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Published in final edited form as: J Neurovirol. 2023 Mar 1;29(3):331–336. doi: 10.1007/s13365-022-01100-4

HIV viremia contributes to neurocognitive impairments in persons who use cocaine

Kareem Al-Khalil 1,*, Sheri L Towe 1, Taylor P Ikner 2, Christina S Meade 1
PMCID: PMC11060036  NIHMSID: NIHMS1983148  PMID: 36857016

Abstract

Persons with HIV (PWH) who use illicit drugs are at elevated risk for neurocognitive impairment (NCI). This study investigated the effects of HIV disease and HIV viremia on NCI among adults who use cocaine. PWH who were not virologically suppressed showed greater global deficits compared to participants with HIV viral suppression and HIV-negative participants, but no differences emerged between the latter two groups. These findings highlight the adverse effects of poorly-controlled HIV disease on NCI, beyond the independent effects of cocaine on cognition, and underscore the importance of strengthening the HIV care continuum for persons who use cocaine.

Keywords: Viral load, viral suppression, HIV, cocaine use, neuropsychological impairment

INTRODUCTION

Neurological complications of HIV disease are a persistent clinical problem, and the resulting neurocognitive impairment (NCI) can adversely impact daily living, including employment, driving, and medication adherence (Anand et al. 2010; Cattie et al. 2012; Laverick et al. 2017). Even with highly effective combination antiretroviral therapy (cART), up to half of patients will experience NCI (Gates & Cysique, 2016; Saylor et al. 2016). In 2019, the United States launched its Ending the HIV Epidemic plan, an ambitious initiative to reduce the burden of HIV in high incidence areas (Fauci et al. 2019). Despite the widespread availability of cART, an estimated 35% of people with HIV (PWH) in the United States do not achieve sustained viral suppression (VS) (CDC, 2020). The HIV care continuum consists of several steps to achieve VS, including HIV diagnosis, linkage to and retention in HIV care, and consistent use of cART (Gardner et al. 2011; Cheever, 2007). Although cognitive deficits remain in PWH who have sustained VS, likely due to persistent low-grade immune activation (Gott et al. 2017), poorly controlled HIV disease may increase the risk of NCI (Heaton et al. 2015).

Cocaine use is prevalent in PWH (Shiau et al. 2017). Persons who use cocaine are more likely to fall out of each step of the HIV care continuum (Parsons et al. 2014; Gwadz et al. 2016; Wechsberg et al. 2017), and are susceptible to virologic failure (Rasbach et al. 2013; Tolson et al. 2018). Chronic cocaine use is associated with deficits in many cognitive domains, including working memory, psychomotor performance, and executive function (Potvin et al. 2014; Spronk et al. 2013). Prior research has also documented the additive effects of HIV disease and cocaine use on cognitive function (Meade et al. 2015). However, these studies did not differentiate the role of unsuppressed HIV viremia independent of underlying effects of cocaine use on cognition, which may be a driver of observed disparities.

The present study investigated the effects of HIV disease on NCI in persons who use cocaine. Comparisons of neurocognitive functioning were made between PWH with (COC+HIV+VS+) and without (COC+HIV+VS-) VS and an HIV-negative comparison group (COC+HIV-). To increase the representativeness of the sample, we utilized community-based recruitment strategies, including peer referral, to reach PWH irrespective of current engagement in HIV care. Our primary hypothesis was that HIV disease would be associated with cognitive deficits, and that these deficits would be greatest in COC+HIV+VS-. A secondary goal was to characterize COC+HIV+VS- to identify potential contributors that could be targeted in interventions to improve HIV clinical outcomes.

METHODS

The sample included 197 adults with and without HIV who were 18–64 years old, had a history of regular cocaine use for >1 year, and used cocaine in the past 90 days. HIV-negative status was verified via an OraQuick® rapid HIV test conducted at the screening, and HIV-positive status was verified by medical record review (to ensure confirmatory testing). Exclusion criteria were: English non-fluency or illiteracy, serious neurological conditions (including opportunistic brain infections), physical disabilities impeding participation, and inability to provide informed consent.

Participants were recruited from the Raleigh-Durham area of North Carolina, USA between October 2016 and December 2018 via flyers posted in community-based organizations and infectious diseases clinics, a contact repository, and participant referrals. After clearing a brief prescreening interview, interested individuals were invited for a comprehensive in-person screening that included clinical interviews, computerized questionnaires, and a urine toxicology screen. Eligible participants returned on another day to complete neuropsychological testing, additional clinical interviews and questionnaires, and another urine drug test. Participants were paid $60 for the screening and $65 for the assessment visit. Study procedures were approved by the institutional review board at Duke University Health System, and participants provided written informed consent.

