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. Author manuscript; available in PMC: 2022 Jul 25.
Published in final edited form as: J Aging Health. 2019 Jun 5;32(7-8):807–816. doi: 10.1177/0898264319853138

Greater Disease Burden, Greater Risk? Exploring Cognitive Change and Health Status among Older Blacks

DeAnnah R Byrd 1, Roland J Thorpe Jr 2, Keith E Whitfield 1
PMCID: PMC9311478  NIHMSID: NIHMS1690554  PMID: 31165660

Abstract

Objective.

Examine the relationships between health status and changes in cognition over time among middle to older aged Blacks.

Method.

Data come from the Baltimore Study of Black Aging—Patterns of Cognitive Aging. At baseline 602 Black participants, ranging from ages 48–95 years old were enrolled. At follow-up, approximately 3 years later, 450 participants were re-interviewed.

Results.

After accounting for baseline cognition, age, sex, and education, a greater number of health conditions was associated with slower perceptual speed (b= −5.099, p= 0.022). Average peak expiratory flow was also associated with improvements in working memory (b= 0.029, p= 0.019) and perceptual speed (b= 0.026, p= 0.026), controlling for model covariates.

Discussion.

Study findings demonstrate that greater disease burden is associated with declines in specific fluid cognitive abilities in middle to later life among Blacks. This finding highlights the importance of reducing health disparities that disproportionately affect Blacks.

Keywords: Black adults, cognitive changes, fluid and crystallized abilities, disease burden, lung function

INTRODUCTION

Numerous studies have examined the relationship between health status and cognition in older adults, and research in this area suggests that poor health, particularly greater cardiovascular burden, is associated with lower cognitive functioning. For instance, chronic conditions such as cardiovascular disease (Aiken Morgan, Sims, & Whitfield, 2010), diabetes (Yeung, Fischer, & Dixon, 2009), and hypertension (Waldstein, 2003) are all linked to poorer cognitive performance across racial and ethnic groups (Aiken Morgan et al., 2010; Carmasin, Mast, Allaire, & Whitfield, 2014; Rexroth et al., 2013; Waldstein, 2003; Whitfield et al., 2000; Yeung et al., 2009). Further, these conditions have been shown to not only damage blood vessels and increase risk of both vascular and Alzheimer’s dementia (Emdin et al., 2016; Wolters et al., 2018); they also increase in middle age (Emdin et al., 2016). Vascular compromise (via high blood pressure or damaged blood vessels), for example, is a known risk factor for cognitive dysfunction and decline (Emdin et al., 2016; Waldstein, 2003). Hence, these vascular risk factors are not only associated with worse cognition they are also linked to changes in cognitive functioning in old age.

In a seminal review of 34 studies on predictors of cognitive change in old age, Anstey and Christian (2000), found that objective indices of health, e.g., lung functioning, blood pressure, and cardiovascular disease including stroke all affect cognitive change in late adulthood. Elevated blood pressure, in particular, affected change in performance on measures of speed and mental status (based on a review of 10 studies) (Anstey & Christensen, 2000). However, these studies were done using primarily older white samples; to date, few studies have examined this association among middle to older age Blacks, exclusively.

The interrelationship between health status and cognition becomes even more relevant when investigating underserved populations such as older Black adults who disproportionately experience poor cognitive health compared to Whites (Aiken Morgan et al., 2010; Weuve et al., 2018). A recent systematic review of dementia incidence and prevalence in the U.S. indicates a higher risk of cognitive disease among Blacks 65 years and older (Mehta & Yeo, 2017). Studies show that older Blacks perform worse than Whites on specific (e.g., the correct naming of the selected 18 items of the Boston Naming Test) and global tests (e.g., SPMSQ) of cognitive functioning and these differences persist after controlling for demographic and health-related factors (Byrd, Gee, & Tarraf, 2018; Whitfield et al., 2000). These findings have been demonstrated in both baseline or cross-sectional differences (Alley, Suthers, & Crimmins, 2007; Masel & Peek, 2009; Weuve et al., 2018; Whitfield et al., 2000; Wilson et al., 2010; Wolinsky et al., 2011) as well as in longitudinal outcomes (Byrd et al., 2018; Sachs-Ericsson & Blazer, 2005; K. Sawyer, Sachs-Ericsson, Preacher, & Blazer, 2008).

