results are clearly affected by schooling (Caramelli et al. 2007). Special attention should be given to the category used. Fruit category proved to be the best VFT as it may be less biased by education, being a more appropriate test across different educational groups (Rosselli et al. 2009). Addenbrooke’s Cognitive Examination—Revised Addenbrooke’s Cognitive Examination–Revised (ACE-R) (Mioshi et al. 2006) is a recent tool designed to detect early stage dementia, which may be especially useful to distinguish AD from frontotemporal dementia. ACE-R assesses five cognitive domains, namely: 1. Orientation (time and space), attention, and concentration (subtract the number 7 from 100 five consecutive times and spell the word “world” backward) 2. Verbal memory (immediate and delayed recall of three words), episodic memory (remember a name and a address), and semantic memory (historical information) 3. Verbal fluency

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4. Language (comprehension of an oral command, write a sentence, repeat words and phrase, name objects, and reading) 5. Visuo-spatial skills (copy drawings) and perceptual abilities The total score ranges from 0 to 100, in that higher scores denote better performance. It takes approximately 10 to 15 minutes to administer. The needed tools to administer the ACE-R are just a sheet of paper, a pencil, and a chronometer. Research studies suggest that the ACE-R is a valid dementia screening test that is sensitive to early cognitive dysfunction (Galton et al. 2005; Mioshi et al. 2006; Larner 2007). Different cut-off points are recommended for different educational levels (García-Caballero et al. 2006; Carvalho, Barbosa, and Caramelli 2010). Other Brief Screening Tests More recently two other screening tests have been proposed to detect early cognitive impairment. The Montreal Cognitive Assessment was designed to assist health professionals to detect mild cognitive impairment (Nasreddine et al. 2005), and several studies have suggested its validity to identify cognitive deficits associated with several neurologic conditions (for a full list of references about this instrument please refer to www.mocatest.org). The Test Your Memory (TYM) instrument has been designed as a cognitive screening test that can be self administered, in the waiting room, in about five minutes. Results have suggested it was accurate in detecting 93% of Alzheimer ’s disease patients compared to 52% when the MMSE was used. This screening test may also help to identify other types of dementia and mild cognitive impairment (Brown et al. 2009). NEUROPSYCHOLOGICAL ASSESSMENT OF MAIN COGNITIVE FUNCTIONS IN THE DEMENTIAS Memory and Learning Assessment Memory comprises a multitude of subsystems and it is possible to observe an uneven age-related decline among them. Therefore, memory assessment requires the examination of different subsystems such as episodic memory, working memory, and prospective memory, among others. The assessment protocol should include tasks based on visual and auditory stimuli, which require immediate and delayed recall. The assessment of the learning curve in consecutive trials and the magnitude of information

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loss in delayed recalls are key aspects of the neuropsychological assessment of episodic memory when one is trying to differentiate age-related decline from early dementia. Memory assessment involves the use of tools that require encoding new information, that is, the formation of new memory traces, with the primary aim of examining the integrity of the medial-temporal region, which includes the hippocampus and entorhinal cortex, directly involved in memory processes. Instruments that include multiple learning trials are of utmost importance, as they enable the assessment of the learning curve. Tests such as Word List from the Wechsler Memory Scale (WMS-III) (Wechsler, “Memory Scale” 1997) and Rey Auditory Verbal Learning Test (RAVLT) (Ivnik et al. 1990) provide five repetitions of the original word list. In the Selective Reminding Test (SRT) (Masur et al. 1990), which involves memorizing a list of 12 words, and in the Fuld Object-Memory Evaluation (FOME) (Fuld et al. 1990), which requires memorizing 10 objects placed in a bag, the examiner repeats only the nonrecalled items in each consecutive recall trial (for references and a detailed description of these instruments see Lezak, Howieson, and Loring 2004). Cognitively unimpaired older adults should improve performance in each trial, and in the delayed recall trial, after 30 minutes, they should remember most of the information recalled in the last trial (around 80%). Dementia patients, on the other hand, do not improve performance with re-exposure to the stimulus list and exhibit significant loss of information in delayed recall trials. The Visual Reproduction and Logical Memory sub-tests, also from the WSM-III battery (Wechsler, “Memory Scale” 1997), are excellent options for assessing visual and auditory episodic memory respectively. In the former, the patient memorizes geometric figures, and in the latter he or she memorizes two stories. Both involve one immediate and one delayed recall, making it possible to examine short- and long-term memory. The Rivermead Behavioral Memory Test (RBMT) (Wilson, Cockburn, and Baddeley 1985) is also a helpful tool in memory assessment, since it encompasses ecological tasks that are seldom explored in other batteries. It informs the examiner about possible difficulties subjects might encounter in their everyday lives, such as being oriented to time and place, recalling a name associated to a person’s face, recalling a route the examiner walks in the room leaving an envelope at a certain spot, remembering to reclaim an object that was lent to someone, or asking a question at the sound of an alarm bell. In a recently published study, low performance in the RBMT screening score was found to be a significant predictor of conversion to Alzheimer ’s disease among normal controls and patients with amnestic mild cognitive impairment (Forlenza et al. 2010).

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For working memory assessment, Digit Span Backward and the LetterNumber Sequencing from the Wechsler Adult Intelligence Scale (WAISIII) battery (Wechsler, “Adult Intelligence” 1997) are useful in addition to a qualitative examination of the older individual’s performance in the Arithmetic subtest from the same battery. Results from memory tests are extremely important when exploring differential diagnosis in dementias. Alzheimer ’s disease is usually associated with a flat learning curve and significant loss in the delayed recall in episodic memory trials (Collie and Maruff 2000). Alzheimer ’s disease patients also frequently recall items that were not presented during learning trials. Dementias related to cerebrovascular diseases can often emerge with no mnestic dysfunction as executive dysfunction may be its most prominent marker (Gainotti et al. 2008). Memory preservation can also be observed in dementias in which the first affected areas are not the temporal-medial but the frontal areas, as can be observed in frontotemporal lobar degeneration associated with semantic dementia or in frontotemporal dementia (Rabinovici and Miller 2010). Attention Assessment Paying attention requires detecting changes in the outside world and simultaneously inhibiting interference from other competing stimuli (Posner and Raichle 1994). Attention is a multidimensional skill, and its components are intrinsically connected with other skills such as memory and executive functions; therefore, it is difficult to assess attention separately. The following tests are simple to apply and can be used to assess different aspects of attention processes: Digit Span Forward and Digit Symbol from the WAIS-III battery (Wechsler, “Adult Intelligence” 1997); Trail A (Ashendorf et al. 2008); Stroop in its several formats; and letter, number, or symbol cancellation tests (Lezak, Howieson, and Loring 2004). The Mental Control subtest of the WMS-III battery can also be used (Wechsler, “Memory Scale” 1997). Attentional deficits are common among aged patients with cerebrovascular diseases. Research studies suggest that a considerable number of subcortical white matter lesions are linked to poorer performance in visual and auditory attention tests and in executive function tests (Van Dijk et al. 2004), as well as to psychomotor slowing (Gainotti et al. 2008). Dementia with Lewy bodies, in turn, is characterized by significant cognitive fluctuation, especially in attention tests (Lezak, Howieson, and Loring 2004). Attention tasks such as inhibitory and mental control (ability to focus) in the face of competing and concomitant stimuli, which are directly related

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to executive functions regarding storage and simultaneous processing of information, are described below. Executive Function Assessment The term executive function denominates a set of skills that are needed for complex behaviors. The executive system is a hypothetical cognitive principle involving planning tasks, organizational tasks, mental flexibility, abstract thinking, avoidance of inappropriate actions, and inhibition of irrelevant information processing. The executive system is also supposed to be in charge of adjusting one’s behavior to solve day-to-day situations such as initiative, management of choices, consequence assessment, decisionmaking, action implementation and control, and course correction and adjustment when needed. The examination of executive functions aims at assessing prefrontal cortex integrity. Several neuropsychological tests may be used to assess different aspects of cognition associated with executive functions. Some of the most frequently used are: (1) verbal fluency during a limited 60-second time frame, restricted semantically, for instance, by a category such as animal and fruit naming (Caramelli et al. 2007; Radanovic et al. 2009), or restricted phonemically, by using words starting with F, A, or S (Controlled Oral Word Association, COWA) (Lezak, Howieson, and Loring 2004) to assess information processing speed; (2) Clock Drawing Test, which assesses visuo-spatial functions as well as planning and self-regulation during execution, as previously described; (3) Trail B, as it demands coordination of two competing information systems (Ashendorf et al. 2008); and (4) Wisconsin Card Sorting Test (WCST) (Lezak, Howieson, and Loring 2004), possibly the most traditional executive function test, when the patient is required to combine 48 cards with one of four model cards that vary according to criteria such as color, shape and number of symbols to assess mental flexibility and abstraction capacity (Modified Card Sorting Test, MCST; Lezak, Howieson, and Loring 2004). In order to assess complex decision making skills, the Iowa Gambling Task may be used as it has already been adjusted and validated for use with older populations (Bechara et al. 1994). The task entails choosing cards from four decks. As the individual verifies gains and losses associated with each card deck, she or he infers which decks are more advantageous and which are not. The subject’s ability to evaluate choices to maximize gains, minimizing losses, is thus surveyed. In recent years, an executive control assessment interview, EXIT-25 (Executive Interview, Royall, Mahurin, and Gray 1992) was developed,

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grouping traditional tests associated with frontal functions. The EXIT-25 is an important battery because performance in it is strongly correlated with activities of daily living (Royall et al. 2007; Pereira et al. 2008). In addition, decline in this scale significantly predicts dependence and need for care (Mann et al. 1992). The Behavioral Assessment of the Dysexecutive Syndrome (BADS) (Lezak, Howieson, and Loring 2004) is also worth mentioning. It simulates daily challenges involving executive functions, such as removing a cork from inside a bottle using a few available tools, working out a route within a zoo, temporal judgment, and managing time for task completion. The advantage of this battery is its resemblance with challenges encountered in the subjects’ everyday lives. Language Assessment Language functions tend to be preserved in healthy aged individuals. Vocabulary remains stable and can even expand with aging, slightly declining after the age of 70. Nevertheless, some language changes have been recorded in healthy elderly individuals. Difficulty in finding words or the “tip of the tongue” experience can be noted more often. From a qualitative standpoint, healthy older adults tend to: (1) use a greater number of words to describe something that could be expressed with only one; (2) describe the function of the object rather than name it; (3) less accurately identify objects due to sensory deficits; (4) make associative semantic errors, when something related to the object is mentioned instead of the object itself (Woodruff-Pak 1997). These changes could suggest difficulty or slowing in semantic access. Complex sentence comprehension and formulation, as well as speech organization and accuracy, could also moderately decline in healthy aged individuals. The Vocabulary subtest from the WAIS-III battery (Wechsler, “Adult Intelligence” 1997) is among the tasks that are frequently used for language assessment among older adults, as it enables the examiner to verify whether the patients’ word knowledge is compatible with his or her level of schooling. Qualitatively, the examiner may also notice if the definitions provided are accurate, and if access to these definitions is fast and easy. Another frequently used test is the Boston Naming Test (Steinberg et al. 2005), when the patient is asked to name 60 pictures, increasingly difficult, and the examiner may need to judge whether semantic and/or phonological clues are helpful. Vocabulary and naming trials are usually complemented by verbal fluency tasks, also used to measure executive function. An additional common practice is to ask the subject to complete

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and interpret proverbs. This task not only examines language comprehension but also assesses semantic memory and abstract thinking. In case of suspected aphasia, the Boston Diagnostic Aphasia Examination (BDAE) (Goodglass, Kaplan, and Barresi 2000) is commonly used. The traditional “cookie theft picture” of this battery can be used separately to examine the quality of oral and narrative speech. Language assessment may be relevant in differential diagnosis of dementias. In the course of healthy aging, language abilities are expected to be preserved, with just mild changes typical of healthy aging (Woodruff-Pak 1997). Significant decline in this function may suggest that lesions in language-related regions (Broca’s and Wernicke’s areas) might be present, or that neurodegenerative disorders that manifest with losses in this function, such as semantic dementia or nonfluent progressive aphasia, might be present (Rabinovici and Miller 2010). Visuo-Spatial Ability Assessment Visuo-spatial abilities are needed to perform tasks such as copying figures, assembling objects, interpreting maps, dimensioning spatial relationships among objects. They also involve spatial orientation to perform complex actions, along with several other abilities (Woodruff-Pak 1997). Among the most widely used trials to assess visuo-spatial ability in neuropsychological practice are the following: the above-mentioned Boston Naming Test, which in addition to naming capability also examines visual perception; the Hooper Visual Organization Test; the Rey-Osterrieth Complex Figure copying; the Necker Cube copying; and the Clock Drawing Test (for references and detailed description of these tools see Lezak, Howieson, and Loring 2004). The latter three instruments involve visuoconstructive abilities, as they require copying or drawing of complex figures. There are motor demands in addition to visual integration and organization. In addition, the Block Design subtest from the WAIS-III battery (Wechsler, “Adult Intelligence” 1997) is also regularly used to assess visual-perceptual and visuo-constructive abilities. In this subtest, the patient is asked to use blocks to form a three-dimensional representation of a bidimensional picture in a card. Visuo-spatial ability loss can be observed in patients with dementias with different etiologies, usually in advanced stages. Patients with focal lesions in the parietal and occipital lobes tend to show significant impairment in visuo-spatial tasks. It must be pointed out that patients suffering from dementia with Lewy bodies display considerable visuo-spatial deficits even at early stages of the disease (Lezak, Howieson, and Loring

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2004). Nevertheless, when assessing visuo-spatial abilities, the educational and occupational background of the older individual must be taken into account. In cases when the individual reports to have always performed poorly in visuo-constructive tasks, a below-average result should not be given much weight. On the other hand, for patients who used to sew, who used to develop carpentry projects, or with a history of complex craftsmanship, poor results in these tasks should be highlighted. Neuropsychological tests are usually strongly influenced by educational experience. Therefore, in developing countries, where older adults’ education profile tends to be diverse, and when assessing minority seniors in developed nations, the assessment protocol should be planned carefully. In a recent study (Yassuda et al. 2009) involving older adults with heterogeneous educational backgrounds, the RBMT, the FOME, and verbal fluency with animal category were not significantly influenced by education and therefore they should be used in such cases. Functional Assessment in the Context of the Dementias One vital aspect in the neuropsychological assessment of older individuals concerns gathering proxy measures of performance in daily living tasks. In the context of aging, assessment of functionality is of utmost importance because it draws the line between normal and pathological cognitive aging. The concept of functional status receives a variety of definitions and it is assessed in many different ways by health professionals. According to the World Health Organization (WHO), it is a key factor for the definition of physical and mental health. Due to the wide array of concepts and terminologies used, the WHO published in 2001 the International Classification of Functioning, Disability and Health, also known as ICF. According to this classification, functional status is considered a broad concept that encompasses a number of body functions and structures, as well as activity and participation in a socio-environmental context (Buñuales, Diego, and Moreno 2002; WHO 2001). Functional capacity concerns the ability to maintain the physical and mental skills needed for independent living, valuing autonomy and selfdetermination (Gordilho et al. 2000). Functional capacity is an index of how a given activity is carried out in everyday life, what people do in their environment, thus including involvement in real-life situations. It is a key aspect in the concept of independence, which implies the ability to function effectively without the help of others in activities of daily living. Functional disability can be denoted by the presence of difficulties

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in performing certain activities of daily living, or even by the complete inability to perform them unaided (Rosa et al. 2003). Autonomy is related to the practice of self-governing and includes the following elements: individual freedom, privacy, free will, and harmony regarding one’s own feelings and needs. The greater the independence, the greater the likelihood of having autonomy, though under partial dependency conditions the individual can keep his or her autonomy, depending on the social arrangements he or she manages to make. Functional decline is the result of a complex interaction among several elements, and cognitive performance is one of the most important of them. The tools and batteries used to assess impairments in dementias focus primarily on cognitive performance. Usually there is less interest in apprehending how cognitive changes interfere with the person’s functioning. However, functional assessment provides information about daily performance, which is vital for health professionals who constantly have to counsel family member regarding patients’ ability to live independently. Functional assessment is also relevant as functional impairment distinguishes mild cognitive impairment (MCI) from dementia syndromes (Petersen et al. 2001). Current studies have investigated whether MCI patients present functional deficits and which kind of functional loss is typical of older adults with MCI (Farias et al. 2006). Some researchers claim that a modest decline in functioning should be part of the set of criteria for MCI diagnosis (Perneczky et al. 2006). It is well established in the literature that functional decline in the dementias follows a gradient in which basic activities of daily living (BADL) are affected after more complex activities have already deteriorated. Among MCI patients, the focus of functional assessment is on complex instrumental activities of daily living (IADL). Following this line of research, international papers point out that decline in four IADLs as strong predictors of dementia: ability to use the telephone, use of means of transportation, management of one’s own medications, and ability to handle finances (Barberger-Gateau et al. 1999), the latter usually being the first to be impaired (Griffith et al. 2007). In a Brazilian sample of older adults with heterogeneous cognitive profiles assessed with a direct measure of functional status (Direct Assessment of Functional Status, DAFS-BR), MCI patients had significantly lower scores in two domains, dealing with finances and shopping skills, compared to normal controls (Pereira et al. 2010). Despite the clinical relevance of studying the relationship between cognitive and functional performance, few research studies have actually examined this relationship (Royall et al. 2007). It is not yet clear, for

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instance, to what extent functional performance fluctuation can be directly attributed to cognition, or whether both are dependent upon noncognitive variables, such as socioeconomic, cultural, or personal aspects (Mor et al. 1989). Rosa et al. (2003) found that the attributes that have the strongest association with functional decline in a sample of Brazilian older adults were illiteracy, retirement, being a pensioner, being a housewife, being over 65, having a multigenerational family structure, being hospitalized in the last six months, not having the habit of visiting friends or relatives, having visual disabilities and a history of stroke, and having a pessimistic view with regards to one’s own health when compared to peers. Executive functioning has been highlighted as the key factor regarding functional performance among older individuals with cognitive decline. It must be pointed out that the degree of the correlation between executive functions and functionality is greater than that observed between executive functions and memory. Executive dysfunction directly affects IADLs, whereas working memory may be preserved in early dementia, starting to be compromised only in more advanced stages (Royall et al. 2005; Pereira et al. 2008). In the context of dementias, functional assessment may involve direct observation (performance trials) and the use of scales or questionnaires filled in by informants or the patient. Up to the present moment, no consensus has been reached regarding the best method to assess an older individual’s functional performance (Royall et al. 2007). In clinical practice, functioning is assessed by reports of a family member or caregiver concerning the difficulties the patient has encountered in carrying out daily living activities. Even though this approach is often adopted, abundant empirical evidence suggests that information provided by third parties is possibly biased, for example, by mood, caregiver personality and burden, resulting in over- or underestimation of functional deficits (Onor et al. 2006; Tierney et al. 1996; Glass 1998; Loewenstein et al. 2001). Research suggests that objective functional assessments (based on observed performance) are more accurate in identifying functional limitations, in addition to making it possible to devise compensatory strategies (Mangone et al. 1993; Farias et al. 2006; Onor et al. 2006; Pereira et al. 2010). The most frequently used tools to assess functional status are the Barthel Index (Mahoney and Barthel 1965), Activities of Daily Living Scale outlined by Katz et al. (1963), IADL Scales by Lawton and Brody (1969) and by Pfeffer et al. (1982), Brazilian OARS Multidimensional Functional Assessment Questionnaire (BOMFAQ) (Blay, Ramos, and Mary 1988), and Functional Independency Measurement (MIF) (Riberto et al. 2001).

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Among other scales, objective functional assessment of aged individuals can be carried out with the use of the Direct Assessment of Functional Status–Revised (DAFS-R) (Loewenstein and Bates 2006). This assessment relies on direct observation of the individual while he or she undertakes activities that replicate IADLs—for instance, orientation to time, communication, ability to handle finances, aptitude to take care of shopping needs, and BADLs, for instance, dressing ability, personal hygiene, and nutrition. The DAFS-R is used in seven countries around the world (Loewenstein, Amigo, and Duara 1989). A recent paper (Pereira et al. 2008) found a strong correlation between executive control (EXIT-25, Royall et al. 1992) and performance in the DAFS-BR (the Brazilian version of DAFS-R). And as stated earlier, this instrument may help identify MCI cases (Pereira et al. 2010). In conclusion, brief cognitive screening, neuropsychological assessment, and assessment of functional status may significantly contribute to the early diagnosis of cognitive impairment. Early diagnosis may facilitate the implementation of pharmacological and psychosocial interventions that might contribute to the stabilization of cognitive losses. Cognitive testing may also assist in evaluating the effectiveness of treatment protocols.

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Chapter 8

Does Poor Sleep Quality in Late Life Compromise Cognition and Accelerate Progression of the Degenerative Dementias? Peter Engel

Cycles of sleep and wakefulness begin in utero and persist throughout life. As such, sleep is both a personally familiar experience and a biologically essential activity. At the same time, the fundamental links between sleep, health, and disease remain elusive. Several decades of sleep research now link rapid eye movement (REM) and non–rapid eye movement (nREM) sleep with new learning and consolidation of both motor and episodic memory. While other functions of sleep escape full understanding, sleep appears to be essential for survival through regulation of growth, development, maintenance, and repair of the brain as well as the entire organism (Diekelmann and Born 2010; Garcia-Rill et al. 2008; Vassalli and Dijk 2009). Hence, the connections between sleep, learning, and memory may have implications for brain development and maintenance during early and mid-life, and cognitive decline associated with aging and degenerative brain disease in late life. These potential late life relationships will be the principal focus of this chapter. Over the lifespan crisp transitions between wakefulness and sleep and their tight circadian regulation diminish in intensity and precision. Sleep-stage transitions become more fragmented, sleep efficiency diminishes, and slow-wave sleep declines with age (Redline et al. 2004; Ohayon

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et al. 2004; Bloom et al. 2009; Dijk et al. 2010) (see Figure 8.1). These changes are enhanced in dementing disorders that are characterized by profound impairments of learning and memory. In some cases of dementia in which sleep disturbances are profound, components of wakefulness may infiltrate sleep (Cajochen et al. 2006; Espiritu 2008; Gagnon et al. 2008). In Alzheimer ’s disease (AD), the dementia in which sleep and circadian disturbances are best studied, the major components of REM and nREM sleep appear to be relatively preserved despite progressive fragmentation of sleep architecture and varying degrees of cell loss in sleep-regulating regions of the brainstem, basal forebrain and hypothalamus. In Lewy body dementia (LBD), sleep and arousal mechanisms may be profoundly disturbed and REM sleep may be disrupted by intrusion of components of the waking state. In REM sleep behavior disorder (RBD), the profound hypotonia of REM sleep fails to occur and patients appear to act out their dreams, often with violent gestures and vocalizations. In LBD and Parkinson’s disease, a related synucleopathy, RBD may precede the dementing illness by 10 years or more (Mahowald, Schenck, and Bornemann 2007; McKeith et al. 2005; Boeve et al. 2007; Iranzo, Santamaria, and Tolosa 2009; Postuma et al. 2009). In this chapter we will consider two possible associations between decrements in sleep quality, age-related memory decline, and dementia: first, that age and dementia-related loss of sleep integrity directly contribute to cognitive impairment, and second, that age and disease-associated circadian and sleep disturbances enhance brain injury and accelerate disease progression. To address these questions the discussion will concentrate on AD, the most thoroughly studied dementia for which animal models are available, and LBD, given ample data on RBD and cognitive deficits associated with this disorder. To investigate this broad area, identify gaps, and suggest future directions we will begin by examining sleep architecture in relation to aging, AD, and LBD and relate these to neuroanatomical correlates of sleep mechanisms to the extent that they are understood. An exploration of human and animal research that links sleep to learning and memory consolidation will follow. This background will form the basis for an investigation of the hypotheses proposed above. Three points deserve mention early in this discussion. First, this chapter represents an attempt to identify connections between several broad and disparate areas of research in sleep, learning, and dementia. Second, treatment of sleep disorders in dementia both pharmacological and otherwise are well considered elsewhere and will not be addressed here (Espiritu

Figure 8.1

Hypnograms characteristic of young adults and elderly individuals. Decrements in sleep architecture in the elderly are characterized by delayed sleep onset, sleep fragmentation, early morning awakening, and decreased slow wave sleep (stages 3 and 4). (Reprinted with permission from “Sleep Problems in the Elderly,” May 1, 1999, American Family Physician. Copyright ©1999 American Academy of Family Physicians. All rights reserved.)

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2008; Boeve, “Update” 2008; Dauvillers 2007; Deschenes and McCurry 2009). Third, uncertainties related to the neuropathological and clinical features of AD and LBD point to the difficulties in the study of aging and dementia. The neuropathological differences between age-related change, Alzheimer ’s disease and Lewy body dementia are hardly distinct, and clinical variability in disease expression from one individual to another can be substantial. Alzheimer and Lewy body pathology commonly cooccur with the predominant features determining a specific diagnosis (Jellinger 2008; Kazee and Han 1995). These realities begin to frame the inherent limitations associated with any attempt to understand the relationships between sleep, aging, and the degenerative dementias. But such a caveat need not dissuade this exploration. Since the earliest neuropathological change in AD begins in the allocortex and cortex with variable involvement of sleep and arousal systems in brainstem and midbrain, sleep disturbances in AD might be considered an exaggerated form of normal aging. These medial temporal lobe and cortical changes likely account for impairments of learning, memory, and abstract reasoning, and reduced fluency that characterize early stages of this disorder (Dubois et al. 2007; McKhann et al. 1984; American Psychiatric Association 2000; Braak and Braak 1991; Thal et al. 2002; Schneider et al. 2009). LBD, in contrast, may be a “bottom up” dementia in which the earliest pathological changes occur in the brainstem, or systemically in the gut and spread rostrally with early involvement of sleep-regulating nuclei. This sequence may explain the presence of RBD as a common precursor of LBD with later evidence of deficits of attention, executive function, and visuospatial ability often with fluctuating cognition, visual hallucinations, and features of Parkinsonism (McKeith et al. 2005; Phillips et al. 2008; Braak et al. 2003). Memory may be spared early on in LBD, distinguishing this condition from AD. In relation to the two hypotheses under consideration, only the first, a possible relationship between sleep disturbances and cognitive impairment of LBD, will gain scrutiny here. Exploration of the second is precluded by an insufficient understanding of the factors that modulate the pathological processes associated with this disease. SLEEP AND AROUSAL: NEUROBIOLOGY AND NEUROANATOMY Arousal is mediated by the reticular activating system situated in the upper brainstem near the pons-midbrain junction where cholinergic and monaminergic cell groups constitute the central components. The cholinergic pedunculopointine (PPT) and laterodorsal (LDT) tegmental nuclei in the mesopontine tegmentum project to thalmocortical nuclei and

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the reticular nucleus to facilitate thalmocortical sensory transmission. Cholinergic basal forebrain projections to the cortex are implicated in waking and electroencephalographic (EEG) desynchronization. Lesions of the basal forebrain produce coma, underscoring the critical importance of this area in arousal. The monoaminergic arousal system comprised of the noradrenergic locus coeruleus, dopaminergic ventral preaqueductal gray, serotoninergic raphe nuclei, and histaminergic tuberomammillary neurons project to the thalamus, lateral hypothalamus (LH), basal forebrain, and cortex. Orexin-secreting neurons in the LH project reciprocally to the brainstem monaminergic systems. Orexin, a peptide that promotes arousal and appetite increases firing rates in the monoaminergic arousal system while melanin-concentrating hormone neurons from LH inhibit the arousal system and mediate REM sleep homeostasis (Fuller, Gooley, and Saper 2006; Saper, Scammell, and Lu 2005; Schwartz and Roth 2008) (see Figure 8.2). During sleep, the hypothalamic ventrolateral preoptic nucleus (VLPO) inhibits arousal circuits, an effect mediated by gamma amino buteric acid (GABA) and galanin. VLPO lesions produce profound insomnia in animals. Loss of VLPO neurons as occurs in aging and AD may contribute to insomnia and sleep fragmentation that can occur in these conditions. VLPO and arousal systems are mutually inhibitory with functions comparable to an electronic “flip-flop” switch imparting a degree of stability to either the waking or sleeping state (Saper and Scammell 2005). Cell loss also occurs in other sleep-modulating hypothalamic and brainstem nuclei in a variety of neurodegenerative diseases but shows no clear relationship with the associated sleep disturbances (Boeve et al. 2007; Benarroch et al. 2009; Jellinger 1988; Mufson, Mash, and Hersh 1988; Ransmayr, Faucheux, and Nowakowski 2000; Saper and German 1987; Schmeichel et al. 2008; Szymusiak, Gvilia, and McGinty 2007; Zhang et al. 2005; Zweig et al. 1989). Transition from nREM to REM sleep is also mediated by a proposed “flip-flop” switch control of which appears to be progressively compromised by aging and degenerative neurological diseases. Mutually inhibitory cholinergic “REM-on” PPT neurons complement serotonergic dorsal raphe and noradrenergic locus coeruleus “REM-off” cells (Hobson 2009; Monti and Monti 2007). Dopaminergic and cholinergic nuclei are active during REM. In the rat REM-on activity in the subcoeruleus region is associated with electromyographic atonia and appears to effect this state through hyperpolarization of spinal cord anterior horn motor neurons during REM sleep. Loss of atonia as occurs in RBD suggests focal brainstem injury to these anterior horn cell inhibitory circuits (Boeve, “Update” 2008).

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Figure 8.2

Simplified schematic of major sleep-regulating regions. Brainstem nuclei for arousal and REM/slow-wave sleep regulation; locus coeruleus (norepinephrine), raphe (serotonin), tuberomammilliary nucleus (histamine), ventral periaqueductal grey (dopamine), pedunculopontine and laterodorsal tegmental nuclei (acetylcholine). Hypothalamus; ventrolateral preoptic nucleus promotes sleep (GABA, galanin, inhibitory transmitters). Orexin from lateral hypothalamus promotes wakefulness. SCN, suprachiasmatic nucleus. (Modified with permission from Macmillan Publishers, Ltd.: Emmanuel Mignot, Shahrad Taheri, and Seiji Nishino, “Sleeping with the hypothalamus: Emerging therapeutic targets for sleep disorders,” Nature Neuroscience 5: 1071–1075. Copyright © 2002.)

In general, all cell groups fire more during wakefulness than nREM sleep whereas REM sleep is a cholinergically hypermodulated and aminergically demodulated state. Hence the cholinergic deficiency associated with both AD and LBD may contribute to the fragmentation of REM sleep periods (Hobson 2009).

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The suprachaismic nucleus of the hypothalamus exerts overall circadian control of sleep and wakefulness. In aging as well as AD and other degenerative dementias substantial cell loss occurs in the suprachaismic nucleus that is associated with reduction in the amplitude of various circadian rhythms and a phase advance, particularly for sleep. Reduction in sleep quality and increased sleep fragmentation are likely related to these changes (Braak and Braak 1992; Goudsmit et al. 1990; Mirmiran et al. 1992). SLEEP ARCHITECTURE, AGING, AND ALZHEIMER’S DISEASE Polysomnographic studies indicate that sleep efficiency and depth diminish with age, sleep becomes fragmented, and nocturnal arousals occur more frequently. These changes are more prominent in AD as are circadian disturbances in sleep-wake activity (Vassalli and Dijk 2009; Redline et al. 2004; Ohayon et al. 2004; Bloom et al. 2009; Dijk et al. 2010; Cajochen et al. 2006; Espiritu 2008; Gagnon et al. 2008; Bliwise 2004). Normal aging and AD are associated with diminished slow-wave sleep (SWS) and a reduced number of sleep spindles during stage 2 sleep (see Figure 8.1 above). Sleep spindles have been implicated in hippocampal-related learning. In addition, both human and animal studies indicate that sleep deprivation, sleep fragmentation, and reduction in REM or nREM sleep can impair motor and episodic learning (Chee and Chuah 2008; Walker 2008; Banks and Dinges 2007; Schabus et al. 2007; Sterpenich et al. 2007). None of these investigations included aged subjects. Do Age-Related Changes in Sleep Produce Memory Impairment? Age and AD-related changes in sleep are not readily comparable to sleep disturbances produced in experimental paradigms but several parallels may be of interest. An extensive literature has defined the learning impairments associated with sleep deprivation and the need for sleep following learning for consolidation and enhancement of procedural and declarative memory. Both REM and nREM sleep enhance the consolidation of motor and episodic memory as well as other forms of learning. Current sleep research suggests that SWS supports system consolidation while REM sleep mediates synaptic consolidation. SWS slow oscillations, sleep spindles, and high-frequency hippocampal ripple oscillations are purported to coordinate reactivation and redistribution of hippocampal dependent memories to the neocortex, a phenomenon that occurs during low cholinergic states.

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During REM, high cholinergic activity may promote synaptic consolidation. Optimum benefits of sleep for memory consolidation appear to occur when SWS is followed by REM sleep, the usual sequence in young humans and animals. Moreover, cholinergic activation strengthens long-term potentiation (LTP) in the hippocampal-medial prefrontal cortex pathway. These observations introduce the possibility that the cholinergic deficiency of AD and LBD combined with disruption of sleep architecture and loss of SWS compromise this dynamic of sleep-induced memory consolidation (Diekelmann and Born 2010; Walker 2009; Stickgold and Walker 2007). Electrophysiological studies suggest that sleep spindles characteristic of stage 2 nREM sleep provide trains of depolarizations to cortex and hippocampus that are comparable to spike trains of in-vitro LTP, a synaptic mechanism implicated in learning (Steriade 1999). Such observations may be linked to impaired learning associated with the reduced number of sleep spindles both in aging and Alzheimer ’s disease, the smaller number of fast spindles in AD patients, and a positive correlation between the intensity of fast spindles and post-sleep immediate recall (Rauchs et al. 2008). Two preliminary investigations suggest a relationship between sleep, learning, and aging. In the first, compared to their younger counterparts, middle-aged subjects demonstrated a decrement in declarative memory that correlated with reduced SWS time (Backhaus et al. 2007). The second showed that improved episodic memory tracked with total sleep time but not specific sleep stage in both young and old subjects (Aly and Moscovitch 2010). Hence age-associated loss of sleep integrity that is exaggerated in Alzheimer ’s disease may be a direct contributor to clinically evident cognitive impairments through decrements in sleep-related memory consolidation and learning processes. Concurrently, age-related reduction in sleep quality combined with age-associated disease may accelerate the pathogenic processes of Alzheimer ’s disease. We will specifically explore this possibility by examining potential relationships between aging, agerelated disorders, and the production of amyloid beta (A-beta). Does the Loss of Sleep Integrity Accelerate Alzheimer ’s Disease Pathogenic Processes? A predominant hypothesis of AD pathogenesis posits the toxicity of A-beta a 40-42 amino acid peptide produced by neurons as a fundamental pathogenic process. Substantial experimental evidence shows that A-beta 42 is neurotoxic, particularly in its soluble, oligameric forms and

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that A-beta is either produced in excess or inefficiently catabolized such that the peptide concentrations reach toxic levels. The neurotoxic effects of A-beta oligomers include depression of LTP, enhancement of long-term depression (LTD), inhibition of synaptogenesis, neuronal cell death and apoptosis (Yang, Hsu, and Kuo 2009). The relationship between A-beta and phosphorylated tau paired helical filaments that accumulate in dying Alzheimer disease neurons is less clear (Querfurth and LaFerla 2010). A-beta is released into the synaptic space during depolarization, raising the possibility that it functions as a neuromodulator of LTP and synaptogenesis (Wasling et al. 2009). The assumption of a physiological function for A-beta is suggested by recent reports of enhanced memory retention and acetylcholine production in hippocampus in response to low doses of A-beta, impaired learning in normal mice following inhibition of A-beta expression, and in-vitro inhibition of hippocampal and dentate gyrus LTP in the presence of A-beta antibodies (Morley et al. 2010). In complementary studies, A-beta 42 in picomolar concentrations facilitated hippocampal LTP whereas nanomolar quantities had the opposite effect. Concurrently picomolar levels of A-beta improved reference and fear memory (Puzzo et al. 2008). Other products of amyloid precursor protein have potential physiological functions. Knockout mice, deficient in neprilysin an amyloid degrading endopeptidase, showed increased A-beta concentrations in the brain. Aged NEP knockout animals demonstrated significantly improved learning and memory and improved LTP in hippocampus and amygdala. This improvement may reflect increased levels of A-beta or other neuropeptides usually metabolized by NEP (Walther et al. 2009). Secreted amyloid precursor protein alpha (sAPP), a product of alpha secretase that incorporates a portion of the A-beta peptide facilitates LTP in rat dentate gyrus in vitro. Preferred production of A-beta at the expense of sAPP alpha may be another potential contributor of the memory deficits of AD (Kim and Tsai 2009; Lauren et al. 2009; Taylor et al. 2008; Bissette 2009). A key point is that dysregulation of A-beta production may be the principal mediator of both immediate adverse effects on cognition through impairment of LTP and longer-term injury-inducing effects in brain areas with the greatest synaptic plasticity. In AD the earliest pathological changes occur in the hippocampus and parahippocampal gyrus, regions that are critical for learning and memory and like the cortical association areas demonstrate considerable synaptic plasticity (Braak and Braak 1991). Disruption of the tightly regulated function of A-beta as a modulator of synaptic plasticity could result in toxic concentrations of this peptide through a variety of mechanisms, some of

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which may be related to disturbances of sleep integrity, others to ageassociated diseases, brain injury by various mechanisms, and the aging process itself. The fact that Alzheimer neuropathology first evolves in brain areas of high synaptic plasticity representing components of the memory retrieval network (posterior cortical regions, including posterior cingulate, retrosplenial, and lateral parietal cortex) where atrophy is prominent in AD implicates the relative vulnerability of the most dynamic parts of the brain to AD-related injury (Buckner et al. 2005, 2009). These observations lead to a second question: might age and dementia-related decrements in sleep integrity disrupt tightly regulated A-beta mechanisms, facilitate A-beta production, increase neurotoxicity, and promote disease progression? Preliminary information provides initial support for this idea. First, both wild type and Amyloid precursor protein Tg2576 transgenic mice demonstrate diurnal variation in A-beta levels as shown by brain in-vivo microdialysis. Comparable changes have been observed in the spinal fluid of human volunteers. Higher levels of A-beta occur during periods of wakefulness, an effect that is likely mediated by orexin. In transgenic mice sleep deprivation, a form of physiologic stress, results in increased A-beta levels and enhanced amyloid plaque deposition. Notably restraint stress acutely increases A-beta concentrations, an effect mediated by corticotrophin releasing factor (Kang et al. 2009). Second, studies of sleep and circadian abnormalities in Tg2576 Alzheimer model mice show changes that mimic those in aged humans and patients with AD (Zhang et al. 2005; Bliwise 2004). These animals demonstrated a blunted increase in electroencephalographic delta power (i.e., a loss of slow-wave sleep EEG frequencies) following sleep deprivation, longer periods of wheel running activity during dark (wake) periods, and a shift in EEG power to higher frequencies during nREM sleep as compared to normal controls (Wisor et al. 2005; Volicer et al. 2001). Whether dysregulation of A-beta production in these animals accounts for these sleep changes or the other way around remains to be seen. If we accept the possibility that excess A-beta is a significant participant in the pathogenesis of AD, that sleep disruption increases A-beta production, and that sleep becomes lighter and more fragmented with age then it may be of value to explore potential relationships between aging, sleep, cognitive decline and dementia. As previously discussed, both learning and memory consolidation occur during REM and SWS (Diekelmann and Born 2010; Walker 2009; Stickgold and Walker 2007). Disruption of normal sleep patterns compromises these functions. Sleep deprivation impairs hippocampal LTP and synaptic plasticity while enhancing LTD (Tadavarty, Kaan, and Sastry 2009; Kopp et al. 2006; Guzman-Marin et al.

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2006; McDermott et al. 2006). Similar disturbances occur following REM sleep deprivation and sleep fragmentation (Ravassard et al. 2009; Ishikawa et al. 2006; Tartar et al. 2006). These neurobiological effects are largely reiterated in studies of the effects of A-beta peptide on hippocampal function. Soluble oligomers of A-beta peptide inhibit LTP and enhance LTD in the CA1 region of rat hippocampus, an effect mediated in part by inhibition of glutamate reuptake, and by disruption of LTP through inhibition of the N-methyl-D-apartate receptor-dependent LTP induction (Li et al. 2009; Yamin 2009). A-beta induces dendritic spine loss, while A-beta protein fragments 25-35 and 31-35 potentiate hippocampal CA1 LTD in vivo (Hsieh et al. 2006; Cheng et al. 2009). To date there is no direct evidence that decrements in sleep quality associated with aging and dementia directly contribute to deficits in learning and memory, depression of LTP, and acceleration of neurodegenerative processes that occur in sleep-deprived animals. Nonetheless, a potential connection may be more likely in late life if age-related change and ageassociated disease enhance brain vulnerability. Of particular interest are age-related changes in the adrenal-hippocampal, pituitary axis, the proinflammatory state of late life, impaired cerebrovascular autoregulation, and age-related disorders such as sleep apnea, periodic leg movements during sleep, and restless leg syndrome.

COMPONENTS OF AGING AND AGE-RELATED DISEASE PROCESSES THAT MAY ENHANCE THE PRODUCTION OF A-BETA Glucocorticoids Basal cortisol levels increase with age, an effect that is enhanced in AD with the diurnal peak in serum cortisol occurring early in the morning. Hippocampal glucocorticoid receptors mediate the effects of cortisol on the hippocampus that include inhibition of LTP in response to acute elevations and atrophy following chronic elevations. The electrochemical response to glucocorticoids may be directly mediated by elevated A-beta levels, resulting from increased production and blunted metabolism of the peptide. Moreover, stress levels of glucocorticoids increase A-beta and tau pathology in a mouse model of Alzheimer ’s disease (Catania et al. 2009; Green et al. 2006; Kulstad et al. 2005; Magri et al. 2006; McAuley et al. 2009; Sotiropoulos et al. 2008; Yao et al. 2007). Notably, the elevations of cortisol, catecholamines, and inflammatory markers characteristic of

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aging are induced in younger individuals by sleep deprivation (Galvao Mde et al. 2009; Leproult and Van Cauter 2010; Mullington et al. 2009; Stamatakis and Punjabi 2010). These, in turn may be associated with increased A-beta production (Kang et al. 2009). Immune Function, Sleep and A-beta Production Intrinsic immunity becomes increasingly pro-inflammatory with age, as reflected by a shift in the mix of circulating cytokines from an anti-inflammatory Th-1 to a Th-2 pro-inflammatory pattern. A similar pro-inflammatory cytokine shift occurs in the aging brain where neuro-inflammation is marked by increased numbers of activated and primed microglia that are hyperresponsive to systemic inflammatory signals or a stressor (Goshen and Yirmiya 2009; Dilger and Johnson 2008; Godbout and Johnson 2009; Sparkman and Johnson 2008). Peripheral inflammatory activity may be enhanced by sleep deprivation although the effect of sleep deprivation or fragmentation on the CNS inflammatory response has not been clearly defined (Irwin et al. 2008; Yehuda et al. 2009). Proinflammatory mediators increase brain A-beta production. Such is the case for systemic administration of lipopolysaccharide to APPswe transgenic mice in which A-beta production is increased threefold. Prostaglandin E2 stimulates A-beta production in vitro and possibly in vivo. In-vitro, glial interferon gamma and tumor necrosis factor enhance A-beta production, directly stimulate the beta-site APP cleaving enzyme (BACE 1), and suppress A-beta degradation (Sheng et al. 2003; Hoshino et al. 2009; Yamamoto et al. 2007; Hoshino et al. 2007). While there are many gaps in the data, one might consider the possibility that inefficient, fragmented, slow-wave-deficient sleep of old age contributes to a chronic stress response that results in increased A-beta production, an effect that is exaggerated within systemic and central nervous system environments of inflammatory hyperresponsiveness. Any type of brain injury enhances production of A-beta, raising the possibility that this or other APP-derived peptides have another physiologic function in the acute response to injury. The injury response includes activation of inflammatory mediators. Traumatic brain injury is a well-established risk factor for AD such that dysregulation of the injury response may facilitate later development of a neurodegenerative disease. The relationships between the neurophysiologic and neurotoxic effects of A-beta and dysregulation of A-beta function through age associated changes in sleep, stress and inflammatory responses suggest a link

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between these variables that deserves further investigation (Mattson et al. 1997; Truettner, Suzuki, and Dietrich 2005; Van Den Heuvel, Thornton, and Vink 2007; Uryu et al. 2007). Do Age-Related Conditions Enhance Brain Vulnerability to Degenerative Changes? Sleep apnea, restless leg syndrome (RLS), and periodic leg movements during sleep (PLMS) are common in late life (Tarasiuk et al. 2008; Al Lawati, Patel, and Ayas 2009; Karatas 2007). Episodic hypoventilation during sleep apnea may be associated with oxygen desaturation. RLS and PLMS delay sleep onset or disrupt sleep continuity. The recurrent hypoxia associated with sleep apnea may be related to animal studies of cerebral hypoperfusion and ischemia, both of which have been associated with depression of LTP as well as increased A-beta production (Zhang et al. 2007; Li et al. 2010; Gasparova, Jariabka, and Stolc 2008; Guglielmotto et al. 2009). Cerebrovascular dysfunction that occurs in AD has been related to deposition of A-beta in blood vessels in cerebral amyloid angiopathy (CAA). CAA occurs to varying degrees in nearly all patients with Alzheimer ’s disease and in 60–75% of normal octogenarians. CAA results in impaired vasodilator as well as vasoconstrictor responses, capillary occlusions, and microbleeds, all of which could potentially accelerate A-beta production in vulnerable areas of the brain (Shin et al. 2007; Thal et al. 2008). Virtually no convincing evidence links age-related elevations in glucocorticoids to accelerated brain aging, the proinflammatory state of aging to neurodegeneration, or age-related reductions in sleep efficiency to decrements in learning and memory. These intriguing possibilities deserve further experimental exploration. REM SLEEP BEHAVIOR DISORDER AND LEWY BODY DEMENTIA In contrast to AD, the neuropathological processes of LBD and the other synucleopathies are first evident in the brainstem and disseminate rostrally, although recent reports cite a considerable number of exceptions to this pattern (Jellinger 2008; Jost 2010; Braak et al. 2004). To date no pathologically confirmed case of AD has been associated with REM sleep behavior disorder (RBD). This is a remarkable contrast to LBD, Parkinson’s disease, and other synnucleopathies in which RBD prevalence may reach 40%. Sleep architecture is little studied in LBD although preliminary information suggests sleep fragmentation and loss of sleep efficiency

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(McKeith et al. 2005; Jellinger 2008; Boeve, “Polysomnographic evidence” 2008). Many components of sleep-regulatory mechanisms are mediated by the brainstem, basal forebrain, and hypothalamus, but the degeneration of specific cholinergic neurons in the PPT and laterodorsal tegmental nuclei as well as dopamine cell loss in the periaqueductal gray in LBD and multiple system atrophy are not clearly associated with RBD (Benarroch et al. 2009; Schmeichel et al. 2008). Excess loss of dopamine cells may be related to daytime sleepiness observed in LBD. Cognitive deficits are identified in RBD, including poorer working memory, attention, visual and verbal memory, and executive function. Semantic memory, language, and visual perception are preserved. These deficits are mild but significant and are probably unrelated to specific RBD episodes because these typically occur infrequently, once or several times a month (Gagnon et al. 2009; Postuma, Gagnon, and Montplaisir 2008; Terzaghi et al. 2008; Massicotte-Marquez et al. 2008; Plazzi et al. 2005). Limited published data suggests that sleep architecture and sleep efficiency are not significantly altered in RBD, although a single study demonstrated EEG slowing in RBD patients comparable to that occurring in Alzheimer ’s disease both in the waking and sleeping states (Wetter et al. 2001; Fantini et al. 2003). Thus relative sleep deprivation does not appear to be a reasonable explanation for RBD cognitive deficits. Alternatively these findings may reflect the early phase of a more generalized degenerative process. The pattern of cognitive deficits in LBD patients that is distinguished by impairments in attention, executive tasks, and particularly in visuospatial and constructional abilities is more severe and somewhat different than that of RBD. Such differences are useful in distinguishing LBD from AD in which deficits of memory and language are prominent, but provide no obvious connection with the mild deficits of RBD (Tiraboschi et al. 2006). The presumed integrity of sleep architecture in RBD suggests that other manifestations of LBD, particularly visual hallucinations and illusions, are not mediated by the intrusion of sleep states, particularly REM dream material into wakefulness. These distortions of visual perception more likely reflect reduction of cholinergic input and hypometabolism of visual association areas related to cholinergic deafferentation from the basal forebrain (Klein et al. 2010). Similarly, links between the sleep disturbances of LDB and its pathological mechanisms remain unidentified. Alpha synuclein is a nuclear and presynaptic protein that in LBD, Parkinson’s disease, and other synucleopathies aggregates in intracellular inclusions known as Lewy bodies. The means by which aggregated alpha synuclein contributes to neuronal

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injury and death is less well defined as compared to the neurotoxicity of A-beta in AD. It does appear that in common with aggregated A-beta, aggregated alpha synuclein triggers microglial activation, neuroinflammation, and neuronal loss. The common co-occurrence of LBD and AD pathology suggests that progression of both diseases may be mediated by similar inflammatory mediators. Virtually no information relates sleep disturbances, physiological stress, glucocorticoids, or other age-related conditions to cognitive decline and disease progression in LBD (Roodveldt, Christodoulou, and Dobson 2008; Mrak and Griffin 2007; Sawada, Imamura, and Nagatsu 2006; Mackenzie 2000). SUMMARY AND CONCLUSIONS The chapter addresses two hypotheses: first, age-related disruption of sleep architecture, depth and efficiency directly contributes to cognitive changes associated with aging and dementia, particularly memory loss; second these sleep disturbances, combined with aging effects and age-associated diseases, accelerate neuropathological processes of the degenerative dementias. Sleep contributes to learning and memory consolidation. Sleep disruption has the opposite effects and neurophysiologic studies suggest mechanisms such as inhibition of LTP. Parallel investigations of A-beta, a probable mediator of AD, indicate that many A-beta toxic effects are comparable to those associated with sleep disruption, sleep deprivation, in turn, increases A-beta production. The toxicity of A-beta may be enhanced by age-related diseases and the pro-inflammatory environment of an aged brain. A-beta likely has physiologic functions such as modulation of synaptogenesis. Dysregulation of such functions with excess A-beta production results in neurotoxicity. The relatively limited data supporting these ideas may be worthy of further experimental investigations. These speculations are not readily extrapolated to LBD but its frequent association with RBD as the earliest symptom indicates initial involvement of brainstem and midbrain sleep and arousal systems. Such involvement may be reflected by fluctuating cognition and variable levels of arousal and awareness associated with this illness. Such a connection has yet to be verified. Lack of evidence linking visual hallucinations of LBD or fluctuations in cognition and arousal to disturbance in sleep-regulating mechanisms is somewhat surprising. These phenomena are frequently observed in other disorders of sleep regulation such as narcolepsy and in normal individuals with sleep paralysis and hypnagogic hallucinations (Wurtman 2006; Cheyne 2005). Further studies of

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RBD and LBD are likely to clarify relationships between the instability and dysregulation of sleep states, arousal mechanisms, cognition, and other manifestations of LBD that are likely to be more varied and complex than currently appreciated (Mahowald and Schenck 1991; Vetrugno et al. 2009). Even when considered in isolation, mechanisms of aging, functions of sleep, and the fundamental causes of the degenerative dementias escape full understanding. In life the three are related and interactive at various levels of biological organization. Considering them together may provoke new questions and testable hypotheses. ACKNOWLEDGMENT: This chapter is supported in part by the GRECC, VA Boston Healthcare System, Geriatric Research, Educational and Clinical Center, 150 South Huntington Avenue, Boston, MA, 02130. REFERENCES Al Lawati, N. M., S. R. Patel, and N. T. Ayas. 2009. Epidemiology, risk factors, and consequences of obstructive sleep apnea and short sleep duration. Prog Cardiovasc Dis 51: 285–293. Aly, M., and M. Moscovitch. 2010. The effects of sleep on episodic memory in older and younger adults. Memory 18: 327–334. American Psychiatric Association, ed. 2000. Diagnostic and Statistical Manual of Mental Disorders (IV-TR). 4th ed. rev. Washington, DC. Backhaus, J., J. Born, R. Hoeckesfeld, S. Fokuhl, F. Hohagen, and K. Junghanns. 2007. Midlife decline in declarative memory consolidation is correlated with a decline in slow wave sleep. Learn Mem 14: 336–341. Banks, S., and D. F. Dinges. 2007. Behavioral and physiological consequences of sleep restriction. J Clin Sleep Med 3: 519–528. Benarroch, E. E., A. M. Schmeichel, B. N. Dugger, P. Sandroni, J. E. Parisi, and P. A. Low. 2009. Dopamine cell loss in the periaqueductal gray in multiple system atrophy and Lewy body dementia. Neurology 73: 106–112. Bissette, G. 2009. Does Alzheimer ’s disease result from attempts at repair or protection after transient stress? J Alzheimers Dis 18: 371–380. Bliwise, D. L. 2004. Sleep disorders in Alzheimer ’s disease and other dementias. Clin Cornerstone 6 (Suppl 1A): S16–28. Bloom, H. G., I. Ahmed, C. A. Alessi, et al. 2009. Evidence-based recommendations for the assessment and management of sleep disorders in older persons. J Am Geriatr Soc 57: 761–789. Boeve, B. F. 2008. Polysomnographic evidence of sleep fragmentation and poor sleep efficiency in dementia with Lewy bodies. Alzheimer ’s and Dementia 4: T435.

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Chapter 9

Magnetic Resonance Spectroscopy: A Tool for Understanding Brain Chemical Changes in Dementias Jacquelynn N. Copeland and H. Randall Griffith

With the number of older adults in the United States projected to grow larger than ever over the next few decades, prevalence of dementia is also expected to rise. Therefore, early detection of neurodegenerative disease processes is a growing concern. In addition, more accurate diagnosis and treatment planning are imperative. Neuroimaging is a term describing different methods utilized to “visualize” changes in the brain. With the computer revolution over the past several decades, neuroimaging has become an important means of detecting and diagnosing neurological diseases, including those that cause dementias. While all neuroimaging techniques help us to “see” some aspect of the brain, each technique provides distinct information about the brain, its function, and its dysfunction. Generally speaking, neuroimaging can be divided into two broad categories, structural and functional imaging. Structural imaging allows for the anatomy of the brain to be visualized, while functional imaging provides a means of seeing changes in blood flow, metabolism, or chemistry of the brain; both structural and functional neuroimaging have their place in research as well as clinical practice. Some types of neuroimaging that are used for dementia include structural

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imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and diffusion tensor imaging (DTI), along with functional imaging techniques including functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance spectroscopy (MRS).These techniques help identify abnormalities that may aid clinicians in making accurate diagnoses, prescribing the appropriate treatment, examining treatment response, and monitoring brain changes over time. The most common neuroimaging techniques used initially when a patient presents with cognitive impairment or dementia like symptoms are a CT scan or an MRI scan, which both show brain structure. CT is less expensive and used primarily to rule out any potentially reversible causes of dementia such as brain tumor, stroke, bleeding, or normal-pressure hydrocephalus, a condition where excess fluid gradually builds up in the brain, resulting in dementia-like symptoms. In addition, CT can also reveal abnormal atrophy, or deterioration of brain volume, in general, or in particular regions of the brain (Petrella, Coleman, and Doraiswamy 2003; Scheltens et al. 2002). For instance, the medial temporal lobe and hippocampus, integral for learning and memory, along with other temporal and parietal regions and areas of the frontal lobe may be of particular importance to image for a patient with suspected dementia. A standard structural MRI is also sensitive to these conditions; in addition, an MRI can display different aspects regarding the makeup of the brain, such as the grey and white matter, and has the ability to detect other abnormalities such as white-matter hyperintensities, which show up on MRI as ultra-white patches, or lacunar infarcts, small areas of cell death caused by occlusion of small blood vessels in deeper parts of the brain, which both may contribute to a presentation of vascular dementia (Small et al. 2008). Although CT and MRI are commonly used for dementia workups, they are particularly limiting when no structural changes are observable, like in early stages of dementia or mild cognitive impairment (MCI), a stage of cognitive difficulties between normal aging and dementia. In these cases, the neuropathological disease process, or the changes in the brain related to the course of the disease, may not have yet affected the overall structure of brain tissue; however, cognitive difficulties and symptoms may be present due to functional changes in brain tissue chemistry or metabolism that have yet to affect the structure of the brain that can be seen on MRI or CT. Thus, functional imaging can provide valuable information about biological and chemical changes occurring in the brain and can support clinicians in early detection and diagnosis of dementia when structural changes are

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absent. Furthermore, even when structural imaging reveals atrophy or other findings suggestive of dementia, functional imaging gives the doctors a way of understanding how the structural changes have affected the brain’s working. However, it is important to remember that neuroimaging is only one piece of information that clinicians use when conducting a dementia workup, making a diagnosis, and providing treatment. MAGNETIC RESONANCE SPECTROSCOPY Magnetic resonance spectroscopy (MRS) is a functional imaging technique that provides information regarding different metabolites, or molecules that play important roles in the functioning of brain cells. MRS works by the same physical principles as does a standard MRI, that being the use of a strong magnetic field and radio-frequency signals. The primary difference between MRI and MRS is that in MRS scans the data obtained by the MR scanner is interpreted based upon the chemical composition of the area of the body being imaged, while in MRI the radio-frequency signal is reconstructed into an image. MRS works based upon the type of molecule that is being measured; of which several molecules can be “visualized,” including those containing the ions hydrogen (1H), phosphorus (31P), carbon (13C), and fluorine (18F). The most common form of MRS measures brain chemicals based upon the presence and number of the hydrogen ions in different molecules (Minati, Grisali, and Bruzzone 2007), specifically referred to as proton magnetic resonance spectroscopy (1H MRS) to define use of the hydrogen nucleus in contrast to other forms of MRS. Research supports the use of 1H MRS with suspected dementia cases because of its accessibility, sensitivity to detection by an MR signal, and good spatial resolution (Jones and Waldman 2004; Ross and Bluml 2001). Before conducting an MRS scan, the clinician chooses a region of interest; these regions are usually areas of the brain where metabolic changes often occur, or in some instances can be a whole slice of the brain, similar to what one snapshot from an MRI scan would show. The MRS scan then measures the concentrations of measurable brain chemicals in this region by sending in radio-frequency (RF) signals to vibrate the ions, which then respond back with their own identifiable RF, based upon the quantity of hydrogen ions in the molecules in that region. Instead of being constructed into an image as in an MRI, the data received from the scanner is plotted out into a fourier transform graph. This graph displays the relative concentrations, based on the RF signal intensity of the molecule, where higher signal intensity (peaks on the graph) indicates higher relative concentration of the brain chemicals, known as metabolites

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as they are the by-products of brain metabolism. Of note, concentrations are most often expressed as a ratio rather than absolute concentrations due to quantification and measurement difficulties. In addition, metabolites have different spectral patterns, such that some may have one peak, two peaks, or multiple peaks. The parameters of the the MRI RF scanning sequence, such as echo time (TE), also result in slightly different outputs, while increasing the magnetic strength of MRI scanner, such as doubling the magnet strength from 1.5 Tesla (or 30,000 times the strength of the Earth’s magnetic field) to 3 Tesla (or 60,000 times the strength of the earth’s magnetic field), helps separate the peaks and improves the ability to distinguish among metabolites. Common metabolites measured in 1H MRS include choline (Cho), creatine (Cr), glutamate-glutamine (Glx), myo-Inositol (mI), scyllo-Inositol (sI), and N-acetyleaspartate (NAA). Each metabolite is thought to represent a metabolic process (or processes) occurring in the brain, such as those associated with integrity of brain cells or breakdown of brain cell tissue. In the brain, most of these metabolites can be compared with the concentration of Cr, which is usually present at constant levels in all living tissue. Using these ratios, it is possible to establish levels of normal and abnormal concentration of metabolites in certain brain areas, such that abnormally low or high MRS ratios may indicate presence of brain diseases, such as Alzheimer ’s disease. Thus, MRS can measure biochemical information of a chosen area of the brain of a patient with suspected dementia, which can be compared directly to the typical metabolite ratios found in a sameage adult with no cognitive complaints or brain abnormalities. Abnormal metabolite ratios can serve as additional evidence supporting a specific diagnosis along with information collected from patient medical history, cognitive functioning, and structural imaging. Furthermore, MRS can be repeated over time and can be valuable to measure progression of the disease as well as response to potential medication treatments. Advantages of MRS The data provided by MRS is relatively unique and the technique is generally safe and readily available. Although other functional imaging techniques such as PET measure metabolic processes in the brain (such as use of glucose), MRS is the only technique that provides data concerning multiple metabolites within the same scan. Furthermore, MRS is particularly advantageous as it can be obtained within specified regions of the brain and is particularly useful when a specific area of the brain is commonly targeted by neuropathological disease processes, such as the

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posterior areas of the brain in Alzheimer ’s disease. MRS is noninvasive and does not require injection of radioactive materials or any surgical procedures. In many instances this type of data can be obtained along with a clinical MRI scan and does not add unreasonable time burdens on the patient. Thus, information regarding the brain’s structure and function can be collected in one session and used together to evaluate disease. MRS can also be conducted on many of the clinically available MRI scanners, which means it has the potential to be utilized at many hospitals in the United States and worldwide.

Limitations of MRS Most studies using MRS have measured metabolic ratios in specific regions of interest rather than examining whole brain ratios. Other functional imaging techniques may be more beneficial for examining overall brain functioning. Overall, low specificity is a main disadvantage of MRS. That is, information and interpretation of metabolites can be somewhat unclear because some metabolites are very close to one another on the spectrum, especially with lower magnetic strength scanners, such as 1.5 Tesla. Additionally, MRS is very susceptible to distortion by nonbraintissue signals, such as signals arising from bone and air in the sinuses and fat deposits in the scalp. Choosing the area of the brain from which the MRS signal will be measured thus requires avoiding areas close to the skull and scalp, as well as close to the base of the brain, where there is signal contamination from the sinuses. However, future advances in technology will likely address some of these limitations. In addition, MRS is currently considered a new and investigational technique, but has the potential to become more commonly used clinically to aid in dementia workups and diagnosis. In combination with other MRI imaging techniques, MRS has the potential to become an important adjunct in the clinical diagnosis of dementias.

