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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: Psychol Aging. 2015 May 11;30(2):279–285. doi: 10.1037/pag0000024

Cognitive Aging in Older Black and White Persons

Robert S Wilson 1, Ana W Capuano 1, Joel Sytsma 1, David A Bennett 1, Lisa L Barnes 1
PMCID: PMC4481321  NIHMSID: NIHMS676635  PMID: 25961876

Abstract

During a mean of 5.2 years of annual follow-up, older Black (n=647) and White (n=647) persons of equivalent age and education completed a battery of 17 cognitive tests from which composite measures of 5 abilities were derived. Baseline level of each ability was lower in the Black subgroup. Decline in episodic and working memory was not related to race. Decline in semantic memory, perceptual speed, and visuospatial ability was slower in Black persons than White persons, and in semantic memory and perceptual speed this effect was stronger in older than younger participants. Racial differences persisted after adjustment for retest effects. The results suggest subtle cognitive aging differences between Black persons and White persons.

Keywords: cognitive aging, racial differences, longitudinal study, retest learning

Introduction

Most knowledge about cognitive aging comes from research on White persons. In the past 15 years, however, several longitudinal studies have compared change in cognitive function in older Black and White persons. Some studies have found more rapid cognitive decline in Black persons (Lyketsos, Chen, & Anthony, 1999; Sachs-Ericsson & Blazer, 2005; Sawyer, Sachs-Ericsson, Preacher, & Blazer, 2009; Wolinsky et al., 2011), some have found more rapid cognitive decline in White persons (Sloan & Wang, 2005; Alley, Suther, & Crimmins, 2007; Karlamangla et al., 2009; Early et al., 2013), and some have found no difference (Atkinson et al., 2005; Masel & Deek, 2009; Castora-Binkley, Peronto, Edwards, & Small, 2013; Marsiske et al., 2013). The factors contributing to these inconsistent findings are uncertain. One issue is that some studies have assessed cognition at only 2 points (Lyketsos et al., 1999; Sachs-Ericsson & Blazer, 2005; Atkinson et al., 2005; Wolinsky et al., 2011). This makes it difficult to separate level of cognitive function from rate of change which is problematic given Black-White differences in cognitive level. Second, with few exceptions (Early et al., 2013; Marsiske et al., 2013), previous studies have assessed cognition with brief global measures such as the Short Portable Mental Status Questionnaire (Sachs-Ericsson & Blazer, 2005; Sawyer et al., 2009), Telephone Interview for Cognitive Status (Sloan & Wang, 2005; Alley et al., 2007; Karlamangla et al., 2009; Masel & Peek, 2009; Wolinsky et al., 2011; Castora-Binkley, et al., 2013), and Mini-Mental State Examination (Lyketsos et al., 1991; Atkinson et al., 2005), which lack measurement precision and the ability to characterize specific domains of cognitive function. Third, retest learning (Yang, Reed, & Kuan, 2012), defined as improved cognitive performance due to repeated test administration, is known to affect estimates of late-life cognitive decline (Wilson, Li, Bienias, & Bennett, 2006; Yang et al., 2012), but it is not known whether there are racial differences in retest learning that are affecting comparisons of cognitive trajectories between racial groups. Finally, it is possible that the comparability of Black and White participants has varied from study to study.

The present analyses are based on older Black and White participants in 3 longitudinal cohort studies. Subgroups of older Black and White persons without dementia at enrollment (each n=647) were selected to be equivalent in age, education, and number of cognitive assessments using propensity matching. At annual intervals for a mean of more than 5 years, they completed a battery of 17 cognitive tests from which previously established composite measures of 5 cognitive domains were derived. We used mixed-effects models to test for racial differences in level of function and rate of change in each cognitive domain.

