Epigenetic Clocks: Biological Aging, Disease, and Clinical Applications

Abstract Epigenetic clocks are DNA methylation-based models that estimate biological age and provide a quantitative framework for investigating interindividual differences in aging. Unlike chronological age, which reflects the passage of time, epigenetic age may capture the cumulative effects of genetic background, environmental exposure, lifestyle, inflammation, and metabolic stress on cellular function. Over the past decade, these models have evolved from tools designed primarily to predict chronological age into increasingly sophisticated biomarkers of morbidity, mortality, and the pace of physiological decline.This review summarizes the conceptual and methodological development of epigenetic clocks and discusses their relevance to aging research and clinical medicine. First-generation clocks, including the Horvath and Hannum models, were developed to estimate chronological age with high accuracy. Second-generation clocks, such as PhenoAge and GrimAge, incorporated clinical bio-markers, mortality-related variables, and plasma protein surrogates, thereby improving their ability to reflect health status and disease risk. More recent models, particularly DunedinPACE, focus on the rate of aging rather than cumulative biological age. Advances in principal component-based clocks, deep learning approaches, and single-cell methylation analyses have further expanded the analytical capacity of the field.Particular attention is given to epigenetic age acceleration and its intrinsic and extrinsic components, as well as the genetic architecture underlying these measures. Variants involving TERT , SELP , HLA, APOE , POU5F1 , and cytochrome P450-related pathways support the close relationship between epigenetic aging, telomere biology, inflammation, immune function, metabolism, and neurodegeneration. Evidence from cancer, clonal hematopoiesis, neurodegenerative disorders, and cardiometabolic disease suggests that epigenetic clocks may serve as useful biomarkers for risk stratification and longitudinal monitoring. Nevertheless, broader clinical implementation will require improved standardization, validation across diverse populations, and careful interpretation in tissue- and context-specific settings.