COMPARISON OF MRS TO OTHER NEUROIMAGING TECHNIQUES Comparison of MRS to Structural Techniques MRS and structural MRI use the same hardware, a magnetic resonance scanner, and use similar RF sequences to excite or “pulse” molecules. The critical difference is in how the resulting RF signal from the body tissue being studied is compiled and analyzed. In structural MRI, RF signal

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data is constructed to display images of the brain’s physical structures in high resolution, while MRS measures resonances of RF signal at different frequencies, which are “shifted” from the reference signal (most commonly the H20 signal) depending on the concentration of metabolites. It is this “chemical shift,” measured in parts-per-million (ppm), by which the metabolites are identified and quantified. Unlike MRS, which most often focuses on a region of interest, images created from an MRI typically (but not always) display the whole brain; however, images are created at different angles, called planes, each with a varying number of slices depending on the pulse sequence used during the MRI scan. Various anatomical structures can be identified depending on the particular slices examined and the plane of imaging. Furthermore, structural MRI analysis techniques can be used to measure volumes of particular structures using MRI images. Another type of MR structural imaging, DTI, measures how water diffuses across membranes in the brain to create images of the nerve bundle pathways in the brain, which connect different parts of the brain to one another. DTI is useful for identifying these nerve bundle paths (or “white matter” paths, referred to because the fatty sheath that lines the nerves makes these tissues white compared to the brain cells, or neurons, which appear “grey” to the naked eye) because water diffuses along these whitematter paths in a uniform fashion, as compared to a random diffusion within the grey matter. Unlike MRI, which images both white and grey matter in the brain, DTI focuses solely on white-matter tracts and supplies information regarding their integrity and orientation. Thus, in contrast to MRS, which can reveal abnormal metabolite ratios in a chosen area of the brain, DTI provides information regarding changes in connectivity in the brain, which may indicate less efficiency or diminished ability for communication between various aspects of the brain. However, structural MRI and DTI only provide information about structure, and cannot provide information regarding brain function. MRS Compared to Functional Imaging Techniques In addition to MRS, another functional technique that utilizes MR hardware is called fMRI. Although fMRI and MRS both utilize magnetic fields to supply information regarding the brain’s functioning, fMRI measures changes in signal intensity to represent changes in oxygen content of the blood as it is used by the brain cells. fMRI mostly is obtained while an active cognitive task is completed within the scanner so the difference

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in blood oxygenation concentration can be compared when the brain is engaged in a cognitive task versus when at “rest.” Areas of the brain that show more oxygen use during a task are considered to be “activated.” Like MRS, though, fMRI has the ability to potentially identify functional abnormalities not seen on standard structural imaging. Finally, practical difficulties caused by the cognitive paradigms used during fMRI make it somewhat difficult for patients with dementias to maintain focus during the long periods required to complete a full fMRI scanning session. Recent advances in resting fMRI may obviate the need to use cognitive tasks within the scanner and may be able to provide information regarding changes in the efficiency of brain function. When used clinically, fMRI is potentially less expensive and is less invasive than PET (Small et al. 2008). PET, on the other hand, applies a different type of functional neuroimaging technology that requires intravenous injection of a radioactive tracer and imaging of this tracer in the brain, such that cerebral blood flow, utilization of glucose, or other metabolic processes can be measured. PET imaging can employ various types of radioactive tracers. A commonly used tracer in clinical practice, flourodeoxyglucose (FDG)-PET, identifies regional glucose metabolism in the brain, indirectly representing activity of neurons in the grey matter. However, other types of PET can examine specific aspects of the brain or brain pathology. For instance, PiB-PET has been developed to examine amyloid plaques, one of the neuropathological markers of Alzheimer ’s disease (for a review, see Noble and Scarmeas 2009). Therefore, in contrast to MRS, PET cannot provide information concerning several metabolites at one time as in MRS, but PET can detect significant changes in brain neurochemical activity, including hypometabolism, which can signify underactivity in particular regions of the brain. In addition, the invasive and radioactive nature of injected tracers, along with limited availability of the equipment needed to make these radioisotopes on site, are some current disadvantages of PET imaging. Another functional imaging technology, SPECT, measures blood flow in the brain by detecting an intravenously injected single-photon radioactive tracer. This technology indirectly reveals brain activity by examining blood flow in the brain; it is less expensive and the tracer is more readily available than PET. Nevertheless, similar disadvantages in PET apply to SPECT when comparing MRS; it is more invasive and requires additional technology. In further contrast to MRS, SPECT can only provide information about one biochemical at a time and cannot provide comparative information on metabolism as in MRS.

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RESEARCH WITH 1H MRS IN DEMENTIA AND MILD COGNITIVE IMPAIRMENT Main Metabolites of Interest The following metabolites are the most commonly used in dementia research.

N-Acetyleaspartate N-acetyleaspartate (NAA), resonating at 2.02 parts per million (ppm), represents the highest peak on a normal spectrum, due to its high proton metabolic concentration in the brain (Kwock 1998; Valenzuela and Sachdev 2001). NAA is found primarily in the main cell body of neurons, the primary signaling cell of the brain, and their axons, the processes of neurons that send information from cell to cell. Furthermore, NAA is present throughout the brain and, due to its location in neurons and axons, found in both grey and white matter. Although the exact function of NAA is unknown, this metabolite serves as a neuronal marker, signifying the density, integrity, and viability of neurons. Measuring NAA in MRS contributes information regarding the number of functioning brain cells; thus, lower concentration ratios of NAA are considered an indication of less neuronal viability and integrity, presumably caused by neuronal death, or loss or injury of axons and dendrites (Birken and Oldendorf 1989). Choline With a peak at 3.22 ppm, choline (Cho) metabolite ratios represent several related neurochemicals in the brain containing choline, such as free Cho, phosphorylcholine, and glycerophosphorylcholine, and to a small extent acetylcholine (Firbank, Harrison, and O’Brien 2002; Kantarci 2007; Valenzuela and Sachdev 2001). The greatest amount of choline in the brain is found in the phospholipids of cellular membranes (Kantarci 2007), which is the outmost layer of a cell that serves as a selective barrier; in addition, choline molecules are related to synthesis and turnover of these membranes (Minati, Grisoli, and Bruzzone 2007). Therefore, metabolite ratios in MRS are considered a crude metabolic marker of membrane density, integrity, and turnover. Furthermore, in 1 H MRS the Cho peak can signify inflammation (Mueller, Schuff, and Weiner 2006).

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Creatine Resonating at 3.02 ppm, creatine (Cr) includes both creatine and phosphocreatine. In short, these metabolites represent the energy metabolism or storage of energy in a cell (Minati, Grisoli, and Bruzzone 2007; Valenzuela and Sachdev 2001). Since creatine concentrations are relatively stable across individuals, they are considered constant and used to calculate ratio concentrations (i.e., NAA/Cr; Cho/Cr). Myo-Inositol A naturally occurring sugar alcohol found in the brain, the myoinositol (mI) peak resonates at 3.56 ppm. Not occurring in neurons, the main signaling cell of the brain, mI is found in higher concentrations in glial cells, brain cells that support and protect neurons. Thus, the mI peak is presumed a glial marker (Castillo et al. 1998; Downes and Macphee 1990; Garcia-Perez and Burg 1991) An increase in mI ratio concentrations may reflect an increase in the amount of glial cells or increased cell size, which is thought to represent inflammation or gliosis, an accumulation of cells as a response to damaged neurons (Brand, Richter-Landsberg, and Leibfritz 1993; Rosen and Lenkinski 2007; Strange et al. 1994; Valenzuela and Sachdev 2001). Scyllo-Inositol In addition to mI, scyllo-inositol, a related metabolite resonating at 3.342 ppm is also presented as a peak on normal MRS spectrum. Less is known about sI’s chemical functions in the brain and how it differs from mI; however, sI is a product of mI metabolism (McLaurin et al. 2000). Glutamate-Glutamine The GLX peak (2.1–2.4 ppm) includes both metabolites: glutamate (Glu) and glutamine (Gln) since the concentration of each substance cannot be separated on lower magnetic strength scanners, such as a 1.5 Tesla. However, improved differentiation of Glu and Gln and their specific concentrations may be revealed using a higher field strength scanner, like a 3 Tesla (Kantarci et al. 2003; Kantarci 2007; Schubert et al. 2004). Glu is the most abundant excitatory neurotransmitter in the brain, thus it is a chemical that is transferred from neuron to another to increase signaling and activity between cells.

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As a precursor of Glu, Gln is believed to have an important neurobiological function related to the detoxification and regulation of Glu (Valenzuela and Sachdev 2001). Glu and Gln are involved in neuronal function, metabolism, and plasticity (Antuono et al. 2001); thus, these metabolites are related to efficient transmission of information, energy production, and adaptability and repair of the brain. 1

H MRS Findings in Alzheimer ’s Disease

Alzheimer ’s disease (AD) is the most common cause of dementia affecting older adults (McMurtray et al. 2006). The disease is characterized by two particular abnormal findings in the brain called amyloid plaques and neurofibrillary tangles. Amyloid plaques are accumulations of beta amyloid protein that occur outside of neurons in the brain, and neurofibrillary tangles refer to twisted and tangled protein fibers that make up a neuron. Both result in death of neurons in the brain, particularly in a pattern with early involvement of areas very important for memory, the medial temporal lobes and hippocampus, but also with further spread into other regions of the brain related to additional cognitive abilities (Braak and Braak 1991). However, examination of the brain at the cellular level is needed for identification of these hallmark brain abnormalities, thus a definitive diagnosis of AD can usually only be made after death (Cummings et al. 1998). Therefore, MRS may help display a specific pattern of abnormal concentrations of metabolites that may represent the cellular changes occurring because of the underlying brain disease in AD, and has the potential to aid in differentiating this pattern from other types of dementias (Kantarci et al. 2008). In studies examining Alzheimer ’s disease, the most common finding is reduction of NAA ratio concentrations (Adalsteinsson et al. 2000; Chantal et al. 2002; Christiansen, Schlosser, and Henriksen 1995; Ernst et al. 1997; Heun et al. 1997; Parnetti et al. 1997; Rose et al. 1999; Schuff et al. 1997; Watanabe et al. 2002). In research where a particular region of interest was examined in patients with AD, decreased NAA/Cr ratios were consistently found in many studies in the hippocampus and medial temporal lobe (Chantal et al. 2002, 2004; Dixon et al. 2002; Jessen et al. 2000; Schuff et al. 1997; Watanabe et al. 2002), along with other specific areas of the temporal lobes (Frederick et al. 1997; Kantarci et al. 2000; Herminghaus et al. 2003; Parnetti et al. 1997) and parietal lobes (Antuono et al. 2001; Griffith, den Hollander, et al. 2007; Hattori et al. 2002; Herminghaus et al. 2003; Kantarci et al. 2000; Kantarci, Xu, et al. 2002; Kantarci, Smith, et al. 2002; Kantarci et al. 2003; Martinez-Bisbal et al. 2004; Rose et al. 1999).

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Furthermore, other regions of the brain including the occipital lobes (Kantarci et al. 2000; Moats et al. 1994; Shonk et al. 1995; Waldman et al. 2002; Watanabe et al. 2002; Weiss et al. 2003) and frontal lobes (Chantal et al. 2002, 2004; Christiansen, Schlosser, and Henriksen 1995; Herminghaus et al. 2003; Parnetti et al. 1997) showed similar findings of depleted NAA/ Cr. Many studies have also displayed NAA reductions in white matter (Catani et al. 2001; Hattori et al. 2002; Herminghaus et al. 2003; Heun et al. 1997; Meyerhoff et al. 1994; Moats et al. 1994); however, a few studies did not find these results (Catani et al. 2002; Watanabe et al. 2002). Additionally, research using MRS techniques to examine larger areas of the brain or whole brain metabolites generally demonstrate a reduction of NAA in AD (Adalsteinsson et al. 2000; Pfefferbaum et al. 1999). Thus, MRS studies in AD seem to support NAA as a marker of both functioning ability of neurons (grey matter) and their axons (white matter). Overall, widespread reductions in NAA are consistent with disease progression and the neurofibrillary tangles present in AD, which may first occur in early stages of the disease in the medial temporal lobes and hippocampus but then spread to areas responsible for vision, sensory, and motor abilities such as the occipital lobe and parietal lobes in later stages of the disease (Braak and Braak 1991). So far, only a few studies have investigated changes in NAA ratios in AD over time. Four studies examined NAA ratios in patients with AD over a one-year period; two of them generally found NAA decreases over time (Adalsteinsson et al. 2000; Kantarci et al. 2007) in individuals with AD, while two other studies did not show declines (Dixon et al. 2002; Jessen et al. 2001). However, in one of the studies with negative results, patients with AD displayed lower ratios of NAA than older controls at both time points (Dixon et al. 2002). Thus, there is a possibility that greater reductions in NAA may indicate later stages of AD and may signify greater amount of brain abnormalities over time due to the disease; nonetheless, more studies are needed to support these results and further examine NAA ratios over longer amounts of time. Abnormalities in the Cho peak have been documented in some studies (Chantal et al. 2002,. 2004; Jessen et al. 2000; Kantarci et al. 2000, 2003; Lazeyras et al. 1998; MacKay et al. 1996; Meyerhoff et al. 1994); however, results have been conflicting. When comparing patients with AD to control participants, some studies find elevations of Cho (Lazeyras et al. 1998; MacKay et al. 1996; Meyerhoff et al. 1994), while others displayed reductions in Cho levels (Chantal et al. 2002, 2004; Jessen et al. 2000; Kantarci et al. 2000, 2003). The meaning behind these inconsistent results is unknown, although there are several hypotheses. Higher levels of Cho

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may be the result of the loss of functioning neurons during AD resulting in increased membrane turnover, or could be related to an increase in the response to help compensate for reduced acetycholine levels seen in AD (Kantarci et al. 2007). There is also the possibility that common medications for AD that are cholinergic, like donapezil, may indirectly result in abnormal Cho levels found in MRS (Griffith et al. 2008; Kantarci et al. 2007). Alternatively, variable results across studies could also be due to differences between methods using 1H MRS, such that variations in length of echo times or particular areas of interest chosen may influence the chance of finding increased or decreased Cho levels (Griffith, Stewart, and den Hollander 2009). Abnormal mI elevations have been found in areas consistent with regions of the brain most affected by the plaques and tangles and neuronal loss in AD, including aspects of the brain such as the temporal-parietal area (Chantal et al. 2002, 2004; Ernst et al. 1997; Parnetti et al. 1996), posterior cingulate gyrus/mesial parietal lobe (Griffith, den Hollander, et al. 2007; Herminghaus et al. 2003; Kantarci et al. 2000, 2003; Kantarci, Xu, et al. 2002; Lazeyras et al. 1998; Martinez-Bisbal et al. 2004; Rose et al. 1999; Waldman and Rai 2003), parietal white matter (Herminghaus et al. 2003; Moats et al. 1994), and occipital lobes (Moats et al. 1994; Shonk et al. 1995; Waldman et al. 2002). Furthermore, frontal lobes (Chantal et al. 2002, 2004; Herminghaus et al. 2003; Parnetti et al. 1997) and subcortical regions (Catani et al. 2001, 2002; Hattori et al. 2002; Heun et al. 1997), which are less commonly affected by the disease, less often display abnormal mI levels. Along with decreased NAA concentrations, increased mI levels are one of the most prominent and consistent findings in 1H MRS among AD patients. Thus, both metabolites seem to serve as separate indicators of the effects of AD on the brain’s functioning with NAA serving as a neuronal marker and mI representing as a glial marker. Cr, sI, and Glx have been less explored in 1H MRS studies with patients who have AD. Cr is consistently used as the denominator of ratios because of its suggested stability even in brain disease (Valenzuela and Sachdev 2001); moreover, AD patients generally show stable Cr levels compared to controls (Ernst et al. 1997; Pfefferbaum et al. 1999; Schuff et al. 1997). Investigation of sI has also been less common; one study revealed sI/Cr elevations in AD patients (Griffith et al. 2006), while another study showed raised concentrations in the normal aging brain (Kaiser et al. 2005). Elevations in sI may occur for the same reasons mI concentrations are increased since they are directly related; however, more research needs to be conducted on sI to support these findings and their possible explanation for elevations. Overall, findings regarding Glx in AD patients using 1H MRS

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are inconsistent. Some studies have shown decreased Glx levels in specific posterior aspects of the brain (Antuono et al. 2001; Hattori et al. 2002) and specific regions in the temporal lobe (Herminghaus et al. 2003) and occipital lobe (Moats et al. 1994). Further research has been conducted to examine the ability for studies using 1H MRS to distinguish patients diagnosed with probable AD from healthy older adults. Several studies have demonstrated that NAA levels can aid in discrimination of normal controls from AD patients (Antuono et al. 2001; Schuff et al. 1997; Shonk et al. 1995). Furthermore, the addition of NAA metabolite levels can improve discrimination of AD patients when information from structural imaging is available (Dixon et al. 2002; Ernst et al. 1997; Kantarci, Xu, et al. 2002; MacKay et al. 1996). Using an NAA/mI ratio, in contrast to other metabolite ratios, seems to best discriminate AD patients from normal controls (Kantarci et al. 2007). Research has also been performed to investigate how cognitive abilities, everyday functioning, and brain abnormalities observed after death relate to 1H MRS findings in AD. The majority of studies have found correlations between 1H MRS and scores on a common mental status screening, the Mini Mental Status Examination (MMSE) (Antuono et al. 2001; Dixon et al. 2002; Doraiswamy, Charles, and Krishnan 1998; Ernst et al. 1997; Heun et al. 1997; Jessen et al. 2000, 2001; Parnetti et al. 1997; Rose et al. 1999; Waldman and Rai 2003). The ability to perform everyday activities, like finances, has also been related to metabolite ratios found in 1H MRS (Griffith, Okonkwo, et al. 2007). Finally, NAA and mI findings from patients with probable AD using 1H MRS before and after death have been found to relate to the extent of damage seen in the brain as a result of senile plaques and neurofibrillary tangles after death (Kantarci et al. 2008). 1

H MRS Findings in Frontotemporal Dementia

Frontotemporal dementia (FTD) is a broad term that refers to a varied group of clinical syndromes that involve deterioration primarily of the frontal and/or temporal lobes of the brain by processes such as neuronal loss and gliosis. Moreover, FTD is characterized by early personality changes and behavior changes including apathy and disinhibition (Coulthard et al. 2006; Ernst et al. 1997). 1 H MRS studies with FTD patients have revealed metabolic abnormalities in several regions of the brain. Lower NAA/Cr ratios in the primarily diseased regions of the brain, the temporal and frontal lobes, was found in one study. In addition, mI/Cr ratios were increased in the frontal lobe region of interest; however, no metabolic abnormalities were present

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on the 1H MRS of a selected parietal lobe region (Coulthard et al. 2006). Furthermore, other research comparing FTD and AD patients supports differing regional patterns of metabolite abnormalities. That is, areas primarily more susceptible to reduced functioning and neuronal death in each disease may show abnormalities on the metabolite spectrum, such that findings in particular regions, such as the midfrontal grey matter in FTD and the temporoparietal grey matter in AD, may help distinguish between these two types of dementia (Ernst et al. 1997; Mihara et al. 2006). Moreover, clinical and cognitive features, such as severity of dementia using the Clinical Dementia Rating and global mental status measured by MMSE scores, were also associated with metabolic abnormalities in the frontal region and temporoparietal regions in a group of FTD, AD, and healthy controls (Ernst et al. 1997). 1

H MRS Findings in Vascular Dementia

Another common form of dementia, vascular dementia (VaD), pertains to a syndrome related to one or more cerebrovascular mechanisms, those which are related to the blood supply to the brain, that cause neuronal death and deterioration of brain functioning. Types of cerebrovascular problems in VaD include infarcts, which are areas of dead neurons and tissue due to deprivation of blood supply and oxygen, white matter lesions, which refer to areas of dead axons, and consequently, atrophy, or shrinkage of the brain (Wiederkehr et al. 2008). Because of VaD’s various causes, different presentation of symptoms, and high co-occurrence with characteristics of AD, diagnosis is particularly difficult (Holmes et al. 1999; Jones and Waldman 2004). Thus, 1H MRS may be useful clinically to distinguish VaD from other dementias, if a pattern of abnormal metabolite findings could be established in research. Research studies using 1H MRS in VaD have demonstrated widespread metabolite abnormalities. For instance, Herminghaus et al. (2003) explored metabolite ratios in five regions of interest including grey and white matter in the mid-parietal, mid-frontal and temporal gyrus. In contrast to controls, NAA/Cr ratios were reduced in all five regions of interests suggesting global areas of neuronal death. Furthermore, elevations of mI/Cr ratios were found in the parietal grey and white matter, frontal white matter, and the temporal lobe, while Glx ratios were also abnormally elevated in parietal grey matter and temporal lobe (Herminghaus et al. 2003). Results from a study by Kantarci et al. (2004) also displayed lower ratios of NAA/Cr in patients with VaD, although these reductions were found in the posterior cingulate region of the brain. However,

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mI/Cr and Cho/Cr ratios were comparable to healthy controls in this brain area (Kantarci et al. 2004). Together, the pervasive abnormal metabolite findings suggest many brain regions directly related and indirectly related to those involved in vascular pathology, such as infarcts and white matter lesions, are disrupted in VaD. Moreover, the NAA/Cr decreases and mI/Cr elevations suggest that both neuronal dysfunction/death and damage to axons are occurring along with gliosis. Finally, abnormal NAA findings in regions such as the posterior cingulate, far away from areas with vascular pathology, are hypothesized to be indirectly related to the degeneration of neurons in these areas with vascular infarcts and lesions (Kantarci et al. 2004). 1

H MRS Findings in Dementia with Lewy Bodies

Dementia with Lewy Bodies (DLB) is named for the abnormal protein formations, Lewy bodies, which develop in neurons and are found at the cellular level when examining the brain after death. This type of dementia is characterized by worsening of cognitive dysfunction over time, accompanied by fluctuations in alertness and attention. Furthermore, visual hallucinations and motor symptoms like those seen in Parkinson’s disease, such as stiffness and rigidity or loss of the ability to initiate and maintain movement, are also main features of DLB (McKeith et al. 2005). Overall, less research has focused on 1H MRS in DLB; furthermore, findings from these studies are generally inconsistent at this time. Molina et al. (2002) found decreased NAA/Cr, Cho/Cr, and Glx/Cr ratios in patients with DLB compared to controls in one region of white matter, but did not find differences in grey matter in the mid-parietal lobe. Moreover, clinical, cognitive, and motor measures were not related to any of these abnormal metabolite findings (Molina et al. 2002). Studies examining other regions of the brain such as the hippocampus and posterior cingulate gyrus in patients with DLB revealed contradicting results. That is, in one study elevated NAA/Cr ratios were found in the hippocampi of DLB patients in contrast with controls; however, Cho/Cr ratios did not show group differences (Xuan, Ding, and Gong 2008). While another study showed Cho/Cr elevations in the posterior cingulate gyrus but no group differences in NAA/Cr or mi/Cr ratios of DLB patients versus healthy controls (Kantarci et al. 2004). Several explanations could explain variability of 1H MRS findings in patients with DLB. First of all, only a few studies have been conducted, and their methods vary considerably; thus differences in patient characteristics or imaging methods may have an effect on the data obtained.

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However, studies examining damage and loss of neurons in patients with DLB after death have also been inconsistent (Cordato et al. 2000; GomezIsla et al. 1999). Thus, the nature of DLB, itself, and the way it affects the brain may vary, such that it produces different results. 1

H MRS Findings in Parkinson’s Disease Dementia

Parkinson’s disease, primarily categorized as a movement disorder, many times also involves cognitive changes which may later develop into dementia. Parkinson’s disease dementia (PDD) is a specific dementia in patients with Parkinson’s disease which involves onset of worsening cognition and functional impairment after at least one year after initial onset of the movement disorder (McKeith et al. 2005). PDD is thought of as a separate dementia with characteristics that can be distinguished from patients with DLB (Benecke 2003) and AD with late motor complications (Dickson 2000). Many studies examining PDD use two comparison groups: a nondemented PD group and a healthy control group. Overall, 1H MRS findings displayed abnormal brain metabolism in PDD patients when compared to both groups. Metabolite levels were first examined in the occipital cortex in PDD patients, where NAA levels were reduced when compared to non-demented PD patients, but not healthy controls (Summerfield et al. 2002). The posterior cingulate gyrus in PDD patients has displayed cellular changes and damage in brains when examined after death (Braak et al. 2004), in addition to abnormal blood flow (Osaki et al. 2005) and neurochemical changes (Brooks and Piccini 2006) in functional imaging studies. Because of these abnormalities, this region of interest was examined in a recent 1H MRS study. Providing further evidence of abnormal chemical changes in the brain, PDD patients displayed reduced NAA/Cr ratios when compared to healthy controls and nondemented PD patients. Furthermore, Glu/Cr ratios were also reduced compared to healthy controls (Griffith et al. 2008). Low NAA levels are thought to signify limited ability for neurons in the cingulate gyrus and occipital lobes to function in PDD, while Glu reductions could possibly relate to other disease processes. This pattern of metabolic abnormalities obtained from 1H MRS in the posterior cingulate gyrus seems to distinguish PDD patients with dementia from those without. In addition, the reduction of NAA/Cr ratios seems to not only discriminate PDD from PD without dementia, but also from normal healthy controls. Furthermore, mental status and cognitive function as measured by the MMSE and Dementia Rating Scale (DRS), also seem to be related to both NAA and Glu levels (Griffith et al. 2008). Ultimately,

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1

H MRS studies examining brain metabolic abnormalities over time in patients with PD who may or may not develop dementia will need to be conducted to understand more about when these brain changes occur in comparison to clinical symptoms. 1

H MRS Findings in Amnestic Mild Cognitive Impairment

Amnestic Mild Cognitive Impairment (MCI) is a classification used to diagnose a stage of cognitive difficulties between normal cognition with aging and AD. There are specific criteria that need to be met for a diagnosis of amnestic MCI, which include: (1) complaints of memory loss by the patient, if possible confirmed by others; (2) impairment on memory testing when compared to performance of adults with the same age and education; (3) generally, normal performance on tests in other domains of cognition; and (4) overall, maintained ability to function in activities of daily living (Petersen et al. 2001). Furthermore, individuals diagnosed with MCI have a higher yearly rate of developing probable AD than cognitively normal peers (Ganguli et al. 2004; Petersen et al. 2001). Overall, 1H MRS studies examining patients with amnestic MCI crosssectionally (i.e., at only one period in time) have found consistent metabolic brain abnormalities between the levels seen with normal healthy controls and AD. For instance, one study found that mI/Cr ratios in the posterior cingulate in patients with MCI were significantly increased compared to controls; however, mI/Cr was significantly lower in MCI patients in contrast to patients with AD. Thus, increased ratios of mI in patients with MCI may have reflected early brain changes, such as gliosis, occurring before indication of neuronal damage or loss as usually seen by decrements in NAA (Kantarci et al. 2000). Additional studies have also found mI elevations in patients with MCI compared to controls in the posterior cingulate (Kantarci et al. 2003; Rami et al. 2007), left hippocampus (Franczak et al. 2007), and other areas of the brain including white matter (Catani et al. 2001) and the parietotemporal cortex (Chantal et al. 2004; Rami et al. 2007). However, some studies have not found differences between mI levels in AD and MCI (Catani et al. 2001; Chantal et al. 2004; Garcia Santos et al. 2008; Kantarci et al. 2003). mI elevations are not the only abnormal metabolite findings in crosssectional studies with MCI. Some studies have also found NAA and Cho abnormalities. Cho was found significantly increased in the right frontal cortex and posterior cingulate, but decreased levels of Cho and NAA were discovered in the left medial temporal lobe in patients with MCI (Chantal et al. 2004; Kantarci et al. 2003). More specifically, NAA decreases were

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seen in the right hippocampus of a small sample of MCI patients (Franczak et al. 2007), and NAA/Cr was found to be as equally reduced in the hippocampus of MCI patients and AD patients in another study (Ackl et al. 2005). In contrast, when the posterior cingulate region was examined MCI patients showed abnormal reduction in NAA, but not as severe as declines seen in AD in one study (Ackl et al. 2005; Kantarci et al. 2003), while in another, only NAA abnormalities were seen in AD patients (Ackl et al. 2005). Some studies have found no evidence of NAA changes in MCI (Garcia Santos et al. 2008; Kantarci, Smith, et al. 2002). However, the overall findings from 1H MRS cross-sectional studies generally suggest that abnormal metabolite ratios found in patients with MCI reflects the transitional phase between normal cognitive aging and Alzheimer ’s disease. That is, MCI patients, in general, do not display the normal metabolism seen in healthy older adults but also do not exhibit as severe abnormalities as found in studies with AD. This conclusion is further supported by findings concerning early cellular brain changes seen in AD (Braak and Braak 1991; Markesbery et al. 2006). In contrast with cross-sectional studies, very few 1H MRS studies have investigated metabolic changes over time in patients with MCI. One such study reported NAA/Cr decreases in the posterior cingulate region of the brain in MCI and AD patients one year later. Furthermore, MCI patients who converted to AD and those who did not convert to AD showed a similar rate of decline in NAA, while interestingly, Cho/Cr levels only declined in the nonconverters. Thus, it was hypothesized that some sort of cholinergic mechanism may have been functioning to help compensate for neuronal damage in MCI patients who remained stable. Of note, no mI/Cr abnormalities were found (Kantarci et al. 2007). Quite the opposite results were found by Bartnik Olson et al. (2008), where the posterior cingulate gyrus of MCI patients showed increased mI concentrations but no change in NAA or Cho concentrations approximately 11 months later. These diverging results may be due to different methodological techniques for measuring abnormal metabolite concentrations; nevertheless, they demonstrate the need for more longitudinal 1H MRS studies in MCI patients to better understand the chemical brain changes occurring in this particular stage of cognitive dysfunction. 1 H MRS studies have also explored relationship between abnormal metabolite findings and cognition in MCI patients. Posterior cingulate 1 H MRS ratios in MCI have been found to correlate with different cognitive abilities, such as global cognition and learning (Kantarci, Smith, et al. 2002), and hippocampal NAA/Cr in MCI patients was related to verbal fluency and naming ability (Ackl et al. 2005). Furthermore, the ability to

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predict “conversion,” or worsening of diagnosis from MCI to dementia, from 1H MRS findings has also been investigated. In general, NAA/Cr baseline ratios and reductions show the most promise in predicting conversion from MCI to dementia (Metastasio et al. 2006; Modrego, Fayed, and Pina 2005); nevertheless, more research needs to be conducted using similar diagnostic criteria and methodological techniques to examine MCI participants and the chemical brain changes that occur over time in converters and nonconverters. CLINICAL APPLICATIONS OF MRS One of the most important purposes for conducting research is to eventually apply the findings to individual patients in clinical situations. Although MRS is still considered experimental and investigational at this point in time, research has expanded our knowledge of abnormal metabolite patterns associated with different dementia types. Future studies using 1 H MRS with dementia and MCI patients can confirm results and provide further evidence for the utility of MRS when conducting dementia evaluations in clinical situations. Potentially, consistent metabolite patterns found in research with dementias could possibly serve as a clinical biomarker for the type of neurodegenerative disease in the future. Furthermore, with technological advances such as stronger magnetic field strength of scanners, MRS may become more powerful and more commonly used clinically. Although the prospect of applying MRS clinically is very hopeful, MRS will only be one of many techniques used for information gathering for a dementia evaluation. MRS findings will be considered only an additional tool in an extensive dementia assessment that will supplement other structural and functional imaging results, patient medical history, report of cognitive and functional difficulties, and so on. In addition to its use in initial dementia evaluations and establishing a diagnosis, MRS also has the possibility to aid in monitoring of disease progression in those with a probable or suspected diagnosis of dementia. As more longitudinal research is conducted, more evidence will demonstrate types of changes in metabolites over time. Consistent patterns of abnormalities may develop that will give patients and their families a better idea of the course and progression of the disease and the accompanied brain changes. Since MRS is not intrusive and does not include exposure to radiation, it is easily repeatable over time. In our experience, it is also well tolerated by patients with dementias in the mild and moderate stages, and in those with movement disorders. Moreover, MRS also has further potential to act as a treatment indicator, to help clinicians monitor

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the value and effectiveness of different medications. That is, MRS could help track the patient’s response to certain medications by examining the chemical changes occurring in the brain to see if the disease process may have slowed or stabilized over time. Because of the widespread commercial availability of MRI scanners from which MRS data can potentially be obtained, MRS has the possibility to provide information to clinicians that could greatly benefit many future patients with dementia. More 1H MRS studies in patients with MCI and different dementias will hopefully establish better discrimination between converters and non-converters and between different types of dementia. Thus, future research advances in MRS and dementia studies may provide unique information to facilitate diagnosis and treatment of dementia in the 21st century.

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About the Contributors

PAUL M. BUTLER, MTS, is an MD-PhD candidate at Boston University School of Medicine. His research interests and publications include topics at the interstices of evolution, medicine, and the humanities. JACQUELYNN N. COPELAND is a graduate student in the University of Alabama at Birmingham’s Medical/Clinical Psychology doctoral program. She received her Bachelor of Science degree in psychology and graduated summa cum laude from the University of Florida in 2006. Her main area of interest is geriatric neuropsychology, with particular focus on dementia and aging. Dr. PETER ENGEL is Geriatric Internist and currently a staff physician in the Geriatric Research, Education and Clinical Center of the VA Boston Healthcare System. Dr. Engel has appointments as Lecturer on Medicine at Harvard Medical School and Adjunct Instructor in Medicine, Boston University. He has a long-standing interest in dementia and degenerative brain disorders of late life. Previously, he was an Associate Professor of Medicine, Albany Medical College, Director of the Memory Clinic at the Albany VA, and co-director of the Partners in Dementia Care Project of the Upstate New York VA Healthcare System. Dr. Engel moved to Boston in 2009. MARIANA KNEESE FLAKS graduated from the Pontific Catholic University of São Paulo, São Paulo, Brazil, with a bachelor ’s degree in psychology and clinical psychology license in 2000. From 2002 to 2004, she attended a hospital psychology specialization on neuropsychology and personality evaluation at the Psychiatry Institute of the Faculty of Medicine of the

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University of São Paulo. Since 2003, she has dedicated herself to scientific research in the field of cognitive effects of aging and the differential diagnosis to detect, at the very beginning, cases that are turning into mild cognitive impairment or dementia. She pursued her doctoral degree in science at the same institution between 2004 and 2008, focusing on validation and diagnosis properties of cognitive screening tests for attention and memory. In 2009 she initiated her postdoctoral studies on neuropsychological factors associated with resilience and vulnerability to post-traumatic stress disorder at the Federal University of São Paulo. LAURA FRATIGLIONI is the director of the Aging Research Center (ARC) and currently employed as a professor at the Karolinska Institutet. She is a medical doctor, specialized in both neurology and epidemiology. She has scientific, clinical, and pedagogic commitments. Under her supervision, 11 PhD students and two postdocs have completed their studies since 1996. She is currently supervising four PhD students. She regularly serves as a reviewer for various clinical and epidemiological journals. Since 1996, as principal investigator, she has regularly received grants from several of the major research councils in Sweden. She has been awarded the Luigi Amaducci Award by the Italian Neurological Association and has been recognized by the Swedish Society of Medicine. She is the scientific coordinator of the Kungsholmen Project on Aging and Dementia, co-investigator for the project “Harmony: A Twin Study on Dementia,” and the principal investigator for the SNAC-Kungsholmen population study. Her scientific production has led to 161 original publications, 31 review articles in peerreviewed journals, 17 chapters in edited volumes, and eight reports. H. RANDALL GRIFFITH, PhD, is a clinical neuropsychologist in a private practice in Birmingham, Alabama. He received his PhD in psychology from Rosalind Franklin University/Chicago Medical School and completed a postdoctoral fellowship in the University of Alabama (UAB) Department of Neurology where he worked for several years with the UAB Alzheimer ’s Center. His research interests include using neuroimaging to better understand changes in cognition and changes in everyday activities of persons with neurodegenerative dementias. HANS-HELMUT KÖNIG, MD, MPH, is Professor of Health Services Research and Health Economics, and co-chair of the Department of Medical Sociology and Health Economics at the University Medical Centre Hamburg-Eppendorf. Before joining the faculty of the University of Hamburg in 2010, he was Professor of Health Economics at the University

About the Contributors

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of Leipzig. Hans-Helmut König studied medicine at the Universities of Tübingen, London and Oxford, received his doctoral degree from the University of Tübingen in 1993 and a master ’s degree in public health from Yale University in 1995. His main research fields are cost-of-illness studies, and empirical and model-based cost-effectiveness analyses, as well as the measurement of preferences for health and health care, with a special focus on mental health care. HANNA LEICHT is a research associate at the Department of Medical Sociology and Health Economics at the University Medical Centre Hamburg-Eppendorf. She completed a BA in philosophy, politics and economics at Oxford University in 2000 and graduated from the University of Potsdam with a diploma in psychology in 2006. She has worked as a research assistant at the University of Leipzig at the Department of Psychiatry, studying insight into illness in Alzheimer ’s disease patients for a dissertation on this subject and at the Health Economics Research Unit. Her publications cover both issues from her dissertation work and topics in cost-of-illness analysis. MELANIE LUPPA is a research fellow at the Department of Psychiatry and Psychotherapy, University of Leipzig (Public Health Research Unit). She holds a degree in medical sciences and is about to complete her graduate diploma of psychotherapy (CBT). She studied psychology at the University of Leipzig, where she graduated in 1998. Her primary expertise lies in the field of epidemiology and health economics of mental disorders. Dr. LAURA E. MIDDLETON’s work is motivated by the goal of decreasing the risk of cognitive impairment and dementia in old age. Her research has focused on the identification of modifiable lifestyle risk factors for dementia. She is particularly interested in empowering people to decrease their own risk of cognitive impairment outside of the health care system. Dr. Middleton’s PhD (Dalhousie University, Halifax, NS, Canada) and postdoctoral fellowship (University of California, San Francisco) examined the relationship between physical activity and cognitive change in old age. It appears that physical activity not only decreases the risk of dementia but also increases the chance of improved cognition in old age. One of her recent studies indicated that being physically active in teenage years reduced the likelihood of cognitive impairment in old age. She is currently conducting studies evaluating the relationship between daily activity (exercise, chores and other movement) and cognition. In addition, she is evaluating how rehabilitation programs might be able to improve

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cognitive and physical outcomes in patients who have mild cognitive impairment or who have suffered a ministroke. FERNANDA SPEGGIORIN PEREIRA graduated with a bachelor ’s degree in psychology from the University of Santa Catarina, Santa Catarina, Brazil, in 2002. She has been a neuropsychology specialist since 2004, and in March 2010 she completed her doctoral degree in psychiatry. Her studies, under the supervision of Dr. Orestes Forlenza and Dr. Mônica Yassuda at the Laboratory of Neuroscience at the University of São Paulo, focused on executive functions and functionality in the context of normal and pathological aging. She was then particularly interested in the relationship of executive dysfunction and instrumental activities of daily living. She teaches and conducts research on topics related to cross-cultural validation of neuropsychological instruments, cognitive and functional assessment and rehabilitation of the elderly. CHENGXUAN QIU, MD, PhD, a research scientist, received his medical degree from Shandong Medical University (China, 1980–1985), master ’s degree in medical epidemiology from Tianjin Medical University (China, 1987–1990), and doctoral degrees (PhD) in epidemiology and biostatistics from Tongji Medical University (China, 1996–1999) and in geriatric epidemiology from Karolinska Institutet (Sweden, 2001–2004). He completed research training as a post-doctoral fellow and visiting scientist at the National Institute on Aging (NIA)/National Institutes of Health (NIH) (2005–2006, 2008), USA. He is currently employed as a research scientist by Karolinska Institutet. Since 1999 Dr. Qiu has been with Karolinska Institutet focusing on epidemiology of dementia and brain aging. His research is based on several population-based databases, for example, the Kungsholmen Project, the Swedish National Study on Aging and Care (SNAC) in Kungsholmen, and the Swedish Brain Power Initiatives. Dr. Qiu’s research topics include the genetic (e.g., APOE genotype and familial aggregation), environmental (education, occupational exposures, and lifestyle factors), and biological (blood pressure, diabetes, and heart disease) factors and their interactions for dementia, Alzheimer ’s disease, and brain lesions (brain regional atrophy, infarcts, white matter changes, and cerebral microbleeds). Dr. Qiu’s research also involves collaboration with the U.S. NIA/NIH (Project: The Age, Gene/Environment Susceptibility-Reykjavik Study) and the National Institute for Health and Welfare in Helsinki, Finland (Project: The Cardiovascular Risk Factors in Dementia).

About the Contributors

233

WILM QUENTIN is a research fellow at the Department of Health Care Management at Berlin Technical University. He is a medical doctor and completed an MSc in health policy, planning and financing at the London School of Hygiene and Tropical Medicine and the London School of Economics in September 2009. He studied medicine and political sciences in Würzburg, Munich, Madrid, Leipzig, and Marburg, where he graduated in 2007. He has worked as a research assistant at the department of Health Economics of the University of Leipzig and published articles on a broad range of topics ranging from tobacco control policies over costs of HIV/ AIDS treatment to cost-of-illness of dementia. STEFFI G. RIEDEL-HELLER is working as a professor for public health at the University of Leipzig. She is a physician, specialized in psychiatry and psychotherapy, and obtained her master of public health degree from Johns Hopkins University, Baltimore, Maryland. Her scientific interest lies in the interface of public health and psychiatry, especially in the field of epidemiology of mental disorders in old age and health service research. She has profound experience in conducting cohort studies in old age. She is also chief editor of a German scientific journal, Psychiatrische Praxis. Dr. MICHAEL J. VALENZUELA is a Research Fellow in Regenerative Neuroscience at the School of Psychiatry, University of New South Wales (UNSW). His background is in psychology, clinical medicine, and neuroscience research. Dr. Valenzuela’s PhD focused on the topic of brain reserve and for this work he was awarded the prestigious Eureka Prize for Medical Research in 2006. Dr. Valenzuela’s current research interests are aimed at understanding the competing forces of brain plasticity and degeneration in the human brain. In particular, he is interested in how we can use the science of neuroplasticity to help prevent dementia in the first place. He has published over 30 scientific papers, gained over $1 million in research funds, and is the author of the best-selling popular science book It’s Never Too Late to Change Your Mind, which details the latest medical thinking about what you can do to avoid dementia (ABC Books, 2009). Dr. ART WALASZEK is a board-certified geriatric psychiatrist and Associate Professor of Psychiatry at the University of Wisconsin School of Medicine and Public Health. He received his medical degree from Northwestern University Medical School, completed psychiatry training at the University of Washington, and completed a fellowship in geriatric psychiatry at

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Northwestern Memorial Hospital in Chicago. Dr. Walaszek is currently the Director of Psychiatry Residency Training at the University of Wisconsin Hospital and Clinics. He directs the CME activities of the University of Wisconsin Department of Psychiatry and is the chair of the CME Committee of the Wisconsin Psychiatric Association. He is a member of the editorial board of Academic Psychiatry and is on the executive council of the American Association of Directors of Psychiatry Residency Training. The Association for Academic Psychiatry has recognized Dr. Walaszek’s educational contributions with the 2007 AAP/Forest Junior Faculty Career Award. As a member of the Wisconsin Geriatric Psychiatry Initiative, he speaks extensively on geriatric topics in various medical and nonmedical settings across the state. He has coauthored articles and book chapters on late-life emotional and behavioral problems, anxiety disorders in longterm care, and late-life depression. His clinical practice involves caring for primarily older adults with depressive disorders, anxiety disorders, and dementia, and their families. MÔNICA SANCHES YASSUDA graduated from the University of São Paulo, São Paulo, Brazil, with a bachelor ’s degree in clinical psychology in 1990. She later moved to Gainesville, Florida, where she pursued her master ’s and doctoral degrees in developmental psychology. Her studies, under the supervision of Dr. Robin Lea West, focused on metamemory and memory training in the context of normal cognitive aging. She was then particularly interested in investigating the possibility of changing negative beliefs about cognition and aging and developing techniques for memory improvement among healthy seniors. Since 2005 she has been an assistant professor at the University of São Paulo. She teaches and conducts research in topics related to neuropsychological markers of pathological cognitive decline, memory interventions, frailty, and cognition.

About the Series Editor

PATRICK MCNAMARA, PhD, is Associate Professor of Neurology and Psychiatry at Boston University School of Medicine (BUSM) and is Director of the Evolutionary Neurobehavior Laboratory in the Department of Neurology at the BUSM and the VA New England Healthcare System. Upon graduating from the Behavioral Neuroscience Program at Boston University in 1991, he trained at the Aphasia Research Center at the Boston VA Medical Center in neurolinguistics and brain-cognitive correlation techniques. He then began developing an evolutionary approach to problems of brain and behavior and currently is studying the evolution of the frontal lobes, the evolution of the two mammalian sleep states (REM and NREM) and the evolution of religion in human cultures.

Index

A-beta, 184–189 Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes Study (ACCORDMIND), 86 ACTIVE trial, 77, 112 Adenbrooke’s Cognitive ExaminationRevised (ACE-R), 157–158 age, 75; A-beta production and, 187–188; health and, 111–113; sleep and, 183–184, 186, 187 adipocytokines, 88 alcohol consumption, 13 Alzheimer ’s disease (AD), 5–17; decisional capacity and, 126–127; delayed onset of, 76; 1 H MRS findings and, 210–213; incidence, 4; occurrence, 1–5; prevalence, 2; prevention, 17–20, 76–90, 105, 108–109; sleep patterns and, 178, 180, 184–188. See also prevention amnestic, 217–219 amyloid plaques, 210 amyloid precursor protein, 6, 185, 186 anosognosia, 131 antemortem cognitive tests, 101 antidepressants, 137 antihypertensive therapy, 8, 18, 83

anti-inflammatory drugs, 12 antioxidants, 11, 82 antipsychotics, 133–134, 137–138 aphasia, 163 aphorisms, 69 APOE ε4 allele, 6, 8, 124. See also apolipoprotein E testing apolipoprotein E testing, 124, 125 arousal, sleep, 180 artificial hydration and nutrition, 141 assessment tools, 103, 104 (figure), 127–128, 130, 138–139. See also neuroimaging; neuropsychiatric inventory (NPI) score; neuropsychological tests; screening tools, cognitive atherosclerosis, 9 attention assessment, 160–161 atypical antipsychotics, 133–134, 137–138 autonomy, 123, 128, 165. See also decisional capacity basic activities of daily living (BADL), 165, 167 behavioral and psychological symptoms of dementia (BPSD), 133–135, 137

238

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Behavioral Assessment of the Dysexecutive Syndrome (BADS), 162 β-amyloid accumulation, 79, 88 Beyond Good and Evil (Nietzsche), 69 Binswanger, Otto, 64 bipolar disorder, 67–68. See also mood disorder blood-brain barrier, 88 body mass index (BMI), 9–10, 87 Boston Diagnostic Aphasia Examination (BDAE), 163 Boston Naming Test, 162, 163 brain abnormality, 212, 213 brain derived neurotrophic factor (BDNF), 106 brain injury, 17, 188 brain reserve capacity, 99–101; behavioral perspective, 103–105; cognitive perspective, 101–102; computational perspective, 102–103; neurocentric perspective, 100–101 B12, vitamin, 11 brain volume, 107 brain vulnerability, 189 brain weight, 101 Cache County Study, 4 CADASIL (cerebral autosomal dominant arteriopathy with subgcortical infarcts and leukoencephalopathy), 58, 64–71. See also CARASIL (Maeda’s syndrome) CAMDEX-R (Cambridge Examination for Mental Disorders of the Elderly), 154 capacity improvement, 130–131 CARASIL (Maeda’s syndrome), 67. See also CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy)

cardiovascular disease, 9, 18, 87 Cardiovascular Health Study (CHS), 9 caregiver burden, 37, 50–51, 75 cellular mechanisms, 107 Centers for Medicare an Medicaid Services (CMS), 137 CERAD battery (Consortium to Establish a Registry for Alzheimer ’s Disease), 154 cerebral amyloid angiopathy (CAA), 189 cerebrovascular problems, 9, 214 Charles Bonnet syndrome, 59, 61 Cho/Cr ratio, 215, 218 cholesterol, 88. See also serum cholesterol choline (Cho), 208, 211–212, 217. See also Cho/Cr ratio cholinergic activity, 184 cholinergic deficiency, 182 cholinesterase inhibitors (ChEI), 114, 132 cigarettes, 12–13 clock drawing test (CDT), 115, 161, 163 cognitive activity, 76–77, 107 cognitive enhancers, 130, 131–133 cognitive impairment, 113–114, 165, 202, 217–219 cognitive lifestyle, 100; beneficial mechanisms of, 105–109; defined, 103; enrichment and, 109–116; implications of, 100–105 cognitive tests, 101, 127–128 cognitive training, 77, 109–111, 115–116; computer based, 112–113; randomized controlled trials and, 111–114, 112 (figure); research challenges and, 114–115 complex mental activity, 15–16 computed tomography (CT), 202 consent, 126, 128, 131, 138–139 Controlled Oral Word Association (COWA), 161

Index cost categories, 42, 45 cost-effectiveness, 132 cost-estimation technique, 40, 45–46, 47, 49 cost-of-illness (COI) study, 35–36; health economics and, 36–37; literature review of, 44–50, 48–49 (table); methodological characteristics, 37–44, 45–46, 46–47 (table); role of neuropsychiatric symptoms and, 50–53, 52 (table), 53 (table); variation and, 38 (table), 38–39, 41 (table), 41–42, 49–50 C-reactive protein (CRP), 12 creatine (Cr), 209, 212. See also Cho/ Cr ratio; Glu/Cr ratio; mI/Cr ratio; NAA/Cr ratio data collection, 39–40 decisional capacity, 125–128 decision making. See decisional capacity dementia, 1, 39, 110 (figure). See also Alzheimer ’s disease (AD); cost-of-illness (COI) study; frontotempral dementia (FTD); Lewy body dementia (LBD); vascular dementia (VaD) Dementia Rating Scale, 216 depression, 17, 59, 62, 106 determinants, 5–17. See also specific determinants Deussen, Paul, 69 diabetes, 8, 84 (table), 86 diagnosis, medical, 57–58, 124–125 diagnostic categories, 58 diet, 10–11, 80–83 diffusion tensor imaging (DTI), 202, 206 Digit Span Forward, 160 Digit Symbol, 160 Direct Assessment of Functional Status-Revised (DAFS-R), 167

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disease burden, 110 driving, 129 dyslipidemia, 84 (table), 87 education, 14–15, 76, 164 elder abuse, 135–136 electroencephalographic (EEG) desynchronization, 181 electrophysiological studies, 184 end-of-life care, 140–142 enrichment, 105, 106, 107 epidemiological research, 2–4, 18 epilepsy, 66 ethics, medical, 123–125, 142; clinical research and, 138; cognitive enhancer use and, 131–133; decisional capacity and, 125–130; diagnosis challenges and, 124–125; incapacity and, 126, 130–131; long-term care and, 136–138; symptom treatment and, 133–135 etiological factors, 19 (table) executive control assessment interview (EXIT-25), 161–162 executive function assessment, 161–162 executive functioning, 161–162, 166 exercise, 16, 105 EXIT-25. See executive control assessment interview (EXIT-25) extremely low-frequency electromagnetic fields (ELF-EMFs), 17 finances, 128. See also cost-of-illness (COI) study Financial Capacity Instrument, 128 folate, 11 Framingham cohort study, 11 French Three-City Study, 11 Frontal Assessment Battery (FAB), 156–157 frontotempral dementia (FTD), 213–214

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Fuld Object-Memory Evaluation (FOME), 159 functional assessment, 164–167 functional imaging, 206–207. See also functional magnetic resonance imaging (fMRI); positron emission tomography (PET); single photon emission computed tomography (SPECT) functional magnetic resonance imaging (fMRI), 202, 206–207

Honolulu-Asia Aging Study, 8 Hooper Visual Organization Test, 163 hormone replacement therapy, 16 hospitalization, 134 hydrogen ions (1H), 203 hypertension, 7–8, 83, 84 (table), 85. See also antihypertensive therapy Hypertension in the Very Elderly Trial (HYVET), 85 hypnograms, 179 (figure) hypomania, 59, 62

gene mutations, 6 genetic counseling, 125 genetic risk factors, 6 glucocorticoids, 187–188 glucose-labeled PET studies, 107–108 Glu/Cr ratio, 216 glutamate (Glu), 209. See also Glu/Cr ratio glutamine (Gln), 209–210 Glx ratio, 214 glycemic control, 86–87, 88

identification of patients, 39 immune function, 188 incapacity, 126, 130–131 incidence, of dementia, 1, 3–4, 5 (table) independence, 123, 128, 165 inflammation, 12 informal care, 43–44 inheritance patterns, 66, 68 institutionalization, 134–135, 136–138 instrumental activities of daily living (IADL), 165, 167 insulin, 86, 88 intervention strategies, 18 involuntary hospitalization, 134 Iowa Gambling Task, 161 ischemic episodes, 65

hallucinations, 215 headache, 58–59, 61–62 Health and Retirement Study, 139 health economics, 36–37 Hegel, George Wilhelm Friedrich, 57 high blood pressure, 7–8, 83, 84 (table), 85. See also antihypertensive therapy hippocampus, 185, 210 historical research, 57–58 1 H MRS findings, 203, 204; Alzheimer ’s disease and, 210–213; dementia with Lewy bodies and, 215–216; frontotempral dementia (FTD) and, 213; mild cognitive impairment and, 217–219; Parkinson’s disease and, 216–217; vascular dementia (VaD) and, 214–215 homocysteine, 11–12

Kohlman Evaluation of Living Skills (KELS), 128 language assessment, 162–163 lateral hypothalamus (LH), 181 laterodorsal (LDT) tegmental, 180 learning assessment, 158–160 Lewy body dementia (LBD), 178, 180, 189–190, 215–216 Lifestyle Interventions and Independence for Elders (LIFE) Study, 79 Lifetime of Experience Questionnaire (LEQ), 103, 104 (figure) literature review, cost, 44–50

Index long-term care facilities, 136 long-term depression (LTD), 106 long-term potentiation (LTP), 106, 185, 187 MacArthur Competence Assessment Tool for Treatment (MacCAT-T), 127, 138–139 magnetic resonance imaging (MRI), 202, 206 magnetic resonance spectroscopy (MRS), 203–205; vs. alternative neuroimaging techniques, 205–207; clinical applications of, 219–220. See also1H MRS mania, 59, 62 medical diagnosis, 57–58 medical ethics. See ethics, medical Mediterranean diet, 11, 81 memory, 106, 107–108, 154–157; learning assessment and, 158–160; sleep disturbance and, 180, 183–184 mentally stimulating activity, 15–16 metabolic syndrome, 85 (table), 87 metabolite, 204, 205, 208–210; abnormalities in, 214–215, 216–218. See also specific metabolites mI/Cr ratio, 213, 214, 215, 217, 218 migraine, 59, 61–62, 67 mild cognitive impairment (MCI), 113–114, 165, 202 Mini Mental State Examination (MMSE), 39, 128, 132, 154, 216 mitochondrial myopathyencephalopathy-lactic acidosisstroke syndrome (MELAS), 68 Montreal Cognitive Assessment, 158 mood disorder, 59–60, 62 mortality rate, and dementia, 4–5, 140–141 motor skills, 155 myo-inositol (mI), 209

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NAA/Cr ratio, 213, 214, 215, 216, 218, 219 N-acetyleaspartate (NAA), 208, 212, 216, 217, 218; ratios, 210–211, 213. See also NAA/Cr ratio naming trails, 162–163 Necker Cube copying, 163 net-cost studies, 42, 44 neuritic plaques, 7–8, 13 neurodegenerative disease, 12, 88. See also Alzheimer ’s disease (AD); dementia; frontotemporoal dementia (FTD); Lewy body dementia (LBD); vascular dementia (VaD) neurofibrillary tangles, 210 neurogenesis, 107 neuroimaging, 7, 12, 13, 16, 108, 151, 201–203. See also magnetic resonance spectroscopy (MRS) neuronal numbers, 101, 102 neuropathological data, 8, 9, 13, 15 neuropeptide (NEP), 185 neuroplasticity, 100, 108–109 neuropsychiatric inventory (NPI) score, 52 (table), 53 (table) neuropsychiatric symptoms (NPS), 50–53, 52 (table), 53 (table) neuropsychological tests, 113, 152, 158–167. See also specific tools neurotoxicity, 184–185 new cases, 2 NHS Economic Evaluation Database (NHSEED), 44 Nietzsche, Friedrich, 58; brilliance of, 70–71; diagnosis of, 64–68, 65 (table), 68–69; family history of, 60, 66, 68, 69; medical history of, 58–63; mood disorder and, 59–60, 62; pain of, 70–71 Nietzsche, Karl Ludwig, 60 NOTCH3 gene mutation, 64, 65, 66 nREM sleep, 177, 182, 184, 186

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nursing home placement, 51 nutrition, 10–11, 141 obesity, 85 (table), 87 OBRA (federal regulations for nursing homes), 137–138 occupational exposure, 17 occurrence, 1, 2–5 ocular disturbance, 60–61 omega-3 polyunsaturated fatty acids (PUFAs), 11, 82 orexin, 181 palliative care decisions, 141 parahippocmpal gyrus, 185 Parkinson’s disease, 61, 178, 190, 216–217 pedunculopointine (PPT) tegmental, 180, 190 pharmaceuticals, 75, 77, 151; antidepressants, 137; anti-inflammatory, 12; atypical antipsychotics, 133–134, 137–138; cognitive enhancers, 132; cost of, 132; pro-inflammatory, 188; psychotropic, 137 physical activity, 16, 77–79, 105 physical restraints, 137 plaque, 106, 210 positron emission tomography (PET), 202, 207 prevalence, of dementia, 1, 2–3, 3 (figure) prevalence cost, 42 prevention, 17–20, 75–76, 81 (figure), 89 (figure), 89–90, 105, 108–109; activity and, 76–77; diet and, 80–83; physical activity and, 77–79; social engagement and, 79–80; and vascular risk factors, 83–88 pro-inflammatory medication, 188 PS1 mutation, 125

PS2 mutation, 125 psychiatric hospital, 63, 134–135 psychological factors, 14–16, 19 (table), 19–20 psychotropic medications, 137 pyramidal neurons, 101 radio frequency (RF), 203, 205–206 regional variation, 2–3 REM sleep, 177, 181, 182 REM sleep behavior disorder (RBD), 178, 180, 181, 189–191 research studies: consent and, 138–140; decisional capacity and, 126–128; hypertension and, 85; physical activity of and, 78–79. See also cognitive training; cost-of-illness (COI) study; data collection; electrophysiological studies; neuroimaging; neuropathological data; specific studies resource use, 36–37, 42–44 Resource Utilisation in Dementia (RUD) Instrument, 40 retinal inflammation, 59 REVEAL (Risk Evaluation and Education for Alzheimer ’s Disease) study, 124, 125 Rey Auditory Verbal Learning Test (RAVLT), 159 Rey-Osterreith Complex Figure copying, 163 risk factors, 75, 80; cognitive lifestyle and, 103–105; genetic, 6; vascular, 7–10, 83–88, 84–85 (table) Rivermead Behavioral Memory Test (RBMT), 159 screening tools, cognitive, 153–158 scyllo-inositol, 209 secreted amyloid precursor protein alpha (sAPP), 185

Index self-neglect, 135 serum cholesterol, 10 serum homocysteine, 11–12 sexual relations, 129 single photon emission computed tomography (SPECT), 202, 207 sleep: architecture, 184–187, 190; arousal, 180–183; aging and, 183–184, 186, 187; disturbances, 177–180, 186–187, 191–192; memory and, 183–184; nREM, 177, 182, 184, 186; REM, 177, 181, 182, 186; slow wave, 183–184. See also REM sleep behavioral disorder (RBD) sleep-regulating regions, 181–183, 182 (figure) slow wave sleep (SWS), 183–184 smoking, 12–13 social engagement, 15, 79–80 social justice, 124, 132 sources of information, 40 sphenoid meningioma, 67 statin therapy, 10 statistical data, 2. See also prevalence, of dementia stroke, 8–9, 57–58, 64 structural imaging, 201, 205–206. See also diffusion tensor imaging (DTI); magnetic resonance imaging (MRI); magnetic resonance spectroscopy (MRS) study design, 40 surrogate decision maker, 130–131, 139, 140, 141 survival time, 5 Swedish Kungsholmen Project, 4 symptom treatment, 133–135 Syndrom Kurztest (SKT), 155–156 syphilis, 58, 67 temporal lobes, 210 10/66 Dementia Research Group, 2

243

testamentary capacity, 129–130 Test Your Memory (TYM) instrument, 158 Tg2576, 186 TIAs (transient ischemic attacks), 65, 66 total-cost studies, 42, 44 total homocysteine (tHcy), 11, 12 toxicity, 184–185 traumatic brain injury, 17 treatment, 133–135. See also pharmaceuticals; specific treatments valuation, 36–37, 43–44 vascular dementia (VaD): cost-ofillness (COI) studies and, 39; determinants, 8, 9, 10; 1H MRS findings and, 214–215; incidence, 4; prevalence, 4; prevention of, 76–83 vascular factors, 13–14, 18–19, 19 (figure) vascular risk factors, 7–10, 82–88 ventrolateral preoptic nucleus (VLPO), 181 verbal fluency tests (VFT), 157, 161 Visual Reproduction and Logical Memory sub-tests, 159 visuo-spatial ability assessment, 163–164 vitamin B12, 11 vitamin D, 83 voluntarism, 126 voluntary hospitalization, 134 voting, 129 Wechsler Adult Intelligence Scale (WAIS-III) battery, 159, 160, 162 Wechsler Memory Scale (WMS-III), 159 welfare, 123 Women’s Health Initiative Memory Study (WHI-MS), 16 World Heath Organization (WHO), 164

DEMENTIA Volume 2: Science and Biology Patrick McNamara, Editor

Brain, Behavior, and Evolution Patrick McNamara, Series Editor

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Copyright 2011 ABC-CLIO, LLC All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except for the inclusion of brief quotations in a review, without prior permission in writing from the publisher. Library of Congress Cataloging-in-Publication Data Dementia / Patrick McNamara, editor. p. cm.—(Brain, behavior, and evolution) Includes bibliographical references and index. ISBN 978-0-313-38434-9 (hard copy : alk. paper)—ISBN 978-0-313-38435-6 (ebook) 1. Dementia. 2. Alzheimer ’s disease. I. McNamara, Patrick, 1956– II. Series: Brain, behavior, and evolution [DNLM: 1. Dementia. WM 220] RC521.D4524 2011 616.8’3—dc22 2010041082 ISBN 978-0-313-38434-9 EISBN 978-0-313-38435-6 15

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This book is also available on the World Wide Web as an eBook. Visit www.abc-clio.com for details. Praeger An Imprint of ABC-CLIO, LLC ABC-CLIO, LLC 130 Cremona Drive, P.O. Box 1911 Santa Barbara, California 93116-1911 This book is printed on acid-free paper Manufactured in the United States of America

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Contents

Series Foreword Preface: Hopeful Trends in Meeting the Challenge of the Dementias Patrick McNamara

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Volume 2: Science and Biology Chapter 1. The Genetics of Alzheimer ’s Disease Lars Bertram Chapter 2. Epigenetic and Gene Imprinting Effects in Parkinson’s Disease: A Kinship Theory of Gene Conflict in Aging and Dementia Paul M. Butler and Patrick McNamara Chapter 3. Oxidative Stress, Mitochondrial and Insulin Signaling Dysfunction: A Redoubtable Trio in Alzheimer ’s Disease Pathogenesis Sónia C. Correia, Renato X. Santos, Cristina Carvalho, Susana Cardoso, and Paula I. Moreira Chapter 4. Alzheimer ’s Disease: Increased Neurogenesis and Possible Disease Mechanisms Related to Neurogenesis Philippe Taupin Chapter 5. Pathophysiology of Behavioral and Psychological Disturbances in Dementia Anna Burke

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Chapter 8.