Methods

Participants

Participants were drawn from 3 ongoing longitudinal cohort studies with nearly identical protocols. The Minority Aging Research Study began in 2004 and involves annual clinical evaluations of older Black persons in the Chicago area recruited from the community and the clinical core of the Rush Alzheimer’s Disease Core Center (Arvanitakis, Bennett, Wilson, & Barnes, 2010; Barnes, Shah, Aggarwal, Bennett, & Schneider, 2012). The Rush Memory and Aging Project began in 1997 and involves annual clinical evaluations of older persons in the Chicago area; participants are mostly White but approximately 6% are Black (Bennett, Schneider, Buchman, Mendes de Leon, & Wilson, 2005; Bennett et al., 2012). The Religious Orders Study began in 1994 and involves annual clinical evaluations of Catholic nuns, priests, and monks from across the United States; approximately 7% are Black (Wilson, Bienias, Evans, & Bennett, 2004; Bennett, Schneider, Arvanitakis, & Wilson, 2012). In all 3 studies, participants signed informed consent forms after a thorough discussion about the project. Each project was approved by the institutional review board of Rush University Medical Center.

Eligibility for these analyses required absence of dementia at baseline and completion of at least 1 annual follow-up evaluation. At the time of these analyses, 708 Black persons and 2,311 White persons met these criteria. With the use of propensity scores, we identified subgroups of 647 Black participants (Minority Aging Research Study, 487; Rush Memory and Aging Project, 76; Religious Orders study, 84) and 647 White participants (Rush Memory and Aging Project, 429; Religious Orders Study, 218) that were balanced in relation to age, education, and number of cognitive assessments because age is related to cognitive decline, education is related to cognitive level, and the duration of observation and number of follow-ups impact the ability to reliably characterize cognitive trajectories. We used a greedy 5-to-1 digit algorithm in SAS to match propensity scores and identify the subgroups (Rassen et al., 2012). As a result of the propensity balancing, the Black subgroup was similar to the White subgroup in age at baseline (73.5 vs 73.6, t[1,292] = 0.2, p=0.839), years of education (15.2 vs 15.4, t[1,292]=0.9, p=0.370), and number of annual cognitive assessments (6.3 vs 6.1, t[1,292] =−0.6, p=0.548). The subgroups did not differ in gender (percent women: 77.1 vs 73.6, χ2 [1] = 2.2, p=0.138).

Clinical Evaluation

At baseline and annually thereafter, participants had a structured clinical evaluation that included a medical history, cognitive testing, and a neurologic examination. On the basis of this evaluation, an experienced clinician diagnosed dementia using the criteria of the joint working group of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association which require a history of cognitive decline and evidence of impairment in at least two cognitive domains (McKhann et al., 1984). Individuals who met these criteria at baseline were not included in the present analyses.

Assessment of Cognitive Function

Cognitive function was annually assessed with a battery of 17 individual tests in a session lasting approximately 1 hour. To reduce measurement error, particularly floor and ceiling artifacts, we used composite measures of two or more cognitive tests in longitudinal analyses. Supported by previous factor analyses (Wilson et al., 2002; Wilson, Barnes, & Bennett, 2003; Wilson et al., 2005; Wilson, Aggarwal et al., 2009; Krueger, Wilson, Bennett, & Aggarwal, 2009), we constructed measures of episodic memory (based on 7 tests: immediate and delayed recall of Logical Memory Story A [Wechsler, 1987] and the East Boston Story [Albert et al., 1991; Wilson et al., 2002] and Word List Memory, Word List Recall, and Word List Recognition ([Welsh et al., 1994]), semantic memory (3 tests: 15-item Boston Naming Test [Kaplan, Goodglass, & Weintraub, 1983; Welsh et al., 1994], a 15-item word reading test [Wilson et al., 2002], and a measure of verbal fluency involving naming examples of animals and vegetables in 60 second trials [Welsh et al., 1994; Wilson et al., 2002]), working memory (3 tests: Digit Span Forward, Digit Span Backward, and Digit Ordering [Wechsler, 1987; Wilson et al., 2002]), perceptual speed (2 tests: oral form of the Symbol Digit Modalities Test [Smith, 1982] and a modified version [Wilson et al., 2002] of Number Comparison [Ekstrom, French, Harman & Kermen, 1976]), and visuospatial ability (2 tests: 16-item version of Standard Progressive Matrices [Raven, Court, & Raven, 1992] and a 15-item version of Judgment of Line Orientation [Benton, Sivan, Hamsher, Varney, & Spreen, 1994]). We also constructed a composite measure of global cognition based on all 17 tests. Raw scores on individual tests were converted to z scores using the baseline mean and SD in all 3 parent cohorts combined. The z scores of component tests were averaged to yield composite scores. Additional information on the individual tests and composite scores is contained in earlier publications (Wilson et al., 2002; Wilson, Barnes, et al., 2003; Wilson et al., 2005).

