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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: Appl Neuropsychol Adult. 2020 Oct 14;29(5):993–1002. doi: 10.1080/23279095.2020.1832095

Cognitive reserve attenuates the association between HIV serostatus and cognitive performance in adults living in the Deep South

Caitlin N Pope 1, Pariya L Fazeli 2, David E Vance 2, Sylvie Mrug 3, Karlene K Ball 3, Despina Stavrinos 3
PMCID: PMC8044258  NIHMSID: NIHMS1660211  PMID: 33054407

Abstract

Cognitive reserve has shown evidence of mitigating HIV-related effects on cognition in people living with HIV (PWH). In a sample of adults residing in the Deep South, an underrepresented subgroup in the neuroAIDS literature, we assessed the association between HIV serostatus and age on processing speed, visual attention, executive function, and episodic memory and the attenuating effect of cognitive reserve. Adults (n = 138; 72 PWH; Mage = 58.7 years, SD = 7.9 years; 75% non-White race) were recruited from a university clinic and the community. Verbal abilities served as a proxy for cognitive reserve. Regressions accounting for race, alcohol usage, and depressive symptoms were conducted for each cognitive outcome. Indirect effects were tested using the PROCESS macro. Being HIV seropositive was associated with worse executive function (b = -1.04, SE = 0.38, p = .007) and episodic memory (b = -39.94, SE = 12.54, p = .002) performance. Every year of age above the mean and non-White race was associated with worse cognitive performance (ps < .05). The addition of cognitive reserve to the model attenuated the HIV serostatus associations with executive function (BC 95% CI -0.770, -0.001) along with most associations between race and cognitive outcomes. Age associations remained for all cognitive outcomes (ps < .05). Findings highlight the importance of including verbal ability proxies of cognitive reserve when assessing cognition in PWH. Highlighting modifiable cognitive processes, such as cognitive reserve, will further the development of targeted cognitive interventions in this at-risk population.

Keywords: HIV/AIDS, aging, Cognitive Reserve, Executive Function, Episodic Memory

Introduction

Currently, there are more than 1 million people with HIV (PWH) in the United States (U.S.) and dependent areas (Centers for Disease Control and Prevention [CDC], 2020). In 2018, approximately 51% of the U.S. and dependent areas HIV disease population were aged 50 and older (CDC, 2020), and this number is expected to increase to 70% by 2030 (Wing, 2017). This shift in the population demographic is largely due to advancements in medicine, including antiretroviral therapy (ART; Wing, 2016). As PWH continue to age, investigation of the effects of normative aging and the chronicity of this disease is warranted (Sundermann et al., 2019).

Prior studies have shown that 30-50% of PWH are at risk for HIV-related cognitive changes or difficulty (HIV-associated neurocognitive disorders [HAND]; see Heaton et al., 2011; Woods, Moore, Weber, & Grant, 2009) outside of normative aging (Craik & Bialystok, 2006). Cognitive changes associated with HIV can vary in severity and remain evident when PWH are taking ART and have achieved undetectable viral load (Heaton et al., 2015; Woods et al., 2009). This is problematic for PWH as these changes may precede or exacerbate cognitive difficulties seen in normative aging (Pathai, Bajillan, Landay, & High, 2013; Sheppard et al., 2017; Woods et al., 2009). Given the persistence of these cognitive changes and that PWH continue to report cognitive difficulties in mid-life as a frequent symptom during the ART era (Greene et al., 2015), further assessment of cognitive performance, associated factors, and prevention of cognitive decline are of great importance in this at-risk population.

Among the many factors that may contribute to and explain changes in cognitive performance seen in PWH, education is of frequent interest (Ramírez, Ford, Stewart, & Teresi, 2005). Education, a key social determinant of health (Braveman, Egerter, & Williams, 2011), has been consistently associated with cognitive performance across the lifespan (Jefferson et al., 2011; Seeman et al., 2005; Zahodne et al., 2011). Education is also highly correlated with reserve, a potential compensatory mechanism for mitigating cognitive decline. Reserve is thought to serve as a protective factor, aiding individuals in compensating and coping with cognitive deficit and decline (caused by disease or injury) by both brain reserve (passive and fixed) and cognitive reserve (active and modifiable) in a synergistic manner (Satz, 1993; Satz, Cole, Hardy, & Rassovsky, 2011; Stern, 2002, 2009). Although the operational definition of cognitive reserve and what proxies best represent it have been debated (Barulli & Stern, 2013; Cabeza et al., 2018), a common proxy is verbal abilities (e.g., vocabulary knowledge, reading recognition; see Chapko, McCormack, Black, Staff, & Murray, 2017; Satz et al., 2011; Stern, 2002, 2009).

