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Datasets for Special Series on Cognitive Reserve

Published online by Cambridge University Press:  04 May 2011

David A. Bennett*
Affiliation:
Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
Lisa L. Barnes
Affiliation:
Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
*
Correspondence and reprint requests to: David A. Bennett, Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S. Paulina, Suite 1028, Chicago, Illinois 60612. E-mail: david_a_bennett@rush.edu
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Abstract

Type
Special Series
Copyright
Copyright © The International Neuropsychological Society 2011

Introduction

The Sixth Annual Advanced Psychometrics Methods Workshop was held August 30 to September 4, 2009 at the University of Washington's Friday Harbor Laboratories in the San Juan Islands, WA. The theme, Cognitive Reserve, focused on identifying variables that influence the association of neuropathology with cognitive function. Functional physiologic systems, for example, pulmonary, renal, hepatic, cardiac, are redundant and a considerable amount of tissue destruction must take place before the system is compromised to the point that signs and symptoms of dysfunction become clinically apparent. The nervous system, though functionally and structurally more complex, has long been recognized to exhibit some type of reserve. Evidence of cognitive reserve comes from a variety of sources. For example, several clinical–pathologic studies have reported widespread Alzheimer's disease (AD) pathology among persons without obvious cognitive impairment suggesting the presence of factors that must influence cognition separate from neuropathology or influence the association of neuropathology with cognition (Crystal et al., Reference Crystal, Dickson, Sliwinski, Lipton, Grober, Marks-Nelson and Antis1993; Davis, Schmitt, Wekstein, & Markesbery, Reference Davis, Schmitt, Wekstein and Markesbery1999; Katzman et al., Reference Katzman, Terry, DeTeresa, Brown, Davies, Fuld and Peck1988; Morris et al., Reference Morris, Storandt, McKeel, Rubin, Price, Grant and Berg1996). Several factors have been reported to be associated with cognitive reserve such as years of formal education and occupation (Zhang et al., Reference Zhang, Katzman, Salmon, Jin, Cai, Wang and Lui1990; Stern et al., Reference Stern, Gurland, Tatemichi, Tang, Wilder and Mayeux1994).

Studies providing data to this workshop included data on a) indicators of cognitive reserve, b) detailed cognitive function, and c) neuropathologic indices. Data were primarily from two studies: The Rush Religious Orders Study and the Rush Memory and Aging Project (MAP). Both are cohort studies of risk factors for common chronic conditions of aging that include organ donation at death. Together, more than 2,500 persons have undergone annual detailed clinical evaluations with up to 17 waves of data, and more than 850 brain autopsies have been performed. Prior work from these studies has reported the relation of neuropathology, including measures of AD pathology, cerebral infarctions, Lewy bodies, and amyloid angiopathy, to cognition (Arvanitakis, Leurgans, Barnes, Bennett, & Schneider, Reference Arvanitakis, Leurgans, Barnes, Bennett and Schneider2011; Arvanitakis, Leurgans, et al., Reference Arvanitakis, Leurgans, Wang, Wilson, Bennett and Schneider2010; Bennett, Schneider, Bienias, Evans, & Wilson, Reference Bennett, Schneider, Bienias, Evans and Wilson2005; Bennett, Schneider, Arvanitakis, et al., Reference Bennett, Schneider, Arvanitakis, Kelly, Aggarwal, Shah and Wilson2006; Schneider, Wilson, Bienias, Evans, & Bennett, Reference Schneider, Wilson, Bienias, Evans and Bennett2004; Schneider, Boyle, Arvanitakis, Bienias, & Bennett, Reference Schneider, Boyle, Arvanitakis, Bienias and Bennett2007; Schneider, Arvanitakis, Bang, & Bennett, Reference Schneider, Arvanitakis, Bang and Bennett2007; Schneider, Arvanitakis, Leurgans, & Bennett, Reference Schneider, Arvanitakis, Leurgans and Bennett2009; Wilson, Leurgans, Boyle, Schneider, & Bennett, Reference Wilson, Leurgans, Schneider and Bennett2010). In addition, several experiential and psychological factors have been reported to be related to cognition separate from neuropathology such as neuroticism, loneliness, depression, and cognitive activities (Bennett, Wilson, Schneider, Bienias, & Arnold, Reference Bennett, Wilson, Schneider, Bienias and Arnold2004; Wilson et al., Reference Wilson, Evans, Bienias, Mendes de Leon, Schneider and Bennett2003; Wilson, Scherr, Schneider, Tang, & Bennett, Reference Wilson, Scherr, Schneider, Tang and Bennett2007; Wilson, Arnold, Schneider, Li, & Bennett, Reference Wilson, Arnold, Schneider, Li and Bennett2007; Wilson, Krueger, et al., 2007). Furthermore, several factors have been reported to modify the relation of neuropathology to cognition including years of formal education, social networks, processing resources, conscientiousness, and sex (Barnes et al., Reference Barnes, Wilson, Bienias, Evans, Schneider and Bennett2005; Bennett, Schneider, Wilson, Bienias, & Arnold, Reference Bennett, Schneider, Wilson, Bienias and Arnold2005; Bennett, Schneider, Arnold, Tang, & Wilson, Reference Bennett, Schneider, Arnold, Tang and Wilson2006; Bennett, Wilson, et al., Reference Bennett, Wilson, Schneider, Evans, Mendes de Leon, Arnold and Bienias2009; Boyle, Wilson, Schneider, Bienias, & Bennett, Reference Boyle, Wilson, Schneider, Bienias and Bennett2008; Wilson, Schneider, Arnold, Bienias, & Bennett Reference Wilson, Schneider, Arnold, Bienias and Bennett2007). Finally, data collection methods include a large subset of data collected with identical methods in both studies allowing for data to be pooled for clinical and clinical–pathologic analyses in which larger samples are needed to enhance power for associations (Bennett, Schneider, Arvanitakis, et al., Reference Bennett, Schneider, Arvanitakis, Kelly, Aggarwal, Shah and Wilson2006; Bennett, Schneider, Aggarwal, et al., Reference Bennett, Schneider, Aggarwal, Arvanitakis, Shah, Kelly and Wilson2006b; Bennett, De Jager, Leurgans, & Schneider, Reference Bennett, De Jager, Leurgans and Schneider2009; Schneider, Arvanitakis, Bang, & Bennett, Reference Schneider, Arvanitakis, Bang and Bennett2007; Wilson, Schneider, Boyle, et al., Reference Wilson, Schneider, Boyle, Arnold, Tang and Bennett2007).