Statistical Analysis

We analyzed change in cognitive function in a series of mixed-effects models (Laird & Ware, 1982). Time was treated as years since baseline. Models included terms for age at baseline, sex, education, race, and their interactions with time. Random effects accounted for individual differences in the initial level of cognitive function and the rate of cognitive change over time. In separate subsequent models, we tested whether race modified the associations of age or education with cognitive trajectories.

We assessed retest effects in two ways. First, we constructed a mixed-effects model that allowed the slope to change at some variable point. We constrained the change point to occur within 6 years of baseline to avoid the acceleration in cognitive decline known to occur in the last few years of life (Sliwinski et al., 2006; Wilson, Segawa, Hizel, Boyle, & Bennett, 2012). Because some preliminary models with cognitive domain measures had difficulty converging, we used a previously established composite index of global cognition based on all 17 tests (Wilson et al., 2002) to maximize measurement precision in the analysis. This approach allowed us to separate cognitive trajectories into components differentially influenced by retesting and to test the relation of race to each component. Second, because the change point model suggested that retest effects were strongest during initial follow-up evaluations, we repeated the core analyses, first eliminating the baseline evaluation and then again eliminating baseline plus the first year follow-up.

Results

At baseline, the subgroups had similar Mini-Mental State Examination scores though the mean level of performance was lower in the Black subgroup compared to the White subgroup (27.9 vs 28.5, t[1,272.5] = 5.5, p<0.001). The Black subgroup also had lower scores than the White subgroup in each cognitive domain (episodic memory 0.014 vs 0.204, t[1,255.1]=5.3, p<0.001; semantic memory −0.076 vs 0.276, t[1,292]=8.6, p<0.001; working memory −0.126 vs 0.136, t[1,279.8]=6.3, p<0.001; perceptual speed −0.128 vs 0.274, t[1,285]=8.3, p<0.001; visuospatial ability −0.343 vs 0.289, t[1,284]=14.2, p<0.001).

To characterize trajectories of change in cognitive function in the two subgroups, we constructed separate mixed-effects models for each cognitive outcome (Table 1). The composite measure of episodic memory declined a mean of 0.048-unit per year, as shown by the term for time in the table. With the baseline difference in episodic memory performance accounted for, as shown by the term for Black race, there was no racial difference in rate of episodic memory decline, as shown by the term for the interaction of race x time. Analysis of working memory yielded similar results, with lower performance in the Black subgroup at baseline but no difference between the subgroups in rate of cognitive decline.

Table 1.

Relation of Demographic Variables to Trajectories of Change in Cognitive Functions

