This Article Abstract Full Text (PDF) Services Download to citation manager Citing Articles Citing Articles via HighWire PubMed PubMed Citation

Education and APOE-e4 in Longitudinal Cognitive Decline: MacArthur Studies of Successful Aging

¹ Department of Medicine, University of California, Los Angeles.
² Department of Preventive Medicine
³ Andrus Gerontology Center, University of Southern California, Los Angeles.
⁴ National Institutes of Health, National Institute on Aging, Bethesda, Maryland.

	Abstract

TOP Abstract Methods Results Discussion References

 
Longitudinal data from the MacArthur Study of Successful Aging were used to test for interactions between education and apolipoprotein E (APOE) genotype with respect to time trends in cognitive performance. Interactions between education, APOE-e4 status, and time were found for overall cognitive function, and for subscales measuring memory and naming: The presence of the e4 allele was associated with steeper declines in cognition for those with a greater than eighth-grade education. For those with an eighth-grade education or less, time trends did not differ by APOE genotype. A measure of cognitive impairment (i.e., scores of 7 on the Short Portable Mental Status Questionnaire) yielded parallel though weaker evidence for a similar interaction with respect to risk of cognitive impairment. These findings suggest that the presence of at least one e4 allele appears to reduce the protective effects of education for those with at least a ninth-grade education or more, resulting in steeper cognitive declines with age.

THE RAPIDLY growing size of our older population, with their elevated risks for cognitive decline, if not frank cognitive impairment, underscores the importance of efforts to understand factors that influence risks for declines in cognitive functioning. Factors ranging from genetic polymorphisms such as the apolipoprotein E (APOE) genotype to potentially more modifiable factors such as educational attainment have been a focus of research. More recently, the possibility of interactions between genetic characteristics and other, potentially modifiable, characteristics of the individual or environment has gained attention. One such possible interaction is that of APOE genotype with educational attainment in relation to risk for cognitive decline.

Consistent evidence documents the link between greater educational attainment, better cognitive function, and lower risk for cognitive decline (Albert et al., 1995b; Cagney & Lauderdale, 2002; Lee, Kawachi, Berkman, & Grodstein, 2003), as well as lower risks for dementia and Alzheimer's disease (Evans et al., 1997; Gilleard, 1997; Letenneur et al., 2000). Precise mechanisms for this effect of education remain unclear. Proposed mechanisms have included possible confounding with general socioeconomic conditions such that education serves largely as a marker for other education-related factors, including general health status and health behaviors, which are also known to influence cognition. Education, however, has been shown to remain a significant predictor of cognitive aging, independent of factors such as health status and health behaviors (e.g., Albert et al, 1995b; Lee et al., 2003). Other hypothesized mechanisms have included more direct effects of education on the brain; these theories focus on the possibility that education promotes brain development, resulting, for example, in greater dentritic branching, which may in turn result in greater "brain reserve." With aging, such a reserve allows the individual to maintain higher levels of cognitive functioning in the face of declines in underlying brain structure or function (Katzman, 1993; Stern, 2002). Such arguments are consistent with evidence from both animal and human studies showing that exposure to enriched environments results in a greater number of synapses (Diamond, 1988; Jacobs, Schall & Scheibel, 1993). Other hypotheses regarding the education–cognition relationship have posited that educational attainment may be a marker for (or consequence of) greater initial, genetically mediated brain reserve (Pedersen, Reynolds, & Gatz, 1996). A third hypothesis posits that the protective effects of educational attainment are the result of education-related differences in activities that entail greater ongoing mental or cognitive stimulation throughout the life course, including participation in occupations or leisure activities that entail more mental demands and stimulation (Richards, Hardy, & Wadsworth, 2003; Schooler, Mulatu, & Oates, 1999; Stern et al., 1995; Wilson et al., 1999, 2002).

APOE-e4 represents another, more recently identified, risk factor for cognitive aging. Initially identified as a risk factor for both familial (Corder et al., 1993; Poirier et al., 1993) and sporadic (Myers et al., 1996; Saunders et al., 1993) cases of Alzheimer's disease (AD), the APOE-e4 genotype has also been shown to be associated with increased risks for poorer functional status (Albert et al., 1995a) and lower cognitive performance (Bretsky, Guralnik, Launer, Albert, & Seeman, 2003; Dik et al., 2001; Fillenbaum et al., 2001). Though the precise mechanisms through which the APOE-e4 allele confers risk for AD, and for cognitive decline more generally, are not clearly known, the presumption is that it contributes to the accumulation of the cortical atrophy and senile plaques that underlies AD and possibly other types of brain dysfunction that result in decline in cognitive function with aging. APOE plays a central role in both the metabolism and transportation of the lipids that are key to the maintenance of synaptic plasticity as well as in synaptogenesis (Dallongeville, Lussier-Cacan, & Davignon, 1992; Soininen & Riekkinen, 1996). Thus, individuals differing in their APOE genotype likely also differ in their ability to initiate neuronal repair as well as in their susceptibility to degeneration.

