Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
463
Citations
1.8m
Views
746
Articles
REVIEWS (Open Access)
Epigenetic Clocks and Biological Ageing: From Biomarker to Intervention Target
Monika Barsagade 1*, Ravi kishore Agrawal 1
Integrative Biomedical Research 10 (1) 1-8 https://doi.org/10.25163/biomedical.10110885
Submitted: 19 February 2026 Revised: 07 April 2026 Accepted: 16 April 2026 Published: 18 April 2026
Abstract
Chronological age is a poor proxy for the true physiological state of an individual, and this mismatch has driven a decade-long search for molecular biomarkers capable of tracking biological age directly. Among the candidates that have emerged from genomics, transcriptomics, proteomics and metabolomics, biomarkers derived from DNA methylation—so-called epigenetic clocks—have proved the most reproducible and the most clinically informative. Here we trace their evolution from first-generation estimators trained to predict chronological age, through second-generation clocks trained on clinical morbidity and mortality outcomes, to third-generation “pace of ageing” metrics and, most recently, resilience-oriented multi-omics frameworks such as EpiAge-R that attempt to separate degenerative methylation noise from active repair signal. We show how epigenetic age acceleration tracks mortality, cardiovascular disease, cancer, neurodegeneration and frailty across large cohorts, how minimalist targeted-CpG assays are beginning to make biological age screening scalable outside specialist laboratories, and how randomized and pilot trials of multidomain lifestyle, dietary and micronutrient interventions have repeatedly demonstrated measurable deceleration—and in some cohorts apparent reversal—of biological age. We argue that epigenetic clocks are best understood not as static descriptive biomarkers but as dynamic, intervention-sensitive endpoints, and we outline the technical noise, tissue specificity and population-reference biases that currently stand between this promise and its responsible use in individual patients.
Keywords: epigenetic clocks; DNA methylation; biological age; geroscience; epigenetic age acceleration; longevity intervention; EpiAge-R
References
Apsley, A. T., Etzel, L., Ye, Q., & Shalev, I. (2025). From population science to the clinic? Limits of epigenetic clocks as personal biomarkers. Epigenomics, 17(18), 1447–1461. https://doi.org/10.1080/17501911.2025.2603880
Baker, G. T., & Sprott, R. L. (1988). Biomarkers of aging. Experimental Gerontology, 23(4–5), 223–239. https://doi.org/10.1016/0531-5565(88)90025-3
Belsky, D. W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D. L., Gao, X., Hannon, E., Harrington, H. L., Rasmussen, L. J., Houts, R., & Moffitt, T. E. (2020). Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. eLife, 9, e54870. https://doi.org/10.7554/eLife.54870
Belsky, D. W., Caspi, A., Corcoran, D. L., Sugden, K., Poulton, R., Arseneault, L., Baccarelli, A., Chamarti, K., Gao, X., Hannon, E., Harrington, H. L., Houts, R., Kothari, M., & Moffitt, T. E. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife, 11, e73420. https://doi.org/10.7554/eLife.73420
Belsky, D. W., Moffitt, T. E., Cohen, A. A., Corcoran, D. L., Levine, M. E., Prinz, J. A., Schaefer, J., Sugden, K., Williams, B., Poulton, R., & Caspi, A. (2018). Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: Do they measure the same thing? American Journal of Epidemiology, 187(6), 1220–1230. https://doi.org/10.1093/aje/kwx346
Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011). Epigenetic predictor of age. PLoS ONE, 6(6), e14821. https://doi.org/10.1371/journal.pone.0014821
Carreras-Gallo, N., Dargham, R., Thorpe, S. P., Warren, S., Mendez, T. L., Smith, R., Macpherson, G., & Dwaraka, V. B. (2025). Effects of a natural ingredients-based intervention targeting the hallmarks of aging on epigenetic clocks, physical function, and body composition: A single-arm clinical trial. Aging, 17(5), 206221. https://doi.org/10.18632/aging.206221
