Degree of Polypharmacy and Cognitive Function in Older Women with HIV
Publication: AIDS Research and Human Retroviruses
Volume 38, Issue Number 7
Abstract
The number of people with HIV (PWH) experiencing age-associated comorbidities including those treated with medications and cognitive impairment is increasing. We examined associations between polypharmacy and cognition in older women with HIV (WWH) given their vulnerability to this comorbidity. Cross-sectional analysis capitalizing on Women's Interagency HIV Study data collected between 2014 and 2017. WWH meeting the following criteria were analyzed: age ≥50 years; availability of self-reported non-antiretroviral therapy (ART) medications data; and neuropsychological data. The number of non-ART medications used regularly in the prior 6 months was summed. Polypharmacy was categorized as none/low (0–4), moderate (5–9), or severe (≥10). Multivariable linear regression analyses examined polypharmacy-cognition (T-score) associations in the total sample and among virally suppressed (VS; < 20 copies/mL)-WWH after covariate adjustment for enrollment site, income, depressive symptoms, substance use (smoking, heavy alcohol, marijuana, crack, cocaine, and/or heroin), the Veterans Aging Cohort Study index (indicators of HIV disease and organ system function, hepatitis C virus serostatus), ART use, nadir CD4 count, and specific ART drugs (efavirenz, integrase inhibitors). We included 637 women (median age = 55 years; 72% Black). Ninety-four percent reported ART use in the past 6 months and 75% had HIV RNA <20 copies/mL. Comorbidity prevalence was high (61% hypertension; 26% diabetes). Moderate and severe polypharmacy in WWH were 34% and 24%. In WWH, severe polypharmacy was associated with poorer executive function (p = .007) and processing speed (p = .01). The same pattern of findings remained among VS-WWH. Moderate polypharmacy was not associated with cognition. Moderate and severe polypharmacy were common and associated with poorer executive function and processing speed in WWH. Severe polypharmacy may be a major contributor to the persistence of domain-specific cognitive complications in older WWH above and beyond the conditions that these medications are used to treat.
Introduction
Access and adherence to modern antiretroviral therapy (ART) has led people with HIV (PWH) to achieve life expectancies similar to those without HIV.1 Consequently, ∼50% of PWH in the United States are 50 years of age and older; this number is projected to exceed 70% by the year 2030.2,3 The “graying of the HIV epidemic” has led to emergence of new public health and clinical care challenges associated with survival to older ages. One of the primary challenges has been addressing a marked increase in age-associated comorbidities and conditions in PWH 50 years of age and older, including multimorbidity,4 polypharmacy, and cognitive and functional impairment.5,6 Notably, the proportion of PWH with multimorbidity, defined as two or more comorbidities, has nearly tripled since 2000.7 Accordingly, use of multiple medications is increasingly common among aging PWH,8 particularly among those with multimorbidity.
Polypharmacy, often defined as use of five or more medications,9 is associated with many adverse conditions in the general aging population, including mortality, falls, global cognitive impairment, and increased hospitalizations, length of stay, and number of readmissions.10–14 In the geriatric literature, polypharmacy is a strong predictor of serious adverse drug events and drug–drug interactions. Moreover, increasing numbers of medications are associated with proportionally greater risks of harm,15,16 leading some to further characterize use of ≥10 medications as “excessive,” “major,” or “severe” polypharmacy or “hyperpolypharmacy”.15,17–20
Older individuals are at particularly high risk for adverse medication effects due to age-associated metabolic changes such as decreased renal and hepatic function and altered body composition, including increased body fat and loss of lean body mass.21–23 These effects have even greater significance for older PWH, who are more likely to suffer from age-associated changes than people without HIV.24 At present, little is known about the effects of polypharmacy on comorbidities in PWH ≥50 years of age.25,26
Among PWH, polypharmacy is highly prevalent (up to 96% among older PWH27) and often occurs prematurely, with greater lifetime exposure to multiple mediations. Among PWH and persons without HIV, a dose–response relationship was demonstrated between non-ART polypharmacy and adverse outcomes such as hospitalization and mortality with no interaction by HIV-serostatus.28 However, few studies evaluating polypharmacy and its consequences including cognitive impairment in PWH focus on women. In the general population, women appear to be at higher risk for adverse drug-related events compared with men, and may require lower medication dosing due to pharmacokinetic and pharmacodynamic sex differences leading to lower clearance rates.29–31 These sex differences may assume even greater importance as women age.