A third study, the Minority Aging Research Study, also contributed clinical data to the workshop. This is a cohort study of risk factors for common chronic conditions with more than 350 older African Americans. Data collection methods include a large subset of data collected with identical methods as those used in the Religious Orders Study and the Rush Memory and Aging Project, which include nearly 200 African Americans, allowing for data to be pooled for clinical analyses across race (Arvanitakis, Bennett, Wilson, & Barnes, Reference Arvanitakis, Bennett, Wilson and Barnes2010; Boyle, Barnes, Buchman, & Bennett, Reference Boyle, Barnes, Buchman and Bennett2009; Boyle, Buchman, Barnes, James, & Bennett, Reference Boyle, Buchman, Barnes, James and Bennett2010; Buchman, Boyle, Barnes, Leurgans, & Bennett, Reference Buchman, Boyle, Barnes, Leurgans and Bennett2010).

This special Series on Cognitive Reserve in the Journal of the International Neuropsychological Society highlights four manuscripts produced by conference participants. The first, by Jones, et al. examined latent variable modeling approaches for quantifying reserve, with an emphasis on their application to data from observational studies. The second paper, by Dowling, et al., used latent variable modeling to examine clinical–pathologic relationships with data from the Religious Orders Study and Rush Memory and Aging Project. The third paper, by Reed et al., also used a latent variable modeling approach to estimate domain-specific cognitive reserve and then sought to identify potential determinants of reserve using data from the same two studies. Finally, the paper by Fyffe et al., assessed differential item functioning, demographic characteristics, and factors associated with cognitive reserve to determine whether they account for racial differences in episodic memory performance using data from the Rush Memory and Aging Project and the Minority Aging Research Study.

Cohorts providing data to workshop

Rush Religious Orders Study

The Religious Orders Study enrolls nuns, priests, and brothers without known dementia, from across the United States. Participants must agree to an annual detailed clinical evaluation and brain donation at the time of death. Each subject signs a consent form and an Anatomical Gift Act. The study was approved by the Institutional Review Board of Rush University Medical Center. The study primarily recruits clergy living in more than 40 communities, including three predominantly African American communities, and Hispanic sisters primarily from communities in San Antonio. Clinical data collection began in January of 1994. The study has a rolling admission with participants enrolling each year. To date, more than 1150 participants have enrolled, approximately 30% are men and approximately 90% are white, non-Hispanic. The mean age at entry is approximately 75 years with an average education of approximately 18 years. Follow-up clinical evaluations are conducted annually with a 95% follow-up rate of survivors. More than 500 brain autopsies have been performed with an autopsy rate nearly 95%.