Screening measures.

The Addiction Severity Index-Lite assessed lifetime substance use and associated impairments (McLellan et al. 1992). Timeline follow-back methodology was used to assess frequency of substance use in the past 90 days (Robinson et al. 2014; Sobell & Sobell, 1992). Self-report of recent drug use was corroborated with an 11-panel urine toxicology screen for cannabis, cocaine, opioids, methamphetamine, amphetamine, benzodiazepines, barbiturates, methadone, oxycodone, buprenorphine, and ecstasy. Module E of the Structured Clinical Interview for DSM-5 was administered to identify substance use disorders in the past 12 months (First et al. 2015). For cocaine use disorder (CUD), the total number of symptoms endorsed, ranging from 0 to 11, was summed to indicate severity. The Word Reading subtest of the Wide Range Achievement Test-4 was used as a screen for literacy (Wilkinson & Robertson, 2006). PWH reported HIV diagnosis dates, acquisition routes, and current antiretroviral (ARV) treatment. Adherence to ARV medications in the past 30 days was assessed using a visual analogue scale (VAS) of 0–100% (Giordano et al. 2004), and a cutoff of ≥90% was used for adherence (Garofalo et al. 2016). Finally, demographics and medical histories were obtained from computerized surveys.

Neuropsychological testing.

Participants completed the following neuropsychological tests:

  1. Grooved Pegboard Test, dominant hand – number of seconds to completion (Klove 1963).

  2. Trail Making Test, Part A – number of seconds to completion (Reitan and Wolfson 1993).

  3. Hopkins Verbal Learning Test – Revised (HVLT-R) –number of words recalled on trials 1–3 (Brandt and Benedict 2001).

  4. Stroop Color and Word Test, interference score – difference between actual and predicted score on the Color-Word trial (Golden 1978).

  5. FAS letter fluency – number of words generated (Benton et al. 1983).

  6. Paced Auditory Serial Addition Task-50 – number correct (Diehr et al. 2003).

Although brief to minimize burden on participants, the chosen neuropsychological battery covers the primary domains affected by HIV, including executive function, information processing speed, and motor skills (Reger et al., 2002; Heaton et al., 2004). Using up-to-date published norms, raw scores were converted to demographically corrected T-scores (M=50, SD=10) (Diehr et al. 2003; Heaton et al. 2004; Stern and White 2009). Each case was scored by two research assistants with any discrepancies resolved by a third research assistant re-scoring the case. T-scores were converted to deficit scores using a 0–5 rating, with 0 reflecting no impairment and 5 reflecting severe impairment (T ≥40 = 0, 35–39 = 1, 30–34 = 2, 25–29 = 3, 20–24 = 4, and <20 = 5). By assigning a zero-point value to T-scores within or above one standard deviation of the mean, deficit scores give less weight to performances that are within normal limits and provide a more accurate characterization of impairment. Global deficit scores (GDS) were computed by averaging the deficit scores for each assessment, and then normalized with square-root transformations.

Medical record review.

Participants provided a release for us to obtain copies of their healthcare records. This information was used to confirm no exclusionary conditions, and to abstract relevant data, including HIV disease characteristics, such as VL and CD4 T-cell counts. VS was defined as <200 copies/mL (Phillips 2020). Additionally, Veterans Aging Cohort Study (VACS) Index 2.0 scores were calculated using relevant measures from the medical records, with higher scores indicating greater mortality risks in PWH (Tate et al. 2019).

Data analysis.

To characterize the sample and identify potential confounding variables, we compared the groups using one-way Analysis of Variance (ANOVA) and chi square for continuous and categorical variables, respectively. Due to the zero-inflation, cognitive deficit scores were analyzed using Tobit models in R Studio (R: Tobit Regression [r-project.org]). Using left-censoring of zeros, a Tobit regression was conducted with GDS as the dependent variable and group as the independent variable with COC+HIV+VS− as the reference. Post-hoc analyses were also conducted to compare COC+HIV+VS+ with COC+HIV-. Age, CUD symptoms, and days of cocaine use were added as covariates because these variables significantly differed between groups.

RESULTS

Sample characteristics (Table 1).

Table 1.

Sample characteristics by study grouping.

