In this work our aim was to identify early biomarkers in plasma samples associated with mortality in children with perinatal HIV treated early in life, to potentially inform early intervention targeting this vulnerable group. 20/215 children (9.3%) with perinatal HIV, enrolled within 3 months of age died prematurely within the first year of the study, despite early ART initiation. Using a propensity score, we selected 40 alive study participants having similar clinical and virological records compared to the deceased group. 13 HIV unexposed (HU) healthy children were additionally used as controls. Baseline plasma samples were analyzed using a targeted proteomic approach, and to assess pathogen-associated and damage-associated molecular patterns (PAMPs, DAMPs) levels. Data from deceased participants were compared to both control groups, with multivariate logistic regression models used to evaluate the association between mortality and plasma proteins. We developed a machine learning model to predict mortality risk, finding that IL-6 and CXCL11 not only were higher in deceased children than Matched-children with HIV (p < 0.001 and p = 0.0034) but also predictive of mortality (accuracy of 77%); levels of PAMPs were higher in deceased children (p = 0.0016). Thus, measuring early inflammatory biomarkers, particularly IL-6, could help mortality risk prediction and potentially guide targeted interventions.
Keywords: HIV infection; IL-6; Inflammatory biomarkers; PAMPs and DAMPs; Pediatric population; Predictive model.
© 2024. The Author(s).