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Dementia in Parkinson Disease: Current Concepts in Neuropathology, Neuroanatomy, and Neurochemistry Raymon Durso

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Aspects in Neuropsychology: Depression and Dementia Ilan Halperin and Amos D. Korczyn

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Relation of Apathy to Dementia in Patients with Parkinson’s Disease Erica Harris

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Vascular Cognitive Impairment and Dementia Howard S. Kirshner

Chapter 10. Neuropsychological Profile of Dementia with Lewy Bodies Haruhiko Oda, Yasuji Yamamoto, and Kiyoshi Maeda

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Chapter 11. Sleep Disorders in Dementia Kesha Wilford and Sanford Auerbach

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Chapter 12. Cognitive Impairment in Parkinson’s Disease Brooke K. Walter and James B. Leverenz

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About the Contributors

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About the Series Editor

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Index

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Series Foreword

Beginning in the 1990s, behavioral scientists—that is, people who study mind, brain, and behavior—began to take the theory of evolution seriously. They began to borrow techniques developed by the evolutionary biologists and apply them to problems in mind, brain, and behavior. Now, of course, virtually all behavioral scientists up to that time had claimed to endorse evolutionary theory, but few used it to study the problems they were interested in. All that changed in the 1990s. Since that pivotal decade, breakthroughs in the behavioral and brain sciences have been constant, rapid, and unremitting. The purpose of the Brain, Behavior, and Evolution series of titles published by ABC-CLIO is to bring these new breakthroughs in the behavioral sciences to the attention of the general public. In the past decade, some of these scientific breakthroughs have come to inform the clinical and biomedical disciplines. That means that people suffering from all kinds of diseases and disorders, particularly brain and behavioral disorders, will benefit from these new therapies. That is exciting news indeed, and the general public needs to learn about these breakthrough findings and treatments. A whole new field called evolutionary medicine has begun to transform the way medicine is practiced and has led to new treatments and new approaches to diseases, like the dementias, sleep disorders, psychiatric diseases, and developmental disorders that seemed intractable to previous efforts. The series of books in the Brain, Behavior, and Evolution series seeks both to contribute to this new evolutionary approach to brain and behavior and to bring the insights emerging from the new evolutionary approaches to psychology, medicine, and anthropology to the general public. The Brain, Behavior, and Evolution series was inspired by and brought to fruition with the help of Debora Carvalko at ABC-CLIO. The series editor,

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Dr. Patrick McNamara, is the director of the Evolutionary Neurobehavior Laboratory in the Department of Neurology at Boston University School of Medicine. He has devoted most of his scientific work to development of an evolutionary approach to problems of sleep medicine and to neurodegenerative diseases. Titles in the series will focus on applied and clinical implications of evolutionary approaches to the whole range of brain and behavioral disorders. Contributions are solicited from leading figures in the fields of interest to the series. Each volume will cover the basics, define the terms, and analyze the full range of issues and findings relevant to the clinical disorder or topic that is the focus of the volume. Each volume will demonstrate how the application of evolutionary modes of analysis leads to new insights on causes of disorder and functional breakdowns in brain and behavior relationships. Each volume, furthermore, will be aimed at both popular and professional audiences and will be written in a style appropriate for the general reader, the local and university libraries, and graduate and undergraduate students. The publications that become part of this series will therefore bring the gold discovered by scientists using evolutionary methods to understand brain and behavior to the attention of the general public, and ultimately, it is hoped, to those families and individuals currently suffering from those most intractable of disorders— the brain and behavioral disorders.

Preface: Hopeful Trends in Meeting the Challenge of the Dementias Patrick McNamara

It is estimated that 24.3 million people around the world have dementia and that, with an estimated 4.6 million new cases every year, we can expect about 43 million people and their families to face the challenge of dementia by 2020. There are several forms of dementia, with the most common being Alzheimer ’s disease (40% of cases), vascular dementia with or without Alzheimer features (25%), and dementia with Lewy bodies (25%), the latter being related to the increasingly important form of dementia associated with Parkinson’s disease. The annual healthcare costs for Alzheimer ’s disease alone is estimated at about $155 billion in the United States. A substantial portion of these costs is due to behavioral and neuropsychiatric disturbances associated with the dementing process— yet these neuropsychiatric and behavioral problems have only recently become the focus of study and treatment in the biomedical communities. The successes of neuropsychiatric approaches to the dementias is measured in reduced suffering for patients and their families and reduced healthcare costs for the system as a whole. The authors of the chapters in these three volumes, devoted to emerging trends in dementia studies, have virtually all emphasized identification, study, and treatment of behavioral and neuropsychiatric problems of patients and their families. The reason they have done so is the dawning realization in both the biomedical and caregiving communities that targeting behavioral and neuropsychiatric problems of dementia leads to some pretty effective scientific studies of mechanisms and very effective and low-cost treatment programs that act to alleviate both patients’ suffering and caregivers’ burdens.

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Preface

Although the standard, it has long been established that dementia most commonly occurs in older people, and that primary symptoms are memory impairment (both short- and long-term), deficits in executive functions, and impairments of abstract thinking and judgment. It has now become crystal clear that some of the best and earliest predictors of dementia risk are mood and personality changes, which all too often are misdiagnosed as depression or some other common mood disorder. Family members may express concern to a primary care physician, but these concerns too often get ignored or shunted aside as a standard mood disorder. It is vitally important to take reports of significant behavioral changes seriously as identification of cognitive components of a dementing process—may be a later-occurring symptom than the behavioral changes. Although the three-step diagnostic process (single question about memory, MMSE, neuropsychological testing) has high positive predictive value, it only detects 18% of future dementia cases. It is the behavioral and neuropsychiatric disturbances, along with incipient cognitive changes, that may yield better detection rates for dementia. Tremendous progress has been made in identification of biomarkers for dementia. The use of functional imaging, proteomic, genetic, biochemical and electrophysiological markers, including sleep polysomnographic techniques, has meant that our ability to detect dementia early on has vastly improved. In addition, the new appreciation of the importance of behavioral and psychiatric problems in dementia as well as validated assessment tools to measure these behavioral problems suggests that it is time to deploy all these new techniques to identify those at risk for dementia so as to prevent or to slow onset of the disorder in these individuals. What is needed are large-scale, multisite, comparative studies that can evaluate optimal use and validity of these various techniques for detecting and selecting asymptomatic people at risk for dementia. The recent Leon Thal Symposium 2009 in Las Vegas, Nevada, explored algorithms, biomarkers, and assessment tools for identifying asymptomatic individuals at elevated risk for dementia. The consensus recommendations of symposium participants included: 1. Establishment of a National Database for Longitudinal Studies as a shared research core resource; 2. Launch of a large collaborative study that will compare multiple screening approaches and biomarkers to determine the best method for identifying asymptomatic people at risk;

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3. Initiation of a Global Database that extends the concept of the National Database for Longitudinal Studies for longitudinal studies beyond the United States; and 4. Development of an educational campaign that will promote healthy brain aging. (Khachaturian et al. 2010) These are all laudable recommendations. But behavioral and neuropsychiatric assessment tools must be included in these large multisite studies of at-risk individuals. A perusal of the essays in these volumes (volume 1 focuses on epidemiologic, descriptive, historical, and diagnostic innovations in dementia; volume 2 focuses on biobehavioral mechanisms of dementia; and volume 3 focuses on emerging treatment strategies including treatments for behavioral problems of dementia) leaves one with a sense of hope and confidence that the daunting challenges of the dementias, both for patients and for families, are finally being effectively addressed. REFERENCE Khachaturian, Z. S., D. Barnes, R. Einstein, et al. 2010. Developing a national strategy to prevent dementia: Leon Thal Symposium 2009. Alzheimer’s and Dementia 6 (2): 89–97.

Chapter 1

The Genetics of Alzheimer’s Disease Lars Bertram

Alzheimer ’s disease (AD) is the most common form of age-related dementia and is characterized by progressive and insidious neurodegeneration of the central nervous system eventually leading to a gradual decline of cognitive function and dementia in affected individuals. The key neuropathological features of AD are abundant amounts of neurofibrillary tangles and β-amyloid (Aβ) in the form of senile plaques and blood vessel deposits, both prerequisites for a confirmed diagnosis of AD (Price et al. 1998) Although the knowledge of the molecular mechanisms leading to neuronal cell death still remains incomplete, it is now established that genes play a predominant role in determining predisposition for the disease and its clinical progression. Although largely indistinguishable in their clinical presentation or neuropathology, AD cases show a dichotomy of familial (i.e., rare) versus seemingly nonfamilial (i.e., common) forms (Tanzi 1999). The latter are also frequently described as “sporadic,” although it has become clear that genes also play a major role in determining onset and progression of these latter cases (Gatz et al. 2006; Bertram and Tanzi 2008). Similar to many other common adult-onset disorders, these genetic factors (a.k.a. “susceptibility genes”) are likely to be numerous, displaying intricate patterns of interaction with each other as well as with nongenetic factors, and—unlike classical Mendelian (“simplex”) disorders—exhibit no simple or single mode of inheritance. Therefore, the genetics of these diseases has been labeled as “complex.” AD is a typical example of a genetically complex disease. Early-onset familial AD (EOFAD), often transmitted as an autosomal

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Dementia

dominant trait with onset ages usually below 65 years of age, is caused by rare, but highly penetrant mutations in at least three genes (APP, PSEN1, PSEN2; see following section). Overall, however, these cases probably represent not more than 5% of all AD cases, and the vast majority of AD is actually of late-onset (LOAD, i.e. usually beyond 65 years), most often not showing any obvious pattern of familial segregation. Despite intensive efforts in characterizing the genetic underpinnings of LOAD over the past three decades, to date genetic variants in only one gene (APOE, encoding apolipoprotein E) have been established to significantly modify the risk and onset age of LOAD across a multitude of independent samples and different ethnicities (Saunders et al. 1993; Strittmatter et al. 1993). Despite the considerable complexities of AD genetics, tremendous progress toward a better understanding of the etiological and pathophysiological mechanisms leading to neurodegeneration has been made. This chapter will outline a brief history of the genetics of AD and discuss its current status and future outlook, in particular focusing on recent findings suggesting the existence of several novel AD genes by means of genomewide association analysis (GWAS). EARLY-ONSET FAMILIAL ALZHEIMER’S DISEASE (EOFAD) GENES Only 5% (or fewer) of all AD cases can be explained by EOFAD (Raux et al. 2005). Despite its rarity, genetic studies of this form of AD are actually facilitated by the availability of large multigenerational pedigrees allowing genetic linkage analysis and subsequent positional cloning, which is usually not possible in LOAD families where fewer relatives survive the family-specific onset age and genetic information from parents is almost always lacking (see below). The search for causative mutations is expected to be vastly facilitated by means of only recently developed massively parallel sequencing technologies enabling to decipher an individual’s whole genome in one experiment at decreasing cost (Tucker, Marra, and Friedman 2009; Lupski et al. 2010). Using conventional genetics analyses, data was reported in 1987 that showed EOFAD linkage to the long arm of chromosome 21 encompassing a region that harbored the gene encoding the amyloid precursor protein (APP; gene: APP), a compelling candidate gene for AD (Tanzi et al. 1987). In 1991 the first APP missense mutation in a family with EOFAD was described (Goate et al. 1991). Since then, over 30 additional ADcausing mutations have been reported in APP which, in total, account for probably not more than one-tenth of all early-onset autosomal dominant

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AD (see Table 1.1; for an up-to-date overview of AD mutations visit the “AD and FTD Mutation Database” at http://www.molgen.ua.ac.be/ ADMutations). Interestingly, most of the APP-mutations occur near the putative γ-secretase site between residues 714 and 717, suggesting that especially the γ-cleavage event of APP and/or its (dys-) regulation are critical for the development of AD. Recently, two additional APP variants were suggested to cause AD via increased levels of the wildtype protein: first, duplications of the APP-containing chromosomal segment causing AD with cerebral amyloid angiopathy (Rovelet-Lecrux et al. 2006), and second, promoter Table 1.1 Summary of Genetic Findings for Early-Onset Autosomal-Dominant Forms of AD

Gene (protein)

Chromosomal location

Number of pathogenic mutations Relevance to AD (affected families)a pathogenesis

APP (β-amyloid precursor protein)

21q21.3

32 (89)

PSEN1 (presenilin 1)

14q24.3

179 (394)

PSEN2 (presenilin 2)

1q31-42

14 (23)

Increase in Aβ production or Aβ42/Aβ40-ratio; mutations in Aβ or close to γ-secretase site; locus duplications Increase in Aβ42/ Aβ40-ratio; mutations throughout molecule; enzymatic role in γ-secretase complex Increase in Aβ42/ Aβ40-ratio; mutations throughout molecule; enzymatic role in γ-secretase complex

Source: Modified and updated based on Bertram and Tanzi 2008. a

(Source: “AD & FTD Mutation Database” [URL: http://www.molgen.ua.ac.be/ADMutations/], current on May 1, 2010).

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Dementia

mutations leading to a possible increase in mRNA levels (Brouwers et al. 2006; Theuns et al. 2006). Both were found to be segregating with the disease in an autosomal-dominant fashion in several unrelated families. While more data is needed to estimate the overall contribution of these variants to the prevalence of EOFAD, these discoveries are in line with the decades-old observation that AD neuropathology almost invariably develops in patients with trisomy 21 (Down’s syndrome), in which the extra copy of APP leads to increases in the expression of APP and deposition of Aβ (Wisniewski et al. 1985; Rumble et al. 1989). Only one year after the discovery of the first APP mutation, a second AD linkage region—on chromosome 14q24—was reported almost simultaneously by four independent laboratories (Mullan et al. 1992; Schellenberg et al. 1992; St. George-Hyslop et al. 1992; Van Broeckhoven et al. 1992). It took three more years to clone the responsible gene (PSEN1) and identify the first AD-causing mutations (Sherrington et al. 1995). It is now known that PSEN1 encodes a highly conserved polytopic membrane protein, presenilin 1 (PS1), that plays an essential role in mediating intramembranous, γ-secretase processing of APP to generate Aβ from APP (Steiner, Fluhrer, and Haass 2008). Even more than a decade after the original description of PSEN1, there are several new AD-causing mutations reported in this gene every year, currently counting a total of nearly 180 (see Table 1.1). Soon after the discovery of PSEN1 as an AD gene, a second member of the presenilin family of proteins was identified via searching the then-available databases. It displayed significant homology to PSEN1 at the genomic as well as at the protein level (Levy-Lahad et al. 1995; Rogaev et al. 1995), and therefore, this gene was named PSEN2 (protein: PS2). It maps to the long arm of chromosome 1 and mutations in this gene account for the smallest fraction of all EOFAD cases. On average, mutations in PSEN2 also display a later age of onset and slower disease progression than APP or PSEN1 mutations. In conclusion, while the currently known AD-causing mutations occur in three different genes located on three different chromosomes, they all share a common biochemical pathway, that is, the altered production of Aβ leading to a relative overabundance of the Aβ42 species, which eventually results in neuronal cell death and dementia. Collectively, these discoveries provided the essential connection between the long-known familial aggregation of early-onset AD and the increase in Aβ production observed in the brains of autopsied AD patients, which originally gave rise to the “amyloid hypothesis of AD” (reviewed in Tanzi and Bertram 2005).

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OTHER POTENTIAL EOFAD GENES Although no additional EOFAD gene has been unequivocally identified since the discovery of PSEN2 in 1995, several lines of evidence suggest that further genetic factors remain to be identified for this form of AD: (1) Numerous early-onset families do not show mutations in APP, PSEN1, or PSEN2 despite extensive sequencing efforts of open reading frames and adjacent intronic regions (Arango et al. 2001; Lleo et al. 2002; Rademakers et al. 2002; Raux et al. 2005); (2) Beyond APP and PS1 there are several additional key proteins involved in the γ- and β-secretase cleavage events and other aspects leading to the aggregation and deposition of Aβ (e.g., nicastrin, aph-1, pen-2, BACE), as well as the hyperphosphorylation of tau and the development of neurofibrillary tangles; (3) A full genome linkage screen performed by our group has identified at least four early-onset AD linkage regions in addition to the chromosomal location of PSEN1 on 14q24 (Blacker et al. 2003). Despite the to-date overall unsuccessful quest for novel EOFAD genes, recent—and still preliminary—reports have indicated the presence of disease-causing mutations in at least three additional genes, two of which are also strong biochemical candidates for an involvement in AD pathogenesis. First, a linkage study in a large and multigenerational clinically defined multiplex AD family from Belgium indicated the presence of an AD locus near the gene encoding tau on chromosome 17q (MAPT). Subsequently, a nonsynonymous mutation in exon 13 of MAPT (R406W) was reported to cosegregate with AD dementia in this family (Rademakers et al. 2003). While this same mutation was also reported in at least one other family with dementia resembling AD (Ostojic et al. 2004), the majority of cases affected by R406W appear to develop a syndrome fulfilling the criteria of frontotemporal dementia (FTDP-17; Rosso et al. 2003). It therefore remains to be determined whether at autopsy the clinically assessed Belgian AD family will prove to show pathological features in agreement with a definite diagnosis of AD. Second, the same group recently reported evidence of significant linkage with EOFAD to chromosome 7q36 in an extended multiplex AD family from the Netherlands (Rademakers et al. 2005). The same ∼10cM haplotype was also found to cosegregate with AD in three additional multiplex families, suggesting the presence of a disease-causing mutation in this chromosomal region. A synonymous mutation (Ala626) in the gene encoding PAX transcription activation domain interacting protein (PAXIP1), located ∼400,000bp downstream of the shared haplotype region, was discovered in AD patients of the index family, but absent

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Dementia

from 320 control individuals. However, since it was also absent from the three additional 7q36 haplotype-sharing families, and—according to preliminary analyses—did not show evidence for functional abnormalities in mutation carriers, the overall evidence supporting PAXIP1 as a novel EOFAD gene remains relatively weak. Finally, a recent study reported the presence of an amino-acid changing mutation (Asp90Asn) in the gene encoding one of the γ-secretase components, pen-2 (PEN2; Sala Frigerio et al. 2005). In addition to being a strong pathophysiological candidate, this gene is also interesting positionally as it maps close to a highly significant linkage region on chromosome 19, approximately 9 Mb proximal of APOE (Bertram et al. 2004). However, as the familial transmission of this mutation with AD could not be determined due to a lack of DNA specimen, and since preliminary functional analyses did not reveal an effect of this mutation on APP metabolism in vitro, this finding can be considered the least convincing of these putative novel EOFAD loci. It can be expected that the now available ultra-high throughput, wholegenome sequencing approaches will allow the identification of additional AD-causing mutations in individual samples or small families that were otherwise not eligible for conventional analyses, that is, linkage followed by positional cloning. These technologies have already been successfully applied to a number of phenotypes (Tucker, Marra, and Friedman 2009), including neurodegenerative disorders (Lupski et al. 2010). LATE-ONSET ALZHEIMER’S DISEASE (LOAD) In contrast to EOFAD, late-onset Alzheimer ’s (LOAD) is characterized by a considerably more complex pattern of genetic and nongenetic factors that remains only poorly understood. Adding to the complexity are methodological difficulties inherent to common diseases in general, and late-onset diseases like AD in particular. Family data, for instance, is more often than not only incomplete (e.g., owing to relatives who died before the family-specific age of risk and/or the lack of genotypic information for parents). Another complication is the unknown number of “phenocopies,” that is, subjects with a nongenetic form of the disease or subjects suffering from other forms of age-related cognitive decline. These and other characteristics largely reduce the power to detect new loci in reasonably sized samples and continue to hamper the independent replication of proclaimed associations. This is demonstrated by the fact that even more than a decade after the discovery of APOE in AD (Saunders et al. 1993; Strittmatter et al. 1993), no other genetic risk factor has been found to consistently confer susceptibility to disease risk, despite intensive efforts in

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many laboratories worldwide (Bertram et al. 2007). And although almost none of the over nearly 700 genes that have been tested for association with AD over the past 30 years have yielded consistent results to date, several lines of evidence suggest that further gene-hunting may indeed be worthwhile. First, there are a number of chromosomal regions showing evidence for genetic linkage from full-genome linkage studies (Bertram and Tanzi 2004; Butler et al. 2009). Second, a number of loci have recently emerged from genome-wide association studies, that is, studies for which the entire genome is interrogated by a relatively dense set of common genetic markers (Bertram and Tanzi 2009). While overlap in results across independent GWAS is still sparse, a few new loci have emerged that stand a rather good chance of representing true novel AD risk genes. Finally, systematic meta-analyses on all published AD genetic association studies show significant summary odds ratios for a few genes when all available genotype data is summed across studies (Bertram et al. 2007; see section below and Table 1.2). While many of these may still represent false-positive findings, it is interesting to note that several of the significantly associated variants are actually nonsynonymous or regulators of gene expression, which at least indirectly implies a functional basis for the observed statistical associations. APOLIPOPROTEIN E (APOE)— THE ONLY ESTABLISHED AD RISK GENE The first proof of principle for successful application of the “positional candidate gene strategy” (i.e., testing biologically plausible candidate genes in promising linkage regions) in AD was provided by the identification of APOE as a risk gene. The positional evidence was delivered almost simultaneously with the identification of the first APP mutations, when Pericak-Vance and colleagues reported linkage of chromosome 19q to cases of predominantly LOAD (Pericak-Vance et al. 1991). Two years later, after Aβ was found to bind apoE (which was also the first evidence for a potential biochemical involvement of apoE in AD), a common polymorphism in APOE, which maps near the 19q linkage region, was tested and shown to be associated with increased risk for AD (Saunders et al. 1993; Strittmatter et al. 1993). In contrast to all other genetic association reports in AD, this result has been overwhelmingly replicated in a large number of studies across many ethnic groups worldwide (Farrer et al. 1997; Bertram et al. 2007). Three major alleles occur at the APOE locus—ε2, ε3 and ε4—which translate into combinations of two amino acid changes at residues 112 and 158 of the apoE-protein (ε2: Cys/Cys; ε3: Cys/Arg; ε4: Arg/Arg,

Gene

APOE CLU PICALM SORL1 GWA_14q32.13 TNK1 ACE IL8 LDLR CST3 CR1 hCG2039140 CHRNB2 SORCS1 TNF CCR2 DAPK1 GAB2 TF MTHFR LOC651924 OTC ADAM10

Rank

1* 2* 3* 4 5* 6* 7 8 9 10 11* 12 13 14 15 16 17 18* 19 20 21* 22 23

Model

APOE_e2/3/4 e4 vs. e3 rs11136000 T vs. C rs541458 C vs. T rs2282649 T vs. C rs11622883 A vs. T rs1554948 A vs. T rs1800764 C vs. T rs4073 A vs. T rs5930 A vs. G rs1064039 A vs. G rs6656401 A vs. G rs1903908 T vs. C rs4845378 T vs. G rs600879 m vs. M rs4647198 C vs. T rs1799864 A vs. G rs4878104 T vs. C rs2373115 T vs. G rs1049296 C2 vs. C1 rs1801133 T vs. C rs6907175 A vs. G rs5963409 (F) A vs. G rs17269348 G vs. A

Polymorphism All All All A All All C All All C All All All All A C All All All All All All All

36 9 6 3 5 5 4 4 4 8 7 4 4 4 3 3 7 12 16 26 6 5 4

9413 26690 21915 1666 3568 3712 1565 1593 1501 3014 17181 2865 1363 2856 771 1426 5789 12577 9233 8604 4882 2864 10982

Ethnicity N studies N samples‡

Table 1.2 Summary of Genetic Findings for Late-Onset Complex Genetics Forms of AD

3.69 (3.30−4.12) 0.86 (0.82−0.89) 0.87 (0.83−0.91) 1.30 (1.13−1.50) 0.84 (0.77−0.93) 0.84 (0.76−0.93) 0.79 (0.68−0.92) 1.27 (1.08−1.50) 0.85 (0.72−0.99) 1.16 (1.00−1.33) 1.19 (1.09−1.29) 1.23 (1.06−1.44) 0.67 (0.50−0.90) 1.24 (1.04−1.48) 1.37 (1.05−1.79) 0.73 (0.56−0.97) 0.88 (0.82−0.96) 0.85 (0.76−0.94) 1.18 (1.06−1.31) 1.13 (1.04−1.23) 0.89 (0.82−0.97) 1.17 (1.04−1.33) 1.15 (1.03−1.28)

OR (95% CI)† <1×10−20 3.3×10−16 3.5×10−10 1.7×10−4 3.4×10−4 5.6×10−4 0.00217 0.00381 0.0386 0.0434 3.1×10−5 0.00703 0.00729 0.0168 0.0185 0.0239 0.00156 0.00227 0.00248 0.00311 0.00439 0.00842 0.00981

P-value A A A A A A A A A A B B B B B B C C C C C C C

Credibility

NEDD9 CH25H LOC439999 CALHM1 GRN IL33 IL1B PGBD1 THRA ENTPD7 TFAM IL1A ECE1 PRNP GAPDHS

rs760678 rs13500 rs498055 rs2986017 rs5848 rs7044343 rs1143634 rs3800324 rs939348 rs911541 rs2306604 rs1800587 rs213045 rs1799990 rs4806173

G vs. C T vs. C G vs. A T vs. C A vs. G C vs. T T vs. C A vs. G T vs. C G vs. A G vs. A T vs. C A vs. C G vs. A G vs. C

All All All C All All C All All All All C All C All

8 7 7 10 4 4 6 7 6 4 6 19 5 11 4

6697 3413 5285 8441 3711 9865 2720 6670 5841 7172 2336 8212 3691 6301 3462

0.89 (0.81−0.98) 1.44 (1.07−1.94) 1.15 (1.02−1.30) 1.18 (1.03−1.35) 1.13 (1.02−1.25) 0.85 (0.73−0.98) 1.16 (1.02−1.32) 1.21 (1.02−1.45) 1.10 (1.01−1.19) 1.10 (1.01−1.21) 0.86 (0.75−0.99) 1.09 (1.00−1.18) 0.86 (0.75−1.00) 0.92 (0.85−1.00) 0.87 (0.75−1.00)

0.0116 0.0137 0.0161 0.0162 0.0173 0.0213 0.0214 0.0279 0.0296 0.0336 0.0364 0.0386 0.0443 0.0445 0.0478

C C C C C C C C C C C C C C C

Indicates loci/variants originally identified by GWAS (see also Table 1.3).

Summary ORs and 95% confidence intervals (CI) are based on random-effects allelic contrasts comparing minor vs. major alleles at each polymorphism.

†

Number of samples refers to the number of independent case-control samples used in the meta-analyses; multiple samples may be reported in the same publication and are considered separately if they are independent, i.e., nonoverlapping. Samples overlapping across publications are only used once, usually by including the datasets with the largest number of available genotypes.

‡

*

Source: Modified after content on the AlzGene website (http://www.alzgene.org; current on May 1, 2010). List of loci, in descending order of genetic effect size, containing at least one polymorphism showing nominally significant (P-value ≤0.05) summary ORs. To be considered as “Top Result,” summary OR needs to be significant across samples from all ethnic backgrounds (“ALL”) or ethnicity-specific strata (“C” Caucasians only, “A” Asian only). Note that AlzGene is continuously updated, so results displayed online may differ from the results above; consult the AlzGene website for up-to-date numbers and additional meta-analyses in these and other loci.

24 25 26 27 28 29 30 31* 32 33 34 35 36 37 38

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Dementia

respectively). While the ε4 allele has been demonstrated to significantly increase the risk for AD, its minor allele—ε2—has been (less consistently) associated with a decreased risk for AD (Corder et al. 1994). Although the association between ε4 and AD is robust, it is not specific. In contrast to the genetic effects in EOFAD, the presence of ε4 is neither necessary nor sufficient to actually cause the disease. Rather, it appears to be a modifier of disease susceptibility, predominantly by decreasing the age of onset in a dose-dependent manner, in the sense that homozygous carriers on average show a younger onset age than carriers of just a single copy (Blacker et al. 1997; Meyer et al. 1998). In terms of disease risk, this effect translates into an increased relative risk (as measured by the odds ratio) of roughly threefold for heterozygous, and ∼fifteen-fold for homozygous ε4-carriers as compared to the ε3/3 genotype (Farrer et al. 1997). Similar trends can be seen across different ethnic groups, ranging from odds ratios of around twofold in Hispanic to more than thirty-threefold in Japanese ε4/4 individuals. Interestingly, APOE ε4, or genetic markers being significantly correlated with this allele, are also by far the most significant signal emerging from any of the currently published genomewide studies on AD (see Table 1.3), suggesting that probably all other AD risk-alleles either exert much smaller effects or are much less common in the general population. Despite the overwhelming support from genetic data suggesting an important contribution of this gene to AD risk or onset age variation, the potential biological consequences of the APOE polymorphisms remain only incompletely understood. The most straightforward hypothesis proposes that the different variants directly influence Aβ-accumulation (Strittmatter et al. 1993). Several lines of evidence support this notion, showing an increased number of Aβ-plaques in the brain of ε4-allele carriers and marked differences in the deposition of Aβ-plaques depending on the presence or absence of human APOE in transgenic mice overexpressing APP (for recent reviews on APOE function, see Bu 2009; Vance and Hayashi 2010). Furthermore, there is evidence for an APOE dependent onset age variation in EOFAD caused by PSEN1 mutations (Nacmias et al. 1995). However, no consensus has yet been reached regarding the predominant mechanism(s) underlying these effects. A large number of in vitro and in vivo studies have suggested direct or indirect effects of the different apoE isoforms on Aβ production, Aβ clearance, Aβ fibrillization, tangle formation, synaptic plasticity and repair, and neuronal toxicity (Bu 2009; Vance and Hayashi 2010). Furthermore systemic dysfunction in lipid transport and, more specifically, cholesterol homeostasis is a possible pathway. Interestingly, cholesterol has also been shown to both increase

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Table 1.3 Overview of All Published Genome-Wide Association Studies (GWAS) in AD

GWAS

Design

Population

Grupe CaseUSA and et al. 2007 control UK

No. SNPs 17,343

Reiman CaseUSA, Neth- 502,627 et al. 2007 control erlands Li et al. CaseCanada 469,438 2008 control and UK USA Poduslo Familyet al. 2009 based and casecontrol Abraham CaseUK‡ # et al. 2008 control Bertram FamilyUSA et al. 2008 based

489,218

561,494 484,522

No. AD GWAS (followup)†

380 (1428) 396 (1666) APOE*, ACAN, BCR, CTSS, EBF3, FAM63A**, GALP, GWA_14q32.13, GWA_7p15.2, LMNA, LOC651924, MYH13, PCK1, PGBD1, TNK1, TRAK2, UBD 446 (415) 290 (260) APOE*, GAB2 753 (418)

9 (199)

736 (249) APOE*, GOLM1, GWA_15q21.2, GWA_9p24.3 10 (225) TRPC4AP

1239 APOE*, LRAT (1400) 941 (1767) 404 (838) APOE*, ATXN1, CD33, GWA_14q31 492 (238) 496 (220) APOE*, FAM113B 844 (1547) 1255 APOE*, (1209) PCDH11X 1082 (−)

532,000 Beecham CaseUSA‡ et al. 2009 control CaseUSA 313,504 Carrascontrol quillo et al. 2009 Harold CaseEurope and 529,205 3941 (2023) et al. 2009 control USA‡ Lambert CaseEurope‡ et al. 2009 control

No. CTRL GWAS (follow- “Featured” up)† Genes

537,029 2032 (3978)

7848 APOE*, CLU (APOJ), (2340) PICALM 5328 APOE*, CLU (3297) (APOJ), CR1 (Continued)

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Dementia

Table 1.3 (Continued) Potkin CaseUSA et al. 2009 control (ADNI)

Heinzen CaseUSA‡ et al. 2009 control

516,645

172 (−)

n.g.

331 (−)

209 (−) APOE*, ARSB, CAND1, EFNA5, MAGI2, PRUNE2 368 (−) APOE*, CHRNA7

Source: Modified after content on the AlzGene website (http://www.alzgene.org; current on May 1, 2010). Studies are listed in order of publication date (determined by PubMed ID).“Featured Genes” are those genes/loci that were declared as “associated” in the original publication, note that criteria for declaring association may vary across studies. *

In many studies, surrogate markers were used for APOE.

**

This locus was originally named “THEM5.”

†

Numbers of “AD GWAS” and “CTRL GWAS” refers to sample sizes used in initial GWA screening, whereas “Follow-up” refers to follow-up samples (where applicable).

‡

Some subjects and datasets overlap across these studies; see AlzGene website for more details.

#

This study is based on a pooled genotyping approach which may lead to invalid allele frequency estimates and results.

Aβ-production and to stabilize the peptide in the brains of transgenic AD mice. Thus, it is possible that APOE-ε4 confers risk for AD via mechanisms that are shared with its effects on cardiovascular disease, for example, by increasing a carrier ’s risk for hypercholesterolemia, as this would also elevate accumulation of Aβ. SYSTEMATIC FIELD SYNOPSIS AND META-ANALYSES OF AD GENETIC ASSOCIATION STUDIES Due to the increasingly large number of genetic association studies in the field of AD, it has become virtually impossible to systematically follow, evaluate, or interpret these findings. In an attempt to facilitate this situation, we have created a publicly available database, “AlzGene” (http://www.alzgene.org), which systematically collects, summarizes and meta-analyzes all genetic association studies published in the field of AD (Bertram et al. 2007). After thorough and ongoing searches of the available scientific literature, studies published in peer-reviewed journals that are available in English are included in AlzGene. Key variables (such as ancestry, type of AD diagnosis, sample size, onset age, and genotype

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distributions) are extracted from the original publications. Furthermore, published genotype data from independent case-control samples are systematically meta-analyzed. Because this approach quantitatively synthesizes all of the published genotype data for each polymorphism, it facilitates the overall interpretation of association findings: rather than relying on the either positive (“significant”) or negative (“insignificant”) outcomes of individual studies, it produces summary odds ratios that take into consideration all data at once and account for within and between study variation. At the day of this writing (May 2010), AlzGene includes more than 1,300 individual studies and showcases the results of nearly 300 individual meta-analyses. In these, nearly 40 genes that are not related to the wellestablished APOE ε4-allele show nominally significant risk effects (see Table 1.2). Interestingly, about one-fourth of these were originally implicated by one of the twelve published genome-wide association studies (GWAS; see below), although independent replication by other groups is still lacking for most of these findings. As can be seen in Table 1.2, the average allelic summary odds ratio (OR), for non APOE-related effects are very modest (∼1.2 for “risk” alleles and ∼0.8 for “protective” alleles) compared to an OR of ∼3–4 for a single copy of the APOE ε4-allele. These modest effect sizes are in good agreement with those found in other genetically complex diseases (Ioannidis et al. 2003; Lohmueller et al. 2003; Allen et al. 2008) and have important (and well-known) implications for the design of future genetic association studies in AD. For instance, sample sizes will need to be vastly increased in order to detect or exclude ORs of ∼1.2 or below with sufficient confidence. In addition to the calculation of summary ORs for eligible polymorphisms, AlzGene also assesses the “epidemiologic credibility” of all nominally significant meta-analysis findings using guidelines recently proposed by investigators from the Human Genome Epidemiology Network (HuGENet; (Ioannidis et al. 2008; Khoury et al. 2009). In brief, these guidelines take into account the power, degree of heterogeneity, and evidence for bias across each meta-analysis, and subsequently assign a grade categorizing the overall degree of epidemiologic credibility for each polymorphism. The grading scheme distinguishes between “strong” (grade A), “moderate” (grade B), and “weak” (grade C) epidemiologic credibility. Of all 38 genes and loci currently showing nominally significant summary ORs in AlzGene meta-analyses, 10 are classified as displaying “strong” epidemiologic credibility based on these guidelines (see Table 1.2).

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GENOME-WIDE ASSOCIATION STUDIES (GWAS) IN AD An alternative to the traditional candidate gene approach is afforded by recent advances in large-scale genotyping technologies that now enable researchers to perform comprehensive and largely hypothesisfree genome-wide association studies (GWAS; Craddock, O’Donovan, and Owen 2008; McCarthy et al. 2008). For many genetically complex diseases, the GWAS approach has yielded an unexpectedly large number of genome-wide significant findings that were confirmed by independent follow-up studies (reviewed in McCarthy et al. 2008; Ioannidis, Thomas, and Daly 2009). In many instances, these findings promise to advance our understanding of the pathogenetic forces underlying the investigated phenotypes, hopefully enabling researchers and clinicians not only to improve diagnostic accuracy, but also to deliver a whole array of new drug targets hopefully leading to more efficient treatment and disease prevention options in the not too distant future. In AD, GWAS results have thus far proved to be less consistent (see Table 1.3 and Bertram and Tanzi 2009 for review), with the exception of the APOE locus, whose association with AD was identified in all but one study, and always found to be orders of magnitude more significant than any of the other, newly implicated loci. Regardless of the currently observed lack of consistency across studies, the jury is still out as to how many and which of the potential new AD loci will replicate in independent follow-up studies as most of the currently published GWAS have only appeared within the past two years. Based on the data available at the time of this writing, the most compelling novel GWAS signals are observed in CLU (clusterin; a.k.a. apolipoprotein J [APOJ]), CR1 (complement component (3b/4b) receptor 1), GAB2 (GRB2-associated binding protein 2), and PICALM (phosphatidylinositol binding clathrin assembly protein), followed by less consistently replicated signals in PGBD1 (piggyBac transposable element derived 1), and TNK1 (tyrosine kinase, nonreceptor 1). Finally, there are at least three replicated loci in hitherto uncharacterized genomic intervals on chromosomes 14q32.13, 14q31.2 and 6q24.1 likely implicating the existence of novel AD genes in these regions. It is important to emphasize that while the statistical support for GWAS signals is—by virtue of their design—very strong, the observed effect sizes (ORs) may not be. For instance, the GWAS signal with the strongest statistical support (lowest P-value) after APOE is elicited by genetic variants in the CLU gene. However, the effect exerted by these variants is only very modest, suggesting a mere ∼15% decrease in AD risk in carriers versus noncarriers of the minor alleles.

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CURRENT TOP-RANKED AD CANDIDATE GENES Taking into account all of the available association data from both GWAS and candidate gene studies, current meta-analysis results implicate some three dozen loci to show at least nominally significant association with risk for AD (see Table 1.2). Applying recently proposed guidelines for the cumulative assessment of genetic association data in complex disorders suggests that at least 10 of these loci show particularly strong “epidemiologic credibility” (grade A) when all of the available genotype data is taken into consideration. These loci and their potential pathogenetic implications in AD are briefly discussed in the following paragraph. The gene currently showing the strongest association with AD risk after APOE is CLU (clusterin; a.k.a. apolipoprotein J [APOJ]), a 75 kDa chaperone protein that is expressed at high levels in the brain and binds to and promotes the clearance of Aβ, a function potentially shared with apoE (Nuutinen et al. 2009). Besides APOE, CLU currently represents the only locus to show genome-wide significant association in more than one GWAS (Harold et al. 2009; Lambert et al. 2009). Another recent GWAS locus (Harold et al. 2009) is located close to PICALM (phosphatidylinositol binding clathrin assembly protein), which encodes for a protein that is involved in VAMP2 trafficking—a process involved in synaptic neurotransmitter release (Harel et al. 2008)—and which may also be linked to the production of Aβ via clathrin-mediated endocytosis (Harold et al. 2009). SORL1 (sortilin-related receptor) belongs to a family of sorting receptors that contain a VPS10 (vacuolar protein sorting protein 10) domain, via which these proteins mediate a variety of intracellular sorting and trafficking functions (Yamazaki et al. 1996). Biochemical evidence suggests that the protein encoded by SORL1 directly influences the production of Aβ by affecting the processing/trafficking of APP (Andersen et al. 2005; Offe et al. 2006) via binding to a complement-type repeat (CR) domain located near 3’ half of the gene (Andersen et al. 2006), which collectively also shows the strongest association with AD risk (Rogaeva et al. 2007). The GWA_14q32 locus was identified in one of the first GWAS published in AD (Grupe et al. 2007) and has not yet been assigned to a specific gene. Until further genetic experiments uncover the functional basis for the observed association(s) their potential pathogenetic relevance (if any) in AD remains unclear. TNK1 (tyrosine kinase, non-receptor, 1), also originally identified in the GWAS by Grupe et al. (2007), is a nonreceptor tyrosine kinase originally known as “thirty-eight-negative kinase 1” that could be linked to AD via its reported role to enable tumor necrosis factor alpha (TNF-α)-induced apoptosis (Azoitei et al. 2007). Thus, TNK1

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may act as a novel molecular switch that can determine the properties of TNF-α signaling and, potentially, neuronal cell death. ACE (angiotensin converting enzyme 1) is a ubiquitously expressed zinc metalloprotease that is involved in blood pressure regulation. Of potential relevance for AD is the observation that the ACE-protein is able to degrade naturally secreted Aβ in vitro (Hu et al. 2001; Hemming and Selkoe 2005), which could explain the observed increase in AD risk. In addition, its risk-exerting effect could also be the result of ACE’s prominent role in blood pressure regulation, as some epidemiological studies suggest that high mid-life blood pressure may increase the risk for AD in later life (Takeda et al. 2008). IL8 (interleukin 8) is a member of the CXC chemokine family and represents a major mediator of inflammatory responses. In AD, IL8 (a.k.a. CXCL8) has been reported to be increased in the brains of AD patients versus controls in some studies (Mines, Ding, and Fan 2007). Besides potentially playing a role in mediating inflammatory responses accompanying AD neuropathology, a more specific role has recently been suggested in a study showing that the receptor for IL8 (CXCR2) may be involved in APP metabolism and Aβ production via modulation of γ-secretase activity (Bakshi et al. 2008). LDLR (low-density lipoprotein receptor) is a membrane-spanning glycoprotein that plays a critical role in removing low-density lipoproteins (LDL and VLDL) from the blood and is the main receptor for apoE in neurons. Recent in vitro and in vivo experiments suggest that LDLR may have an important function in AD neuropathogenesis, in particular the clearance and deposition of Aβ (Kim et al. 2009; Abisambra et al. 2010). Finally, CST3 (cystatin C), which is the most abundant extracellular inhibitor of cysteine proteases, was found to bind Aβ (Vinters et al. 1990) and to inhibit Aβ-fibril formation in a concentration-dependent manner in vitro (Sastre et al. 2004) and in vivo (Kaeser et al. 2007). These findings, together with the observation that there is a general reduction in neuroprotection when levels of CysC are decreased in vitro may be the functional correlates of the observed epidemiological association between this gene and increased risk in AD (Crawford et al. 2000). While many of the proposed biochemical mechanisms outlined above appear plausible and well supported by functional genetics data, it should be added that the majority of the genes currently highlighted in AlzGene were originally tested for genetic association precisely because they emerged as promising functional candidates. This is even true for the top-ranking non-APOE GWAS hit, CLU, which owing to its functional relatedness with apoE actually represents one of the first studied candidate genes in AD

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(Tycko et al. 1996). At the time, however, lack of power had precluded an earlier recognition of this locus as an AD risk gene. Thus, more functional and molecular genetic experiments are necessary to decipher the precise role of the implicated sequence variants in AD pathogenesis and to determine their potential usefulness in diagnostic or pharmacogenetic settings. OUTLOOK TO FUTURE GENETIC STUDIES OF AD While the systematic collection and analysis of data already existing in the AD literature clearly facilitates the interpretation and follow-up of the hitherto proposed candidate genes, the loci investigated to date represent only a fraction of the sequence currently known to exert functional roles in the human genome. The ongoing development and availability of better and more affordable genotyping and high-throughput sequencing technologies will allow for a much more systematic and efficient assessment of the remainder of the genome in the search for common and rare disease modifiers over the coming years. Whole genome sequencing, in particular, will reveal whether or not the currently observed association results all point to the same underlying disease-modifying allele (as is likely the case for ε4 in APOE), or whether they merely represent a common chromosomal background for much less common mutational events that cannot currently be interrogated by any of the available GWAS microarrays. Due to the vastly increased amount of data generated, however, the outcome of such studies will even more critically than before depend on a careful study design, which includes choosing appropriate ascertainment strategies, maintaining a high phenotyping accuracy, devising a careful analysis plan able to handle the immense multiple testing problem, and successfully and consistently replicating the observed effects in independent samples from different populations before drawing any further-reaching conclusions. Based on the experiences gathered from AlzGene and other related projects (Ioannidis et al. 2003; Lohmueller et al. 2003; Allen et al. 2008; Khoury et al. 2009), however, the single most important factor in identifying genuine and reproducible susceptibility variants will be the use of sufficiently sized, and thus: sufficiently powered, samples. Assuming that most of the still elusive genes will exert small effects, that is, increasing or decreasing disease risk by ∼25% or less, combined sample sizes of 6,000 to over 25,000 cases and controls (for minor allele frequencies between 50% to 5%, respectively) may be necessary to achieve 80% power to detect genome-wide significance (i.e., a P-value of ∼5 × 10-8). Considering that far less than 5% of all studies in the field of AD genetics published to

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date actually have enough power to detect such small effects, a concerted collaborative effort among individual laboratories is needed in order to arrive at the required sample sizes and to allow these promising new technologies to reach their full potential. CONCLUSION Despite the great progress in the field of AD genetics that has led to the discovery and confirmation of three autosomal-dominant early-onset genes (APP, PSEN1, PSEN1) and one late-onset risk-factor (APOE), strong evidence exists suggesting the presence of additional AD genes for both forms of the disease. The hunt for these genes is aggravated by factors that generally complicate the identification of complex disease genes: locus and/or allelic heterogeneity, small effect sizes of the underlying variants, unknown and difficult to model interaction patterns, population differences, insufficient sample sizes and/or sampling strategies, and linkage disequilibrium among polymorphisms other than those initially associated with the disease. The emergence of more powerful and efficient genotyping and sequencing technologies (e.g., whole genome sequencing) as well as analysis tools (e.g., systematic and continuously updated meta-analyses) should enable us to better disentangle the genetics of AD and other complex diseases. Eventually, the insights gained from such studies will lead to a better understanding of the pathophysiological mechanisms leading to neurodegeneration and dementia. This knowledge will lay the foundation to developing new treatment strategies that will ultimately allow curing, delaying, or even preventing this devastating disease. REFERENCES Abisambra, J. F., T. Fiorelli, J. Padmanabhan, P. Neame, I. Wefes, and H. Potter. 2010. LDLR expression and localization are altered in mouse and human cell culture models of Alzheimer ’s disease. PLoS One 5: e8556. Allen, N. C., S. Bagade, M. B. McQueen, J. P. Ioannidis, F. K. Kavvoura, M. J. Khoury, R. E. Tanzi, and L. Bertram. 2008. Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: The SzGene database. Nat Genet 40: 827–834. Andersen, O. M., J. Reiche, V. Schmidt, M. Gotthardt, R. Spoelgen, J. Behlke, C. A. von Arnim, et al. 2005. Neuronal sorting protein-related receptor sorLA/LR11 regulates processing of the amyloid precursor protein. Proc Natl Acad Sci USA 102: 13461–13466. Andersen, O. M., V. Schmidt, R. Spoelgen, J. Gliemann, J. Behlke, D. Galatis, W. J. McKinstry, et al. 2006. Molecular dissection of the interaction between

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Theuns, J., N. Brouwers, S. Engelborghs, K. Sleegers, V. Bogaerts, E. Corsmit, T. De Pooter, C. M. van Duijn, P. P. De Deyn, and C. Van Broeckhoven. 2006. Promoter mutations that increase amyloid precursor-protein expression are associated with Alzheimer disease. Am J Hum Genet 78: 936–946. Tucker, T., M. Marra, and J. M. Friedman. 2009. Massively parallel sequencing: The next big thing in genetic medicine. Am J Hum Genet 85: 142–154. Tycko, B., L. Feng, L. Nguyen, A. Francis, A. Hays, W. Y. Chung, M. X. Tang, et al. 1996. Polymorphisms in the human apolipoprotein-J/clusterin gene: Ethnic variation and distribution in Alzheimer ’s disease. Hum Genet 98: 430–446. Van Broeckhoven, C., H. Backhovens, M. Cruts, G. De Winter, M. Bruyland, P. Cras, and J. J. Martin. 1992. Mapping of a gene predisposing to earlyonset Alzheimer ’s disease to chromosome 14q24.3. Nat Genet 2: 335–339. Vance, J. E., and H. Hayashi. 2010. Formation and function of apolipoprotein E-containing lipoproteins in the nervous system. Biochim Biophys Acta 1801 (8): 806–818. Vinters, H. V., G. S. Nishimura, D. L. Secor, and W. M. Pardridge. 1990. Immunoreactive A4 and gamma-trace peptide colocalization in amyloidotic arteriolar lesions in brains of patients with Alzheimer ’s disease. Am J Pathol 137: 233–240. Wisniewski, K. E., A. J. Dalton, C. McLachlan, G. Y. Wen, and H. M. Wisniewski. 1985. Alzheimer ’s disease in Down’s syndrome: Clinicopathologic studies. Neurology 35: 957–961. Yamazaki, H., H. Bujo, J. Kusunoki, K. Seimiya, T. Kanaki, N. Morisaki, W. J. Schneider, and Y. Saito. 1996. Elements of neural adhesion molecules and a yeast vacuolar protein sorting receptor are present in a novel mammalian low density lipoprotein receptor family member. J Biol Chem 271: 24761–24768.

Chapter 2

Epigenetic and Gene Imprinting Effects in Parkinson’s Disease: A Kinship Theory of Gene Conflict in Aging and Dementia Paul M. Butler and Patrick McNamara

In this chapter we review epigenetic factors that influence Parkinson’s disease pathophysiology and that might predict Parkinson’s disease dementia (PDD). We emphasize effects of imprinted genes on developing catecholaminergic systems, mitochondrial functioning, and neurobehavioral functioning. We identify five key imprinted genes, 23 potentially imprinted genes (identified via bioinformatics modeling), five X-linked, mtDNA haplogroups, and seven PD-associated genes with loci in known imprinting centers that significantly influence PD pathophysiology. These analyses yield a major and novel prediction concerning effects of the DGK-theta gene on production of the etiopathogenesis of PD. We integrate these analyses into a theoretical framework of evolutionary theories of aging, intergenerational transfer of resources, and the kinship theory of intragenomic conflict. Our kinship theory of conflict in aging predicts that to increase inclusive fitness the matrilineal genome functions to extend life beyond reproductive capacity, whereas patrilineal genes antagonize the process of aging.

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Dementia

Within this theoretical framework, PD can be understood as an aberrant side effect of epigenetic dysregulation over genes influencing aging, with PD susceptibility increasing with either full or partial loss-of-maternal or gain-of-paternal gene function. OVERVIEW OF THE CLINICAL AND NEUROPATHOLOGICAL HALLMARKS OF PD Parkinson’s disease (PD) is a progressive neurodegenerative disorder that disrupts proper functioning of neostriatal and mesocortical catecholaminergic systems (Lotharius and Brundin 2002). The best predictors of PD onset are age and sex. The average age of onset is 61 years and males are twice as likely as females to get PD (Standaert and Cantuti-Castelvetri 2008). The cardinal clinical features of PD are bradykinesia postural instability, rigidity, resting tremor, and/or gait disturbances (Hardy et al. 2006). Response to dopaminergic medication provides further support for the diagnosis of clinical PD (Litvan et al. 2007). Neurobehavioral features of PD include mood, social cognitive, and executive function deficits (McNamara, Durso, and Harris 2007, 2008; Williams-Gray et al. 2007; Huang et al. 2007). A shift in personality has also been noted with elderly (rather than early-onset) PD patients characterized as harm avoidant, overly conscientious, moralistic, ambitious, and low on novelty-seeking behaviors (McNamara, Durso, and Harris 2007, 2008). Between 30% and 80% of persons with PD are at risk for developing dementia as the disease progresses. The cholinesterase inhibitor rivastigmine has been shown to slow (but unfortunately not prevent) decline in selected cognitive functions and to reduce psychiatric disturbances (hallucinations or apathy) that often accompany dementia. Amantadine may increase the time from onset of Parkinson’s disease to dementia in persons at risk for dementia. One of the risk factors for dementia is signs of cognitive or thinking problems in early stages of the disease. If we can identify persons at high risk for dementia in PD we can offer these high-risk individuals early treatment with one of these drugs so as to slow down progression to dementia. Identifying new genetic risk factors is therefore of extreme clinical importance. The pathological features of Parkinson’s disease result primarily from the loss of dopaminergic neurons in the ventrolateral substantia nigra (Dauer and Przedborski 2003). The London Brain Bank criteria require the presence of Lewy bodies in the substantia nigra for the pathological diagnosis of PD. Of those individuals clinically diagnosed with PD, over 90%

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27

that come to autopsy demonstrate Lewy body formation in substantia nigral tissue (Hughes et al. 1992). Lewy bodies are intracellular inclusion bodies of proteinaceous aggregates comprised mainly of alpha-synuclein. They are typically 15 micrometers in diameter, eosinophilic, and spherical. These intra-cytoplasmic bodies are composed of alpha synuclein, parkin, ubiquitin, and neurofilaments, and contain a dense hyaline core surrounded by a clear halo (Bras et al. 2008). Besides the substantia nigra, Lewy body formation can occur in the locus coeruleus, dorsal raphe, nucleus basalis of Meynert, dorsal motor nucleus of vagus, cingulate cortex, entorrhinal cortex, olfactory bulb, and autonomic nervous system (Francis and Perry 2007). The clinical correlates of dopamine (DA) dysfunction are best understood despite the involvement of other neurotransmitter systems, such as the noradrenergic and serotonergic systems. Breakdown of neurotransmitter systems other than DA are thought to occur in later stages of the condition and are perhaps age-related (Dauer and Przedborski 2003). GENETICS OF PARKINSON’S DISEASE In his 1817 monograph, James Parkinson suggested that the “shaking palsy” was due to stress (Parkinson 1817). Decades later, Gowers (1888) hypothesized a familial connection inherent to PD based on his observation that PD seemed to intermittently afflict members within extended families. Until the 1990s, a genetic component to PD vulnerability was largely dismissed. The first breakthrough came in the mid-1990s when Polymeropoulos et al. (1996, 1997) demonstrated genetic linkage to human chromosome 4 in the Contursi Greek/Italian kindred. The so-called PARK1 gene was localized to 4q21-23 and shown to code for the alpha-synuclein protein (SNCA). Albeit rare, numerous other mutations in SNCA were discovered subsequently (Athanassiadou et al. 1999; Bostantjopoulou et al. 2001; Kruger et al. 1998). Since these initial discoveries, researchers have turned to investigation of epigenetic factors to identify causal mechanisms of PD. The pathogenesis of PD is thought to relate to two fundamental processes: (1) misfolded and aggregate proteins lead to cell death in substantia nigra pars compacta (SNpc) dopaminergic neurons, and (2) mitochondrial dysfunction and consequent oxidative stress induces toxic and oxidized DA species (Dauer and Przedborski 2003). These biochemically intertwined processes result in cells selectively targeted for programmed cell death. The connection between misfolded proteins, production of reactive oxygen species (ROS; e.g., superoxides O2–.), and apoptosis is only partially elucidated. Both genetic

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Dementia

and toxin-induced models of PD have helped to illuminate several of the final common biochemical pathways leading to PD. Monogenic PD: Alpha-Synuclein, LRRK2, DJ-1, PINK1, and Parkin Overproduction of SNCA is directly related to age-of-onset of PD. The greater the accumulation of alpha-synuclein protein beyond what is necessary for normal cellular function, the earlier PD-type symptoms emerge (Ross et al. 2008). The relationship between levels of excess alpha synuclein and cell death is dose-sensitive. Alpha-synuclein is part of a family of related proteins expressed in the central nervous system. SNCA is concentrated in presynaptic nerve terminals (Jakes, Spillantini, and Goedert 1994). Evidence suggests it plays a prominent role in synaptic plasticity through its facilitation of vesicular release of neurotransmitters (Liu et al. 2004). Alpha-synuclein binds lipid membranes, which alters the conformation of a previously unfolded N-terminus into an alpha-helix. This allegedly stabilizes curvature of lipid membranes, thereby modulating neurotransmitter-holding vesicular function (Lotharius and Brundin 2002). Evidence suggests that SNCA aggregation is selectively cytotoxic in DA-ergic neurons (Xu et al. 2002). Through interaction with ROS, oligomeric accumulation of alpha-synuclein in human dopamine neurons leads to apoptosis. Non-DA-ergic neurons do not exhibit the same sensitivity to oligomeric alpha-synuclein prompted cytotoxicity. Xu et al. (2002) showed that dopamine-related cytotoxicity is mediated by 54-83-kD soluble protein aggregates that contain alpha-synuclein and a specific 14-3-3 protein. Substantia nigra cells in PD patients selectively express elevated levels of these proteins. Aggresomes of soluble alpha-synuclein and 14-3-3 protein complexes render endogenous intracellular dopamine toxic. This evidence points to a mechanism to explain the tissue selectivity of neuronal loss in PD. It is well established that aggregated alpha-synuclein containing inclusion bodies (Lewy bodies) form lesions that result in the pathologic features of many neuro-degenerative diseases. Several cellular scenarios can lead to the accumulation of alpha-synuclein aggregates: (a) gene mutations that increase alpha synuclein production, (b) gene mutations coding for proteins involved in degrading and turning over misfolded proteins (e.g., parkin/ubiquitin), and (c) exposure to ROS or nitration, which can lead to alpha-synuclein misfolding and aggregation (Giasson et al. 2000). Levels of oxidized proteins increase with age, and DA metabolism generates ROS (Xu et al. 2002). Chaperones (e.g., heat shock protein 70) target proteins for proteasomal degradation by polyubiquination. This process

Epigenetic and Gene Imprinting Effects in Parkinson’s Disease

29

becomes less efficient in aging. So, a deleterious feedback cycle emerges in normal aging—an inevitable decrease in efficiency in cell handling of misfolded proteins, which subsequently increases the interaction time with ROS giving way to more misfolded proteins and eventual sequestration and inclusion body formation. Indeed, Lewy body formation and neuronal loss in subcortical areas is apparent in nondemented older adults based on neuropathological cohort studies (Byford et al. 2009). Critical cellular regulation of ROS is under dynamic control by mitochondrial homeostatic mechanisms. During acute states of elevated ROS levels, prolonged periods of mild ROS elevation, or loss of efficacious enzymatic machinery to deal with ROS, mitochondrial homeostasis can become disturbed leading to fission and spilling of ROS and pro-apoptotic modulators (e.g., cytochrome c) into the cytoplasm. Release of cyctochrome c from mitochondrial cristae induces alpha-synuclein aggregation (Chu 2009). Further, cytochrome c is a potentiator of the apoptotic intrinsic pathway (Skulachev 1998). Correlates of apoptosis include Bax, Caspase-8 and -9, and are all found in higher levels in SNpc neurons in post-mortem tissue samples of PD patients (Ho et al. 2009). Caspase-8 is located in deafferented presynaptic terminals and once activated might lead to a retrograde “dying back” of DA neurons in the striatum (Bernheimer et al. 1973). Abnormalities in Complex I of the mitochondrial electron transport chain are also found in PD neurons and peripheral platelet tissue samples (Betarbet et al. 2006). Both complex I and III of the transport chain are key generators of ROS during oxidative phosphorylation. Abnormalities in handling ROS lead to compensatory mitochondrial fusion mechanisms (Chu 2009). When these compensatory systems cannot handle the ROS load, and protective mitochondrial fusion fails, this can lead to mitochondrial fission with subsequent spilling of bioactive and pro-apoptotic chemicals into the cytoplasm. Mitochondrial fission-fusion dynamics are essential then for controlling ROS, cytotoxicity, and apoptosis. Besides increase in intracellular ROS and alpha-synuclein overproduction, dysregulation in other cellular machinery can lead to PD. Mutations in the leucine-rich repeat kinase 2 gene (LRRK2/PARK8) are believed to cause between 2% and 40% (depending on the population) of PD cases (Bonifati 2006). All of the functions of LRRK2 are not understood. It is a large, multidomain protein with GTPase kinase activity, and it associates with mitochondrial outer membranes and lysosomal vesicles (Marin 2006). Several lines of research have illuminated the role mutations in LRRK2 play in PD histopathology. For instance, LRRK2 might link tau and alphasynuclein misfolding through phosphorylation of alpha-synuclein. Tau and alpha-synuclein can bind one another and stimulate phosphorylation

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Dementia

and aggregation of homopolymers, which eventually become cytotoxic (Devine and Lewis 2008). The most common LRRK2 mutation, the G2019S, causes an increase in autophosphorylation and kinase activity (Saha et al. 2009). Hyperphosphorylated alpha-synuclein aggregates comprise PD-associated Lewy bodies. Also, LRRK2 interacts with death receptor Fas-associated protein with death domain (FADD). LRRK2 inhibits FADD and caspase-8 (extrinsic death pathway). Mutations increase interaction with FADD and increase cell death via caspase 8 recruitment (Ho et al. 2009). Allegedly then, deviation in LRKK2 function yields PD patterns of neuronal brain damage because of its influence over phosphorylation of alpha-synuclein and tau, and control of the extrinsic apoptotic pathway. PARK7 (DJ-1) mutations are loss-of-function alterations associated with early-onset PD (Bonifati et al. 2006; Taira et al. 2004). Experimental data with drug-induced PD models demonstrate that rotenone disturbs mitochondrial function, which results in aberrant DJ-1 and alpha-synuclein regulation (Betarbet et al. 2006). DJ-1 is thought to localize to mitochondria during times of oxidative stress in order to serve as a protective redox sensor. PINK1 (PARK6) codes for PTEN-induced kinase 1, which contains an N-terminal localization signal for the mitochondria (Valente et al. 2004). Research demonstrates that PINK1 protects against proteasome-mediated apoptosis in neuronal cell populations (Chu 2009; Dagda et al. 2009). The proposed mechanisms include PINK1 protecting against LRRK2 neurite injury, promotion of mitochondrial fusion as a protective mechanism against oxidative stress, and maintenance of cristae membrane structure and Ca2+ balance. PINK1 deficiency leads to abnormal cristae structure and membrane potential, mitochondrial calcium dysregulation, and oxidative stress. This is believed to increase mitochondrial fragmentation via a perturbation of fission-fusion homeostasis. Parkin (PARK2) codes for an ubiquitin-associated protein that leads to early-onset PD with loss-of-function mutations (Sun et al. 2006). Parkin is an ubiquitin-protein ligase involved in the polyubiquination pathways that tag proteins for degradation in proteasomes (Shimura et al. 2000). Regulating protein turnover, namely alpha-synuclein, is of homeostatic importance to prevent cytotoxicity. Inability to recycle misfolded proteins increases exposure time to ROS, which can induce protein aggregation and ultimately cytotoxicity and apoptosis. Before moving on to discuss the role of epigenetics (namely gene imprinting) in PD risk, it is important to note that preliminary evidence suggests involvement of epigenetic dysregulation, such as hypomethylation of DNA, in PD susceptibility (Urdinguio, Sanchez-Mut, and Esteller 2009).

