The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi‐Omics Integration

Aging clocks are tools that quantify biological aging through the integration of multi-omics data, encompassing epigenetic, transcriptomic, proteomic, metabolic, and microbial information, together with functional biomarkers. These tools show significant potential for use in preventive medicine, early detection of chronic conditions, and monitoring the effectiveness of interventions designed to enhance population health. The advancement of artificial intelligence has facilitated the widespread adoption of ensemble learning and deep learning techniques in constructing aging clocks. Such methods are capable of efficiently synthesizing and analyzing high-dimensional, multi-modal biological data, thereby uncovering deeper insights and promoting a transition from epigenetic-based aging prediction to the development of multi-omics and multi-modal aging clocks. Aging clocks built on large-scale data and artificial intelligence have demonstrated notable progress in terms of accuracy, interpretability, and generalizability. Consequently, they provide a substantive foundation for understanding mechanisms of aging and contribute meaningful guidance for clinical practices aimed at delaying age-related diseases and fostering healthy aging.