Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Aging by the clock and yet without a program

Nature Aging volume 5, pages 1946–1956 (2025)Cite this article

Subjects

Abstract

The mechanisms of aging are becoming increasingly well mapped; however, there remains ongoing debate about the ultimate and proximate causes of aging. The recent development of highly precise aging clocks led to a resurgence of arguments in support of a biological program of aging. However, the declining force of natural selection after the onset of reproduction means that cellular function could deteriorate without requiring a specific program. Here, we argue that aging clocks do not imply an intrinsic program but rather reflect the stochastic accumulation of molecular errors and damage. Damage accumulates due to insufficient maintenance and repair and contributes to system-wide entropy. In support of this, cross-species comparisons indicate that enhanced DNA repair capacity is a key determinant of exceptional longevity in mammals. By better understanding the nature of the stochasticity that governs the aging process, we will have a stronger mechanistic basis for developing geroprotective interventions to promote healthy aging in humans.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Molecular mechanisms that are subject to stochasticity.
Fig. 2: Epigenetic changes and transcription.
Fig. 3: Mechanisms of DNA methylation maintenance and sources of variation.

Similar content being viewed by others

References

  1. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: an expanding universe. Cell 186, 243–278 (2023).

    PubMed  Google Scholar 

  2. Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin.74, 12–49 (2024).

    PubMed  Google Scholar 

  3. Ahadi, S. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26, 83–90 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhang, W. B. et al. Extended twilight among isogenic C. elegans causes a disproportionate scaling between lifespan and health. Cell Syst. 3, 333–345 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Li, X. et al. Systems properties and spatiotemporal regulation of cell position variability during embryogenesis. Cell Rep. 26, 313–321 (2019).

    CAS  PubMed  Google Scholar 

  6. Ruby, J. G. et al. Estimates of the heritability of human longevity are substantially inflated due to assortative mating. Genetics 210, 1109–1124 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. Shenhar, B. et al. Heritability of human lifespan is about 50% when confounding factors are addressed. Preprint at bioRxiv https://doi.org/10.1101/2025.04.20.649385 (2025).

  8. Issa, J. Aging and epigenetic drift: a vicious cycle. J. Clin. Invest. 124, 24–29 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Tarkhov, A. E. et al. Nature of epigenetic aging from a single-cell perspective. Nat. Aging 4, 854–870 (2024).

    PubMed  Google Scholar 

  10. Schumacher, B., Pothof, J., Vijg, J. & Hoeijmakers, J. H. J. The central role of DNA damage in the ageing process. Nature 592, 695–703 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ren, P., Zhang, J. & Vijg, J. Somatic mutations in aging and disease. GeroScience 46, 5171–5189 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Cagan, A. et al. Somatic mutation rates scale with lifespan across mammals. Nature 604, 517–524 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Hamilton, W. D. The moulding of senescence by natural selection. J. Theor. Biol. 12, 12–45 (1966).

    CAS  PubMed  Google Scholar 

  14. Moorad, J., Promislow, D. & Silvertown, J. Evolutionary ecology of senescence and a reassessment of Williams’ ‘extrinsic mortality’ hypothesis. Trends Ecol. Evol. 34, 519–530 (2019).

    PubMed  PubMed Central  Google Scholar 

  15. Rose, M. R. Evolutionary Biology of Aging (Oxford Univ. Press, 1994).

  16. Medawar, P. B. An unsolved problem of biology. Med. J. Aust. https://doi.org/10.5694/j.1326-5377.1953.tb84985.x (1952).

  17. Williams, G. C. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11, 398–411 (1957).

    Google Scholar 

  18. Lemaître, J.-F., Moorad, J., Gaillard, J., Maklakov, A. A. & Nussey, D. H. A unified framework for evolutionary genetic and physiological theories of aging. PLoS Biol. 22, e3002513 (2024).

    PubMed  PubMed Central  Google Scholar 

  19. Bulut-Karslioglu, A. et al. Inhibition of mTOR induces a paused pluripotent state. Nature 540, 119–123 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Iyer, D. P. et al. mTOR activity paces human blastocyst stage developmental progression. Cell 187, 6566–6583 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Szathmáry, E. The origin of replicators and reproducers. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 1761–1776 (2006).