Despite the fact that risks for cognitive decline linked to APOE genotype, and to educational attainment, have each been linked in turn to their postulated effects on brain structure or function, there has been little research on the possibility of an interaction between these two significant risk factors. If education, as has been hypothesized, is associated with more complex brain development, including development of greater synaptic density, then this relatively greater richness in synapses and in neural networks provided by educational experience might well serve as a counterweight to cognitive deterioration induced by APOE-e4. Such a finding would be consistent with related recent findings that education modifies the relation between extent of neuritic and diffuse plaques (measured at autopsy) and the level of cognitive function, with better education resulting in a reduction in the deleterious effects of senile plaques on cognitive function (Bennett et al., 2003). In a parallel vein, one might hypothesize that more highly educated individuals enjoy greater, or at least longer, protection against risks incurred by the presence of the APOE-e4 allele(s).

In the following analyses, we seek to test this hypothesized interaction between education and APOE genotype. There is, to our knowledge, only one prior study (Kalmijn, Feskens, Launer, & Kromhout, 1997) that has tested this hypothesis, and it used a sample composed only of elderly men. Though the actual test for interaction was not significant (possibly because of the small sample of poorly educated men, n = 43 vs 313 for better educated), the pattern of the findings in that study suggested that presence of the e4 allele may attenuate the benefits of education, as education did not appear to be associated with differences in cognitive decline in the presence of the e4 allele whereas it was associated with such risks among those without the e4 allele.

Using longitudinal data from the MacArthur Study of Successful Aging, we seek to extend these earlier results, examining the joint effects of educational attainment and APOE genotype on cognitive decline in a larger cohort of older men and women. The strengths of the MacArthur study include the following: first, there are extensive longitudinal data on cognitive changes based on the Short Portable Mental Status Questionnaire (SPMSQ), along with more detailed assessments of cognitive functioning in multiple major domains; second, there is a community-based sample of both men and women, with ethnic and sociodemographic diversity.

	METHODS

TOP Abstract Methods Results Discussion References

 
Study Population
Subjects were participants in the MacArthur Successful Aging Study, a population-based prospective investigation focusing on a cohort of initially relatively high-functioning men and women, aged 70–79 years (Berkman et al., 1993). Briefly, participants were subsampled on the basis of age and both physical and cognitive functioning from three community-based cohorts located in Durham, NC, East Boston, MA, and New Haven, CT (Cornoni-Huntley, Brock, Ostfeld, Taylor, & Wallace, 1986) with the goal of identifying those in the top third of the population. Baseline data were collected between May 1988 and December 1989 and included a 90-min, face-to-face interview assessing (a) physical and cognitive performance; (b) health status; (c) demographic, social, and psychological characteristics, including educational attainment; and (d) other lifestyle characteristics.

Age-eligible men and women (n = 4,030) were screened on the basis of physical and cognitive criteria with the intention of identifying high-functioning individuals (i.e., those in the top third of the age group). Physical selection criteria were developed from pilot data and included the following: (a) no reported disability on the seven-item Activities of Daily Living (ADL) scale (Katz, Ford, Moskowitz, Jackson & Jaffe, 1963); (b) no more than one reported mild functional limitation on eight items assessing gross mobility and range of motion (Nagi, 1976; Rosow, Breslau, & Guttman, 1966); (c) ability to hold a semitandem balance for at least 10 s; and (d) ability to stand from a seated position five times within 20 s. The cognitive criteria included (a) scoring six or more correct on the nine-item SPMSQ (Pfeiffer, 1975) and (b) remembering three or more of six elements on a delayed recall of a short story. Of relevance to the current analyses, these cognitive screening tools were relatively simple and so excluded only those with frank impairments.

Of the 4,030 eligible men and women, a cohort of 1,313 met all screening criteria; 1,189 (90.6%) agreed to participate and provided informed consent. After baseline data collection, the cohort was subsequently reevaluated in 1991 (n = 1,118) and 1995 (n = 916).