However, research also shows that the factors that explain cognitive performance in older Whites differs from that of older Blacks. For instance, Whitfield and colleagues (2000) using a sample of individuals in their 80’s, report that depressive symptoms are important in explaining cognitive performance among Whites, whereas the number of chronic conditions explains performance among Blacks. Specifically, increased levels of depression reduce performance on a cognitive naming task in Whites but not in Blacks. In contrast, a greater number of health conditions reduce performance in Blacks, such that those with more health conditions obtained a lower naming score (as measured by the 18-item abbreviated Boston Naming Test), but is irrelevant for Whites as it has no impact on their performance (Whitfield et al., 2000). Likewise, Aiken Morgan and colleagues (2010) examined the cross-sectional relationship between cardiovascular health and cognitive functioning using a sample of older Blacks. They also found that self-reported cardiovascular health contributes to variability in late life cognition (i.e., specific aspects of verbal learning performance and general intelligence) in older Blacks (Aiken Morgan et al., 2010). Specifically, cardiovascular problems were associated with decreases on domain-specific cognitive measures such as memory and abstract reasoning (e.g., general intelligence). Studies have further found a link between greater vascular burden among older Black adults and cognitive change (Carmasin et al., 2014). Yet these studies are limited by the small number of cognitive domains included and the cross-sectional examination of the health-cognition relationship. Most, for example, have examined two domains such as naming deficits and memory (Boston Naming Test and incidental recall) (Whitfield et al., 2000), or global cognition (MMSE) and processing speed (The Wechsler Adult Intelligence Scale—Revised Digit Symbol task) (Carmasin et al., 2014). Others that have included multiple measures (i.e.,12 different cognitive tests) and domains of cognition (i.e., learning and memory, global cognition, attention, short-term or working memory, abstract reasoning, and visuospatial test of mental rotation of geometric shapes) have investigated the relationship between health status and cognition at one time point (Aiken Morgan et al., 2010). Hence, few studies have examined the degree to which within-group variability impacts observed racial differences in cognitive burden over time. Individual variability in health risk factors such as greater disease burden and high blood pressure may better elucidate the differential patterns of cognitive aging between older Blacks and Whites. Thus, studies that examine health status and changes in multiple cognitive domains over time among older Blacks are needed.

The purpose of this study was to examine the relationships between health status and changes in cognition over time among middle-aged and older Blacks. We hypothesize that health status (as measured by the number of health conditions) will differentially impact change in psychometric dimensions of cognition over time. Specifically, we hypothesize that reporting more health conditions will be related to declines in fluid abilities (e.g., inductive reasoning, memory, working memory, and perceptual speed), but unrelated to changes in crystalized abilities (e.g., vocabulary). These hypotheses are based on Horn and Cattell’s classic theory of intellectual decline in older adulthood, which distinguishes between fluid and crystallized abilities (Gf-Gc) (Horn & Cattell, 1966). Fluid abilities involve thinking flexibly, reasoning abstractly, concept formation, and novel problem-solving. This ability is considered independent of learning, experience, acculturation, and education. Crystallized abilities, on the other hand, represent the depth and breadth of one’s knowledge, particularly verbal knowledge that has been accumulated throughout life. These abilities are influenced by educational and cultural factors that are rooted in experiences.

Although there a multiple determined pathways for cognitive decline in aging, the Gf-Gc theory remains dominant in the study of cognitive abilities and intellect, given its ability to make predictions regarding the processes of cognitive aging and changes in intellectual performance (Horn & Cattell, 1966). A review of the literature on cognitive functioning in older adults show that cognitive abilities decline with age on fluid measures such as slower processing speed; however, these declines are not seen for crystallized abilities (Fillit et al., 2002; Horn & Cattell, 1967). These findings have also been replicated in samples of Black elders. For example, Whitfield and colleagues (2010) report that mean levels of performance on most fluid measures (i.e., inductive reasoning and processing speed) are significantly lower for older Blacks (ages 62–79 years) compared to younger ones (ages 50–61 years), whereas performance on measures of crystallized abilities (verbal ability and numerical ability) do not differ by age (Whitfield, Allaire, Gamaldo, & Bichsel, 2010). Although mean levels of performance on some fluid measures differed by age, the factor structure of the cognitive abilities did not (i.e., the structure of the latent ability factors were equivalent between younger and older Blacks) (Whitfield et al., 2010).