Verbal abilities are highly related to premorbid and crystallized intelligence (Blair & Spreen, 1989; Cattell, 1941; Gershon et al., 2014; Horn, 1998; Horn & Cattell, 1966; Whalley, Deary, Appleton, & Starr, 2004), years of education (Richards & Sacker, 2003), education quality (Manly, Jacobs, Touradji, Small, & Stern, 2002), and various lifespan experiences and opportunities (Jefferson et al., 2011). Additionally, within minority subgroups of older adults, verbal ability is considered a more appropriate method of measuring education as years of education may not fully account for differences associated with the quality of the education or learning environment (Crowe, Clay, Sawyer, Crowther, & Allman, 2007; Manly et al., 2002; Ramírez et al., 2005; Shavers, 2007; Zajacova & Lawrence, 2018). This is an important consideration for HIV, given that it disproportionally occurs in specific geographic regions (e.g., the Deep South; CDC, 2020), in economically disadvantaged areas, and within individuals of minority status (e.g., Black race, Hispanic ethnicity; CDC, 2020), factors also associated with constrained resources and education inequalities (Zajacova & Lawrence, 2018). Understanding how verbal abilities are associated with cognitive performance in the context of cognitive reserve is imperative not only to PWH, but also to the overall aging population. Prior research has shown that environmental constraints and being of minority status is associated with worse cognitive performance and an increased risk for Alzheimer’s disease (AD; Kiely, Anstey, & Butterworth, 2019; Parikh et al., 2011; Sisco et al., 2015; Weuve et al., 2018; Zeki Al Hazzouri et al., 2017). For example, Weuve et al. (2018) found the associations between Black race, cognitive performance, and AD risk was partially mediated by educational attainment (years of formal education), attesting to the importance of assessing cognitive reserve in the context of disparity.

There is evidence that lower cognitive reserve (typically operationalized by years of education and verbal IQ) in PWH is associated with worse cognitive performance, (Kaur, Dendukuri, Fellows, Brouillette, & Mayo, 2019; Stern, Silva, Chaisson, & Evans, 1996), HAND diagnoses (Milanini et al., 2016; Morgan et al., 2012), and inefficient compensatory activation mechanisms related to top-down attentional control networks (Chang, Holt, Yakupov, Jiang, & Ernst, 2013). Foley et al. (2012) also found that in older PWH, higher cognitive reserve in older age was a protective factor against cognitive impairment. A recent meta-analysis by Kaur et al. (2019) found that cognitive reserve studies in samples of PWH showed a strong association between cognitive reserve and cognitive performance. Whereas the overall effect as strong there was evidence of unexplained heterogeneity across studies and methodological limitations such as differences in the operationalization of cognitive reserve and inconsistencies with chosen neuropsychology test batteries.

Given the disproportionate rates of HIV/AIDS (incidence and prevalence; CDC, 2019, 2020) and underrepresentation of neuroAIDS research from the Deep South (Fazeli, Woods, & Vance, 2020) combined with inconsistencies of how cognitive reserve is associated with cognitive performance in HIV samples, more research is needed to better understand how education-related proxies of cognitive reserve are associated with cognitive performance in PWH in this particular geographical area hit hard by the HIV/AIDS epidemic. Specifically, can cognitive reserve act as a protective factor in the context of having HIV in older age? Using a sample of adults ages 50 and older, living in the Deep South, we assessed the association between HIV serostatus, age, and proxies of cognitive reserve (verbal abilities) with multiple domains of cognitive performance (processing speed, visual attention, executive function, and episodic memory). It was hypothesized that an additive effect would be seen for HIV and age after controlling for empirically identified demographic and health covariates (i.e., race, alcohol usage, and depression symptom severity; (see Green, Saveanu, & Bornstein, 2004; Thames et al., 2012; Weuve et al., 2018)), such that worse cognitive performance would be associated with being HIV seropositive (HIV+) and older age. Additionally, it was expected that higher cognitive reserve would be associated with better cognitive performance and would attenuate the associations between cognitive performance with HIV serostatus and age.

Methods

Participants and Procedure

Adults aged 50 and older, with and without HIV, were recruited from a metropolitan area in the Deep South. PWH were recruited via targeted flyers at a university-affiliated HIV clinic and HIV- controls were recruited from a sample of community-dwelling adults, through a self-selection recruitment method. Given 59% (n = 81) of the sample were recruited for a previous study, (see Fazeli, Woods, Pope, Vance, & Ball, 2017 for further detail on the parent study), all participants met the self-report inclusion criteria of: (1) if having a HIV diagnosis being a current patient at the university affiliated HIV clinic, (2) being aged 50 and older, (3) having a stable address (e.g., not homeless), (4) being able to speak and understand English, (5) having no self-reported mental impairments (e.g., Alzheimer’s disease, dementia, or intellectual disability), (6) not being legally blind or deaf, (7) not currently undergoing radiation or chemotherapy, (8) no history of a brain trauma with loss of consciousness greater than 30 min, (9) no neurological problems (e.g., schizophrenia, bipolar disorder, migraines, history of stroke, epilepsy, or history of seizures), (10) not having untreated hypertension, (11) no intracranial metal plates, implants, or biomedical devices, and (12) being right-handed (see Fazeli et al., 2017). The current sample consisted of 138 adults, 72 PWH and 66 HIV seronegative (HIV-) comparisons.

