Measuring biological aging-the underlying rate of physiological decline-has become increasingly important in predicting disease risk, guiding personalized health strategies, and advancing age-targeted therapeutics. Unlike chronological age, biological age provides a more accurate reflection of an individual's functional health and longevity potential. DNA methylation-based epigenetic clocks are established tools for estimating biological age, but existing assays often require hundreds to thousands of CpG sites, resulting in high costs, complex analysis, and poor scalability. These limitations hinder their practical use in large-scale population screening and routine clinical applications. To address this gap, we developed EpiClock, a streamlined and cost-effective biological age prediction model based on just eight age-associated CpG markers (ASPA, FHL2, MIR29B2CHG, Chr16q24.1, SLC12A5, SST, LDB2, and COL1A1) from blood-derived DNA. The model showed high accuracy (R = 0.9332; mean absolute deviation (MAD) = 3.78 years), strong inter-operator reproducibility (R = 0.9667), and robust intra-assay precision (CV < 6 %, SD ≤ 0.05), with optimal performance using buffy coat samples. By combining a minimal marker set with high analytical reliability, EpiClock enables scalable, high-throughput biological age assessment-supporting its use in drug development, population-level screening, and accessible precision aging diagnostics. 2 2
Longevity & Aging