Yet, there have been few longitudinal studies focusing on Black elders exclusively and the individual variation in health risk factors that predict changes in fluid-crystallized distinctions of cognitive abilities. Following this line of reasoning, this study examined whether health status is more associated with cognitive changes in fluid abilities rather than crystallized domains among older Blacks.

METHODS

The goal of the Baltimore Study of Black Aging—Patterns of Cognitive Aging (BSBA-PCA) (Duke University IRB #1610) was to understand changes in cognitive, health, and psychosocial factors over time (Aiken-Morgan, Gamaldo, Sims, Allaire, & Whitfield, 2014). Baseline data collection yielded 602 Blacks (ages 48–95; mean = 69.1; SD = 9.8), while longitudinal follow-up at 33 months resulted in 450 participants (ages 51–96; mean = 71.4; SD = 9.2). At follow-up, data collection repeated the same interview and testing procedures performed at baseline. The analysis for the present study included 450 BSBA-PCA participants who completed both waves of data collection.

Attrition

There were 152 participants lost due to attrition between baseline and follow-up (i.e., death, refusal, relocation, sickness). To address losses to follow-up we conducted multiple sensitivity analyses on key demographics and cognitive variables. Similar to other studies (Aiken-Morgan et al., 2014; Gamaldo et al., 2014), we found while there were no significant differences in age, sex, education, and median income between completers and dropouts, there were differences on the cognitive measures. Specifically, dropouts had significantly lower working memory t (596) =−1.99, p=.047, perceptual speed t (598) =−2.64, p=.009, verbal memory t (597) =−2.26, p=.024, vocabulary t (588) =−3.70, p=.000, and inductive reasoning t (595) =−2.37, p=.018 scores at baseline compared to participants who completed both waves of the study.

Measures

Cognitive Status

Five domains of cognition were assessed at baseline and follow-up. These measures have typically been shown to load on a fluid factor (e.g., inductive reasoning, memory, working memory, and perceptual speed) or a crystalized factor (e.g., vocabulary) (Whitfield et al., 2010). Composite scores were developed for all five domains by summing the items of each individual test.

Inductive Reasoning assessing how well a participant can find patterns in strings of letters, numbers, and words was determined by three measures: Number Series (Schaie & Thurstone, 1985). Participants are shown a series of numbers (e.g., 10, 11, 12, 13, 14, ___) and asked to select from five possible numbers that will continue the series (i.e., 15). Participants are given 4.5 minutes to complete 15 number series, and the number of correct responses (coded as 1) are summed to create a total score. Letter Series (Schaie & Thurstone, 1985). Participants are shown are series of letters (e.g., cdcdcd) and asked to identify the next letter in the series (i.e., c). This timed test allows 4 minutes to complete 30 letter series. The total score for this measure is calculated by summing the number of correct responses. Shipley Institute of Living Scale Abstraction Test (Shipley, 1986) is a 20-item test assessing abstract thinking. Participants are shown a series of letters, numbers, and words (e.g., North South, up down, high ____) and then asked to identify the most logical item that would follow in the series, (i.e., low). The total number of correct responses, coded as “1”, are summed to provide an overall score on this measure.

Memory measures one’s ability to remember and learn a list of words: Immediate Recall (Zelinski, Gilewski, & Schaie, 1993) asks participants to silently study a list of 20 words for 3.5 minutes, then write down all the words they recall from the list. Rey Auditory Verbal Learning Test (Rey, 1941) reads participants a list of words once, and then instructs each participant to remember and write down as many recalled words. Hopkins Verbal Learning Test (Brandt, 1991) measures one’s ability to remember a list of words.

