方法对比

	Epigenetic Clock (DNA Methylation Age)	Biological Age Estimation
领域	Social Gerontology	Social Gerontology
方法族	Regression model	Regression model
起源年份	2013	2006
提出者	Steve Horvath	Petr Klemera and Stanislav Doubal
类型	Penalized-regression predictor of age from DNA methylation	Estimator of biological age from a panel of age-related biomarkers
开创性文献	Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), R115.	Klemera, P., & Doubal, S. (2006). A new approach to the concept and computation of biological age. Mechanisms of Ageing and Development, 127(3), 240-248.
别名	DNAm Age, Horvath Clock, DNA Methylation Clock, Methylation Age Predictor	KDM Biological Age, Klemera-Doubal Method, Biomarker-Based Biological Age, Physiological Age Estimation
相关	4	4
摘要	An epigenetic clock is a statistical predictor that estimates age from patterns of DNA methylation, the chemical marks on the genome that change in a regular way over the life course. The most influential is Steve Horvath's 2013 multi-tissue clock, which predicts chronological age from methylation levels at 353 specific CpG sites using a penalized regression model. Methylation is measured as a beta-value between zero and one at each site, representing the fraction of cells in which that site is methylated, and the clock combines a weighted set of these values into a predicted DNA methylation age, or DNAm age. Remarkably, Horvath's clock works across many tissues and cell types from the same individual, suggesting it captures a fundamental aging process rather than a tissue-specific quirk. The difference between predicted DNAm age and actual chronological age, known as epigenetic age acceleration, serves as a biomarker of biological aging. Age acceleration predicts mortality and a range of age-related conditions, which has made epigenetic clocks central to modern aging research.	Biological age estimation seeks to measure how old a person's body actually is, as distinct from the number of years since their birth. The most influential statistical approach is the Klemera-Doubal method (KDM), introduced in 2006, which derives a single biological-age value from a panel of age-related biomarkers. The central idea is that many physiological measures change predictably with age, so by regressing each biomarker on chronological age in a reference sample one can learn how each one tracks aging and then combine them to infer an individual's underlying biological age. Klemera and Doubal showed mathematically that treating biological age as a latent quantity estimated from all biomarkers jointly, weighted by how strongly and how cleanly each tracks age, yields a more accurate estimate than simply regressing chronological age on the biomarkers. The gap between estimated biological age and chronological age, often called biological age acceleration, indicates whether a person is aging faster or slower than average. This deviation predicts mortality and morbidity beyond chronological age, which is what makes the estimate useful.