Fixed effects Episodic Memory Semantic Memory Working Memory Perceptual Speed Visuospatial Ability
Estimate SE p Estimate SE p Estimate SE p Estimate SE p Estimate SE p
Time −0.048 0.006 <0.001 −0.078 0.007 <0.001 −0.048 0.005 <0.001 −0.090 0.006 <0.001 −0.042 0.005 <0.001
Age −0.034 0.002 <0.001 −0.032 0.003 <0.001 −0.015 0.003 <0.001 −0.040 0.003 <0.001 −0.018 0.003 <0.001
Male gender −0.185 0.036 <0.001 −0.075 0.040 0.063 −0.017 0.042 0.681 −0.229 0.047 <0.001 0.201 0.044 <0.001
Education 0.046 0.004 <0.001 0.054 0.005 <0.001 0.061 0.005 <0.001 0.082 0.006 <0.001 0.058 0.005 <0.001
Black race −0.177 0.031 <0.001 −0.348 0.035 <0.001 −0.299 0.036 <0.001 −0.419 0.040 <0.001 −0.595 0.038 <0.001
Age x time −0.006 0.001 <0.001 −0.006 0.001 <0.001 −0.004 0.001 <0.001 −0.006 0.001 <0.001 −0.003 0.001 <0.001
Gender x time −0.005 0.009 0.561 0.007 0.010 0.460 0.004 0.007 0.577 0.002 0.008 0.790 0.003 0.006 0.624
Education x time −0.003 0.001 0.004 −0.004 0.001 0.002 −0.003 0.001 <0.001 −0.003 0.001 0.003 −0.001 0.001 0.064
Race x time 0.010 0.008 0.199 0.021 0.009 0.012 0.004 0.006 0.442 0.040 0.007 <0.001 0.019 0.005 <0.001
Random effects Variance Variance Variance Variance Variance
Intercept 0.251 0.303 0.357 0.455 0.381
Slope 0.010 0.012 0.004 0.008 0.002
Error 0.121 0.179 0.162 0.140 0.198
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Note. From 5 Separate mixed-effects models. SE, standard error.

A different pattern emerged in the remaining cognitive domains. Baseline levels of semantic memory, perceptual speed, and visuospatial ability were each lower in the Black subgroup compared to the White subgroup, but the annual rate of decline in each domain was slower in the Black subgroup compared to the White subgroup. On average, the annual rate of decline in semantic memory was 27% slower (0.021/0.078) in Black persons than White persons, decline in perceptual speed was 44% slower (0.040/0.090), and decline in visuospatial ability was 45% slower (0.019/0.042).

The annual rate of decline in each cognitive domain was more rapid in older participants compared to younger ones, as shown by the terms for the interaction of age x time in Table 1. To test whether race modified this association, we repeated each analysis with terms added for the 2-way interaction of race x age and the 3-way interaction of race x age x time. As shown in Table 2, the 3-way race x age x time interactions for semantic memory and perceptual speed were significant. This means that the age related acceleration in decline in these abilities was more pronounced in White persons than in Black persons. To visually examine these interactions, we divided the baseline age range into 5-year intervals and plotted the predicted 5-year paths of cognitive decline during the study for each baseline age subgroup. The figure shows that the association of older age with faster rate of decline in semantic memory (upper panel) and perceptual speed (lower panel) was reduced in Black persons compared to white persons suggesting racial differences in how these abilities age.

Table 2.

Interactive Effects of Race and Age on Change in Different Cognitive Functions

Fixed effects Episodic Memory Semantic Memory Working Memory Perceptual Speed Visuospatial Ability
Estimate SE p Estimate SE p Estimate SE p Estimate SE p Estimate SE p
Time −0.050 0.006 <0.001 −0.080 0.007 <0.001 −0.049 0.005 <0.001 −0.093 0.006 <0.001 −0.042 0.005 <0.001
Age −0.040 0.003 <0.001 −0.034 0.004 <0.001 −0.017 0.004 <0.001 −0.037 0.004 <0.001 −0.015 0.004 <0.001
Male gender −0.184 0.036 <0.001 −0.074 0.040 0.067 −0.017 0.042 0.694 −0.228 0.047 <0.001 0.201 0.044 <0.001
Education 0.045 0.004 <0.001 0.054 0.005 <0.001 0.061 0.005 <0.001 0.082 0.006 <0.001 0.059 0.005 <0.001
Black race −0.174 0.031 <0.001 −0.349 0.035 <0.001 −0.299 0.037 <0.001 −0.426 0.040 <0.001 −0.599 0.038 <0.001
Age x time −0.007 0.001 <0.001 −0.008 0.001 <0.001 −0.004 0.001 <0.001 −0.007 0.001 <0.001 −0.004 0.001 <0.001
Gender x time −0.006 0.009 0.526 0.006 0.010 0.517 0.003 0.007 0.624 0.001 0.008 0.882 0.003 0.006 0.640
Education x time −0.003 0.001 0.004 −0.004 0.001 0.001 −0.003 0.001 <0.001 −0.003 0.001 0.003 −0.001 0.001 0.064
Race x time 0.013 0.008 0.109 0.026 0.009 0.003 0.007 0.006 0.277 0.046 0.007 <0.001 0.020 0.006 <0.001
Race x age 0.011 0.005 0.013 0.004 0.005 0.420 0.004 0.005 0.413 −0.006 0.006 0.320 −0.007 0.006 0.222
Race x age x time 0.002 0.001 0.062 0.003 0.001 0.029 0.001 0.001 0.278 0.003 0.001 0.003 0.000 0.001 0.662
Random effects Variance Variance Variance Variance Variance
Intercept 0.249 0.304 0.357 0.455 0.382
Slope 0.010 0.012 0.004 0.008 0.002
Error 0.121 0.179 0.162 0.140 0.198
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Note. From 5 separate mixed-effects models. SE, standard error