    PubMed  PubMed Central  Google Scholar 

  22. Piette, B. M. A. G. & Heddle, J. G. A peptide–nucleic acid replicator origin for life. Trends Ecol. Evol. 35, 397–406 (2020).

    PubMed  Google Scholar 

  23. Miller, S. L. A production of amino acids under possible primitive earth conditions. Science 117, 528–529 (1953).

    CAS  PubMed  Google Scholar 

  24. Miller, S. L. & Urey, H. C. Organic compound synthesis on the primitive earth. Science 130, 245–251 (1959).

    CAS  PubMed  Google Scholar 

  25. Dattani, J. & Barahona, M. Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization. J. R. Soc. Interface 14, 20160833 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. Seeger, M., Flöttmann, M. & Klipp, E. A dynamical stochastic model of yeast translation across the cell cycle. Heliyon 9, e13101 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Leote, A. C., Lopes, F. & Beyer, A. Loss of coordination between basic cellular processes in human aging. Nat. Aging 4, 1432–1445 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Cohen, A. A., Coste, C. F. D., Li, X., Bourg, S. & Pavard, S. Are trade-offs really the key drivers of ageing and life span? Funct. Ecol. 34, 153–166 (2020).

    Google Scholar 

  29. Wensink, M. J. & Cohen, A. A. The Danaid theory of aging. Front. Cell Dev. Biol. 9, 671208 (2022).

    PubMed  PubMed Central  Google Scholar 

  30. Gems, D. & Kern, C. C. Biological constraint, evolutionary spandrels and antagonistic pleiotropy. Ageing Res. Rev. 101, 102527 (2024).

    PubMed  Google Scholar 

  31. Lynch, M. Evolutionary layering and the limits to cellular perfection. Proc. Natl Acad. Sci. USA 109, 18851–18856 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Mitteldorf, J. An epigenetic clock controls aging. Biogerontology 17, 257–265 (2016).

    CAS  PubMed  Google Scholar 

  33. Pamplona, R., Jové, M., Gómez, J. & Barja, G. Programmed versus non-programmed evolution of aging. What is the evidence? Exp. Gerontol. 175, 112162 (2023).

    PubMed  Google Scholar 

  34. Goldsmith, T. C. Evolution of aging theories: why modern programmed aging concepts are transforming medical research. Biochemistry 81, 1406–1412 (2016).

    CAS  PubMed  Google Scholar 

  35. Weismann, A. Ueber Die Dauer Des Lebens; Ein Vortrag (G. Fischer, 1882).

  36. Libertini, G. The programmed aging paradigm: how we get old. Biochemistry 79, 1004–1016 (2014).

    CAS  PubMed  Google Scholar 

  37. Lidsky, P. V. & Andino, R. Could aging evolve as a pathogen control strategy? Trends Ecol. Evol. 37, 1046–1057 (2022).

    PubMed  Google Scholar 

  38. Kirkwood, T. B. L. & Melov, S. On the programmed/non-programmed nature of ageing within the life history. Curr. Biol. 21, R701–R707 (2011).

    CAS  PubMed  Google Scholar 

  39. Cohen, A. A. Physiological and comparative evidence fails to confirm an adaptive role for aging in evolution. Curr. Aging Sci. 8, 14–23 (2015).

    PubMed  Google Scholar 

  40. de Magalhães, J. P. Ageing as a software design flaw. Genome Biol. 24, 51 (2023).

    PubMed  PubMed Central  Google Scholar 

  41. Gems, D., Virk, R. S. & de Magalhães, J. P. Epigenetic clocks and programmatic aging. Ageing Res. Rev. 101, 102546 (2024).

    CAS  PubMed  Google Scholar 

  42. de Magalhães, J. P. Programmatic features of aging originating in development: aging mechanisms beyond molecular damage? FASEB J. 26, 4821–4826 (2012).