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31

SNCA can translocate to the nucleus and associate with histones, modifying the acetylation process (Kontopoulos, Parvin, and Feany 2006). SNCAmediated toxicity is thought to interrupt a major regulator of aging, sirtuin 2 (Outeiro et al. 2007).

GENOMIC IMPRINTING AND PD PD Susceptibility, a “Multi-Hit” Hypothesis, and Functional Haploidy The pathways leading to PD susceptibility over time are likely influenced by the function of several key proteins. An increased risk for developing PD could emerge given a hypothetical individual with increased ROS due to normal aging, high intrinsic DA turnover, and a SNP polymorphism in a polyubiquinator important to DA-ergic neuronal function that delays protein recycling. Much like Knudson’s “multi-hit” hypothesis for cancer risk (Knudson 1971), there are likely many subtle changes in numerous aspects of cellular machinery that enhance risk of PD. Subtle change in protein function at several key bioregulatory nodes, rather than complete loss-of-function mutations, can increase the probability of PDrelated brain damage accumulating over time. Knudsen’s “multi-hit” hypothesis applied to PD susceptibility refers to both overt mutational events leading to loss- or gain-in-function protein expression patterns and polymorphism-induced alterations in expression levels. A recent metabolomics study by Gieger et al. (2008) demonstrated that a single nucleotide polymorphism in the PARK2 (parkin) gene was one of five key proteins to dramatically alter concentrations of many core amino acids measured in human serum. While the complete loss-of-function mutation in parkin results in juvenile PD, one can imagine the impact of a functional polymorphism in parkin in conjunction with changes in function of several other vital enzymatic regulators. Indeed, subtle changes in the function of key bioregulatory nodes increase risk of PD. For example, polymorphisms in key mtDNA genes can lead to either a protection from, or vulnerability to PD as a function of age (Pyle et al. 2005; Wild and Dikic 2010). With the “multi-hit: framework in mind, functionally haploid gene systems are increasingly vulnerable as loci for disease susceptibility from a probabilistic perspective because haploid gene expression cannot rely on compensatory mechanisms of biallelic gene dosage. For males, X-linked gene expression is functionally haploid and with over 90% of mtDNA being derived from a primary oocyte, functional haploidy generally applies to mtDNA-associated risk (Giles et al. 1980). Epigenetic processes,

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Dementia

such as genomic imprinting, represents another type of functional haploidy currently neglected in studies of PD genetic risk. Genomic imprinting refers to the epigenetic silencing of alleles in an individual according to the parent-of-origin of the allele (Burt and Trivers 2006; Haig 2004). Classic imprinted gene mechanisms (complete silencing of either paternally or maternally received alleles) results in functional haploid gene expression such that mutant alleles are exposed to more direct selection. If alteration in imprinting mechanisms occurs, then this functional haploidy increases the risk for complete loss-of-function or extreme gain-of-function (Wilkins and Haig 2003, “What good”; Ubeda and Wilkins 2008). Imprinted genes often regulate growth and development such that alteration in gene expression can amplify dysfunction in cellular proliferation, apoptosis, or differentiation. PD Susceptibility, Gene Imprinting, and the Kinship Theory of Genomic Imprinting Apart from functional haploid gene expression increasing likelihood for mutational or dysregulatory events across time, several other lines of research suggest that gene imprinting is likely involved in PD susceptibility. Evidence is accumulating that epigenetic approaches are crucial to understand many neuropsychiatric conditions and neurological disorders that, like PD, involve a diathesis stress etiological model of exposure (e.g. to an environmental toxin) with underlying genetic risk (Abdolmaleky, Thiagalingam, and Wilcox 2005; Bertram et al. 2000; Lamb et al. 2005; Mayeux et al. 2002). Many of the genes known to be imprinted are expressed in the brain (Davies, Isles, and Wilkinson 2005). The well-described syndromes of gene imprinting dysfunction, which include Prader-Willi, Angelman’s, Beckwith-Widemann, and Silver-Russell syndromes (dysregulated imprinting at 15q11-13, 11p15.5, 7p11.2, or 5q35), greatly alter behavior and cognition (Davies, Isles, and Wilkinson 2001; Eliot and Maher 1994; Flint 1992; Whittington et al. 2004). An overwhelming amount of evidence connects genomic imprinting with brain development, modulation of behavior, and neuropsychotic spectrum disorders and neurodegenerative disease, including Alzheimer ’s disease, autism, epilepsy, schizophrenia, ADHD, bipolar disorder, and Huntington’s disease (Bassett, Avramopoulos, and Fallin 2002; Crespi 2008; Goos and Silverman 2006; Lamb et al. 2005; Ottman et al. 1988; Wilkinson, Davies, and Isles 2007). The kinship theory of genomic imprinting suggests that genes are selected for expression or silencing depending on their parent-of-origin and their effects on the growth and fitness of mothers, their progeny, and

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other genetically asymmetrical kin (Day and Bonduriansky 2004; Haig 1997; Haig 2000, “Genomic imprinting”; Wilkins and Haig 2003, “What good”). Genes that enhance offspring fitness at a cost to the mother and other maternal kin will more likely be paternally expressed and maternally silenced through nucleotide methylation, posttranslational histone modification, or noncoding RNA (ncRNA) mechanisms. Contrarily, maternally expressed genes will likely restrain extraction of maternal resources by offspring (e.g., restrain placental growth) and silence paternal alleles. As predicted by Haig’s theory of intragenomic conflict, numerous imprinted genes have been discovered across differing mammalian species that can be understood to express this genetic tug-of-war over maternal resources and growth of offspring. From this theoretical perspective intragenomic conflict is essentially about a struggle over the pace and bio-flow of energy resources. After presenting evidence for the presumed involvement of imprinted genes in PD susceptibility, we draw from the kinship theory of intragenomic conflict to address the evolutionary dynamics behind this connection. Methodological Approach The list of known human imprinted genes is constantly growing. Bioinformatics models have enhanced our ability to predict novel loci. Algorithms designed to identify differences between imprinted and nonimprinted genes have been powerful tools in the effort to locate imprinted loci and genes. A methodological caveat is important to note at this point. The genes discussed as either maternally or paternally imprinted in the discussion to follow do not take into full consideration possibilities of more complex imprinting patterns, such as partial imprinting, in which paternal and maternal allele expression differ by degree (Morison, Ramsay, and Spencer 2005; Wolf et al. 2008). Imprinted genes may express a parentof-origin effect selectively in certain tissue (e.g., hippocampal neurons vs. cardiac myocytes) or cell lineages, at particular developmental stages (e.g., fetal vs. postnatal weaning), or exhibit pleiotrophic effects via polymorphic imprinted states, reciprocal patterning, multiple splice variants, etc. (Buetner et al. 2004; Kishino 2006; Sandovici et al. 2003; Weinstein 2001). Biochemical mechanisms to underlie gene imprinting can be quite diverse—differential methylation of DNA elements at imprinting clusters inducing an enhancer or blocker effect on allele expression by parent of origin, cis-mediated silencing of a given allele by a noncoding RNA element, or via methylation of micro-imprinted domains of promoter regions

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in oocyte-derived genetic material (Wood and Oakey 2006). Also, complex imprinting patterns of bipolar homozygous or heterozygous bipolar dominance and callipgye-type expression patterns exist (Kim et al. 2004; Wolf et al. 2008). The mechanisms of gene imprinting are just beginning to be understood in their full complexity. Take, for example, imprinted noncoding RNA—ncRNA genes are a group (e.g., Piwi-interacting RNAs, microRNAs, and C/D small nucleolar RNAs) of untranslated transcripts that are expressed in a parent-of-origin pattern, which can function as cisacting silencers of chromatin at imprinting centers or as anti-sense transacting gene expression regulators. ncRNA imprinting has already been implicated in Prader-Willi syndrome, several types of cancers, and neural plasticity (Royo and Cavaille 2008). Another important note of caution is that while many of the genes discussed in the sections to follow are known to be imprinted in humans, the genes we draw upon from bioinformatics predictions (Luedi et al. 2007) will need empirical validation before full inclusion in this model of PD risk is warranted. Empirical investigations of bioinformatically identified imprinted genes have yielded mixed results (Pollard et al. 2008; Ruf et al. 2007; Wolf et al. 2008). These partial failures to empirically confirm bioinformatically identified genes may be due to several factors: (1) not all major tissue types were assessed for imprinting, (2) varying developmental stages were not sampled, (3) gender effects were usually not addressed, and (4) human tissue was not tested. Despite the impressive cross-validation check of the linear algorithm used in many bioinformatics models, the false positive rates of prediction from the Luedi et al. (2007) bioinformatics method will need future empirical confirmation. Whether or not the predictions of novel imprinted genes by bioinformatics end up fully or partially accurate, the evidence we present here (not entirely dependent on prediction models) suggests that genetic imprinting plays a significant role in the pathophysiology of PD.

IMPRINTED GENES AND DEVELOPING MIDBRAIN DA SYSTEMS FOXA2: Forkhead Box Protein A2; Maternally Imprinted Foxa2 is a member of a larger class of proteins that serve as transcriptional factors vital for prenatal development of various tissue types, cell survival, and longevity (Greer and Brunet 2005; Tothova and Gilliland 2007). Foxa2 is located at human chromosome 20p11.21 and is predicted to be maternally imprinted (Luedi et al. 2007). Foxa2 is a forkhead transcription factor known to play a critical role in the development of endoderm,

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midline structures, notochord, and floor plate (Ang and Rossant 1994; Epstein, McMahon, and Joyner 1999; Sasaki and Hogan 1994). Using a mouse model, Kittappa et al. (2007) demonstrated that the foxa2 gene controls both the birth and death of dopaminergic neurons in old age. They showed that midbrain dopaminergic neurons are derived from the floor plate under the tight regulation of the Foxa2 gene. Further, mice deficient in foxa2 late in life developed motor problems evidenced histopathologically by selective neuronal loss in the substantia nigra. This study is of particular importance because of its use of a complete lifespan model. Most models of PD currently focus on inducing abnormalities via toxin exposure in animals after assumed normal development of dopaminergic systems. Assessing the overall growth, maintenance, and trajectory of dopaminergic midbrain neurons seems an improvement in scope and applicability. LMX1B: LIM Homeobox Transcription Factor 1, Beta; Maternally Imprinted Foxa2 gene is an important, albeit not exhaustive, regulator of mesencephalic dopaminergic neurodevelopment. Other important contributions are made by numerous genes, including but not limited to sonic hedgehog (SHH), LIM homeobox transcription factor 1 alpha and beta (LIM1A and LIM1B), fibroblast growth factor 8 (Fgf8), paired-like homeodomain transcription factor 3 (Pitx3), bicoid class homeobox transcription factor 1 and 2 (Otx1 and Otx 2), and the proneural basic helix-loop-helix transcription factor neurogenin 2 (Ngn2) (Bergman et al. 2009; Fuchs et al. 2009; Lin et al. 2009). Many of these genes crucial for dopaminergic system building and maintenance are predicted with high confidence to be imprinted: maternally imprinted Foxa2 at 20p11.21, paternally imprinted SHH at 7q36.3, maternally imprinted LMX1B at 9q33.3, and maternally imprinted Otx1 at 2p15 (see Smidt and Burbach 2007 for review of genes; Luedi et al. 2007). Table 2.1 contains an extended list of the imprinted genes (most predicted) with known protein products that exert a neurotrophic effect on mesodiencephalic DA neurons. Both LMX1A and LMX1B are important for proper differentiation of midbrain dopamine neuron birth and development (Alavian, Scholz, and Simon 2008; Smidt et al. 2000). Bergman et al. (2009) assessed SNPs in both LIM homeobox genes and PD susceptibility in 357 Swedish PD subjects. Three SNPs in LMX1A and one in LMX1B were associated with PD. Additionally, the risk for PD by gender varied by SNP type. Lin et al. (2009) investigated the relationship between LMX1B and Foxa2 in development of mesodiencephalic dopaminergic neurons. Their

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Table 2.1 Imprinted Genes Predicted by Bioinformatics Models (Luedi et al. 2007) with Known Protein Products with Trophic Effects on Embryonic Mesodiencephalic DA Neurons Locus (gene)

Chromosomal locus

GDNF

5p13.2

Neurod2 Pitx2

17q12 4q25

Pitx1

5q31.1

GFRA4

20p13

BMP1

8p21.3

BMP8

1p34.3

EGFL3/MEGF6

1p36.33

EGFL7

9q34.33

SHH FGFR3

7q36.3 4p16.3

BMP4

14q22.2

Foxa2 LMX1B

20p11.21 9q33.3

Otx1

2p15

Encoded protein Glial cell linederived neurotrophic factor Neurogenin 2 Paired-like homedomain transcription factor 2 Paired-like homedomain transcription factor 1 GDNF family receptor alpha-4 receptor Bone morphogenetic protein 1 Bone morphogenetic protein 8 Multiple epidermal growth factorlike domain 6 Epidermal growth factor-like 7; vascular endothelial statin Sonic hedgehog Fibroblast growth factor receptor 3 Bone morphogenetic protein 4; BMP2B1 Forkhead box A2 LIM homeobox transcription factor 1, beta Orthodenticle 1, homolog of drosophila

Mode of inheritance Paternal

Paternal Paternal

Paternal

Paternal

Paternal Paternal Paternal

Paternal

Paternal Maternal Maternal

Maternal Maternal

Maternal

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Table 2.1 (Continued) Pax6 CDKNN1C

11p13 11p15.5

TFAP2A

6p24.3

Paired box gene 6 Cyclin dependent kinase inhibitor 1C Transcription factor AP 2 alpha reg

Maternal Maternal* Maternal

* Denotes known imprinted gene

research revealed that Foxa2 functioned as an upstream modulator of LMX1B positively modulating gene expression and dopamine neuron development. The study also demonstrated that tyrosine hydroxylase (TH; maternally imprinted) is a target for Foxa2. The cooperative, feedforward modulation between Foxa2, TH, and LMX1B is interesting given that both are known and predicted to be maternally imprinted genes, respectively. The interactions between these imprinted genes promoting DA-ergic midbrain neuron development involve cross-regulation from other imprinting centers elsewhere in the genome. For instance, the imprinting zone at 11p15.5, which includes TH, IGF2, and H19, becomes activated during dopaminergic cell differentiation (Freed et al. 2008). Freed and colleagues examined gene expression in human embryonic stem cells through the process of cell differentiation into dopamine neurons. A number of genes previously known to be correlated with dopamine neuron development were identified, such as MSX1, CDKN1C, Pitx1, and Pitx2, in addition to several novel loci, which covered the 11p15.5 imprinting center expressing genes H19, TSSC4, TH, and IGF2. Other Imprinted Genes and DA System Control Table 2.1 displays several imprinted genes (mostly predicted) that contribute to development and longevity of midbrain dopaminergic neurons. While the initial growth and establishment of the DA system receives greater paternal genetic contributions, the control of programmed DA cell death seems to be largely under maternal control. From Brainstem Building to Catecholamine Regulation—AP-2α (TFAP2A), Transcription Factor AP-2 alpha, Maternally Imprinted Apparent from the above discussion regarding the role of genetic elements in building dopaminergic pathways in the midbrain, the impact of

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differential gene expression can extend into adulthood and aging (e.g., programmed DA cell death). Transcription factor AP-2 alpha (AP-2α) is an example of a gene with substantial regulatory control of brainstem development and continued impact across development. Activating enhancer-binding protein 2 contains a family of transcription factors with five isoforms described to date: AP-2α, AP-2β, AP-2γ, AP-2δ, and AP-2ε (Eckert et al. 2005; Hensch et al. 2008). The AP-2α and AP-2β isoforms have been shown to regulate gene expression during embryonic development of neural crest cell lineages and neuroectodermal cells, including those in the midbrain (Moser, Ruschoff, and Buetnner 1997). Extending into mammalian adulthood, AP-2α expression specifically correlates with levels of catecholamine metabolites in the frontal cortex and midbrain (e.g., DOPAC, HVA, 5-HIAA; Damberg et al. 2001). Additionally, transcription of AP-2α and AP-2β seems necessary to prevent cell specific activation of apoptotic pathways (Brewer et al. 2004; Moser et al. 1997). Thus, in addition to its important neurodevelopmental role, AP-2α is an important determinant for maintenance, functional characteristics, and regulation of target catecholamine gene expression. Several catecholamine-related genes involved in CNS neurotransmitter function contain AP-2 binding sites in their regulatory regions, for example, DβH, AADC, 5-HT receptor 2A, and TH (Du et al. 1994; Greco et al. 1995; Hahn et al. 1993; Kim et al. 2001; Kobayashi et al. 1989; McMahon and Sabban 1992). AP-2α, located at human chromosome 6p24.2, is predicted to be maternally imprinted (Luedi et al. 2007). Because this gene interacts with other catecholaminergic regulatory genes that are known to be imprinted (see next section) the confidence level in the imprinting status of AP-2α is increased. Transcriptional activation mediated by AP-2 can be induced by two different signal transduction pathways: the phorbol ester and diacylglycerol-activated protein kinase C or the cAMP-dependent protein kinase A pathways (see DGK-theta section below for a discussion on the potential role of DGK-theta in PD etiopathogenesis). IMPRINTED GENES AND CATECHOLAMINE METABOLISM Figure 2.1 depicts an overview of the intragenomic conflict over important regulators of catecholamine metabolism. The discussion below details more specifically the biochemical role of each contributor. TH: Tyrosine Hydroxylase; Maternally Imprinted TH is the rate-limiting catalyst in the conversion of L-tyrosine to L-DOPA. It is vital to the production of catecholamines. Four different types of TH are

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Figure 2.1

Conflict over regulation and gene expression in the catecholamine system: depiction of metabolic pathways with imprinted genes. ** denotes ratelimiting step in DA metabolism, and * denotes X-linked gene. TH – tyrosine hydroxylase; L-DOPA – L-3,4-dihydroxyphenylalanine; DDC – DOPA decarboxylase; DβH – dopamine beta-hydroxylase; MAO-B – monamine oxidase type B; COMT – catechol-O-methyltransferase; 5-HT – serotonin; DA – dopamine; DRD4 – dopamine receptor 4; 5HTR2A – serotonin receptor type 2A; DOPAC – 3,4-dihydroxyphenylacetic acide; HVA – homovanillic acid; 5-HIIA – 5-hydroxyindoleacetic acid; ♀ – female imprinted gene; ♂ – male imprinted gene. Note that all genes are known imprinted human genes, except AP-2α and DβH, which are both predicted to be maternally imprinted genes by bioinformatics models (Luedi et al. 2007).

expressed in the human brain and adrenal medulla (Grima et al. 1987). In the brain, TH is most highly concentrated in the substantia nigra and locus coeruleus. Various TH isoforms exist to contribute to the functional diversity of TH. One mechanism is alternative splicing of TH mRNA, which is implicated in some neurologic diseases (Nagatsu and Ichinose 1991). Polymorphisms in the TH gene, a maternally imprinted gene located at 11p15.5, are associated with male longevity (De Benedictis et al. 1998).

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TH is also an independent marker for size at birth and obesity. Zhou et al. (1995) showed that dopamine-deficient mice become hypoactive after birth and stop feeding. Treatment with L-DOPA resolves these behavioral shortcomings within minutes. Continued treatment with L-DOPA leads to normal growth, movement, and feeding. TH gene mutations have been implicated in PD (Ludecke, Dworniczak, and Bartholome 1995; Swaans et al. 2000). Most evidence suggests that TH gene mutations causing significant loss of enzymatic function are related to infantile or juvenile onset of parkinsonism. L-DOPA medication was very effective at managing these cases. More recently, haplotypes associated the IGF2-INS-TH cluster at 11p15.5 were associated with both risk of and protection from PD (Sutherland et al. 2008). DBH: Dopamine Beta-Hydroxylase; Maternally Imprinted DBH is a 290-kDa copper containing oxygenase that metabolizes dopamine to produce norepinephrine (Rush and Geffen 1980). Located at 9q34.2, DBH is predicted to be a maternally imprinted gene (Luedi et al. 2007). Healy et al. (2004) demonstrated in a study of 809 PD patients that the DBH SNP polymorphism -1021 T/T genotype was underrepresented in PD’s versus controls. These results suggest some type of protective effect in the T/T SNP against PD. DBH gene promoter polymorphism -1021 C/T appears to regulate plasma DBH activity. A T/T SNP at -1021 lowers DBH activity, consequently increasing serum dopamine levels (Zabetian et al. 2001). Lower levels of DBH activity (-1021 T/T genotype) have a neuroprotective effect against PD. DDC: DOPA Decarboxylase; Paternally Imprinted DDC, generally known as aromatic L-amino acid decarboxylase (AAAD), is a homodimeric, lyase enzyme responsible for synthesizing dopamine and serotonin (Ichinose et al. 1989). The biosynthetic pathway for dopamine involves the dietary ingestion of the essential amino acid phenylalanine and subsequent conversion into L-tyrosine by the enzyme phenylalanine hydroxylase. The rate-limiting step is controlled by the maternally imprinted tyrosine hydroxylase, which converts L-tyrosine to L-DOPA. In the periphery and in the central nervous system AAAD catabolizes L-DOPA to dopamine. Dopamine is found in numerous areas of the brain with high concentrations in the basal ganglia (Hyland et al. 1992; Pons et al. 2004). AAAD is not the rate-limiting step in dopamine and serotonin synthesis. AAAD becomes the rate-limiting enzyme in individuals treated

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with L-DOPA or 5-HT for conditions, such as PD (LeWitt 2008). Tehranian et al. (2006) discovered molecular interaction between alpha-synuclein and AAAD. Dopaminergic cells overexpressing alpha-synuclein display reduced AAAD and TH activity. Alpha-synuclein acted to diminish phosphorylation and activity of both TH and AAAD. This evidence furthered the argument that alpha-synuclein is directly involved in dopamine synthesis and metabolism. DeLuca et al. (2003) showed that AAAD polymorphisms (to attenuate paternal allele expression/function) contribute to longevity in drosophila. Also, polymorphisms in tyrosine hydroxylase are implicated in longevity in C. elegans species (DeLuca et al. 2001). Menheniott et al. (2008) demonstrated genomic imprinting in the DDC gene with a paternal pattern of expression. The neighboring Grb10 (see section below) displays a reciprocal maternal imprinting pattern. Perturbations in the multifunctional enzyme DDC have been reported for a range of neurodegenerative and psychiatric conditions, such as Parkinson’s disease and bipolar disorder (Borglum et al. 2003). Ishikawa et al. (2009) discovered an interaction between DDC, TH, and DJ-1. In human dopaminergic neurons DJ-1 directly bound to TH and DDC and positively regulated their activities. Mutant forms of DJ-1 possessing Cystein-106 with SO(2)H and SO(3)H types inactivated the regulatory control that DJ-1 maintained over TH and DDC. This interesting finding indicates an essential role that DJ-1 might have in DA metabolism. While the imprinting status of DJ-1 (PARK7) is not currently known, its locus at 1p36.3 is surrounded by over 30 alleged imprinted genes, which are mostly predicted to be maternally imprinted (Luedi et al. 2007). While many genes in imprinting centers are impacted by imprint mechanisms, one cannot assume the effects of imprinting by mere proximity. However, loci within imprinting centers do raise greater suspicion regarding imprinting status. 5-HTR2A: Serotonin Receptor, 2A; Maternally Imprinted In PD there can be extensive loss of serotonergic function due to loss of neurons in the dorsal raphe nucleus (Francis and Perry 2007). Loss of serotonergic terminal boutons can be compromised by 40–50% in projections to the frontal and temporal cortex and putamen (D’Amato et al. 1987). Because PD primarily impacts DA-ergic neurons, other neurotransmitters systems have been lesser studied in relation to PD susceptibility. One investigation demonstrated a significant correlation between sporadic PD and a SNP (rs6311) in the 5-HTR2A gene in a Russian population (Shadrina et al. 2008).

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Conflicting data exists regarding the imprinting status of the 5-HTRA2 gene (Bunzel et al. 1998; Fukuda et al. 2006). However, sufficient evidence exists to demonstrate that gene expression varies across tissue type and developmental stage with a predominately monoallelic maternal expression pattern. Allelic variation has been repeatedly confirmed using real-time quantitative polymerase chain reaction techniques, despite the fact that 5-HTR2A at 13q14 is not located in a known imprinting center (Lo et al. 2003). The current data on 5-HTR2A gene imprinting in humans demonstrates that gene expression between alleles (paternal or maternal) can change across time, tissue type, and perhaps via epigenetic idiosyncrasies dependent on individuals’ internal milieu and environmental exposures.

IMPRINTED GENES AND MITOCHONDRIAL FUNCTION NDUFS4: NADH-Ubiquinone Oxidoreductase Fe-S Protein 4; Paternally Imprinted Complex I, or NADH ubiquinone oxidoreductase, is a multimeric protein comprised of at least 41 subunits. Seven subunits are controlled by the mitochondrial genome while the remaining 34 are under nuclear genomic control. Complex I is a critical component of the mitochondrial electron transport chain and essential to subunit assembly (Lazarou et al. 2007). In properly functioning mitochondria, Complex I removes electrons from NADH and passes them to ubiquinone via a chain of protein-associated redox controllers. This process begins an ensemble of mitochondrial actions that is pivotal to ATP formation (van den Heuvel et al. 1998). One important subunit vital to the function of NADH oxidation is NADH-ubiquinone oxidoreductase Fe-S Protein 4 (NDUFS4). Proper functioning of NDUFS4 is essential to the machinations of Complex I because of its association with the iron-sulfur clusters, which mediate electron transfer. Feng, Bussiere, and Hekimi (2001) demonstrated that inhibition or mutation (loss of function) at the Fe-S subunits of the mitochondrial transport chain complexes in C. elegans increased life span. Evidence suggests that mutations in the NDUFS4 gene lead to Complex I deficiencies (Ugalde et al. 2004; van den Heuvel et al. 1998) and is implicated in neurologic disease (Hekimi and Guarente 2003; Papa et al. 2009). Severe Complex I deficiencies can lead to Leigh syndrome, which is an early onset neurodegenerative disorder with characteristic neuropathology consisting of brain stem, basal ganglia, cerebellar, and thalamic focal lesions (Dahl 1998; David,

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Gomez, and Okazaki 1970). Indeed, mutations specific to NDUFS4 have been described to induce Leigh syndrome (Budde et al. 2000). Dysfunctional NDUFS4 decreases Complex I function in addition to Complex III function through an unknown mechanism. Important to note, complex I and III are potent generators of ROS (Murphy 2009). NDUFS4 is located at 5q11.1 (Emahazion et al. 1998) and is predicted to be paternally imprinted (Luedi et al. 2007). NDUFA4: NADH-Ubiquinone Oxidoreductase Protein A, 4; Paternally Imprinted Another important gene coding for a functionally important subunit of Complex I is NDUFA4. Located at 1p13.3, this gene is predicted to be paternally imprinted as with the previously discussed NDUFS4 (Luedi et al. 2007). Without traversing territory already explored, suffice it to say that Complex I mitochondrial function is vital for understanding PD susceptibility. While many of the Complex I–associated genes have been investigated as relevant to PD, NDUFA4 is of particular interest for the following reasons. First, the hydrophobicity profile demonstrates that this protein is an inner mitochondrial membrane spanning subunit vital for electron transfer dynamics (Lazarou et al. 2007). Next, recent work by Hayashi et al. (2009) demonstrates a clear chemical interaction between DJ-1 and NDUFA4. In their study, Hayashi et al. found that DJ-1 bound directly to NDUFA4 and ND-1, nuclear and mitochondrial DNA-encoding subunits of Complex I. In DJ-1 knockdown models, complex I activity was reduced. Mitochondrial Genes Contributing to Complex I Function; Maternal Control As previously mentioned, seven mitochondrial genes contribute to Complex I function (mt-nd1, mt-nd2, mt-nd3, mt-nd4, mt-nd4l, mt-nd5, and mt-nd6) in addition to the gene for coenzyme Q, or complex III, function (mt-cyb). An X-linked gene, NDUFA1 (at X24), is important for Complex I function. As stated previously, Complex I and III are the most important generators of ROS production. MtDNA controls the important pro-apototic retaining complex IV, cytochrome c oxidase (mt-co1, ct-co2, and mt-co3). An important struggle over mitochondrial function is demonstrated in these examples of paternally imprinted nuclear and maternally controlled x-linked and mtDNA-associated genetic loci. Elstner et al. (2009) presented evidence for dysfunction in NADH-ubiquinone oxidoreductase, subunit 2 (mt-nd2) is a major risk factor for PD. Also, studies

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have correlated several polymorphic changes in mtDNA (e.g., haplotype cluster UKJT) with decreased risk for PD with a potential gender effect (Gaweda-Walerych et al. 2008; Pyle et al. 2005). Luoma et al. (2004) demonstrated a connection between parkinsonism and mitochondrial DNA polymerase γ (POLG) mutations in a clinical and molecular genetics study. Mutations in POLG, an enzyme that synthesizes all mtDNA, were found in all of the familial PD cases studied. In a study of a multigenerational PD family, Swerdlow et al. (1998) showed maternally inherited complex I gene dysfunction from mtDNA in all PDaffected members. Compared to cell lines cultured with paternal mtDNA, cybrid lines containing maternal mtDNA from this affected multigenerational family exhibited lower complex I activity, increased ROS production, increased radical scavenging enzymatic activity, and higher numbers of abnormally shaped mitochondria. These findings were present in all asymptomatic young maternal descendants in the familial study—note the differential impact by sex. Thus, while female mitochondrial function was affected, males were more severely impacted, allegedly because an equilibrium-maintenance or homeostatic line of compensatory protective mechanistic processes was crossed.

IMPRINTED GENES AND LEWY BODY FORMATION GATA2: GATA-Binding Protein 2; Maternally Imprinted GATA genes express transcription factors with zinc fingers in their DNA binding domains (Tsai et al. 1994). GATA transcription factors are a family of proteins that regulate gene expression in hematopoietic cells. Depending on type, GATA proteins function prominently in nonhematopoietic cell interactions also. GATA2, in particular, is expressed in hematopoietic progenitor cells as well as nonhematopoietic embryonic stem cells (Lee et al. 1991). GATA2 is expressed in white adipocyte precursor cells and terminally traps development in the pre-adipocyte stage (Tong et al. 2000). To trap nascent adipocyte cells, GATA2 suppresses peroxisome proliferators-activated receptor-gamma (PPARG), which is predicted to be a paternally imprinted gene on chromosome 3p25.2. It is not surprising that GATA2 is a growth retarding maternally imprinted gene (Luedi et al. 2007). These two directly acting genes oppose one another in growth, development, and imprinting status. GATA2 deficiency is associated with obesity (Tong et al. 2000). This interaction between the maternally imprinted GATA2 growth retardant and PPARG the paternally imprinted growth promoter is aligned with predictions from the kinship theory of gene imprinting (Haig 2004). Similar to other imprinted genes, GATA2 expression plays a prominent role in placental and

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embryonic development, placental angiogenesis, and mother to fetus control of nutrients (Hemberger and Zechner 2004; Ma et al. 1997). Recently, Scherzer et al. (2008) discovered a relationship between GATA genes and alpha-synuclein (SNCA) expression. They found high levels of SNCA expression in red blood cells and erythropoietic precursors. Further, they discovered that both GATA1 (x-linked) and GATA2 (maternally imprinted) regulate expression of SNCA. GATA2 is highly expressed in substantia nigra and frontal lobe cells, occupying intron-1 of SNCA modulating its expression in dopaminergic cells. This demonstrated a link between SNCA expression and GATA2 in PD vulnerable brain areas. The major protein aggregate in Lewy bodies is alpha-synuclein. The relative expression of SNCA directly relates to the pathology of both familial and idiopathic PD. Increase in SNCA gene expression has been consistently demonstrated in PD. Even small increases in SNCA expression can have an additive effect over time leading to accumulation of cytotoxic phosphorylated oligomers of alpha-synuclein in dopaminergic neurons. GATA2 regulation serves as a single mechanism underlying the relative expression of SNCA in both hematopoietic and neuronal cells. Neuronal cells with silenced GATA2 induced a 28% decrease in SNCA mRNA transcription and a 46% decrease in SNCA protein production (Scherzer et al. 2008). Contrastingly, cells with GATA2 or GATA1 expression caused a 62-fold increase in mRNA SNCA transcripts and a 6.9-fold increase in SNCA protein translation. The complete function of GATA2 is difficult to obtain in vivo experimentation because knock-out GATA2 mice leads to embryonic lethality. Figure 2.2 provides an overview of the relationship between imprinted genes and Lewy body formation, catecholamine metabolism, and mitochondrial function. PREDICTION: DGK-θ: DIACYLGLYCEROL KINASE, THETA; MATERNALLY IMPRINTED Our review of the effects of imprinted genes on PD pathogenesis leads us to predict a major role for a newly discovered PD susceptibility gene called DGK-θ in production of PD neurobehavioral symptomology. The rationale for this prediction follows. The largest genome-wide association study to date to investigate susceptibility genes for familial PD identified a novel locus on the petit arm of chromosome 4 (Pankratz et al. 2009). The strongest evidence of association (p < 6 * 10–6) was with three SNPs in the GAK/DKG-θ region. The study assessed 1,119 familial PD cases, and identified this novel locus in addition to confirming two previously known susceptibility regions, one near SNCA on 4q21 and MAPT on 17q21.

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Figure 2.2

Schematic diagram of intracellular biochemical interaction between imprinted genes and PD-related pathways. ♀ – female imprinted gene; ♂ – male imprinted gene; TH – tyrosine hydroxylase; DDC – dopamine decarboxylase; DβH – dopamine beta-hydroxylase; 5-HTR2A – Serotonin Receptor type 2A; DJ-1 – PARK7 gene; NDUFA4 – NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4; NDUFS4 – NADH dehygrogenase (ubiquinone) iron-sulfur protein, 4; mtDNA – mitochondrial DNA; ROS – reactive oxygen species.

The statistical significance in disease susceptibility with SNPs at 4p16.3 in the GAK/DGK-θ region exceeded that of SNPs proximal to the genes for SNCA and MAPT. The evidence of the potential association between GAK (cyclin G associated kinase, a cell cycle modulator) and PD susceptibility is plausible because it is one of the 137 genes differentially expressed in PD mid-brain tissue. Grunblatt et al. (2004) demonstrated a 1.56-fold increase in GAK expression in substantia nigra pars compacta tissue in PDs compared to controls. Whether altered regulation of GAK is a compensatory change to loss of DA-ergic neurons or a primary cause of a final biochemical pathway leading to PD histopathological changes remains unknown. Beside the role of cyclin-dependent kinases (CDKs) in cell cycle processes, some evidence supports a role for CDKs in both pro-apototic and anti-apoptotic pathways (Kwon et al. 2008; Padmanabhan et al. 1999; Toiber, Greenberg, and Soreq 2009). No research to date has examined the role of GAK in nigrostriatal neurons in relation to apoptosis. At this point, it is not possible to speculate whether upregulation of GAK in substantia nigra pars compacta PD tissue is causative of cell death or a compensatory preventative cell reaction.

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A potential case for the involvement of DGK-θ in PD-related neuronal cell death is more likely given available evidence. The complete cellular workings of diacylglycerol kinase type theta (DGK-θ) are not understood. Important to note, DGK-θ is predicted to be a maternally imprinted gene in a cluster of 10 imprinted genes at the 4p16.3 locus (Luedi et al. 2007). The evidence for imprinting is strengthened by two lines of research. First, deletions in the 4p16.3 region that include DGK-θ lead to Wolf-Hirschhorn syndrome, a disorder dependent on parent-of-origin effects and a hemizygous deletion of the distal short arm of chromosome 4 (Anvret et al. 1991; Hirschhorn, Cooper, and Firschein 1965; Thies et al. 1992; Wolf et al. 1965). Wolf-Hirschhorn syndrome is characterized clinically by severe growth retardation and mental defects, microcephaly, “Greek helmet” facies, cleft lip and/or palate, cardiac septal defects, and coloboma of the eye. Animal models with radiation-induced deletion of a region syntenic to human 4p16.3 lead to growth retardation, susceptibility to seizures, midline structural abnormalities, craniofacial and ocular disturbances, cerebellar hypoplasis, and shortened cerebral cortex (Naf et al. 2001). Another line of research also increases suspicion of gene imprinting of DGK-θ. Imprinted gene products often interact biochemically with geneproducts from other imprinted genes, such as the interaction between the paternally imprinted insulin-like growth factor 2 (11p15.5) and the maternally imprinted insulin-like growth factor 2 receptor (6q25). Likewise, DGK-θ contains a Ras-associated binding domain, and Ras (HRAS gene at 11p15.5) has known imprinted gene expression across varying stages of development (Kratz et al. 2007). So, allegedly the maternally imprinted DGK-θ interacts chemically with paternally imprinted HRAS and insulin (11p15.5), which has been shown to increase DGK-θ activity in cerebral cortex and synaptosomes in adult rat brain tissue (Zulian, Ilincheta de Boschero, and Giusto 2006). This taken in conjunction with the bioinformatics predictive models, and the hemizygous parent-of-origin impact of 4p16.3 deletions, greatly increase the probability that DGK-θ is subject to gene imprinting effects. The primary structure of the DGK-θ isotype contains a proline-rich region, three cysteine-rich domains, a Ras-associating domain, a pleckstrin homology domain, and a catalytic region (Houssa et al. 1997). DGK-θ is a member of a larger class of enzymes called diacylglycerol kinases (DGKs), which are known to be important regulators of cellular signaling and homeostasis (Merida, Avila-Flores, and Merino 2008; Sakane et al. 2007). DGK phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Maintaining a balance between these two bioactive lipids, DAG and PA, is a primary function for DGKs. The pleckstrin domain within DGK-θ allows binding to phosphatidylinositol lipids in membranes, G-proteins,

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and protein kinase C (Haslam, Koide, and Hemmings 1993). Recent research suggests that DGK-θ can also localize to the nucleus within neurons and regulate gene transcription (Tu-Sekine and Raben 2009). The question arises, how might alterations in the function of DGK-θ lead to PD pathology? One potential explanation involves the relationship between DGK-θ, dopamine metabotropic signaling, and lipid peroxidation. Regulating levels of DAG is key to the intracellular signaling of DA, which utilizes metabotropic second messenger systems (Girault and Greengard 2004). DA binding activates adenylyl cyclase and phospholipase C (PLC) via the D1/ D5 receptor mechanism, and inhibits via the D2/D3/D4 receptor system. This, in turn, regulates intracellular levels of second messengers, such as, cyclic adenosine 3′, 5′-monophosphate (cAMP), DAG, and intracellular Ca2+ concentration (Nishino et al. 1993). Proper functioning of DGK is essential for DA signaling homeostasis because of its regulatory role in phosphorylating DAG. Dysregulation of intracellular DAG levels can lead to oxidative stress and ignition of inflammatory pathways by way of the following mechanism. All neural membranes are comprised of phospholipids high in eicosanoids (e.g., arachidonic acid, docosahexaenoic acid, etc.). These polyunsaturated fats (PUFA) promote membrane fluidity, permeability, and normal cell function. Peroxidation of membrane phospholipids causes release and loss of embedded PUFAs and subsequent loss of membrane flexibility (Catala 2009). Lipid peroxidation can result from interaction with hydroxyl free radicals or activation of the phospholipase A2 or phospholipase C/diacylglycerol lipase pathways (Farooqui and Horrocks 1994). Several factors can exaggerate hydroxyl free radical formation, such as intracellular exposure to heavy metals like iron and copper, which catalyze hydroxyl radical formation. Both catabolic lipase pathways, the phospholipase A2 and phospholipase C/diacylglycerol pathways, are inducible by excess excitatory amino acid receptor binding or excess intracellular levels of DAG (Farooqui and Horrocks 1994, 1998). Following DA depletion in nigrostriatal neurons of PD tissue, protective homeostatic mechanisms respond by alterations in transcriptional upregulation of several excitatory receptor subunits (e.g., glutamatergic) in attempt to compensate for the decreased DA-ergic activity (Mallet et al. 2006; Meurers et al. 2009; Wilson and Kawaguchi 1996). This, in turn, can begin a deleterious feed-forward biochemical cycle: compensatory upregulation of excitatory receptor activity on DA nigrostriatal neurons leads to an increase in activity in the phospholipase A2 and phospholipase C/ diacylglycerol pathways, which gives way to an increase in lipid catabolism and peroxidation, loss of membrane fluidity, destabilization of bioelectric currents (namely Ca2+ balance), and activation of pro-apoptotic pathways, cell loss and aggravation of DA-ergic cell loss.

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The brain consumes approximately 20% of body oxygen despite constituting only 2–3% of human body mass (Moreira et al. 2005). Across cell types, brain cells are particularly vulnerable to oxidative stress due to the high level of oxygen consumption and processing. This vulnerability is heightened by the high levels of PUFAs in neural cell membranes in relation to comparatively low levels of antioxidant enzymes (Mariani et al. 2005). Aging is associated with increased oxidative stress due to elevated levels of ROS within the brain accumulating over time, which induces protein misfolding, aggregation, and formation of inclusion bodies (Cardoso et al. 2005). Couple the increase in age-associated oxidative stress with a polymorphic version of the DGK-θ protein to decrease its efficacy— depending on cellular compensatory mechanisms via alternate pathways, over time peroxidation of membrane lipids becomes increasingly likely with an age-associated increase in oxidative stress and functionally dysregulated DAG/PA ratios due to less efficacious DGK-θ enzymatic action. As previously stated, dysregulation of DAG levels can lead to the release of polyunsaturated fatty acids (PUFAs) via the phospholipase A2 and phospholipase C/diacylglycerol lipase pathway, decreasing membrane fluidity, loss of membrane integrity, and resultant cell death via any number of pathways, such as Ca2+ dependent and Ca2+ independent apoptosis. Homeostasis of DAG levels is crucial for controlling lipid peroxidation (Farooqui and Horrocks 1998; Nishino et al. 1989). Additionally, the regulation of DAG levels by DGK-θ is important in controlling the activation of protein kinase C (PKC) induced NADPH oxidase activity (Miller et al. 2009). DAG or phorbol esters interact with protein kinase C to induce production of free radicals through activation of NADPH oxidase (Kozikowski et al. 2003; Nishino et al. 1989). Ubiquitous to neural cells, PKC isoforms regulate a wide array of biochemical activity, such as ion channel and receptor balance, neurotransmitter release, and synaptogenesis (Battaini 2001; Catarsi and Drapeau 1997; Wagey et al. 2001). Aberrant PKC enzymatic activity has been noted in several neurodegenerative conditions, such as Alzheimer ’s and PD (Ahlemeyer et al. 2002; Kozikowski et al. 2003; Ran et al. 2003). NADPH-oxidase is upregulated in the substantia nigra of PD patients (Wu et al. 2003). One potential mechanism to connect these lines of evidence involves dysfunctional DGK-θ activity. If DGK-θ fails to efficiently convert DAG to PA and cellular levels of DAG rise, then DAG binding to PKC should increase inducing an increase in the NADPH-oxidase activity. NADPH activity increases ROS and mediates neurotoxicity of alpha-synuclein (Zhang et al. 2005). The aggregate-inducing interaction between ROS and alpha-synuclein is well described. Another potential aggravator are PUFAs, which as

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previously highlighted are exposable to intracellular contents given fluctuating DAG/PA ratios via DGK dysfunction. Sharon et al. (2003) identified a cellular pool of soluble oligomers of alpha-synuclein in normal mesencephalic human brain tissue. Exposure to PUFAs increased amounts of soluble alpha-synuclein oligomers, whereas exposure to saturated fatty acids decreased oligomer levels. In mouse models, soluble oligomers of alphasynuclein were demonstrated to increase with age. This fits with the finding that patients with PD or other Lewy body–associated conditions have higher levels of lipid-interacting soluble oligomers of alpha-synuclein. The process leading to alpha-synuclein aggregation in neurodegenerative disease is likely preceded by PUFAs and soluble stores of alpha-synuclein increasingly interacting over time. Finally, there is a very close biochemical connection between DAG pathways and ceramide synthesis (Liu, Kleine, and Herbert 1999). Ceramide is an important second messenger implicated in numerous signal transduction pathways mediating cell growth, differentiation, inflammatory response, and apoptosis (Cutler and Mattson 2001). DA-ergic cell signaling, DAG, and ceramide biochemical pathways have been demonstrated to interact dynamically (Liu et al. 2003) and emerging pathways in the genetics of Parkinson’s disease implicate ceramide metabolism (Bras et al. 2008). In sum, the largest genome-wide association study to date identified a novel PD susceptibility area with SNPs in the GAK/DGK-θ region on human chromosome 4p16.3 (Pankratz et al. 2009). This is notable because DGK-θ is predicted to be a maternally imprinted gene and our confidence in the accuracy of this prediction is increased because parent-of-origin transmission effects from its locus at 4p16.3 have already been demonstrated. As suggested, DGK-θ could potentially be involved in modulating lipid peroxidation, production of ROS, and maintaining neuronal membrane stability. Further, DGK-θ is important for regulating DAG-related ceramide biosynthetic pathways, which are potentially involved in Parkinson’s disease pathology. Figure 2.3 furnishes an overview of the potential pathways that DGK-θ regulates that relate to PD cellular dysfunction.

COMPLEXITY OF IMPRINTING EFFECTS: GRB10, GROWTH FACTOR RECEPTOR BOUND PROTEIN 10; MATERNAL IMPRINTING IN THE PERIPHERY AND PATERNAL IMPRINTING IN THE BRAIN We now illustrate the flexibility and complexity of imprinting effects on physiologic and PD-related pathophysiologic systems. GRB10 inhibits receptor-bound insulin signaling pathways (Liu and Roth 1995). GRB10

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Figure 2.3

Biochemical pathways associated with the maternally imprinted gene DGKtheta, a complex intracellular signaling molecule implicated in PD susceptibility by a recent genome-wide assoiation study. DGK-θ – diacylglycerol kinase type theta; PKC – protein kinase type C; NADPH oxidase – nicotinamide adenine dinucleotide phospate-oxidase; DAG – diacylglycerol; PA – phosphatidic acid; PUFA – polyunsaturated fatty acid; ♀ – female imprinted gene.

binds with high affinity to autophosphorylated insulin receptors bound by insulin. Formation of this GRB10-insulin-insulin receptor complex inhibits insulin-dependent phosphorylative activation of IRS1 and a GTPase activating protein. GRB10 can also inhibit tyrosine kinase activity involved in growth promotion caused by IGF1. Another biochemical activity of GRB10 involves downstream reactions in insulin-mediated pathways. In response to insulin stimulation, GRB10 migrates from the cytosol to the cellular membrane. GRB10 can also bind activated platelet-derived growth factor receptor, epidermal growth factor receptor, and growth hormone (Frantz et al. 1997). Blagitko et al. (2000) demonstrated that GRB10 is imprinted in a tissue-specific manner. Maternal expression predominates in fetal skeletal

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muscle and paternal expression dominates in brain tissue. Charalambous et al. (2003) confirmed that GRB10 is imprinted with the majority of total protein expression belonging to the maternal allele. Blocking the maternal allele caused a 30% increase in both embryonic and placental overgrowth in mice. Maternal GRB10 alleles are growth inhibitors in the periphery capable of suppressing tyrosine kinase mediated growth. The paternally imprinted gene IGF2 is a growth-promoting gene not impacted by downstream effects of GRB10 growth-inhibition. This is consistent with the gene-conflict model of offspring development. GRB10 is a gene of interest to PD research because of its chemical interaction with PARK11. Located at 2q37, it is also known as GIGYF2 (GRB10 Interacting GYF Protein 2). Lautier et al. (2008) demonstrated a genetic association between GIGYF2 mutations and PD in a study of 250 European individual with a family history of PD. Homologous proteins, GIGYF2 and GIGYF1, bind GRB10 through the GYF domain and interact with insulin and IGF signaling, which is vital to brain function. Giovannone et al. (2003) provided evidence that overexpression of GIGYF1 bound to IGF1 receptor complexes formed cytotoxic inclusion bodies. Dysregulation of either GRB10 or GIGYF2 could lead to formation of inclusion bodies in the brain. One potential contributory mechanism could be the formation of cytotoxic inclusion bodies because of over expression of GIGYF2 or GRB10. Because GRB10 is paternally expressed in brain tissue, unrestrained growth promoting effects could lead to formation of inclusion bodies via overload of protein expression across time that overwhelm cellular machinery that handle protein turnover. Reciprocally, a loss of maternal function to restrain growth could lead to excess GIGYF2 or GRB10 binding to IGF receptors and subsequent formation of cytotoxic protein aggregates. With this potential etiological mechanism, PD emerges as an effect of dysregulated growth in brain tissue based on paternal growth promotion outpacing maternal growth restraint. Either a maternal loss-of-function in growth restraint mechanisms or a paternal gain-of-function in growth and protein expression could set up the conditions for accumulation of proteinaceous inclusion bodies, such as Lewy bodies, over time (see Table 2.2). PD ASSOCIATED GENES IN IMPRINTING CENTERS Many regions of the genome are associated with risk for developing PD. In this brief section, we present several “suspect” genes with either known or unknown imprinting status. The proximity of these target areas to imprinting centers increases the likelihood that epigenetic mechanisms influence gene expression.

Gene

NDUFA4

TH

Mitochondrial Function

Catecholamine Metabolism

DA System FOXA2 Building and Longevity DA System LMX1B Building and Longevity Mitochondrial NDUFS4 Function

Relation to PD pathology

11p15.5

1p13.3

5q11.1

9q33.3

20p11.21

Forkhead transcription factor, 2A LIM Homeobox transcription factor 1 beta NADH-ubiquinone oxdore ductase Fe-S protein 4 NADHubiquinone oxidoreductase 1 alpha subcomplex 4 Tyrosine hydroxylase

Chromosome Encoded locus protein

Rate-limiting catalyst in the conversion of L-tyrosine to L-DOPA

Pivotal for Complex I mitochondrial function; demonstrated to bind DJ-1 to preserve mitochondrial function

Plays a role in early development of the notochord; demonstrated to play a role in longevity via regulation of dopaminergic mid-brain neurons Important for the development of mesenchephalic dopaminergic neurons; SNPs associated with PD risk in women Crucial for Complex I mitochondrial function; electron transfer

Putative function

Table 2.2 Imprinted Genes and Predicted Imprinted Genes Associated with PD

(Continued)

Maternally imprinted *

Paternally imprinted

Paternally imprinted

Maternally imprinted

Maternally imprinted

Mode of inheritance

3q21.3

GATA2

Diacylglycerol kinase, theta Growth factor receptor bound protein 10; maternally expressed gene 1

L-amino acid decarboxylase Dopamine β-hydroxylase 5-HT receptor, 2A GATA binding protein 2 Polymorphisms in this 5-HT receptor have been linked to PD susceptibility and phenotype Zinc-finger transcription factor; regulates expression of α-synuclein in substantia nigra and frontal lobes Modulate balance between diacylglyerol (DAG) and phosphatidic acid (PA) Involved in insulin signaling pathways; interacts with park11 /GIGYF2 (GRB10 interacting GYF protein 2)

Enzyme responsible for synthesizing dopamine and serotonin Enzyme responsible for synthesizing dopamine and serotonin

Maternally imprinted Maternally * expression in peripheral tissue and Paternal * expression in brain

Maternally imprinted * Maternally imprinted

Paternally imprinted * Maternally imprinted

* Denotes known imprinted gene; remaining genes are predicted to imprinted by the bioinformatics model from Luedi et al. (2007).

DGKQ 4p16.3 (DGK-θ) GRB10 7p12 (MEG1)

13q14

5-HTR2A

Catecholamine Metabolism Lewy Body Formation

Lewy Body Formation Lewy Body Formation

DDC 7p12 (AAAD) DBH 9q34.2

Catecholamine Metabolism Catecholamine Metabolism

Table 2.2 (Continued)

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11p15.5 Two genes located in this imprinting center are related to PD susceptibility but their imprinting status is not known: DRD4 and CTSD. The dopamine-four-receptor (DRD4) is a metabotropic, G protein-coupled receptor categorized in the D2-like receptor family. This class of receptors all function to inhibit adenylyl cyclase (Oldenhaf et al. 1998). DRD4 is differentially expressed across varied regions of the brain with the prefrontal cortex containing the highest density of receptors that act to inhibit neural firing (Dulawa et al. 1999). Additionally, high expression of DRD4 messenger ribonucleic acid (mRNA) has been detected in the medulla, midbrain, and amygdala (D’Souza et al. 2004). Linkage disequilibrium analysis demonstrates linkage to nearby imprinted genes, such as tyrosine hydroxylase and the Harvey RAS oncogene (Gelernter et al. 1992). DRD4 shows evidence of maternal transmission patterns in some cases of bipolar disorder and paternal patterns in cases of ADHD (Hawi et al. 2005; Muglia et al. 2002). However, no direct tissue-by-tissue assessment of imprinting across varying developmental periods has been completed so the imprinting status of DRD4 is still speculative but with high suspicion based on parent-of-origin effects in neuropsychiatric populations. Ricketts et al. (1998) demonstrated an association between PD and long variants (>6) of the variable number tandem repeats (VNTR) in exon III of DRD4. A recent study of north and south Indian populations revealed an association between DRD4 120-bp duplication markers and PD (Juyal et al. 2006). Both long VNTR variants in exon III and 120-bp duplication makers functionally reduce the activity or expression of the DRD4 receptor. Because of its inhibitory function, loss-of-DRD4 receptor function results in phenotypic disinhibition. Beyond genetic susceptibility to PD, DRD4 polymorphisms have been implicated in sleep attacks in PD. Paus et al. (2004) found an association between VNTRs in exon III and sleep attacks in PD. A forthcoming article by Eisenegger et al. (2009) implicates DRD4 polymorphisms in L-DOPA responsiveness and pathological gambling behavior. Another gene located at 11p15.5 related to PD is cathepsin D (CTSD; Latourelle et al. 2009). Cathepsins are lysosomal proteases, and CTSD is an important lysosomal aspartyl protease comprised of two disulfideconnected polypeptide chains (Hasilik and Neufeld 1980). Cullen et al. (2009) demonstrated the CTSD degrades alpha-synuclein in dopaminergic cells and that mutations in CTSD lead to lysosomal storage dysfunction, alpha-synuclein misprocessing, and alpha-synuclein toxicity.

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15q13 A genome wide study of genetic susceptibility to PD implicated the 15q13 loci, which is the region connected to the imprinting disorders of Prader-Willi and Angelman syndrome (Morison, Ramsay, and Spencer 2005; Srinivasan et al. 2008). Ubiquitin-mediated proteolysis was implicated in this study with loss of function to the maternally imprinted ubiquinating gene (UBEA3) as the most likely molecular target. 1p36 This region of the human genome contains at least 26 imprinted genes, mainly of maternal origin (Luedi et al. 2007; Morison et al. 2005). PD associated genes of interest located in this 1p36 region are PINK1 (PARK6) and DJ-1 (PARK7), and ATP13A2 (PARK9). Additionally, a genome-wide association study assessing the impact of genetics on age-of-onset identified an SNP in the 1p36.32 region coding for the maternally imprinted gene peroxisome biogenesis factor 10, also known as Pex10 (Latourelle et al. 2009; Luedi et al. 2007). While the association did not meet conservative criteria for genome-wide significance, it was still an important predictor of PD susceptibility in certain populations. Pex10 encodes a protein important for importing peroxisomal matrix proteins. Loss of maternal function in Pex10 likely increases oxidative stress within select cellular environments (Warren et al. 1998). X-LINKED GENES AND PD Beside the impact of autosomal DNA on PD susceptibility, several loci on the X chromosome correlate with PD risk. While numerous reports of several X-linked genes exist in the literature on PD susceptibility, Table 2.3 presents an abridged list of the most frequently replicated X-linked loci associated with PD risk. The X-linked genes might help to explain the sex-difference in PD risk. Other explanations for the male to female ratio difference include (1) sex hormone impact on disease susceptibility, (2) differences in male versus female DA network function (e.g., DA receptor density and recycling, DA metabolic rates, etc.), and (3) male versus female behavior differences that lead to differential environmental exposures leading to increased risk of PD. We will suggest another potential explanatory option related to gene imprinting in the following section. Imprinting might be important for explaining parent-of-origin influences on X-linked gene susceptibility loci for any given condition. Murine

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Table 2.3 X-Linked Genes Associated with PD Locus (gene)

Chromosomal Encoded locus protein

GATA1

Xp11.23

MAO-B

Xp11.23

LAMP2

Xq24

PARK12

Xq21-q25

PASD1

Xq28

Putative function

GATA binding Regulates ALAS2 protein 1 and SNCA gene expression in addition to FECH, the catalyst for the final step of heme biosynthesis, which adds iron into protoporphyrin IX Monoamine Mitochondrial oxidase B enzymes responsible for catabolizing biogenic amines (e.g., dopamine) in brain and platelets Lysosomal glycoLysosomalassociated protein receptor membrane necessary for protein 2 protein degradation via chaperone-mediated autophagy; mutant α-synuclein blocks LAMP2 function Genetic linkage Unknown evidence suggene gests this region product is a susceptibility locus for PD PAS domain Associated with containing 1 PD

Mode of inheritance X-linked

X-linked

X-linked

X-linked

X-linked

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models show the genes most important for X-inactivation: X-ist and Tsix are paternally and maternally imprinted, respectively (Marahrens et al. 1997). However, evidence of X-linked imprinting in humans was inconclusive until the past few years. Recently, because of the increased prevalence of gene-imprinting associated diseases in babies born with assisted reproductive technology (ART) therapy, research to support a role for imprinting in humans developed. Kobayashi et al. (2009) present evidence for paternal imprinting of the X-ist gene in humans. Gene imprinting might help to explain X-linked risk of PD based on male or female descent due to the differential inactivation of X chromosome via epigenetic regulation. EVOLUTIONARY PERSPECTIVES In this final section, we situate our findings within a larger theoretical framework that draws on Haig’s well-supported kinship theory of gene imprinting, and on evolutionary approaches to aging. Based on phylogenetic comparisons, genetics of menopause and aging, the free radical theory of aging, intergenerational resource transfer models, models of maternal care and genomic imprinting, and the implication of imprinting in the etiology of numerous neuropsychiatric and neurodegenerative diseases, we suggest that aging and longevity are significantly influenced by genetic conflict. To the extent that some aspects of PD pathophysiology are linked with aberrant aging processes, PD too will be influenced by genetic conflict. We suggest age-related aspects of PD are influenced by epigenetic shifts toward loss-of-function and gain-of-function in matrilineal and patrilineal alleles, respectively. Summary of Relevant Aspects of the Kinship Theory of Gene Imprinting The kinship theory of genomic imprinting attributes imprinted expression of a locus to a conflict of interests between alleles of maternal and paternal origin (Burt and Trivers 1998; Haig 1992; Haig 2000a; Haig 2004; Haig and Westoby 1989). Conflicts arise when the expression of gene G in offspring Z exerts fitness consequences for other individuals to whom offspring Z has differential matrilineal and patrilineal coefficients of relatedness (Haig 2004; Normark 2006). That is, levels of expression of gene G in offspring Z come under selective pressure when either greater or lesser expression of G differentially affects matrilineal or patrilineal inclusive fitness. As an example and stated more concretely, genes of paternal

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origin are expected to promote increased demands on mothers throughout pregnancy, whereas genes of maternal origin are predicted to restrain fetal demand on the mother (Haig 2000b; Haig 2004). This “tug-of-war” within the developing embryo is essentially a genetic scuffle over resource withdrawal from the gravid female. Evidence comes from conditions associated with loss of either paternal or maternal imprinting, evidenced in Prader-Willi and Silver-Russell syndrome (both loss of paternal gene expression or maternal uniparental disomy) or in Angelman and Beckwith-Widemann syndrome (both due to loss of maternal gene expression or excess paternal gene function) (Eggermann, Eggermann, and Schonherr 2008; Haig 2010). In addition to fetus-mother struggles over resources, the kinship theory is relevant to all of a given individual’s interactions with asymmetric kin (Haig 1997; Haig 2000b). Table 2.4 outlines the paternal versus maternal allelic effects of known imprinted genes on growth schedules and behavior across varying life stages. The terms madumnal and padumnal are used to denote the allelic influence of maternally and paternally derived alleles within a given individual, respectively (Wilkins and Haig 2003, “Inbreeding”). In an infinitely outbreeding population, all children of a given mother have madumnal alleles perfectly in common (relatedness of 1) whereas, given paternity uncertainty (either due to serial monogamy or polygamous sexual practice), the padumnal alleles among kin of this same mother are asymmetrically related (Relatedness Range: 0–1) (Haig 2004). Gene imprinting arises when equal expression of maternally derived and paternally derived alleles fails to remain evolutionarily stable (Wilkins and Haig 2003, “Inbreeding”). Evolutionary instability and selection can drive preferential allele expression based on parent-of-origin until the allele of lesser expression becomes silenced via mechanisms of nucleic acid methylation, histone acetylation, ncRNA action, etc. In the section to follow we propose a kinship theory of genomic conflict in aging, which represents a provisional attempt to make sense of the reason (1) Parkinson’s disease exists, (2) male susceptibility is higher, and (3) why PD is primarily a condition of aging. Kinship Theory of Genomic Conflict in Aging The kinship theory of gene imprinting predicts intragenomic conflict whenever levels of gene expression exert asymmetric effects on the fitness of patrilineal and matrilineal kin. Based on models of inbreeding (Wilkins and Haig 2003a; Haig 1999), we derive the source of conflict in aging from the differential rate in change of coefficients of relatedness (padumnal

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Table 2.4 Overview of Parent-of-Origin Gene Strategies to Increase Respective Inclusive Fitness by Developmental Stages Life stage

Madumnal alleles

Prenatal [~9 months]

Retard growth schedules Promote fetal overgrowth Decrease placental blood Increase placental blood flow by elevated blood flow by reduced blood pressure to lessen nutri- pressure to promote nutrient transfer to fetus ent transfer to fetus Hasten partuition Delay partuition

Postnatal and Preweaning [~0–3 years]

Lessen intensity suckling Heighten suckling intensity Stronger appetite for Lowered appetite for supplemental foods supplemental foods Earlier weaning Prolong weaning Shorten lactational Lengthen lactational amenorrhea amenorrhea Decrease demanding Increase demanding behavior behavior

Postnatal, postweaning, and preadrenarche [~3–6 years]

Accelerate past “selfcentered,” no sharing behavior Greater acceptance of responsibility

Adrenarche, prepubescence, and pregonadarche [~6–12 years]

Decrease behaviors of Increase behaviors of social reciprocity and social reciprocity and increase in food stealing food sharing Attenuated appetite and Hyperphagia and increase slowed growth in growth rate Continued reliance on Shift from feeding from maternal “local” feeding “local” feeding pot to “communal” feeding pot pot

Pubescence and early adulthood [> 13+ years] Adulthood and postreproductive aging [~25–75 years]

Quickened shift from nonreproductive consumer to reproductive producer ?