Working Memory was assessed using three measures: Operation Span (Turner & Engle, 1989) testing one’s ability to solve arithmetic problems while remembering words. Alpha Span assessing short-term memory (Craik, 1990). Backward Digit Span measuring short-term or working memory requires participants to repeat backwards a series of digits that were presented orally (Wechsler, 1981).

Perceptual Speed representing information processing speed was assessed using three measures: Identical Pictures Test (Ekstrom & Harman, 1976), Digit Symbol Substitution Test (Wechsler, 1981), and Number Comparison Test (Ekstrom & Harman, 1976).

Vocabulary or verbal meaning tests one’s knowledge of word meanings and was assessed using two measures: Verbal Ability Test (Ekstrom & Harman, 1976) is a 36-item test broken into two 18-item subtests that contains a prompt word and five-choice synonyms to that word. Participants are given a total of 8 minutes to complete both halves of the test, 4 minutes per 18-item subtest. The number of correct responses from both subtests are summed to generate the total score. Shipley Institute of Living Verbal Meaning Test (Shipley, 1986). This 40-item vocabulary test ask participants to underline the one word out of four that is most similar to the prompting word. Each correct response is scored as “1” and are summed to generate a total score.

Health Status

A summary health status measure was created by summing participants’ self-report of whether a physician or nurse had informed them that had any of the following conditions: diabetes, cardiovascular disease, high blood pressure/hypertension, arthritis, stroke, heart attack, angina, and asthma. Each of the conditions was coded as binary variables (0 = absent and 1 = present). Due to the small number of participants reporting having had a heart attack or angina, a dichotomous variable representing heart troubles was created, as done by other studies (Thorpe, Clay, Szanton, Allaire, & Whitfield, 2011). All seven conditions were summed to create a variable representing the total number of health conditions, which was then dichotomized as having two or more conditions (1) compared with one or none (0) (Thorpe et al., 2011). We dichotomized this variable, rather than using the individual health conditions given that other studies examining health status and/or disease burden, cognition and other health outcomes among Blacks have dichotomized this variable (Carmasin et al., 2014; Thorpe et al., 2011). These studies as well as others that have left this variable continuous (Aiken Morgan et al., 2010) suggest that the greater the number of self-reported health conditions, the greater the cognitive or mobility difficulties among Blacks. For example, Mak and colleagues (2006) report an association between diabetes and hypertension on cognitive aging in an African-Caribbean population. They postulate that the association of both diseases creates greater risk for poor cognitive functioning above and beyond either alone (Mak, Kim, & Stewart, 2006). Moreover, older people often suffer from comorbidity, or several chronic conditions simultaneously (Verbrugge, Lepkowski, & Imanaka, 1989). The goal of this study was to better understand whether greater disease burden in terms of reporting more health conditions is associated with domain-specific cognitive changes; hence, this dichotomous classification was utilized to identify participants who had multiple, i.e., two or more health conditions versus those with one or none.

Other measures of health status included average peak expiratory flow to assess lung functioning and blood pressure to objectively assess basic cardiovascular health. Average peak expiratory flow was calculated by taking the mean of three pulmonary function readings using a mini-Wright peak flow meter (Cook et al., 1991). At each reading, participants were asked to blow as hard as possible into the end of a peak flow meter after taking a deep breath for one second (1 s). Using an oscillometer automated device (A&D model UA- 767) three readings of orthostatic blood pressure (BP) were taken (Beevers, Lip, & O’Brien, 2001) and mean systolic (SBP) and diastolic (DBP) values were calculated for each individual. Similar to another study we included both mean BP level and hypertensive status (Elias, Robbins, Elias, & Streeten, 1998).

Control Variables

Control variables included age, sex, and years of education. Age was measured in years and treated as continuous. Sex was dichotomized with male as the reference category. Years of education was measured by the highest grade attained and treated as continuous.