APOE Genotype
APOE genotyping was performed by use of genetic material isolated from peripheral lymphocytes (Saunders et al., 1993). Frequency of the e4 allele was 0.13, and the relative frequency of the e4/e4 homozygote was quite low, only 1.7% (n = 17). Because both neuronal activity–functional studies and epidemiological studies suggest that the presence of either one or two APOE-e4 alleles influences risk (Corder et al., 1993; Dubelaar et al., 2004), we used a dichotomous classification of individuals as either e4 carriers or noncarriers for our analyses—an approach consistent with previous research examining APOE and cognition (Tang et al., 1998; Wilson et al. 1999).

Education
Analyses reported here examined educational attainment in terms of four categories reflecting major stages of educational attainment within the cohort: grade school only (0–8 years), some high school (9–11 years), completed high school (12 years), and some college or more (13+ years). Three dichotomous variables indicating each of the three higher categories were used in the analyses to allow for assessment of potential nonlinearities in the association of different stages of educational attainment with patterns of cognitive aging; the least educated group serves as the reference in all cases.

Cognitive Function
Detailed, performance-based assessments of cognitive function were obtained at each interview, permitting examination of longitudinal patterns of change in domains such as these: (a) abstraction (based on 4 items from the Similarities subtest of the Wechsler Adult Intelligence Scale–Revised, or WAIS-R; range = 0–16; see Wechsler, 1981), (b) language (confrontation naming that uses a modified, 18-item version of the Boston Naming Test, or BNT; range = 0–18; see Kaplan, Goodglass, & Weintraub, 1983), (c) delayed incidental recall (delayed recall of confrontation-naming items); (d) delayed spatial recognition (range = 0–17; see Moss, Albert, Butters, & Payne, 1986), and (e) spatial ability (figures; range = 0–20; see Rosen, Mohs, & Davis, 1984). Two summary scores were created based on these various subscales. One summary (global) cognition score was created to measure overall cognitive performance (sum of scores on each of the five standard tests listed above; range 0–89; see Inouye, Albert, Mohs, Sun, & Berkman, 1993). A second "memory" subscale score (range = 0–35) was created by summing scores from the two delayed recall tasks (i.e., delayed incidental recall and delayed spatial recognition). These instruments were administered at baseline and at each of the two subsequent follow-ups. Distributions were approximately normally distributed at all time points for all of these more detailed assessments.

The SPMSQ (Pfeiffer, 1975) was used at baseline to screen for study eligibility (scores of 6–9 correct were required for inclusion in the initial cohort). This questionnaire was also repeated at the 1991 and 1995 interviews. Although all cohort members had scores of 6 or more at baseline, declines in cognitive function over time result in considerably wider ranges of scores in 1991 and 1995 (range = 0–9). Because the distribution of SPMSQ scores was highly skewed toward better performance even at the 1991 and 1995 follow-ups, a dichotomization indicating presence or absence of "cognitive impairment" was developed for analysis. Fillenbaum, Heyman, Williams, Prosnitz, and Burchett (1990) have shown that 90% of individuals with scores of 6 or less meet criteria for at least moderate cognitive impairment. Because of the initial selection criteria for a high-functioning cohort, however, less than 10% of participants had scores of 6 or less as of 1991 and fewer than 25% of participants met criteria by 1995. As a result, models testing the desired three-way interactions between education, APOE genotype, and time would not converge. Models using a somewhat more liberal definition of impairment based on the criterion of scores of 7 or less did converge and are reported here.

Covariates
Variables considered for inclusion in the multivariable analyses as potential confounders included demographic characteristics, prevalent and incident comorbidities, health status factors, and health behavior characteristics that have previously been shown to influence patterns of cognitive performance. Demographic variables included age (measured in years as of the baseline interview), gender, and ethnicity (coded White vs African American). Comorbidities included self-reported prevalent and incident myocardial infarction, stroke, diabetes, and hypertension. Additional health status factors that were evaluated included body mass index (based on self-reported height and weight) and self-rated health. Health behavior characteristics of interest included smoking status (current, past, or never smoker), alcohol consumption (measured in terms of average monthly alcohol; see Armor, Polich, & Stambul, 1975), and physical activity (reported strenuous and moderate recreational activities, as well as strenuous and moderate work activities; see Paffenbarger, Hyde, Wing, & Hsieh, 1986; Taylor et al., 1978).