Cognitive impairment is a known adverse consequence of polypharmacy in the general aging population; yet relatively little is known about the impact of polypharmacy on cognition in PWH, and even less in older WWH. The few studies conducted to date have been in samples of mostly men with HIV, and indicate that polypharmacy is associated with lower global cognitive function32,33 as well as with lower learning, memory, and verbal fluency.33
Thus, the primary aim of this analysis was to examine the potential cognitive burden of polypharmacy among older WWH (≥ 50 years of age). Our general hypothesis was that polypharmacy would have broad negative associations with cognitive performance above and beyond medical and psychiatric comorbidities. We further sought to determine whether the pattern of associations would parallel previous findings in older people (i.e., associate only with learning, memory, and executive function)34,35 or findings seen in WWH across a wide age range (i.e., associate only with learning, fluency, speed, attention/working memory, and motor function).36
Methods
Participants
All participants were enrolled in the Women's Interagency HIV Study (WIHS), a prospective multisite cohort study of WWH and HIV-negative women (women without HIV).37–39 In brief, study enrollment initially occurred at six U.S. sites (Brooklyn, NY; Bronx, NY; Chicago, IL; Washington DC; Los Angeles, CA; and San Francisco, CA) in three waves: 1994–95, 2001–02, and 2011–12. In 2013, the WIHS closed its Los Angeles site and added four southern U.S. sites: Atlanta, GA; Chapel Hill, NC; Miami, FL; and Birmingham, AL/Jackson, MS. WIHS methods and cohort characteristics have been described previously.37–39
Participants completed semiannual visits, which included physical examinations, biospecimen collection, and a face-to-face interview for the collection of clinical, behavioral, and sociodemographic characteristics. Neuropsychological (NP) assessments were integrated into WIHS visits and collected every 2 years beginning in 2009 for the initial six study sites and beginning in 2013 for the southern sites.40 Participants included in this analysis were WWH ≥50 years of age, who had available data on self-reported medication between 2014 and 2017, and completed one NP assessment during the 2014 and 2017 time period when polypharmacy was computed.
Measures
Medication assessments
At each WIHS visit, participants were asked to recall non-ART medications taken in the prior 6 months. These medications were summarized only for data collected between 2014 and 2017. Polypharmacy, the number of non-ART medications used regularly in the past 6 months was quantified and categorized as none/low (0–4 medications), moderate (5–9 medications), or severe (≥10 medications). In addition, non-ART medication data were reviewed with specific drugs categorized as neurocognitive acting effects (NC-AE) if there were known adverse cognitive effects.36,41 NC-AE medications were quantified as none, one, or more than one.
These medications were also categorized (any vs. none) into specific drug classes (opioids, anticonvulsants, anticholinergics, antianxiety, antihistamine, gastrointestinal agents, beta-blockers, antidepressants, antipsychotics, and muscle relaxants). Finally, as in our previous study,36,41 medications reported by participants were categorized by whether they had anticholinergic properties according to the Anticholinergic Risk Scale.42 The purposes of this additional drug categorization was to better understand the drug classes that participants were most commonly prescribed as well as to be used in secondary analyses when polypharmacy-cognition associations were present in WWH.
Cognitive function
Global and domain-specific NP cognitive function was assessed every 2 years. We chose the last NP assessment that occurred between 2014 and 2017 so it could be matched in time with the timeframe in which polypharmacy was determined. The comprehensive NP battery included the following: Hopkins Verbal Learning Test-Revised (HVLT-R), Letter-Number Sequencing, Trail Making (TMT), Stroop Test, Symbol Digit Modalities Test (SDMT), Controlled Oral Word Association Test (COWAT), Category Fluency Test (Animals), and Grooved Pegboard (GPEG).
Performance on these tests were used to assess the following seven domains: learning (total learning across HVLR-T trials), memory (delayed free recall on HVLT-R), attention/working memory (total correct on LNS control and experimental conditions), processing speed (total correct on SDMT, time to completion on Stroop Trial 2), executive function (time to completion on TMT Part B and Stroop Trial 3), fluency (total correct on COWAT and category fluency), and motor skills (total time to completion for each hand on GPEG). All timed outcomes were log transformed to normalize the data distributions and reverse scored so that higher scores always reflected better performance.
As in our previous analyses,40,43 demographically adjusted T-scores (M [mean] = 50; SD [standard deviation] = 10) were created for each test and these T-scores were then used to create domain-specific T-scores and a global T-score. Domains measured by more than a single test were derived by averaging the T-scores. If only one test in a domain was completed, the T-score for that test outcome was used. A global NP score was also derived for individuals with T-scores on at least four of the seven cognitive domains by averaging the T-scores across domains. Impairment on a domain or globally was defined using a T-score cutoff of 1 standard deviation (T ≤ 40).
Covariables
The following variables were identified as confounders (related to polypharmacy and cognition but does not exist in the causal pathway): depressive symptoms assessed using the Center for Epidemiologic Studies Depression Scale (CES-D, score ≥16 indicating depressive symptoms),44 smoking (current, former, and never), heavy alcohol use (>7 drinks per week45), recent marijuana use (yes vs. no), recent crack, cocaine, and/or heroin use (yes vs. no), and the Veterans Aging Cohort Study (VACS) index, which combines routinely monitored indicators of HIV disease (age, current CD4 count, and HIV viral load) and organ system function indicators (hemoglobin, Fibrosis-4, estimated glomerular filtration rate), as well as hepatitis C virus serostatus.46,47
HIV-specific variables included as confounders were nadir CD4 count (continuous), ART use (yes vs. no), and ART medications often associated with cognition, specifically efavirenz and integrase inhibitors dolutegravir, elvitegravir, and raltegravir.48–50 Each ART medication was treated as a separate binary variable.
Statistical analyses
Univariable and multivariable linear and logistic regression analyses were used to examine the association between degree of polypharmacy (moderate vs. none/low and severe vs. none/low) and cognitive burden in the total sample and among virally suppressed (VS)-WWH (HIV RNA <20 cp/mL). Separate models were created for each cognitive domain and for global NP function. Linear regression analyses were used when absolute T-score was the outcome, whereas logistic regression analyses were used when impairment was the outcome.
