Integrating AI and Causal Genetics to Prioritize Therapeutic Targets for Aging and Age-Related Diseases

Aging is increasingly viewed as a pathological 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 artificial intelligence (AI)-driven target discovery framework that integrates large-scale multi-omic datasets to prioritize therapeutic targets shared between aging and 12 ARDs spanning four major disease areas: neurological, inflammatory, metabolic, and fibrotic disorders. We identified 29 high-confidence and 16 previously unrecognized aging-associated targets implicated across the selected disease areas, together with convergent pathway perturbations characterized by robust upregulation of interferon and inflammatory signaling, alongside coordinated downregulation of MYC-driven proliferative programs, consistent with heightened inflammatory activation and reduced anabolic activity during aging. Age-dependent transcriptomic alterations across tissues were observed for all identified target genes. Hallmarks of aging assessment revealed chronic inflammation as the most enriched hallmark across aging and ARDs. Mendelian randomization analyses 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-localization analyses suggested that the same genetic variants influencing IL6R expression are also associated with parental survival, a proxy for human lifespan, supporting a role for IL-6 signaling in longevity, and indicated a shared genetic signal at the NOS2 locus associated with rheumatoid arthritis risk. Together, our findings outline a scalable AI-guided multi-omic framework for identifying causal and repurposable therapeutic targets for aging and ARDs.