Conole, E. L. S., Robertson, J. A., Smith, H. M., Cox, S. R., & Marioni, R. E. (2025). Epigenetic clocks and DNA methylation biomarkers of brain health and disease. Nature Reviews Neurology, 21(8), 411–421. https://doi.org/10.1038/s41582-025-01105-7
Deelen, J., Kettunen, J., Fischer, K., van der Spek, A., Trompet, S., Kastenmüller, G., Boyd, A., Zierer, J., van den Akker, E. B., Ala-Korpela, M., Amin, N., Demirkan, A., Ghanbari, M., van Heemst, D., Ikram, M. A., van Klinken, J. B., Mooijaart, S. P., Peters, A., Salomaa, V., … Slagboom, P. E. (2019). A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nature Communications, 10, 3346. https://doi.org/10.1038/s41467-019-11311-9
Diebel, L. W. M., & Rockwood, K. (2021). Determination of biological age: Geriatric assessment vs biological biomarkers. Current Oncology Reports, 23(9), 104. https://doi.org/10.1007/s11912-021-01097-x
Fiorito, G., Caini, S., Palli, D., Bendinelli, B., Saieva, C., Ermini, I., Valentini, V., Assedi, M., Rizzolo, P., Ambrogetti, D., Ottini, L., & Masala, G. (2021). DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: The DAMA study. Aging Cell, 20(10), e13439. https://doi.org/10.1111/acel.13439
Fitzgerald, K. N., Hodges, R., Hanes, D., Stack, E., Cheishvili, D., Szyf, M., Henkel, J., Twedt, M. W., Giannopoulou, D., Herdell, J., Logan, S., & Bradley, R. (2021). Potential reversal of epigenetic age using a diet and lifestyle intervention: A pilot randomized clinical trial. Aging, 13(7), 9419–9432. https://doi.org/10.18632/aging.202913
Fransquet, P. D., Wrigglesworth, J., Woods, R. L., Ernst, M. E., & Ryan, J. (2019). The epigenetic clock as a predictor of disease and mortality risk: A systematic review and meta-analysis. Clinical Epigenetics, 11(1), 62. https://doi.org/10.1186/s13148-019-0656-7
Galkin, F., Mamoshina, P., Kochetov, K., Sidorenko, D., & Zhavoronkov, A. (2021). DeepMAge: A methylation aging clock developed with deep learning. Aging and Disease, 12(5), 1252–1262. https://doi.org/10.14336/AD.2021.0511
Galow, A.-M., & Peleg, S. (2022). How to slow down the ticking clock: Age-associated epigenetic alterations and related interventions to extend lifespan. Cells, 11(3), 468. https://doi.org/10.3390/cells11030468
Gensous, N., Sala, C., Pirazzini, C., Ravaioli, F., Milazzo, M., Kwiatkowska, K. M., Marasco, E., De Fanti, S., Giuliani, C., Pellegrini, C., & Bacalini, M. G. (2022). A targeted epigenetic clock for the prediction of biological age. Cells, 11(24), 4044. https://doi.org/10.3390/cells11244044
Guerville, F., De Souto Barreto, P., Ader, I., Andrieu, S., Casteilla, L., Dray, C., Fazilleau, N., Guyonnet, S., Langin, D., Liblau, R., Parini, A., Valet, P., Vergnolle, N., Rolland, Y., & Vellas, B. (2020). Revisiting the hallmarks of aging to identify markers of biological age. The Journal of Prevention of Alzheimer’s Disease, 7(1), 56–64. https://doi.org/10.14283/jpad.2019.50
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., & Zhang, K. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell, 49(2), 359–367. https://doi.org/10.1016/j.molcel.2012.10.016
Higgins-Chen, A. T., Thrush, K. L., Wang, Y., Minteer, C. J., Kuo, P.-L., Wang, M., Niimi, P., Sturm, G., Lin, J., Moore, A. Z., & Levine, M. E. (2022). A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking. Nature Aging, 2(7), 644–661. https://doi.org/10.1038/s43587-022-00248-2
Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), R115. https://doi.org/10.1186/gb-2013-14-10-r115
Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 19(6), 371–384. https://doi.org/10.1038/s41576-018-0004-3
Horvath, S., Oshima, J., Martin, G. M., Lu, A. T., Quach, A., Cohen, H., Felton, S., Matsuyama, M., Lowe, D., Kabacik, S., Wilson, J. G., Reiner, A. P., Maierhofer, A., Flunkert, J., Aviv, A., Hou, L., Baccarelli, A. A., Li, Y., Stewart, J. D., … Raj, K. (2018). Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging, 10(7), 1758–1775. https://doi.org/10.18632/aging.101508