Rush Memory and Aging Project

The Memory and Aging Project enrolls older lay persons from across northeastern Illinois (Bennett, Buchman, et al., 2005). Participants must agree to an annual detailed clinical evaluation and donation of brain, spinal cord, nerve, and muscle at the time of death. Each subject signs a consent form and an Anatomical Gift Act. The study was approved by the Institutional Review Board of Rush University Medical Center. The study primarily recruits from more than 40 communities. It also recruits from Section 8 and Section 202 housing, retirement homes, and through local Churches and social service agencies serving minorities and low-income elderly. Clinical data collection began in the fall of 1997. The study has a rolling admission with participants enrolling each year. To date, more than 1450 participants have enrolled, approximately 30% are men and approximately 90% are white, non-Hispanic. The mean age at entry is approximately 80 years with an average education of approximately 14 years with more than a third with 12 or fewer years of education. Follow-up clinical evaluations are conducted annually with a follow-up rate in excess of 90% of survivors. More than 375 autopsies have been performed with an autopsy rate over 80%.

Rush Minority Aging Research Study

The Minority Aging Research Study recruits older African Americans from the Chicago area using the same methodology as used by the Rush Memory and Aging Project. Each subject signs a consent form agreeing to an annual detailed clinical evaluation and participation in an organ donation component is encouraged but not required. The study was approved by the Institutional Review Board of Rush University Medical Center. Clinical data collection began in 2004. The study has a rolling admission with participants enrolling each year. To date, more than 350 participants have enrolled, approximately 30% are men. The mean age at entry is approximately 73 years with an average education of approximately 14 years with more than a third with 12 or fewer years of education. Follow-up clinical evaluations are conducted annually with a follow-up rate in excess of 90% of survivors.

Data used in papers in special issue

Three types of data were used in analyses: (a) indicators of cognitive reserve, (b) detailed cognitive function, and (c) neuropathologic indices. These are summarized in Tables 13 for each study.

Table 1 Demographic characteristics, measures of reserve, and clinical diagnoses in the Religious Orders Study (ROS), Memory and Aging Project (MAP), and the Minority Aging Research Study (MARS)

Table 2 Cognitive function tests in the Religious Orders Study (ROS), Memory and Aging Project (MAP), and the Minority Aging Research Study (MARS)

Table 3 Neuropathologic indices available in the Religious Orders Study (ROS) and Memory and Aging Project (MAP)

Demographics, Indicators of Reserve, and Clinical Diagnoses

Sex, race, and ethnicity are ascertained using the 1990 U.S. Census questions.

Indicators of reserve included direct and indirect measures. Direct measures included years of formal education, and early, mid-, and late life cognitive activities (Barnes, Wilson, Mendes de Leon, & Bennett, Reference Barnes, Wilson, Mendes de Leon and Bennett2006; Bennett, Schneider, Wilson, Bienias, & Arnold, Reference Bennett, Schneider, Wilson, Bienias and Arnold2005; Bennett, Wilson, et al., Reference Bennett, Wilson, Schneider, Evans, Aggarwal, Arnold and Bienias2003; Wilson, Mendes de Leon, et al., Reference Wilson, Mendes de Leon, Barnes, Schneider, Bienias, Evans and Bennett2002; Wilson, Scherr, Schneider, Tang, & Bennett, Reference Wilson, Scherr, Schneider, Tang and Bennett2007). We also measure height (Buchman, Schneider, Wilson, Bienias, & Bennett, Reference Buchman, Schneider, Wilson, Bienias and Bennett2006). Indirect measures included parental education, income, occupation, and father's occupation; in addition, birth addresses were linked to 1920 census data to determine early-life socioeconomic status including literacy rates and Duncan socioeconomic status of head of household for county or census track (Wilson et al., Reference Wilson, Scherr, Hoganson, Bienias, Evans and Bennett2005; Wilson, Scherr, Schneider, Tang, & Bennett, Reference Wilson, Scherr, Schneider, Tang and Bennett2007).

The diagnostic process for Alzheimer's disease and mild cognitive impairment included a decision tree designed to mimic expert clinical judgment, implemented by computer (Bennett, Schneider, Aggarwal, et al., Reference Bennett, Schneider, Aggarwal, Arvanitakis, Shah, Kelly and Wilson2006). It combines data reduction techniques for the cognitive performance testing (see below) with a series of discrete clinical judgments made in series by a neuropsychologist and a clinician. The evaluation is designed to reduce costs and enhance uniformity of diagnostic decisions over time and space (Weir et al., Reference Weir, Wallace, Langa, Plassman, Wilson, Bennett and Sano2011).

Apolipoprotein E allele status was performed by Agencourt Bioscience Corporation (Beverly, MA) using high throughput sequencing (Bennett, De Jager, Leurgans, & Schneider, Reference Bennett, De Jager, Leurgans and Schneider2009).