Analysis
We used mixed models for analyses in order to take advantage of all three waves of data. We used SAS Proc GENMOD to implement the generalized estimating equations (GEE) approach (Zeger & Liang, 1986) for analyses of the dichotomous longitudinal data for the SPMSQ. The GEE approach is particularly suited to the analysis of such dichotomous, non-Gaussian longitudinal data as it avoids the need for multivariate distributions by assuming a functional form for the marginal distribution at each time point. Using this approach, one need only specify the relationship between the mean and variance and hypothesized correlation structure. The "link" function used in the GEE analysis is determined by the specified relationship between the mean and variance; in our case, we used a logit link for the dichotomous outcome. We modeled the working correlation structure among the 1988, 1991, and 1995 outcome measures as exchangeable. For the more normally distributed, continuous outcomes, we used SAS Proc Mixed to model patterns of change in cognitive performance over the three assessments (1988, 1991, 1995; see Byrk & Raudenbush, 1992). We modeled effects of education category, time–follow-up, genotype, and ethnicity as fixed effects. We specified an unstructured covariance structure for the random intercept models. For both the GEE and Proc Mixed models, we tested for main and two-way interactive effects of education and APOE genotype (i.e., differences at baseline), as well as three-way interactions with time (i.e., differences in slope over time). For both the GEE and Proc Mixed models, we modeled time as two intervals, comparing each of the two follow-ups (i.e., 1991 and 1995) to the 1988 baseline to allow for evaluation of possible nonlinearities in the patterns of change over time.

We retained demographic characteristics (i.e., age, gender, and ethnicity) in all models regardless of level of statistical significance. For other covariates considered as potential confounders, we used a criterion of p <.05 to determine whether they should be included in the multivariable models for the various cognitive outcomes. For the SPMSQ, we retained physical activity, smoking status, and presence of diabetes or stroke in the final models. For all other cognitive outcomes, none of the potential confounders met criteria for inclusion, so those multivariable models include our primary predictors of interest (education and APOE) along with only age, gender, and ethnicity.

Analyses reported here include 895 participants with complete data for predictors as well as at least one wave of follow-up data (i.e., 1991, 1995, or both). As shown in Table 1, comparisons of these individuals with those excluded because of missing data (n = 289 because of missing APOE data, and 5 because of missing ethnicity information) indicated that the latter were generally similar to those included in the analyses, differing only in terms of their lower percentage of Blacks and greater preponderance of individuals with 12 years of education. With the exception of one cognitive subscale (spatial recognition), there were no significant differences in terms of baseline cognitive performance (see Table 1). We used SAS version 8.0 for all analyses (SAS Institute, 1999).

	RESULTS

TOP Abstract Methods Results Discussion References

Table 2 presents results of analyses testing for main and interaction effects of education, APOE, and time, for the global cognitive performance measure as well as its component measures of memory, naming, spatial recognition, similarities–abstraction and figures–copying. Significant main effects for education were found for each of the measures of cognitive performance, with increasingly better performance as educational attainment increases. In addition to these main effects for education, however, analyses also yielded evidence for the hypothesized interaction between education and APOE status. Specifically, the analyses revealed significant three-way interactions between education, APOE genotype, and time for the global measure of total cognition as well as the memory and naming subscales, indicating that time trends (1988–1991 and 1988–1995) for the education groups differed by e4 status (see Table 2). Figures 1 and 2 provide graphical illustrations of these interactions, showing time trends for the total cognitive score (Figure 1) and the memory score (Figure 2) for each of the e4–education groupings. In both figures, scores are shown separately for those with and without the e4 allele in each education group; symbols for a given education group are the same for those with and without the e4 allele, but for those without the e4 allele, the symbol is open whereas a closed symbol is used to delineate their counterparts who have the e4 allele. As shown in Figure 1, total cognitive scores for those with the least education (i.e., 0–8 years) showed no difference between those with and without the e4 allele, and these two groups exhibited the poorest performance at all times. By contrast, for those with the most education (13+ years), presence of the e4 allele was associated with significantly greater decline over time, resulting in a greater disparity in overall cognitive performance by the final 1995 assessment (p <.05). This same pattern was seen for those with 9–11 years of education (p <.01). Among those who completed high school (i.e., 12 years education), a comparison of participants with and without the e4 allele revealed a somewhat surprising convergence of scores at the 1991 assessment (those with the e4 allele showing a surprising increase in performance from 1988 to 1991; p <.10). These two groups, however, appeared to be diverging again by 1995, with the e4 group declining somewhat more than those without the e4 allele, though this difference was not statistically significant. For this same education group, the evidence for divergence by 1995 between those with and without the e4 allele was greater for the memory subscale (see Figure 2) and achieved marginal statistical significance (p <.10). Though not shown graphically, the pattern for the naming scores paralleled that shown in Figure 1 for the total cognitive score. For the remaining cognitive outcomes (spatial recognition, figures–copying, similarities–abstraction), the only significant effects were for education (as already noted) and time, indicating parallel declines across the different educational groups (see Table 2). APOE genotype was unrelated to changes in performance on all tests except the similarities where a marginal association with poorer performance was seen for those with the e4 allele (p =.08; Table 2).