Ibáñez-Cabellos, J. S., García-Giménez, J. L., Escobar, J., Pallardó, F. V., & Mena-Mollá, S. (2026). From the lab to lifestyle: Epigenetic clocks in personalized aging and health. Biogerontology, 27, 101–120. https://doi.org/10.1007/s10522-026-10447-8
Ibáñez-Cabellos, J. S., Sandoval, J., Pallardó, F. V., García-Giménez, J. L., & Mena-Mollá, S. (2025). A sex-specific minimal CpG-based model for biological aging using ELOVL2 methylation analysis. International Journal of Molecular Sciences, 26(7), 3392. https://doi.org/10.3390/ijms26073392
Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., Franceschi, C., Lithgow, G. J., Morimoto, R. I., Pessin, J. E., & Rando, T. A. (2014). Geroscience: Linking aging to chronic disease. Cell, 159(4), 709–713. https://doi.org/10.1016/j.cell.2014.10.039
Kim, H., Park, A.-H., Kwon, M., Lee, K. J., Kim, M.-J., & Lee, M.-S. (2025). EpiClock; biological age measurement from blood DNA methylation using a minimal CpG marker set for high-throughput iPlex mass spectrometry assay for screening in drug development and population health. Experimental Gerontology, 211, 112918. https://doi.org/10.1016/j.exger.2025.112918
Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., Whitsel, E. A., Wilson, J. G., Reiner, A. P., Aviv, A., Lohman, K., Liu, Y., Ferrucci, L., & Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. https://doi.org/10.18632/aging.101414
Li, A., Koch, Z., & Ideker, T. (2022). Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging. Journal of Internal Medicine, 292, 733–744. https://doi.org/10.1111/joim.13533
Liang, R., Tang, Q., Chen, J., & Zhu, L. (2024). Epigenetic clocks: Beyond biological age, using the past to predict the present and future. Aging and Disease, 16(6), 3520–3545. https://doi.org/10.14336/AD.2024.1495
Liebich, A., Zheng, S., Schachner, T., Mair, J., Jovanova, M., Müller-Riemenschneider, F., & Kowatsch, T. (2024). Non-pharmaceutical interventions and epigenetic aging in adults: Protocol for a scoping review. PLoS ONE, 19(8), e0301763. https://doi.org/10.1371/journal.pone.0301763
Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194–1217. https://doi.org/10.1016/j.cell.2013.05.039
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). Hallmarks of aging: An expanding universe. Cell, 186(2), 243–278. https://doi.org/10.1016/j.cell.2022.11.001
Lu, A. T., Binder, A. M., Zhang, J., Yan, Q., Reiner, A. P., Qin, H., Whitsel, E. A., Fornage, M., Baccarelli, A. A., Assimes, T. L., Murabito, J. M., Just, A. C., Wang, C., Bandinelli, S., Wilson, J. G., Ferrucci, L., & Horvath, S. (2022). DNA methylation GrimAge version 2. Aging, 14(23), 9484–9549. https://doi.org/10.18632/aging.204434
Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., Hou, L., Baccarelli, A. A., Li, Y., Stewart, J. D., Whitsel, E. A., Assimes, T. L., Ferrucci, L., & Horvath, S. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging, 11(2), 303–327. https://doi.org/10.18632/aging.101684
Maddock, J., Castillo-Fernandez, J., Wong, A., Ploubidis, G. B., Kuh, D., Bell, J. T., & Hardy, R. (2021). Childhood growth and development and DNA methylation age in mid-life. Clinical Epigenetics, 13(1), 155. https://doi.org/10.1186/s13148-021-01138-x
McCrory, C., Fiorito, G., Hernandez, B., Polidoro, S., O’Halloran, A. M., Hever, A., Ni Cheallaigh, C., Lu, A. T., Horvath, S., Vineis, P., & Kenny, R. A. (2021). GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality. The Journals of Gerontology: Series A, 76(5), 741–749. https://doi.org/10.1093/gerona/glaa286
McGreevy, K. M., Radak, Z., Torma, F., Jokai, M., Lu, A. T., Ake, T., Marioni, R., Irincheeva, I., Correia, N., Kabacik, S., Raj, K., & Horvath, S. (2023). DNAmFitAge: Biological age indicator incorporating physical fitness. Aging, 15(10), 3904–3938. https://doi.org/10.18632/aging.204538