Figure 1.

Figure 1

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Predicted trajectories of decline in semantic memory (upper panel) and perceptual speed (lower panel) during 5 years in the study for typical Black (blue lines) and White (green lines) participants who began the study at ages 55, 60, 65, 70, 75, 80, 85, or 90 years old, from a mixed-effects model that also adjusted for gender and education.

Although Black and White participants were matched on years of education, it is quite possible that there were racial differences in quality of education. To account for this possibility, we repeated the initial analyses with terms added for the 2-way interaction of race x education and the 3-way interaction of race x education x time. There were no 2-way or 3-way interactions in any cognitive domain, suggesting no racial differences in the association of education with cognition.

Performance on cognitive tests tends to improve with repeated administration of the tests (Wilson et al., 2006; Yang et al., 2012). We conducted additional analyses to determine whether retest learning influenced comparisons of the Black and White subgroups. Because retest effects are strongest during the first and second re-exposures to the test (Bartels, Wegrzyn, Wiedl, Ackermann, & Ehrenreich, 2010), we used a change point model to identify an early inflection after which decline began to accelerate. To minimize measurement error, we used a measure of global cognition based on all 17 tests (baseline mean=0.065, SD=0.570). In this analysis (Table 3), there was gradual improvement in global cognition at the rate of 0.348-unit per year for a mean 0.448-year after which global cognition declined at the rate of 0.079-unit per year. Black race was not related to the initial slope or the inflection point but was associated with slower decline after the inflection point.

Table 3.

Relation of Demographic Variables to Retest Effects Estimated from a Change Point Model with a Composite Measure of Global Cognition

Effect Estimate (95% confidence interval)
Intercept
 Mean 0.196 (0.157,0.238)
 Age −0.029 (−0.033,−0.025)
 Education 0.053 (0.046,0.06)
 Male gender −0.084 (−0.141,−0.02)
 Black race −0.294 (−0.346,−0.241)
Initial slope
 Mean 0.348 (0.254,0.468)
 Age 0.005 (−0.003,0.013)
 Education year 0.004 (−0.008,0.018)
 Male gender 0.006 (−0.1,0.127)
 Black race −0.077 (−0.173,0.011)
Change-point
 Mean 0.448 (0.358,0.536)
 Age −0.005 (−0.01,0)
 Education, years 0.004 (−0.005,0.012)
 Male gender −0.07 (−0.144,0.003)
 Black race 0.027 (−0.045,0.1)
Slope after change point
 Mean −0.079 (−0.09,−0.067)
 Age −0.006 (−0.007,−0.005)
 Education, years −0.003 (−0.005,−0.001)
 Male gender 0.004 (−0.014,0.021)
 Black race 0.022 (0.008,0.037)
Random effects
 Intercept 0.18 (0.163,0.199)
 Initial slope −0.049 (−0.077,−0.024)
 Change point 0.005 (0.002,0.007)
 Slope after change point −0.016 (−0.024,−0.009)
 End of learning phase (change-point) 0.044 (0.032,0.056)
 Error
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Note. The mean represents the effect when all covariates are zero.