Padumnal alleles

Prolong “self-centered,” no sharing behavior Reticence to accept responsibility

Tarried shift from nonreproductive consumer to reproductive producer ?

Note: Evidence comes from study of syndromes resulting from loss- or gain- of paternal or maternal gene function in Prader-Willi, Silver-Russell, Angelman’s, Beckwith-Widemann, and Temple syndromes.

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versus madumnal) as a function of female reproductive life spans. Modeling of empirical data on aging in humans (from recent and ancestral hunter-gatherer groups) supports our claim that intergenerational transfers predict low offspring mortality especially as female fertility declines with age (Lee 2003). To increase inclusive fitness, madumnal allele expression favors life extension beyond reproductive capacity. We suggest that PD results when epigenetic mechanisms controlling aging become dysregulated. Source of Gene Conflict in Aging Because ancestral populations did not represent infinitely outbred, genetically unrelated kin, the genetic relatedness differed by group, that is, inbreeding to some degree was occurring in all reproductive acts (Wilkins and Haig 2003b). For example, if a female mates with a related male her two alleles may have different probabilities of being present in the spermatozoa that fertilize her oocyte. Geographic structure and higher male reproductive variance imply that earlier matings by reproductively viable females tend to produce kin that are more padumnally related compared to madumnally related. In time, if a mother disperses to a new locale, older potential mates with high patrilineal relatedness die off or the group splits, then the average madumnal relatedness increases in kin bred from later stages of a given females reproductive lifespan—thus, madumnal inclusive fitness is maximized. Reciprocally, padumnal relatedness is maximized in kin bred from earlier female reproductive stages. So, inbreeding and higher male reproductive variance leads to differentially favorable padumnal inclusive fitness with maximal resource demands incurred to mothers of such kin (Haig 1999). The formalization of the model (Wilkins and Haig 2003b) is summarized below. Let rm1 and rp1 represent the average relatedness of current offspring for a female’s madumnal and padumnal alleles, respectively, and let rm2 and rp2 represent the average relatedness of future offspring for a female’s madumnal and padumnal alleles, respectively. The most favorable allocation of maternal resources, from the “perspective” of an allele, is governed by the ratio (V) of current relatedness to mean future relatedness, for madumnal (Vm = rm1/rm2) and padumnal alleles (Vp = rp1/rp2). In unbounded outbreeding population, Vm will always equal Vp (e.g., rm1 = rm2 = rp1 = rp2 = ½). Mating conditions in ancestral populations, however, were not under conditions of complete symmetry. Asymmetry arose, and consequently evolutionary selective processes, when populations exhibited size-boundedness based on the environmental carrying capacity of a given geographic locale, with male/female differences in reproductive

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variance, and with inbreeding and/or incest rates changing over the course of a female’s reproductive life. Resultantly, the rate of change in the probability of relatedness (V) differs for madumnal and padumnal alleles. Wilkins and Haig (2003b) suppose that the age-related decline in rm and rp could be modeled as an exponential decay process changing in time toward (1 + F)/2, where F is a female’s own degree of inbreeding. We find an exponential decay process to be reasonable because evidence suggests that maternal compared to paternal dispersal was likely more common in ancestral populations (Selelstad, Minch, and Cavalli-Sforza 1998). Figure 2.4 demonstrates the differential decay process between madumnal and padumal alleles, which selects for different time preferences for investment. In relation to the genetic control of aging, we will make the case that madumnal alleles will promote longevity, especially in maternal grandmothers, in order to transfer the cost of maternal investment “now” to aged-others “later”. Phylogenetic Comparisons Interbirth intervals, longevity and postreproductive life histories in humans stand in stark contrast to our most phylogenetically related species, pan troglodytes and pan paniscus (chimpanzees and bonobos, respectively). Both species from the pan genus have approximate life spans of 40 years in the wild with reproductive cycles every 6 to 8 years (Tarou et al. 2002). On average both chimpanzee and bonobo mothers can have 5–6 offspring total throughout their reproductive life. A well-supported human female can reproduce every 2–3 years over the course of a 30-year reproductive life span (~age 16 to 46), which potentially results in 10 offspring precluding multiple births. This strategy is only successful with resource transfers from “mothers to others” (preferably to those with high coefficients of madumnal relatedness). Compared to the phylogenetically related great apes, then, humans exhibit a prolonged life after reproductive viability, an extended juvenile stage of dependence, and support of resource provision by older postreproductive individuals and/or reproductively active male resource contributors (Kaplan et al. 2000). Intergenerational Transfers Human ontogenetic development is marked by a series of passages through differing patterns of resource transfer. Resource transfer occurs from placenta to embryo, breast to baby, and foraging and food pots to child. Measures of “resources” include food, warming (group thermoregulation via burning brown adipose; see Haig 2007), fanning, guarding, carrying,

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teaching, and leading. Modeling of empirical data regarding offspring mortality rates with the aforementioned resource-type provisioning from modern-day and hunter-gatherer populations demonstrates that life histories are shaped by intergenerational transfer of resources provided by post-reproductive individuals (Lee 2003). The most optimized strategy, then, for investment payoff is the intergenerational transfer effect. This explains the necessity of postreproductive survivorship and why juvenile mortality declines with age. In other words, a three-generation model of resource transfer is the only plausible explanatory device. Madumnal gene interests in longevity promote inclusive fitness by transferring the costs of resource provision for offspring to grandparents, especially grandmothers (or any other aged individuals with high madumnal relatedness coefficients). Further, relatedness asymmetries, based on the model of Wilkins and Haig (2003b) predict that reproduction by mothers at times later in their reproductive life favors madumnal inclusive fitness. Offspring from partuition later in a mother ’s reproductive lifespan will escape high mortality rates if proper provisioning of Figure 2.4

Average Relatedness 0.76

0.72

y

0.68

Average paternal relatedness curve

0.64

Average maternal related curve 0.6

0.56

Time, fraction of female’s reproductive life

0.52 0

x 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

A depiction of rate of decay of madumnal and padumnal allele relatedness across a given female’s reproductive lifespan. Note that both values decrease as the female’s mating pool includes fewer related males due to death and dispersal. Relatedness decay asymptotically approaches (1 + F)/2 given F, the female’s inbreeding coefficient (adapted from Wilkins & Haig, 2003).

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resources is available by contributors to both the “local” pot (from kin with high madumnal relatedness) and “community” pot (kin with either unknown or low relatedness to madumnal alleles). Nonparental helpers are absolutely necessary for offspring survival—for example, 11 people on average help to raise a single child in the African Ituri Forest hunter-gather society and in other instances kin relationships influence the degree to which a helper contributes to the local (increased madumnal relatedness) compared to the community resource pot (decreased madumnal relatedness) (Ivey 2000; Pashos and McBurney 2008). We suggest longevity promoted by madumnal alleles provides the occasion for grandmothers (of maternal descent most probabilistically) to provide care and food for the local pot, and grandfathers that contribute to the “community” pool. Due to paternity uncertainty, grandfathers are increasingly less likely to identify kin of higher padumnal relatedness, so contribution to the community reserves remains the most likely “average” scenario (Haig 2010). Genomic Conflict over Aging If madumnal inclusive fitness improves with later-stage reproductive efforts, then why did not selective process merely extend the reproductive lives of human females? In fact, regression models from empirical data on rates of follicular attrition predict that human female reproductive capacities should extend to 70 years (Cant and Johnstone 2008). However, a rapid drop-off in fertility occurs after the fourth decade of life largely under genetic and perhaps epigenetic control (Liu et al. 2010; Murabito et al. 2005; van Asselt et al. 2004; Voorhuis et al. 2010). Formal modeling of human reproductive life cycles shows how menopause and prolonged nonreproductive life stages decreases reproductive competition among females and boosts matrilineal inclusive fitness. The genetics underlying this process of “physiological decay in fertility” was codified in a common primate ancestral species (Thompson et al. 2007) so the genetically programmed life-extension beyond reproductive viability is a distinctly Homo feature that needs explaining. If (1) offspring from early female reproductive life cycles favors padumnal inclusive fitness and offspring from late reproductive efforts enhances madumnal inclusive fitness, (2) menopause is genetically controlled (if not epigenetically) to extinguish conditions high in maternal competitiveness, (3) madumnal alleles favor shorter interbirth intervals and padumnal alleles favor longer interbirth intervals, (4) offspring viability (especially from later reproductive female stages) requires intergenerational transfers for success, then our kinship theory of gene conflict in aging predicts

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(a) matrilineal control over enhancing longevity, (b) longevity distinctly benefiting madumnal inclusive fitness, and (c) counter strategies and antagonizing effects of aging beyond reproductive viability deriving from padumnal alleles. We propose that PD (and perhaps other neurodegenerative conditions) can result when epigenetic dysmodulation of the alleles involved in this intragenomic conflict over aging become biochemically salient to phenotypic expression. Matrilineal genetics tend to be in control of longevity, based on the most prevalent theory of aging in mammals, the free radical theory of aging (Harman 1956) and telomere-length. Evidence for the free radical theory compiled over the decades show that (1) mitochondrial production of ROS and/or damage to mtDNA increase with age, (2) introduction of chemically uncoupled or aged mitochondria into young cells leads to cellular degeneration, (3) overexpression of enzymatic controllers of ROS reduces mtDNA damage and ROS thus extending lifespan, and (4) caloric restraint reduces mitochondrial ROS production and mtDNA mutation and thus prolongs cellular life (for a review see Benz and Yau 2008; DePinho 2000). Epigenetics are implicated in the modulation of free radical production and control (Allen and Balin 1989; Guarente 2006; Hitchler and Domann 2007; Imai et al. 2000; Orr et al. 2005; Sohal and Allen 1985; Stover et al. 2000). Another mechanism involves the connection between telomere length and aging. Two of the major regulators are dyskerin (DKC1, at X28 maternally controlled) and telomerase reverse transcriptase (TERT), which is located at 5p15.33—a site flanked by 10 predicted maternally imprinted genes (Calado and Young 2009; Luedi et al. 2007). So two major longevity associated systems exhibit maternal control—via mtDNA, X-linked, and epigenetic mechanisms. Numerous genes have been associated with longevity, including, apolipoprotein epsilon (APOE, De Benedictis et al. 1997; Kervinen et al. 1994), TH (De Benedictis et al. 1998), and DDC (loss of paternal function, DeLuca et al. 2001, 2003). Variations in APOE are of interest to the present discussion because of its deep connection to the pathophysiology of neurodegenerative diseases of aging, such as Alzheimer ’s and PD (Bu 2009; Gallardo, Schluter, and Sudhof 2008; Saunders et al. 1993). And as previously discussed variations in the maternally imprinted TH and paternally imprinted DDC are associated with PD risk. Because, TH and DDC impact resource conflict at prenatal and early postnatal stages, it is interesting that an impact on aging is present. Once a given locus has been silenced, new functions acquired by that locus that maximize inclusive fitness of the controlling genome (madumnal or padumnal) can emerge as pleiotropic effects (Haig 2000a).

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Given that life extension is predominately controlled by maternally controlled gene systems, how and why might these mechanisms favor greater female versus male life expectancy? Difference in life span between males and females is a complex phenomenon because several possible environmental, epigenetic, and genetic overlapping factors can impact phenotypic outcomes. Controlling for environmental factors, quantitative genetic analyses have revealed that the genetic architecture of aging varies in males and females (Fox et al. 2006; Lai et al. 2007; Veumeulen, Bijlsma, and Loeschcke 2008). Gene imprinting, sex-linked genetics, and maternally controlled mtDNA appear to all contribute to both aging and the gender difference in longevity. Asymmetric inheritance of mtDNA from maternal lineages in humans precludes the possibility for paternal optimization processes to engage natural selective processes to increase inclusive fitness (Tower 2006). A recent study assessed the impact of X-linked genes on aging in Drosophila by analysis of sex-specific levels of transcript variation (Wayne et al. 2007). Variation in males resulted mainly from additive allele interaction, while in females allele expression was largely nonadditive and subject more to epistatic control, especially through X-associated gene regulation. As mentioned above, life span can be extended by dietary restriction (e.g., lower mitochondrial energy metabolism, thus less ROS over time). Paternally imprinted genes can lower life span with gain-of-function growth factor expression or extend life span when loss-of-function mutations ensue. For example, C. elegans, Drosophila, and murine models demonstrate that loss-of-function in insulin (paternally imprinted at 11p15) and IGF1 signaling increases life span (Chandrashekar and Bartke 2003; Partridge, Gems, and Withers 2005). Curiously, insulin-like growth factor-binding protein, acid-labile subunit (IGFALS at 16p13.3), which binds IGF,1 shows a monoallelic pattern of expression suggestive of imprinting (Sano et al. 2001). Also, elderly men exhibit increased rates of metabolism, caloric consumption, and levels of sympathetic nervous system activity, which all tend to accelerate the effects of aging (Poehlman et al. 1997). As predicted earlier, if madumnal inclusive fitness increases with longevity promotion and the creation of intergenerational transfer resource providers for later-stage partuition, then padumnal line counterstrategies to enhance inclusive fitness ought to also be apparent. We suggest that the reproductive male preference for women in younger stages of reproductive histories is such a counterstrategy because this maximizes the padumnal relatedness inherent (through inbreeding, higher male reproductive variance, and incest) in the maternal genome. This is in fact the case that human males (Buss 2006) sexual preference in “short-term investments” are young fertile females in ripe ovulatory cycles—even at a subliminal

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level perhaps communicated by pheromones (Miller, Tybur, and Jordan 2007). It is not at all obvious or given that human males should desire young females. To illustrate, bonobo males prefer mating with older, more experienced females (Small 2002). These matriarchal-based bonobo social societies are madumnally inbred, so the rate in change of relatedness asymmetries does not follow the same course as in human reproductive life spans, thus, the age-preferred sexually appetitive behavior differs. PD as a Disease of Evolutionary Conflict over Aging Our kinship theory of gene conflict in aging predicts that alleles to affect the aging process exhibit asymmetric fitness consequences for matrilineal and patrilineal kin. Dysregulation leading to loss of function of matrilinecontrolled longevity-promoting allele expression will increase PD susceptibility if the downstream biochemical phenotype involves mitochondrial dysfunction, increase in ROS, increased protein aggregation, or pro-apoptoic pathway activation. We made an extended case for the involvement of imprinted genes on PD susceptibility. The conflict over mitochondrial function is fundamental to the case that PD risk is impacted by intragenomic conflict. The seven maternally controlled subunits of complex I (via mtDNA) and one X-linked gene are juxtaposed with the at least two paternally imprinted nuclear DNA subunits (NDUFS4 and NDUFA4) pivotal for energy production. Further we demonstrated a genetic struggle over building and maintaining a dopaminergic diencephalon/midbrain, regulating behavior and longevity via the catecholaminergic system, and protein growth and restraint. We showed that females benefit most in the struggle over longevity because of the X-linked and epigenetic gene control of aging. Our kinship theory of aging predicts that this is the case because female aged helpers (especially with high madumnal relatedness to kin) are prerequisite for madumnal inclusive fitness via intergenerational transfers, which enable reproductive females to reproduce more frequently. This leaves males more susceptible to acceleration in aging either by design or as a side effect of the evolved gene system regulation over longevity. This scenario taken in consideration with the vulnerable functional haploidy that imprinted genetics involves, presents us with a theoretical tool to understand why PD even exists, and why aged males are most susceptible. CONCLUSION We situated our findings within a larger theoretical framework, the kinship theory of gene imprinting, which has successfully explained the phenotypic

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expression of dozens of imprinted genotypes. We provisionally extend the theory to include the domain of aging as a ground for intragenomic conflict. Based on phylogenetic comparisons, genetics of menopause and aging, free radical theory of aging, resource transfer models, models of maternal care and genomic imprinting, and the implication of imprinting in the etiology of numerous neuropsychiatric and neurodegenerative diseases, we suggest that not only is aging and longevity subject to the influence of gene conflict, but that PD results when the epigenetic struggle between matrilineal and patrilineal inclusive fitness becomes dysregulated—namely shifts toward loss of function and gain of function in matrilineal and patrilineal alleles, respectively. Thus, while still retaining its identity as a heterogeneous condition with complex etiopathogenesis, PD can also be studied within an evolutionary perspective for clues to evolution of aging itself. That is, PD exists as a uniquely human disease because of dysregulation in the evolutionarily recent struggle to maintain an evolutionarily stable strategy to maximize inclusive fitness of both padumnal and madumnal gene lineages.

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Chapter 3

Oxidative Stress, Mitochondrial and Insulin Signaling Dysfunction: A Redoubtable Trio in Alzheimer’s Disease Pathogenesis Sónia C. Correia, Renato X. Santos, Cristina Carvalho, Susana Cardoso, and Paula I. Moreira

Alzheimer ’s disease (AD) represents the most common form of dementia among people age 65 and older, affecting more than 35 million people worldwide and representing 50–56% of cases at autopsy and in clinical series. Clinically, AD is characterized by a progressive cognitive deterioration, together with impairments in behavior, language, and visuospatial skills, culminating in the premature death of the individual typically 3–9 years after diagnosis (Querfurth and LaFerla 2010, 329). The great majority of AD cases are sporadic in origin with a late onset, while a small proportion (< 1%) has genetic origin and involves mutations in amyloid β protein precursor (AβPP) and presenilins 1 and 2 (PS1 and PS2), leading to autosomal dominant familial AD with an early onset. Additionally, the allelic abnormalities of the apolipoprotein E (APOE) gene on chromosome 19 are responsible for both anticipated onset and increase in severity of inherited and sporadic AD (Rocchi et al. 2003, 1). Neuropathologically, AD has as main hallmarks the selective neuronal and synaptic loss, the deposition of

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extracellular senile plaques, mainly composed of amyloid-β (Aβ) peptide and the presence of intracellular neurofibrillary tangles (NFT) containing hyperphosphorylated tau protein (Selkoe 2001, 75; Moreira et al. 2006, 97; Moreira, Santos, and Oliveira 2007, 1621; Moreira et al. 2009, 741). Aβ peptide consists of 39–43 amino acid residues, derived from the proteolytic cleavage of AβPP by the β- and γ-secretases (Uemura, Kuzuya and Shimohama 2004, 1). The most common resulting fragments are either 40 or 42 amino acids in length (Aβ1-40 and Aβ1-42). Over the last decades many efforts have been made to uncover the molecular mechanisms underlying the pathogenesis of AD with several hypothesis being proposed to answer to one of the most exciting question of the actuality: What is the trigger/early event(s) of AD? In 2004, the “mitochondrial cascade hypothesis” emerged to explain many of the biochemical, genetic and pathological features of sporadic AD (Swerdlow and Khan 2004, 8). This hypothesis postulates that (1) inheritance determines mitochondrial baseline function and durability; (2) mitochondrial durability influences how mitochondria change with age; and (3) when mitochondrial alterations reaches a threshold, AD histopathology and symptoms ensue (Swerdlow and Khan 2009, 308). In addition, mitochondria have been shown to be targets of the deleterious effects of Aβ (LaFerla, Green, and Oddo 2007, 499), potential sites of Aβ production (Hansson et al. 2004, 654; Keil et al. 2004, 50310) and triggers of the disease (Nunomura et al. 2001, 759; Pratico et al. 2001, 4183; Hauptmann et al. 2009, 1574), which provide stronger evidence supporting the “mitochondrial cascade hypothesis.” Thus, it is plausible that mitochondrial-dependent pathogenic mechanisms have a central stage in the onset and progression of AD (Figure 3.1). Oxidative stress has also been implicated in the pathogenesis of AD occurring prior to the onset of symptoms, the oxidative changes being pervasive throughout the body and detected peripherally (Ghanbari et al. 2004, 41; Moreira, Harris, et al. 2007, 195; Perry et al. 2003, 552) and associated with the vulnerable regions of the brain affected in disease (Nunomura et al. 1999, 1959; Nunomura et al. 2001, 759). The complex nature and genesis of oxidative damage in AD could be the result of mitochondrial abnormalities that can trigger oxidative stress. Interestingly, also disturbances in insulin metabolism, especially insulin resistance, have been suggested to be involved in AD, attributing a role to the disruption of insulin signaling in AD pathophysiology (Moreira, Santos, et al. 2007, 1621; Cardoso et al. 2009, 483). In light of this evidence, the present review is devoted to discuss the current knowledge concerning the role of oxidative stress, mitochondria, and insulin signaling deregulation in the onset and progression of AD.

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Figure 3.1

Mitochondrial dysfunction in Alzheimer ’s disease. Mitochondrial dysfunction is intimately involved in the pathogenesis of AD. Amyloid β peptide (Aβ) has been documented to impair the activity of respiratory chain complex IV, leading to increased reactive oxygen species (ROS) levels. Indeed, Aβ peptide was shown to be imported into mitochondria via the translocase of the outer membrane (TOM) import machinery and localized to mitochondrial cristae, thus promoting mitochondrial dysfunction and oxidative damage. Furthermore, Aβ also interacts with cyclophilin D, a critical molecule involved in mitochondrial permeability transition pore (MPTP) formation and cell death. The opening of MPTP leads to the release of pro-apoptotic factors such as cytochrome c (Cyt c) to the citosol, and consequently to the induction of the apoptotic cell death. Additionally, mitochondrial DNA (mtDNA) mutations have also been implicated in mitochondrial dysfunction that occurs in AD. ADP- adenosine diphosphate; Cyt c- cytochrome c; IM- inner membrane; IMSintermembrane space of mitochondria; NAD+- oxidized nicotinamide adenine dinucleotide; NADH- reduced nicotinamide adenine dinucleotide; H+- proton; OM- mitochondrial outer membrane.

Unraveling the mechanisms involved in the etiopathogenesis of AD could provide new insights that can be translated to potential pharmacological interventions aimed to treat this neurodegenerative disease.

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OXIDATIVE STRESS AND MITOCHONDRIAL DYSFUNCTION AS TRIGGERS OF NEURODEGENERATION IN ALZHEIMER’S DISEASE Mitochondria are ubiquitous and dynamic organelles that house many crucial cellular processes in eukaryotic organisms being considered “gatekeepers of life and death.” Major functions of mitochondria include the production of over 90% of cellular ATP through the tricarboxylic acid cycle (TCA) cycle and oxidative phosphorylation, regulation of intracellular calcium (Ca2+) and redox signaling and the arbitration of apoptosis (Green and Kroemer 2004, 626; Beal 2005, 495; Mattson, Gleichmann, and Cheng 2008, 748). Therefore, the importance of mitochondria for neuronal function and survival is notorious since neurons are cells with extremely high energy demands, mitochondrial oxidative phosphorylation being essential for neurons to meet their high energy requirements. In line with this, neurons are very vulnerable to bioenergetic crisis and dysfunction of mitochondrial machinery (Murphy, Fiskum, and Beal 1999, 231; Moreira et al. 2009, 741). Indeed, dysfunction of mitochondrial energy metabolism culminates in ATP production and Ca2+ buffering impairment, and exacerbated generation of reactive oxygen species (ROS) (Beal 2005, 495). ROS, in turn, cause cell membranes damage through lipid peroxidation and accelerates the high mutation rate of mitochondrial DNA (mtDNA). Additionally, accumulation of mtDNA mutations enhances oxidative damage, causes energy crisis and increases ROS production, in a vicious cycle (Petrozzi et al. 2007, 87). Moreover, the brain is especially prone to oxidative stress-induced damage due to its high levels of polyunsaturated fatty acids, high oxygen consumption, high content in transition metals and poor antioxidant defenses (Nunomura et al. 2006, 82323). The next subsections explore the role of oxidative stress in AD as well as the contribution of mitochondrial malfunctions to the pathophysiology of the disease. Oxidative Stress: A Critical Player in Alzheimer ’s Disease For a long time, oxidative stress was defined as the imbalance between the formation of ROS and the antioxidant defense mechanisms. Meanwhile, a new concept of oxidative stress emerged to account for two different mechanistic outcomes, macromolecular damage and disruption of thiol redox circuits, which lead to aberrant cell signaling and dysfunctional redox control (Jones 2006, 1865). Increased oxidative stress has been observed in age and age-related neurodegenerative diseases, mitochondria being both targets and sources of ROS (Lin and Beal 2006, 787). In

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fact, accumulating evidence demonstrates that oxidative damage marked by high levels of lipid, protein, and nucleic acid oxidation is increased in vulnerable neurons in AD (Castellani et al. 2001, 175; Nunomura et al. 1999, 1959; Nunomura et al. 2001, 759; Smith et al. 1997, 2653; Straface et al. 2005, 2759). Nucleic acid oxidation is marked by increased levels of 8-hydroxy-2-deoxyguanosine (8-OHdG) and 8-hydroxyguanosine (8-OHG) (Nunomura et al. 1999, 1959; Nunomura et al. 2001, 759). Protein oxidation is marked by elevated levels of protein carbonyl and widespread nitration of tyrosine residues in the susceptible neurons (Smith et al. 1996, 120; Smith et al. 1997, 2653). Lipid peroxidation is marked by higher levels of thiobarbituric acid reactive substances (TBARS), malondialdehyde (MDA), 4-hydroxy-2-nonenal (HNE), isoprostanes and altered phospholipid composition (Sayre et al. 1997, 2092). Also, modifications to sugars are observed via increased glycoxidation and glycation (Smith et al. 1994, 5710; Smith et al. 1995, 172) that are responsible for the formation of advanced glycation endproducts (AGEs) such as Nε-(carboxymethyl) lysine (CML), pentosidine and pyralline. Additionally, it has been proposed that oxidative stress precedes all the other pathological hallmarks of AD pathogenesis. Indeed, the secretion and deposition of Aβ within vulnerable AD neurons have been suggested to be compensatory mechanisms developed by cells to protect themselves against oxidative damage (Hayashi et al. 2007, 1552; Nakamura et al. 2007, 12737; Smith et al. 2002, 1194). Accordingly, Aβ was demonstrated to follow the appearance of oxidative stress markers in AD (Petersen et al. 2007, 143) and it was shown that this peptide protects lipoproteins from oxidation in cerebrospinal fluid and plasma (Atwood et al. 1998, 12817; Atwood et al. 2003, 249–266; Cuajungco et al. 2000, 19439; Kontush et al. 2001, 119). Similarly, in AβPP transgenic mouse models of AD (Tg2576), it was also observed that oxidative stress appears before than Aβ deposition (Pratico et al. 2001, 4183; Smith et al. 1998, 2212). In light of this evidence, Zhu and colleagues proposed the “Two-Hit hypothesis,” which postulates that the early and progressive oxidative damage to neurons elicits a compensatory response such that the cell can exist in the overly oxidizing environment. Furthermore, this “oxidative steady state,” with the initial purpose to afford protection, makes the cell more vulnerable to additional insults, such as Aβ deposition and NFT formation (Zhu et al. 2001, 39; Zhu et al. 2004, 219; Zhu et al. 2007, 494). Cellular oxidative damage also promotes cell cycle aberration and tau hyperphosphorylation, leading to the NFT formation (Castegna et al. 2002, 1524; Castegna et al. 2003, 1394; Lee et al. 2004, 1; Lee et al. 2005, 164; Mark

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et al. 1997, 255). Consequently, damaged cells succumb to the degenerative process, or exist in a dysfunctional state, the ultimate manifestation of which is the cognitive decline and dementia descriptive of AD. Along with increased oxidative damage, impaired antioxidant defenses have also been proposed to be prominent features of AD (Smith et al. 1997, 2653; Straface et al. 2005, 2759). Indeed, decreased activities of the antioxidant enzymes copper/zinc superoxide dismutase (Cu/ZnSOD) and catalase (CAT) were found in the frontal and temporal cortex of AD patients (Marcus et al. 1998, 40). The total antioxidant capacity was also significantly decreased in AD as well as in mild cognitive impairment (MCI) but not in patients with vascular dementia (Straface et al. 2005, 2759), being shown a negative correlation between the total antioxidant capacity and disease duration, in AD patients (Guidi et al. 2006, 262). It has also been proposed that oxidative stress-mediated neuronal loss could be initiated by a decline in glutathione (GSH), which acts as a scavenger of free radicals and is the most abundant thiol-reducing agent in mammalian tissues (Bains and Shaw 1997, 335). In fact, altered GSH levels were observed in specific regions of the central nervous system of AD patients (Gu et al. 1998, 24). Similarly, it was found a reduced GSH content in lymphoblasts carrying AβPP, PS1, and PS2 gene mutations when compared to controls (Cecchi et al. 1999, 152). More recently, it has been reported that erythrocytes of AD and MCI patients present a decrease in GSH levels and GSH/ GSSG ratio compared to age-matched control subjects (Bermejo et al. 2008, 162). Accordingly, a study from our laboratory showed low levels of GSH in the triple transgenic model of AD (3xTg-AD), accompanied by a decrease in vitamin E levels and high levels of lipid peroxidation (Resende et al. 2008, 2051). Mitochondrial abnormalities have also been implicated in the etiopathogenesis of AD, which can be triggered by oxidative disturbances. Compelling evidence demonstrates that AD patients present reduced metabolic activity, which is believed to be the result of oxidative damage to vital mitochondrial components (Aksenov et al. 1998, 151; Aliev et al. 2003, 209–238; Anderson, Cummings, and Cotman 1994, 286; Hirai et al. 2001, 3017). As mentioned above, besides being essential ATPproducing organelles, mitochondria are also one of the major intracellular sources of potentially pathogenic ROS, including hydrogen peroxide (H2O2), hydroxyl (HO–) and superoxide (O2–), particularly in highly metabolically active organs such as the brain (Wallace 1999, 1482). Excessive mitochondrial ROS generation damages several cellular targets including mitochondrial components themselves (lipids, proteins, and DNA) (Moreira et al. 2009, 741). Indeed, mitochondrial ROS induce mutations

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in the mitochondrial DNA (mtDNA), which in turn impair the oxidative phosphorylation system. This impairment results in an exacerbation of ROS generation, promoting the augment of the number of mtDNA mutations in a vicious positive feedback cycle (Fukui and Moraes 2008, 251). Also the lack of histones in mitochondrial DNA (mtDNA) and diminished capacity for DNA repair render mitochondria an easy target to oxidative stress events (Moreira et al. 2009, 741). Overall, oxidative stress-induced cellular damage has been long recognized as a culprit in degenerative processes that occur in AD. The next subsection is devoted to explore the intimate connection between mitochondrial impairment and oxidative stress. Mitochondrial Anomalies and Oxidative Stress: Side by Side in Alzheimer ’s Disease Accumulating data from in vitro, in vivo and human studies argue that mitochondrial dysfunction and bioenergetics failure are early events implicated in AD pathogenesis (Moreira et al. 2010, 2) (see Figure 3.1). Impaired activities of the three key TCA enzyme complexes, pyruvate dehydrogenase (PDH), isocitrate dehydrogenase, and α-ketoglutarate dehydrogenase (KGDH) have been documented in postmortem AD brain and fibroblasts from AD patients (Huang et al. 2003, 309; Bubber et al. 2005, 695). Data from our laboratory also demonstrated that the levels of both PDH and KGDH are decreased in AD brains (Moreira et al. 2007, “Autophagocytosis of mitochondria,” 525). Furthermore, Bubber and collaborators (2005, 695) tested whether impairments in TCA cycle enzymes correlate with disability in AD brains. The authors observed that all the changes in TCA cycle activities (specifically that of PDH complex) correlated with the clinical state, suggesting a coordinated mitochondrial alteration (Bubber et al. 2005, 695). These enzymes are known to be highly susceptible to oxidative modification and are altered by exposure to a range of pro-oxidants (Tretter and Adam-Vizi 2000, 8972). In addition, a decline in respiratory chain complexes I, III, and IV activities was found in platelets and lymphocytes from AD patients and postmortem AD brain tissue (Kish et al. 1992, 776; Parker et al. 1994, 1086; Bosetti et al. 2002, 371; Valla et al. 2006, 323), further emphasizing that mitochondrial abnormalities are present at the earliest symptomatic stages of the disease. Similarly, in vitro studies demonstrated that pheochromocytoma cells (PC12) exposed to Aβ1-40 and Aβ25-35 present mitochondrial dysfunction characterized by the inhibition of complexes I, III, and IV of the mitochondrial respiratory chain (Pereira, Santos and Oliveira 1998, 1749).

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More recently, Fattoretti and collaborators (2009), in order to establish a link between AD and mitochondrial dysfunction, investigated succinic dehydrogenase (SDH) (mitochondrial respiratory complex II) activity in mitochondria of hippocampal CA1 pyramidal neurons obtained from 3xTg-AD mice. The authors observed a decreased density (number of mitochondria/μm3 of cytoplasm) of SDH-positive mitochondria in 3xTg-AD mice. Data from our laboratory also revealed that AD fibroblasts present high levels of oxidative stress and apoptotic markers when compared with young and age-matched controls. Moreover, AD-type changes could be generated in control fibroblasts using N-methylprotoporphyrin to inhibit cytochrome c oxidase (COX) assembly, which indicates that the observed oxidative damage was associated with mitochondrial dysfunction. Additionally, the effects promoted by the N-methylprotoporphyrine were reversed or attenuated by lipoic acid and N-acetyl cysteine (Moreira, Harris, et al. 2007, 195). Overall, these findings suggest that mitochondria are important in oxidative damage that occurs in AD and that antioxidant therapies may be promising. mtDNA mutations have also been implicated in mitochondrial dysfunction in the pathogenesis of AD (see Figure 3.1). For instance, 20 point mutations were detected in the mitochondrial-encoded cytochrome c oxidase subunits I, II, and III genes in AD patients (Hamblet et al. 2006, 398). Qiu and collaborators (2001, 261) also identified two missense mutations in the mtDNA of COX in a patient with AD. Further, a high aggregate burden of somatic mtDNA mutations was observed in postmortem brain tissue from AD patients (Lin et al. 2002, 133; Coskun, Beal and Wallace, 2004, 10726). Accumulating evidence also indicates that Aβ and AβPP could directly target mitochondria. For instance, Aβ was found to impair cellular respiration, energy production, and mitochondrial electron chain complexes activity in human neuroblastoma cells (Rhein, Baysang, et al. 2009, 1063). Moreover, cultured neurons isolated from Tg mice that overexpress a mutant form of AβPP and Aβ-binding alcohol dehydrogenase (ABAD) (Tg mAβPP/ABAD) display spontaneous generation of H2O2 and O2–, decreased ATP, release of cytochrome c and induction of caspase 3-like activity followed by DNA fragmentation and loss of cell viability. Furthermore, generation of ROS is associated with dysfunction at the level of COX (Takuma et al. 2005, 597). Similarly, Crouch and colleagues (2005, 672) found that Aβ1-42 can disrupt mitochondrial COX activity in a sequenceand conformation-dependent manner. In an in vitro study designed to explore the effect of the AβPP Swedish double mutation (K670M/N671L) on oxidative stress-induced cell death mechanisms in PC12 cells, increased activity of caspase 3 was observed due to an enhanced activation of both

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intrinsic and extrinsic apoptotic pathways, including activation of the JNK pathway. Moreover, apoptosis was attenuated by SP600125, a JNK inhibitor, through protection of mitochondrial dysfunction and reduction of caspase 9 activity (Marques et al. 2003, 28294). These findings corroborate the hypothesis that the massive neurodegeneration at an early age in familial AD patients could be a result of an increased vulnerability of neurons through the activation of different apoptotic pathways as a consequence of elevated levels of oxidative stress. In addition, mitochondrial dysfunction was also linked to the accumulation of full-length and carboxy-terminally truncated AβPP across mitochondrial import channels in brain tissue from AD patients. The authors observed that this accumulation of AβPP inhibited the entrance of nuclear-encoded COX subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased H2O2 levels (Devi et al. 2006, 9057). Similarly, Anandatheerthavarada and colleagues (2003) reported an accumulation of full-length AβPP in the mitochondrial compartment in a transmembrane-arrested form that impaired mitochondrial functionality and energy metabolism. Also, a progressive accumulation of Aβ monomers and oligomers was detected within the mitochondria of both transgenic mice overexpressing mutant AβPP and postmortem brain from AD patients (Caspersen et al. 2005, 2040; Crouch et al. 2005, 672; Devi et al. 2006, 9057; Manczak et al. 2006, 1437). A direct link between Aβ-induced toxicity and mitochondrial dysfunction in AD pathology has been suggested by the interaction between mitochondrial Aβ and ABAD (Yan and Stern 2005, 161; Lustbader et al. 2004, 448). Moreover, this interaction was found to induce mitochondrial failure via changes in mitochondrial membrane permeability and a reduction in the activities of enzymes involved in mitochondrial respiration (Lustbader et al. 2004, 448). More recently, Hansson Petersen and collaborators (2008, 13145) showed that Aβ peptide is imported into mitochondria via the translocase of the outer membrane (TOM) import machinery and localized to mitochondrial cristae (see Figure 3.1). Thus, it has been proposed that Aβ species transport to mitochondria cause mitochondrial dysfunction and oxidative damage, and consequently damage neurons both structurally and functionally (Caspersen et al. 2005, 2040; Crouch et al. 2005, 672; Devi et al. 2006, 9057; Manczak et al. 2006, 1437; Hansson Petersen et al. 2008, 13145). Previous studies from our laboratory also reported an increased susceptibility to mitochondrial permeability transition pore (MPTP) induction promoted by Aβ peptides (Moreira et al. 2001, 789; Moreira et al. 2002, 257) (Figure 3.1). In accordance, it was provided a plausible mechanism underlying Aβ-induced mitochondrial dysfunction, in which Aβ interacts with cyclophilin D, a critical

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molecule involved in MPTP formation and cell death. Du and collaborators (2008) showed that the interaction of cyclophilin D with mitochondrial Aβ potentiates mitochondrial, neuronal and synaptic stress. Conversely, cyclophilin D ablation protects neurons from Aβ-induced MPTP formation and the resultant mitochondrial and cellular stresses. Additionally, cyclophilin D deficiency substantially improves learning and memory and synaptic function in an AD mouse model and alleviates Aβ-mediated reduction of long-term potentiation (LTP) (Du et al. 2008, 1097). Another study reported that the presequence protease (PreP) is responsible for the degradation of the accumulated Aβ in mitochondria, further supporting the association of Aβ with mitochondria and mitochondrial dysfunction in AD (Falkevall et al. 2006, 29096). However, the key role of mitochondria in AD pathogenesis was recently highlighted, as well as the close interplay of this organelle with the two main pathological features of the disease. Rhein, Song, and collaborators (2009, 20057) demonstrated that Aβ and tau synergistically impair mitochondrial function and energy homeostasis in 3xTg-AD mice. Accordingly, a previous study demonstrated that Tg mice overexpressing the P301L mutant human tau protein present alterations of metabolism-related proteins including mitochondrial respiratory chain complexes, antioxidant enzymes and synaptic proteins that are associated with increased oxidative stress. Moreover, mitochondria from these Tg mice displayed increased vulnerability toward Aβ insult, which reinforce a possible synergistic action of tau and Aβ pathology on the mitochondria. The authors also suggest that tau pathology involves a mitochondrial and oxidative stress disorder possibly distinct from that caused by Aβ (David et al. 2005, 23802). These findings may contribute to a better understanding of the biochemical pathways underlying mitochondrial dysfunction in AD and may help lead to the development of novel mitochondrial-targeted therapeutic strategies. Ultrastructural alterations in mitochondrial morphology such as reduced size and broken internal membrane cristae were also documented in brains from AD patients (Hirai et al. 2001, 3017; Baloyannis 2006, 119). One reasonable explanation for these observations could be the increased mitochondrial autophagy found in AD (Moreira et al. 2007, “Autophagocytosis of mitochondria,” 525; Moreira et al. 2007, “Increased autophagic degradation,” 614). Another consequence of Aβ on mitochondria is the induction of dynamic changes, including mitochondrial fission/fusion perturbations. Wang and collaborators (2008, “Dynamin,” 470) reported abnormal mitochondrial fission and fusion in fibroblasts from sporadic AD patients, marked by lower levels of dynamin-related protein 1 (Drp1), a key regulator of mitochondrial fission. The authors also observed that AD fibroblasts display elongated

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mitochondria which form collapsed perinuclear networks (Wang et al. 2008, “Dynamin,” 470; Wang et al. 2009, “Role of abnormal mitochondrial dynamics,” 153). Accordingly, AβPP overexpression in M17 neuroblastoma cells resulted in predominantly fragmented mitochondria, decreased Drp1 and optic atrophy protein 1 (OPA1) levels, and a defect in neuronal differentiation (Wang et al. 2008, “Amyloid-beta overproduction,” 19318). Moreover, reduced expression levels of Drp1, OPA1, mitofusin (Mfn)1 and 2 and increased mitochondria fission protein 1 (Fis1) levels were found in hippocampal tissues from AD patients compared with age-matched controls (Wang et al. 2009, “Impaired balance,” 9090). These results suggest that AD is characterized by mitochondrial fission/fusion imbalance, and consequently mitochondrial fragmentation and abnormal distribution, which potentiates mitochondrial and neuronal dysfunction in this neurodegenerative disease. BRAIN GLUCOSE TRANSPORT AND METABOLISM AND INSULIN SIGNALING DEREGULATION IN ALZHEIMER’S DISEASE Glucose is the main source of energy required for normal brain function. Since neurons are incapable to synthesize or store glucose, they are dependent on glucose transport across the blood-brain barrier (BBB), which is mediated by glucose transporters (GLUTs) (Scheepers, Joost, and Schurmann 2004, 364). An impairment of glucose metabolism in the brain of AD patients has been observed by positron emission tomography (PET) imaging studies (Azari et al. 1993, 438; Small et al. 1996, 70; Davis et al. 1997, 4526). Moreover, this impairment seems to be a cause, rather than a consequence, of neurodegeneration in AD (Hoyer 2004, 541). Insulin-mediated neuronal insulin receptor (IR) and/or insulin-like growth factor-1 receptor (IGF-1R) activation (Kahn et al. 1993, 291; Noh et al. 1999, 263) underlies a complex and important role in the regulation of brain metabolism (Gasparini and Xu 2003, 404; Santos, Pereira, and Carvalho 1999, 33; Shah and Hausman 1993, 151; Yang, Raizada, and Fellows 1981, 1050), neuronal growth and differentiation (Schechter et al. 1998, 270; Gasparini and Xu 2003, 404; Plitzko, Rumpel, and Gottmann 2001, 1412), or neuromodulation (Gasparini and Xu 2003, 404; Kremerskothen et al. 2002, 153; Shuaib et al. 1995, 809; Vilchis and Salceda 1996, 1167). Originally, the brain was believed to be an insulin-insensitive organ. However, biochemical evidence for the presence of insulin and IRs in the brain and expression of insulin-sensitive GLUT-4 in neurons confirm the idea that the brain is in fact a target organ for insulin (El Messari et al.

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2002, 225). Indeed, IRs are widely expressed throughout the brain in both neurons and glia (Wozniak et al. 1993, 1), with the highest levels in the olfactory bulb, cerebral cortex, hippocampus, cerebellum, and hypothalamus (Havrankova, Roth, and Brownstein 1978, 636; Van Houten et al. 1979, 666). In the adult brain, insulin derives primarily from its synthesis in pancreatic β-cells, being transported by cerebrospinal fluid (CSF) into the brain. This transport occurs mainly via a carrier-mediated, saturable, regulatable, and temperature-sensitive active process (Erol 2008, 241; Salkovic-Petrisic and Hoyer 2007, 217; Banks 2004, 5; Burns et al. 2007, 1094). Additionally, previous studies showed that insulin could be synthesized de novo in the brain. This idea was confirmed by the observation of the existence of preproinsulin I and II mRNA within rat fetal brain and in cultured neurons, and insulin immunoreactivity in the endoplasmatic reticulum (ER), Golgi apparatus, cytoplasm, axon, dendrites and synapses of neuronal cells (Adamo, Raizada, and LeRoith 1989, 71; Craft et al. 1996, 123; Schechter et al. 1996, 16; Schechter et al. 1998, 270; Zhao et al. 1999, 34893). This was further supported by the high levels of insulin detected in brain extracts (Havrankova et al. 1979, 636), the presence of insulin in immature nerve cell bodies (Schechter et al. 1992, 27; Schechter et al. 1996, 16), the observation that, despite peripherally injected, insulin can enter the CSF rapidly (Freude et al. 2005, 3343), and that less than 1% of the hormone crosses the BBB in dogs and rodents (Banks and Kastin 1998, 883). Hoyer (2004, 135) suggested that the impaired glucose utilization observed in AD brains is a consequence of diminished glucose breakdown in brain tissue, caused by a disturbance in the control of glucose utilization at the level of insulin signal transduction. Furthermore, it has been shown that type 2 diabetes mellitus is a risk factor for AD and that AD patients have a higher risk to develop type 2 diabetes (Cole and Frautschy 2007, 10; Moreira et al. 2009, 741). More recently, it was proposed that AD can be an “insulin-resistant brain state” or even a “type 3 diabetes” (Rivera et al. 2005, 247; Steen et al. 2005, 63; Craft et al. 1998, 164). Indeed, it was observed an age- and AD-related decrease in insulin mRNA and protein levels (Lester-Coll et al. 2006, 13; Rivera et al. 2005, 247; Steen et al. 2005, 63), IR and IGF-1R expression (Frolich et al. 1999, 290; Moloney et al. 2010, 224), insulin receptor substrate-1 (IRS-1) and IRS-2 levels and phosphatidylinositol 3-kinase (PI3-K) and extracellular-regulated kinase 1/2 (ERK1/2) activities. Furthermore, AD patients show increased fasting plasma insulin levels, decreased CSF insulin levels, and/or decreased CSF/plasma insulin ratio, besides increased Aβ levels (Watson and Craft 2004, 97), suggesting a decrease in insulin clearance, which may provoke

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an elevation of plasma Aβ levels (Li and Holscher 2007, 384). It was also found that insulin modulates AβPP processing both in vivo and in vitro. Insulin has also been proposed to increase the extracellular concentration of Aβ by two independent mechanisms: stimulation of Aβ secretion by the enhancement of its trafficking from the ER and trans-Golgi network, the main site for Aβ generation, to the plasma membrane, which significantly reduces the intracellular concentration of Aβ derivatives (Aβ40 and Aβ42); or inhibition of extracellular degradation of Aβ by insulin-degrading enzyme (IDE), a metalloprotease enzyme responsible for insulin degradation and is also the main soluble Aβ degrading enzyme at neutral pH (Gasparini et al. 2001, 2561). This last hypothesis is supported by (1) a decrease in IDE activity and mRNA and protein levels in the AD brain; (2) impaired brain Aβ and insulin degradation in knockout mice lacking IDE (Frolich et al. 1999, 290; Hong and Lee 1997, 19547; Lucas et al. 2001, 27); (3) increased IDE immunoreactivity around senile plaques; and (4) enhanced IDE activity in IDE and AβPP double transgenic mice associated with a decrease in Aβ and prevention of AD (Leissring et al. 2003, 1087). Meanwhile, brain IR does not desensitize, thus IDE may constitute a negative feedback loop that controls insulin action (van der Heide, Ramakers, and Smidt 2006, 205; Zhao et al. 2004, 71). Since AD has been recognized as an “insulin-resistant brain state,” the intracerebroventricular (icv) injection of diabetogenic streptozotocin (STZ) has been shown to produce neurochemical and brain glucose metabolism changes, as well as long-term and progressive deficit in learning, memory, and cognitive behavior, that resemble those found in the brain of patients with AD (Grünblatt et al. 2007, 757; Salkovic-Petrisic and Hoyer 2007, 217). It was found that icvSTZ administration promotes a significant decrease in IRs expression in cortex and hippocampus, insulin-1 mRNA in hippocampus, insulin-2 mRNA in cortex and a significant increase of tau phosphorylation in the hippocampus, these alterations being associated with the impairment of memory and learning. These findings suggest that alterations of neuronal insulin signaling severely affect learning and memory processes. Additionally, icvSTZ administration was shown to induce brain atrophy, mainly due to neuronal and oligodendroglial cell loss mediated by apoptosis, mitochondrial dysfunction, neuroinflammation, and oxidative stress (Lester-Coll et al. 2006, 13). Furthermore, icvSTZ administration causes abnormalities in brain glucose metabolism, including reduction of glucose utilization in 17 of 35 brain areas (Duelli et al. 1994, 737) and decreased activities of glycolytic enzymes, leading to a decline in the levels of energy-rich compounds, ATP and creatine phosphate (Lannert and

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Hoyer 1998, 1199). De la Monte and collaborators (2006, 89) also reported an increase of AβPP and acetylcholinesterase expression, GSK-3β activity, phospho-tau and ubiquitin levels and decreased expression of choline acetyltransferase in icvSTZ-treated animals. Collectively, these findings support the idea that dysfunctional insulin signaling is critically involved in the pathogenesis of AD. Disturbance of tau phosphorylation seems to be another mechanism by which insulin is implicated in AD pathology. Indeed, insulin has been shown to activate the major kinases involved in tau phosphorylation, including glycogen synthase kinase 3β (GSK-3β), ERK1/2 and cyclindependent kinase 5 (Cdk-5) (Li and Hölscher 2007, 384; de la Monte and Wands 2005, 23802; van der Heide, Ramakers, and Smidt 2006, 205). Conversely, it has been reported that insulin and IGF-1 also inhibit abnormal tau hyperphosphorylation by stimulating Akt-induced phosphorylation/ inactivation of GSK-3β in both human and animal neurons (Li and Holscher 2007, 384; de la Monte and Wands 2005, 23802; Moloney et al. 2010, 224; Hong and Lee 1997, 19547). Accordingly, it was previously shown that insulin and IGF-1 reduce tau phosphorylation promoting its binding to microtubules by inhibition of GSK-3β via the PI3-K pathway (Ho et al. 2004, 902). In primary cortical neurons it was also observed that insulin or IGF-1 transiently increases phosphorylation of specific tau residues by activation of GSK-3β (Lesort and Johnson 2000, 305). Thus, disturbed insulin and/or IGF-1 signaling pathways could potentiate abnormal tau hyperphosphorylation leading to NFT formation (Cheng et al. 2005, 5086). Furthermore, hyperphosphorylated tau fails to be transported into axons, accumulating and aggregating into NFTs in neuronal perikarya, which promote mitochondrial dysfunction, oxidative stress, apoptotic or necrotic death (de la Monte and Wands 2005, 45). FINAL REMARKS Oxidative stress and mitochondrial abnormalities have been proposed to play a central role in the pathogenesis of AD. Mitochondria are pivotal in controlling cell survival and death, since they generate the majority of cellular ATP, buffer intracellular Ca2+, integrate apoptotic signaling pathways and represent one of the major sources of pathogenic ROS. Thus, perturbations in the physiological functions of these organelles inevitably culminate in oxidative damage and disturbed mitochondrial and neuronal function. Oxidative stress, in turn, potentiates mitochondrial and neuronal dysfunction in a vicious cycle. Converging evidence also posits that impaired brain glucose metabolism and abnormalities in the insulin

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Chapter 4

Alzheimer’s Disease: Increased Neurogenesis and Possible Disease Mechanisms Related to Neurogenesis Philippe Taupin

Alzheimer ’s disease (AD) is a neurodegenerative disease for which there is no cure. Aging is the major contributing factor for the increased risk of developing AD. The risk of developing AD doubles every 5 years after the age of 65 and the disease affects more 30% of individuals over the age of 80 (Ferri et al. 2006). There are two forms of the disease, the late-onset AD (LOAD) and the early-onset AD (EOAD). LOAD is diagnosed after the age of 65 and most cases of LOAD are sporadic forms of the disease. LOAD is the most common form of the disease, accounting for over 93% of all cases of AD (Burns, Byrne, and Maurer 2002). EOAD is diagnosed at younger than 65 and most cases of EOAD are inherited forms of AD or familial Alzheimer ’s disease (FAD). It is a rare form of the disease. Genetic, acquired, and environmental risks factors are believed to be causative factors for LOAD, whereas genetic inherited mutations are causal factors for EOAD (Zilka, Ferencik, and Hulin 2006). Among the genetic factors that are established risk factors for LOAD is the presence of certain alleles of the apolipoprotein E gene (ApoE) in the genetic makeup of the individual. These risk factors increase the probability of developing AD. Mutations causative for EOAD concern a number of genes, some of which have been characterized. These genes are referred to as familial Alzheimer genes, among which is the gene of beta-amyloid precursor protein (APP). About 200 families in the world

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carry genetic mutations that lead to the development of the disease. Rare cases of sporadic form of EOAD occur, with no family history and no identified causal genetic mutations. The diagnosis of AD is primarily performed by symptoms, like cognitive impairments and behavioral changes, and by the assessments of risk factors (Dubois et al. 2007; Patterson et al. 2008). The average life expectancy of patients diagnosed with AD is 8.5 years. Current treatments consist in drug and occupational therapies (Scarpini, Scheltens, and Feldman 2003). Recent advances in adult neurogenesis and neural stem cell (NSC) research open new opportunities for our understanding of and for developing new treatments and cures for AD. Neurogenesis, the generation of nerve cells, occurs in the adult brain and NSCs reside in the adult central nervous system (CNS) of mammals, including in humans (Gage 2000). NSCs are the self-renewing multipotent cells that have the ability to give rise to the main phenotypes of the nervous system, nerve cells, astrocytes, and oligodendrocytes. In the adult brain, neurogenesis occurs primarily in two regions, the dentate gyrus (DG) of the hippocampus and the subventricular zone (SVZ) along the ventricles (Eriksson et al. 1998; Taupin 2006; Curtis et al. 2007). In the DG, newly generated neuronal cells in the subgranular zone (SGZ) migrate to the granule cell layer, where they differentiate into granule-like cells and extend axonal projections to the CA3 region of the Ammon’s horn (Gould et al. 1998; Taupin 2009, “Characterization”). Newly generated neuronal cells in the anterior part of the SVZ migrate through the rostro-migratory stream to the olfactory bulb, where they differentiate into interneurons (Lois and Alvarez-Buylla 1994; Doetsch and Alvarez-Buylla 1996). It is postulated that newly generated neuronal cells in the adult brain originate from NSCs. Because of their potential to generate the main phenotypes of the nervous system, NSCs represent a promising model for cellular therapy for treating a vast array of neurological diseases and injuries, and particularly neurodegenerative disease like AD (Taupin 2008, “Adult neural stem cells”). The stimulation of locally endogenous neural progenitor or stem cells in the adult brain or the transplantation of neural progenitor and stem cells, isolated from the adult brain and propagated in vitro, are proposed to repair and restore the degenerated or injured nerve pathways. The confirmation that adult neurogenesis occurs in the adult brain and NSCs reside in the adult CNS, reveals that the adult brain may be amenable to repair. The contribution of adult neurogenesis and newly generated neuronal cells to the physiopathology and functioning of the nervous system remains the source and center of intense interest and research. Reports show that neurogenesis is enhanced in the brain of patients with AD (Jin, Peel, et al. 2004). Aneuploidy would underlie the process of

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neurodegeneration and amyloid formation. The process of adult neurogenesis holds the potential to generate populations of cells that are aneuploids, particularly in the hippocampus. Do adult neurogenesis and NSCs contribute to the pathology of neurological diseases like AD? Do adult neurogenesis and newly generated neuronal cells of the adult brain contribute to pathogenesis of AD? In the following sections, we will review and discuss the potential involvement of adult neurogenesis and newly generated neuronal cells of the adult brain in the pathology and pathogenesis of AD. ETIOLOGY AND PATHOLOGY OF ALZHEIMER’S DISEASE Alzheimer ’s disease is a neurodegenerative disease. It is associated initially with the loss of nerve cells in areas of the brain that are vital to memory and other mental abilities, like the enthorhinal cortex, hippocampus, and neocortex. As the disease advances, other regions of the brain are affected, including the medial temporal area, lateral hemisphere, basal forebrain, and locus coeruleus, leading to severe incapacities (Burns et al. 2002). As the disease and neurodegeneration further progress, so do the disabilities and impairments, leading ultimately to death. AD was described by Alois Alzheimer in 1906, who reported first the histopathological features of AD: the presence of amyloid plaques and neurofibrillary tangles in the brain of patients with severe dementia (Alzheimer 1906). Amyloid Plaques and Neurofibrillary Tangles Amyloid plaques and neurofibrillary tangles are the hallmarks of AD. Amyloid plaques are extracellular deposits of proteins surrounded by degenerating nerve cells, in the brain of patients with AD (Anderson et al. 2004). They are composed of amyloid fibrils. Amyloid fibrils are aggregates of protein beta-amyloid. Protein beta-amyloid is a 40 amino acid beta-peptide. It is synthesized and secreted by nerve cells, by posttranscriptional maturation of APP (Kang et al. 1987). APP is processed by alpha-, beta- and gamma-secretase enzymes. Protein beta-amyloid is an amyloidogenic protein. These proteins are soluble in their physiological state. Under pathological conditions, they form insoluble extracellular aggregates or deposits of amyloid fibrils (Serpell, Sunde, and Blake 1997). In physiological conditions, APP is cleaved by the alpha- and gamma-secretase enzymes into a 40 amino acid betapeptide. Certain pathological conditions, like the presence or expression of amyloid-promoting factors or certain gene mutations, including in

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APP, cause excessive cleavage of APP by the beta- and gamma-secretase enzymes, resulting in an increase production of a 42 amino acid betaamyloid peptide. This latter form of protein beta-amyloid aggregates into insoluble amyloid deposits, particularly in the brain, forming aggregates and deposits of amyloid fibrils. Amyloid plaques are thought to be the first histological change that occurs in the brain of patients with AD (St. George-Hyslop 2000). The density of amyloid plaques increases as the disease advances. They are distributed throughout the brain of those patients, particularly in the region of degeneration, like the entorhinal cortex, hippocampus, temporal, frontal, and inferior parietal lobes. The role and contribution of amyloid plaques in the pathology of AD remain unclear and the source of controversies. On the one hand, it is proposed that deposits of protein beta-amyloid may be a causative factor of AD. According to this hypothesis, referred as amyloid hypothesis, as the amyloid deposits in the brain, brain cells start dying, and the signs and symptoms of the disease begin (Hardy and Selkoe 2002; Meyer-Luehmann et al. 2006). On the other hand, the correlation between the density of amyloid plaques and the severity of the dementia is not clearly established (Terry 1996). The deposit of protein beta-amyloid would be a consequence rather than a cause of AD. Neurofibrillary tangles are deposits of proteins present inside neuronal cells in the brain of patients with AD. They are composed of hyperphosphorylated tau proteins (Fukutani et al. 1995). Tau protein is a microtubuleassociated phosphoprotein. It is involved in the formation of microtubules (Kim, Jensen, and Rebhun 1986). The hyperphosphorylation of tau proteins result in their aggregation and in the breakdown of microtubules (Iqbal et al. 1998). This leads to the formation of neurofibrillary tangles and cell death (Alonso et al. 2001). As the disease advances, the regions of the brain affected expand, leading to severe incapacity and death (Brun and Gustafson 1976). Genetic Factors and Mutations There are two forms of the diseases, sporadic and inherited. Most cases of LOAD are sporadic forms of the disease and are diagnosed after the age of 65. EOAD is diagnosed at younger age than 65 and most cases of EOAD are inherited forms of AD. LOAD is believed to be caused by genetic, acquired, and environmental factors, among them the presence of certain alleles in the genetic makeup of the individuals, hypertension and diabetes, and neuroinflammation and oxidative stress (Cankurtaran et al. 2008). The presence of the apolipoprotein E varepsilon 4 allele (ApoE4) is the

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best established genetic risk factor for LOAD. ApoE is a plasma protein; it participates in the transport of cholesterol and other lipids in the blood (Mahley 1988). There are four major isoforms of the gene coding for ApoE encoded by different alleles in humans, ApoE, ApoE2, ApoE3 and ApoE4. ApoE accounts for the vast majority of causes and risks to develop LOAD: up to 50% of people who have AD have at least one ApoE4 allele. Neuronal sortilin-related receptor (SORL1) belongs to a family of proteins termed retromer (Raber, Huang, and Ashford 2004). Retromers are involved in intracellular trafficking. Reduced expression of the gene coding for SORL1 (SORL1) is associated with an increase in density of amyloid plaques in the brain and increased risk for LOAD. The variants of SORL1 may promote AD by suppressing the activity of the gene. This may affect the processing of APP and increase its production (Rogaeva et al. 2007). Other genes have been linked with the occurence of LOAD, among them variants for the genes coding for alpha2-macroglobulin, monoamine oxidase A, myeloperoxidase and cystatin C (CST3) (Finckh et al. 2000). These risk factors increase the probability of developing the disease. So far, three genes have been identified as carrying genetic mutations underlying the development of EOAD. These genes are also known as FAD genes. These genes are the APP gene, the presenilin-1 gene (PSEN1) and the presenilin-2 gene (PSEN2) (Schellenberg 1995). APP is a 695-770 amino acid protein coding for the protein beta-amyloid. The PSEN proteins are components of the gamma-secretase complex. These enzymes play a role in the maturation of APP into the 42 protein beta-amyloid (Nishimura, Yu, and St. George-Hyslop 1999). Mutations in PSEN1 and PSEN2 lead to excessive cleavage by gamma-secretase enzyme, resulting in increased production and aggregation of protein beta-amyloid (Newman, Musgrave, and Lardelli 2007). Mutations in these genes almost always result in the individual developing the disease (Hardy 2001). Aneuploidy Aneuploidy is an abnormal number of chromosomes in the cells of the body. It is a common cause of genetic disorders. Several studies report that cells of patients with AD elicit aneuploidy, particularly for chromosome 21, 13, and 18. Lymphocytes of patients with LOAD present an elevation in aneuploidy for chromosomes 13 and 21 (Migliore et al. 1999). Preparations of lymphocytes of patients with sporadic and inherited forms of AD elicit a two-fold increase in the incidence of aneuploidy for chromosomes 18 and 21 (Geller and Potter 1999). In regions of degeneration 4–10% of neurons, like the hippocampus, are aneuploids and express proteins of

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the cell cycle in the brain of patients with AD (Busser, Geldmacher, and Herrup 1998; Yang, Geldmacher, and Herrup 2001). The adult brain contains a substantial number of cells that are aneuploids; estimated at 5–7% of the cells in the brain of adult mice (Rehen et al. 2005). The genetic imbalance in aneuploid cells signifies that they are fated to die (Herrup et al. 2004). The relatively high percentage of aneuploid cells in regions of degeneration in AD brains suggests that they undergo a slow death process. These cells may live in this state for months, possibly up to 1 year (Herrup and Arendt 2002; Yang, Mufson, and Herrup 2003). This supports their involvement in the slow and progressive neurodegenerative process of AD. Cyclin B, the marker of the phase G2 of the cell cycle, is also expressed in neurons in regions of degeneration, particularly the hippocampus, in patients with AD (Vincent, Rosado, and Davies 1996). In the adult brain, most nerve cells are post-mitotic cells. The characterization of aneuploidy and cyclin B in nerve cells in the region of degeneration reveal that cell cycle re-entry and DNA duplication, without cell division, precedes neuronal death in the brain of patients with AD. The deregulation and/or re-expression of proteins of the cell cycle in nerve cells triggering cycle re-entry, with blockage in phase G2, and aneuploidy would underlie the neurodegenerative process and pathogenesis of AD. Enhanced Neurogenesis The expression of markers of immature neuronal cells, like doublecortin and polysialylated nerve cell adhesion molecule, is enhanced in the hippocampus, particularly the DG, in the brain of AD patients, most likely with LOAD (Jin, Peel, et al. 2004). In animal models, neurogenesis is decreased in the DG of adult mice deficient for PSEN1 and/or APP, in the DG of adult transgenic mice over expressing variants of APP or PSEN1, and in the DG of adult PDAPP transgenic mice, a mouse model of AD with age-dependent accumulation of protein beta-amyloid (Wen et al. 2002; Donovan et al. 2006; Verret et al. 2007; Zhang et al. 2007; Rodríguez et al. 2008). It is increased in the DG of adult transgenic mice that express the Swedish and Indiana APP mutations (Jin, Galvan, et al. 2004). Mice deficient for or over expressing variants of APP or PSEN1, and transgenic mice that express the Swedish and Indiana APP mutations, a mutant form of human APP, are transgenic mice that express variants of FAD genes. Transgenic mice deficient for APP and PSEN1 provide information on the activities and functions of the proteins involved in EOAD. They are not representative of complex diseases, like LOAD. They do not represent the disease. The aggregation of protein beta-amyloid affects adult

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neurogenesis (Heo et al. 2007). It may have adverse effects on neurogenesis during development in transgenic mice for APP, affecting the adult phenotype. In all, the discrepancies of the data observed on adult neurogenesis in autopsies and animal models of AD may originate from the validity of the animal models used in those studies, as representative of AD and to study adult phenotypes (German and Eisch 2004). The discrepancies of the data observed on adult neurogenesis may also originate from the validity of the protocols used as a paradigm to study adult neurogenesis, like the immunohistochemistry for markers of the cell cycle and for the thymidine analog bromodeoxyuridine (BrdU). Most of the studies conducted in autopsies and animal models of neurological diseases and disorders use either immunoshistochemistry for markers of the cell cycle or the BrdU labeling paradigm, to study and quantify adult neurogenesis in situ. Proteins of the cell cycle, like cyclin B—the marker of the phase G2—are expressed in neurons in regions where neurodegeneration occurs. Some at-risk neurons in regions of degeneration are aneuploids in the brain of patients with AD (Busser, Geldmacher, and Herrup 1998; Yang, Geldmacher, and Herrup 2001). Cell cycle re-entry and DNA duplication, without cell division, precedes neuronal death in degenerating regions of the CNS. This suggests that when using immunohistochemistry for proteins of the cell cycle, to study adult neurogenesis, this paradigm does not allow discrimination between cells undergoing DNA duplication, without cell division, as part of their pathological fate and newly generated neuronal cells (Taupin 2007). BrdU is used for birth dating and monitoring cell proliferation (Miller and Nowakowski 1988). There are pitfalls and limitations over the use of thymidine analogs, and particularly BrdU, for studying neurogenesis (Nowakowski and Hayes 2000; Gould and Gross 2002). BrdU is a thymidine analog. It is not a marker of cell proliferation; it is a marker for DNA synthesis. Studying and quantifying neurogenesis with BrdU require distinguishing cell proliferation and neurogenesis from other events involving DNA synthesis, like DNA repair, abortive cell cycle re-entry and cell cycle re-entry and gene duplication, without cell division, leading to aneuploidy (Taupin 2007). In addition, BrdU has a number of side effects. It is a toxic and mutagenic substance. It alters DNA stability and lengthens the cell cycle. BrdU has mitogenic, transcriptional, and translational effects on cells that incorporate it. It triggers cell death and the formation of teratomes. Hence, data involving the use of immunohistochemistry for proteins of the cell cycle and BrdU labeling, as paradigms for studying adult neurogenesis in neurological diseases and disorders, and particularly in AD, must be carefully assessed, analyzed, and discussed.

