PROJECT SUMMARY/ABSTRACT Life expectancy is increasing, and consequently, the burden of chronic age-related disease is also increasing. Interventions and treatments that target the fundamental biological process of human aging have the potential to mitigate risk of multiple diseases faced by our aging population. To develop interventions targeting the aging process, one must identify predictive factors and biomarkers associated with the aging clinical endpoints. By definition, the development of aging-based conditions and diseases takes time and requires a great deal of follow-up time. To accelerate research in human aging, biomarkers of human aging and prognostic biomarkers of healthy human aging are desperately needed. Without reliable biomarkers, early-stage drug development is severely limited. In this application, we propose a framework to identify biomarkers of healthy human aging using advanced Artificial Intelligence (AI) methods applied to a wide range of deeply phenotyped studies that collected data from humans and non-humans. We have assembled a team with deep expertise in clinical research of aging, genetic epidemiology, biology of aging, and AI. To identify biomarkers of aging through the integrative analysis of omic data with AI, we propose the following specific aims: Aim 1 (R21, first stage). Assemble datasets from the Framingham Heart Study (FHS) and the Longevity Consortium (LC) with multiple omics to test AI methods and to identify biomarkers associated with human aging. Aim 2 (R21, first stage). Test biologically informed AI Deep Neural Network (DNN) models with FHS and LC data to integrate omic data, predict outcomes, and identify predictive omic features. Aim 3 (R33, second stage). Apply models from public data onto exceptional longevity (EL) data. Aim 4 (R33, second stage). Establishing causal relationship between biomarkers and longevity phenotypes through Mendelian Randomization (MR) analysis and cell culture experiments.