Statistical Analyses

Descriptive statistics were calculated to examine differences in the demographic and health characteristics of the sample at baseline and follow-up. Ordinary least squares (OLS) linear regression was used to examine the relationships between the number of health conditions at baseline (two or more vs. one or none) and cognitive change, controlling for baseline cognition, age, sex, and education. This approach examines the effects of health status on changes in cognition and has been used by other longitudinal studies on older Black adults (Byrd, 2017). Below is the regression equation used to predict change in the five domains of cognition:

Y (Follow-up Cognition) = B0 + B1 (Baseline Cognition) + B2 (Controls) - B3 (Two or More Health Conditions) + B4 (Lung Functioning) - B5 (Blood Pressure) + εi

All p-values <.05 were considered statistically significant. Analyses were conducted using STATA, version 15, software (StataCorp, 2017).

RESULTS

Sample Characteristics

Table 1 shows the demographic characteristics of the BSBA-PCA sample at baseline and follow-up. The average age of participants at baseline was 69.12 years (range 48–95 years, SD 9.75), compared to a mean age of 71.4 years at follow-up (range 51–96 years; SD 9.2). The majority of the baseline sample was female (25.42% male) and had an average education of 11.64 years (SD 2.95, range 3–20 years). The majority of baseline participants reported having high blood pressure (82.89%), arthritis (65.28%) and two or more health conditions (78.24%). The average peak expiratory flow at baseline was 276.59 liters/min (range 0–646.67 L/min; SD 94.13) and the mean systolic (SBP) and diastolic (DBP) blood pressure readings were 146.17 mmHg (range 82–230.33 mmHg; SD 23.82) and 86.61 mmHg (range 51–136.67 mmHg; SD 12.86), respectively. There were also clear differences between baseline (wave 1) and follow-up (wave 2) such that participants at follow-up were significantly older, less likely to be men, reported lower levels of education and fewer health conditions, and reported a lower average peak expiratory flow and diastolic blood pressure level. Further, the two waves differed significantly on certain cognitive measures, including working memory, vocabulary and inductive reasoning.

Table 1.

Baseline Characteristics for Adult Respondents. Baltimore Study of Black Aging--Patterns of Cognitive Aging, At Baseline (N=602) and Follow-Up (N=450).

Wave 1 Wave 2 Wave Difference Baseline Values for Completers (N=450)
Characteristics n (%) n (%) p value n (%)
Demographic
Age (years), mean (SD) 69.1 (9.75) 71.4 (9.21) <.0001 68.7 (0.43)
Female 449 (74.58) 348 (57.81) <.0001 344 (76.44)
Education <.0001
 Grade school (8 years or less) 80 (13.29) 52 (8.64) 54 (12.00)
 High school (9–12 years) 336 (55.81) 285 (47.34) 259 (57.56)
 College, vocational or trade school (13–16 years) 161 (26.74) 94 (15.61) 118 (26.22)
 Graduate school (17+ years) 24 (3.99) 17 (2.82) 19 (4.22)
Health Status
Health conditions
 Diabetes 205 (34.05) 162 (26.91) <.0001 145 (32.51)
 Cardiovascular disease 157 (26.08) 134 (22.26) <.0001 107 (24.21)
 High blood pressure 499 (82.89) 373 (61.96) <.0001 373 (83.45)
 Arthritis 393 (65.28) 294 (48.84) <.0001 304 (68.01)
 Stroke 98 (16.28) 87 (14.45) <.0001 74 (16.44)
 Heart trouble 120 (19.93) 97 (16.11) <.0001 89 (19.82)
 Asthma 113 (18.77) 89 (14.78) <.0001 86 (19.11)
Two or more health conditions 471 (78.24) 353 (58.64) <.0001 353 (78.44)
Average peak expiratory flow, mean (SD) 276.6 (94.13) 239.9 (98.59) <.0001 275.7 (89.23)
Systolic blood pressure (SBP)a mean (SD) 146.2 (23.82) 145.3 (24.56) = 0.97 145.4 (22.94)
Diastolic blood pressure (DBP)a mean (SD) 86.6 (12.86) 84.4 (13.66) = 0.0007 87.0 (12.14)
Cognitive Measures
Inductive reasoning, mean (SD) 132.9 (41.00) 146.1 (31.37) <.0001 135.2 (40.24)
Memory, mean (SD) 147.7 (27.22) 147.4 (24.7) = 0.15 149.2 (27.37)
Working memory, mean (SD) 138.1 (33.14) 148.2 (23.00) <.0001 139.69 (32.72)
Perceptual speed, mean (SD) 148.5 (28.27) 148.7 (26.01) = 0.12 150.3 (27.24)
Vocabulary, mean (SD) 145.9 (33.36) 98.4 (19.68) <.0001 148.9 (31.69)
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Note. Tests of differences between waves was based on a paired t-test for age, average peak expiratory flow, SBP, DBP, and all cognitive measures; Pearson’s chi-square for gender, education and all health conditions. SD = standard deviation.

a

SBP range = 82–230.3 mmHg and DBP range = 51–136.7 mmHg.

