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Investigating Pace of Biological Aging as a Mental and Physical Health Biomarker in Youth with Perinatally-Acquired HIV
Conference proceeding

Investigating Pace of Biological Aging as a Mental and Physical Health Biomarker in Youth with Perinatally-Acquired HIV

Hansoo Chang, Ashni Kumar, Joshua Suh, Kevin Street, Ana Ferariu, Alexei Taylor, Kunjal Patel, Sean Brummel, Paige Williams, Lei Wang, …
Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp 1-1
12 Oct 2025

Abstract

Applied computing -- Life and medical sciences -- Health informatics
HIV and its treatment may affect the growth and development of organ function and the mental health of youth who are living with perinatally-acquired HIV (PHIV). Recent research has investigated utilizing biological age as a quantitative tool that can accurately predict a broad range of physical and mental health outcomes. However, few studies have utilized a longitudinal approach to analyze biological age in targeted populations such as those with PHIV. Even fewer studies have investigated the trends of biological aging in children since most biological aging algorithms have been developed with an older population in mind. To fill the gap, we propose a new approach to quantify the pace of biological aging as a weighted combination of the temporal trajectory of each individual biomarker. The resulting metric is called individualized pace of biological aging (IPOA). Compared to existing methods such as Belsky's pace of biological aging (BPOA), and biological age gap (BAG), our proposed IPOA allows researchers to effectively account for each participant's baseline health condition as these serve as weights in the model. Our results show that IPOA was significantly associated with future HIV viral load and multiple health outcomes, outperforming previously validated biological aging and pace of aging methods. Overall, our proposed method allows for a more accurate, data-driven method of calculating pace of aging for adolescents by utilizing a weighted combination of the starting health condition of each individual that may be clinically useful as a predictive tool of whole person health.

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Mathematical & Computational Biology
Medical Informatics
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