Aging clocks based on accumulating stochastic variation
Open accessPublished version of a preprint
Abstract
Aging clocks have provided one of the most important recent breakthroughs in the biology of aging, and may provide indicators for the effectiveness of interventions in the aging process and preventive treatments for age-related diseases. The reproducibility of accurate aging clocks has reinvigorated the debate on whether a programmed process underlies aging. Here we show that accumulating stochastic variation in purely simulated data is sufficient to build aging clocks, and that first-generation and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. We find that accumulating stochastic variation is sufficient to predict chronological and biological age, indicated by significant prediction differences in smoking, calorie restriction, heterochronic parabiosis and partial reprogramming. Although our simulations may not explicitly rule out a programmed aging process, our results suggest that stochastically accumulating changes in any set of data that have a ground state at age zero are sufficient for generating aging clocks.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
fastp: an ultra-fast all-in-one FASTQ preprocessor
(2018) · doi:10.1093/bioinformatics/bty560 · Cited by 29,721 · see citing works
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826 · see citing works
Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N., et al. (2020) · RePEc: Research Papers in Economics · Cited by 17,844 · see citing works
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 9,969 · see citing works
seaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310 · see citing works
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075 · see citing works
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441 · see citing works
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270 · see citing works
An epigenetic biomarker of aging for lifespan and healthspan
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., et al. (2018) · Aging · doi:10.18632/aging.101414 · Cited by 3,675 · see citing works
Epigenetic differences arise during the lifetime of monozygotic twins
Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestar, M. L., Heine-Suñer, D., Cigudosa, J. C., Urioste, M., Benitez, J., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500398102 · Cited by 3,602 · see citing works
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175 · see citing works
DNA methylation-based biomarkers and the epigenetic clock theory of ageing
Horvath, S., & Raj, K. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0004-3 · Cited by 3,095 · see citing works
DNA methylation GrimAge strongly predicts lifespan and healthspan
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., et al. (2019) · Aging · doi:10.18632/aging.101684 · Cited by 2,540 · see citing works
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Soneson, C., Love, M. I., & Robinson, M. D. (2016) · F1000Research · doi:10.12688/f1000research.7563.2 · Cited by 1,936 · see citing works
Pleiotropy, natural selection, and the evolution of senescence
Williams, G. C. (1957) · Evolution · doi:10.2307/2406060 · Cited by 1,912 · see citing works
Pingouin: statistics in Python
Vallat, R. (2018) · Journal of Open Source Software · doi:10.21105/joss.01026 · Cited by 1,780 · see citing works
Functional roles for noise in genetic circuits
Eldar, A., & Elowitz, M. B. (2010) · Nature · doi:10.1038/nature09326 · Cited by 1,440 · see citing works
The central role of DNA damage in the ageing process
Schumacher, B., Pothof, J., Vijg, J., & Hoeijmakers, J. H. J. (2021) · Nature · doi:10.1038/s41586-021-03307-7 · Cited by 907 · see citing works
Self-citationAging of blood can be tracked by DNA methylation changes at just three CpG sites
Weidner, C. I., Lin, Q., Koch, C., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D., Jöckel, K., Erbel, R., Mühleisen, T., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r24 · Cited by 849 · see citing works
Varoquaux, G. P., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., & Mueller, A. (2015) · GetMobile Mobile Computing and Communications · doi:10.1145/2786984.2786995 · Cited by 663 · see citing works
