Aging is a complex biological process characterized by progressive functional decline and increased risk of chronic diseases. In recent years, DNA methylation-based epigenetic clocks have emerged as some of the most robust biomarkers for estimating biological age. Initial research clocks, such as those developed by Horvath and Hannum, provided highly accurate chronological age predictions. Subsequent models, including PhenoAge, GrimAge, and DunedinPACE, improved upon this by incorporating health-related variables and functional measures, expanding their relevance to disease risk and pace of aging. Importantly, these multi-CpG clocks have demonstrated strong predictive accuracy, but their reliance on large numbers of CpG sites and high-throughput technologies limits their clinical scalability due to cost, complexity, and sample processing requirements. In this review, we critically evaluate the current landscape research-based epigenetic clocks, and their transition into direct-to-consumer testing. We discuss their key strengths, limitations, and translational potential, with particular emphasis on the growing demand for simplified, cost-effective, and analytically accessible epigenetic clocks, which should maintain predictive accuracy while enabling broader implementation in clinical and epidemiological settings. Special attention is given to ELOVL2-based clocks, which exemplify minimalistic yet robust models that can facilitate large-scale studies and democratize access to biological aging assessment. Ultimately, we argue that the next generation of epigenetic clocks should prioritize both analytical simplicity and validation across diverse populations to support personalized interventions for healthy aging.
Longevity & Aging