The procedure was reviewed and approved by a university Institutional Review Board. Participants were screened via telephone for eligibility and recruited from March 2015 to January 2017. Eligible participants were scheduled for an appointment and informed consent was obtained by a trained research assistant. The appointment lasted no longer than 2.5 hours and participants were offered a break to minimize fatigue or boredom. Participants were administered self-report questionnaires and computerized cognitive testing and compensated for their participation.

Materials

Demographics

Demographics questionnaire.

Participants self-reported pertinent sociodemographic information such as age (measured in years), gender (0 = ‘female’, 1 = ‘male’), race (1 = ‘White’, 2 = ‘Black’, 3 = ‘Other’), and years of formal education (measured in years). Socioeconomic status (SES) was measured by annual household income before taxes (1 = ‘0–10,000 dollars’, 2 = ‘10,001–20,000 dollars’, 3 = ‘greater than 20,000 dollars’). Alcohol usage was confirmed by having participants self-report to a single question ‘How many drinks containing alcohol do you have on a typical day when you are drinking?’ (0 = ‘no drinks/not applicable’, 1 = ‘one to two’, 2 = ‘three to four’, 3 = ‘five to six’, 5 = ‘ten or more’). PWH self-reported HIV characteristics such as HIV diagnosis (0 = HIV-, 1 = HIV+), estimated years with HIV, and viral load. Current CD4 cell count, Nadir CD4 cell count, and currently being on ART was obtained from clinic records.

Cognitive Performance Battery

Useful Field of View (UFOV®) task.

The UFOV® task was administered to measure visual attention processes through three subtests: 1) stimuli identification, 2) divided attention, and 3) selective attention (Ball & Owsley, 1991). A score was generated for each subtest, which represents the speed in milliseconds (ms) that the individual obtains 75% accuracy of the stimuli presented. Scores ranged from 16.7–500 ms and were summed across the three subtests to derive an overall score of visual attention (UFOV® Total Score; Edwards et al., 2005). Scores were subtracted from the total possible score of 1500 ms so that higher values indicated better performance.

The NIH Toolbox – Cognition Battery (NIHTB-CB) – Computer Version.

The NIHTB-CB is a comprehensive but brief computerized battery (approximately 30 minutes to complete) appropriate for ages 3 to 85 (Gershon et al., 2013; Weintraub et al., 2014). Four cognitive domains were used: processing speed (Pattern Comparison Test), episodic memory (Picture Sequence Memory Test), working memory (List Sorting Test), and executive function (inhibitory control - Flanker Test, cognitive flexibility - Dimensional Change Card Sort [DCCS] Test). Raw accuracy scores were reported for the Pattern Comparison Test and List Sorting Test, computed scores adjusting for reaction time were reported for the Flanker Test and DCCS Test, and computer adaptive testing for the Picture Sequence Memory Test (Fazeli et al., 2017; Slotkin et al., 2012). To account for the multiple executive function measures, the individual test scores from the Flanker Test, DCCS Test, and List Sorting Test were converted to z-scores and summed into an executive function score. On all NIHTB outcomes, higher values indicated better cognitive performance.

Cognitive Reserve

The NIHTB-CB was also used to measure verbal abilities: vocabulary knowledge (Picture Vocabulary Test) and word reading recognition (Oral Reading Recognition Test). Computer adaptive test scores were reported for verbal abilities (Fazeli et al., 2017; Slotkin et al., 2012) which were converted to z-scores and summed into a cognitive reserve score. A higher value indicated better verbal ability.

Depression Symptom Severity

Beck Depression Inventory-II (BDI-II).

The BDI-II is comprised of 21 questions that assessed the presence and severity of depression symptom severity over the preceding two weeks, including the day of administration (Beck, Steer, & Brown, 1996). Each question had four levels (0 to 3) that increased with severity for that specific symptom (e.g., worthlessness). The BDI-II has been psychometrically validated in older adult samples with evidence of high convergent and discriminate validity (Dozois, Dobson, & Ahnberg, 1998; Segal, Coolidge, Cahill, & O'Riley, 2008). The questions were summed to create a total depression symptom severity score ranging from 0 to 63, (α = .87). Higher values indicated more severe depression symptoms.

Statistical Analysis

All analyses were conducted using SPSS 25 (IBM Corp., 2017) with statistical significance denoted by p < .05.