    PubMed  PubMed Central  Google Scholar 

  43. Maklakov, A. A. & Chapman, T. Evolution of ageing as a tangle of trade-offs: energy versus function. Proc. Biol. Sci. 286, 20191604 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

    CAS  PubMed  Google Scholar 

  45. Jaenisch, R. & Bird, A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat. Genet. 33, 245–254 (2003).

    CAS  PubMed  Google Scholar 

  46. Smith, Z. D., Hetzel, S. & Meissner, A. DNA methylation in mammalian development and disease. Nat. Rev. Genet. 26, 7–30 (2024).

    PubMed  Google Scholar 

  47. Seisenberger, S., et al. Reprogramming DNA methylation in the mammalian life cycle: building and breaking epigenetic barriers. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368, 20110330 (2013).

    PubMed  PubMed Central  Google Scholar 

  48. Vanyushin, B. F., Nemirovsky, L. E., Klimenko, V. V., Vasiliev, V. K. & Belozersky, A. N. The 5-methylcytosine in DNA of rats. Tissue and age specificity and the changes induced by hydrocortisone and other agents. Gerontologia 19, 138–152 (1973).

    CAS  PubMed  Google Scholar 

  49. Wilson, V. L. & Jones, P. A. DNA methylation decreases in aging but not in immortal cells. Science 220, 1055–1057 (1983).

    CAS  PubMed  Google Scholar 

  50. Issa, J. P. et al. Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nat. Genet. 7, 536–540 (1994).

    CAS  PubMed  Google Scholar 

  51. Rakyan, V. K. et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 20, 434–439 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Christensen, B. C., et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 5, e1000602 (2009).

    PubMed  PubMed Central  Google Scholar 

  53. Bocklandt, S., et al. Epigenetic predictor of age. PLoS ONE 6, e14821 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

    PubMed  PubMed Central  Google Scholar 

  55. Koch, C. M. & Wagner, W. Epigenetic-aging-signature to determine age in different tissues. Aging 3, 1018–1027 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

    CAS  PubMed  Google Scholar 

  57. Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging 10, 573–591 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Föhr, T. et al. Does the epigenetic clock GrimAge predict mortality independent of genetic influences: an 18 year follow-up study in older female twin pairs. Clin. Epigenetics 13, 128 (2021).

    PubMed  Google Scholar 

  60. Moqri, M. et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell 186, 3758–3775 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Meyer, D. H. & Schumacher, B. BiT age: a transcriptome-based aging clock near the theoretical limit of accuracy. Aging Cell 20, e13320 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Schumacher, B., Gallrein, C. & Meyer, D. Neuron-type specific aging-rate reveals age decelerating interventions preventing neurodegeneration. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-4360587/v1 (2024).

  63. Kriukov, D. et al. ComputAgeBench: epigenetic aging clocks benchmark. In Proc. 31st ACM SIGKDD Conf. on Knowledge Discovery and Data Mining V.2 https://doi.org/10.1145/3711896.3737382 (ACM, 2025).

  64. Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Nat. Aging 3, 1144–1166 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Seale, K., Horvath, S., Teschendorff, A., Eynon, N. & Voisin, S. Making sense of the ageing methylome. Nat. Rev. Genet. 23, 585–605 (2022).

    CAS  PubMed  Google Scholar 

  66. Raj, K. & Horvath, S. Current perspectives on the cellular and molecular features of epigenetic ageing. Exp. Biol. Med. 245, 1532–1542 (2020).

    CAS  Google Scholar 

  67. Blagosklonny, M. V. Aging is not programmed: genetic pseudo-program is a shadow of developmental growth. Cell Cycle 12, 3736–3742 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Meyer, D. H. & Schumacher, B. Aging clocks based on accumulating stochastic variation. Nat. Aging 4, 871–885 (2024).

    PubMed  PubMed Central  Google Scholar 

  69. Chan, J., Rubbi, L. & Pellegrini, M. DNA methylation entropy is a biomarker for aging. Aging 17, 685–698 (2025).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Kabacik, S. et al. The relationship between epigenetic age and the hallmarks of aging in human cells. Nat. Aging 2, 484–493 (2022).

    PubMed  PubMed Central  Google Scholar 

  71. Perez, K., et al. DNA repair-deficient premature aging models display accelerated epigenetic age. Aging Cell 23, e14058 (2024).