View larger version (17K): [in this window] [in a new window]   Figure 1. Time trends for total cognitive performance: Education x APOE genotype interactions. (Legend: Ed13+ = no e4 allele, 13+ years education; Ed12 = no e4 allele, completed high school; Ed9–11 = no e4 allele, some school; Ed0–8 = no e4 allele, no high school; e4/Ed13+ = as at least one e4 allele, 13+ years education; e4/Ed12 = has at least one e4 allele, completed high school; e4/Ed9–11= has at least one e4 allele, some high school; e4/Ed0–8 = has at least one e4 allele, no high school.)

View larger version (19K): [in this window] [in a new window]   Figure 2. Time trends for memory performance: Education x APOE genotype interactions. (Legend: Ed13+ = no e4 allele, 13+ years education; Ed12 = no e4 allele, completed high school; Ed9–11 = no e4 allele, some high school; Ed0–8 = no e4 allele, no high school; e4/Ed13+ = has at least one e4 allele, 13+ years education; e4/Ed12 = has at least one e4 allele, completed high school; e4/Ed9–11= has at least one e4 allele, some high school; e4/Ed0–8 = has at least one e4 allele, no high school.)

View this table: [in this window] [in a new window]   Table 3. Main and Interaction Effects of Education and APOE Genotype With Respect to Risks for Cognitive Impairment (Scores of 7 on the SPMSQ).

View larger version (15K): [in this window] [in a new window]   Figure 3. Joint effects of education and APOE on the odds of cognitive impairment over time. (Legend: Ed13+ = no e4 allele, 13+ years education; Ed12 = no e4 allele, completed high school; Ed9–11 = no e4 allele, some high school; Ed0–8 = no e4 allele, no high school; e4/Ed13+ = has at least one e4 allele, 13+ years education; e4/Ed12 = has at least one e4 allele, completed high school; e4/Ed 9–11= has at least one e4 allele, some high school; e4/Ed 0–8 = has at least one e4 allele, no high school.)

	DISCUSSION

TOP Abstract Methods Results Discussion References

Analyses of risk for cognitive impairment (defined as scoring 7 or less on the SPMSQ) revealed significant main effects of education, the more highly educated groups being significantly less likely to exhibit cognitive impairment at baseline. The absence of any interaction with time also indicated that these differences persist through the 1991 and 1995 follow-ups. By contrast, for APOE, there was no significant difference in the likelihood of cognitive impairment at baseline, but a marginally significant interaction with time suggested the possibility of a growing difference over time such that those with at least one e4 allele were marginally more likely to exhibit cognitive impairment by the final 1995 assessment. Tests for a possible three-way interaction between APOE, education, and time also yielded only marginal statistical evidence, suggesting that presence of the e4 allele may reduce the protection afforded by a given level of education. These latter analyses bear replication; the relatively high functioning status of the entire cohort at baseline may have reduced the power of our current analyses to detect interactions with respect to cognitive impairment, as relatively few members of the cohort met criteria for impairment even at the final assessment in 1995. Testing for these same interactions in a more broadly representative cohort of older adults might provide stronger evidence for possible difference in time trends for cognitive impairment by APOE and education status.

An evaluation of these findings requires acknowledgment of some limitations of the study. One such limitation relates to the lack of comprehensive neurological evaluations at baseline that might have revealed evidence for preclinical dementia in some cohort members. Mitigating against this, however, eligibility criteria for the study did require a baseline score of 6 or above on the SPMSQ for entry, placing participants in what is usually considered the normal range. Further, all participants were required to score 3 or above on delayed recall of a story at baseline, and those included in the current analyses (i.e., who remained alive and had complete follow-up data on cognitive performance) were among the cohort members with better baseline cognitive scores. All of these factors reduce the likelihood that the cohort examined here included cases of preclinical dementia, although the lack of complete neurologic screening for the cohort does not rule out the possibility that otherwise undetected preclinical dementia cases were included and were possibly differentially represented among the educational categories. Another potential limitation is the initial restriction of this cohort to those with no evident physical or cognitive impairments at study inception. As a result, the degree to which findings from this cohort may (or may not) generalize beyond such relatively high-functioning subgroups of older adults remains to be determined. Further, the initial selection of a relatively high-functioning cohort may have reduced the statistical power of the current analyses, because the range of cognitive performance at baseline was restricted to relatively higher levels of performance and, although declines were certainly evident over time, considerably greater declines would probably have been seen with a more broadly representative cohort of older adults. Indeed, as noted, analyses of the SPMSQ, which measures more severe forms of impairment, yielded the weakest evidence for the hypothesized interaction of education and APOE, and this may have been due, at least in part, to the fact that we could not model impairment based on the usual criterion of scoring 6 or less because of the relative dearth of participants who met this criterion even by the final follow-up in 1995. One might postulate that similar assessments over time in a more broadly representative cohort could yield stronger evidence for the hypothesized interaction with respect to cognitive impairment.