Descriptive Analyses

Descriptive characteristics were assessed for the entire sample. HIV serostatus group differences were conducted on demographic variables (t-test for continuous variables, chi-square and likelihood ratio test for categorical variables, and Mann-Whitney U test for ordinal variables). Group differences were also conducted on cognitive reserve proxies (Picture Vocabulary Test and Oral Reading Recognition Test) and cognitive performance tests (Pattern Comparison Test, UFOV® Total Score, Flanker Test, DCCS Test, List Sorting Test, and Picture Sequence Memory Test). Cohen’s d (Cohen, 1988) was calculated to assess the effect size of the mean difference between PWH and the HIV- comparison group. Of the variables of interest for analyses, only 1% of the total data points were missing. Specifically, missing data points were found on the following variables: BDI depression symptom severity score (n = 3), UFOV® Total Score (n = 2), DCCS test (n = 1), Oral Reading Recognition Test (n = 4), and the Picture Vocabulary Test (n = 5). Participants with any missing values were more likely to be of younger age (p = .006), African American race (p = .013), and had worse performance on the DCCS Test (p = .021) and Oral Reading Recognition Test (p = .047).

Regression Analyses

A series of hierarchical linear regressions were conducted for each cognitive performance outcome (processing speed, visual attention, executive function, and episodic memory). In step one, demographic and health covariates (race, depression symptom severity, and alcohol usage) were added simultaneously to the model. In step two, HIV serostatus and age (mean-centered to aid in interpretability) were added to the model to assess the additive effect on cognitive performance outcomes. Lastly, in step three, cognitive reserve was added to the model to investigate the attenuating role of cognitive reserve on the associations between HIV serostatus and age with cognitive performance. The PROCESS macro (Version 3.4; Hayes, 2019) was used to test the mediating effect of cognitive reserve. Bootstrapping with 5,000 resamples was used to calculate bias-corrected 95% confidence intervals (Preacher & Hayes, 2008) for the indirect effect. Unstandardized beta estimates (b) and standard errors (SE) are reported for all model independent variables. Percent of variance explained by the model was measured by ΔR2 and total model R2. Missing data were handled for regression analyses by listwise deletion, yielding a total sample size of n = 130 for each model.

Results

Descriptive Analyses

The total sample age ranged from 50.1 to 87.6 (M = 58.7 years, SD = 7.9 years), was 52.9% (n = 73) male, and 74.6% (n = 103) African American race (see Table 1). The majority of the sample (n = 92, 67.2%) reported an annual income of $20,000 dollars or less before taxes and an average of 13.3 years of education (SD = 2.2, range 5 to 20 years). PWH were on average five years younger, reported two years less of formal education, were more likely to be African American, and reported less household income before taxes when compared to the HIV- group (ps < .05). Regarding HIV characteristics, the estimated median years with HIV was 16 years [IQR = 10, 21]. The majority of the sample had undetectable viral load (90.4%), was on ART (94.4%), had a current median CD4 of 655 [IQR = 418.25, 898.75], and a median Nadir CD4 of 66 [IQR = 22, 321.50].

Table 1.

Sample characteristics.

Total Sample (n = 138) PWH (n = 72) HIV− (n = 66)
Variable M (SD) M (SD) M (SD) t df p
Age (years) 58.65 (7.86) 56.20 (4.44) 61.33 (9.73) 3.92 89.19 < .001
Education (years)a 13.28 (2.24) 12.31 (1.81) 14.33 (2.21) 5.88 125.82 < .001
BDI-II 8.61 (7.89) 10.85 (8.47) 6.14 (6.39) −3.66 129.13 < .001
% (n) % (n) % (n) χ2 df p
Gender (male) 52.9 (73) 58.3 (42) 47.0 (31) 1.79 1 .182
% (n) % (n) LR df p
Race 14.14 2 .001
 African American 74.6 (103) 87.5 (63) 60.6 (40)
 White 24.6 (34) 12.5 (9) 37.9 (25)
 Other 0.7 (1) 0 (0) 1.5 (1)
% (n) % (n) % (n) U p
Household Income (before taxes) a 992.50 < .001
 0-10,000 36.2 (50) 51.4 (37) 19.7 (13)
 10,001-20,000 30.4 (42) 41.7 (30) 18.2 (12)
 > 20,000 32.6 (45) 5.6 (4) 62.1 (41)
Alcohol Usage (per day)b 2323.0 .805
 No drinks/not applicable 44.2 (61) 44.4 (32) 43.9 (29)
 1 to 2 42.8 (59) 40.3 (29) 45.5 (30)
 3 to 4 10.9 (15) 12.5 (9) 9.1 (6)
 5 to 6 2.2 (3) 2.8 (2) 1.5 (1)
HIV Characteristics Median [IQR]
Estimated years with HIV -- 16 [10, 21] --
Current CD4 -- 655 [418.25, 898.75] --
Nadir CD4 c -- 66 [ 22, 321.50] --
% (n)
Viral load (% undetectable) 90.4 (66)
ART (% currently taking) 94.4 (68)
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Note. PWH = people with HIV; HIV- = HIV seronegative; BDI-II = Beck Depression Inventory-II, ART = Antiretroviral therapy.

a

Income missing for one PWH.

b

Self-reported ‘How many drinks containing alcohol do you have on a typical day when you are drinking?’

c

n = 69.