OLS Regression

The association between all five cognitive measures and health status, controlling for baseline cognition, age, sex and education is displayed in Table 2, Models 1–5. Across all models, baseline cognition was positively associated with change in all five cognitive domains.

Table 2.

Regression Analysis for Health Status, Lung Functioning and Blood Pressure Predicting Changes in Cognitive Status among Black Adults. Baltimore Study of Black Aging--Patterns of Cognitive Aging.

Model 1 Inductive Reasoning Model 2 Memory Model 3 Working Memory Model 4 Perceptual Speed Model 5 Vocabulary
B(SE) B(SE) B(SE) B(SE) B(SE)
Baseline Cognition 0.180***(0.044) 0.453***(0.038) 0.213***(0.033) 0.607***(0.040) 0.290***(0.027)
Age −0.027(0.189) −0.445***(0.126) −0.025(0.127) −0.207(0.116) 0.022(0.100)
Female 0.214(3.760) 6.961**(2.535) 6.296**(2.509) 3.549(2.345) 5.154**(2.002)
Education 1.246*(0.611) 0.152(0.368) 1.2778***(0.384) 0.049(0.350) 1.013***(0.320)
Two or more health conditionsa 2.335(3.636) 1.224(2.471) −2.590(2.480) −5.099*(2.226) −2.635(1.957)
Average peak expiratory flow 0.019(0.019) 0.019(0.013) 0.029*(0.013) 0.026*(0.012) 0.007(0.010)
Mean systolic blood pressure −0.204*(0.092) −0.004(0.062) −0.067(0.062) 0.011(0.056) −0.054(0.049)
Mean diastolic blood pressure 0.239(0.170) 0.077(0.116) 0.101(0.115) 0.015(0.104) 0.065(0.091)
R2 0.109 0.328 0.215 0.509 0.326
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Note. B = unstandardized coefficient; SE = standard error.

a

Reference category is one or no health conditions.

*

p < .05

**

p < .01

***

p < .001

Model 1 illustrates that higher education is associated with better inductive reasoning (b= 1.246, p= 0.042). In addition, high mean systolic blood pressure was predictive of lower inductive reasoning (b= −0.204, p= 0.026).

Model 2 indicates that older age is related to decreased (verbal) memory (b= −0.445, p< 0.001); while being female is associated with greater (verbal) memory (b= 6.961, p= 0.006). Consistent with our hypotheses, cognitive performance declines with increasing age in a variety of fluid domains.

Model 3 shows that average peak expiratory flow (a measure of lung functioning) was positively associated with changes in working memory (b= 0.029, p= 0.019). As expected individuals with higher baseline lung functioning had better cognitive functioning at follow-up. Further, Model 1 demonstrates that being female (b= 6.296, p= 0.012) and having more years of education (b= 1.278, p= 0.001) was associated with increased working memory. This finding supports the notion that higher levels of education are strongly associated with improved cognitive functioning.

Model 4 shows that a greater number of health conditions are associated with change in perceptual speed, such that Blacks with two or more health conditions have significantly slower speed than those with one or no conditions (b= −5.099, p= 0.022). Higher average peak expiratory flow is predictive of faster perceptual speed (b= 0.026, p= 0.026) at follow-up.

Model 5 demonstrates that a greater number of health conditions are not associated with changes in vocabulary and supports our hypotheses that disease burden is unrelated to changes in crystallized abilities. This model also shows that being female (b= 5.154, p= 0.010) and having higher levels of education (b= 1.013, p= 0.002) is associated with increased vocabulary scores.