    CAS  PubMed  Google Scholar 

  72. Yang, J. H. et al. Loss of epigenetic information as a cause of mammalian aging. Cell 186, 305–326 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Moqri, M. et al. PRC2-AgeIndex as a universal biomarker of aging and rejuvenation. Nat. Commun. 15, 5956 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Laugesen, A., Højfeldt, J. W. & Helin, K. Role of the Polycomb repressive complex 2 (PRC2) in transcriptional regulation and cancer. Cold Spring Harb. Perspect. Med. 6, a026575 (2016).

    PubMed  PubMed Central  Google Scholar 

  75. Corley, M. & Kroll, K. L. The roles and regulation of Polycomb complexes in neural development. Cell Tissue Res. 359, 65–85 (2015).

    CAS  PubMed  Google Scholar 

  76. Horvath, S., Zhang, J., Haghani, A., Lu, A. T. & Fei, Z. Fundamental equations linking methylation dynamics to maximum lifespan in mammals. Nat. Commun. 15, 8093 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. McLain, A. T. & Faulk, C. The evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging 10, 561–572 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Mayne, B., Berry, O., Davies, C., Farley, J. & Jarman, S. A genomic predictor of lifespan in vertebrates. Sci. Rep. 9, 17866 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Jenkinson, G., Pujadas, E., Goutsias, J. & Feinberg, A. P. Potential energy landscapes identify the information-theoretic nature of the epigenome. Nat. Genet. 49, 719–729 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Koch, Z., Li, A., Evans, D. S., Cummings, S. & Ideker, T. Somatic mutation as an explanation for epigenetic aging. Nat. Aging 5, 709–719 (2025).

    CAS  PubMed  Google Scholar 

  81. Sziráki, A., Tyshkovskiy, A. & Gladyshev, V. N. Global remodeling of the mouse DNA methylome during aging and in response to calorie restriction. Aging Cell 17, e12738 (2018).

    PubMed  PubMed Central  Google Scholar 

  82. Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol. 18, 57 (2017).

    PubMed  PubMed Central  Google Scholar 

  83. Bertucci, E. M., Mason, M. W., Rhodes, O. E. & Parrott, B. B. The aging DNA methylome reveals environment-by-aging interactions in a model teleost. Preprint at bioRxiv https://doi.org/10.1101/2021.03.01.433371 (2021).

  84. Koike, Y., et al. Age-related demethylation of the TDP-43 autoregulatory region in the human motor cortex. Commun. Biol. 4, 1107 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Meyer, D. H. & Schumacher, B. The stochasticity of biological aging. Nat. Med. 31, 1735–1736 (2025).

    CAS  PubMed  Google Scholar 

  86. Rieckher, M., Garinis, G. A. & Schumacher, B. Molecular pathology of rare progeroid diseases. Trends Mol. Med. 27, 907–922 (2021).

    CAS  PubMed  Google Scholar 

  87. Panier, S., Wang, S. & Schumacher, B. Genome instability and DNA repair in somatic and reproductive aging. Annu. Rev. Pathol. 19, 261–290 (2024).

    CAS  PubMed  Google Scholar 

  88. Milholland, B. et al. Differences between germline and somatic mutation rates in humans and mice. Nat. Commun. 8, 15183 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Bujarrabal-Dueso, A. et al. The DREAM complex functions as conserved master regulator of somatic DNA-repair capacities. Nat. Struct. Mol. Biol. 30, 475–488 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Bujarrabal-Dueso, A. Targeting DNA damage in ageing: towards supercharging DNA repair. Nat. Rev. Drug Discov. https://doi.org/10.1038/s41573-025-01212-6 (2025).

  91. de Lima Camillo, L. P., Lapierre, L. R. & Singh, R. A pan-tissue DNA-methylation epigenetic clock based on deep learning. NPJ Aging 8, 4 (2022).

    PubMed Central  Google Scholar 

  92. Wang, S., Meyer, D. H. & Schumacher, B. Inheritance of paternal DNA damage by histone-mediated repair restriction. Nature 613, 365–374 (2023).