When assessing depression symptom severity, PWH reported greater severity (M = 10.85) when compared to the HIV- group (M = 6.14, p < .001). Alcohol usage was reported in both groups with PWH reporting more instances of drinking three to four and five to six drinks on a typical day while consuming alcohol when compared to the HIV- group, although this difference was not statistically significant (p = .805). Regarding cognitive performance, an intermediate effect size was found on the Flanker Test (d = 0.52), DCCS Test (d = 0.55), and Picture Sequence Memory Test (d = 0.54). Intermediate effect sizes were also found for cognitive reserve proxies: Picture Vocabulary Test (d = 0.65) and Oral Reading Recognition Test (d = 0.65). These findings suggest, unadjusted, PWH have significantly lower scores, or worse performance, compared to the HIV- group (see Table 2) on tests of inhibitory control, cognitive flexibility, episodic memories, and verbal abilities. No significant group differences were seen on the Pattern Comparison Test, UFOV® Total Score, or List Sorting Test.

Table 2.

HIV serostatus group differences on cognitive performance and cognitive reserve.

Total sample PWH HIV-
Variable M (SD) M (SD) M (SD) t df p d
Cognitive Performance
  Processing Speed
    Pattern Comparison Test 44.67 (11.12) 43.75 (10.68) 45.67 (11.58) 1.01 136 .314 0.17
  Visual Attention
    UFOV® Total (ms)* 1056.36 (256.92) 1040.45 (279.67) 1073.74 (230.44) 0.75 134 .452 0.13
  Executive Function
    Flanker Test 7.42 (1.18) 7.14 (1.29) 7.73 (0.96) 3.08 130.31 .003 0.52
    List Sorting Test 14.25 (2.99) 13.85 (3.26) 14.68 (2.62) 1.65 136 .101 0.28
    DCCS Test 6.18 (1.55) 5.79 (1.66) 6.61 (1.31) 3.23 131.75 .002 0.55
  Episodic Memory
    Picture Sequence Memory Test 417.91 (71.31) 399.82 (62.79) 436.84 (75.16) 3.09 131 .002 0.54
Cognitive Reserve Proxies
  Vocabulary Knowledge
    Picture Vocabulary Test 1610.94 (252.46) 1533.31 (240.81) 1689.74 (240.91) 3.75 131 <.001 0.65
  Word Reading Recognition
    Oral Reading Recognition Test 1939.04 (363.46) 1828.01 (361.93) 2053.44 (330.30) 3.76 132 <.001 0.65
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Note. UFOV® = Useful Field of View; DCCS = Dimension Change Card Sorting.

*

Score subtracted from the total possible score of 1500 ms so that higher values indicate better cognitive performance.

Regression Analyses

In step one of the model, race associations with all cognitive performance outcomes were statistically significant (ps < .05; Table 3). Being non-White race was associated with worse performance on tests of processing speed, visual attention, executive function, and episodic memory. No statistically significant associations were seen for depression symptomology or alcohol usage with any of the cognitive performance outcomes (ps > .05). In step two of the model, the addition of HIV serostatus and age revealed an additive effect for executive function and episodic memory. For every additional year of age, above the average of the total sample, there was a 0.07 and 2.11-point reduction on executive function and episodic memory test scores, respectively. For HIV serostatus, being HIV+ was associated with a 0.72 and 34.54-point reduction on executive function and episodic memory test scores, respectively. For tests of processing speed and visual attention, older age was associated with worse performance, but the associations with HIV serostatus were not statistically significant (ps > .05). The associations between all cognitive performance outcomes and race remained statistically significant, with worse performance evident for those of non-White race (ps < .05).

Table 3.

Hierarchical linear regression models.