All multivariable models adjusted for study site, annual household income, depressive symptoms, smoking, heavy alcohol use, current self-reported marijuana and crack, cocaine, and/or heroin use, the VACS index, nadir CD4 count, ART use, and efavirenz, dolutegravir, elvitegravir, and raltegravir use. Based on the pattern of polypharmacy-cognition associations, secondary multivariable linear regression analyses were conducted to determine whether the associations were strengthened when restricting the medication count to NC-AE medications including those with anticholinergic-acting properties. All statistical analyses were conducted in SAS version 9.4 (SAS Institute, Inc., Cary, NC). Results were considered significant at p < .05 (two-sided).
Results
A total of 637 women were included in the analysis. Table 1 includes sociodemographic, behavioral, and clinical characteristics of the sample. Among the total sample of WWH, 93% reported ART use and 75% had an undetectable HIV viral load. The overall prevalence of comorbidities was as follows: 61% with hypertension, 32% with depressive symptoms, and 26% with type 2 diabetes.
| Characteristic, N (%) | Total sample (N = 637) | VS (N = 481) |
|---|---|---|
| Age, years, median (IQR) | 55 (52, 59) | 55 (52, 58) |
| Education level high school or greater | 444 (70) | 336 (70) |
| Annual household income ≤$12,000/year | 341 (54) | 248 (52) |
| WIHS site | ||
| Bronx/Manhattan | 94 (15) | 69 (14) |
| Brooklyn | 93 (15) | 67 (14) |
| Washington, DC | 86 (13) | 62 (13) |
| San Francisco | 101 (16) | 69 (14) |
| Chicago | 85 (13) | 76 (16) |
| Southern sites | 178 (28) | 138 (29) |
| Race/ethnicity | ||
| White, non-Hispanic | 108 (17) | 86 (18) |
| Black, non-Hispanic | 457 (72) | 344 (72) |
| Hispanic/Other | 69 (11) | 49 (10) |
| Current smoker | 225 (35) | 178 (37) |
| Recent cocaine, crack, or heroin use | 53 (8) | 30 (6) |
| Recent marijuana use | 103 (16) | 77 (16) |
| Recent heavy alcohol use (>7 drinks/week) | 56 (9) | 33 (7) |
| Postmenopausal statusa | 583 (91) | 434 (90) |
| Comorbidities | ||
| Hepatitis C Virus infectionb | 191 (30) | 135 (28) |
| Diabetes mellitus | 168 (26) | 134 (28) |
| Hypertension | 388 (61) | 293 (61) |
| Renal dysfunction (eGFR <60) | 117 (18) | 83 (17) |
| Depressive symptoms (CES-D ≥16) | 199 (32) | 140 (30) |
| Obesity (body mass index ≥30 kg/m2) | 273 (43) | 220 (46) |
| VACS index, median (IQR) | 33 (22, 40) | 28 (22, 38) |
| HIV disease-related characteristics | ||
| History of AIDS | 246 (39) | 171 (36) |
| Current CD4 cell count (cells/μL), median (IQR) | 645 (429, 850) | 664 (483, 880) |
| Nadir CD4 cell count (cells/μL), median (IQR) | 258 (133, 442) | 277 (158, 472) |
| Undetectable HIV RNA viral load (<20 copies/mL) | 481 (75) | 481 (100) |
| Current ART usec | 602 (94) | 465 (97) |
a
Postmenopausal status is defined by self-reported amenorrhea at two consecutive visits for women aged ≥45 years and no resumption of menses.
Recent is defined as in the past 6 months.
b
Defined as antibody positive, RNA unknown and active infection, RNA+.
c
Defined as therapy used since last visit (∼ use in the past 6 months).
ART, antiretroviral therapy; eGFR, estimated glomerular filtration rate; CES-D, Center for Epidemiologic Studies Depression Scale; VS, virally suppressed.
The prevalence of moderate and severe polypharmacy in the total sample of WWH was 34.5% and 23.9% and in VS-WWH 35.8% and 24.1%, respectively (Table 2). Approximately half of WWH (49%) were prescribed NC-AE medications with the most common being antidepressants (25%) followed by anxiolytics (13%) and opioids (12%). In addition, 31% of WWH were on NC-AE medications with anticholinergic properties. The mean T-scores in the sample were in the average range (means ∼50).