DISCUSSION

The goal of the present study was to examine the relationships between health status and changes in cognition over time in a sample of middle-aged and older Blacks.

The main findings show that better health status, in terms of fewer health conditions and increased expiratory flow, is associated with change in cognition over time among older aged Blacks. More specifically, this study found that greater disease burden, i.e., reporting more health conditions was associated with slower perceptual speed at 3-year follow-up. Higher lung functioning was positively associated with cognitive changes, such that individuals with high baseline average peak expiratory flow had increases in working memory and perceptual speed over time. These findings demonstrate that greater disease burden at baseline is associated with decline in specific fluid domains of cognitive ability at follow-up, while better health status leads to stability and small improvements in cognitive performance over time. These findings suggest that poor health and disease burden are important to consider when examining cognitive changes in fluid-crystallized abilities among older Blacks.

This is consistent with previous work that shows health status is predictive of cognitive test performance among middle age and older Blacks (Aiken Morgan et al., 2010; Byrd, 2017; Carmasin et al., 2014; Sims et al., 2015; Whitfield et al., 2000). It is also consistent with studies linking lung functioning to cognitive decline in older adults (Albert et al., 1995; Swan, LaRue, Carmelli, Reed, & Fabsitz, 1992) and those that report an association between lung function and cognitive performance in older Blacks (Allaire, Tamez, & Whitfield, 2007; Sims et al., 2015). For instance, Whitfield and colleagues (2000) linked both the number of chronic conditions and lung functioning to specific fluid domains of cognitive test performance among Blacks. They found that Blacks with more health conditions reported a lower naming score, as measured by the 18-item abbreviated Boston Naming Test, compared to those with fewer conditions. They further found that higher levels of peak flow were associated with better performance on specific measures of cognitive functioning, including confrontational naming, recall and proportional recall tasks (Whitfield et al., 2000). However, this study was limited in that it used a relatively high-functioning cohort of older adults, i.e., those approximately in the top third of their age group in terms of physical and cognitive functioning (Whitfield et al., 2000, pp. 72–73). The present study included a large and diverse population of Blacks that were heterogeneous in terms of their educational, economic and societal resources.

Although this study found a strong link between lung functioning and cognitive decline, some studies have found no association (Pathan et al., 2011). Lower lung functioning has been associated with cognitive decline and dementia risk through different mechanisms, such as chronic hypoxia that can arise from numerous cardiorespiratory disorders or the development of a pro-inflammatory state (Dodd, Getov, & Jones, 2010; Peers et al., 2009).

Previous research further indicates that vascular risk factors are predictive of cognitive changes in middle and later-life among Blacks, which is consistent with the present study findings. Carmasin and colleagues (2014), for example, examined the link between greater vascular burden (as measured by high-risk for those with two or more conditions compared to those with low-risk who had one or none of the conditions) and cognitive change among Black elders. They report that vascular risk factors, e.g., diabetes, cardiovascular disease, high blood pressure, history of heart attack, angina, circulation problems, and history of stroke, increase risk for mild changes in processing speed (as measured by a Digit Symbol task). They conclude that vascular risk factors are predictive of worsening cognition in fluid domains, i.e., slower processing speed among middle-aged and older Blacks (Carmasin et al., 2014). However, that study was limited in that it used only two domains of cognition: global cognition and processing speed. Like the current study, a high baseline health burden (more chronic conditions) was also associated with poorer cognitive functioning in fluid abilities over time. One strength of the current study is that it assessed multiple domains of cognitive functioning, providing further support that health status differentially influences domain-specific cognitive changes among older Blacks.

Moreover, the physiological pathways linking health status and vascular risk factors to cognitive changes has been well elucidated. For instance, vascular risk factors such as high blood pressure have been associated with increased white matter hyperintensities (WMHs) and other cerebrovascular changes (Carmasin et al., 2014), including disruption of cerebral white matter integrity and greater risk for white matter infarcts, greater brain atrophy, and silent strokes (DeCarli et al., 2001; Longstreth et al., 2005; Seshadri et al., 2004; Vermeer et al., 2003). These vascular changes visible through neuroimaging have further been associated with global and domain-specific cognitive declines, including memory, processing speed (Digit Symbol Substitution Test), and general cognition (MMSE) (Longstreth et al., 2005; Moorhouse & Rockwood, 2008; Kathryn Sawyer, Corsentino, Sachs-Ericsson, & Steffens, 2012; Sheline et al., 2006; Vermeer et al., 2003). There are potentially many mechanisms through which high blood pressure and other vascular risk factors exerts a deleterious effect on brain and cognitive health (Goldstein, Levey, & Steenland, 2013).

