Epigenetic Clocks

What They Measure

Epigenetic clocks estimate biological age using DNA methylation patterns. These patterns change predictably with age across many tissues, allowing models to infer an age-related signature. [1] [2] [3]

How Clocks Are Built

Most clocks are trained on large datasets to predict chronological age or health outcomes. The accuracy of a clock depends on the population used for training and the tissue measured. First-generation models target chronological age, while later clocks incorporate clinical risk factors to better predict morbidity and mortality. [4] [5] [6]

Use in Research

Epigenetic clocks are widely used to study ageing trajectories, compare interventions, and identify factors associated with accelerated or decelerated ageing. Measures of epigenetic age acceleration and pace-of-ageing predict mortality and disease risk in large cohorts and are increasingly used as trial endpoints. [7] [8] [9]

Limitations and Interpretation

Clocks can be sensitive to cell composition, inflammation, and technical variation. A change in clock age does not necessarily mean a change in disease risk for an individual, and different clocks often capture partially distinct biological processes. [3] [10] [11]

Summary

Epigenetic clocks are influential tools for ageing research, offering a molecular estimate of biological age. They are powerful for population studies but require cautious interpretation in individuals. [8] [9]

References

1. Hannum, G., et al. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Genome Research, 23(12), 2124-2132. https://pmc.ncbi.nlm.nih.gov/articles/PMC12539533/
2. Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), R115. https://onlinelibrary.wiley.com/doi/10.1111/acel.13229
3. Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 19(6), 371-384. https://onlinelibrary.wiley.com/doi/full/10.1002/ajhb.23488
4. Levine, M. E., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 10(4), 573-591. https://pmc.ncbi.nlm.nih.gov/articles/PMC12539533/
5. Lu, A. T., et al. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY), 11(4), 1183-1199. https://pmc.ncbi.nlm.nih.gov/articles/PMC11404624/
6. Higgins-Chen, A. T., et al. (2022). A computational solution for bias and noise in epigenetic clocks. Clinical Epigenetics, 14(1), 117. https://pmc.ncbi.nlm.nih.gov/articles/PMC12539533/
7. Belsky, D. W., et al. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. Nature Medicine, 28(10), 2086-2095. https://pmc.ncbi.nlm.nih.gov/articles/PMC11404624/
8. Chen, B. H., et al. (2016). DNA methylation-based measures of biological age: meta-analysis predicting mortality. Aging Cell, 15(4), 686-698. https://onlinelibrary.wiley.com/doi/10.1111/acel.13229
9. Liu, J., et al. (2024). Quantification of epigenetic aging in population studies. Clinical Epigenetics, 16(1), 175. https://pubmed.ncbi.nlm.nih.gov/36206857/
10. Lehallier, B., et al. (2022). Hallmarks of aging in human cells. Nature Aging, 2(5), 441-453. https://www.nature.com/articles/s43587-022-00220-0
11. Zhang, Y., et al. (2020). Epigenetic clocks: theory and applications in human biology. American Journal of Human Biology, 33(1), e23488. https://pmc.ncbi.nlm.nih.gov/articles/PMC10411856/