    CAS  PubMed  Google Scholar 

  93. Gladyshev, V. N. The ground zero of organismal life and aging. Trends Mol. Med. 27, 11–19 (2021).

    CAS  PubMed  Google Scholar 

  94. Totska, K., Barata, J. C. V. V., Sandt, W., Meyer, D. H. & Schumacher, B. Age deceleration and reversal gene patterns in dauer diapause. Preprint at bioRxiv https://doi.org/10.1101/2025.04.14.648662 (2025).

  95. Kawamura, K. et al. Resilience and restoration from fasting–refeeding mediated by a nutrient-regulated linker histone. Preprint at bioRxiv https://doi.org/10.1101/2025.04.14.648802 (2025).

  96. Benjamin, H. Biologic versus chronologic age. J. Gerontol. 2, 217–227 (1947).

    CAS  PubMed  Google Scholar 

  97. Hollingsworth, J. W., Ishii, G. & Conard, R. A. Skin aging and hair graying in Hiroshima. Geriatrics 16, 27–36 (1961).

    CAS  PubMed  Google Scholar 

  98. Hollingsworth, J. W., Hashizume, A. & Jablon, S. Correlations between tests of aging in Hiroshima subjects—an attempt to define ‘physiologic age’. Yale J. Biol. Med. 38, 11–26 (1965).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Hollingsworth, J. W., Hamilton, H. B. & Ishii, G. Age-related changes in erythrocyte agglutinability in Hiroshima subjects. J. Appl. Physiol. 16, 1093–1096 (1961).

    CAS  PubMed  Google Scholar 

  100. Webster, I. W. & Logie, A. R. A relationship between functional age and health status in female subjects. J. Gerontol. 31, 546–550 (1976).

    CAS  PubMed  Google Scholar 

  101. Furukawa, T., Inoue, M., Kajiya, F., Inada, H. & Takasugi, S. Assessment of biological age by multiple regression analysis. J. Gerontol. 30, 422–434 (1975).

    CAS  PubMed  Google Scholar 

  102. Costa, P. T. & McCrae, R. R. Measures and markers of biological aging: ‘a great clamoring… of fleeting significance’. Arch. Gerontol. Geriatr. 7, 211–214 (1988).

    PubMed  Google Scholar 

  103. Costa, P. T. & McCrae, R. R. in Principles of Geriatric Medicine 30–37 (McGraw-Hill, 1985).

  104. Wilson, D. L. Aging hypotheses, aging markers and the concept of biological age. Exp. Gerontol. 23, 435–438 (1988).

    CAS  PubMed  Google Scholar 

  105. Salthouse, T. A. in Age, Health, and Employment 78–92 (Prentice-Hall, 1986).

  106. Hochschild, R. Improving the precision of biological age determinations. Part 1: a new approach to calculating biological age. Exp. Gerontol. 24, 289–300 (1989).

    CAS  PubMed  Google Scholar 

  107. Zhang, Q. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 11, 54 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Sluiskes, M. H., et al. Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data. BMC Med. Res. Methodol. 24, 58 (2024).

    PubMed  PubMed Central  Google Scholar 

  109. Belsky, D. W., et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife 11, e73420 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Kriukov, D., Kuzmina, E., Efimov, E., Dylov, D. V. & Khrameeva, E. E. Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects. Aging Cell 23, e14283 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Higgins-Chen, A. T. et al. A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking. Nat. Aging 2, 644–661 (2022).

    PubMed  PubMed Central  Google Scholar 

  112. Ying, K. et al. Causality-enriched epigenetic age uncouples damage and adaptation. Nat. Aging 4, 231–246 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Schrödinger, E. What Is Life? The Physical Aspect of the Living Cell & Mind and Matter (Cambridge Univ. Press, 1974).

  114. Clausius, R. Ueber verschiedene für die Anwendung bequeme Formen der Hauptgleichungen der mechanischen Wärmetheorie. Ann. Phys. 201, 353–400 (1865).