Measures of Cognition

A battery of cognitive performance tests is administered each year (Barnes et al., Reference Barnes, Wilson, Mendes de Leon and Bennett2006; Boyle, Barnes, Buchman, & Bennett, Reference Boyle, Barnes, Buchman and Bennett2009; Wilson, Beckett, et al., Reference Wilson, Beckett, Barnes, Schneider, Bach, Evans and Bennett2002). The Mini-Mental State Examination (MMSE) is primarily used to describe the cohort. Eleven tests are used for diagnostic classification, including complex ideational material. The remaining tests assess a range of cognitive abilities and are used to construct separate summary measures of five cognitive domains, including episodic memory, semantic memory, working memory, perceptual speed, and visuospatial ability. Semantic memory can be separated into word knowledge and word generation (fluency). There are nineteen tests in common across the three studies. Seven tests can be administered by telephone. This version yields composites for episodic memory, semantic memory, and working memory (Wilson & Bennett, Reference Wilson and Bennett2005).

Measures of Neuropathology

The brain is removed and weighed. The post-mortem neuropathologic evaluation includes a uniform structured assessment of AD pathology, cerebral infarcts, Lewy body disease, and other pathologies common in aging and dementia. Brain tissue stained with modified Bielschowky is used to count neuritic plaques, diffuse plaques, and neurofibrillary tangles in five brain regions, including the mesial temporal lobe (hippocampal formation and entorhinal cortex) and neocortex (superior frontal and middle temporal gyri, and parietal lobe) (Bennett, Schneider, Arnold, Tang, & Wilson, Reference Bennett, Schneider, Arnold, Tang and Wilson2006; Bennett et al., Reference Bennett, Wilson, Schneider, Evans, Aggarwal, Arnold and Bienias2003). The location, size, and age of each macroscopic infarct are recorded as described (Schneider et al., Reference Schneider, Wilson, Bienias, Evans and Bennett2004; Schneider, Boyle, et al., Reference Schneider, Boyle, Arvanitakis, Bienias and Bennett2007). Microscopic infarctions are identified on H&E stained sections (Arvanitakis et al., Reference Arvanitakis, Leurgans, Barnes, Bennett and Schneider2011). Lewy bodies are identified on alpha-synuclein immunostained sections from six brain regions (Bennett, Schneider, et al, Reference Bennett, Schneider, Wilson, Bienias and Arnold2005; Schneider, Arvanitakis, et al, Reference Schneider, Boyle, Arvanitakis, Bienias and Bennett2007; Schneider, Arvanitakis, et al., Reference Schneider, Arvanitakis, Bang and Bennett2007).

Advantages of These Cohort Studies for Examining Cognitive Reserve

The neurobiologic basis of cognitive reserve is not well understood. Although there is evidence to suggest that the modest association between cognition and neuropathology may reflect the inter-individual variability of cognitive reserve, it has been challenging to operationalize or measure reserve directly. As noted by Jones et al. (this issue), if reserve is defined as the difference between expected and observed impairment for a given level of pathology, then good measures of performance and pathology are needed to measure reserve. The observational studies used in three of the current papers have several advantages that advance the study of reserve and the sophisticated latent variable modeling techniques used by the authors allow a better understanding of both the neural underpinnings of reserve and the experiential factors that may influence reserve. The combination of large, clinically well-characterized older adults with high rates of follow-up participation, comprehensive well-established cognitive performance measures, and a wide spectrum of neuropathology in prospective longitudinal studies provide a unique opportunity to directly link common indicators of reserve (e.g., education, occupation, cognitive activities) to measures of neuropathology. Furthermore, the substantial overlap in clinical and cognitive measures across the three epidemiologic studies allows investigators to pool data across studies increasing power to not only examine clinical–pathologic correlations but to address issues across race and ethnicity as well. Together, the set of papers in this special series represents an important contribution to the study of cognitive reserve.

Acknowledgments

The authors thank the participants of the Religious Orders Study, Rush Memory and Aging Project, and Minority Aging Research Study, and the staff of the Rush Alzheimer's Disease Center. These studies are supported by National Institute on Aging Grants P30AG10161, R01AG15819, R01AG17917, and R01AG22018.

References

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Figure 0

Table 1 Demographic characteristics, measures of reserve, and clinical diagnoses in the Religious Orders Study (ROS), Memory and Aging Project (MAP), and the Minority Aging Research Study (MARS)

Figure 1

Table 2 Cognitive function tests in the Religious Orders Study (ROS), Memory and Aging Project (MAP), and the Minority Aging Research Study (MARS)

Figure 2

Table 3 Neuropathologic indices available in the Religious Orders Study (ROS) and Memory and Aging Project (MAP)