| N (%) | Total sample (N = 637) | VS (N = 481) |
|---|---|---|
| Polypharmacy (number of non-ART medications) | ||
| None/low (0–4) | 265 (42) | 193 (40) |
| Moderate (5–9) | 220 (34) | 172 (36) |
| Severe (≥10) | 152 (24) | 116 (24) |
| Neurocognitively active non-ART medications | 312 (49) | 235 (49) |
| Opioids | 76 (12) | 54 (11) |
| Anticonvulsants | 42 (7) | 34 (7) |
| Anticholinergics | 5 (1) | 3 (1) |
| Antianxiety | 81 (13) | 62 (13) |
| Antihistamine | 65 (10) | 47 (10) |
| Gastrointestinal agents | 28 (4) | 21 (4) |
| Beta-blockers | 20 (3) | 16 (3) |
| Antidepressants | 157 (25) | 121 (25) |
| Antipsychotics | 43 (7) | 30 (6) |
| Muscle relaxants | 36 (6) | 22 (5) |
| Anticholinergic-acting non-ART medications | 197 (31) | 142 (29) |
| NP test performance (T-score), M (SD) | ||
| Executive function | 48.5 (10.3) | 48.9 (10.4) |
| Processing speed | 49.0 (9.6) | 49.4 (9.5) |
| Attention/working memory | 48.5 (9.7) | 48.7 (9.8) |
| Learning | 50.1 (9.9) | 50.5 (9.7) |
| Memory | 49.6 (10.2) | 49.4 (10.3) |
| Motor | 49.6 (10.8) | 50.2 (10.6) |
| Fluency | 49.3 (9.5) | 49.7 (9.9) |
| Global NP function | 49.2 (6.4) | 49.6 (6.4) |
| NP impairment | ||
| Executive function | 121 (19) | 90 (19) |
| Processing speed | 96 (15) | 68 (14) |
| Attention/working memory | 119 (19) | 87 (18) |
| Learning | 94 (15) | 67 (14) |
| Memory | 108 (17) | 85 (18) |
| Motor | 103 (16) | 72 (15) |
| Fluency | 91 (14) | 67 (14) |
| Global NP function | 57 (9) | 41 (9) |
M, mean; SD, standard deviation.
Cognitive burden of polypharmacy
Overall, degree of polypharmacy was associated with domain-specific cognitive performance in WWH (Table 3). Specifically, in the total sample of WWH, severe but not moderate polypharmacy was associated with poorer performance on executive function, processing speed, and motor function in univariable analyses (p's < 0.05). However, in multivariable models in all WWH, severe but not moderate polypharmacy was only associated with poorer performance on executive function (B [unstandardized beta coefficient] = −2.95, SE [standard error] = 1.08, p = .00 7) and processing speed (B = −2.46, SE = 0.99, p = .01) (Fig. 1 left). The same pattern of associations were seen among VS-WWH in both univariable and multivariable models (Table 3 and Fig. 1 right).
| Moderate polypharmacy (5–9 medications) | Severe polypharmacy (≥10 medications) | |||||||
|---|---|---|---|---|---|---|---|---|
| Total sample of WWH | VS-WWH | Total sample of WWH | VS-WWH | |||||
| Domain | B (SE) | p-value | B (SE) | p-value | B (SE) | p-value | B (SE) | p-value |
| Executive function | ||||||||
| Univariable | −1.3 (0.9) | .17 | −1.5 (1.1) | .16 | −3.1 (1.0) | .003 | −3.2 (1.2) | .008 |
| Multivariable | −0.9 (1.0) | .34 | −0.9 (1.1) | .45 | −2.9 (1.1) | .007 | −2.9 (1.3) | .02 |
| Processing speed | ||||||||
| Univariable | −0.3 (0.9) | .72 | −1.1 (1.0) | .25 | −3.0 (1.0) | .002 | −3.7 (1.1) | .001 |
| Multivariable | 0.1 (0.9) | .92 | −0.6 (1.0) | .56 | −2.4 (1.0) | .01 | −2.7 (1.2) | .02 |
| Attention/working memory | ||||||||
| Univariable | −0.6 (0.9) | .52 | −1.0 (1.0) | .34 | 0.2 (1.0) | .86 | 0.2 (1.1) | .77 |
| Multivariable | −0.7 (0.9) | .47 | −0.8 (1.1) | .44 | 0.1 (1.0) | .92 | −0.1 (1.2) | .94 |
| Learning | ||||||||
| Univariable | −1.1 (0.9) | .21 | −0.4 (1.0) | .70 | 0.5 (1.0) | .64 | 1.4 (1.1) | .22 |
| Multivariable | −1.1 (0.9) | .23 | −0.1 (1.1) | .90 | 0.4 (1.0) | .70 | 1.0 (1.2) | .40 |
| Memory | ||||||||
| Univariable | 0.4 (0.3) | .69 | 0.8 (1.1) | .47 | 1.9 (1.0) | .07 | 1.9 (1.2) | .11 |
| Multivariable | 0.3 (0.9) | .72 | 0.7 (1.1) | .51 | 1.8 (1.1) | .10 | 1.4 (1.3) | .27 |
| Motor | ||||||||
| Univariable | −0.4 (1.0) | .66 | 0.2 (1.1) | .88 | −2.5 (1.1) | .02 | −2.6 (1.2) | .03 |
| Multivariable | 0.2 (1.0) | .87 | 0.9 (1.1) | .41 | −1.7 (1.1) | .13 | −1.6 (1.3) | .23 |
| Verbal fluency | ||||||||
| Univariable | −0.1 (0.9) | .89 | 0.1 (1.0) | .93 | −1.4 (1.0) | .13 | −1.1 (1.1) | .33 |
| Multivariable | 0.2 (0.9) | .79 | 0.7 (1.1) | .52 | −1.7 (1.0) | .08 | −1.4 (1.2) | .26 |
| Global NP function | ||||||||
| Univariable | −0.5 (0.6) | .39 | −0.4 (0.7) | .53 | −1.1 (0.6) | .09 | −1.0 (0.7) | .19 |
| Multivariable | −0.3 (0.6) | .64 | −0.0 (0.7) | .99 | −0.9 (0.6) | .15 | −0.9 (0.8) | .25 |
Multivariable linear models were adjusted for WIHS study city, income, depression, smoking, heavy alcohol use, recent marijuana use, recent crack, cocaine, and/or heroin use, the VACS index score, nadir CD4 count, ART use, and ART medications often associated with cognition—efavirenz and the following integrase inhibitors-dolutegravir, elvitegravir, and raltegravir. Findings significant at p < .05 are bolded.