Despite these limitations, there are some major strengths of the study. They relate primarily to the range of cognitive measures and the length of follow-up. Evaluating cognition in older individuals is enhanced by the availability of tests reflecting a spectrum of functional domains. When one is evaluating changes in functioning, a particular concern is reliability in assessing change over time, where delineation of signal versus noise in such tests can be problematic. One study has shown that measurement of signal (vs. noise) is enhanced by the length of time over which an individual has been evaluated as well as the number of repetitions of the test (Van Belle, Uhlmann, Hughes, & Larson, 1990). The current study included a variety of cognitive tests, assessed performance at three points in time, and included a 7-year follow-up period for a cohort moving from their 70s into their 80s.

This study shows that, over a 7-year follow-up period, educational attainment remains a primary predictor of patterns of cognitive aging, with greater educational attainment generally associated with better performance on all measured outcomes. However, the current findings also suggest the potential importance of the APOE genotype in modifying patterns of cognitive decline over time, particularly for those with greater than an eighth-grade education. For these latter groups (i.e., those with at least some high school, if not more), results of the current analyses suggested that the presence of the e4 allele may be associated with steeper declines in cognitive performance relative to their education-matched counterparts without the e4 allele. These results, pointing as they do to the potential that there are differential protective effects of greater education for those with and without the e4 allele, underscore the importance of further research to clarify the underlying biologic (and other) mechanisms through which genetic factors as well as educational attainment serve to protect against decline in cognitive functioning as people grow older. The importance of understanding these various mechanisms is highlighted by the fact that our ability to develop more effective strategies to prevent or retard cognitive declines in the rapidly growing population of older adults depends on our ability to gain a more complete understanding of the multiple and likely interactive factors that have an impact on patterns of cognitive aging.

	Acknowledgments

 
Work on this article was supported by the MacArthur Research Network on Successful Aging and the MacArthur Research Network on SES and Health through grants from the John D. and Catherine T. MacArthur Foundation, the Epidemiology, Demography and Biometry Office, and grants AG-17056, AG-17055, and AG-17265 from the Behavioral and Social Research Program of the National Institute on Aging.

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication January 27, 2004. Accepted for publication September 22, 2004.

	References

TOP Abstract Methods Results Discussion References

 

