Integrating AI and causal genetics to prioritize therapeutic targets for aging and age-related diseases

Abstract

Aging is increasingly viewed as a pathologic process and a principal driver of diverse age-related diseases (ARDs). Framing aging as a disease offers an opportunity to identify therapeutic targets capable of modifying multiple chronic disorders simultaneously. Here, we developed an AI-driven target discovery framework that integrates large-scale multi-omic datasets to prioritise therapeutic targets shared between aging and 12 ARDs across four major disease areas: neurological, inflammatory, metabolic, and fibrotic disorders. We identified 29 high-confidence and 16 previously unrecognised aging-associated targets implicated across selected disease areas, together with convergent pathway perturbations characterized by robust upregulation of interferon and inflammatory signalling, alongside coordinated downregulation of MYC-driven proliferative programs, consistent with heightened inflammatory activation and reduced anabolic activity during aging. Hallmarks of aging assessment revealed chronic inflammation as the most enriched hallmark across aging and ARDs. Mendelian randomisation provided genetic causal support for IL6, IL6R, NLRP3, NOS2, TLR4, and GLP1R in aging-related traits and multiple ARDs, highlighting potential opportunities for drug repurposing. Co-localisation analysis further demonstrated a shared genetic signal at the IL6R locus between gene expression levels and parental survival. Together, our findings outline a scalable AI-guided multi-omic framework for identifying causal and repurposable therapeutic targets for aging and ARDs.