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In all, AD is a neurodegenerative disease that affect mostly individuals over 65 years of age. There are two forms of the disease, sporadic and inherited. It is characterized by widespread neurodegeneration, amyloid deposits and neurofibrillary tangles, aneuploidy and enhanced neurogenesis, though this latter observation remains to be fully established. It is proposed that enhanced neurogenesis may be a result, rather than a cause, of the illness (Taupin 2008, “Adult neurogenesis pharmacology”; Taupin 2008, “Adult neurogenesis and drug therapy”). Enhanced neurogenesis in the DG of the brain with neurological diseases and disorders, particularly neurodegenerative diseases, may contribute to a regenerative attempt, to compensate for the neuronal loss. POSSIBLE MECHANISMS RELATED TO ADULT NEUROGENESIS The confirmation that adult neurogenesis occurs in the adult brain and NSCs reside in the adult CNS not only brings new opportunities for the treatment of AD, but also raises the question of the involvement of adult neurogenesis and newly generated neuronal cells of the adult brain in the etiology and pathogenesis of the disease. Amyloid plaques, neurofibrillary tangles, aneuploidy and enhanced neurogenesis are landmarks of the pathology of AD, but their role and contribution to AD remain to be fully elucidated and established, this particularly in light of and relation to recent developments in adult neurogenesis and NSC research. Aneuploidy in AD Patients Aneuploidy may originate from the nondisjunction of chromosomes during mitosis or meiosis. It may originate from cell cycle re-entry with cells undergoing DNA duplication without cell division and from cell fusion (Alvarez-Dolado et al. 2003; Torres, Williams, and Amon 2008). Cells that are the most likely to develop aneuploidy are dividing cells. Lymphocytes of patients with EOAD and LOAD elicit an elevation in aneuploidy for chromosome 13, 18, and 21 (Geller and Potter 1999; Migliore et al. 1999). Hence, the nondisjunction of chromosomes, particularly of chromosomes 13, 18 and 21, in stem cells and/or populations of somatic cells that retain their ability to divide is at the origin of aneuploidy in patients with AD (Potter 1991). In the adult brain, most nerve cells are post-mitotic. The characterization of cyclin B and aneuploidy in neurons suggests that cells re-entered the cell cycle and underwent DNA replication, but did not complete the cell cycle, in regions of degeneration in the brain of patients with AD

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(Vincent, Rosado, and Davies 1996; Busser, Geldmacher, and Herrup 1998; Yang, Geldmacher, and Herrup 2001). AD is associated with the loss of nerve cells initially in areas of the brain, like the enthorhinal cortex, hippocampus, and neocortex. As the disease advances, other regions of the brain are affected by neurodegeneration, including the medial temporal area, lateral hemisphere, basal forebrain, and locus coeruleus. The genetic imbalance in aneuploid cells signifies that they are fated to die and that they undergo a slow death process (Yang, Mufson, and Herrup 2003; Herrup et al. 2004). Cell cycle re-entry and DNA replication, without mitosis, is at the origin of aneuploidy in nerve cells of the adult brain. It is an underlying factor in the neurodegenerative process and pathogenesis of AD. Aneuploidy for Chromosome 21 and Amyloid Deposits Amyloid plaques are deposits of protein amyloid (Anderson et al. 2004). Deposit of protein amyloid is one of the histopathological features of AD and one the probable cause for the pathogenesis of AD. The gene for APP is located on chromosome 21 (21q21) (Goldgaber et al. 1987; Schellenberg et al. 1992). Cells of patients with AD elicit aneuploidy, particularly for chromosome 21 (Geller and Potter 1999; Migliore et al. 1999). Aneuploidy for chromosome 21 would result in the overexpression of APP and promote the formation of amyloid plaques. In patients with FAD, with mutation of the APP gene, it would result in the overexpression of mutant form of amyloid protein in aneuploid cells and amyloid formation. In patients with the sporadic form of AD, it would result in the overexpression of wild type amyloid protein in aneuploid cells and amyloid formation, under certain conditions or risk factors. According to the amyloid hypothesis, aneuploidy for chromosome 21 would underlie cell death and the pathogenesis of AD. In support of this contention, Down’s syndrome has for pathogenic cause trisomy for the chromosome 21. Patients with Down’s syndrome develop, during their thirties and forties, dementia and neuropathology that share some characteristics with AD, particularly with regard to amyloid formation and deposits (Glenner and Wong 1984). Aneuploidy for chromosome 21 would underlie the pathogenesis and pathology of the dementia that occurs in patients with Down’s syndrome and AD. Aneuploidy for chromosome 21 has been proposed as one of the mechanisms underlying the formation of amyloid deposits and the pathogenesis of AD and Down’s syndrome (Potter 1991). Protein beta-amyloid induces cell cycle re-entry and neuronal death (Chen et al. 2000). Hence, aneuploidy

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for chromosome 21 in neurons in regions of degeneration would underlie the pathogenesis of AD, not only by promoting the formation of amyloid plaques, but also by promoting cell cycle re-entry and DNA duplication, without cell division, leading to aneuploidy and neuronal cell death. Aneuploidy for Chromosome 17 and Neurofibrillary Tangles Formation Neurofibrillary tangles are one of the histopathological features of AD and one the probable cause for cell death in AD. Neurofibrillary tangles are deposits of proteins present inside neuronal cells (Alonso et al. 2001). They are composed of hyperphosphorylated tau proteins (Fukutani et al. 1995). The tau gene is located on chromosome 17 (17q21.1) (Iqbal et al. 1989). Aneuploidy for chromosome 17 would result in the overexpression of tau protein. It would underlie the pathogenesis of AD, by promoting the formation of neurofibrillary tangles and cell death. Aneuploidy for Chromosomes 1, 14 and 19 and Pathogenesis of AD The PSEN1 and PSEN2 genes carry genetic mutations underlying the development of EOAD (Schellenberg 1995). The PSEN proteins, components of the gamma-secretase complex, play a role in the maturation of APP into protein beta-amyloid (Nishimura, Yu, and St. George-Hyslop 1999). Mutations in PSEN1 and PSEN2 lead to excessive cleavage by the gamma-secretase enzyme. This results in increased production and aggregation of protein beta-amyloid, leading to the development of the EOAD (Newman, Musgrave, and Lardelli 2007). The PSEN1 and PSEN2 genes are located on chromosome 14 (14q24.3) and 1 (1q31–q42), respectively (Nishimura et al. 1999). The presence the ApoE4 allele is a genetic risk factor for LOAD. The ApoE gene is located on chromosome 19q13.2. Aneuploidy for chromosomes 1, 14 and 19, and more generally for chromosomes carrying genes involved in the development of AD, including SORL1 and CST3 genes, would contribute to the pathogenesis of the disease, EOAD or LOAD, depending on the gene involved in the disease. In support of this contention, people who have two ApoE4 alleles have a higher risk of being diagnosed with AD, after age of 65 (Strittmatter et al. 1993). Aneuploidy and Adult Neurogenesis Neurogenesis occurs in the adult brain and NSCs reside in the adult CNS. In the adult mammalian brain, neurogenesis occurs primarily in the

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DG and SVZ. The process of adult neurogenesis holds the potential to generate populations of cells that are aneuploids particularly in the neurogenic areas. The nondisjunction of chromosomes during the process of cell division of newly generated progenitor cells of the adult brain could lead to newly generated neuronal cells that are aneuploids or to aneuploid cells that would not proceed with their developmental program (Taupin 2009, “Adult neurogenesis, neural stem cells”; Taupin 2009, “Adult neurogenesis in the pathogenesis”) (see Figure 4.1). Hence, neurogenesis could also be a contributing factor of aneuploidy in AD. In the adult brain aneuploidy may therefore originate both from cycle re-entry and DNA duplication, without cell division, in regions of degeneration including the hippocampus, and from the nondisjunction of chromosomes in neural progenitor and stem cells of the adult brain, and their progenies, that retain their ability to divide in neurogenic areas particularly in the hippocampus. This reveals that adult neurogenesis could be an underlying factor in the neurodegenerative process and therefore pathogenesis of AD. It is estimated that 0.004% of the granule cell population is generated per day in the DG of adult macaque monkeys (Kornack and Rakic 1999). Despite neurogenesis being an event with relatively low frequency in the adult mammalian brain, the fact that cells that are the most likely to develop aneuploidy are dividing cells and that neurogenesis occurs in the adult hippocampus, a region of the brain particularly and among the first affected in AD, suggests that aneuploidy originating from adult neurogenesis may play a critical role in the process of degeneration and pathogenesis in AD. Such aneuploidy, particularly for chromosomes 17 and 21 and other genes involved in AD, would further contribute to the pathogenesis of AD, by promoting the formation of amyloid deposits and neurofibrillary tangles in the neurogenic areas, particularly the hippocampus. In all, adult neurogenesis may play a critical role in the pathogenesis of AD, in the process of neurodegeneration, amyloid deposits and neurofibrillary tangles formation.

Factors Promoting Aneuploidy in Alzheimer ’s Disease Hyperphosphorylation of Tau Protein Hyperphosphorylated tau protein is a component and promotes the formation of neurofibrillary tangles (Alonso et al. 2001). Tau is a microtubule-associated protein, involved in the formation of microtubules (Kim, Jensen, and Rebhun 1986). The hyperphosphorylation of tau by kinases leads to the dissociation of tau and tubulin, and to the breakdown of

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Figure 4.1 Fate of Newly Generated Neural Progenitor and Neuronal Cells in the Adult Brain

Neurogenesis occurs in the adult brain, primarily in the DG of the hippocampus and SVZ. Adult NSCs represent a promising model for cellular therapy for treating a vast array of neurological diseases and injuries, and particularly neurodegenerative diseases like AD. The role and contribution of adult neurogenesis and newly generated neuronal cells of the adult brain to the physiopathology and functioning of the nervous system remain to be elucidated. NSCs are the self-renewing multipotent cells that have the ability to give rise to the main phenotypes of the nervous system; they generate a large number of progenies through an intermediate population of the cells, the neural progenitor cells (in light gray). (1. Apoptosis) Cell death is a normally occurring process in the neurogenic zones, as a significant proportion of newly generated cells are believed to undergo apoptosis rather than achieving maturity. (2. Normal development) Newly generated neuronal cells that survive, survive for an extended period of time, at least 2 years in humans, and extend functional projections. They may be involved in plasticity and contribute to regenerative attempts in the diseased and injured nervous system, particularly in AD. (3. Aneuploidy) Aneuploidy is a landmark of the pathology of AD and contributes to the pathogenesis of the disease. The process of adult neurogenesis holds the potential to generate populations of neural progenitor cells that are aneuploids (in dark gray). The nondisjunction of chromosomes during the process of cell division of newly generated neural progenitor cells of the adult brain could lead to newly generated neuronal cells that are aneuploids (A) or to newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program (B). The genetic imbalance in aneuploid cells signifies that they are fated to die. Newly generated neuronal cells that are aneuploids and newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program in the adult brain may contribute to the pathogenesis of AD. They may contribute to the process of neurodegeneration, amyloid deposits and neurofibrillary tangles formation, particularly in the hippocampus. This reveals that adult neurogenesis would not only be beneficial for the adult brain, but it may also be involved in the pathogenesis of neurological diseases, particularly in AD.

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microtubles (Iqbal et al. 1998). This causes the disruption in the mitotic spindle, promoting aneuploidy during mitosis. Hyperphosphorylated tau protein may contribute to the pathogenesis of AD, not only by the polymerization and aggregation of tau proteins, resulting in the formation of neurofibrillary tangles and cell death, but also by promoting the nondisjunction of chromosomes and aneuploidy in dividing cells. Hyperphosphorylated tau protein could be a contributing factor in the generation of newly generated neuronal cells that are aneuploids or to newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program in the adult brain, particularly in the hippocampus (Taupin 2009, “Adult neurogenesis, neural stem cells”). Mutation in PSEN1 The PSEN1 proteins are components of the gamma-secretase complex and play a role in the maturation of APP into protein beta-amyloid (Nishimura, Yu, and St. George-Hyslop 1999). Mutated forms of PSEN1 are detected in the centrosomes and interphase kinetochores of dividing cells. Mutated PSEN1 proteins may be involved in the segragation and migration of chromosomes during cells division (Li et al. 1997). Mutated PSEN1 proteins may contribute to the pathogenesis of EOAD, not only by promoting the formation of deposits of amyloid fibrils, but also by promoting the nondisjunction of chromosomes and aneuploidy in dividing cells (Boeras et al. 2008). Mutated PSEN1 proteins could be a contributing factor in the generation of aneuploid newly generated neuronal cells or to aneuploid newly generated neural progenitor cells that would not proceed with their developmental program in the adult brain, particularly in the hippocampus (Taupin 2009, “Adult neurogenesis, neural stem cells”). Modulation of Adult Neurogenesis Neurogenesis is modulated in the adult brain, particularly in the hippocampus, by a broad range of environmental, physio- and pathological stimuli and processes, trophic factors/cytokines and drugs (Taupin 2007). The stimulation of neurogenesis in the adult brain may contribute to the generation of newly generated neuronal cells that are aneuploids or to newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program in the neurogenic regions of the adult brain, particularly the hippocampus. It is reported that neurogenesis is enhanced in the hippocampus, particularly the DG, in the brain of patients with AD (Jin, Peel, et al. 2004). Enhanced neurogenesis in the DG of the brain with neurological diseases and disorders, particularly

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neurodegenerative diseases, may contribute to a regenerative attempt to compensate for the neuronal loss (Taupin 2008, “Adult neurogenesis pharmacology”). Hence, enhanced neurogenesis in the DG of the brain of patients with AD could be a contributing factor in the generation of newly generated neuronal cells that are aneuploids or of newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program in the adult brain and therefore could be a contributing factor to the pathogenesis of AD (Taupin 2009, “Adult neurogenesis, neural stem cells”). Oxidative Stress Oxidative stress is an environmental risk factor for LOAD. Oxidative stress induces cell cycle re-entry and neuronal death (Langley and Ratan 2004). It promotes aneuploidy, particularly for chromosome 17 that carries the tau gene (Ramírez et al. 2000). Oxidative stress may promote the pathogenesis of LOAD, not only by promoting cell cycle re-entry and DNA duplication, without cell division, in the brain, leading to neuronal death and the process neurodegeneration, but also by promoting the generation of aneuploid cells fated to die and the formation of neurofibrillary tangles, leading to cell death (Taupin 2009, “Adult neurogenesis, neural stem cells”). CONCLUSION AND PERSPECTIVES The confirmation that adult neurogenesis occurs in the adult brain and NSCs reside in the adult CNS of mammals reveals that the adult brain has the potential for self repair. It opens new opportunities for the treatment of a broad range of neurological diseases and injuries, including neurodegenerative diseases like AD, cerebral strokes, and spinal cord injuries. The role and contribution of adult neurogenesis and newly generated neuronal cells to the physio- and pathology of the adult brain remain to be elucidated, particularly in neurodegenerative diseases like AD. Neurogenesis is enhanced in the hippocampus of patients with AD. Though these data remain to be confirmed and validated, it suggests that adult neurogenesis would contribute to a regenerative attempt to compensate for the neuronal loss in AD. Aneuploidy is a landmark of the pathology of AD and contributes to the pathogenesis of the disease. Aneuploidy contributes directly and indirectly to the processes of amyloid deposits, neurofibrillary tangles formation and neurodegeneration in the brain. Cells that are the most likely to develop aneuploidy are dividing cells. Hence, the process of adult

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neurogenesis holds the potential to generate populations of aneuploid cells particularly in the hippocampus, a region where neurogenesis occurs in the adult brain and that is primarily affected in AD. Newly generated neuronal cells of the adult brain that are aneuploids or newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program may contribute to the pathogenesis of AD, in the process of neurodegeneration, amyloid deposits, and neurofibrillary tangle formation. This reveals that adult neurogenesis would not only elicit a beneficial effect for the adult brain, but it may also be involved in the pathogenesis of neurological diseases and disorders, particularly in AD. Adult neurogenesis may be the target of new drugs aimed at treating AD, by promoting neuroregeneration and decreasing the risk of the generation of newly generated neuronal cells of the adult brain that are aneuploids or of newly generated neural progenitor cells that are aneuploids and would not proceed with their developmental program in the adult hippocampus. Future studies will aim at characterizing the role and contribution of adult neurogenesis and NSCs in the pathogenesis and pathology of AD and other neurological diseases. Results from such studies will lead to a better understanding of neurological diseases and disorders, and to novel and more effective treatments and cures for these diseases, particularly AD.

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Chapter 5

Pathophysiology of Behavioral and Psychological Disturbances in Dementia Anna Burke

Although dementia is frequently thought of as an impairment in cognition, a common and often overwhelming dilemma facing many families and clinicians is the presence of behavioral and psychological disturbances during the course of the illness. These can include depressive signs and symptoms, agitated behaviors, and psychotic symptoms. It is estimated that as many as 40% to 90% of individuals diagnosed with dementia will experience behavioral or psychological disturbances during the course of their illness (Teri et al 1992; Zuidema et al 2006). The presence of such symptoms is detrimental to patients and their caregivers and results in increased rates of caregiver burn-out, depression, and earlier institutionalization of the affected patient (Kaufer et al. 1998; Donaldson, Tarrier, and Burns 1998; Fuh et al. 2001; Mourik et al 2004; Buhr, Kuchibhatla, and Clipp 2006; Aarsland et al. 2007). Such disturbances also pose a quandary for clinicians as their etiology remains poorly understood and effective treatment options remain few. Behavioral and psychological impairments in dementia may also hinder appropriate treatment of other medical conditions, further frustrating clinicians and families while putting patients at greater medical risk. The focus of this chapter will be to provide a framework for the greater understanding of the changes occurring in the neurotransmitter systems of the brain and how they relate to clinical symptom manifestations in dementia, as well as possible treatment approaches. Since Alzheimer ’s

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dementia (AD) constitutes an estimated 60–80% of all diagnosed dementias, it will serve as the model for the discussion. NEUROTRANSMITTER CHANGES IN DEMENTIA Alzheimer ’s disease is the most common form of dementia, characterized by an insidious onset and a progressive decline in cognitive and functional abilities as a result of the loss of neurons in the cerebral cortex and certain subcortical regions. The disease commonly results in alterations of personality, mood, behaviors, and reality testing, which may occur at any stage of the illness. Despite the prevalence of behavioral and psychotic disturbances in dementia (BPSD), their underlying neurobiology has received far less attention than the neurobiology of cognitive features such as memory impairment in AD. However, disturbances in several neurotransmitter systems measured via serum levels, cerebrospinal fluid levels, and tissue levels have been demonstrated to be of clinical significance in symptom manifestation. Clues as to the underlying mechanisms have also been provided through a greater understanding of other clinical syndromes displaying similar symptoms. Changes in neurotransmitter biosynthesis, biodegradation, presynaptic and postsynaptic receptor binding sites, and second messenger systems, as well as decreased central nervous system uptake of neurotransmitter precursors have all been implicated in BPSD. The most extensively studied neurotransmitter system has been the cholinergic system. However, GABAergic dysfunction and disturbances of the serotonergic and noradrenergic systems have become a prominent focus and treatment target. Acetylcholine Cholinergic deficits are the best-established neurotransmitter changes in AD. In the central nervous system (CNS), acetylcholine is a major neurotransmitter vital for the formation and retention of new memories. It also acts as a neuromodulator, affecting and regulating CNS excitability, arousal, and reward systems. Current scientific theories hypothesize that dementia-related memory issues stem from the increased breakdown of acetylcholine by acetylcholinesterase, an enzyme that degrades acetylcholine, as well as decreased production of the neurotransmitter due to neuronal cell death in dementia. Acetylcholine deficiency in the CNS has been associated with the archetypal symptoms of dementia and Alzheimer ’s disease: short-term memory loss, disorientation, sleep disturbances, as

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well as development of psychotic and agitated behaviors. Symptom formation may vary depending on the region of the brain affected, hence the diversity of clinical pathology seen in individuals diagnosed with dementia. Decreased concentrations of choline acetyltransferase, the enzyme that synthesizes acetylcholine, in the neocortex and hippocampus and neuronal atrophy in the nucleus basalis of Meynert, have been well established. Postmortem, histopathological studies of brain tissue of patients with AD and visualization via positron emission tomography (PET) in living subjects reveal reductions in acetylcholinesterase. Degeneration in specific subtypes of cholinergic receptors has also been identified. Although postsynaptic muscarinic receptors appear to be well preserved, a prominent deterioration in presynaptic muscarinic receptors as well as in certain presynaptic nicotinic cholinergic receptors is noted. Aside from its vital function in memory, cholinergic deficiency has been linked to the development of behavioral disruptions frequently seen in dementia. Sunderland et al. (1987) observed that when scopolamine, an anticholinergic agent, was given intravenously to AD patients, agitation and hostility ensued. Gorman and colleagues (1993) in a double-blind, crossover study comparing the cholinesterase inhibitor physostigmine to haloperidol, found that both agents significantly reduced agitation with approximately equivalent effects. Cholinergic receptor agonists, such as Xanomeline, have also been reported to reduce agitated behaviors (Bodick et. 1997). Research has also suggested that there is a “cholinergic component” of conscious awareness. The cholinergic system controls activities that depend on selective attention, which are an essential component of conscious awareness. This awareness can be altered by various pharmacological agents and by the disease process and neuronal loss itself. For example, drugs that antagonize muscarinic receptors induce hallucinations and reduce the level of consciousness, while the nicotinic receptors are implicated as being involved in the mechanism of action of general anesthetics. It is theorized that the excessive loss of cortical acetylcholine may allow “irrelevant” information to enter the conscious awareness and hence produce hallucinations. The role of the cholinergic system in impairment of reality testing is further supported by pharmacological studies indicating an increase in psychotic features with anticholinergic agents and reduction of psychosis with cholinergic agents. Psychosis is common in conditions such as anticholinergic delirium. Psychotic symptoms in this disorder are readily reversed by treatment with cholinesterase inhibitors or physostigmine.

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Sunderland et al. (1987) reported that scopolamine increased the incidence of thought disorders in AD patients. Bodick et al. (1997) observed that xanomeline decreased psychosis in AD patients. Cummings, Gorman, and Shapira (1993) also reported benefit in reduction of psychotic symptoms in AD patients after administration of physostigmine. Given evidence of the detrimental consequences of cholinergic deficiencies, use of medications that modify the cholinergic system may be of benefit. Several studies of cholinesterase inhibitors, typically used to treat cognitive impairment in dementia, reveal evidence of modest clinically relevant psychotropic effects in some patients with dementia. Fundamentally, these studies were not designed to address behavioral outcomes as the primary goal. However, they did evidence reduced behavioral symptoms, particularly mood disturbances and delusions, in patients with AD with relatively severe psychopathology. A review of 24 trials involving nearly 5,800 participants receiving donepezil found benefits of treatment noted on measures of activities of daily living and behavior. Galantamine, donepezil, and rivastigmine also demonstrated effects in people with mild, moderate or severe Alzheimer ’s dementia in 10 randomized doubleblind, placebo-controlled trials (Birks and Harvey 2006). No significant differences in efficacy were noted between study medications. Similarly, a modest benefit was revealed in a 2003 meta-analysis of 29 parallel-group or cross-over randomized, placebo-controlled trials of outpatients diagnosed with mild to moderate AD meant to quantify the efficacy of these cholinesterase inhibitors for neuropsychiatric and functional outcomes. No difference in efficacy among various cholinesterase inhibitors was observed (Trihn et al. 2003). A 2006 exploratory analysis of data pertaining to the efficacy of donepezil in severely behaviorally disturbed patients with AD suggested that the medication reduces behavioral symptoms, particularly mood disturbances and delusions, in patients with AD with relatively severe psychopathology (Cummings, McRae, and Zhang 2006). Unlike prior studies, which had involved low-level psychopathology, the authors focused on more severe BPSD manifestations, further substantiating the potential benefits of cholinesterase inhibitors at various stages of the disease. As previously mentioned, in neurodegenerative diseases such as AD, alterations in consciousness and symptom development are associated with regional deficits in the cholinergic system. Symptom formation may vary depending on the region of the brain affected, hence the diversity of clinical pathology seen in individuals diagnosed with dementia. In AD, hypoactivity of cholinergic projections to the hippocampus and cortex

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results in loss of explicit memory, the memory responsible for conscious, intentional recollection of previous experiences and information. Symptoms of apathy and withdrawal appear to be related to functional impairments in the prefrontal and anterior temporal cortices (Craig et al. 1996). Agitation, aggression, and psychosis, which tend to be more prevalent during the more advanced stages of the disease, have been associated with hypometabolism present in the frontal and temporal lobes (Jeste et al. 1992; Sultzer et al. 1995). Cholinergic neuron degeneration in the nucleus basalis of Meynert (NBM) results in circadian rhythm disturbance and “sundowning” in Alzheimer ’s disease. NBM neurons modulate the activity of the mainly cholinergic suprachiasmatic nucleus (SCN), the core circadian pacemaker in the body, and the induction of non-REM sleep (Klaffke and Staedt 2006). SCN dysregulation alters the core body temperature, heart rate, and hormone secretion. These changes may manifest as disturbed sleep and agitation associated with “sundowning.” Suprachiasmatic nucleus volume and cell number have been found to be decreased in those between the ages of 80 and 100 (Swaab, Fliers, and Partiman 1985). The NBM also carries many afferents from the limbic system and is therefore believed to play a role in communicating the emotional state of the organism to the cerebral cortex. Though many hypotheses regarding cholinergic effects on mood have been put forth, evidence remains inconsistent. Janowsky, Risch, and Gillin (1983) hypothesized that cholinergic disturbances were closely tied to dysregulation of other neurotransmitter systems related to affective states. Cholinergic predominance was viewed as resulting in depression, while cholinergic depletion and resulting adrenergic predominance were responsible for mania. This was further supported by several studies of cholinergic agents used to treat AD which were found to increase depressive symptoms in patients (Davis et al 1987; Sunderland, Tariot, and Newhouse 1988). However, recent research data from clinical trials reveal no depressant effects related to the medications, and in some cases mild euphoria has even been noted (Sunderland et al 1987; Bodick et al. 1997; Morris et al. 1998; Davis et al. 1992; Elgamal and MacQueen 2008; Holtzheimer et al. 2008). Overall, cholinesterase inhibitors appear to have no significant destabilizing effects on mood and may in fact be of modest benefit in improving symptoms such as apathy and withdrawal. In summary, CNS cholinergic deficits appear to play a role in the development of behavioral disturbances in some patients with AD as evidenced by pharmacological studies indicating an increase in psychotic features with anticholinergic agents and reduction of psychosis with cholinergic

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agents. Data suggests that pharmacological agents such as cholinesterase inhibitors may be of benefit in ameliorating these symptoms. However, further investigation is warranted. Norepinephrine Disruptions of the central noradrenergic system have been implicated as an important factor in the development of BPSD in AD. Norepinephrine (NA) is a primary neurotransmitter of the sympathetic nervous system, which is involved at a basal level in maintaining the body’s homeostasis, as well as mobilizing the body’s resources under stress to induce the “fightor-flight” response. Most of the CNS noradrenergic neurons are located in the locus coeruleus (LC) and are associated with cognitive function. The LC is also involved in regulation of levels of arousal, agitation, anxiety, sleep-wake cycle, levels of vigilance, emotional control, and aggression (Thoa et al. 1972; Geyer and Segal 1974; Torda 1976; Chan-Palay and Asan 1989; Peskind et al. 1995; Arnsten, Steere, and Hunt 1996; Anand and Charney 2000). Neurons originating in the ventral tegmental area, which have projections to the forebrain, are associated with sexual and feeding behaviors. Traumatic injuries to this area of the brain have been linked to violent behaviors, rage, and behavioral dyscontrol. Noradrenergic neurons project diffusely to various areas of the brain including the thalamus, hypothalamus, midbrain, and cerebellum and thus exert influence on numerous functions. Postmortem studies have consistently revealed decreased NA levels in the LC of patients with AD. Subcortical and cortical decreases in the thalamus, hypothalamus, hippocampus, amygdala, cingulate gyrus, frontal medial gyrus, temporal superior gyrus, putamen, and caudate have also been noted (Bondareff, Mountjoy, and Roth 1982.; Mann, Yates, and Marcyniuk 1984; Moll et al. 1990; Hoogendijk, Sommer et al. 1999; Matthews et al. 2002). These decreases in tissues may not necessarily correlate with functional noradrenergic transmission. The intraneuronal metabolite, MHPG, may be a better measure of actual noradrenergic function. In postmortem studies, despite decreased levels of NA, MHPG was frequently unchanged or high in patients with AD when compared to controls (Gottfries et al. 1983; Arai, Kosaka, and Iizuka 1984; Raskind et al. 1984; Francis et al. 1985; Herregodts et al. 1989; Nazarali and Reynolds 1992; Hoogendijk, Feenstra et al. 1999). This suggests a possible compensatory mechanism for LC NA loss. Remaining LC neurons may become overactivated to counterbalance noradrenergic losses (Hoogendijk et al. 1999).

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Alzheimer ’s disease severity and noradrenergic neuronal losses appear to be linked. A study comparing 19 subjects with AD and 10 nondemented controls found a correlation between NA neuron counts in the LC and disease severity. The group of more severely impaired subjects was noted to have an 81% decrease in LC neurons when compared to nondemented subjects. The less severely impaired group was found to have a 20% decrease in LC neurons (Bondareff, Mountjoy, and Roth 1982). Because of its diffuse NA axonal dissemination, LC neuronal losses may influence a variety of bodily functions. The link between NA disruption and behavioral disturbances has been studied in a variety of psychiatric conditions including depression, mania, anxiety, aggression, and agitation. Decreased NA receptor sensitivity and increased NA turnover have been noted in conditions such as generalized anxiety disorder and posttraumatic stress disorder (Abelson et al. 1991; Geracioti et al. 2001; Nutt 2001). High levels of NA have been associated with aggressive behaviors in depressed and manic patients, patients with personality disorders, and healthy subjects. Indications that noradrenergic hyperactivity may correlate with increased agitation are further evidenced by elevated cerebrospinal fluid MHPG levels in AD patients with motor restlessness (Brane et al. 1989). A possible link between agitated and aggressive behavior and beta-adrenergic receptor binding was suggested by Russo-Neustadt and Cotman (1997). The authors found a modest, but significant increase in total beta-adrenergic receptor concentrations in the cerebella of aggressive patients with AD when compared to nonaggressive patients with AD and healthy controls, though tyrosine hydroxylase immunoreactive innervation of the cerebella was preserved. Alpha2-receptors may also play a role in modulating aggression. A postmortem study of the hypothalamus, frontal cortex, and cerebellum of agitated patients with AD, nonagitated patients with AD, and elderly healthy control subjects demonstrated that levels of Alpha2receptors in the cerebellum of aggressive patients were 70% higher than their nonagitated counterparts (Russo-Neustadt and Cotman 1997). These levels were also elevated in the nonagitated subjects, but not significantly higher than levels in healthy controls. Frontal cortex and hypothalamic levels were not found to vary significantly. In vivo studies measuring cerebrospinal fluid NA levels were performed by Anden and colleagues to address a potential link between BPSD and NA. Yohimbine, a substance known to increase release of NA, was administered to subjects with AD, healthy elderly subjects, and younger subjects (Anden, Pauksens, and Svensson 1982; Peskind et al. 1998). AD patients displayed increased sensitivity to the agent when compared to the other

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groups. A substantial portion of participants with AD developed agitation, hyperarousal, and psychotic signs and symptoms. This may be a result of increased postsynaptic noradrenergic sensitivity in AD (Raskind et al. 1984; Peskind et al. 1995). Though psychosis may also be linked to noradrenergic dysregulation, evidence of such a correlate remains inconsistent. Higher NA concentrations have been found in the sustantia nigra of psychotic patients with AD by Zubenko et al. (1991). Förstl et al. (1994) also demonstrated significantly higher neuron counts in the parahippocampal gyrus and lower dorsal raphe nucleus neuron counts in AD patients experiencing auditory hallucinations when compared to nonpsychotic AD patients. At this point, evidence of a link between psychosis and the noradrenergic system remains unclear and further investigation is warranted. Depressive symptoms and apathy, which tend to predominate in the early stages of AD have also been associated with noradrenergic dysfunction. A reduction in NA cortical levels has been observed in depressed AD patients (Zubenko, Moossy, and Kopp 1990). Several studies revealed a significantly higher level of LC degeneration in depressed patients with dementia as opposed to their nondepressed counterparts (Zubenko et al. 1988; Zweig et al. 1988; Förstl et al. 1994). However, such evidence has been contradicted by Hoogendijk et al. (1999), who discovered no significant differences in LC degeneration between depressed and nondepressed AD patients. Given the possibility that reducing noradrenergic neurotransmission in the CNS may benefit agitated and aggressive behaviors, potential therapeutic interventions have been explored. However, β-blocking agents have not been subject to rigorous study. Most of the evidence comes from open trials conducted more than 10 years ago (Rosenquist, Tariot, and Loy 2000). Propranolol may be helpful specifically for aggression and uncooperativeness. Modest benefits were illustrated in a recent study evaluating the efficacy of the β-adrenergic antagonist propranolol for treatment-resistant disruptive behaviors and overall behavioral status in nursing home residents AD (Peskind et al. 2005). Short-term propranolol augmentation treatment appeared modestly effective and well tolerated for overall behavioral status in patients with disruptive behaviors. However, the usefulness of propranolol in this very old and frail population was limited by the high frequency of relative contraindications to β-blocker treatment. Prazosin, a nonsedating medication used for hypertension and benign prostatic hypertrophy, antagonizes NA effects at brain postsynaptic α-1 adrenoreceptors. A recent small, double-blind, placebo-controlled, parallel group study examined the efficacy and tolerability of prazosin for

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behavioral symptoms in patients with agitation/aggression in AD (Wang et al. 2009). Those receiving prazosin displayed a significant improvement in behavioral symptoms as compared to the placebo group with no significant increase in adverse effects. These encouraging preliminary results require confirmation in larger controlled studies. Substantial evidence from neuropathological studies and pharmacotherapeutic studies suggests that the noradrenergic system plays a role in modulating behavior, particularly aggression and agitation, though a direct link requires further investigation. This provides an argument for additional comprehensive research into the clinical utility of noradrenergic agents in the pharmacological treatment of BPSD in AD. Serotonin Disruptions in serotonergic neurotransmission in dementia are also believed to result in the clinical manifestations of BPSD. The serotonergic system has widespread connections throughout the brain enabling it to regulate mood, feeding, aggression, sleep, temperature, sexual activity, and motor activity. Serotonergic deficits are well documented in AD and appear to be more pronounced in patients displaying behavioral disturbances (Zubenko, Moossy, and Kopp 1990). Loss of neurons in the brainstem serotonergic nuclei have consistently been reported (Mann and Yates 1983; Yamamoto and Hirano 1985). Serotonin (5-HT) is also well known to be responsible for symptom formation in various other clinical entities that display similar behavioral and psychological symptoms as those seen in dementia, such as depression, anxiety disorders, eating disorders, substance abuse, and psychotic disorders. This neurotransmitter has also been implicated in the pathophysiology of aggression and impulsivity in nondemented individuals (Greenberg and Coleman 1976). The relationship between specific behaviors and decreased serotonin levels in AD patients has been studied. Postmortem studies found that 5-HT levels in AD patients with psychosis were lower than those in AD patients without psychosis (Zubenko, Moossy, and Kopp 1990). However, another study looking at serotonin levels in the temporal cortex of psychotic versus nonpsychotic patients was unable to confirm these results (Lawlor et al. 1995). Serotonin has also been linked to nonpsychotic behavioral symptoms, such as agitation, anxiety, panic and depression in both clinical studies and postmortem analyses. Decreased cortical levels of 5-HT were found post mortem in patients with a history of aggression compared with nonagitated patients, whereas normal numbers of 5-HT2 receptors were found

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in nonaggressive patients. Recently, two neuroendocrine studies found an increased response to the 5-HT releasing agent fenfluramine in agitated aggressive patients when compared with nonagitated aggressive patients. Both neuroendocrine BPSD studies excluded AD patients with significant depression. One study also found a significant gender effect (Mintzer et al. 1998; Lanctôt et al. 1998, 1999). Gender had not been controlled for in other studies looking at the serotonergic system and may be an important contributor to variations in the serotonergic system throughout the life span. A positive correlation between CSF levels of the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA) was seen in patients exhibiting symptoms such as anxiety and panic. The loss of serotonergic neurons in the raphe nucleus and most major areas of the brain has also been associated with depressive features in AD (Zubenko 1992). Postmortem studies have similarly found reduced serotonin levels in the frontal and temporal cortex of depressed individuals with AD (Chen et al. 1996). Additional indirect evidence of the influence of the serotonergic system on behavioral symptoms has been gained from clinical manipulation of this neurotransmitter system through pharmacotherapy. A number of clinical trials have addressed the efficacy of serotonergic agents in AD patients exhibiting depressive or disruptive behaviors. For example, studies conducted by Nyth and Gottfries (1990) on citalopram, which among the SSRIs has the greatest in vitro selectivity ratio for the serotonergic versus the noradrenergic system, revealed positive evidence of the impact of serotonin on behavior. Citalopram was administered for four weeks to patients with mild to moderate AD or multi-infarct dementia in a multicenter, placebo-controlled, parallel group study. Significant improvements were noted in the AD patients on a geriatric rating scale in emotional bluntness and in all six BPSD (irritability, restlessness, anxiety, fear/panic, confusion, and depressed mood) for baseline versus citalopram scores. Irritability and depression improved significantly for citalopram versus placebo groups. There was no significant treatment effect in the multiinfarct group. The patients in this study had very mild BPSD at baseline, with mean scores of less than 2 out of 6 on each symptom. A similar positive correlation between pharmacological manipulation of the serotonergic system and BPSD in AD has been reported as a result of atypical antipsychotic use. Most of the new antipsychotics have strong antagonistic affinity for the 5-HT2 receptor. These agents have frequently been used off-label with good effect in AD patients with BPSD. Numerous studies have revealed significant efficacy of these agents on symptoms such as agitation, aggression, and psychosis (Burke and Tariot 2009). However, it is unclear whether this result is secondary to serotonergic or

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dopaminergic antagonism, since the typical antipsychotics have also demonstrated a similar benefit. Though an effect of serotonin on behavior is apparent, the complexity of the serotonergic system in the brain, as well as its interdependence on other neurotransmitter systems, results in difficulty studying it in isolation. For instance, serotonin and acetylcholine interact extensively in the brain. Serotonin inhibits release of ACh from cortical and hippocampal cholinergic nerve terminals, possibly via 5-HT1B receptors in the hippocampus. The 5-HT3 receptors may also inhibit the release of ACh, whereas 5-HT1A receptors may mediate an increase in ACh release. Thus, disruptions in serotonin have the potential to influence an already impaired cholinergic system in individuals with AD. Similarly, an interplay between the noradrenergic and serotonergic systems is present in the human brain. Serotonin is a co-transmitter with NA, and uptake of 5-HT and NA can be accomplished by either 5-HT or NA neurons. The serotonergic system also inhibits the release of NA via 5-HT1 receptors, hence influencing behaviors such as the sleep-wake cycle, level of vigilance, and emotion. The loss of serotoninergic neurons affects the dopaminergic system since 5-HT neurons from the raphe nuclei are known to synapse with dopaminergic neurons and to control dopamine release in the midbrain, striatum, and nucleus accumbens. Serotonergic neurons may either inhibit (via 5-HT1A) or increase release of DA, influencing depression, agitation, and psychotic behaviors. GABA, the main inhibitory neurotransmitter in the brain, has been shown to regulate behaviors such as aggression. Increased GABA levels have been associated with decreased aggression. Serotonin acts as a co-transmitter with GABA, impacting its effect on such behaviors (Eichelman 1987). Gamma- aminobutyric Acid (GABA) GABA is the primary inhibitory neurotransmitter in the CNS. It is a local inhibitory interneuron for other neurotransmitters that are key in controlling behavior, including serotonin and dopamine. In addition to these local circuit actions, GABAergic neurons project from the striatum to the lateral globus pallidus and from the cerebellar inferior olivary nucleus to the vestibular nucleus. Consequently, GABA exerts additional influence on behavior through interactions with serotonin. GABA modulates behaviors such as fear, phobias, anxiety, and depression. It is thought to play a vital role in the development of psychiatric disorders such as anxiety disorders and depression, which share some

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common behavioral symptoms with AD. Evidence from postmortem studies, antemortem studies, neuroimaging studies, and markers of CNS GABA indicate a relationship between AD and GABAergic dysfunction. There are 22 studies examining GABA concentrations and GABA benzodiazepine binding in patients with AD (Lanctôt et al. 2004). The postmortem studies reveal a reduction of GABA levels throughout key cortical areas: frontal lobe (24–29%), temporal lobes (19–47%), and parietal lobes (21–47%). Binding studies using GABA or benzodiazepines show decreased binding in the temporal region. Limbic areas affected include amygdala (17–28%), thalamus (28–36%), and the cingulate (26–36%). The hippocampus, caudate, nucleus accumbens, and putamen appear to be unaffected. Neuroimaging studies, including positron emission tomography (PET) and single-photon emission tomography (SPECT), illustrate cortical and subcortical GABA deficits (Foster et al. 1987; Meyer et al. 1995; Wyper, Kelly, and Patterson 1998; Ohyama et al. 1999). In particular, the parietal cortex appears to be most affected. Investigation of markers of CNS GABA in the CSF and in plasma of patients with AD has yielded varied results. Of the 12 studies examining CSF GABA levels, only four found significant reductions (40–77%) (Enna et al. 1977; Manyam et al. 1980; Zimmer et al. 1984; Mohr et al. 1986). A single study of plasma GABA levels found a 51% decrease in AD patients (Jimenez-Jimenez et al. 1998). However, two other studies failed to detect any significant variation. Unfortunately, the link between GABAergic dysfunction and BPSD in AD remains unclear. Of the above mentioned studies, only two described BPSD. Procter et al. (1992) reported no significant differences in cortical GABA concentrations between AD patients and control subjects with or without behavioral symptoms. An imaging study conducted by Wyper, Kelly, and Patterson (1998) failed to show a correlation between behaviors and cortical GABA deficits. However, clinical experience supports GABA as a therapeutic target for treatment of BPSD in AD. Benzodiazepines, which specifically affect the GABA receptors, have been widely used to ameliorate behavioral disturbances in AD. Anticonvulsants, which are believed to directly and indirectly modulate GABAergic neurotransmission, have also shown promise in treatment of BPSD. However, many of the above-mentioned treatments are limited by potential toxicity and side-effect profiles. In summary, the role of GABA in AD requires further study. Currently, clinical research trials examining the influence of GABA specifically on behaviors have been limited in number. However, it is highly probable

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that this neurotransmitter is directly or indirectly involved in the development of behavioral symptoms associated with dementia and that it may provide a target for future therapeutic intervention. Glutamate Approximately 70% of cortical neurons use glutamate (Glu) as a neurotransmitter. Hence it is the primary excitatory neurotransmitter in the cortex and hippocampus that is involved in many higher mental functions. A link between presynaptic and postsynaptic glutamatergic losses and cognitive dysfunction in AD has been well established. This neurotransmitter plays a pivotal role in learning and memory. Excessive glutamate levels in the brain can, however, lead to neurotoxicity which in turn may result in both cognitive and behavioral symptoms of dementia. Pertubations in glutamate homeostasis have been implicated in the development of both cognitive and behavioral symptoms in Alzheimer ’s dementia. Glutamate release and uptake are chronically decreased in AD leading to impaired neural activity in many cortical regions (Francis et al. 1993; Francis 2003). Neuropathologic studies have documented reduced levels of glutamate reuptake in the frontal and temporal cortices of patients with Alzheimer ’s disease. Vesicular glutamate transporters (VGluT) have also been noted to be lost in various cortical regions. These alterations lead to inefficient removal of free glutamate from the synapse resulting in the presence of abnormally high synaptic glutamate levels under resting condition. Because the reduction in vesicular glutamate uptake causes less glutamate to be stored in the vesicles, neurons are left with fewer neurotransmitter molecules to release into the synaptic cleft in times of neuronal activity. This results in two major pathophysiological consequences. First, the presence of elevated neurotransmitter levels in the synapse under resting conditions creates a constant “background signal,” leading to chronic lowlevel activation of glutamatergic receptors on postsynaptic neurons and potential neurotoxicity. Second, because of this background signal, as well as the fact that neurons are left with smaller amounts of neurotransmitter to release into the synapse during neuronal firing, the difference between synaptic glutamate concentration during neuronal activity and synaptic glutamate concentration under resting conditions, referred to as the “peak signal,” is attenuated, leading to suboptimal neurotransmission as exemplified by a lack of long-term potentiation (LTP). Though glutamate exerts its effects through four types of receptors, this neural process relies heavily upon neurotransmission mediated by N-methyl-D-aspartate (NMDA) receptors and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid

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(AMPA) receptors, as these receptors allow the influx of large amounts of stimulatory Ca2+ into the postsynaptic cell. If synaptic glutamate levels and intracellular calcium levels become significantly elevated, neurotoxicity and neurodegeneration ensue. The majority of evidence related to the effects of altered glutamate transmission and resulting neurotoxicity in psychosis stem from the glutamate hypothesis in illnesses such as schizophrenia. This hypothesis originated from the observed psychomimetic action of phencyclidine (PCP), a noncompetitive NMDA receptor antagonist. An interdependence between dopamine and glutamate in the pathophysiology of psychosis in schizophrenia and AD has been postulated. NMDA hypofunction in the prefrontal cortex may result in dysregulation of the dopamine system and this in turn affects the glutamate-mediated systems (Tamminga 1998; Laruelle et al. 2005). There is also evidence supporting the influence of glutamate neurotransmission in the cause and treatment response of depression (Paul and Skolnick 2003). As noted previously, many studies link serotonergic dysfunction to aggressive behaviors, although the postsynaptic location of most serotonergic receptors, including those positioned on glutamatergic pyramidal neurons, suggests a role for glutamate neurotransmission. Further indication as to the potential effect of altered glutamate neurotransmission on behavioral manifestations of AD is suggested by evidence from clinical trials of pharmaceuticals, such as memantine, which act on glutamate receptors. Memantine is a noncompetitive inhibitor of NMDA receptors that may permit normal memory formation but block their excitotoxic activation (Winblad, Mobius, and Stoffler 2002). A review of double-blind, parallel group, placebo-controlled, randomized trials of memantine in people with dementia conducted by McShane, Areosa Sastre, and Minakaran (2006) reported a slight decrease in development of agitation in patients taking memantine. This effect was slightly larger, but still small, in moderate to severe AD. There was no evidence about whether memantine had an effect on agitation which is already present. A significant advantage for memantine over placebo was also supported by a pooled analysis of 3 studies of patients with symptoms of aggression/agitation, delusions, and hallucinations (Wilcock et al. 2008) Although further study is indicated to establish the full impact of glutamatergic disruption in the human brain, initial evidence points to a probable link between behavioral and psychotic manifestations of AD and glutamate induced excitotoxicity. Additional research is currently underway to establish the significance of modification of non-NMDA receptors,

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such as the AMPA receptors, as possible targets for new anti-agitation therapies. Neuropeptides Little is known about the possible clinical consequences of alterations in neuropeptide levels in the brains of patients with AD. Most neuropeptides, including vasoactive intestinal peptide and cholecystokinin appear to remain unchanged. However, others, such as beta-endorphin in the cerebrospinal fluid (CSF), galanin in the nucleus basalis, and substance P in the neocortex, are significantly altered. Somatostatin and its receptors, typically found in high concentrations in the hypothalamus, neocortex, and limbic system, have been shown to be reduced in the hippocampus, the frontal and temporal cotices, and the superior temporal gyrus of patients with AD in postmortem studies (Gabriel et al. 1996; Tamminga et al. 1987). Somatostatin is involved in the regulation of hormone release from the anterior pituitary and may act as a neurotransmitter-modulator. One of its roles also appears to be that of induction and maintenance of long-term potentiation in the hippocampus, which is vital for the formation of new memories. Though the consequences of somatostatin deficiency are not yet fully understood, decreased CSF somatostatin levels have been displayed in several psychiatric and neurodegenerative disorders. Neuropeptide Y, a potent anxiolytic, has also been implicated as a potential contributor to the development of behavioral symptoms in dementia. Neuropeptide Y and somatostatin coexist in the amygdala, basal ganglia, and cerebral cortex. Minthon et al. (1997) analyzed the CSF levels of somatostatin-like immunoreactivity and NPY-like immunoreactivity of patients with AD and with frontotemporal dementia (FTD). They correlated these levels to 54 different clinical items, such as restlessness, anxiety, irritability and depression. The CSF levels of somatostatin and neuropeptide Y were significantly correlated in FTD, a neurodegenerative disorder whose hallmark features are behavioral disturbances, but not in AD. Several significant correlations to the clinical signs were found; disorientation and dyspraxia in AD and agitation, irritability, and restlessness in FTD. Somatostatin showed a significant negative correlation with severity of dementia in AD (Minthon, Edvinsson, and Gustafson 1997). Corticotropin-releasing hormone (CRF) immunoreactivity reductions, corresponding to interneuron losses in the neocortex, have also been consistently reported in AD. CRF reductions are not unique to AD and develop in numerous neurodegenerative processes and psychiatric illnesses. The

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role of CRF deficits in AD is not fully understood. Nevertheless, evidence from disorders such as depression and anxiety indicates a clear relevance of CRF homeostasis in the physiological and behavioral responses to stress (Todorovic et al. 2005). CRF release is stimulated by norepinephrine and acetylcholine, while its release is inhibited by GABA. Limited evidence from current studies and from analyses of other disorders manifesting with behavioral and psychotic symptoms suggests that neuropeptide alteration play a role in modulating behavior, particularly aggression and agitation, though a direct link requires further investigation. Dopamine Dopamine (DA) has long been implicated as a vital neurotransmitter affecting human behaviors, cognition, movement, motivation, mood, sleep, attention, reward/punishment, and memory. Excessive dopaminergic activity in the striatum has been viewed as a common final pathway for the development of psychotic symptoms in numerous psychiatric conditions, including schizophrenia, bipolar disorder, and psychotic depression. Dopamine has also been implicated as playing a critical role in determining response rates in reward-related behaviors that are essential for effortful choices such as pleasure-seeking behaviors. Hence this neurotransmitter plays a role not only in psychosis, but also in affective states and addictions. However, the role of dopamine in the development of BPSD in AD remains unclear. Central dopaminergic dysfunction has been reported in a subgroup of AD patients. However, in the absence of Lewy body pathology, AD appears to be associated with only mild dopaminergic dysfunction. The delicate interplay between the cholinergic, serotonergic and dopaminergic systems within the corticostriatal neurocircuitry, have been hypothesized to lead to relative striatal hyperdopaminergia and hence to psychosis. Dopamine D1 receptors appear to be only modestly reduced in the neocortex and basal ganglia, while more consistent losses of D2 receptors are evident. In vivo studies in small numbers of patients with moderately severe AD have reported higher D2 receptor availability in association with wandering behavior and lower striatal D2 receptor availability in more behaviorally disturbed patients (Tanaka et al. 2003; Meguro et al. 1997). More recently, higher striatal DA transporter availability has been correlated with apathy (“loss of initiative”) in a larger sample that included eight patients with Lewy body dementia (David et al. 2008). Apathy is believed to represent a deficit in corticostriatal (ventral striatum, prefrontal cortex)

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processing, characterized by a reduced ability to process emotionally significant stimuli. Despite some indication of dopaminergic involvement in disturbances of mood and behavior, a clear link cannot be established. Zubenko (1992) found that despite greater cytopathology in the substantia nigra of AD patients with major depression, dopamine levels were relatively intact when compared to AD patients without major depression. Decreased levels of homovanillic acid (HVA), a major dopamine metabolite, were associated with greater depression in some, but not all studies. Several postmortem and in vivo studies found a correlation between increased presynaptic dopamine levels and aggression in AD patients (Lopez et al. 1996).

SUMMARY Much remains to be learned about the neurochemistry of psychological and behavioral function in normal states and in dementia. Despite a rapidly developing understanding of the underlying neuropathology and neurochemistry, most evidence of explicit links between particular neurotransmitter abnormalities and a single behavior or behavior clusters remains inconsistent. This is likely related to the extensive interaction between many neurotransmitters and neuropeptides altered in dementia. Further, it is unclear whether changes in a particular neurotransmitter or neuropeptide are primary or secondary to changes in another. Adding to the confusion is the dynamic nature of BPSD. As opposed to static structural alterations, changes in behaviors are fluctuating and often intermittent in nature. They frequently involve biochemical and structural changes rather than static structural alterations. The study of the neurochemical etiology of BPSD presents many challenges. It is essential for future work to focus on identifying coexisting neuropeptide and neurotransmitter changes through the progression of the disease. Future research in this area must integrate neurobiological, neuroimaging, and clinical investigation. However, thorough study of BPSD clusters and their parallel neurochemical abnormalities may allow us to develop future patient-specific, evidence-based therapeutic interventions for dementia.