• Albert, S. M., Gurland, B., Maestre, G., Jacobs, D. M., Stern, Y., Mayeux, R. (1995a). APOE genotype influences functional status among elderly without dementia. American Journal of Medical Genetics, 60,583-587.[Medline]
• Albert, M. S., Jones, K., Savage, C. R., Berkman, L., Seeman, T., Blazer, D., et al. (1995b). Predictors of cognitive change in older persons: MacArthur Studies of Successful Aging. Psychological Aging, 10,578-589.
• Armor, D. J., Polich, M., Stambul, H. B. (1975). Alcoholism and treatment (Appendix A). New York: Wiley.
• Bennett, D. A., Wilson, R. S., Schneider, J. A., Evans, D. A., Mendes de Leon, C. F., Arnold, S. E., et al. (2003). Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology, 60,1909-1915.[Abstract/Free Full Text]
• Berkman, L. F., Seeman, T. E., Albert, M., Blazer, D., Kahn, R., Mohs, R., et al. (1993). High, usual and impaired functioning in community-dwelling older men and women: Findings from the MacArthur Foundation Research Network on Successful Aging. Journal of Clinical Epidemiology, 46,1129-1140.[Medline]
• Bretsky, P., Guralnik, J. M., Launer, L., Albert, M., Seeman, T. E. (2003). The role of APOE-epsilon4 in longitudinal cognitive decline: MacArthur Studies of Successful Aging. Neurology, 60,1077-1081.[Abstract/Free Full Text]
• Byrk, A. S., Raudenbush, S. W. (1992). Hierarchical linear models. Newbury Park, CA: Sage.
• Cagney, K. A., Lauderdale, D. S. (2002). Education, wealth, and cognitive function in later life. Journal of Gerontology: Psychological Sciences, 57B,P163-P172.
• Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., et al. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science, 261,921-923.[Abstract/Free Full Text]
• Cornoni-Huntley, J., Brock, D. B., Ostfeld, A. M., Taylor, J. O., Wallace, R. B. (1986). Established populations for epidemiology studies of the elderly: Resource data book (NIH Publication No. 86-2443). Bethesda MD: National Institute on Aging.
• Dallongeville, J., Lussier-Cacan, S., Davignon, J. (1992). Modulation of plasma triglyceride levels by ApoE phenotype: A meta-analysis. Journal Lipid Research, 33,447-454.
• Diamond, M. C. (1988). Enriching heredity: The impact of the environment on the anatomy of the brain. New York: Free Press.
• Dik, M. G., Jonker, C., Comijs, H. C., Bouter, L. M., Twisk, J. W., van Kamp, G. J., et al. (2001). Memory complaints and APOE-epsilon4 accelerate cognitive decline in cognitively normal elderly. Neurology, 57,17-22.[Abstract/Free Full Text]
• Dubelaar, E. J., Verwer, R. W., Hofman, M. A., Van Heerikhuize, J. J., Ravid, R., Swaab, D. E. (2004). ApoE epsilon4 genotype is accompanied by lower metabolic activity in nucleus basalis of Meynert neurons in Alzheimer patients and controls as indicated by the size of the Golgi apparatus. Journal Neuropathology Experimental Neurology, 63,159-169.
• Evans, D. A., Hebert, L. E., Beckett, L. A., Scherr, P. A., Albert, M. S., Chown, M. J., et al. (1997). Education and other measures of socioeconomic status and risk of incident Alzheimer disease in a defined population of older persons. Archives of Neurology, 54,1399-1405.[Abstract/Free Full Text]
• Fillenbaum, G., Heyman, A., Williams, K., Prosnitz, B., Burchett, B. (1990). Sensitivity and specificity of standardized screens of cognitive impairment and dementia among elderly black and white community resident. Journal of Clinical Epidemiology, 43,651-660.[Medline]
• Fillenbaum, G. G., Landerman, L. R., Blazer, D. G., Saunders, A. M., Harris, T. B., Launer, L. J. (2001). The relationship of APOE genotype to cognitive functioning in African American and White community resident elderly. Journal of the American Geriatrics Society, 49,1148-1155.[Medline]
• Gilleard, C. J. (1997). Education and Alzheimer's disease: A review of recent international epidemiological studies. Aging Mental Health, 1,33-46.
• Inouye, S. K., Albert, M. S., Mohs, R., Sun, K., Berkman, L. (1993). Cognitive performance in a high functioning community dwelling elderly population. Journal of Gerontology: Medical Sciences, 4,M146-M151.
• Jacobs, B., Schall, M., Scheibel, A. B. (1993). A quantitative dendritic analysis of Wernicke's area in human. II Gender, hemispheric and environmental changes. Journal of Comparative Neurology, 327,97-111.[Medline]
• Kalmijn, S., Feskens, E. J. M., Launer, L. J., Kromhout, D. (1997). Longitudinal study of the effect of apolipoprotein e4 allele on the association between education and cognitive decline in elderly men. British Medical Journal, 314,34.[Free Full Text]
• Kaplan, E., Goodglass, H., Weintraub, S. (1983). The Boston Naming Test and scoring booklet. In H. Goodglass & E. Kaplan (Eds.), The assessment of aphasia and related disorders (2nd ed.). Philadelphia: Lea and Febiger.
• Katz, S. C., Ford, A. B., Moskowitz, R. W., Jackson, B. A., Jaffe, M. W. (1963). Studies of illness in the aged—the index of ADL: A standardized measure of biological and psychosocial function. Journal of the American Medical Association, 185,914-919.
• Katzman, R. (1993). Education and the prevalence of dementia and Alzheimer's disease. Neurology, 43,13-20.[Free Full Text]
