Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV.

Despite the success of antiretroviral therapy (ART), individuals with HIV remain at risk for experiencing non-AIDS adverse events (NAEs), including cardiovascular complications and malignancy. Several surrogate immune biomarkers in blood have shown predictive value in predicting NAEs; however, composite panels generated using machine learning may provide a more accurate advancement for monitoring and discriminating NAEs. In a nested case-control study, we aimed to develop machine learning models to discriminate cases (experienced an event) and matched controls using demographic and clinical characteristics alongside 49 plasma immunoproteins measured prior to and post-ART initiation. We generated support vector machine (SVM) classifier models for high-accuracy discrimination of individuals aged 30-50 years who experienced non-fatal NAEs at pre-ART and one-year post-ART. Extreme gradient boosting generated a high-accuracy model at pre-ART, while K-nearest neighbors performed poorly all around. SVM modeling may offer guidance to improve disease monitoring and elucidate potential therapeutic interventions.

Plasma extracellular vesicles and cell-free mitochondrial DNA are associated with cognitive dysfunction in treated older adults with HIV.

Extracellular vesicles (EVs) are nanoparticles with a role in intercellular communication. Cell-free mitochondrial DNA (cf-mtDNA) has been associated with cognitive dysfunction in people with HIV (PWH). We conducted a nested case-control study to test the hypothesis that plasma EVs are associated with cf-mtDNA and cognitive dysfunction in older PWH. A machine learning-based model identified total EVs, including select EV subpopulations, as well as urine cf-mtDNA and 4-meter walk time carry power to predict the neurocognitive impairment. These features resulted in an AUC-ROC of 0.845 + / - 0.109 (0.615, 1.00).