    Google Scholar 

  115. Ben-Naim, A. Entropy and time. Entropy 22, 430 (2020).

  116. Bortz, W. M. 2nd. Aging as entropy. Exp. Gerontol. 21, 321–328 (1986).

    PubMed  Google Scholar 

  117. Sacher, G. The complementarity of entropy terms for the temperature dependence of development and aging. Ann. N. Y. Acad. Sci. 138, 680–712 (1967).

    Google Scholar 

  118. Nikopoulou, C., Parekh, S. & Tessarz, P. Ageing and sources of transcriptional heterogeneity. Biol. Chem. 400, 867–878 (2019).

    CAS  PubMed  Google Scholar 

  119. Cooney, C. A. Are somatic cells inherently deficient in methylation metabolism? A proposed mechanism for DNA methylation loss, senescence and aging. Growth Dev. Aging 57, 261–273 (1993).

    CAS  PubMed  Google Scholar 

  120. Bronner, C., Alhosin, M., Hamiche, A. & Mousli, M. Coordinated dialogue between UHRF1 and DNMT1 to ensure faithful inheritance of methylated DNA patterns. Genes 10, 65 (2019).

    PubMed  PubMed Central  Google Scholar 

  121. Zhou, W. et al. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nat. Genet. 50, 591–602 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Takahashi, S. et al. Embryonic genome instability upon DNA replication timing program emergence. Nature 633, 686–694 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Scherer, M., et al. Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic Acids Res. 48, e46 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Loyfer, N. et al. A DNA methylation atlas of normal human cell types. Nature 613, 355–364 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Meaburn, E. L., Schalkwyk, L. C. & Mill, J. Allele-specific methylation in the human genome: implications for genetic studies of complex disease. Epigenetics 5, 578–582 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012).

    CAS  PubMed  Google Scholar 

  128. Xie, H. et al. Genome-wide quantitative assessment of variation in DNA methylation patterns. Nucleic Acids Res. 39, 4099–4108 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Bertucci-Richter, E. M., Shealy, E. P. & Parrott, B. B. Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation. Aging 16, 1002–1020 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Zhang, X. & Wang, X. MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites. Bioinformatics 38, i307–i315 (2022).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

B.S. acknowledges funding from the Deutsche Forschungsgemeinschaft (Reinhart Koselleck-Project 524088035, FOR 5504 project 496650118, FOR 5762 project 531902955, SFB 1678, SFB 1607, CECAD EXC 2030-390661388, ANR-DFG project 545378328, the DFG-ISF project 561031107 and DFG project grants 558166204, 540136447, 496914708, 437825591, 437407415 and 418036758), the Deutsche Krebshilfe (70114555), Deutsche José Carreras Leukämie-Stiftung (DJCLS 04 R/2023), a John Templeton Foundation Grant (61734), the European Research Council (ERC-2023-SyG 101118919) and the Hevolution Foundation (HF-GRO-23-1199212-35). D.H.M. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG project grant 570621149) and from the Koeln Fortune Program / Faculty of Medicine, University of Cologne. A.A.M. acknowledges funding from the Leverhulme Trust RPG-2023-068 and the Natural Environment Research Council (NE/W001020/1).

Author information

Authors and Affiliations

  1. Institute for Genome Stability in Ageing and Disease, Medical Faculty, University of Cologne, Cologne, Germany

    David H. Meyer & Björn Schumacher

  2. Cologne Excellence Cluster for Cellular Stress Responses in Ageing-Associated Diseases, Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany

    David H. Meyer & Björn Schumacher

  3. School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, UK

    Alexei A. Maklakov

Authors
  1. David H. Meyer
    View author publications

    Search author on:PubMed Google Scholar

  2. Alexei A. Maklakov
    View author publications

    Search author on:PubMed Google Scholar

  3. Björn Schumacher
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization and writing: D.H.M., A.A.M. and B.S.

Corresponding authors

Correspondence to David H. Meyer, Alexei A. Maklakov or Björn Schumacher.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Steven Cummings, Vera Gorbunova, and Alan Cohen for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meyer, D.H., Maklakov, A.A. & Schumacher, B. Aging by the clock and yet without a program. Nat Aging 5, 1946–1956 (2025). https://doi.org/10.1038/s43587-025-00975-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43587-025-00975-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing