Machine Learning Quantifies Accelerated White-Matter Aging in Persons With HIV

Older persons with HIV may undergo changes to the brain's white matter analogous to increased aging. We apply a machine learning method to quantify such changes, examining the roles of viral load and comorbidities in brain aging and cognitive impairment. Background Persons with HIV (PWH) undergo white matter changes, which can be quantified using the brain-age gap (BAG), the difference between chronological age and neuroimaging-based brain-predicted age. Accumulation of microstructural damage may be accelerated in PWH, especially with detectable viral load (VL). Methods In total, 290 PWH (85% with undetectable VL) and 165 HIV-negative controls participated in neuroimaging and cognitive testing. BAG was measured using a Gaussian process regression model trained to predict age from diffusion magnetic resonance imaging in publicly available normative controls. To test for accelerated aging, BAG was modeled as an age x VL interaction. The relationship between BAG and global neuropsychological performance was examined. Other potential predictors of pathological aging were investigated in an exploratory analysis. Results Age and detectable VL had a significant interactive effect: PWH with detectable VL accumulated +1.5 years BAG/decade versus HIV-negative controls (P = .018). PWH with undetectable VL accumulated +0.86 years BAG/decade, although this did not reach statistical significance (P = .052). BAG was associated with poorer global cognition only in PWH with detectable VL (P < .001). Exploratory analysis identified Framingham cardiovascular risk as an additional predictor of pathological aging (P = .027). Conclusions Aging with detectable HIV and cardiovascular disease may lead to white matter pathology and contribute to cognitive impairment.