B, unstandardized beta coefficient; NP, neuropsychological; SE, standard error; WWH, women with HIV.
When examining cognitive impairment in the total sample of WWH in multivariable models, severe polypharmacy, compared with none/low medication use, was associated with a greater odds of having impaired executive function (adjusted odds ratio [AOR] = 1.71, 95% CI [confidence interval]: 0.99–2.94, p = .05] and processing speed (AOR = 1.98, 95% CI: 1.08–3.63, p = .02]. Among VS-WWH, severe polypharmacy, compared with none/low medication use, was associated with a greater odds of processing speed impairment (AOR = 2.10, 95% CI: 0.99–4.43, p = .05); executive function (AOR = 1.47, 95% CI: 0.77–2.81, p = .24).
When restricting non-ART medication count to NC-AE medications, in multivariable models women taking more than one medication but not one medication (vs. none) was associated with poorer performance on executive function (B = −3.21, SE = 1.06, p = .002) and processing speed (B = −3.24, SE = 0.97, p < .001) in the total sample of WWH (Table 4). The same pattern of associations was seen among VS-WWH. When specifically examining NC-AE medications with anticholinergic properties, these medications were associated poorer performance on executive function (B = −2.26, SE = 0.91, p = .01) and processing speed (B = −2.07, SE = 0.84, p = .01) in the total sample of WWH. In VS-WWH, NC-AE medications with anticholinergic properties were only associated with poorer performance on executive function (B = −2.39, SE = 1.09, p = .03).
| Executive function | Processing speed | |||||||
|---|---|---|---|---|---|---|---|---|
| Total sample | VS-WWH | Total sample | VS-WWH | |||||
| B (SE) | p-value | B (SE) | p-value | B (SE) | p-value | B (SE) | p-value | |
| NC-AE | ||||||||
| 1 (vs. none) | −1.3 (1.0) | .21 | −1.5 (1.2) | .23 | −1.2 (0.9) | .19 | −1.3 (1.1) | .23 |
| 1+ (vs. none) | −3.2 (1.1) | .002 | −3.4 (1.2) | .007 | −3.2 (1.0) | <.001 | −3.0 (1.1) | .008 |
| With anticholinergic properties | ||||||||
| Any (vs. none) | −2.3 (0.9) | .01 | −2.4 (1.1) | .03 | −2.1 (0.8) | .01 | −1.5 (0.9) | .11 |
| Antidepressants (vs. none) | −2.3 (1.0) | .02 | −2.1 (1.1) | .07 | −1.7 (0.9) | .06 | −1.1 (1.0) | .30 |
| Anxiolytics (vs. none) | −3.0 (1.3) | .02 | −3.5 (1.5) | .02 | −1.4 (1.2) | .24 | −1.2 (1.4) | .38 |
| Opioids (vs. none) | −0.3 (1.4) | .80 | 0.4 (1.6) | .79 | 1.1 (1.2) | .38 | 1.4 (1.4) | .32 |
Multivariable linear models were adjusted for WIHS study city, income, depression, smoking, heavy alcohol use, recent marijuana use, recent crack, cocaine, and/or heroin use, the VACS index score, nadir CD4 count, ART use, and ART medications often associated with cognition—efavirenz and the following integrase inhibitors-dolutegravir, elvitegravir, and raltegravir. Findings significant at p < .05 are bolded.
B, unstandardized beta coefficient; NC-AE, neurocognitive acting effects.
Of the most commonly used NC-AE medications, antidepressants and anxiolytics but not opioids were associated with these cognitive domains in multivariable models (Table 4). Specifically, antidepressants were associated with poorer executive function in the total sample of WWH (B = −2.29, SE = 0.08, p = .02); similar trend in VS-WWH (B = −2.09, SE = 1.17, p = .07). Similarly, anxiolytics were associated with poorer executive function in the total sample of WWH (B = −2.99, SE = 1.29, p = .02) and among VS-WWH (B = −3.48, SE = 1.51, p = .02). Antidepressants and anxiolytics were not associated with processing speed.
Discussion
The overall prevalence of polypharmacy (≥5 medications) was high (59%) in this cohort of predominantly low-income WWH of color aged ≥50 years; however, only severe, not moderate, polypharmacy was associated with cognitive function after covariate adjustment, which included the VACS index. Among WWH, severe polypharmacy was associated with poorer executive function and processing speed. The same pattern of associations was present among VS-WWH.