Moqri, M., Herzog, C., Poganik, J. R., Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., & Gladyshev, V. N. (2023). Biomarkers of aging for the identification and evaluation of longevity interventions. Cell, 186(18), 3758–3775. https://doi.org/10.1016/j.cell.2023.08.003
Olaso-Gonzalez, G., Millan-Domingo, F., Tarazona-Santabalbina, F. J., Viña, J., & Gomez-Cabrera, M. C. (2026). A multidomain lifestyle intervention reverses frailty and modulates the epigenetic clock and telomere length in older adults: A randomized clinical trial. Aging Cell, 25(2), e13037. https://doi.org/10.1111/acel.70376
Petkovich, D. A., Podolskiy, D. I., Lobanov, A. V., Lee, S.-G., Miller, R. A., & Gladyshev, V. N. (2017). Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metabolism, 25, 954–960.e6. https://doi.org/10.1016/j.cmet.2017.03.006
Polidori, M. C. (2024). Aging hallmarks, biomarkers, and clocks for personalized medicine: (re)positioning the limelight. Free Radical Biology and Medicine, 215, 48–55. https://doi.org/10.1016/j.freeradbiomed.2024.02.012
Polidori, M. C., & Ferrucci, L. (2023). Frailty from conceptualization to action: The biopsychosocial model of frailty and resilience. Aging Clinical and Experimental Research, 35(4), 725–727. https://doi.org/10.1007/s40520-022-02337-z
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022). Measuring biological age using omics data. Nature Reviews Genetics, 23(12), 715–727. https://doi.org/10.1038/s41576-022-00511-7
Satapathy, A., Sarangi, R., & Nath, S. (2026). How old are you really? Epigenetic clocks – A narrative review. Journal of Integrative Medicine and Research, 4(2), 109–116.
Shireby, G. L., Davies, J. P., Francis, P. T., Burrage, J., Walker, E. M., Neilson, G. W. A., Dahir, A., Thomas, A. J., Love, S., Smith, R. G., Lunnon, K., Kumari, M., Schalkwyk, L. C., Morgan, K., Brookes, K., Hannon, E., & Mill, J. (2020). Recalibrating the epigenetic clock: Implications for assessing biological age in the human cortex. Brain, 143(12), 3763–3775. https://doi.org/10.1093/brain/awaa334
Shokhirev, M. N., Torosin, N. S., Kramer, D. J., Johnson, A. A., & Cuellar, T. L. (2024). CheekAge: A next-generation buccal epigenetic aging clock associated with lifestyle and health. GeroScience, 46(3), 3429–3443. https://doi.org/10.1007/s11357-024-01094-3
Wang, K., Liu, H., Hu, Q., Wang, L., Liu, J., Zheng, Z., Zhang, W., Ren, J., Zhu, F., & Liu, G.-H. (2022). Epigenetic regulation of aging: Implications for interventions of aging and diseases. Signal Transduction and Targeted Therapy, 7(1), 374. https://doi.org/10.1038/s41392-022-01211-8
Weidner, C. I., Lin, Q., Koch, C. M., & Wagner, W. (2014). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biology, 15(2), R24. https://doi.org/10.1186/gb-2014-15-2-r24
World Health Organization. (2015). World report on ageing and health. World Health Organization.
Yamada, H. (2025). Epigenetic clocks and EpiScore for preventive medicine: Risk stratification and intervention models for age-related diseases. Journal of Clinical Medicine, 14(10), 3604. https://doi.org/10.3390/jcm14103604
Yamada, H. (2026). Epigenetic clocks, resilience, and multi-omics ageing: A review and the EpiAge-R conceptual framework. International Journal of Molecular Sciences, 27(4), 1908. https://doi.org/10.3390/ijms27041908
Yang, Z., Epel, E. S., & Medzhitov, R. (2016). Correlation of an epigenetic mitotic clock with cancer risk. Genome Biology, 17, 205. https://doi.org/10.1186/s13059-016-1068-z
Zhang, Q., Vallerga, C. L., Walker, R. M., Lin, T., Henders, A. K., Montgomery, G. W., & McRae, A. F. (2019). Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Medicine, 11, 54. https://doi.org/10.1186/s13073-019-0667-1
Article metrics
View details
0
Downloads
0
Citations
15
Views
0
Save
Save
0
Citation
Citation
15
View
View
0
Share
Share