COC+HIV− (n=116) COC+HIV+VS+ (n=62) COC+HIV+VS− (n=19) Statistics p-value
Sample characteristics
Age, M (SD) 48.01 (9.56) 50.16 (8.82) 43.32 (10.62) F(2,194)=3.90 0.022c
Male, n (%) 59 (51%) 44 (71%) 9 (47%) χ(2)2=7.43 0.024a
African American, n (%) 107 (92%) 58 (94%) 18 (95%) FET=0.14 1.000
Education in years, M (SD) 12.00 (1.98) 11.82 (2.15) 11.32 (2.21) F(2,194)=0.94 0.394
Cocaine use characteristics
Route of administration, n (%) FET=1.120 0.568
 Nasal 22 (19%) 16 (26%) 4 (21%)
 Smoking 94 (81%) 46 (74%) 15 (79%)
Cocaine use disorder symptoms, M (SD) 7.05 (2.59) 5.77 (2.99) 6.26 (3.25) F(2,194)=4.38 0.014a
Years of regular cocaine use, M (SD) 18.12 (9.66) 18.23 (11.13) 16.21 (9.52) F(2,194)=0.32 0.727
Days of cocaine use in past 90, M (SD) 38.95 (27.86) 25.58 (23.80) 35.89 (27.48) F(2,194)=5.13 0.007a
Positive UDS, n (%) 95 (82%) 49 (79%) 16 (89%) FET=0.76 0.694
Days of other substance use (past 90)
Alcohol use, M (SD) 37.34 (31.89) 32.89 (29.62) 26.32 (29.30) F(2,194)=1.22 0.297
Marijuana use, M (SD) 21.59 (33.11) 29.19 (35.14) 23.41 (34.68) F(2,194)=1.02 0.362
Opioid, M (SD) 5.91 (17.05) 2.15 (11.02) 1.11 (2.31) F(2,194)=1.86 0.159
Neurocognitive Functioning
Global Deficit Score, M (SD) 0.63 (0.45) 0.58 (0.53) 1.0 (0.72) χ2(2) = 8.79 0.012b,c
HIV Characteristics
Years since HIV diagnosis, Median (IQR) N/A 15.00 (15) 13.50 (17) U = 501.00 0.718
HIV acquisition route, N (%) N/A FET = 1.98 0.568
 MSM 23 (41%) 5 (31%)
 Heterosexual sex 27 (48%) 10 (63%)
 Injection drug use 4 (7%) 0 (0%)
 Other 2 (4%) 1 (6%)
Nadir CD4 count, Median (IQR) N/A 196 (322) 193 (435) U = 576.50 0.889
Proportion with Nadir CD4 < 200, N (%) N/A 31 (50%) 10 (52.6%) χ2(1) = 0.40 0.841
Current CD4 count, Median (IQR) N/A 695 (454) 469 (589) U = 280.50 <0.001
Currently taking ARVs, N (%) N/A 62 (100%) 13 (68%) χ2(1) = 21.15 <0.001
≥90% adherence to ARV, N (%)1 N/A 50 (81%) 5 (39%) χ2(1) = 9.80 0.002
VACS Index 2.0 score, Median (IQR) N/A 46.04 (19.69) 60.93 (24.73) U = 913 <0.001
VACS without VL, Median (IQR) N/A 49.97 (15.81) 57.36 (23.66) U=754 0.066
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Pairwise differences from post-hoc tests:

a

COC+HIV− ≠ COC+HIV+VS+;

b

COC+HIV− ≠ COC+HIV+VS−;

c

COC+HIV+VS+ ≠ COC+HIV+VS−

1

Restricted to participants on ART

The sample consisted of 197 participants (56% men, 93% African American) with a mean age of 48.2 years (SD=9.6) and education of 11.9 years (SD=2.1). The groups were similar in race, education, years of regular cocaine use, route of cocaine administration, and current use of other substances. However, there were group differences on age, biological sex, frequency of current cocaine use, and CUD symptoms. Post-hoc t-tests revealed that the COC+HIV+VS+ group was significantly older than the COC+HIV+VS− group, t(194) = 2.77, p < 0.05, and that they had a greater proportion of men compared to COC+HIV− group, χ2(1) = 6.70, p < 0.05. COC+HIV+VS+ also endorsed fewer CUD symptoms, t(194) = 2.92, p < 0.05, and less frequent cocaine use, t(194) = 3.19, p < 0.01, compared to COC+HIV-.

Neurocognitive deficits (Table 1).

Overall, cognitive impairment was quite high in our sample (untransformed GDS: Mean = 0.65; SD = 0.52). Tobit regression results revealed significantly higher GDS in COC+HIV+VS− relative to both COC+HIV+VS+, B (SE) = 0.30 (0.11), p < 0.01, and COC+HIV-, B (SE) = 0.22 (0.10), p < 0.05. Post-hoc comparisons revealed no differences in cognitive deficit scores between the COC+HIV+VS+ and COC+HIV− groups, B (SE) = −0.07 (0.07), p=0.27. Furthermore, GDS were not significantly associated with cocaine use, (B (SE) = −0.002 (0.001), p=0.17), or with CUD symptoms, (B (SE) = 0.008 (0.011), p=0.49).