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Chapter 6

Dementia in Parkinson Disease: Current Concepts in Neuropathology, Neuroanatomy, and Neurochemistry Raymon Durso

The characterization of dementia in Parkinson disease has undergone over the past four decades a complex evolution parallel to advances in neurobehavioral cognitive sciences, neurochemistry, neuropathology, imaging, and genetics. It continues to be further defined with an ultimate goal of integrating information from these multiple disciplines and discovering unifying concepts that would significantly impact clinical treatment. This chapter will review current ideas concerning dementia in Parkinson disease along with relevant historical concepts that have shaped the field. It will focus on dopamine and acetylcholine as the major neurotransmitters mediating cognitive deficits in Parkinson disease. HISTORICAL PERSPECTIVES REGARDING THE CLINICAL EXPRESSIONS OF DEMENTIA IN PARKINSON DISEASE While initial descriptions of Parkinson disease were focused solely on motor deficits (Parkinson 1817), it became evident with further observation especially in patients with long-term disease that cognitive dysfunction was an undeniable part of this disorder. The occurrence of dementia in the early literature had been highly variable ranging from

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approximately 10–40% of Parkinson patients (Marttila and Rinne 1976; Lieberman et al. 1979; Taylor, Saint-Cyr, and Lang 1985; Tison et al. 1995; Aarsland et al. 1996; Hobson and Meara 1999). Contributions to this variance were primarily a lack of a cohesive approach to define exactly what constitutes dementia in the disorder. An international task force to develop unifying definitions and criteria for dementia associated with Parkinson disease was undertaken in the recent past (Emre et al. 2007). It is a complicated undertaking as it requires the inclusion of multiple disciplines including neuropathology, neurochemistry, neuroanatomy, epidemiology, and neuroimaging all of which continue to advance and provide new sources of unique data. From a clinical point of view, a better clarity regarding the nature of dementia in Parkinson disease and other parkinsonian disorders came with theories that two types of dementia could occur in neurodegenerative disorders. These were coined “subcortical” dementia and “cortical” dementia (Albert, Feldman, and Willis 1974; Cummings 1986). The entity “subcortical” dementia was associated with Parkinson disease and was characterized by problems with attention and deficits in spatial working memory and cognitive planning similar to those seen in animals with prefrontal damage (Mishkin 1957; Shallice 1982). Descriptions of “subcortical dementia” were often ascribed to a slowness of thought or “bradyphrenia” that results in prolonged cognitive processing time most evident when tasks become more complex (Zimmerman et al. 1992; Cooper et al. 1994). “Cortical” dementia had Alzheimer disease as its prototype and centered on language abnormalities and the presence of apraxia and agnosia (Cummings 1986). While the terms “subcortical” and “cortical” dementia are now rarely used, they nonetheless have set a valuable framework to understand how cognitive dysfunction in Parkinson disease might be rationally organized. The best present-day correlate to “subcortical” dementia is executive cognitive dysfunction. Executive cognitive function represents a battery of intellectual functions centered on attention span, concentration, and memory. Inclusive in these functions is an assortment of complicated processes that are involved in decision-making such as anticipation, judgment, motivation, social/ethical appropriateness, and goal-directed behavior. A fundamental requirement of executive cognitive function involves the ability to store and hold information on-line so that it can be continuously accessed in order to plan and change strategies. These functions, as will be discussed later in this chapter, are believed to be mediated through dopaminergic pathways involving midbrain neurons in the substantia nigra and ventral tegmental area that are either directly or indirectly distributed to the prefrontal cortex. The concept of executive cognitive

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function implies the requirement of networks to process information from multiple areas of brain as a necessary part of decision making. At the core of this processing lies “working memory,” which involves prefrontal dopaminergic neurons that have the capacity to maintain firing as a probable method of storing information when tasks are being planned and executed (Goldman-Rakic 1996). One can envision initiating an activity that requires a wide range of information to accomplish. For example, throwing an initial punch in a fight would not only require the gathering of visual, proprioceptive, and motor information but would also process more complicated data such as the ethics and social consequence of such an action. In addition, long-term memory information would be likely included (e.g., What happened when I previously performed this action?). The process of storing all information as it is gathered prior to actual output appears to be an important function of prefrontal neurons mediating working memory. The prefrontal area is ideally situated to receive information from the neocortex as well as the limbic system. A more complete gathering and maintaining of information in prefrontal areas prior to initiating a motor or cognitive task could be viewed as a neurochemical/ neurophysiological correlate for the concept of logic. That is, all input has been considered for a task and consequently the output has been a decision based on the processing of the most complete database possible. Conversely, an inability to employ a full database because either the prefrontal working memory cells cannot receive information from all parts of the brain or they cannot maintain a firing rate as information is gathered would result in an output that has not considered a full complement of potentially available information for planning. The result may be more impulsive appearing behavior that appears illogical. One of the best tests capable of demonstrating executive cognitive dysfunction in Parkinson disease and the role of “working memory” is the Wisconsin Card Sorting Test. In this test “stimulus cards” with shapes of different colors, designs, and quantities are initially presented to the subject. Without informing the subject, the examiner then decides whether additional cards to be given to the subject will be matched to the stimulus cards by color, design, or quantity. The examiner tells the subject only whether the match of the new additional cards to the stimulus cards is right or wrong. Consequently, by trial and error, the subject learns how he or she should be matching the cards. The matching rules are subsequently changed (without informing the subject) and the subject must change strategy to learn and apply new rules. The test measures how long it takes the subject to learn the correct strategy a well as how long it takes to acquire the new strategy (“change sets”). Consequently, the Wisconsin

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Card Sorting Test gives information on how well the subject can plan and change strategies based on goal-directed behavior, all of which are fundamental skills related to executive function. It provides information on both impulsive and perseverative tendencies. It is easy to envision how tasks associated with the Wisconsin Card Sorting Test would depend heavily on maintaining activity in working memory cells since decisions are closely based on information gathered in previous successful matching attempts. In early Parkinson disease patients, deficits are seen in the Wisconsin Card Sorting Test with a prominent feature being the tendency to perseverate; that is, patients find it difficult to learn new strategies when the task has changed (Canavan et al. 1989; Paolo et al. 1996). Clinically, behavioral perseveration is frequently manifested in some patients when they seem unable to change their actions even when such previous behavior has resulted in an unwanted outcome (e.g., persistent falling because of not using an available cane or walker, continuing to drive a car despite previous accidents). Others have also found problems in early Parkinson disease involving visual working memory with a sparing of verbal working memory (Bradley, Welch, and Dick 1989). Of interest is the observation that there appears to be preservation of visual working memory for shapes (Postle, Jonides, and Smith 1997; Owen et al. 1997). It is suggested that the visual working memory deficits become more evident in early Parkinson disease only when more complex executive function tasks are added to the testing paradigms such as set shifting (Owen 2004). Abnormalities in the Ravens Progressive Matrices and Tower of London Test also suggest that the greatest deficits are evident when tasks are changed and new strategies need to be used (Farina et al. 2000; Owen, James, and Leigh 1992). Perseveration of previous strategies and inconsistent performance on newly acquired strategies tend to predominate. The Ravens Progressive Matrices tests a subject’s ability to find patterns in an apparent chaos of visual scenes by having the subject complete the last image in a series. The Tower of London Test and its variations involve a test subject moving colored beads among different pegs on a pegboard in order to replicate the design of the examiner. Subjects are scored by the number of moves undertaken to replicate the examiner ’s design with the fewest moves resulting in the best score. In both tests subjects learn the strategy being employed, which is then changed, and the new strategy needs to be discovered and applied. While working memory deficits in visual spatial tasks have been especially noted in Parkinson disease, verbal memory tests using “working memory” have also proven problematic in Parkinson disease. There is no evidence for language deficits (i.e., aphasia) but rather apparent deficits in the executive function of planning speech. As expected and similar to

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previously described visual spatial testing, deficits are most notable with greater complexity in the task. Testing that requires multiple simultaneous levels of processing results in the greatest deficits. Patients appear to be less able than control subjects to develop strategies that integrate and prioritize information to perform more complex memory tasks. For example, when interference is interjected into a memory testing paradigm deficits become more clearly evident (Taylor, Saint-Cyr, and Lang 1990; Cooper, Sagar, and Sullivan 1993). There would appear to be an inability to maintain information in “working memory” when extraneous information is being introduced. It is evident that executive cognitive dysfunction occurs early in Parkinson disease with the greatest involvement mediated through “working memory” in prefrontal areas. These executive deficits are more evident when tasks become more complicated and especially when new rule changes must be incorporated. The next section discusses the dopaminergic pathways believed to mediate these processes. THE NEUROANATOMICAL AND NEUROCHEMICAL BASIS OF EXECUTIVE COGNITIVE DYSFUNCTION IN EARLY PARKINSON DISEASE: ROLE OF DOPAMINE The nature of the earliest type of cognitive impairment in Parkinson disease as discussed earlier involves executive cognitive dysfunction. While the understanding of the neuroanatomy and neurochemistry of executive cognitive function continues to be investigated and better understood, current research has focused on two neuroanatomical pathways as most important to mediating executive cognitive function where dopaminergic transmission seems to play the major role. These involve (1) nigrostriatalthalamic-frontal cortex circuitry and (2) mesocortical pathways in the brain. Nigrostriatal-thalamic-cortex pathways have been recognized as a potential circuit for linking basal ganglia with cortical function (Alexander, Delong, and Strick 1986). The pathway linking nigra with prefrontal cortex initiates with the nigrostriatal tract which has classically been argued to arise predominantly from the zona compacta (A9) area of the substantia nigra. The long-excepted role of the nigrostriatal tract in mediating normal movement by supplying dopamine to the striatum has been expanded to include cognitive function in recent decades. The ventrolateral portion of the nigra innervates putamen and is most damaged in Parkinson disease. It is this part of the nigra that is believed to mediate the prominent motor dysfunction. Medioventral and dorsal

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areas of the nigra, however, connect to the caudate (Rinne et al. 1989). The caudate nucleus of the striatum has been of particular interest as a structure that plays a role in normal executive function. Some [18F]fluorodopa positron emission tomography (PET) studies in Parkinson disease have reported reduced dopaminergic activity in caudate nucleus that correlates with deficits in neuropsychological performance (Marie et al. 1999; Bruck et al. 2001). A most recent study using single photon emission tomography with [I-123] Iofluplane-CIT, a marker that binds to presynaptic dopamine transporter proteins and hence an indicator of integrity of the nigrostriatal tract, has found a positive correlation between tests of executive cognitive function and caudate uptake in both hemispheres (Nobili et al. 2010). The efferent pathways from striatum to frontal cortex mediated through thalamus represent an indirect effect of caudate dopamine levels on “working memory” neurons in prefrontal cortex. In addition, reciprocal connections between these frontal areas and parietal cortex (Cavada and Goldman-Rakic 1989) provide a neuroanatomical basis for understanding how information required for planning and managing complex tasks in space and time is manipulated. At the level of prefrontal cortex, there is consensus that two areas are most important in mediating executive cognition, the ventrolateral frontal cortex (areas 45 and 47) and the dorsolateral frontal cortex (areas 46 and 9) (Owen 2004). These areas likely differ in the way they process similar memory-related information with ventrolateral areas handling first order processing and dorsolateral frontal cortex further manipulating data in a manner that allows for retention and constant monitoring in order to develop and change strategies when undertaking a complicated dynamic task. The ventrolateral frontal cortex has well-described connections to posterior association areas in parietal and temporal lobes and appears to mediate more simple memory functions such as the intentional retrieval of data. It can also serve to activate working memory that allows for decision making and judgments but only when tasks are not complicated and contain few steps. The second prefrontal processing area for executive cognitive function, the dorsolateral frontal lobes (area 46 and 9), appear to mediate more complex memory processing. Connections between ventrolateral and dorsolateral frontal lobes provide a network for first order memory information handled in the ventrolateral frontal cortex to be transferred to dorsolateral frontal areas for further processing. This more complicated processing is the basis for the development of complex planning and strategy. The dorsolateral frontal cortex is likely fundamental in mediating change in

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strategy based on incoming new information. This ability to “change sets” requires that earlier memories be temporarily stored and monitored in order to be compared to newer experiences representing change. Recognition of this change can then allow for a new strategy to be developed. Imaging data from PET marking cerebrovascular blood flow lends support for these suppositions. Specifically, in healthy normal volunteers who are asked to perform tasks involving short-term retention of five serially presented spatial tasks with no further ongoing manipulation, imaging changes representing increased cerebrovascular blood flow are noted in the right ventrolateral frontal cortex. However, when the task presented becomes more complicated and involves ongoing rule changes requiring continuous updating of information and resultant strategy changes, then mid-dorsolateral frontal cortex activity is noted along with bilateral ventrolateral frontal activation (Owen, Evans, and Petrides 1996). In Parkinson disease patients undergoing PET to examine cerebral blood flow, clear deficits in these frontolateral cortex subdivisions have been difficult to find (Owen et al. 1998). However, functional MRI testing comparing PD patients with mild executive cognitive deficits to cognitively intact PD subjects has supported deficits in caudate and frontolateral cortex in impaired subjects (Lewis et al. 2003). Furthermore, memory testing that required manipulation of presented data (i.e., that which is theorized to both require ventrolateral and dorsolateral frontal processing) showed a greater MRI abnormality in frontal regions than tasks involving more simple memory recall (Lewis et al. 2005). This is consistent with previously discussed neuropsychology research that emphasized cognitive deficits in early PD were in the realm of working memory function that involved more complex processing and manipulation of memory data. More recent functional MRI studies have emphasized that only some executive cognitive tasks, especially those associated with set-shifting, require caudate activity (Monchi et al. 2004, 2007). When such set-shifting tasks are analyzed with functional MRI, reductions in dorsolateral and ventrolateral frontal cortex along with expected diminished caudate activity are evident in Parkinson disease. However, when executive tasks not requiring set-shifting are employed (i.e., tasks not requiring caudate activation), increases in dorsolateral and ventrolateral frontal cortex activity are noted. In addition, posterior cortical areas are activated as well (Samuel et al. 1997; Cools et al. 2002, Monchi et al. 2007). It is believed that such a pattern of increased activation may represent an attempt to recruit more cortical activity in order to compensate for and circumvent existing deficits in Parkinson patients (Samuel et al. 1997; Dagher et al. 2001). These kinds of results have also been argued as evidence that additional systems other

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than nigrostriatal-thalamic-prefrontal circuitry (e.g., the mesocortical dopamine pathway) must exist to mediate executive cognitive function not requiring set-shifting. The second dopaminergic pathway theorized as important for executive cognitive function involves direct dopamine stimulation of the prefrontal cortex from mesocortical tracts arising predominantly from cell bodies in the ventral tegmental area of midbrain. This mesocortical tract supplies prefrontal and basal frontal cortex. Loss of dopaminergic neurons in the mesocortical tracts of Parkinson disease patients has been well described. Using a tyrosine hydroxylase enzyme marker that identifies catecholamine neurons, loss of dopamine cells in the ventral tegmental area of the midbrain was reported in Parkinson disease postmortem tissue (Javoy-Agid and Agid 1980). Similarly, autopsy examination of basal frontal cortex levels of dopamine and its metabolites in Parkinson patients who had not taken levodopa for at least four days prior to death also showed declines as compared to controls (Scatton et al. 1983). Dopamine loss in frontal areas (averaging 40%) tends to be more variable and less severe as compared to that in the striatum (> 80%) (Agid, Javoy-Agid, and Ruberg 1987). [18F]fluorodopa PET has demonstrated in Parkinson disease subjects reductions in frontal dopaminergic activity (presumed to represent mesocortical tracts) of 45% of controls (Rinne et al. 2000). In this same study the importance of both caudate and mesodopaminergic activity in mediating executive cognitive dysfunction was reported. Specifically, significant decreases in caudate [18F]fluorodopa uptake (and not frontal cortex) were correlated with slower performance on tasks requiring subjects to make multiple associations that were not intuitive. Other types of tasks examining executive function involving more simple immediate memory (digit span backwards, phonologic fluency, and paired word associations) were significantly correlated with frontal cortex uptake and not caudate activity. The greater the radioactive fluorodopa uptake in the frontal lobes (indicative of more intact direct mesocortical dopaminergic innervation), the better was the performance on these simpler tasks. Further results emphasizing the importance of direct mesocortical dopaminergic activity in activating dorsolateral frontal cortex after performance of a planning task was reported with positron emission tomography measuring cerebral blood flow (Cools et al. 2002). These authors indicated that in Parkinson patients evaluated both on levodopa medication and off levodopa medication for approximately 18 hours that levodopa-induced increases in cerebral blood flow were significantly diminished in the right dorsolateral prefrontal cortex as a function of poor performance on the planning task. Such a correlation was not seen with basal ganglia blood flow, indicating that the finding was not mediated

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through indirect nigrostriatal-thalamic-frontal pathways. There are also functional MRI studies that support an independent role of the mesocortical dopaminergic pathway in mediating executive cognitive dysfunction in Parkinson disease (Mattay et al. 2002). In examining early Parkinson disease subjects with functional MRI during a working memory task on levodopa and off levodopa for at least 12 hours, investigators found that increased activity in frontal lobe during the working memory task was most evident only when subjects were off levodopa. Under this “no levodopa” condition greater activity in frontal lobes was associated with a greater number of testing errors, suggesting that there was an attempt to recruit more frontal cortex in order to compensate for working memory deficits. They found no difference in frontal activity during working memory testing when patients were taking levodopa. They argued that if nigrostriatal-thalamic-frontal cortex pathways were important during this working memory task, frontal activity should have been increased after administration of levodopa similar to the increase they found in cortical motor regions. In summary, Parkinson disease, even early in the disease process, is associated with impaired cognition. The latter takes the form of executive cognitive dysfunction and is most evident in tasks that require the storing and monitoring of incoming information required to develop strategies of action. Parkinson disease patients appear most disabled in their ability to use such on-line processing in order to “change sets” or change strategies based on new conditions and rules. Their tendency is to perseverate on older strategies. It is believed that such defects come from abnormalities and deficiencies in dopamine processing both through the nigrostriatalthalamic-frontal cortex circuit and the mesocortical dopamine pathway. There is evidence that deficiencies in both of these systems are responsible for the executive cognitive dysfunction seen in Parkinson disease. What remains uncertain is the exact nature of how these different dopaminergic pathways serve specific aspects of working memory and how they integrate with each other to provide for “normal” function. Answers to these questions will be better elucidated as improved imaging techniques become available and better testing strategies evolve to examine potential underlying principles that comprise normal working memory. THE NEUROANATOMICAL AND NEUROCHEMICAL BASIS OF DEMENTIA IN PARKINSON DISEASE: ROLE OF ACETYLCHOLINE As previously discussed, even early in Parkinson disease there is evidence of cognitive impairment in the form of executive cognitive dysfunction.

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From a practical point of view this degree of impairment would likely go unnoticed by family and acquaintances of the affected patient. However, the development of severe dementia also is evident in Parkinson disease. Such dementia can develop into severe cognitive loss rivaling that of Alzheimer disease or dementia with Lewy bodies. Parkinson patients run a five times greater risk of developing dementia as compared to age-matched controls (Hobson and Meara 2004). The most recent reports citing incidence and prevalence of dementia in Parkinson disease remain, like their predecessors, highly variable. Hughes et al. (2000) found the prevalence of dementia in Parkinson disease to be to be 38% after 10 years in a cohort of 83 Parkinson patients without evidence of dementia at time of recruitment. Aarsland, Anderson, et al. (2003), using three different cognitive rating scales (MiniMental State Examination, the mental subscale of the UPDRS [United Parkinson Disease Rating Scale], and the Gottfries-Brane-Steen scale) found that the prevalence of dementia grew in their cohort of 224 Parkinson patients from an initial 26% to 78% after eight years. In a cohort of 86 Parkinson patients with no evidence of dementia, the prevalence of the problem after four years was found to be 35.3% as compared to 7% in control subjects (Hobson and Meara 2004). Finally, the incidence of dementia as measured with a Mini-Mental State Examination in 740 newly diagnosed patients participating in the DATATOP study was 5.8% after five years (Uc et al. 2009). The differences in prevalence among these studies mostly relate to the rigorousness of the diagnostic criteria employed and differences in the prevalence of dementia in the initial cohorts. The most important risk factors for developing dementia have been reported as severity of Parkinson motor signs (Marder et al. 1995; Hughes et al. 2000; Levy et al. 2002; Uc et al. 2009), duration of Parkinson symptoms (Hobson and Meara 2004), age (Hughes et al. 2000; Levy et al. 2002; Hobson and Meara 2004; Uc et al. 2009), and development of hallucinations (Aarsland, Anderson, et al. 2003; Hobson and Meara 2004: Uc et al. 2009). Cognitive function in Parkinson patients with dementia involves numerous deficits not seen in early Parkinson disease. In the latter, as previously discussed, executive cognitive dysfunction is subtle, involving primarily complex working memory tasks that require set changes and online monitoring of associations among incoming memory traces. In demented Parkinson patients, executive cognitive abnormalities become more profound. For example, a simple version of the Wisconsin Card Sorting Test examining visual working memory is abnormal in demented Parkinson disease patients and not in their nondemented counterparts (Paolo et al. 1996). In studies comparing demented Parkinson subjects with Alzheimer disease there is a tendency for demented Parkinson patients to have more severe

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abnormalities in executive cognitive function such as attention and working memory than their Alzheimer counterparts (Litvan et al. 1991; Pillon et al. 1993; Aarsland, Litvan, et al. 2003; Noe et al. 2004). However, it is evident from these same reports that differences tend to blur as dementia severity worsens especially in tests of simple learning and immediate memory. Another tendency seen is that on tests of executive function and memory, Parkinson disease dementia more closely simulates dementia with Lewy bodies than Alzheimer disease (Litvan et al. 1991; Paolo et al. 1995; Aarsland, Litvan, et al. 2003). In addition to executive dysfunction, patients with Parkinson dementia appear to manifest more global deficits that would suggest involvement of additional cortical structures outside prefrontal areas. For example, abnormalities in visual spatial skills are also a feature of dementia in Parkinson disease. These same abnormalities are often described as a hallmark of Alzheimer disease and are presumed to represent early parietal cortical involvement. In Parkinson disease dementia, visual spatial and constructional skills are impaired as compared to normal subjects (Postle, Jonides, and Smith 1997; Farina et al. 2000). When compared to Alzheimer disease these defects are either equally or more severely involved (Paolo et al. 1995; Litvan et al. 1991; Cahn-Weiner et al. 2003; Cormack et al. 2004). In addition to dysexecutive problems, differences in the cognitive profile separating Parkinson disease dementia from Alzheimer disease has been an emphasis of memory dysfunction and language deficits in the latter (Cummings et al. 1988; Emre 2003). Imaging studies to be discussed later in this chapter give credence to the concept that larger areas of cortex including parietal cortex and hippocampus become involved when Parkinson patients become demented. This could explain these findings of significant visual-spatial dysfunction rivaling or even surpassing that seen in Alzheimer disease. It must be remembered, however, that visual spatial and drawing tasks often use both motor skills and complex processing that requires planning and strategy development. As previously discussed these abilities require prefrontal cortex and working memory. Therefore, the known severe executive dysfunction present in demented Parkinson patients almost assuredly plays a significant role in poor visual spatial function and constructional abnormalities even if a superimposed progressive cortical impairment exists. The findings of increased risk for dementia in Parkinson disease have raised the issue as to whether dementia is (1) part of the natural pathological course of Parkinson disease and (2) the natural progression of Parkinson disease involves the co-occurrence of other dementing illnesses notably dementia with Lewy bodies and/or Alzheimer disease.

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DEMENTIA AS A NATURAL PROGRESSION OF PARKINSON DISEASE: SUPPORT FROM IMAGING STUDIES The best support that dementia may be part of the natural course of Parkinson disease is derived from imaging studies. PET was used to image both dopaminergic integrity with [18F]fluorodopa and cholinergic integrity using N-[11C] –methl-4-piperidayl acetate (MP4A) in Parkinson disease patients with and without dementia as compared to normal controls (Hilker et al. 2005). Investigators found decreased striatal fluorodopa uptake in Parkinson patients with and without dementia as compared to controls. This was consistent with the presence of their motor disabilities. In Parkinson patients with dementia, however, cholinergic MP4A global cortical binding showed significant reductions of 29% versus controls while Parkinson patients without dementia demonstrated moderate reductions of 10.7% compared to controls. Most interestingly, patients with dementia had a significant correlation between reductions in striatal fluorodopa update and MP4A diminished cortical binding. These results led the authors to propose that Parkinson disease might represent a complex pathological process involving multiple linked neurotransmitters deficits. They speculated that with advancing disease cholinergic activity is increasingly lost resulting in more severe cognitive dysfunction. The observation that cholinergic loss is present in Parkinson disease patients with dementia is supported by other studies as well. The nucleus basalis of Meynert (NBM) represents the major projecting cholinergic system from basal forebrain to amygdala and neocortex (Whitehouse et al. 1983; Perry et al. 1985). In demented patients with Parkinson disease, a selective loss of neurons within this nucleus has been reported (Whitehouse et al. 1983). Cortical concentrations of choline acetyltransferase, the synthetic enzyme for acetylcholine, represent an excellent marker of cholinergic neuronal integrity. This enzyme is located in cholinergic fibers projecting from cell bodies in the NBM. The finding of severe choline acetyltransferase reductions in temporal neocortex of demented Parkinson patients postmortem with the degree of loss correlating with severity of cognitive impairment further emphasizes the importance of this neurotransmitter loss in contributing to Parkinson disease dementia (Perry et al. 1985). Concentrations of acetylcholinesterase, the enzyme metabolizing acetylcholine, have also frequently been employed to evaluate the cholinergic system. In PET studies using the ligand 1-[11C]methylpiperidin-4-yl propionate that binds to this enzyme, an average reduction of 20% in cortical activity was seen in Parkinson patients with dementia as compared to 12.9% in nondemented Parkinson disease patients (Bohnen et al. 2003). Similar results were

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reported using PET and N-[11C]-methyl-4-piperidyl acetate as a ligand for acetylcholinesterase (Shimada et al. 2009). In the latter study, this enzyme in cerebral cortex was found to be reduced 12% in Parkinson disease without dementia and 27% in Parkinson disease with dementia as compared to controls. All of these imaging studies indicate that cholinergic loss is seen early in Parkinson disease and becomes more severe when dementia develops. In Bohnen and colleagues’s study (2003) the reduction in cholinergic activity in Parkinson patients with dementia was even more severe than patients with mild Alzheimer disease. Cholinergic deficits contributing to cognitive loss in Parkinson disease with dementia is also supported by clinical pharmacology research demonstrating cognitive improvement in these patients (including executive function) after administration of rivastigmine, a drug that inhibits breakdown of acetylcholine (Emre et al. 2004). There is also an indication that interaction between acetylcholine and dopamine in the frontal and hippocampal cortex is required to accomplish some types of working memory tasks (Wisman et al. 2008). Specifically, a working memory task involving preservation of visual spatial memory acquired over several days remains unaffected in rats receiving ventral tegmental lesions to disrupt dopaminergic mesocortical tracts. In a similar fashion, the task also remains intact in animals given lesions of the NBM and/or septohippocampal projection to interrupt cholinergic input to the neocortex and hippocampus. While neither the dopaminergic nor cholinergic lesion alone will affect working memory performance, when both lesions are simultaneously undertaken (specifically those involving ventral tegmental and septohippocampal regions) diminished working memory performance is first noted. These imaging studies lend support for the concept that Parkinson disease may represent a progressive illness that initially involves executive cognitive dysfunction mediated through prefrontal dopaminergic pathology and then expands to involve more diffuse cortical structures (e.g., parietal and hippocampal anatomy) with increasing involvement of the cholinergic system. The basis for this expanding pathology remains unknown. The possibility that either dementia with Lewy bodies or Alzheimer disease are etiologically linked with Parkinson disease dementia is next discussed as possible pathological explanations for this expanding pathology. ARE PARKINSON DISEASE DEMENTIA AND DEMENTIA WITH LEWY BODIES ETIOLOGICALLY LINKED? The premise that Parkinson disease may progress to dementia with Lewy bodies derives from pathological studies in recent years that have

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emphasized that a common link may exist among different neurodegenerative illnesses centered on abnormalities in the protein synuclein. The Lewy body, a cytoplasmic inclusion in neurons and neurites, represents the pathological hallmark of Parkinson disease (Forno 1996). Synuclein is a major component of the Lewy body (Spillatini et al.1997). It is normally a soluble small protein occurring in cytoplasm with evidence of membrane association (Maroteaux and Scheller 1991; Jakes, Spillantini, and Goedert 1994). The pathological form of this protein appears to occur when it aggregates and becomes insoluble resulting in accumulation in neurons and glial cells in a filamentous configuration (Goedert and Spillantini 1998; Baba et al. 1998). Lewy bodies in Parkinson disease are typically concentrated in brainstem neurons, most notably in substantia nigra, locus coeruleus, dorsal motor nucleus of the vagus, and the NBM (Forno 1996). Dementia with Lewy bodies is characterized by an additional presence of Lewy bodies diffusely distributed throughout the cortex. Clinically, while some overlap may occur, the disorder is differentiated from Parkinson disease with dementia by an earlier emergence of dementia and hallucinations, more rapid clinical deterioration, and poorer response to levodopa (McKeith et al. 1996). The Braak hypothesis of staging idiopathic Parkinson disease has argued that Parkinson disease and dementia with Lewy bodies are etiologically linked through the Lewy body and synuclein (Braak et al. 2003, 2004) and represent a continuum of disease. They have proposed six stages of pathology in Parkinson disease that begin with Lewy body formation in brainstem nuclei including the dorsal motor nucleus of the vagus and NBM (stage 1) progressing to involvement of substantia nigra (stages 3 and 4) and ending in a diffuse presence of Lewy bodies throughout the brain (stages 5 and 6) analogous to pathology seen in dementia with Lewy bodies. Multiple studies have found the presence of increased amounts of brain synuclein in various neurodegenerative disorders including Parkinson disease, dementia with Lewy bodies, and multiple system atrophy (Wakabayashi et al. 1997, 1998; Spillantini et al. 1998, “Filamentos”; Spillantini et al. 1998, “Alpha-synuclein”). These reports involved qualitative staining techniques based on antibody immunoreactivity. For multiple system atrophy, in addition to neurons and neurites, staining was seen in glial cells. The term alpha-synucleinopathies was coined as a term to represent the concept that these orders were linked through synuclein (Goedert and Spillantini 1998; Galvin, Lee, and Trojanowski 2001). The association between synuclein and Parkinson disease was first established in two families with familial Parkinson disease (Polymeropoulos et al. 1997; Kruger et al. 1998). However, the link between synuclein and idiopathic

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Parkinson disease with or without dementia continues to be a work in progress. In addition, still absent is the discovery of a distinct pathological mechanism by which synuclein would damage cells. Familial forms of Parkinson disease that are clearly associated with synuclein are extremely rare and other genetic markers such as the PARK genes some of which are associated with synuclein are not present in the vast majority of patients with sporadic occurring disease. The PARK genes tend to be found in young-onset Parkinson disease patients who have considerably less risk of dementia as compared to patients with typical age of onset and no evidence of genetic involvement (Khan et al. 2003; Lohmann et al. 2009). In addition when using more accurate quantitative techniques to measure synuclein, there appears to be a lack of consensus regarding abnormal increases in synuclein in the substantia nigra of “idiopathic” Parkinson disease. Changes reported range from large increases (Devi et al. 2008) to unchanged (Fuchs et al. 2008). A most recent study using a quantitative western blotting methodology did not find synuclein increases in the nigra, striatum, or frontal cortex of Parkinson patients with brainstem predominant Lewy body pathology nor were there any significant correlations between intensity of Lewy bodies and levels of synuclein (Tong et al. 2010). These authors did report marked increases of synuclein throughout the brain including the nigra in a patient with familial Parkinsonism–dementia. They also reported similar large increases of synuclein in the cortex and nigra of patients with multiple-system atrophy. The observed differences in Parkinson disease results as compared to previously cited studies were attributed to differences in technique (qualitative verses quantitative analysis) and the possibility that Parkinson specimens in other studies may have been contaminated with patients having dementia with Lewy bodies (i.e., studies were less rigorous in defining Parkinson disease as only having Lewy bodies isolated to brainstem). In addition, the Braak hypothesis, which has been used as a basis of support for linking Parkinson disease and dementia with Lewy bodies through a common synuclein pathology, has been challenged in recent years (Jellinger 2008). The presence of dementia in patients with early Braak stages, the absence of a caudo-rostral progression over time in young onset Parkinson patients, the lack of a correlation between Parkinson clinical severity and Braak staging, and sparing of caudal regions (medullary nuclei) in Parkinson post-mortem tissue demonstrating synuclein inclusions in the midbrain and cortex consistent with Braak stages 4 and 5 have all been cited deviations questioning validity. Consequently, the role of synuclein in pathologically linking Parkinson disease with other neurodegenerative disorders representing the synucleinopathies remains elusive.

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It would seem that Parkinson disease with dementia and dementia with Lewy bodies still remain as distinct clinical entities although it is clear that pathologically this remains an open question. In the future, better clarification of genetic risk in sporadic Parkinson disease may uncover more unifying genetic pathways that etiologically link Parkinson disease with other neurodegenerative disorders. To date, however, research attempting to determine if genes causing specific monogenic forms of familial Parkinson disease may also serve as susceptibility factors for sporadic Parkinson disease has not yielded clear results (Lesage and Brice 2009). Synuclein may serve as a marker for a common pathology still to be determined. It also still remains possible that synuclein changes may result as a secondary response to other suspected pathophysiologic mechanisms. In that regard, there is some evidence that dopamine and possibly related oxidative metabolism, a longstanding pathological mechanism proposed to underlie Parkinson cell death, can change soluble synuclein into the insoluble form proposed to be linked to neurodegenerative pathology (Kim and Kang 2005; Outeiro et al. 2009).

ARE PARKINSON DISEASE DEMENTIA AND ALZHEIMER DISEASE PATHOLOGIES LINKED? Alzheimer disease neuropathology is characterized by the occurrence of neurofibrillary tangles and amyloid plaques in the brain whereby Parkinson disease is best differentiated pathologically from Alzheimer disease by the occurrence of Lewy bodies in brainstem nuclei. Clinically, at least early in the course of these two diseases, there are differences in cognitive deficits as previously discussed in this chapter that involve abnormalities in executive function in Parkinson disease and more predominant memory and language deficits, agnosia, and apraxias in Alzheimer disease. It should be emphasized, however, that any such differences tend to vanish when the dementias of both disorders progress. When comparing postmortem brains of Parkinson patients with and without dementia, a higher burden of Alzheimer cortical changes are seen in the demented Parkinson disease (Werner and Jellinger 1991: Selikhova et al. 2009). However, it is also noted that the presence of Lewy bodies are a better predictor of dementia than the occurrence of neurofibrillary tangles or plaques (Hurtig et al. 2000; Selikhova et al. 2009). In patients with dementia, when Lewy body and neurofibrillary tangle/amyloid plaque pathologies overlapped faster progression of dementia was noted as compared to patients with only Alzheimer or Lewy body pathology (Kraybill

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et al. 2005). This would suggest an increased cognitive burden when both pathologies co-occur. With regard to genetics, there appear to be only weak links tying Alzheimer disease to Parkinson disease with dementia. Siblings of patients with Parkinson disease and dementia as compared to siblings of normal controls were found to have a three times greater risk of developing Alzheimer disease. No increased risk of developing Alzheimer disease was found among the parents of these demented patients (Marder et al. 1999). The apolipoprotein E 4 genotype that provides an increased risk for developing Alzheimer disease is not associated with risk for dementia in Parkinson disease (Huang, Chen, and Poole 2004; Kurz et al. 2009). At the present time there does not seem to be enough supportive data to link the pathology of Parkinson disease and Alzheimer disease. It seems reasonable to still treat these two disorders as distinct clinical entities. It is possible that they may co-occur in some individuals leading to even greater cognitive decline than either disorder alone. REFERENCES Aarsland, D., K. Anderson, J. P. Larsen, A. Lolk, and P. Kragh-Sorensen. 2003a. Prevalence and characteristics of dementia in Parkinson disease. Archives of Neurology 60: 387–392. Aarsland, D., I. Litvan, D. Galasko, T. Wentzel-Larsen, and J. P. Larsen. 2003b. Performance on the dementia rating scale in Parkinson’s disease with dementia and dementia with Lewy bodies: Comparison with progressive supranuclear palsy and Alzheimer ’s disease. Journal of Neurology, Neurosurgery, and Psychiatry 74: 1215–1220. Aarsland, D., E. Tandberg, P. J. Larsen, and J. L. Cummings. 1996. Frequency of dementia in Parkinson disease. Archives of Neurology 53: 538–542. Agid, Y., E. Javoy-Agid, and M. Ruberg. 1987. Biochemistry of neurotransmitters in Parkinson’s disease. In Movement disorders, ed. C. D. Marsden and S. Fahn, 166–230. London: Butterworth. Albert, M. L., R. G. Feldman, and A. L. Willis. 1974. The subcortical dementia of progressive supranuclear palsy. Journal of Neurology, Neurosurgery, and Psychiatry 37: 121–130. Alexander, G. E., M. R. Delong, and P. L. Strick. 1986. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience 9: 357–381. Baba, M., S. Nakajo, P.-H. Tu, T. Tomita, K. Nakaya, V. M.-Y. Lee, J. Q. Trojanowski, and T. Iwatsubo. 1998. Aggregation of alpha-synuclein in Lewy bodies of sporadic Parkinson’s disease and dementia with Lewy bodies. American Journal of Pathology 152: 879–884.

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Chapter 7

Aspects in Neuropsychology: Depression and Dementia Ilan Halperin and Amos D. Korczyn

A complex association exists between late-life depression, cerebrovascular disease, and poor cognitive outcome, including progressive dementia and especially Alzheimer ’s disease. While neuroimaging evidence suggests that cerebrovascular disease plays a prominent role in causing dementia, it appears that depression may also confer substantial risk for developing dementia. The relationships between the cerebrovascular changes, other structural abnormalities in the brain, specific forms of cognitive dysfunction, and increased risk for developing dementia among those with late-life depression have been difficult to reconcile. Various findings suggest the existence of multiple pathways to poor cognitive outcomes. In this chapter, we present a framework outlining multiple etiologic and pathogenetic links between depression, cognitive impairment and progressive cognitive decline, including dementia. The suggested framework can assist research by synthesizing the knowledge obtained so far on the depression-dementia relationship, underlying the neurobiological mechanisms which contribute to the identification of at-risk individuals and monitor the impact of depression on the clinical status and course of both illnesses. OVERVIEW OF LATE ADULTHOOD: NEUROBIOLOGICAL, PSYCHOLOGICAL AND SOCIOECONOMICS OF OLD AGE Gerontologists divide older ages into two groups: young elders aged 65 to 74 years, and the oldest-old, aged 75 years and beyond. For many

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individuals the passage from midlife to late adulthood is marked also by a shift from the pursuit of wealth to maintenance of health. (The term “late adulthood” usually refers to the stage of the life cycle that begins at age 65 years, replacing the now politically incorrect term “old age.”) The aging body attracts growing concerns, reflecting diminution in function, altered physical appearance and increased incidence of physical illness. Despite these occurrences, the body in late adulthood can still be a source of considerable feeling of well-being in the sense of competence. This is particularly true if attention is paid to maintaining quality of life through involvement in leisure activities, trusting relationships with family and friends and maintenance of physical health. Neurobiological Changes in Old Age The aging process is characterized by a gradual decline in functions of all body systems, including cardiovascular, respiratory, endocrine, and immune. But the common popular belief that old age is associated with profound intellectual infirmity is a myth since many older persons remain fit in their cognitive abilities and physical capacities (Von Faber et al. 2001). The biological changes that accompany aging do not progress in a linear fashion in all systems, nor do they follow a similar pattern of decline in all persons. Each individual is genetically endowed with one or more vulnerable systems, or a system may become vulnerable because of environmental stressors or intentional misuse, such as excessive ultraviolet exposure, smoking or alcohol abuse. Human aging is a multidimensional process of physical, psychological, and social change. In the central nervous system (CNS) changes result in the decrement of both white and gray matter resulting in reduction of overall brain weight. Dementia is a common neurodegenerative syndrome in old age with many different potential causes, including primary degenerative processes. Other influences include particularly vascular factors. In practice, different types of lesions frequently co-occur. In older individuals, a pure vascular or degenerative picture is rarely seen. Thus, mixed dementia is considered by some the most common type of dementia. This observation leads to an important conclusion, mainly that no single measure may be found which will eliminate the disease globally (Korczyn 2002). All epidemiological studies of dementia show increased prevalence of dementia in advanced age. In the age group of 60 to 64 years the prevalence of dementia is below 1%; it then doubles approximately every five years reaching a prevalence of about 50% among those age 90 years or older (Brayne et al. 2006; Korczyn and Vakhapova 2007).

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A variety of neurodegenerative pathologies are associated with the development of late-life dementia. Alzheimer disease (AD) is considered by far the most prevalent pathology before age of 85 years; 60–75% of all cases of dementia are diagnosed as AD (Fratiglioni et al. 2000; Kawas et al. 2000). AD is associated with progressive deterioration of memory and other cognitive functions resulting partly from loss of cholinergic transmission in cortical brain regions innervated by neurons arising in the nucleus basalis of Meynert (NBM). The presence of extracellular plaques containing deposits of amyloid and other proteins, as well as intracellular neurofibrillay tangles, are hallmarks of the AD pathology and are thought to contribute to the cognitive deficit. These may result from destructive processes involving the microtubules and synaptic loss. Such destructive processes could further contribute to neuronal damage and disease progression (Youdim 2006). After AD, vascular dementia (VaD) is considered by some to be the second-leading cause of dementia. The frequency of VaD varies by study population, screening methodology, diagnostic criteria, and time period. VaD is a cerebrovascular disease that leads to neuronal loss and decline in cognitive functioning. It occurs when the blood supply carrying oxygen and nutrients to the brain is interrupted or restricted by a diseased vascular system (Lobo et al. 2000). In the United States and Europe, VaD accounts for 10–20% of cases; However in Asia VaD may be as common or even more common than AD. This is likely related to the high prevalence of vascular risk factors in this region (Ueda et al. 1992). Skoog and colleagues (1993) have shown that the prevalence of VaD was slightly higher (46.9%) then the prevalence of AD (43.5%) in people 85 years of age or over. Unlike AD, VaD is not a unitary nosologic entity, but rather a complex of multisided pathologic mechanisms and clinical presentations (Lobo et al. 2000). The several types of VaD include multi-infarct dementia (MID) and poststroke VaD. MID is caused by a series of small strokes that often go unnoticed. Over time, however, the cumulative damage caused to brain tissue interferes with basic cognitive functions and disrupts everyday functioning (Jellinger 2007). MID causes loss of functioning to specific areas of the brain, impairing neurological and mental functions. Neuropsychological findings in VaD vary with the site and severity of the brain lesion (Tomlinson, Blessed, and Roth 1970). For patients with single or multiple large infarcts, deficits correlate with the site and extent of the infarct. In patients with extensive deep white matter disease, impairments may be observed in psychomotor speed, dexterity, executive functions, and motor aspects of speech (e.g., dysarthria or reduced verbal output).

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Patients with subcortical vascular dementia show reduced ability to set and reach goals with mental slowing and gradual executive dysfunction (de Haan, Nys, and Van Zandvoort 2006). Surprisingly, the risk factors associated with VaD are similar to those associated with AD and with the risk for cardiovascular disease (CVD) (Carlson et al. 2008; Fernández Martínez et al. 2008; Abellan van Kan et al. 2009). Based on Chobanian and colleges (2003), some CVD risk factors can be medically treated (see Table 7.1). Psychological Changes and Quality of Life in Old Age In healthy old age persons usually maintain a level of social activity that is only slightly changed from that of earlier years. For many, old age is a period of continued intellectual, emotional, and psychological stimulation. However, in many cases physical illness or death of friends and relatives may limit social interaction. This isolation from family and friends may lead to the development of depression (Blane, Netuveli, and Montgomery 2008). Mandatory retirement due to age, which was an important social milestone in the nineteenth century, has become increasingly outdated since many old persons still feel very potent. However, in some cases old persons may themselves resent and fear other old persons, discriminate against them, and hold negative stereotypes about old age. This phenomenon, first described by Robert Butler (1969) is known as ageism. Ageism is the result of the society’s popular (but wrong) belief that often old age is associated with senility, general weakness, and infirmity (Kang and Chasteen 2009). Table 7.1 Risk Factors for Cerebrovascular Disease Medical intervention Advanced age Genetic factors Male gender High blood pressure Dyslipidemia (elevated LDL, decreased HDL) Diabetes mellitus Atrial fibrillation Smoking Physical inactivity Obesity

Not possible Not possible Not possible Possible Possible Possible Possible Possible Possible Possible

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The World Health Organization defines Quality of Life (QoL) as “individuals’ perception of their position in life in the context of the culture and the value system in which they live and in relation to their goals, expectations, standards and concerns” (WHOQOL Group 1995). What influences QoL? The relationship between age and functioning, be it psychological, emotional, or social, has been looked at from two perspectives: chronological perspectives and individual difference perspectives (Baltes 1998). Regarding chronological perspectives, we know that stressful factors accumulate over the years and may result in impaired health or productivity. These factors may explain age-related variance in cognition, emotion, social connectedness, and so on. Research has given credence to this assumption. Chronological age was shown to be a proxy to stressful events such as declining health, lowered income, reduced socializing, cognitive impairment, and institutionalization (Andrews, Clark, and Luszcz 2002). However powerful the effect of the latter factors may be, they account for only about 8–15% of the difference in individual subjective feeling of happiness. Thus it appears that in old age people may became more dependent on their individual psychological resources for maintaining QoL (Lyubomirsky 2001; Lyubomirsky, Sheldon, and Schkade 2005). Individual differences refer to the psychological resources and strategies that serve one to adapt positive outcomes of quality of life in the context of ageing, and preservation of one’s self feeling of well-being and quality of life (Baltes and Carstensen 1996). The effect of such psychological resources on everyday competence was studied by Baltes and Lang (1997). These investigators looked at differential daily functioning between resource-poor and resource-rich elders. Four groups of elderly were identified on the basis of two resource factors: a sensorimotor-cognitive factor and a social-personality factor. Resource-rich elderly differed in several indicators of everyday functioning from resource-poor elderly: length of waking day, variety in activities, frequency of leisure and social activities, as well as resting times. These findings suggest that age-associated activities decline more commonly and to a greater extent in the resource-poor than in the resource-rich group. However, it is not that the resource-rich elders do not experience decline, but that they experience less of it or are able to better compensate. Another psychological resource is known as psychological acceptance (PA). As people get older, they may struggle with reduction in their ability to participate in activities they feel to be meaningful. PA is the ability to accept such age-related changes (Hayes et al. 2004). Higher PA also allows one to spend more time living actively, making choices to assist completion of goals or sorting out problems, rather than spending time and mental

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resources on controlling psychological events. Thus it is hypothesized that people with higher PA have better QoL in the areas of health, community participation, and mental health, and less adverse psychological reactions related to these changes (Bond and Bunce 2003). Surprisingly, the amount of scientific data that directly links individual differences in PA and quality of life in the elderly is meager. However, the existing research suggests that elderly people who are able to do things they enjoy, despite age-related limitations, are more satisfied with their lives (Lyubomirsky, Sheldon, and Schkade 2005; Maher and Cummins 2001). Davis and Friedrich (2004) have suggested that elderly subjects who had more knowledge about aging had a better life satisfaction. Presumably increased knowledge enabled them to mentally adapt more readily and accept changes. Socioeconomic Position, Health-Related Quality of Life, and Overall Quality of Life in Old Age Aging is changing in Western communities due to a combination of the increase in life expectancy. Consequently an increasing proportion of the old age population in Western countries can expect to spend from 10 to 20 years after retirement from paid employment pension (Oeppen and Vaupel 2002; Blane et al. 2004). Several socioeconomic factors (SEFs) were investigated in an effort to evaluate their influence on QoL at old age: education, social position, wealth, and gender differences. Education has been widely perceived as one of the most important socioeconomic determinants of health. It is acquired early in life and for most people remains relatively unchanged thereafter. Education may be a surrogate of lifestyle, past-time interests, health behavior, problemsolving abilities, social relations, self-esteem and stress management, in ways which, with regard to health, are to the advantage of the more educated, as well as through income or occupation (Sulander et al. 2006; Winkleby et al. 1992; Minicuci and Noale 2005). Research from a number of countries has shown that this effect is present even at older ages (Martelin, Koskinen, and Valkonen 1998; Silventoinen and Lahelma 2002). Social position is also thought to affect health and mortality in many ways: by influencing attitudes, beliefs and values people use to make life-course choices, through psychosocial stresses, and by influencing lifecourse opportunities. Studies have demonstrated clearly that even marginal class differences can strongly affect health and mortality and that this effect remains even at older ages (Breeze et al. 2001; McMunn et al. 2006).

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However the social classification of older people is ambiguous, because the majority is no longer employed (Grundy and Holt 2001). It has been suggested (Arber and Ginn 1993) that instead of an elderly person’s previous class, an alternative indicator of structural position such as current material circumstances (i.e., wealth) could be used. Wealth is particularly important when studying SEF at older ages since it can reflect an individual’s accumulated lifetime experience (Filakti and Fox 1995). Wealth, especially at older ages, is likely to influence health by providing material resources and a feeling of security and control and by influencing health behaviors (Allin, Masseria, and Mossialos 2009). Wealth is therefore a better measure of economic status than income, especially after retirement (Smith and Kington 1997). Regarding gender differences women experience longer survival and therefore may accumulate more disabilities (Verbrugge 1989). The contribution of SEF to these differences is not clear. Gender may influence health through occupation and social position (which differ especially at older ages) and through employment and earning history, which influence familial wealth (Dahl 1994; Lahelma et al. 1999). The various factors of SEF mentioned above, and their potential effect on QoL at older ages are also explained by their association to other measures of health-related quality of life (HRQoL). HRQoL is a term originally coined as a measure of overall health status evaluating how good or bad it would be in a given health status measured by various domains of health (e.g., pain, impaired physical function and impaired mental health) (Robert et al. 2009). People with lower SEF (low income, education, and assets) have poorer QoL and also poorer HRQoL at all age groups including older ages. However, it was found that specific factors such as low income and low education at midlife ages were specifically strong predictors of worse HRQoL in ages 65 and older (Robert et al. 2009). Understanding the relationships between SEF and HRQoL and their combined effect on QoL in old age is further complicated by the fact that some social variables are theorized to positively affect QoL in old age (further discussed in the study by Netuveli and colleagues), thus conflicting with low SEF and HRQoL and contradicting with the results of Stock et al. (1983). Netuveli and colleagues (2006) concluded that doing volunteer work, being a resident in a neighborhood perceived to be good, having trusting relationships with children, family, and friends, and access to a car or owning a pet yielded positive effects on QoL and health in old age. In conclusion, aging is perceived to decrease QoL, but it is important to look for QoL predictors other than chronological age per se. Many factors

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are suspected; however, little is known about their relative importance and no single factor solitarily determines QoL at old age. Factors like perceived poor financial situation, depression, functional limitation attributable to longstanding illness, and limitations in everyday activities can affect QoL negatively, while those like having trusting relationships with children, family, and friends, and affluence can improve quality of life. These factors are frequently interconnected and interdependent. LATE-LIFE DEPRESSION Major depressive disorder (MDD) is a mood disorder characterized by low mood accompanied by low self-esteem, and loss of interest or pleasure in normally enjoyable activities (Belmaker and Agam 2008). In the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), the diagnosis of MDD requires the presence of single episodes persisting for at least two weeks or recurrent major depressive episodes. Further qualifiers are used to classify both the episode itself and the course of the disorder (American Psychiatric Association 2000). MDD is common in the elderly, with an estimated prevalence of ∼3% in the general population and 15–25% among nursing home residents (Rothschild 1996). Approximately 15% of community-dwelling elderly have clinically significant depressive symptoms, and such symptoms are present in ∼25% of those elderly who have a chronic medical illness (Unutzer et al. 1997). MDD has been chronologically divided into two types: early-onset depression (EOD) and late-life depression (LLD). EOD arises in adults between the ages of 45 to 64 years, while LLD refers to depression starting after age 65 (Lyness et al. 1994). Late- and Early-Onset Depression When compared with elderly individuals with EOD, individuals with LLD have less frequently family history of mood disorders, larger impairment in neuropsychological tests, more neurosensory hearing impairment, enlargement of lateral brain ventricles, more white-matter hyperintensities, and a higher rate of dementia development on follow-up (Alexopoulos, Young, and Shindledecker 1992; Jacoby and Levy 1980). LOD individuals are less likely to have psychiatric co-morbidities, such as personality disorders, substance abuse, or panic disorder, than EOD patients (Lyness et al. 1994) and are more likely to have associated medical co-morbidities (Emery and Oxman 1992) and fewer episodes of depression,

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and experience more often feelings of loss of interest and apathy (Krishnan et al. 1995; Heun, Kockler, and Papassotiropoulos 2000). The importance for identifying and treating LOD stems from the fact that it is considered a risk factor for death; the severity of depressive symptomatology has been found to be a strong predictor for suicide (Alexopoulos 2001). LLD also interferes with the patient’s cooperation and compliance to prescription medication and treatment of his or her depression; this is due to the negative perception and social stigma about depression (DiMatteo, Lepper, and Croghan 2000). QoL is negatively affected by LLD because of lower self-rated health that is beyond the levels predicted by objectively measured physical conditions (Schulberg et al. 1998), psychomotor slowing, emotional lability, crying spells, insomnia, weight loss, and pessimism (McGuire and Rabins 1994). The effect of LLD on HRQoL includes amplified perception of pain (Lynch 2001). Compared to nondepressed old persons, older depressed patients tend to have, or report, more somatic and cognitive symptoms than affective symptoms. Older patients who deny having depressed mood may report a lack of feeling or emotion, or acknowledge a loss of interest and pleasure in activities. The tendency of depressed older adults to report fewer affective symptoms is captured by the concept of “depression without sadness” (Gallo and Rabins 1999). This variant has been identified in elderly primary care populations and consists of apathy, loss of interest, fatigue, difficulty sleeping, and other somatic symptoms, but not sad mood. It is unclear whether “depression without sadness” is an idiopathic depression, a depression secondary to medical illness, or a nonaffective syndrome related to apathy. Screening for Depression While both dementia and LLD are commonly seen in old age, no data exist as to their co-occurrence. This is because epidemiological studies on dementia typically exclude patients diagnosed as being depressed and vice versa, studies on depression exclude people suffering from dementia. Lacking established biological markers for both dementia and LLD prevents a clear distinction between the two entities or studying their simultaneous existence. Yet several features, such as poor concentration and impaired attention, are common in both (Halperin and Korczyn 2007). Commonly used screening instruments measuring the severity of depression include the Beck Depression Inventory (Beck and Steer 1987), the Hamilton Depression Scale (HAMD) (Hamilton 1960), the Geriatric Depression Scale (Yesavage et al. 1983), the General Health Questionnaire

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(Goldberg 1978), the Zung self-rated depression scale (Zung 1965), and others. In a review of 18 studies of major depression, these assessment instruments have been found to have 84% sensitivity and 72% specificity for detecting major depression in depressed old persons (Mulrow et al. 1995). However, the characteristics of old patients who screen positive for depression but who do not have major depression remain to be identified. Some of these patients may have persistent minor depression, dysthymia, or a partially remitted depression. It is largely unknown whether elevated screening scores but no diagnosis (“subsyndromal depression”) are a risk for later affective or cognitive deterioration. One study (Lyness et al. 2006) compared outcomes among patients with minor and subsyndromal depression, major depression, and no depression, to examine putative outcome. Results showed that compared with patients who were not depressed, those who had minor or subsyndromal depression had a 5.5-fold risk for major depression at one year. As discussed below, those individuals are also at increased risk to develop dementia. Correctly identifying depression at early stages may allow more benefit from treatment. Yet most primary care providers do not consistently use screening instruments in their practice. Routine screening, when incorporated into practice, can improve diagnosis; however, screening alone may be insufficient for initiating treatment and does not lead to better outcomes (Coyne et al. 2000). In primary care centers other barriers can decrease the likelihood of successful diagnosis of LLD. Brown and colleagues (1995) found that men are less likely to report mood-related symptoms than women, and primary care physicians are less able to recognize depression in men than in women (Potts, Burnam, and Wells 1991). Furthermore, reports of somatic complaints may lead to misdiagnosis of depression in the primary care setting (Goldman 1997). In summary, making a diagnosis of LLD can be difficult for many reasons including more subtle presentations in older adults or coexisting medical illnesses. Numerous screening instruments exist and such instruments can help detect depressive symptoms. Patients who screen positive should be further interviewed to determine whether they can benefit from treatment. Screening for Dementia Much progress has been made in identifying the typical pattern of cognitive deficits associated with early AD. Yet the boundaries between normal age-related cognitive changes and early signs of AD remain especially difficult to delineate in very elderly individuals (i.e., over the age of 80).

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This is because many of the early structural and functional brain changes of AD overlap with changes observed in normal aging. Normal aging is associated with mild brain atrophy (Jack et al. 1997; Pfefferbaum et al. 1994) and reduced synaptic density (Masliah et al. 1993). These brain changes are thought to mediate age-related decline in information processing speed, executive function, learning efficiency, and retrieval (Hedden and Gabrieli 2004). Because normal aging can detrimentally affect many of the same cognitive abilities affected by AD, specific deficits related to AD may be much less evident in the AD oldest-old patients than in the younger AD patients. As a result, a less-distinct cognitive deficits profile is associated with AD in the oldest-old AD patients. Thus, Bondi and colleagues (2003) noted that despite achieving similar raw scores on all neuropsychological measures, the old and the oldest-old AD patients differed in the severity and pattern of the cognitive deficits in relation to agematched healthy controls. The old AD patients were generally more impaired than the oldest-old AD patients and exhibited worse deficits in memory and executive functions than in other cognitive domains. In contrast, the oldest-old AD patients exhibited a similar level of impairment across all cognitive domains so that their deficits profile lacked the disproportionate saliency of memory and executive function deficits typical of the disease. Thus, aging can significantly affect the severity and pattern of neuropsychological deficits associated with early AD and reduce the saliency of the deficit. Several studies suggest that memory performance may be poor a number of years prior to the development of the dementia and then decline rapidly. Small and colleagues (2000) and Backman and colleagues (2001) found that episodic memory was mildly impaired six years prior to dementia onset. Yet, neuropsychological screening tools are not free from limitation. Some have noted that screening failure rates have been estimated between 50% and 80% for moderate to severe dementia and up to 91% for mild dementia cases (Boustani et al. 2005). International survey revealed that the most frequently used cognitive assessment tests are the Folstein Mini-Mental State Exam (MMSE) and the Clock Drawing Test (CDT) (Shulman et al. 2006). However, the MMSE has been criticized for poor sensitivity and specificity. One study demonstrated that the MMSE has only 80% sensitivity and 86% specificity for correctly identifying dementia (Cherbuin, Anstey, and Lipnicki 2008). Others (Lorentz, Scanlan, and Borson 2002) concluded that the CDT has higher accuracy than the MMSE for identifying dementia. However, in the

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absence of specialist physician examination, the CDT by itself cannot be considered reliable enough for accurate clinical dementia screening purpose. In summary, screening tools for dementia have advanced in delineating patterns of impaired neuropsychological abilities. Future advances may offer clinicians the opportunity to begin early drug treatment soon as it becomes available. DEPRESSION WITH REVERSIBLE DEMENTIA One association between depression and cognitive impairment (CI) has previously been termed “pseudo-dementia.” This term refers to cognitive decline that can be reversed by treating the depression (or other psychiatric symptoms). However, dementia is a syndrome and anything that fulfills its criteria is dementia proper, regardless of cause or reversibility (Korczyn 1991). Moreover, Kiloh (1961) argued that the term “pseudodementia” has no valid diagnostic value since it does not provide any specific diagnostic information. Nevertheless, the term is still widely used implying cognitive impairment that is not due to structural changes of the brain and thus is potentially reversible. Alexopoulos and colleagues (1993) suggested that the CI associated with depression is often not reversible or only partially reversible, and that CI associated with LLD persists despite amelioration of depressive symptoms. Kral and Emery (1989) found that 39 of 44 elderly patients (89%) diagnosed with depressive pseudodementia developed AD within 4 to 18 years (average of 8 years). In a study of 20 depressed patients aged 60 years or over (half had their first episode of depression after 50 years of age), 37% of recovered depressed patients showed residual CI compared to normal control subjects (Abas, Sahakian, and Levy 1990). In order to estimate how often dementia can be reversed, Weytingh and colleagues (1995) carried out a quantitative review of 16 studies comprising 1551 patients. The percentages of reversed dementia varied widely: from 0 to 23% for partial and from 0 to 10% for full recovery. Depression and drug intoxication were the most frequent causes of reversible dementia, followed by metabolic and neurosurgical disorders. The percentage of both partial and full recovery of dementia has dropped in recent years, to less than 1% for both. This decrease could be associated with setting and the use of stricter diagnostic methods. The researchers concluded that reversible dementia is very rare in an outpatient setting when using strict diagnostic methods. These studies indicate that elderly patients suffering a depressive disorder with associated CI are more likely to develop dementia than depressed subjects who are cognitively intact.

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THE SEARCH FOR A POSSIBLE RELATIONSHIP BETWEEN DEPRESSION AND DEMENTIA Depressive symptoms are observed in 35–50% of dementia patients, particularly in the earlier stages of the disease (Nilsson et al. 2002). There seems to be a complex relationship between depression and dementia pointing toward several possible hypotheses, not necessarily mutually exclusive: (1) depression is a mental reaction in patients experiencing cognitive decline; (2) depression may be a characteristic of early dementia; (3) depression is a risk factor for dementia; and (4) dementia and depression share common underlying brain changes or risk factors. These possibilities will be now discussed.

Depression Is a Mental Reaction in Patients Experiencing Cognitive Decline Social events have great impact on a person’s mood. Living alone, having limited social interaction and support, or living in neighborhoods with limited transportation all predispose to cognitive decline (La Gory and Fitpatrick 1992; Dean et al. 1992). Physical illnesses are also known to produce depressive mood, such as is common in victims of cancer and cardiovascular and cerebrovascular diseases (Gruneir et al. 2005). Facing a serious illness like cancer or dementia can result in denial, or in a search for treatment, but it is also stressful and can cause depressed mood. This is true even in cases when the diagnosis is not delivered directly to the patient. Patients with incipient cognitive decline can feel the loss of mental functions and react to it emotionally. Clinicians should be aware of such mental reactions that develop in patients experiencing cognitive decline and strive to look for them even in cases when there are no clear complaints. Poor appetite, insomnia, and loss of libido can be the manifestations of depression, which should be identified and treated (American Psychiatric Association 2000).