The key finding in this study was that severe, but not moderate, polypharmacy was associated with poorer cognitive function in WWH. This finding is consistent with some,51,52 but not all,53,54 studies examining the degree of polypharmacy and cognition. Of note, whereas moderate (18.1%) and severe (4.7%) polypharmacy were related to poorer cognition in Rawle et al.,55 severe polypharmacy was more strongly related to poorer cognition than was moderate polypharmacy. Differences in the pattern of associations across studies could be due to differences in characteristics of the study population (e.g., age, race/ethnicity, and sex), proportion of individuals using NC-AE medications including those with anticholinergic properties, cognitive status (e.g., cognitively intact, mild, dementia), and/or in the assessments used to measure cognitive function.
Although WWH in the present analyses were aged ≥50 years, 50% of the participants were between 52 and 59 years of age, which is younger than other studies in which the mean is often ≥65 years of age.53,55 One possible explanation is that a greater degree of polypharmacy is needed to see any potential adverse effects on cognition among women in their 50s compared with their 60s. In addition, our study used a comprehensive NP test battery to assess global and domain-specific cognitive function, whereas others32,33 only measured cognition using a minimal number of standard NP tests or simply a cognitive screener such as the Mini-Mental Status Examination (MMSE).
Severe polypharmacy was related to specific cognitive domains (vs. global NP function) in WWH. In particular, severe polypharmacy was associated with poorer executive function and processing speed. These findings are in contrast to a recent cross-sectional analysis of primarily white men with HIV in which polypharmacy was found to relate to learning, memory, and verbal fluency as well as global cognitive function.33 Although differences in sociodemographics may in part contribute to the observed differences between studies, the specific medications are associated with cognition may also differ between studies. Although this study was focused on polypharmacy, we did find that the associations were strengthened when restricting to NC-AE medications, including those with anticholinergic properties.
In addition, antidepressants and anxiolytics, two of the most commonly prescribed NC-AE medications, were also associated with executive function. These findings may be specific to older WWH, as in our previous WIHS study, which included WWH ranging from 25 to 87 years of age (mean = 46.99, SD = 8.76), we did not find NC-AE medications or anxiolytics to relate to executive function or processing speed.36 Antidepressants were not examined and opioids were not associated with these cognitive domains, which parallels our findings in older WWH. This is in contrast to a cross-sectional study of white men with HIV whereby anxiolytics, antipsychotics, opioids, and antimicrobials were the most common medication classes relating to poorer cognition.33
Despite the ability to examine the degree of polypharmacy on cognition in a large sample of WWH and women without HIV aged ≥50 years, there were a number of limitations. The primary limitations were that the study was cross-sectional, precluding any inferences of causality, and that non-ART medication data was self-reported. However, studies have demonstrated high correlations between self-reported measures of medication with pharmacy prescription records in low-income older adults.56 In addition, it is difficult to disentangle the effect of medications on cognition from the diseases that those medications are used to treat. Although we adjusted for a number of factors including the VACS index, adjustment may only partially mitigate this potential bias.
Conclusions
In sum, moderate and severe polypharmacy were common in this cohort of aging WWH and were associated with poorer executive function and processing speed. Thus, as PWH age, it will become increasingly important to protect their cognitive function potentially through careful monitoring of their non-ART medications. Further research is warranted to better understand the longitudinal effects of polypharmacy on cognition above and beyond the comorbidities they are used to treat in aging WWH as well as potential medication interactions in these populations.
Acknowledgments
The authors gratefully acknowledge the contributions of the study participants and dedication of the staff at the MWCCS sites.
References
1. Samji H, Cescon A, Hogg RS, et al.: Closing the gap: Increases in life expectancy among treated HIV-positive individuals in the United States and Canada. PLoS One 2013;8:e81355.
2. Smit M, Brinkman K, Geerlings S, et al.: Future challenges for clinical care of an ageing population infected with HIV: A modelling study. Lancet Infect Dis 2015;15:810–818.
3. Wing EJ: HIV and aging. Int J Infect Dis 2016;53:61–68.
4. Althoff KN, McGinnis KA, Wyatt CM, et al.: Comparison of risk and age at diagnosis of myocardial infarction, end-stage renal disease, and non-AIDS-defining cancer in HIV-infected versus uninfected adults. Clin Infect Dis 2015;60:627–638.
5. Rodriguez-Penney AT, Iudicello JE, Riggs PK, et al.: Co-morbidities in persons infected with HIV: Increased burden with older age and negative effects on health-related quality of life. AIDS Patient Care STDS 2013;27:5–16.
6. Kong AM, Pozen A, Anastos K, Kelvin EA, Nash D: Non-HIV Comorbid Conditions and Polypharmacy Among People Living with HIV Age 65 or Older Compared with HIV-Negative Individuals Age 65 or Older in the United States: A retrospective claims-based analysis. AIDS Patient Care STDS 2019;33:93–103.
7. Wong C, Gange SJ, Moore RD, et al.: Multimorbidity among persons living with human immunodeficiency virus in the United States. Clin Infect Dis 2018;66:1230–1238.
8. Gimeno-Gracia M, Crusells-Canales MJ, Javier Armesto-Gomez F, Rabanaque-Hernandez MJ: Prevalence of concomitant medications in older HIV+ patients and comparison with general population. HIV Clin Trials 2015;16:117–124.
9. Gnjidic D, Hilmer SN, Blyth FM, et al.: Polypharmacy cutoff and outcomes: Five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes. J Clin Epidemiol 2012;65:989–995.