HIV characteristics (Table 1).

The PWH in the current sample had been diagnosed with HIV for a median of 15 years, and many had a history of severe immuno-suppression (median nadir CD4 = 193 count/μL; 50.6% <200), but most were currently asymptomatic (median current CD4: 662 count/μL; 93% currently taking cART). There were no group differences in proportion of CD4 nadir < 200, years of HIV diagnosis, or routes of HIV acquisition. As expected, ARV prescription and adherence rates were higher in COC+HIV+VS+ than COC+HIV+VS-, as was current CD4 count. VACS Index 2.0 scores were significantly higher in COC+HIV+VS− compared to COC+HIV+VS+, but these differences were not significant when scores were calculated without viral load.

DISCUSSION

Our primary finding is that VS among PWH who use cocaine may protect against additional cognitive impairment related to HIV disease, independent of deficits related to ongoing cocaine use. PWH who use cocaine and other drugs are more likely to fall out of each step of the HIV care continuum (Gwadz et al. 2016; Wechsberg et al. 2017), resulting in poorer HIV clinical outcomes (Arnsten et al. 2002; Lucas et al. 2002; Baum et al. 2009). Differences in VACS scores between COC+HIV+VS+ and COC+HIV+VS− were driven by viral load differences. Even though cognitive impairment was quite prevalent among our participants, as consistent with their prolonged cocaine use (median lifetime use = 17 years), the current study supports the importance of achieving and maintaining VS among persons who use drugs to protect against additional cognitive impairment.

While abstinence from cocaine and other illicit drugs is recommended to minimize adverse health outcomes, many people continue to use drugs for a variety of reasons, including limited access to substance use treatment (Foster et al. 2010; Priester et al. 2016), high relapse rates (Dutra et al. 2008), and desire to use. Our results demonstrate that many people who use cocaine and other drugs do achieve VS despite ongoing use. In this sample, the majority of PWH (77%) had VS; these participants were more likely to be on cART and to adhere to their medications. Achieving optimal HIV clinical outcomes does not necessitate abstinence from substance use, but interventions focusing on HIV care retention are critical (Pan et al. 2019; Metsch et al. 2016; Stitzer et al. 2017). For example, contingency management strategies, which are effective in supporting abstinence from drugs of abuse, have also been found successful in achieving VS in PWH (Burch et al., 2017). Our results suggest that such interventions may be particularly indicated for younger PWH with more severe CUD.

Research has linked plasma markers of immune activation, inflammation, and dysregulated metabolism with HIV-related NCI (Zayyad & Spludich, 2015; Lentz et al., 2011). Further, cocaine use has detrimental effects on neuro-inflammatory markers in PWH, and consequent HIV-related NCI (Cai et al., 2016; Cisneros et al., 2018) even among those with VS (Chilunda et al., 2019). Indeed, there may some residual impact of cocaine use on immune dysregulation in the COC+HIV+VS+ that could further amplify the risk for NCI.

The urgency to promote treatment access and sustained VS in PWH who use cocaine cannot be overstated. This work provides additional evidence that PWH who use stimulants can achieve VS, they are likely to do so more slowly (Chilunda et al., 2019; Cisneros et al., 2018; Cai et al., 2016) and remain at higher risk for virologic rebound compared to PWH without substance use (Boucoiran et al., 2017). As such, PWH who use cocaine need more intensive intervention to improve linkage and retention to care in order to ensure sustained VS despite ongoing substance use.

A key strength of this study was the use of community-based recruitment strategies to reach a more representative sample of people irrespective of engagement in HIV care. At the same time, the current study is limited by its cross-sectional design, which cannot ascertain the temporal relationship between HIV disease and cognitive deficits. This study also did not include a comparison group of participants who did not use cocaine as the effects of cocaine on cognition are well documented. Another limitation is the small number of COC+HIV+VS+ participants in the current sample, which warrants replication studies.

In sum, among adults with regular cocaine use, PWH who have achieved VS performed equivalently to HIV-negative counterparts on a neurocognitive assessment battery, underscoring the importance of adherence to cART. However, PWH who had unsuppressed viremia did evidence greater cognitive deficits, despite their relatively younger age. Future studies should investigate longitudinal effects of HIV viremia and illicit drug use on cognitive functioning to ascertain more explanatory relationships between these variables.

Acknowledgements:

The authors thank all the individuals who participated in this study.

Funding:

This study was funded by the National Institute of Health.

Footnotes

Conflict of Interest: The authors declare that they have no conflict of interest.

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