Because the change point model suggested that most of retest learning occurred after the initial evaluations, we repeated the original analyses excluding data from baseline and then from baseline plus the first follow-up. As shown by the terms for time in Table 4, excluding the initial visits increased the estimated annual rates of decline, suggesting diminished retest effects. With the effect of retesting reduced, there was no systematic effect on estimated racial differences in level of cognition, as shown by the terms for Black race. Of most interest, the only impact on estimated racial differences in cognitive decline, as shown by interaction terms, was in working memory. With either the first evaluation or first two evaluations excluded, decline in working memory was significantly slower in Black persons relative to White persons.

Table 4.

Effect of Excluding Initial Cognitive Evaluations

Cognitive domain Fixed effects All years included Year 0 excluded Years 0 and 1 excluded
Estimate SE p Estimate SE p Estimate SE p
Episodic memory Time −0.048 0.006 <0.001 −0.068 0.007 <0.001 −0.079 0.008 <0.001
Black race −0.177 0.031 <0.001 −0.165 0.037 <0.001 −0.172 0.045 <0.001
Race x time 0.010 0.008 0.199 0.010 0.009 0.242 0.012 0.009 0.203
Semantic memory Time −0.078 0.007 <0.001 −0.094 0.008 <0.001 −0.110 0.009 <0.001
Black race −0.348 0.035 <0.001 −0.339 0.041 <0.001 −0.368 0.050 <0.001
Race x time 0.021 0.009 0.012 0.024 0.010 0.014 0.028 0.011 0.010
Working memory Time −0.048 0.045 <0.001 −0.059 0.006 <0.001 −0.066 0.007 <0.001
Black race −0.299 0.036 <0.001 −0.345 0.042 <0.001 −0.340 0.050 <0.001
Race x time 0.004 0.006 0.442 0.014 0.007 0.031 0.016 0.008 0.040
Perceptual speed Time −0.090 0.006 <0.001 −0.106 0.006 <0.001 −0.116 0.007 <0.001
Black race −0.419 0.040 <0.001 −0.442 0.046 <0.001 −0.467 0.055 <0.001
Race x time 0.040 0.007 <0.001 0.048 0.009 <0.001 0.047 0.009 <0.001
Visuospatial ability Time −0.042 0.005 <0.001 −0.046 0.005 <0.001 −0.046 0.006 <0.001
Black race −0.595 0.038 <0.001 −0.568 0.044 <0.001 −0.539 0.053 <0.001
Race x time 0.019 0.005 <0.001 0.019 0.006 0.002 0.017 0.007 0.018
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Note. From 15 separate mixed-effects models that also included terms for age, male gender, education, and their interactions. SE, standard error

Discussion

We assessed different domains of cognitive function at annual intervals for a mean of more than 5 years in older Black and White persons matched for age, education, and number of cognitive assessments. Baseline level of cognition in each domain was lower in Black persons compared to White persons. Rates of decline in semantic memory, perceptual speed, and visuospatial ability were slower in Black persons compared to White persons, and the effects in semantic memory and perceptual speed increased with advancing age. There were no racial differences in decline in episodic memory and mixed results for working memory. The results suggest that there may be subtle differences between Black and White persons in profiles of cognitive aging.

That previous studies of racial differences in cognitive aging are approximately evenly split among those showing no difference (Atkinson et al., 2005; Masel & Peek, 2009; Castora-Binkley et al., 2013; Marsiske et al., 2013), more rapid decline in Black persons (Lyketsos, et al 1999; Sachs-Ericsson & Blazer, 2005; Sawyer et al., 2009 Wolinsky et al., 2011), and more rapid decline in White persons (Sloan & Wang, 2005; Alley et al., 2007; Karlamangla et al., 2009; Early et al., 2013) suggests that there are probably not strong racial differences in trajectories of cognitive aging. The present results are consistent with this idea. However, most previous research has been based on global cognitive measures. We are aware of only two previous studies that assessed multiple cognitive domains. One found virtually no differences in cognitive decline (Marsiske et al., 2012) but the other study found slightly more rapid decline in White persons relative to Black persons in semantic memory and executive function with no difference in episodic memory decline (Early et al., 2013). The present results suggest similar racial differences in cognitive aging, with less decline in semantic memory, perceptual speed, and visuospatial ability in Black persons compared to White persons, possible differences in working memory, and no differences in episodic memory.