• Lee, S., Kawachi, I., Berkman, L. F., Grodstein, F. (2003). Education, other socioeconomic indicators, and cognitive function. American Journal of Epidemiology, 157,712-720.[Abstract/Free Full Text]
• Letenneur, L., Launer, L. J., Andersen, K., Dewey, M. E., Ott, A., Copeland, J. R. (2000). Education and risk for Alzheimer's disease: Sex makes a difference. EURODEM pooled analyses. EURODEM Incidence Research Group. American Journal of Epidemiology, 151,1064-1071.[Abstract/Free Full Text]
• Moss, M., Albert, M. S., Butters, N., Payne, M. (1986). Differential patterns of memory loss among patients with Alzheimer's Disease, Huntington's Disease and alcoholic Korskoff's syndrome. Archives of Neurology, 43,239-246.[Abstract/Free Full Text]
• Myers, R. H., Schaefer, E. J., Wilson, P. W., D'Agostino, R., Ordovas, J. M., Espino, A. (1996). Apolipoprotein E 4 association with dementia in a population-based study: The Framingham Study. Neurology, 46,673-677.[Free Full Text]
• Nagi, S. Z. (1976). An epidemiology of disability among adults in the United States. Millbank Memorial Fund Quarterly, 56,439-468.
• Paffenbarger, R. S., Jr., Hyde, R. T., Wing, A. L., Hsieh, C. C. (1986). Physical activity, all-cause mortality and longevity of college alumni. New England Journal of Medicine, 314,605-613.[Abstract]
• Pedersen, N. L., Reynolds, C. A., Gatz, M. (1996). Sources of variation among Mini-Mental State Examination scores, education, and cognitive abilities. Journal of Gerontology: Psychological Sciences, 51B,P55-P63.
• Pfeiffer, E. (1975). A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. Journal of the American Geriatrics Society, 23,433-441.[Medline]
• Poirier, J., Davignon, J., Bouthillier, D., Kogan, S., Bertrand, P., Gauthier, S. (1993). Apolipoprotein epolymorphism and Alzheimer's disease. Lancet, 342,697-699.[Medline]
• Richards, M., Hardy, R., Wadsworth, M. E. J. (2003). Does active leisure protect cognition? Evidence from a national birth cohort. Social Science and Medicine, 56,785-792.
• Rosen, W., Mohs, R., Davis, R. (1984). A new rating scale for Alzheimer's disease. American Journal of Psychiatry, 141,1356-1364.[Abstract/Free Full Text]
• Rosow, I., Breslau, N., Guttman, A. (1966). Health scale for the aged. Journal of Gerontology, 21,556-559.[Free Full Text]
• SAS Institute. (1999). The SAS System for Windows (Version 8.00) [Computer software]. Cary, NC: Author.
• Saunders, A. M., Strittmatter, W. J., Schmechel, D., George-Hyslop, P. H., Pericak-Vance, M. A., Joo, S. H., et al. (1993). Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. Neurology, 43,1467-1472.[Abstract/Free Full Text]
• Schooler, C., Mulatu, S., Oates, G. (1999). The continuing effects of substantively complex work on the intellectual functioning of older workers. Psychology and Aging, 14,483-506.[Medline]
• Soininen, H. S., Riekkinen, P. J. (1996). Apolipoprotein E, memory and Alzheimer's disease. Trends in Neurosciences, 19,224-228.[Medline]
• Stern Y. (2002). What's cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8,448-460.
• Stern, Y., Alexander, G., Prohovnik, I., Stricks, L., Link, B., Lennon, M. C. (1995). Relationship between lifetime occupation and parietal flow: Implications for a reserve against Alzheimer's disease pathology. Neurology, 45,55-60.[Abstract/Free Full Text]
• Tang, M. X., Stern, Y., Marder, K., Bell, K., Gurland, B., Lantigua, R., et al. (1998). The APOE4 allele and the risk of Alzheimer's disease Among African Americans, Whites, and Hispanics. Journal of the American Medical Association, 279,751-755.[Abstract/Free Full Text]
• Taylor, H. L., Jacobs, D. R., Jr., Schucker, B., Knudsen, J., Leon, A. S., Debacker, G. (1978). A questionnaire for the assessment of leisure time physical activities. Journal of Chronic Diseases, 31,741-755.[Medline]
• Wechsler, D. (1981). Adult Intelligence Scale–Revised. New York: Psychological Corporation.
• Wilson, R. S., Bennett, D. A., Beckett, L. A., Morris, M. C., Gilley, D. W., Bienias, J. L., et al. (1999). Cognitive activity in older persons from a geographically defined population. Journal of Gerontology: Psychological Sciences, 54,P155-P160.
• Wilson, R. S., Bennett, D. A., Biennias, J. L., Aggarwal, N. T., Mendes de Leon, C. F., Morris, M. C., et al. (2002). Cognitive activity and incident AD in a population-based sample of older persons. Neurology, 59,1910-1914.[Abstract/Free Full Text]
• Van Belle, G., Uhlmann, R. F., Hughes, J. P., Larson, E. B. (1990). Reliability of estimates of changes in mental status test performance in senile dementia of the Alzheimer's type. Journal of Clinical Epidemiology, 43,589-595.[Medline]
• Zeger, S. L., Liang, K. Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42,121-130.[Medline]

This article has been cited by other articles:

				R. S. Wilson, L. E. Hebert, P. A. Scherr, L. L. Barnes, C. F. Mendes de Leon, and D. A. Evans Educational attainment and cognitive decline in old age Neurology, February 3, 2009; 72(5): 460 - 465. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Services Download to citation manager Citing Articles Citing Articles via HighWire PubMed PubMed Citation