Other characteristics related to cognitive change include older age associated with decreased verbal memory ability (Sheline et al., 2006), female gender (as a significant predictor of working memory, verbal memory and vocabulary, with women performing better than men) (Whitfield et al., 2000), increased education linked to better working memory, vocabulary performance and inductive reasoning (Alley et al., 2007; Gottesman et al., 2017; Sheline et al., 2006; Wilson et al., 2009) and high mean systolic blood pressure (but not diastolic blood pressure) was related to lower inductive reasoning. Similar to the present study, other studies have also shown that among older Blacks higher systolic blood pressure (but not diastolic blood pressure) is associated with lower cognitive status, including slower processing speed and worse global cognition (Whitfield et al., 2008). In general, systolic blood pressure has been associated with worse attention and significant declines in semantic memory (Goldstein et al., 2013) as well as poorer processing speed and reduced recognition memory (Pase et al., 2013). Given the significant correlations between systolic blood pressure and most cognitive measures, it may be a better source of individual variability in cognitive aging among Blacks, rather than diastolic.

Some limitations should be recognized. First, this sample was limited to urban, community-dwelling older Blacks who live in the Baltimore area. Thus, the generalizability of our findings may be limited to Blacks who reside in this particular region of the country. Although, age and education were both related to cognitive changes in some cognitive domains they were not related in others (e.g., perceptual speed). Additionally, the present analyses were based on participants who completed both waves of the BSBA-PCA. Hence, having a larger and more diverse sample may have yielded different findings. Second, the number of health conditions was based on the participant’s report of being told by a physician or nurse whether they had a specific condition or not. These self-reported conditions are hampered by the unreliability of participants’ recall, recall bias, and criterion and construct validity (Dohrenwend, 2006). However, self-reported health status has been shown to be an accurate indicator of disease status in older adults (Ferraro & Wilmoth, 2000; Guralnik et al., 1993). Nonetheless, clinical data or objective measurements of other risk factors may provide more robust assessment of participants’ overall disease burden and should be considered in future studies. Third, the BSBA-PCA did not assess all risk factors or disease severity at baseline, including one’s BMI, smoking history or alcohol intake. Rather, these risk factors were only collected at follow-up (wave 2). The collection of this information at both time points could have expanded the criteria used to determine high versus low disease burden. Other comorbid conditions (e.g., BMI and depression) and disease severity should be considered in future work, since these may be important in understanding how health status is related to changes in cognition over time among Blacks. Disease severity has been linked to cognitive decline over time; specifically, increasing disease duration (i.e., type 2 diabetes) is associated with reduced processing speed, executive function, and delayed word recall over a 12-year follow-up period among middle to older aged adults (Spauwen, Kohler, Verhey, Stehouwer, & van Boxtel, 2013).

Different aspects of health impact specific fluid domains of cognitive change in middle to later life among Blacks. Specifically, better health status, in terms of fewer health conditions and increased lung functioning, is associated with improved fluid cognitive abilities (i.e., increases in working memory and perceptual speed) over time among Blacks. These results reaffirm the idea that within-group studies may better advance our understanding of cognitive aging among Blacks, given that solely looking at between-group differences does not completely explain race differences (Whitfield et al., 2008). Although between-group comparisons have contributed to the understanding of race differences in both the level and rate of change in cognition; nevertheless, the sources of variability within Blacks may be accounting for the observed racial differences in cognitive burden over time. Therefore, in order to address the cognitive health disparities that older Blacks experience, we need to focus on improving the health burden disproportionately affecting Blacks. By doing so, we may better understand the complexity of sources of individual differences in cognitive aging among older Black adults, which may further generalize to other populations.

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