The basis of the observed differences in cognitive decline in Black persons compared to White persons is uncertain. Previous studies that found more cognitive decline in Black persons compared to White persons have suggested that racial differences in vascular disease, socioeconomic level, leisure activities, or diet might be contributing factors (Lyketsos et al., 1999; Sachs-Ericsson & Blazer, 2005; Sawyer et al., 2009), but these factors seem unlikely to explain slower cognitive aging in Black persons. Previous studies finding less decline in Black persons than White persons have focused more on methodologic issues than possible underlying mechanisms (Sloan & Wang, 2005; Alley et al., 2007; Wolinsky et al., 2011; Early et al., 2013). Racial differences in level of cognitive function make it difficult but not impossible to assess racial differences in change in cognitive function. Black and White persons differ not only in quantity of education (i.e. years of schooling) but also in quality of education (Manly, Schupt, Tang, & Stern, 2005), making it difficult to form truly comparable racial subgroups. However, the racial subgroups in the present study had similar cognitive trajectories in some cognitive domains, suggesting that they were reasonably comparable. A substantial proportion of the variability in late-life cognitive decline is attributable to individual differences in levels of dementia related pathologies (Wilson, Leurgans, Boyle, Schneider, & Bennett, 2010; Boyle, Yu, Wilson, Schneider, & Bennett, 2013) and the ability to tolerate these pathologies. It is possible, therefore, that Black and White persons differ in the rate at which dementia related pathologies accumulate in the brain or in how strongly these pathologies impair cognitive function, but little relevant data have been published. Clinic-pathologic research in racial and ethnic minorities is needed to address these issues.

We are not aware of previous research on racial differences in retest learning. Because it is difficult to separate retest effects from aging effects, particularly with uniform retest intervals (Hoffman, Hofer, & Sliwinski, 2011), we used two complementary approaches. In the change point model, there were no racial differences in the extent or timing of the retest effect, and less decline following the change point in the Black group compared to the White group. In the sensitivity analyses, eliminating the first two evaluations, which the change point model identified as the time of maximal retest learning, did not diminish the slightly more favorable profile of cognitive aging shown by the Black group relative to the White group. Together these analyses suggest no substantial racial differences in retest learning.

Although this and previous studies suggest that rates of cognitive aging in older Black and White persons are broadly similar, there are Black-White differences in level of cognition that predate old age (Peoples & Fagan, 1995). As a result, there are higher rates of dementia in Black persons than White persons (Yaffe et al., 2013) because the diagnosis is primarily based on cognitive level (or cognitive decline inferred from cognitive level). The development of normative data for racial and ethnic subgroups might limit such diagnostic misclassification. Recent research suggests that the gap in cognitive test performance between Black and White persons may be narrowing (Huang & Hauser, 2001; Dickens & Flynn, 2006) though the extent and timing of these changes are not securely established (Murray, 2006).

Confidence in these findings is strengthened by several factors. Individuals with dementia were excluded at study onset based on a uniform clinical evaluation and standard criteria applied by an experienced clinician, maximizing diagnostic precision. Cognition was assessed in multiple domains with previously established composite measures and a high rate of follow-up participation allowing us to reliably characterize person-specific trajectories of change. Use of propensity scores enhanced the comparability of the subgroups. An important limitation is that participants were selected, and so it will be important to replicate these findings in other groups.

Acknowledgments

This research was supported by NIH grants R01AG17917, P30AG10161, R01AG36042 and the Illinois Department of Public Health. The funding organizations had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

The authors thank the participants of the Minority Aging Research Study, the Rush Memory and Aging Project, and the Religious Order Study for their invaluable contributions. We thank Charlene Gamboa, MPH; Tracy Colvin, MPH; Tracey Nowakowski, Barbara Eubeler, Karen Lowe-Graham, MS, and Karen Skish, MS, for study recruitment and coordination, and John Gibbons, MS, and Greg Klein, MS, for data management, Alysha Kett, MS, for data analysis, and the staff of the Rush Alzheimer’s Disease Center.

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