10. Wimmer BC, Cross AJ, Jokanovic N, et al.: Clinical outcomes associated with medication regimen complexity in older people: A systematic review. J Am Geriatr Soc 2017;65:747–753.
11. Saedder EA, Lisby M, Nielsen LP, Bonnerup DK, Brock B: Number of drugs most frequently found to be independent risk factors for serious adverse reactions: A systematic literature review. Br J Clin Pharmacol 2015;80:808–817.
12. Shah BM, Hajjar ER: Polypharmacy, adverse drug reactions, and geriatric syndromes. Clin Geriatr Med 2012;28:173–186.
13. Salazar JA, Poon I, Nair M: Clinical consequences of polypharmacy in elderly: Expect the unexpected, think the unthinkable. Expert Opin Drug Saf 2007;6:695–704.
14. Larson EB, Kukull WA, Buchner D, Reifler BV: Adverse drug reactions associated with global cognitive impairment in elderly persons. Ann Intern Med 1987;107:169–173.
15. Maher RL, Hanlon J, Hajjar ER: Clinical consequences of polypharmacy in elderly. Expert Opin Drug Saf 2014;13:57–65.
16. Rambhade S, Chakarborty A, Shrivastava A, Patil UK, Rambhade A: A survey on polypharmacy and use of inappropriate medications. Toxicol Int 2012;19:68–73.
17. Nishtala PS, Salahudeen MS: Temporal trends in polypharmacy and hyperpolypharmacy in Older New Zealanders over a 9-Year Period: 2005–2013. Gerontology 2015;61:195–202.
18. Chan DC, Hao YT, Wu SC: Polypharmacy among disabled Taiwanese elderly: A longitudinal observational study. Drugs Aging 2009;26:345–354.
19. Rochon PA, Stall NM, Holmes HM: Drug Therapy and Frailty: Chicken or the Egg? J Am Geriatr Soc 2019;67:2441–2443.
20. Kann IC, Lundqvist C, Luras H: Polypharmacy Among the Elderly in a List-Patient System. Drugs Real World Outcomes 2015;2:193–198.
21. Kyle UG, Genton L, Hans D, Karsegard L, Slosman DO, Pichard C: Age-related differences in fat-free mass, skeletal muscle, body cell mass and fat mass between 18 and 94 years. Eur J Clin Nutr 2001;55:663–672.
22. Mangoni AA, Jackson SH: Age-related changes in pharmacokinetics and pharmacodynamics: Basic principles and practical applications. Br J Clin Pharmacol 2004;57:6–14.
23. Hilmer SN, McLachlan AJ, Le Couteur DG: Clinical pharmacology in the geriatric patient. Fundam Clin Pharmacol 2007;21:217–230.
24. De Francesco D, Wit FW, Burkle A, et al.: Do people living with HIV experience greater age advancement than their HIV-negative counterparts? AIDS 2019;33:259–268.
25. Edelman EJ, Gordon KS, Glover J, McNicholl IR, Fiellin DA, Justice AC: The next therapeutic challenge in HIV: Polypharmacy. Drugs Aging 2013;30:613–628.
26. Pahwa S, Deeks S, Zou S, et al.: NIH Workshop on HIV-associated comorbidities, coinfections, and complications: Summary and recommendation for future research. J Acquir Immune Defic Syndr 2021;86:11–18.
27. Greene M, Steinman MA, McNicholl IR, Valcour V: Polypharmacy, drug-drug interactions, and potentially inappropriate medications in older adults with human immunodeficiency virus infection. J Am Geriatr Soc 2014;62:447–453.
28. Justice AC, Gordon KS, Skanderson M, et al.: Nonantiretroviral polypharmacy and adverse health outcomes among HIV-infected and uninfected individuals. AIDS 2018;32:739–749.
29. Rochon PA, Petrovic M, Cherubini A, et al.: Polypharmacy, inappropriate prescribing, and deprescribing in older people: Through a sex and gender lens. Lancet 2021;5:E290–E300.
30. Alhawassi TM, Krass I, Bajorek BV, Pont LG: A systematic review of the prevalence and risk factors for adverse drug reactions in the elderly in the acute care setting. Clin Interv Aging 2014;9:2079–2086.
31. Bartz D, Chitnis T, Kaiser UB, et al.: Clinical Advances in Sex- and Gender-Informed Medicine to Improve the Health of All: A Review. JAMA Intern Med 2020;180:574–583.
32. Koenig N, Fujiwara E, Gill MJ, Power C: Montreal cognitive assessment performance in HIV/AIDS: Impact of systemic factors. Can J Neurol Sci 2016;43:157–162.
33. Ances BM, Letendre SL: CROI 2019: Neurologic complications of HIV disease. Top Antivir Med 2019;27:26–33.
34. Eum S, Hill SK, Rubin LH, et al.: Cognitive burden of anticholinergic medications in psychotic disorders. Schizophr Res 2017;190:129–135.
35. Papenberg G, Backman L, Fratiglioni L, Laukka EJ, Fastbom J, Johnell K: Anticholinergic drug use is associated with episodic memory decline in older adults without dementia. Neurobiol Aging 2017;55:27–32.
36. Rubin LH, Radtke KK, Eum S, et al.: Cognitive Burden of Common Non-antiretroviral Medications in HIV-Infected Women. J Acquir Immune Defic Syndr 2018;79:83–91.