Processing Speed Visual Attention Executive Function Episodic Memory
ΔR2 b (SE) p ΔR2 b (SE) p ΔR2 b (SE) p ΔR2 b (SE) p
Step 1 .06 .055 .12 .002 .17 < .001 .18 < .001
  Race (ref = White) −4.59 (2.00) .024 −170.37 (45.63) < .001 −1.85 (0.37) < .001 −62.00 (12.34) < .001
  Depression Symptomology −0.06 (0.12) .605 5.04 (2.80) .074 −0.01 (0.02) .600 0.83 (0.75) .271
  Alcohol Usage −1.32 (1.29) .309 32.35 (30.39) .289 −0.04 (0.24) .863 −5.82 (7.96) .466
Step 2 .18 < .001 .09 .002 .07 .003 .09 .001
  HIV serostatus (ref = seronegative) −3.29 (1.92) .089 −71.07 (46.81) .131 −1.04 (0.38) .007 −39.94 (12.54) .002
  Age (years) −0.63 (0.12) < .001 −10.09 (2.81) < .001 −0.07 (0.02) .006 −2.11 (0.76) .006
  Race (ref = White) −6.16 (1.88) .001 −191.93 (45.24) < .001 −1.86 (0.37) < .001 −61.48 (12.17) < .001
  Depression Symptomology −0.07 (.12) .569 5.42 (2.81) .056 0.00 (.02) .999 1.31 (0.74) .080
  Alcohol Usage −1.84 (1.17) .120 25.81 (29.21) .379 −0.10 (0.23) .677 −7.63 (7.62) .318
Step 3 .04 .011 .08 < .001 .13 < .001 .04 .012
  HIV serostatus (ref = seronegative) −2.39 (1.91) .212 −41.44 (45.47) .364 −0.72 (0.35) .044 −34.54 (12.45) .006
  Age (years) −0.62 (0.11) < .001 −9.87 (2.69) < .001 −0.06 (0.02) .004 −2.09 (0.74) .005
  Race (ref = White) −2.48 (2.33) .290 −71.23 (54.82) .196 −0.51 (0.43) .234 −37.05 (15.26) .017
  Depression symptomology −0.01 (0.11) .899 7.07 (2.73) .011 0.02 (0.02) .374 1.66 (0.74) .027
  Alcohol Usage −2.11 (1.15) .069 16.53 (28.02) .556 −0.20 (0.21) .356 −9.89 (7.50) .190
  Cognitive Reserve 1.84 (0.72) .011 60.18 (16.82) < .001 0.67 (0.13) < .001 12.32 (4.82) .012
Total R2 .28 .28 .38 .31
n 130 129 130 126
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Notes. Processing speed measured by NIHTB-CB Pattern Comparison Test; Visual Attention measured by UFOV Total Score; Executive Function measured by a standardized composite score of NIHTB-CB Flanker Test, NIHTB-CB DCCS Test, NIHTB-CB List Sorting Test; Episodic Memory measured by NIHTB-CB Picture Memory Test; Cognitive Reserve measured by NIHTB-CB Picture Vocabulary Test and NIHTB-CB Oral Reading Recognition Test. Age (years) was mean centered.

Cognitive reserve, comprised of verbal ability cognitive reserve proxies, was added to the model in step three. All associations between cognitive reserve and cognitive performance outcomes were statistically significant (ps < .05). Greater cognitive reserve was associated with an increase test score performance of 1.84 for processing speed, 60.18 for visual attention, 0.67 for executive function, and 12.32 for episodic memory. After the addition of cognitive reserve to both the executive function and episodic memory models, HIV serostatus estimates were reduced, but remained statistically significant. Bootstrapping with 5,000 resamples to calculate bias-corrected 95% confidence intervals was used to test for potential mediation (Preacher & Hayes, 2008). The indirect effect for executive function was statistically significant (b = -0.33, Boot SE = 0.20, BC 95% CI -0.770, -0.001) whereas the indirect effect for episodic memory was not (b = -5.40, Boot SE = 3.64, BC 95% CI -13.420, 0.696). Age association estimates remained consistent in direction, magnitude, and statistical significance after the addition of cognitive reserve to the model. No evidence of mediation with bootstrapping was evident for any of the age models. Additionally, indirect effects for race with cognitive outcomes, except for processing speed (b = -0.33, Boot SE 0.15, BC 95% CI -0.590, 0.017), were statistically significant with bias-corrected bootstrapping (bias-corrected confidence intervals did not contain 0). This suggests that after accounting for cognitive reserve, race no longer was a statistically significant predictor on most domains of cognition.

Accounting for cognitive reserve in step three significantly increased the percent of accounted variance for every cognitive performance outcome. The full model in step three (the addition of cognitive reserve to the model) accounted for 28% of the variance in processing speed (ΔR2 = .04, p = .011), 28% of the variance in visual attention (ΔR2 = .08, p < .001), 38% of the variance in executive functioning (ΔR2 = .13, p < .001), and 31% of the variance in episodic memory (ΔR2 = .04, p = .012).

Discussion

Understanding the influences of social determinants of health within samples of PWH from minority groups and disadvantaged areas, such as the Deep South (Fazeli et al., 2020), is of great public health interest as more PWH begin to face normative aging changes. As disease-related cognitive burden persist in the era of ART, more research into protective factors such as cognitive reserve which are associated with social determinants of health, will help to develop targeted interventions to promote successful cognitive aging (Fazeli, Moore, & Vance, 2019; Fazeli et al., 2014; Kaur et al., 2019; Vance et al., 2019). Using a sample of adults ages 50 and older living in the Deep South, an underrepresented group in the neuroAIDS literature, we investigated the associations between HIV serostatus and age on four cognitive domains: processing speed, visual attention, executive function, and episodic memory. Additionally, we assessed the attenuating influence of cognitive reserve, measured by verbal ability proxies, on the association between cognitive performance with HIV serostatus and age. After adjusting for race, depression symptom severity and alcohol usage, our findings suggested an additive effect of HIV serostatus and age on executive function and episodic memory. Age, but not HIV serostatus, associations with processing speed and visual attention were also statistically significant. The addition of cognitive reserve to the model attenuated HIV serostatus associations with executive function performance, but not age associations, which remained statistically significant and at the same magnitude.

As cognitive difficulty has changed in prevalence and presentation during the era of ART, the fact that cognitive difficulty remains persistent and highly reported among middle-aged PWH is of great concern (Greene et al., 2015; Heaton et al., 2011; Woods et al., 2009). When assessing mean cognitive differences based on HIV serostatus, unadjusted for covariates, our findings were similar in effect size to results reported in a recent meta-analysis on executive dysfunction (Walker & Brown, 2018). In particular, we found intermediate sized effects on measures of inhibition, cognitive flexibility (also referred to as set-shifting), and episodic memory for PWH in comparison to a HIV- group using the NIHTB-CB (Gershon et al., 2013) and UFOV® Total Score (Ball & Owsley, 1991), cognitive batteries not represented in the meta-analysis (Walker & Brown, 2018). Consistent performance deficits on these executive function and memory tests are important to understand as they are thought to measure cognitive processes reliant on the frontal cortex, fronto-striato-thalamic circuits, and parahippocampal cortex and hippocampus; brain areas also known to be impacted by the neuropathogenesis of HIV (Castelo, Sherman, Courtney, Melrose, & Stern, 2006; Melrose, Tinaz, Castelo, Courtney, & Stern, 2008; D. J. Moore et al., 2006; Ortega, Brier, & Ances, 2015; Rubin et al., 2016). No significant HIV serostatus differences were evident on measures of processing speed, visual attention, and working memory, which were not anticipated based on previous study findings (Heaton et al., 2011; Vance, Wadley, Crowe, Raper, & Ball, 2011; Walker & Brown, 2018). Further research is needed to fully understand the individual differences and mechanisms behind HIV-related cognitive performance, by including more comprehensive definitions and precise measures of cognitive processes (e.g., avoiding cognitive tasks that tap into multiple related cognitive processes) and inclusion of key factors such as age, comorbidities, psychosocial factors, and compensatory strategies (Walker & Brown, 2018; Woods et al., 2009). By adopting clear operational definitions, consistency of used cognitive measures and batteries, and inclusion of important factors will help provide reliable statistical estimates across HIV seropositive subsamples that will directly influence the development of targeted cognitive remediation interventions.

A recent systematic review and meta-analysis conducted by Kaur et al. (2019) found a strong effect of cognitive reserve on different cognitive outcomes (e.g., global cognitive ability, cognitive impairment) in HIV seropositive samples, similar to other cognitive reserve studies conducted with older adults with various brain pathologies and injuries (Chapko et al., 2017; Stern, 2009; Whalley et al., 2004). Importantly, there is a degree of uncertainty around these effects as they also found evidence of a large degree of heterogeneity across studies, risk of bias, and varying methodologies, making effects difficult to interpret. Extending the findings from Kaur et al. (2019), our study used verbal abilities (similar to studies reviewed in the meta-analysis) to assess how cognitive reserve may explain, and to some degree attenuate, the association between cognitive performance with HIV serostatus. From our findings, it can be interpreted, that from a cross-sectional perspective, cognitive reserve attenuated the HIV serostatus associations with executive function performance. These findings are promising as they warrant the need for further longitudinal research in diverse HIV subsamples assessing how cognitive reserve may be a protective factor against HIV-related cognitive decline. Additionally, to date, a comprehensive assessment of reserve-related factors is lacking in the neuroAIDS literature, despite research showing the utility of assessing non-education related proxies of cognitive reserve (e.g., occupational complexity, engagement in late-life leisure or mentally stimulating activities, and life experiences; see Stern, Barnes, Grady, Jones, & Raz, 2019).

Additionally, these findings can assist in the development of future interventions to sustain cognitive functioning and to prevent further decline. By increasing opportunities for education and life experiences, especially in individuals at higher risk for cognitive decline (e.g., individuals of minority race; living in geographic areas with disparity; systemic poverty and stressors – (Kiely et al., 2019; Parikh et al., 2011; Weuve et al., 2018; Zeki Al Hazzouri et al., 2017)), we may be able to equip adults with additional resources to promote cognitive functioning in the face of disease and pathology. For example, Lenehan et al. (2016), found in a sample of 459 middle-age and older adults from the Tasmanian Health Brain Project that completing an intervention of at least 12 months of part-time university education was associated with an increase in cognitive reserve (operationalized by IQ capacity and academic ability) over a 4-year period compared to the control group who received no extra education.

Lastly, after accounting for HIV serostatus and empirically relevant health covariates (race, depression symptom severity, and alcohol use – see Green et al., 2004; Thames et al., 2012; Weuve et al., 2018), age associations with processing speed, visual attention, executive function, and episodic memory were statistically significant. Age association estimates were not attenuated after the inclusion of cognitive reserve and remained in the same direction and magnitude for all cognitive performance outcomes. These cross-sectional findings further support findings of an independent (i.e., additive) effect of age and HIV serostatus on the variability of cognitive performance seen in PWH (Canizares, Cherner, & Ellis, 2014; Moore et al., 2014). Future longitudinal assessments of cognitive performance in adults aging with HIV are needed to further determine the separation between accelerated or accentuated aging in PWH and how cognitive reserve may influence this association.

Strengths and Limitations

The current study was strengthened by the focus on PWH from the Deep South given the disproportionate amount of neuroAIDS research on this subgroup hit hard by the HIV/AIDS epidemic (CDC, 2019; Fazeli et al., 2020) in comparison to other areas in the US (e.g., Southern California). The current sample is representative of the race and gender composition of HIV cases in the Southern US (CDC, 2019). Additionally, the use of the NIHTB-CB (Gershon et al., 2013), a comprehensive but brief computerized battery (approximately 30 minutes to complete) appropriate for ages 3 to 85, increases the ability for comparisons of cognitive findings across studies and within meta-analyses, a notable methodological limitation in this area of research (Kaur et al., 2019). Lastly, the use of verbal ability proxies allowed for a more nuanced assessment of cognitive reserve over the commonly used proxy, years of education (Kaur et al., 2019), which may not fully account for key differences associated with disparity (Crowe et al., 2007; Manly et al., 2002; Zajacova & Lawrence, 2018).

Like other studies, there are limitations to consider that may influence the generalizability of the results. First, the cross-sectional design limited the interpreted findings to associations with cognitive performance at one time point. Given cognitive reserve is thought to accumulate over time (Barulli & Stern, 2013; Chapko et al., 2017), cognitive performance over multiple time points with covariates at different times points of development will provide a more comprehensive trajectory of the attenuating influence of cognitive reserve on the association between HIV serostatus and cognitive performance. Second, the study relied on a pre-existing association between HIV serostatus and brain dysfunction and did not incorporate any biomarkers of brain reserve (e.g., neuroimaging, genetic markers). This prevented the ability to assess the protective role of cognitive reserve in the presence of structural or functional brain abnormalities. Third, the HIV- comparison group was recruited through self-recruitment in the community using targeted flyers along with self-reported inclusion criteria. Despite efforts to recruit comparable groups, the HIV- comparison group was on average older, more likely to be White race, had more years of education, and reported more yearly income before taxes compared to the HIV+ group. These differences may have further contributed to the HIV serostatus differences reported. Additionally, it was not known if these individuals lived their entire life in the Deep South or at what point in their life they began residing there. Lastly, although there was only a small percentage of missing data (1% of total data points), individuals with any missing data were more likely to be older in age, African American race, and have worse performance on measures of cognitive flexibility and reading recognition. From this pattern of missingness, it could be assumed that individuals who had missing data may have had a harder time completing the cognitive battery or lower reserve (both verbal tests had missing data points), making the overall estimate of cognitive performance more conservative as individuals with incomplete cases were not represented in the regression analyses because of listwise deletion.

Conclusion and Implications

Cognitive reserve, a potential compensatory mechanism for mitigating cognitive decline associated with brain injury or pathology, was investigated as a protective factor to HIV-related cognitive variability in a sample of individuals residing in the Deep South. Although cognitive reserve has been studied in HIV samples (Kaur et al., 2019), it remains to be fully understood exactly how cognitive reserve is accumulated, it’s role in pathological cognitive aging, and whether it can be modified to sustain and potentially improve cognitive performance, especially in subgroups of PWH who have predisposition to have further difficulty with cognitive aging. These findings highlight the importance of including verbal ability proxies of cognitive reserve when assessing cognitive performance in HIV samples as it attenuates the association between HIV serostatus and cognitive performance. Highlighting this modifiable cognitive process will further the development of targeted interventions on cognitive processes such as executive function in disadvantaged groups of older adults that can be designed to incorporate modifying principles of cognitive reserve (Vance et al., 2019) to promote successful cognitive aging.

Acknowledgments

This article was supported by funding from the National Institute on Aging (K99 AG 048762-01 PI: Fazeli [A Novel Neurorehabilitation Approach for Cognitive Aging with HIV]; P30 AG 022838-11 PI: Ball [Edward R. Roybal Center pilot grant]), National Institute of Mental Health (R25 MH108389 [Sustained Training on Aging and HIV Research; STAHR]), and the U.S. Department of Transportation Federal Highway Administration (FWHA) Dwight D. Eisenhower Graduate Fellowship Program.

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