Effects of clinical, comorbid, and social determinants of health on brain ageing in people with and without HIV: a retrospective case-control study

Abstract

Background: Neuroimaging reveals structural brain changes linked with HIV infection and related neurocognitive disorders; however, group-level comparisons between people with HIV and people without HIV do not account for within-group heterogeneity. The aim of this study was to quantify the effects of comorbidities such as cardiovascular disease and adverse social determinants of health on brain ageing in people with HIV and people without HIV.

Methods: In this retrospective case-control study, people with HIV from Washington University in St Louis, MO, USA, and people without HIV identified through community organisations or the Research Participant Registry were clinically characterised and underwent 3-Tesla T₁-weighted MRI between Dec 3, 2008, and Oct 4, 2022. Exclusion criteria were established by a combination of self-reports and medical records. DeepBrainNet, a publicly available machine learning algorithm, was applied to estimate brain-predicted age from MRI for people with HIV and people without HIV. The brain-age gap, defined as the difference between brain-predicted age and true chronological age, was modelled as a function of clinical, comorbid, and social factors by use of linear regression. Variables were first examined singly for associations with brain-age gap, then combined into multivariate models with best-subsets variable selection.

Findings: In people with HIV (mean age 44·8 years [SD 15·5]; 78% [296 of 379] male; 69% [260] Black; 78% [295] undetectable viral load), brain-age gap was associated with Framingham cardiovascular risk score (p=0·0034), detectable viral load (>50 copies per mL; p=0·0023), and hepatitis C co-infection (p=0·0065). After variable selection, the final model for people with HIV retained Framingham score, hepatitis C, and added unemployment (p=0·0015). Educational achievement assayed by reading proficiency was linked with reduced brain-age gap (p=0·016) for people without HIV but not for people with HIV, indicating a potential resilience factor. When people with HIV and people without HIV were modelled jointly, selection resulted in a model containing cardiovascular risk (p=0·0039), hepatitis C (p=0·037), Area Deprivation Index (p=0·033), and unemployment (p=0·00010). Male sex (p=0·078) and alcohol use history (p=0·090) were also included in the model but were not individually significant.

Interpretation: Our findings indicate that comorbid and social determinants of health are associated with brain ageing in people with HIV, alongside traditional HIV metrics such as viral load and CD4 cell count, suggesting the need for a broadened clinical perspective on healthy ageing with HIV, with additional focus on comorbidities, lifestyle changes, and social factors.

Funding: National Institute of Mental Health, National Institute of Nursing Research, and National Institute of Drug Abuse.

Figure 1.. Spatial correlation map of brain-age gap and volumetric features.

To estimate the importance of volumetric features in the derivation of the brain-age gap by the convolutional neural network DeepBrainNet, correlations between brain-age gap and FreeSurfer volumes were calculated for PWH (A, B) and PWoH (C, D). All significant positive correlations (blue) were for ventricular cerebrospinal fluid compartments and for T₁ white matter hypointensities (not shown), while the strongest negative correlations were subcortical, in the hippocampus (bilateral), amygdala (bilateral), brainstem, and corpus callosum.

Figure 2.. HIV-specific predictors of brain aging.

Univariate associations between potential predictors of brain aging and DeepBrainNet-derived brain-age gap, i.e., the difference between model-estimated age and chronological age. Four factors were considered only in persons with HIV (PWH): plasma HIV viral load (A), hepatitis C co-infection (B), current CD4 T-cells in plasma (C), and lifetime minimum (nadir) CD4 T-cells (D). *=significant at pre-corrected p<0·05. †=significant at p<0·05 after false discovery rate correction.

Figure 3.. Predictors of brain aging for persons with and without HIV.

Univariate associations between potential predictors of brain aging and DeepBrainNet-derived brain-age gap. Seven factors were examined for both persons with and without HIV: area deprivation index (A), early life stressors (B), educational quality (C), educational duration (D), Framingham cardiovascular risk (E), alcohol (F), cocaine (G), tobacco (H), and employment status (I). *=p<0·05. †=significant at p<0·05 after false discovery rate correction.

Figure 4.. Multivariate prediction of brain-age gap in persons with and without HIV.

To identify predictor subsets that best explain the variability in the brain-age gap for persons with HIV (PWH, A and B) and persons without HIV (PWoH, C and D), best-subsets variable selection was performed using Mallow’s C_p as selection criterion. Left panels (A, C) display the best result (lowest C_p) for each number of predictors; shaded panels indicate that the predictor in that column was included. The selected model (highlighted row) for PWH included Framingham cardiovascular risk, hepatitis C, and unemployed status. The model for PWoH included alcohol use, early life stress, WRAT-III reading score, and unemployed status. Right panels (B and D) show model fit across the number of predictors, where the minimum C_p is obtained with three predictors for PWH and four predictors for PWoH.

Figure 5.. Multivariate predictors of brain aging in combined cohort of persons with and without HIV.

Best-subsets selection was also performed to model the brain-age gap for persons with HIV (PWH) and without HIV (PWoH). Panel A displays the best result (lowest C_p) for each number of predictors; shaded magenta panels indicate that the predictor in that column was included. The final model (top row) included male sex, Framingham risk score, lifetime alcohol use, hepatitis C, area deprivation index, and unemployment. Panel B shows the model fit across the number of predictors, where the minimum C_p is obtained with six predictors (asterisk).