37. Adimora AA, Ramirez C, Benning L, et al.: Cohort Profile: The Women's Interagency HIV Study (WIHS). Int J Epidemiol 2018;47:393–394i.
38. Bacon MC, von Wyl V, Alden C, et al.: The Women's Interagency HIV Study: An observational cohort brings clinical sciences to the bench. Clin Diagn Lab Immunol 2005;12:1013–1019.
39. Barkan SE, Melnick SL, Preston-Martin S, et al.: The Women's Interagency HIV Study. WIHS Collaborative Study Group. Epidemiology 1998;9:117–125.
40. Maki PM, Rubin LH, Valcour V, et al.: Cognitive function in women with HIV: Findings from the Women's Interagency HIV Study. Neurology 2015;84:231–240.
41. Radtke KK, Bacchetti P, Anastos K, et al.: Use of nonantiretroviral medications that may impact neurocognition: patterns and predictors in a large, long-term HIV Cohort Study. J Acquir Immune Defic Syndr 2018;78:202–208.
42. Rudolph JL, Salow MJ, Angelini MC, McGlinchey RE: The anticholinergic risk scale and anticholinergic adverse effects in older persons. Arch Intern Med 2008;168:508–513.
43. Rubin LH, Maki PM, Springer G, et al.: Cognitive trajectories over 4 years among HIV-infected women with optimal viral suppression. Neurology 2017;89:1594–1603.
44. LS R: The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Measur 1977;1:385–401.
45. Drinking levels defined: NIAAA Alcohol & Your Health website. Available at www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking, accessed July 24, 2021.
46. Justice AC, Modur SP, Tate JP, et al.: Predictive accuracy of the Veterans Aging Cohort Study index for mortality with HIV infection: A North American cross cohort analysis. J Acquir Immune Defic Syndr 2013;62:149–163.
47. Justice AC, Freiberg MS, Tracy R, et al.: Does an index composed of clinical data reflect effects of inflammation, coagulation, and monocyte activation on mortality among those aging with HIV? Clin Infect Dis 2012;54:984–994.
48. O'Halloran JA, Wang K, Spence AB, et al.: Integrase Strand Transfer Inhibitor Start or Switch Impacts Learning in Women with HIV. J Acquir Immune Defic Syndr 2020;86:593–599.
49. Amusan P, Power C, Gill JM, et al.: Lifetime antiretroviral exposure and neurocognitive impairment in HIV. J Neurovirol 2020;26:743–753.
50. Rubin LH, Li Y, Fitzgerald KC, et al.: Associations between Antiretrovirals and Cognitive Function in Women with HIV. J Neuroimmune Pharmacol 2020; 16:195–206.
51. Jyrkka J, Enlund H, Lavikainen P, Sulkava R, Hartikainen S: Association of polypharmacy with nutritional status, functional ability and cognitive capacity over a three-year period in an elderly population. Pharmacoepidemiol Drug Saf 2011;20:514–522.
52. Rawle MJ, Richards M, Davis D, Kuh D: The prevalence and determinants of polypharmacy at age 69: A British birth cohort study. BMC Geriatr 2018;18:118.
53. Assari S, Wisseh C, Saqib M, Bazargan M: Polypharmacy is associated with lower memory function in African American Older Adults. Brain Sci 2020;10:49.
54. Soysal P, Perera G, Isik AT, et al.: The relationship between polypharmacy and trajectories of cognitive decline in people with dementia: A large representative cohort study. Exp Gerontol 2019;120:62–67.
55. Rawle MJ, Cooper R, Kuh D, Richards M: Associations Between Polypharmacy and Cognitive and Physical Capability: A British Birth Cohort Study. J Am Geriatr Soc 2018;66:916–923.
56. Caskie GI, Willis SL: Congruence of self-reported medications with pharmacy prescription records in low-income older adults. Gerontologist 2004;44:176–185.
Information & Authors
Information
Published In
AIDS Research and Human Retroviruses
Volume 38 • Issue Number 7 • July 2022
Pages: 571 - 579
PubMed: 35357949
Copyright
Copyright 2022, Mary Ann Liebert, Inc., publishers.
History
Published online: 11 July 2022
Published in print: July 2022
Published ahead of print: 26 April 2022
Published ahead of production: 31 March 2022
Topics
Authors
Authors' Contributions
Conceptualization by L.H.R; data curation by Q.S., D.R.H, and B.T.; formal analysis by Q.S. and D.R.H; writing—original draft by L.H.R., A.N., and A.S.; writing—review and editing by all authors.
Author Disclosure Statement
Rubin is a consultant for Digital Artefacts. All other coauthors report no conflicts of interest.
Funding Information
This study was supported by the Johns Hopkins University NIMH Center for novel therapeutics for HIV-associated cognitive disorders (P30MH075773; Haughey, Rubin). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), U01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn Anastos and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D'Souza, Stephen Gange, and Elizabeth Golub), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen and Audrey French), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, Jodie Dionne-Odom, and Deborah Konkle-Parker), U01-HL146192; UNC CRS (Adaora Adimora), U01-HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional cofunding from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD), National Institute on Aging (NIA), National Institute of Dental & Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), National Institute of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR), and P30-MH-116867 (Miami CHARM).
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