Depression May Be a Characteristic of Early Dementia Retrospective studies describe an association between a history of depression and an increased risk for the development of AD. That risk was particularly high when depression appeared in elderly individuals (Jorm et al. 1991). In recent years investigators have been trying to define an intermediate stage between normal aging and AD, using the term mild cognitive impairment (MCI). The common tendency is to view normal

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aging, MCI, and AD as a continuum (Burns and Zaudig 2002). However, it is not necessarily true that every adult will develop MCI or that all those that suffer from MCI will ultimately progress to dementia. Longitudinal studies found that 19–50% of elderly people suffering from MCI will develop dementia during a period of three to four years from their initial diagnosis (Hanninen et al. 2002). Modrego and Ferrandez (2004) found increased risk to develop dementia among subjects with MCI who are also depressed. At the beginning of their study depression or depressive symptoms were diagnosed in 36% of the patients. After an average period of three years, 85% of these patients developed dementia as compared to only 32% of the subjects who were not suffering from depression. These results show the intimate relationship between late onset of depression and incipient cognitive decline and may pave the road in helping to identify subjects that are likely to develop dementia. It is well established that abnormalities in serotonergic neurotransmission are central to the pathophysiology of depression in younger adults, but underlying pathological changes associated with these abnormalities are less clear (McAllister-Williams, Ferrier, and Young 1998), but seem to imply the serotonergic system (Meltzer et al. 1998). In a study by Baumann and colleagues (2002) autopsy examinations were performed on brains of 12 patients with mood disorders in comparison to the brains of 12 normal subjects. Results showed reduction of 31% in the number of neurons of the ventrolateral subnucleus of the dorsal raphe of patients with mood disorders compared with control subjects. This neuronal deficiency in the dorsal raphe may contribute to impaired serotonergic innervations of brain regions which are involved in mood regulation. In conclusion, similar neuropathology related processes might underline the association between AD and depression. This may result in earlier expression of MCI. The additive or synergistic effects of additional pathologic processes associated with depression may result in reduced neuronal reserve leading to earlier symptoms of dementia. Depression Is a Risk Factor for Dementia Depression is an important issue for those working with the elderly. This is because depression affects a large number of elderly subjects and has been associated with increased morbidity and mortality. Kessing and Nilsson (2003) studied the association between affective disorder and subsequent dementia in patients with unipolar or bipolar affective disorders compared to patients with other chronic illnesses (osteoarthritis and diabetes) during the period of 1977 to 2003. Patients with unipolar or bipolar

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affective disorder had an increased risk of developing dementia compared to patients with other illnesses. Nilsson and colleagues found in follow-up cohorts up to 21 years that patients (mean age 77 years) who developed dementia had a prior increased risk of being admitted to a hospital due to major depression or mania (Nilsson et al. 2002). Ownby and colleagues (2006) systematically reviewed and completed a meta-analysis of 20 relevant studies, from an even larger sample, which provided data of 102,172 persons from eight countries. The meta-analysis showed that persons with a history of depression were more likely to be diagnosed as having AD in later life. This finding was robust across analyses stratified sampling type, retrospective versus prospective data collection and strictness of diagnostic criteria used for AD and depression. However, these data did not provide information about why depression and AD may be linked. In conclusion, depression is a risk factor predisposing for future development of dementia. However, whether prior depression is a true etiologic risk factor or rather an early clinical manifestation of dementia is unclear. Dementia and Depression Share Common Underlying Brain Changes or Risk Factors AD is characterized by the formation of neurofibrillary tangles and plaques, and neuronal loss across the CNS (Small et al. 2000). The histopathological changes show a characteristic sequence, with the entorhinal cortex and the hippocampus being among the first affected regions of the brain, followed by selected regions of the neocortex (Braak and Braak 1991). Neuroimaging studies present supporting evidence to neuroanatomical changes in AD brains: Using voxel-based morphometry, a study by Ohnishi and colleagues (2001) found a significant reduction of gray matter volume in the hippocampal formation and entorhinal cortex bilaterally in AD patients’ brains. As imaging techniques developed during the last two decades, evidence began to accumulate that depression is accompanied by structural changes in the brain that have a resemblance to AD. The hippocampus particularly is severely damaged in the early stages of AD (Van de Pol et al. 2006), but Bremner and colleagues (2000) found reduction of hippocampal volume also in nondemented depressed patients. Neuropathological studies such as the postmortem study of Rapp and colleagues (2006) found distinct differences in both neuritic plaques and neurofibrillary tangles in the hippocampus of patients with AD as a function of depression history. Specifically, patients with neuropathologically

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confirmed diagnosis of AD who had a history of major depressive disorder exhibited a larger number of neurofibrillary tangles and neuritic plaques in the hippocampus than patients with AD who never had an episode of major depressive disorder in their life. Such was not the case in the study conducted by Wilson and colleagues (2003) who evaluated prospectively a group of 130 Catholic nuns, priests, and brothers. Brain autopsy at death revealed that the association of depressive symptoms with clinical AD and cognitive impairment appeared to be independent of cortical plaques and tangles. Studies that have pointed out a connection between cardiovascular risk factors and depression have led to the “vascular depression hypothesis” (Alexopoulos et al. 1997). According to this hypothesis, subcortical neuronal tracts and in particular periventricular ones are important for the regulation of mood and motivation. Therefore it is reasonable to assume that subcortical lesions in these areas might inflict mood disorders and depression. Indeed, white-matter changes (WMC) are common among the elderly and are thought to result from damage to small blood vessels due to hypertension, hypercholesterolemia, and diabetes mellitus (de Leeuw et al. 2002). WMC occur in both VaD and AD (Hirono et al. 2000; O’Brien et al. 1996) but were described by Chen and colleagues (2006) also in depressed patients. De Groot and colleagues (2000) showed a relationship between the presence of depressive symptoms and WMC among 1077 nondemented elderly subjects. Subjects with WMC presented more depressive symptoms and their depressive symptoms were more frequent. Subjects with severe WMC had a higher risk (3 to 5 times more) to develop depression compared with subjects who had only lesser degrees of WMC. In summary, the link between dementia and LLD may be mediated by neuropathologic changes including greater hippocampal amyloid plaque neurofibrillary tangle pathology in AD patients with a lifetime history of depression. Additional evidence supporting the notion that vascular disease contributes to depression comes from structural MRI studies that show an association between ischemic brain lesions and depression diagnosis in older persons. These studies suggest a relationship between AD, ischemic WMHs, and depression, as well as AD changes. This raises the possibility that ischemic structural changes in the brain are a common etiologic factor of both the depression and the associated cognitive dysfunction. The data described so far about the shared brain changes suggest that it is reasonable to assume that LLD by itself can predict subsequent development of dementia in individuals with cerebrovascular or AD pathology (Thomas, Kalaria, and O’Brien 2004).

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DEPRESSION IN VASCULAR BRAIN DISEASE The proposed link between depression and vascular disease is not new and was originally proposed as “arteriosclerotic depressive disease” in Gaupp’s article, “Depressive States in Old Age” (1905). Some studies have suggested that cardiovascular risk factors may enhance the risk for depression. However, depression may in turn increase the risk for cardiovascular and cerebrovascular disease (Kales, Maixner, and Mellow 2005). Carney and colleagues (1999) reported that half of the patients suffering from cardiovascular diseases had a history of depressive episodes prior to the diagnosis of cardiovascular disease. Also, one of every five patients had previously been diagnosed as having major depression at the time of diagnostic cardiac catheterization or acute myocardial infarction (Hance et al. 1996). Another one in five had minor depression at these times. Approximately one in three patients developed major depression at some time during the 12 months after a coronary event (FrasureSmith et al. 1999). Mood disturbances may be a specific complication of stroke, and it has been estimated that between 18% of a local community’s elders and 78% of hospital admission poststroke patients suffer from depression (Morris, Robinson, and Raphael 1990; Stern and Bachman 1991), with the period of greatest risk being the 2 years following the stroke event (Parikh et al. 1990). At present, support for vascular brain disease as an underlying etiology of LLD includes the high rate of depression in patients with vascular disease and the frequency of “silent stroke” and white-matter hyperintensities (WMH) in LLD. Structural neuroimaging and neuropathology studies have shown a relationship between frontostriatal impairment and late life depression. Bilateral WMH are prevalent in geriatric (Kumar et al. 2000) and mainly occur in subcortical structures and their frontal projections (Lesser et al. 1996). Subcortical WMH have been found to be associated with executive dysfunction (Boone et al. 1992). Lesions localized in the basal ganglia and their frontal projections are also associated with high incidence of depression and executive dysfunction (Rajkowska et al. 1999). Neuropathological studies identified abnormalities related to frontal impairment. Reduction in glia of the subgenual prelimbic anterior cingulate gyrus has been demonstrated in unipolar depressed patients (Rajkowska et al. 1999; Lai et al. 2000). Abnormalities in neurons of the dorsolateral prefrontal cortex have also been documented in unipolar disorder (Ongur, Drevets, and Price 1998). Yet, the exact relationship between depression and vascular brain disease still remains ambiguous.

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Depression may itself predispose to vascular disease. Musselman and colleagues (1998) proposed a link between depression and cardiovascular disease that included (1) the effects of hypercortisolemia; (2) immune activation; and (3) depression-related platelet aggregation leading to increased thrombosis. Others included (4) depression-induced impairment of arterial endothelial functioning (Broadley et al. 2002) or (5) abnormal folate or homocysteine metabolism (Godfrey et al. 1990). Although these mechanisms have been proposed to relate depression to cardiovascular disease, several could pertain to depression-cerebrovascular disease linkages as well. The association between depression and vascular brain disease could also represent the consequences of a shared underlying etiology such as atherosclerosis. Atherosclerosis could lead to events such as cerebral lesions, which could trigger depression either through disruption of critical pathways implicated in mood regulation or accumulation of lesions exceeding a certain threshold (Alexopoulos et al. 1997). Vascular disease may also be linked to depression via shared genetic risk factors. Although the apolipoprotein ε4 (APOE ε4) allele is an established risk factor for coronary artery disease and for AD, the finding of an increased frequency in LLD is less clear (Krishnan et al. 1996; Steffens et al. 2003). Finally, nonbiological factors may be involved in the brain depression– vascular disease linkage, including the effects of depression on decreasing adherence to treatment regimens for vascular diseases such as hypertension, heart disease, or diabetes.

SHARED CHANGES IN THE NEUROTRANSMITTER SYSTEM BETWEEN DEMENTIA AND DEPRESSION Changes in the Serotonergic System Neurotransmitter changes appear during aging. Serotonin (5-hydroxytryptamine) is widely distributed in the human nervous system. Most of the brain cells containing serotonin are located in the raphe nuclei (RN) at the brain stem and in the hypothalamus. In the brain serotonin has many functions, including involvement in memory and affective processes (Korczyn and Blum 1976). Most of the studies that explore the relationship between depression and the serotonergic system were performed in young adults and animal models, and only a few were done on elderly populations. These few studies suggest that mood disorders in old age are accompanied by disruption in serotonin metabolism (Gareri, De Fazio, and De Sarro 2002).

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Suicide is traditionally considered an extreme response to stress, with the most frequent stress being depressive illness. A biological role for the serotonergic system, possibly associated with a genetic risk factor, has been postulated (Mann et al. 1989). Low levels of 5-HT and 5-HIAA (the 5HT metabolite) have been found in postmortem examinations of brainstem tissues of suicide victims. An increased number of 5-HT1A and 5-HT2 receptors was found in the pre-frontal cortex of suicide victims, implying up-regulation, induced by 5-HT deficiency (Rao et al. 1998). On the other hand, Stockmeier (1997) examined various indices of serotonergic neurotransmission in axonal projection areas such as prefrontal cortex, hippocampus, and cell bodies originated within the dorsal RN. All samples were obtained from postmortem studies of depression and suicide victims. The author concluded that there were no significant differences between suicide victims with major depression and psychiatrically normal control subjects in serotonin-1A or serotonin-2A receptors in the right prefrontal cortex or the hippocampus. However there were region-specific alterations in suicide victims with major depression in G-protein-induced activation of the phosphoinositide signal transduction system and in the levels of G-protein alpha subunits involved in cyclic AMP synthesis. Jones and colleagues (1990) showed lower levels of 5HIAA in the cerebrospinal fluid of patients with a history of suicide attempts. Antidepressant drugs are thought to work by limiting serotonin and noradrenaline reuptake, raising neurotransmitter concentrations in the synapse. Evidence supporting serotonergic involvement in AD stem from findings of reduced reuptake of serotonin in the temporal cortex (Reinikainen, Soininen, and Riekkinen 1990). Other studies focused on the RN itself. The dorsal part of the nucleus contains a large number of dense serotonergic neurons which project to the basal ganglia, thalamus, hypothalamus, and cortex. In AD there is depopulation of neurons in RN and that nucleus is also affected by many neurofibrillary tangles (Curcio and Kemper 1984). However, serotonin reuptake inhibitor drugs do not have a beneficial cognitive effect in AD (Grau-Veciana 2006). Changes in the Noradrenergic System The origin of the noradrenergic system in the brain is the locus ceruleus (LC) located at the brainstem. Axons from the LC reach many brain areas such as the hippocampus, amygdala, and neocortex. Activation of the noradrenergic system enhances the reaction to sensory information by amplifying the response to relevant stimuli.

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McGaugh and colleagues (1996) discovered that applying β noradrenergic receptor agonists enhances memory consolidation while antagonists had an opposite effect. In their study Ordway and colleagues (2003) studied the binding of the radioactive ligand P[125] iodoclonidine to α2– adrenoceptors at the LC of patients suffering from major depression and showed significant elevation in major depression patients compared to matched controls. These are consistent with deficiency of noradrenaline in the LC in subjects with major depression. Typical AD changes such as neurofibrillary degeneration were also found in the LC. During normal aging neurofibrillary tangles develop in the LC but they are more prominent in AD (Grudzien et al. 2007). In this study the investigators also made a comparison between the extent of the cytopathology in the LC to the cognitive state prior to the death of the subjects. Other studies (Chen et al. 2000; Rub et al. 2000) confirmed the presence of neurofibrillary tangles in the LC and RN in AD. From the evidence presented so far it seems that the concentrations of noradrenaline in the brains of AD patients and depressed patients is decreased due to degeneration of the LC (Syed et al. 2005). In summary, there are several similarities between neurotransmitter changes in brains of AD patients and elderly depressed patients. It is not likely that the neurotransmitter changes mentioned above are responsible for dementia, since drugs that increase the activity of such neurotransmitters do not have a strong beneficial cognitive effect in AD despite being useful in treating depression. The presence of such changes in AD should therefore be seen as an expression of the disease and not as a direct cause for the cognitive decline. Changes in Hormonal System The hypothalamic-pituitary-adrenal (HPA) axis is an important feedback system controlling the release of the stress hormone cortisol. Hypercortisolism is typical for depressed patients, probably because of poor feedback inhibition (Holsboer 2000). Abundant glucocorticoid receptors exist in the hippocampus and frontal cortex, the function of which is still unclear. However, prolonged exposure to high cortisol levels can damage neurons in these brain areas. Animal models have shown death of hippocampal neurons following exposure to high concentrations of glucocorticoid hormones (Sapolsky 2000). Hence it was speculated that high cortisol levels can induce cognitive impairments (Van de Pol et al. 2006). This last fact might explain the limited antidepressant effect on cognitive functioning (Butters et al. 2000).

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If a patient’s response to antidepressants is poor, than it is possible that the cognitive symptomatology is related to hypercortisolism and neurodegenerative processes. Elderly individuals may be more vulnerable to this process since hypercortisolism is more common in older as opposed to younger people (O’Brien 1997). If high glucocorticoid levels can cause hippocampal atrophy, this can be of significant importance in the pathogenesis of AD. Wolf and colleagues (2005) studied the relation between subjective cognitive complaints and high levels of cortisol in a sample of healthy middle aged and older subjects (mean age 62) with and without cognitive complaints. Results indicated a significant correlation between high cortisol levels and cognitive impairments, particularly in memory. High cortisol levels with disrupted sensitivity to steroids feedback is also described in VaD (Maeda et al. 1991). Dehydroepiandrosterone (DHEA) and its derivative dehydroepiandrosterone sulphate (DHEAS) are produced by both the brain and the pituitary glands. These hormones have an important role in activating the immune system, particularly under stress. In contrast to only small changes in cortisol levels with aging, there is marked reduction in DHEAS with aging. Cortisol and DHEAS have opposite effects. While exposure to cortisol promotes neuronal death, DHEAS has protective properties. Therefore a change in the ratio between cortisol and DHEAS (in favor to cortisol) is a neurotoxic factor (Ferrari et al. 2001). In summary, corticosteroid changes that occur in depression may cause degenerative changes in the hippocampus, possibly similar to AD.

IS THERE A SPECIAL PROFILE OF DEPRESSION OR COGNITIVE IMPAIRMENT THAT IS A RISK FACTOR FOR DEMENTIA? Depression-Executive Dysfunction Lockwood and colleagues (2002) have suggested that executive dysfunctions (i.e., abstract thinking, planning, executing complex behavior, or difficulties in shifting mental sets) can be seen as central features of LLD. This idea was highlighted again by Alexopoulos and colleagues who introduced the term “depression-executive dysfunction” (DED) (Alexopoulos 2001; Alexopoulos, Kiosses, et al. 2002). DED has been described and conceptualized as an entity with pronounced frontostriatal dysfunction. On a clinical level, DED is characterized by psychomotor retardation, reduced interest in activities, and impaired instrumental activities of daily living (Alexopoulos et al. 2001).

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Krishnan and colleagues (1997) showed that brain infracts affecting frontal-subcortical circuitry would be more common in patients with DED than in patients without DED, and also more common than in patients with depression but without executive dysfunction. These findings support the DED hypothesis presuming that many patients with LLD have an underlying cerebrovascular disease with ischemic WMC, which in turn disrupt both mood regulation and executive function pathways (Alexopoulos, Kiosses, et al. 2002). Alexopoulos, Kiosses, and colleagues (2002) describe the unique clinical presentation of DED in comparison to LLD. Depressive symptomatology and especially psychomotor retardation and loss of interest in activities were observed in DED patients, whereas the latter symptoms did not influence the functioning of depressed patients without executive impairment. Is There a Special Kind of Depression That Is a Risk Factor for Preceding Development of Dementia? Longitudinal studies (Sheline et al. 1999; Chodosh et al. 2007; Devanand et al. 1996; Zubenko, Mossy, and Koop 1990) pointed out that it is the severity of depressive symptoms at the baseline measurement that is associated with future cognitive decline. However it is still not known whether severity of the depressive symptoms over time is also associated with cognitive decline. Rovner and colleagues (2009) tested the hypothesis that variability in Geriatric Depression Scale (GDS) scores over time can predict cognitive decline in a study of 160 participants over 65 years old. Cognitive evaluation and GDS were administrated every six months. Results showed that 23 (14.4%) declined cognitively. Age, low education, baseline GDS score (>5), and also variability in GDS scores (i.e., fluctuations between time points) were associated with cognitive decline. Interestingly, the GDS item “Do you feel you have more problems with memory than most?” was significantly more often endorsed by subjects who later declined cognitively than by those who did not (21% versus 2.6% respectively). These findings suggest that not only the severity of depressive symptoms at baseline, but also that cognitive decline followed by mood variability over time (i.e., fluctuations in GDS scores) may reflect damage of neurobiological mechanisms. Another study (Alexopoulos et al. 2005) examined the relationship of executive impairment to the course of depressive symptoms among elderly patients with major depression. A total of 112 nondemented elderly patients with major depression participated in an eight-week citalopram

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(a selective serotonin reuptake inhibitor [SSRI] trial). Executive functions were assessed with the initiation/perseveration subscale of the Dementia Rating Scale and the Stroop Color-Word test. Medical burden was rated with the Cumulative Illness Rating Scale. The main finding of this study was that both abnormal initiation/perseveration and abnormal Stroop Color-Word scores were associated with an unfavorable response of SSRI. These findings confirm earlier results suggesting that impairment in executive functions predicts adverse outcomes of geriatric depression (Potter et al. 2004; Simpson et al. 1998). The theoretical significance of this finding is that it provides a rationale for CI that appears to be a part of depression rather than an indirect behavioral consequence of depressive symptoms. Moreover, most demented disorders have high rates of depressive symptoms. As such, cohort studies (Devanand et al. 1996; Buntinx et al. 1996) showed that a history of depression increases the risk for later development of dementia. These findings suggest that elderly subjects with MCI are at increased risk for future development of dementia. MDD is associated with specific cognitive deficits including poor attention and concentration and slow mental-processing speed (Boone et al. 1995). However, when cerebrovascular disease is present, it is the unique profile of DED that puts older people at increased risk. DED can thus be considered as the phenotype for the underlying brain vascular pathology predisposing to dementia. It appears that frontostriatal–limbic abnormalities predispose to both depressive symptoms and executive dysfunction in older adults (Alexopoulos, Buckwalter et al. 2002). This view is supported by clinical as well as by brain structure and brain function studies. Degenerative disorders of the basal ganglia, as well as stroke of the caudate head and the left frontal pole, often present with depression and executive dysfunction (Sobin and Sacheim 1997). Structural neuroimaging studies (Krishnan, Hays, and Blazer 1997) have shown that MRI hyperintensities are common in subcortical structures and their frontal and limbic connections of depressed elderly patients with executive dysfunction, and impaired metabolism of the caudate nucleus and the frontal regions accompany the depressive state. POSTDEPRESSION COGNITIVE DECLINE Knowledge is sparse concerning whether recovery from a depressive episode also entails recovery from the accompanying cognitive dysfunction. Available evidence on this issue has mostly focused on the effects of antidepressant treatment of cognitive functioning in the elderly, suggesting

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that cognitive dysfunction may persist after recovery from depression (Weiland-Fiedler et al. 2004). Taylor and colleagues (2002) investigated the relationship of depression severity and CI following antidepressant treatment in 52 elderly depressed patients following a one-year treatment period. The study demonstrated that greater severity of depression at baseline treatment was associated with less improvement in cognitive deficits, even when medication therapy had been successful. In a recent study Bhalla and colleagues (2009) examined both the characteristics and frequency of CI among 109 depressed subjects at remission after treatment, compared to an age-matched group of never depressed elderly subjects. The results showed that despite adequate antidepressant treatment and relative to the control group, twice as many depressed subjects had CI and were diagnosed with MCI or dementia (48% versus 28% respectively). Also, older subjects (age>74, 65%) were more likely to be diagnosed with MCI compared to the younger subjects (age<74, 31%). Subjects with LLD were more likely to be diagnosed with MCI or dementia (56%) than those with EOD (35%). Another study (Butters et al. 2000) indicated that elderly LLD patients with CI may experience improvement in some cognitive domains following antidepressant treatment but may not necessarily reach normal cognitive level of performance particularly in memory and executive functions. Airaksinen and colleagues (2004) examined in a three-year retrospective study the performance of elderly depressed patients. At follow-up, despite the symptomatic improvement and improved social functioning, episodic memory and social functioning did not follow this general recovery trend. Cognitive functions that fail to be normalized after remission from depression can thus be termed as postdepression cognitive decline (PDCD). PDCD represents an increased risk for the development of dementia (Halperin and Korczyn 2009). In Figure 7.1 we present a model for the relation between subtypes of MCI that persist after recovery from LLD and prediction about which kind of dementia will develop over time. CONCLUSIONS Depression and dementia are common in old age. Accumulating epidemiological evidence in recent years found that depression is a risk factor for the appearance of dementia. Yet it is unclear whether MDD in patients with dementia is caused by a mental reaction to the disease, or, do aging and disease related brain abnormalities predispose to late-life depression.

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Figure 7.1

Subtypes of Mild Cognitive Impairment (MCI) persist after recovery from Late-Life Depression (LLD) and subsequent development of specific dementia kind.

In the first case, chronic and negative daily events affect thoughts and emotions adjunct to environmental, physiologic stressor triggers somatic response in the CNS leading to excessive secretion of neurotoxic corticosteroids. In the second case, it might also be postulated that older persons are especially sensitive to stress since their brains respond by excessive secretion of corticosteroids. Whichever interaction of these causes might be, either one of them (and both combined) may causes neurotoxic damage and abnormalities, thus suggesting a possible direct pathophysiologic link between depression and the neuropathologic hallmarks of AD and VaD. A proposed predominant mechanism by which depression increases risk for dementia is depicted in Figure 7.2. Medical intervention and care management calls for HPA normalization. This will presumably have positive effects in the treatment of depression and the risk for future development of dementia. Cerebrovascular disease might predispose, precipitate, or perpetuate some late-life depressive syndromes. This notion is based on the

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Figure 7.2

Pathways linking depression to dementia.

comorbidity of depressive syndromes with cerebrovascular lesions, cerebrovascular risk factors, and the fact that depression often develops after a stroke. Elderly people with vascular depression have greater disability and cognitive impairment than those who are depressed but do not have vascular stigmata. The vascular depression hypothesis is of clinical importance because drugs used for the prevention of cerebrovascular disease might, for example, reduce the risk for vascular depression. Late-life depressive disorders also often arise in the context of psychosocial adversity. Low economic status, poor physical health, disability, social isolation, and relocation often lead to an adjustment disorder with depressed mood or trigger more severe depressive syndromes than previously present.

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Chapter 8

Relation of Apathy to Dementia in Patients with Parkinson’s Disease Erica Harris

Apathy is often defined simply as a general loss of motivation. Marin (1996) added to this definition a reduction in goal-directed behavior and thought with few displays of emotion. Although a consensus within the literature has not been reached regarding whether apathy is a neuropsychiatric syndrome or if it is a syndrome by itself, this will not be covered in this chapter. The reader is referred to previous debates of this topic (Starkstein and Leentjens 2008; Marin 1996). Patients with Parkinson’s disease (PD) are not the only clinical group that manifests apathy. Other groups include stroke patients, Alzheimer ’s disease patients, patients with dementia, major depression (Marin, Biedrzycki, and Firinciogullari 1991), and other disorders involving the basal ganglia such as parkinsonism (Sockeel et al. 2006). The prevalence of apathy in patients with PD is found to vary between 16.5% (Aarsland et al. 1999) to 45% (Isella et al. 2002), with one study even reporting apathy in as many as 51% of their sample of patients with PD (Kirsch-Darrow et al. 2006). In addition, apathy has been found to be the most distressing neuropsychiatric problem to caregivers (Leiknes et al. 2010). In this chapter, we will review the neurobiology of PD. Then we will provide a literature review about studies that have previously been conducted to evaluate apathy in PD and relate apathy to the development of dementia. We will then describe three subtypes of apathy that have been linked to basal ganglia and fronto-subcortical circuits and how a patient

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with PD would present with the respective type of apathy. Is one subtype of apathy predictive of progressive dementia over another? Finally, we will explore how these patients’ quality of life can be increased through better diagnosis and treatment. NONMOTOR SYMPTOMS OF PARKINSON’S DISEASE WITH A FOCUS ON APATHY Parkinson’s disease (PD) is classically regarded as a degenerative movement disorder of unknown etiology due to a loss of dopaminergic neurons in the basal ganglia, particularly within an area known as the substantia nigra pars compacta (SNc). The basal ganglia are a set of structures within the midbrain that are responsible for learning and coordinating movement such as swinging a baseball bat, walking, initiating, maintaining, or carrying out a particular behavior. The loss of dopamine (DA) within the SNc contributes to a variety of motor and nonmotor problems that are associated with PD. The gamut of such nonmotor problems include changes in personality (Poewe et al. 1983; Menza et al. 1993; Menza 2000; McNamara, Durso, and Harris 2008), sleep disorders (Chaudhuri et al. 2002; Lauterbach 2007; Stavitsky et al. 2008), pain (Chaudhuri, Healy, and Schapira 2005), falls (Chaudhuri et al. 2005), anosmia (Cramer, Friedman, and Amick 2010), impulse control disorders related to gambling, sexual paraphilias, buying addictions, or binge eating (Ferrara and Stacy 2008; Lim, Evans, and Miyasaki 2008; Wolters, van der Werf, and van den Heuvel 2008), deficits in executive cognitive dysfunction (McNamara, Durso, and Brown 2003), and mood changes including depression (Lew 2007) and apathy (Lew 2007; Simuni and Sethi 2008; Aarsland, Marsh, and Schrag 2009). More than likely, some of these nonmotor symptoms are present before the actual display of motor symptoms (Siderowf and Stern 2008; Tolosa et al. 2009). In this chapter, we will focus on apathy. Clinicians and neurologists are taking the nonmotor symptoms of PD, including apathy, more seriously since the revised Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS; Goetz et al. 2008), part 1, which addresses nonmotor aspects of daily living in an interview format between the clinician and the patient. One of the questions is about apathy and assesses if the patient feels indifferent to doing activities or to being with people. The clinician and patient decide what the best response is for the patient, 0 indicating there have been no changes in the last week to 4, there is a complete loss of initiative and the patient is passive and withdrawn. Kirsch-Darrow et al. (2009) evaluated this new apathy question on the UPDRS against the Apathy Scale (AS)

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used by Starkstein et al. (1992). Kirsch-Darrow and colleagues found that this one apathy item has low specificity if subjects indicate a 1 and it has low sensitivity with a cut-off of 2 or better. As a result, they recommend that users of this apathy screener question exercise prudence when evaluating patients as there is a high probability of missing patients who need to be managed for clinical apathy. Apathy mimics some of the symptoms of depression but can be differentiated from depression (Marin 1996). Approximately 40% of PD patients evidence some degrees of a depression (Cummings 1992; Cummings and Masterman 1999). Depression in patients with PD is different from depression in psychiatric populations because patients with PD have greater anxiety and less negative self-reflecting, or self-ideation, fewer feelings of guilt, and less suicidal ideation. Moreover, patients with lower levels of 5-HIAA (5-hydroxyindoleacetic acid, a metabolite of serotonin), a past history of depression, and more functional disability usually have a greater risk for developing depression in PD. Being female, having an early age of onset of PD, and greater left-brain involvement may all be risk factors for depression in PD as well (Cummings and Masterman 1999). What is more, those patients who exhibit more bradykinesia and gait instability than tremor are more likely to develop depression (Cummings 1992) whereas the apathy studies have reported there is more tremor and akinesia as the predominant motor symptoms in patients with PD (Kirsch-Darrow et al. 2006). In addition, depressed patients with PD have greater frontal-lobe dysfunction and greater involvement of dopaminergic and noradrenergic systems than nondepressed patients (Cummings 1992; Starkstein et al. 1992). NEUROBIOLOGY OF PARKINSON’S DISEASE In the initial stages of PD, the loss of dopamine within the brain is chiefly localized to either the left or right hemisphere. Depending on which hemisphere has a greater depletion of dopamine, the display of motor symptoms is on the contralateral side of the body (Amick, Grace, and Chou 2006; Djaldetti, Ziv, and Melamed 2006). For example, if there is a greater depletion of dopamine in the left hemisphere, then tremors may first begin in the right hand and vice versa. There are two major dopaminergic pathways within the brain that are affected in PD. These include the nigrostriatal—or movement—pathway and the mesolimbic—or reward— pathway. The nigrostriatal pathway sends dopamine from the substantia nigra to the basal ganglia (Björklund and Lindvall 1975) and the mesolimbic pathway sends dopamine from the ventral tegmental area (VTA) to

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the striatum, the limbic system, and the frontal cortex (Lindvall et al. 1974; Swanson 1982). Apathy can result from lesions to the basal ganglia and to these fronto-subcortical circuits (Levy and Dubois 2006; Dujardin et al. et al. 2007). Understanding the connections and projections of the dopaminergic neurotransmitter system will help us to learn how these neuroanatomical areas can be disrupted and produce apathy. PREVIOUS RESEARCH ON APATHY IN PARKINSON’S DISEASE As described above, apathy is a behavioral syndrome that is the result of a loss of motivation and incentives, a lack of emotion, and no exhibition of goal-directed behavior or thought, with apathy not being attributable to a loss of consciousness (Marin 1996). Levy and Dubois (2006) extend this definition by stating that apathy is the result of a loss of voluntary and intentional behavior on behalf of the self; this behavior can be measured and observed. What are the signs of a patient who is apathetic? This is the patient who does not comply with medication regimens, who says he is going to show up for appointments but does not, and who, in general, just does not do what he is asked to do (Marin 1996). In addition, patients who display apathy are more likely to not complete questionnaires about themselves and exhibit negative personality traits. These patients may make statements such as “I do not care about being around people” or “I do not have a desire to learn anything new.” Several types of scales have been developed to measure apathy, but none have been established as the gold standard (van Reekum, Stuss, and Ostrander 2005). One such scale is the Apathy Evaluation Scale (AES; Marin 1996) with a patient and an informant version. There are four subscales in addition to a Global Severity Score: Cognitive, Behavior, Emotional, and Other. The AES has been validated in Alzheimer ’s disease patients, right hemisphere stroke patients, patients with major depression, and healthy elderly controls, with internal consistency reliability ranging from 0.86 to 0.94 and test-retest reliability fluctuating between 0.76 and 0.94. The convergent validity coefficients range from 0.43 to 0.72 (Marin, Biedrzycki, and Firinciogullari 1991). Starkstein et al. (1992) evaluated a 14-item version of the Apathy Scale (a brief form of the AES) in patients with Parkinson’s disease. They found interrater reliability to be r = 0.81 and internal validity to be alpha = 0.76. Another scale that has been used to measure apathy is the Lille Apathy Rating Scale (LARS; Sockeel et al. 2006). The LARS focuses on four of the core features of apathy including intellectual curiosity, self-awareness, emotion, and action initiation. Between-items reliability was established to be 0.80 whereas between-subscales reliability

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was 0.74. The test-retest coefficient was found to be 0.95 with split-half reliability at 0.84. Concurrent validity was r = 0.87 (comparing the global scores of the AES and the LARS). Moreover, the Neuropsychiatric Inventory (NPI; Cummings 1997) is a tool that is used to measure neuropsychiatric behavioral changes in patients with dementia, but there is only one question that assesses global apathy. And finally, Robert et al. (2002) developed the Apathy Inventory (AI) specifically for those patient groups who have suffered brain damage. Like the NPI, it can be used with a caregiver or next-of-kin. The AI is a global measure of apathy that consists of patient and caregiver versions to measure the core features of apathy: lack of emotion, no initiative, and a loss of interest in previous activities and people. Internal consistency for reliability was found to be alpha = 0.84, but this applies only to the caregiver version of the AI. Interrater reliability was high with Kappa coefficient = 0.99. It has not yet been established whether apathy is related to a stage of disease or to greater motor impairment. There also is not a consensus on whether there is an overall decline in Mini-Mental State Examination (MMSE) scores and general cognitive functioning in patients with PD who suffer from apathy (Levy et al. 1998; Pluck and Brown 2002; Starkstein et al. 1992; Starkstein and Leentjens 2008). On the other hand, some studies have shown that degrees of apathy predict risk for dementia, but these studies have not always specified exact subtypes of apathy. Below is a review of apathy studies previously conducted with patients with PD. Starkstein et al. (1992) assessed apathy in a group of 50 patients with idiopathic PD using the Apathy Scale (AS). They found that only 12% of their sample exhibited apathy without depression whereas 30% had both apathy and depression; patients with apathy exhibited greater difficulty with timed neuropsychological tests and verbal memory tests. Pedersen and colleagues (2009) conducted a longitudinal study with a communitybased sample of 139 patients with PD in western Norway. They found that patients with apathy were demented at baseline and often at follow-up (79 patients of the original 139 patients were reassessed four years later). At follow-up, 29.1% of their sample was demented with 63.6% of patients displaying apathy to also be demented. They concluded that apathy may be a result of the natural, biological course of PD for those patients with more advanced disease but that the emergence of dementia may be related to the nondopaminergic circuits and the fact that the patient sample was older. Pluck and Brown (2002) evaluated apathy in 45 PD patients and in a control group of 17 patients with osteoarthritis. They found that the patients with PD had higher levels of apathy. Those patients with PD

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who were classified as having high apathy performed worse on verbal fluency and the Stroop test than those who scored low on apathy. They hypothesized that those patients, 7.1% of the sample, could possibly be demented since they did so poor on the cognitive battery. Cognitive impairment was also found to be related to apathy, but disease progression or stage of disease was found to be unrelated to levels of apathy, possibly indicating a subtype of apathy. Furthermore, apathy was not related to anxiety scores or to depression and was not predicted by stage of disease or duration of disease. Kirsch-Darrow et al. (2006) investigated apathy in 80 idiopathic patients with PD and in 20 patients with adult-onset dystonia using the short version of the AES. They found that 51% of patients with PD experienced apathy with almost 29% experiencing apathy and depression together. None of the patients with dystonia experienced apathy. They also found a small positive correlation between age and apathy score, which previous studies have not. They concluded that apathy is more than likely a defining feature of PD and that apathy is not a symptom of depression and occurs independently of depression since the anterior cingulate-mesial frontal cortex is more damaged in PD. Finally, Cramer, Friedman, and Amick (2010) examined olfaction in apathy in patients with PD by administering the AES. They found that greater apathy indicated greater olfactory impairment. Their reasoning was that smell is intricately tied to emotions and since olfaction areas are anatomically close to limbic areas within the brain, studying the overlap of these areas is important to understanding how apathy can be a part of the physiological changes that emerge during the course of PD. They further recommend that more research be tailored to deciphering the link between dementia and motor-symptom severity to be sure apathy scores or olfactory test scores are not influenced by these factors. Sockeel et al. (2006) developed the Lille Apathy Rating Scale (LARS). They found that this new measure is a useful tool in separating apathy from depression in patients with PD. Of 159 patients with probable PD, 32.1% were found to have apathy in a study conducted by Dujardin et al. (2007) to validate the LARS using the same patient sample from the Sockeel group. Dujardin and colleagues found that having a low level of cognitive functioning was the main contributing factor to more severe apathy. Motor-symptom severity did not contribute to apathy, and the occurrence of apathy is not dependent on depression. Overall, the LARS was reliable in differentiating various types of apathy among PD patients, the types of apathy including none, slight, moderate, and severe. Dujardin et al. (2009) examined dementia in 20 apathetic patients with PD and

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in 20 nonapathetic patients with PD using the LARS. They administered various tests of executive function including the Stroop, word generation, a letter and number sequencing test, and the oral version of the Symbol Digit Modalities test. Apathetic patients with PD performed much more poorly on this cognitive battery with reduced response inhibition and action initiation. At 18 months follow-up, dementia was much more common in patients who were apathetic, suggesting that apathy can predict later dementia. Only one case of dementia was seen at follow-up in the nonapathetic group. Dujardin et al. (2009) allude to a subtype of apathy known as cognitive-affective apathy (Levy and Dubois 2006) that is present in this sample of patients. More about this subtype of apathy is explained below. Levy et al. (1998) used the Neuropsychiatric Inventory (NPI) to assess apathy in PD patients. They found that depression and apathy were uncorrelated. PD patients had greater cognitive dysfunction as evidenced by the MMSE and this was correlated with apathy. They also had greater levels of depression. Oguru et al. (2010) found in their study that 60% of Japanese patients with PD exhibited apathy and 56% of these patients exhibited depression. Of their sample, 43% displayed apathy and depression together, and 8% displayed dementia. After removing patients with depression and dementia, they found that only 15% of their sample displayed apathy as the sole behavioral disorder. They concluded that it is possible that apathy can occur independent of depression in PD. Those patients with apathy had higher levels of depression and worse cognitive functioning. Oguru and colleagues hypothesize as did Kirsch-Darrow et al. (2006) that impairment to the mesial frontal-anterior cingulate circuits underlie the production of apathy. Pedersen et al. (2010) administered the NPI to a group of communitydwelling PD patients who had never begun taking medicine for their PD. Of their sample, 25 patients were diagnosed solely with apathy whereas 14 patients were diagnosed with depression and apathy together. Apathy in patients with PD was significantly associated with male gender, worse motor scores, high depression, and impairments in attention/executive deficits. No correlation was found between apathy and increased cognitive impairment as has been found in other studies, and no controls were diagnosed with apathy. Newly diagnosed PD patients who had never taken medicine and had apathy indicated that their apathy was more than likely the result of a neurobiological change. Aarsland et al. (1999) found that 4.3% of patients with untreated PD who were assessed with the NPI had apathy and no depression whereas Pedersen et al. (2010) found that 8.3% of untreated patients with PD did.

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Finally, Robert et al. (2002) used the AI to rate apathy in Alzheimer ’s disease patients, mild cognitive impairment patients, and patients with PD. They found that the patients with PD rated their global apathy higher than the other two neurological groups and showed that they were aware of their apathy. This was not seen in the nonapathetic patient group. Robert et al. (2002) recommend that future research try to unravel the cognitive, behavioral, and emotional components of apathy. In summary, it is inconclusive from this previous research if apathy is a result solely of the disability of PD or if it is a neurobiological problem resulting from PD. Most of the researchers conclude that apathy is a multifactorial neuropsychiatric problem and is probably a result of the underlying neurobiology in PD. Apathy, furthermore, appears to be a strong predictor for development of a later dementing process. However, PD is a complicated neurodegenerative disease. There are different ages of onset of disease with different disease courses. Even diagnosing the disease can be difficult with up to 25% of patients being misdiagnosed (Tolosa, Wenning, and Poewe 2006). In addition, it is impossible gauge how much loss of dopamine has occurred in the brainstem and progressed to the midbrain and to the cortex. As a result, even though we have these data on apathy, research groups are still trying to establish the best method to measure apathy in addition to consistently detailing the specific subtypes of apathy we would see in patients with PD based on the type of lesion in the basal ganglia and fronto-subcortical circuits and resulting cognitive batteries. SUBTYPES OF APATHY IN PATIENTS WITH PARKINSON’S DISEASE Levy and Dubois (2006) discuss three models of apathy (based on Stuss, van Reekum, and Murphy 2000) including emotional-affective, cognitive-affective and auto-activation deficit, that are linked with profiles of dysfunction within the basal ganglia and the frontal lobes. Even though these specific subtypes of apathy are described, the occurrence of dementia along with the presentation of apathy in patients with PD is not uniform (Starkstein et al. 1992; Levy and Dubois 2006; Pluck and Brown 2002). However, these models can serve as heuristics to evaluate apathy while dementia is concurrently assessed not only in patients with PD but in other patient populations as well. We revisit these models of apathy for two purposes: (1) to redirect the research community to these subtypes, and (2) to begin thinking of ways by which these subtypes of apathy are expressed in patients with PD.

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Emotional-Affective Apathy Emotional-affective apathy is linked with dysfunction in the orbitalmedial prefrontal cortex (PFC) and the limbic regions of the basal ganglia including the ventral striatum and ventral pallidum (Levy and Dubois 2006). Lesions to the orbital-medial PFC often result in emotional blunting, poor judgment related to actions and consequences, and the performance of actions based on feelings and emotions alone. Robert et al. (2002) report that in their study that mild cognitively impaired (MCI) patients differed on the lack of initiative domain, but this could not be adequately investigated due to the small sample size. They suggest that future studies should investigate whether this domain is indicative of a progressive change toward dementia. In patients with PD who exhibit this type of apathy, we would see the emergence of impulsive behaviors and a reduction in self-initiated behaviors, possibly increased novelty seeking and a lack of response to rewarding behaviors. There would be a lack of disregard for any consequences of behavior. Many of these types of behaviors are seen after these patients have been medicated with dopamine agonists over a long period of time (Weintraub 2008). The neurotransmitter dopamine responds to reward, but these patients are less likely to recognize the reward; therefore, there is no recognition of a rewarding and beneficial outcome (Daw and Shohamy 2008). When we are surprised by a new outcome and our dopamine receptors respond, it is considered that we have learned from this response. Daw and Shohamy (2008) argue that the striatum is where this learning occurs in the basal ganglia. One type of test that can evaluate this form of apathy is the Gambling Task developed by Bachara (Levy and Dubois 2006). This form of apathy is difficult for caregivers to manage because these patients may perform activities that can be quite embarrassing to the family. In addition, there is also a loss of interaction with the family on behalf of the patient. Activities of daily living such as maintaining good hygiene are also neglected.

Cognitive-Affective Apathy Cognitive-affective apathy involves lesions of the dorsolateral PFC and the dorsal caudate nucleus, the internal portion of the globus pallidus, the lateral substantia nigra pars reticulata, and the anterior thalamic nuclei (Levy and Dubois 2006). Patients with PD who suffer from this type of apathy perform poorly on tests of executive cognitive functioning and planning including the Stroop, Trails, categorical verbal fluency, the Tower

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of London, and the Wisconsin Card Sorting Test (Levy and Dubois 2006). Each of these tasks requires the patient to plan, to establish a set of rules, to maintain the set of rules, and then be able to shift the rules to exhibit cognitive flexibility. These patients also have difficulties with working memory in that they forget the mini goals they are working towards in these various tasks (Levy and Dubois 2006). An as example, patients with PD who have this type of apathy are those who more than likely exhibit the greatest deficits on the Tower of London (TOL) planning task and who become frustrated because they cannot do it. From personal testing experience, some apathetic patients with PD have not gotten farther than part five of nine of the TOL task and do extremely poor on word-generation tasks. Much of the research as discussed in the literature review indicates that this subtype of apathy is common. Auto-Activation Deficit Auto-activation deficit is the most debilitating form of apathy. The lesions are located in the medial PFC and may be large frontal lesions or white-matter lesions and in the cognitive and limbic areas of the basal ganglia, particularly in the internal portion of the globus pallidus (Levy and Dubois 2006). In this form of apathy, whatever information is gleaned from the external environment does not elicit any behavior from the basal ganglia to reach the output systems. The basal ganglia and frontal lobes need input to communicate with each other. When this input cannot reach the desired brain areas, such as the pallidum, there is no feedback loop providing information to the frontal lobes about what behavior to perform. Patients with this type of apathy remain motionless and respond to needing a “little” push each day or having someone tell them what to do. If they are given some form of external environmental stimuli, they can move and carry out behaviors for a short period of time, but this is not sustained (Levy and Dubois 2006). As a result, there is no voluntary action that is carried out by the patient. Patients with PD who exhibit this type of apathy would exhibit all of these symptoms. It will be especially important to identify those patients who suffer from this type of apathy early, with treatment interventions aimed towards the patient and the caregiver. SUGGESTIONS FOR TREATMENT AND FUTURE RESEARCH Any treatments for apathy thus far seem to be targeted only to global apathy and not to any specific subtype of apathy. Dopaminergic agents such as bromocriptine and amantadine, in addition to amphetamines,

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atypical antipsychotics, and acetylcholinesterase inhibitors, might be beneficial for some patients with apathy. However, which drug is effective for which type of apathy has yet to be described (van Reekum, Stuss, and Ostrander 2005). It could be possible that one type of medication may not be suitable for emotional-affective apathy but is ideal for cognitive-affective apathy and vice versa. As Stuss, van Reekum, and Murphy (2000) point out, until we have a better grasp as to the underlying neurobiological and neurochemical manifestations of apathy, any pharmacological or behavioral therapy will not be useful. As several authors recommend (Kirsch-Darrow et al. 2006, 2009; Starkstein and Leentjens 2008), better measurement and methodologies of studying apathy can assist pharmaceutical companies in developing better medications to assist with the problems associated with apathy including motivation and emotion. What are the risk factors for apathy? How can we better predict those patients who will develop apathy versus those who do not? As we have discussed above, however, there are various subtypes of apathy that patients may endure. It is problematic that there are no specific treatments tailored toward these individual subtypes of apathy. Since the definition of apathy is still not ideally defined, what we may be categorizing as apathy within PD may be something entirely different (Starkstein and Leentjens 2008). However, we need to find other avenues of treatment that are nonpharmacologic, especially for patients with PD since they are already taking very detailed medication regimens. One of the first steps is for clinicians to become better educated about the signs and symptoms of apathy (van Reekum, Stuss, and Ostrander 2005). Furthermore, educating caregivers and patients that apathy is a symptom of PD will help them to be better prepared in the event the patient becomes apathetic (Kirsch-Darrow et al. 2006). Better education can then possibly lead to rehabilitative programs of which there currently are none specifically tailored to patients with PD and apathy. Stuss et al. (2000) report on a study that attempted therapy for apathetic patients who were not patients with PD. Kopelowicz and colleagues (1997) enrolled a total of six subjects with schizophrenia, three with and without deficit syndrome. In other words, they exhibited the amotivational syndrome that is typically found in schizophrenia. The authors implemented a social-skills training program that lasted for 12 weeks. The schizophrenic patients who did not have the deficit syndrome improved, but those with the deficit syndrome were not influenced by the social-skills training. Other types of rehabilitation that were discussed refer to teaching compensatory strategies and training executive cognitive functions (Stuss, van Reekum, and Murphy 2000). The paucity of research on rehabilitation of patients with

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apathy indicates that better emotional and cognitive therapies need to be developed (Aarsland et al. 1999). In the end, though, it will take dedicated caregivers and clinicians to work with these patients. One factor not addressed in current studies of apathy in patients with PD is the impact of PD subtype on apathy. Patients with left-onset disease are known to significantly differ along an array of clinical variables from PD patients with right-onset disease. For example, there are differences in language production among left-versus right-onset PD patients. Could the language production among these patients be linked to specific types of apathy (Pluck and Brown 2002)? What type of apathy is seen in patients with left-onset versus right-onset PD? Knowing this information may tell us something special about the various forms of apathy that are seen in these patients. More than likely, there is a different neurobiology and neurochemical basis depending upon the side of onset exhibiting a different presentation of apathy. Additionally, although it is clear that apathy is a strong predictor of later dementia, it is uncertain as to whether any particular subtype of apathy more strongly predicts dementia than the other subtypes of apathy. Researchers investigating apathy in patients with PD should report their data with respect to side of onset to provide additional information by which the tangled web of apathy can be addressed. Although not all patients with PD will have apathy, this neuropsychiatric symptom is not to be ignored. As Cummings (1997) indicates, those patients who suffer from dementia may be a subgroup with neurobiological changes occurring at a different rate than those without dementia. This equally applies to our patients with PD who have apathy. They are a special subgroup of patients with PD who need to be treated as such. These patients are an interesting group to study to learn more about how such symptoms can develop from a neurodegenerative disease and can help us eventually find better ways to treat and to manage apathy in patients with PD and to possibly predict whether apathy definitely predicts later dementia. REFERENCES Aarsland, D., J. P. Larsen, N. G. Lim, C. Janvin, K. Karlsen, E. Tandberg, and J. L. Cummings. 1999. Range of neuropsychiatric disturbances in patients with Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry 67: 492–496. Aarsland, D., L. Marsh, and A. Schrag. 2009. Neuropsychiatric symptoms in Parkinson’s disease. Movement Disorders 24 (15): 2175–2186.

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Amick, M. M., J. Grace, and K. L. Chou. 2006. Body side of motor symptom onset in Parkinson’s disease is associated with memory performance. Journal of Neurology, Neurosurgery and Psychiatry 12: 736–440. Björklund, A., and O. Lindvall. 1975. Dopamine in dendrites of substantia nigra neurons: Suggestions for a role in dendritic terminals. Brain Research 83: 531–537. Chaudhuri, K. R., D. G. Healy, and A. H. V. Schapira. 2005. Non-motor symptoms of Parkinson’s disease: Diagnosis and management. The Lancet Neurology 5 (3): 235–245. Chaudhuri, K. R., S. Pal, A. DiMarco, C. Whatley-Smith, K. Bridgman, R. Mathew, F. R. Pezzela, B. Hogl, and C. Trenkwalder. 2002. The Parkinson’s disease sleep scale: A new instrument for assessing sleep and nocturnal disability in Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry 73: 629–635. Cramer, C. K., J. H. Friedman, and M. M. Amick. 2010. Olfaction and apathy in Parkinson’s disease. Parkinsonism and Related Disorders 16: 124–126. Cummings, J. L. 1992. Depression and Parkinson’s disease: A review. American Journal of Psychiatry 149 (4): 443–454. Cummings, J. L. 1997. The Neuropsychiatric Inventory: Assessing psychopathology in dementia patients. Neurology 48 (Suppl. 6): S10–16. Cummings, J. L., and D. L. Masterman. 1999. Depression in patients with Parkinson’s disease. International Journal of Geriatric Psychiatry 14: 711–718. Daw, N. D. and D. Shohamy. 2008. The cognitive neuroscience of motivation and learning. Social Cognition 26: 593–620. Djaldetti, R., I. Ziv, and E. Melamed. 2006. The mystery of motor asymmetry in Parkinson’s disease. Lancet Neurology 5: 796–802. Dujardin, K., P. Sockeel, M. Delliaux, A. Destée, and L. Defebvre. 2009. Apathy may herald cognitive decline and dementia in Parkinson’s disease. Movement Disorders 24 (16): 2391–2397. Dujardin, K., P. Sockeel, D. Devos, M. Delliaux, P. Krystkowiak, A. Destée, and L. Defebvre. 2007. Characteristics of apathy in Parkinson’s disease. Movement Disorders 22 (6): 778–784. Ferrara, J. M., and M. Stacy. 2008. Impulse-control disorders in Parkinson’s disease. CNS Spectrums 13: 690–698. Goetz, C. G., B. C. Tilley, S. R. Shaftman, G. T. Stebbins, S. Fahn, P. Martinez-Martin, W. Poewe, et al. 2008. Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Movement Disorders 23 (15): 2129–2170. Isella, V., P. Melzi, M. Grimaldi, S. Iurlaro, R. Pioliti, C. Ferrarese, L. Frattola, and I. Appollonio. 2002. Clinical, neuropsychological and morphometric correlates of apathy in Parkinson’s disease. Movement Disorders 17: 366–371. Kirsch-Darrow, L., H. F. Fernandez, M. Marsiske, M. S. Okun, and D. Bowers. 2006. Dissociating apathy and depression in Parkinson disease. Neurology 67: 33–38.

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Pedersen, K. F., G. Alves, K. Brønnick, D. Aarsland, O-B. Tysnes, and J. P. Larsen. 2010. Apathy in drug-naïve patients with incident Parkinson’s disease: The Norwegian ParkWest study. Journal of Neurology 257: 217–223. Pluck, G. C., and R. G. Brown. 2002. Apathy in Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry 73: 636–642. Poewe, W., E. Karamat, G. W. Kemmler, and F. Gerstenbrand. 1983. The premorbid personality of patients with Parkinson’s disease: A comparative study with health controls and patients with essential tremor. Advances in Neurology 53: 339–342. Robert, P. H., S. Clairet, M. Benoit, J. Koutaich, C. Bertogliatic, O. Tible, H. Caci, M. Borg, P. Brocker, and P. Bedoucha. 2002. The Apathy Inventory: Assessment of apathy and awareness in Alzheimer ’s disease, Parkinson’s disease and mild cognitive impairment. International Journal of Geriatric Psychiatry 17: 1099–1105. Siderowf, A., and M. B. Stern. 2008. Premotor Parkinson’s disease: Clinical features, detection, and prospects for treatment. Annals of Neurology 64: S139–147. Simuni, T., and K. Sethi. 2008. Nonmotor manifestations of Parkinson’s disease. Annals of Neurology 64: S65–80. Sockeel, P., K. Dujardi, D. Devos, C. Denève, A. Destée, and L. Defebvre. 2006. The Lille apathy rating scale (LARS), a new instrument for detecting and quantifying apathy: Validation in Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry 77: 579–584. Starkstein, S. E., and A. F. G. Leentjens. 2008. The nosological position of apathy in clinical practice. Journal of Neurology, Neurosurgery, and Psychiatry 79: 1088–1092. Starkstein, S. E., H. S. Mayberg, T. J. Preziosi, P. Andrezejewski, R. Leiguarda, and R. G. Robinson 1992. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. Journal of Neuropsychiatry 4 (2): 134–139. Stavitsky, K., P. McNamara, R. Durso, E. Harris, S. Auerbach, and A. Cronin-Golomb. 2008. Hallucinations, dreaming and frequent dozing in Parkinson’s disease: Impact of right-hemisphere neural networks. Cognitive and Behavioral Neurology 21: 143–149. Stuss, D. T., R. van Reekum, and K. J. Murphy. 2000. Differentiation of states and causes of apathy. In The neuropsychology of emotion, ed. J. C. Borod, 340-63. Oxford: Oxford University Press. Swanson, L. W. 1982. The projections of the ventral tegmental area and adjacent regions: A combined fluorescent retrograde tracer and immunofluorescence study in the rat. Brain Research Bulletin 9: 321–353. Tolosa, E., C. Gaig, J. Santamaria, and Y. Compta. 2009. Diagnosis and the premotor phase of Parkinson disease. Neurology 72: S12–20. Tolosa, E., G. Wenning, and W. Poewe. 2006. The diagnosis of Parkinson’s disease. Lancet Neurology 5: 75–86. van Reekum, R., D. T. Stuss, and L. Ostrander. 2005. Apathy: Why care: Journal of Neuropsychiatry and Clinical Neurosciences 17: 7–19.

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Chapter 9

Vascular Cognitive Impairment and Dementia Howard S. Kirshner

Vascular disease in the brain is a common cause of late-life cognitive impairment. It is estimated that up to 30% of stroke survivors have disabling cognitive deterioration at one year after stroke. Vascular risk factors, transient ischemic attacks, both silent and clinically evident strokes, and “white-matter ischemic changes” on magnetic resonance imaging (MRI) studies all correlate with dementia. Vascular dementia, or mixed vascular and degenerative dementia, may be second only to Alzheimer ’s disease in prevalence of dementing illnesses. Historically, vascular diseases were thought to represent the majority of dementia cases; senile dementia was commonly referred to as “cerebral arteriosclerosis” or “hardening of the arteries.” Vascular dementia has been recognized since the writings of Thomas Willis (Roman 2002). The German physician Otto Binswanger wrote about vascular lesions associated with dementia, in the same period that Alois Alzheimer described the plaques and tangles associated with dementia, that led to the name “Alzheimer ’s disease” (AD). In the 1960s, the pendulum of thought about dementing illnesses shifted away from vascular disease, toward the concept of primary neuronal degeneration, unrelated to vascular disease; this has since been the central way of thinking about Alzheimer ’s disease. Over the past decade, the pendulum has shifted back somewhat, toward vascular diseases as prominent causes of dementia. Several lines of evidence, which I will discuss in this chapter, have supported the increased emphasis on vascular diseases. First, in pathological series of dementia cases, vascular diseases are second only to Alzheimer ’s

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disease as causes of dementia, and many cases have both cerebral infarcts and changes of AD. Second, the risk factors for stroke have also been shown to be the principal risk factors for dementia, and even for Alzheimer ’s disease specifically. Third, the pathology of Alzheimer ’s disease, besides the formation of senile plaques containing a central core of amyloid protein in the neuropil and “tangles” in the remaining neurons, includes deposition of amyloid protein in cerebral vessels. Many patients who qualify for the diagnosis of AD have changes on MRI scan and at autopsy of deep whitematter ischemic changes, likely related to the amyloid vasculopathy. Cerebral amyloidosis also results in brain hemorrhages (Zhang-Nunes et al. 2006). Fourth, many types of cerebrovascular disease cause or contribute to the development of dementia. This heterogeneity of vascular disorders makes vascular dementia a complex topic. How do vascular lesions contribute to dementia? The presence of ischemic infarctions in the brain undoubtedly affects cognitive function. The relationship between vascular lesions and cognitive impairment, however, is complex. The degree of impairment likely relates to both the quantitative amount of tissue damage and to the strategic locations of the infarctions. These relationships will be discussed in some detail. In this chapter I shall discuss the definition and diagnostic criteria for vascular cognitive impairment; the evidence for the association of vascular risk factors and vascular lesions with dementia; and finally, the prevention and treatment of vascular dementia. To date, only a limited number of clinical trials have addressed the prevention and treatment of vascular dementia, many fewer than those concerning Alzheimer ’s disease. DEFINITION AND DIAGNOSTIC CRITERIA FOR VASCULAR DEMENTIA Dementia can be defined as a loss of cognitive functions significant enough to cause functional disability in everyday life. Vascular dementia is a dementia caused at least in part by vascular lesions such as infarctions. The difficulty implicit in this definition lies in teasing out the contributions of strokes, or vascular lesions, and neurodegenerative changes in a patient with dementia. The presence of AD changes in elderly stroke survivors cannot be excluded by imaging studies alone. Hachinski originally coined the term “multi-infarct dementia” to reflect cases of dementia caused by an accumulation of ischemic stroke damage (Hachinski et al. 1975). This term has not remained useful, for at least two reasons. First, single, strategic infarctions can be associated with dementia, as in the case of a patient with a large left middle cerebral artery territory stroke and

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global aphasia, or a posterior cerebral artery territory stroke with damage to the hippocampus and loss of short-term memory. Second, strokes can affect cognitive functions without producing dementia. In 1975 Hachinski advanced the “Hachinski ischemic score,” originally for use in research studies. This score remains the only quantitative measure of vascular dementia, used in numerous research trials and verified with pathology (Rosen et al. 1980). Table 9.1 shows the Hachinski ischemic score. Note that vascular risk factors such as hypertension, evidence of atherosclerosis, or a history of strokes are important, as are the mode of presentation (acute onset of cognitive difficulties or a stepwise rather than gradually progressive deterioration) and associated features. The terms “vascular dementia” (VaD) (Hachinski 1994, 5) or “vascular cognitive impairment” have largely replaced “multi-infarct dementia.” Tables 9.2 and 9.3 summarize two sets of diagnostic criteria for vascular dementia, the California (Chui et al. 1992) and NINDS-AIREN (Roman et al. 1993) criteria. These criteria are similar, in that they require evidence of strokes, both clinically and by imaging studies (and not just white matter changes on MRI), and also evidence of cognitive impairment. Both make clear that definite vascular dementia can be diagnosed only with neuropathology, usually an autopsy study, so that the most a clinician can diagnose is “probable” or “presumed” vascular dementia. Both sets Table 9.1 Hackinski Ischemic Score Feature Abrupt onset Stepwise deterioration Fluctuating course Nocturnal confusion Relative preservation of personality Depression Somatic complaints Emotional incontinence History of hypertension History o