Immunological Age Prediction in HIV-Infected, ART-Treated Individuals

Abstract Anti-retroviral therapy (ART) improves life expectancy in people living with HIV (PWH), but it remains unclear how chronic infection affects normal aging of the immune system. Plasma cell-free protein expression and phenotypes were assessed blood from ART treated PWH (19–77yrs, n=106) age-matched, HIV-negative controls (HC, n=103). Age-associated parameters identified HC independently by univariate Spearman correlation (p<0.05) to obtain a reduced dataset for downstream statistical modeling. Machine learning algorithms applied generate predictive models age as continuous variable. biomarker analysis resulted 8-parameter inflammatory index that was higher compared correlated age. Combining flow plasma data generated 277 491 age-associated (out total 1,357) PWH, respectively. exhibited different immunological profiles associated HC. However, CD38 on T cells declined both PWH. Predictive modeling using 14-parameter signature 76.9% accuracy predict chronological HC, however this model failed (18.6% accuracy) highlighting effect aging. A 25-parameter (IMmunological Age Prediction, IMAP-25) showed 70% 53% used show an accelerated rate 5.6 yrs We approach signatures, easily monitored human samples, could be indicator one’s ‘immunological age’ during ART-treated infection.