Aging effects on DNA methylation modules in human brain and blood tissue
Horvath, S., Zhang, Y., Langfelder, P., Kahn, R., Boks, M., van Eijk, K., van den Berg, L. H., & Ophoff, R. (2012) · Genome Biology · doi:10.1186/gb-2012-13-10-r97 · Cited by 635 · see citing works
Increased cell-to-cell variation in gene expression in ageing mouse heart
Bahar, R., Hartmann, C. H., Rodriguez, K. A., Denny, A. D., Busuttil, R. A., Dollé, M. E. T., Calder, R. B., Chisholm, G. B., Pollock, B. H., Klein, C. A., et al. (2006) · Nature · doi:10.1038/nature04844 · Cited by 603 · see citing works
Teschendorff, A. E., Breeze, C. E., Zheng, S., & Beck, S. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1511-5 · Cited by 548 · see citing works
Universal DNA methylation age across mammalian tissues
Lu, A. T.-H., Fei, Z., Haghani, A., Robeck, T. R., Zoller, J. A., Li, C. Z., Lowe, R., Yan, Q. J., Zhang, J., Vu, H., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00462-6 · Cited by 495 · see citing works
Zhang, Q., Vallerga, C. L., Walker, R. M., Lin, T., Henders, A. K., Montgomery, G. W., He, J., Fan, D., Fowdar, J., Kennedy, M., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0667-1 · Cited by 487 · see citing works
Padovan-Merhar, O. M., Nair, G. P., Biaesch, A. G., Mayer, A., Scarfone, S., Foley, S. W., Wu, A. R., Churchman, L. S., Singh, A., & Raj, A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.03.005 · Cited by 434 · see citing works
Aging and epigenetic drift: a vicious cycle
Issa, J.-P. (2014) · Journal of Clinical Investigation · doi:10.1172/jci69735 · Cited by 419 · see citing works
Using DNA methylation profiling to evaluate biological age and longevity interventions
Petkovich, D. A., Podolskiy, D. I., Lobanov, A. V., Lee, S.-G., Miller, R. A., & Gladyshev, V. N. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.03.016 · Cited by 398 · see citing works
Lee, R. D. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530303100 · Cited by 395 · see citing works
Identification of genetic elements that autonomously determine DNA methylation states
Lienert, F., Wirbelauer, C., Som, I., Dean, A., Mohn, F., & Schübeler, D. (2011) · Nature Genetics · doi:10.1038/ng.946 · Cited by 385 · see citing works
Recruitment of DNA methyltransferase I to DNA repair sites
Mortusewicz, O., Schermelleh, L., Walter, J., Cardoso, M. C., & Leonhardt, H. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0501034102 · Cited by 380 · see citing works
Repression of the Heat Shock Response Is a Programmed Event at the Onset of Reproduction
Labbadia, J. P., & Morimoto, R. I. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.06.027 · Cited by 330 · see citing works
Correlation of an epigenetic mitotic clock with cancer risk
Yang, Z., Wong, A., Kuh, D., Paul, D. S., Rakyan, V. K., Leslie, R. D., Zheng, S. C., Widschwendter, M., Beck, S., & Teschendorff, A. E. (2016) · Genome Biology · doi:10.1186/s13059-016-1064-3 · Cited by 325 · see citing works
Tsaprouni, L. G., Yang, T.-P., Bell, J., Dick, K. J., Kanoni, S., Nisbet, J., Viñuela, A., Grundberg, E., Nelson, C. P., Meduri, E., et al. (2014) · Epigenetics · doi:10.4161/15592294.2014.969637 · Cited by 319 · see citing works
Replication timing-related and gene body-specific methylation of active human genes
Aran, D., Toperoff, G., Rosenberg, M., & Hellman, A. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddq513 · Cited by 304 · see citing works
Caloric restriction delays age-related methylation drift
Maegawa, S., Lu, Y., Tahara, T., Lee, J. T., Madžo, J., Liang, S., Jelı́nek, J., Colman, R. J., & Issa, J. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00607-3 · Cited by 278 · see citing works
Making sense of the ageing methylome
Seale, K., Horvath, S., Teschendorff, A., Eynon, N., & Voisin, S. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00477-6 · Cited by 274 · see citing works
Nuclear Retention of mRNA in Mammalian Tissues
Bahar Halpern, K., Caspi, I., Lemze, D., Levy, M., Landen, S., Elinav, E., Ulitsky, I., & Itzkovitz, S. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.11.036 · Cited by 258 · see citing works
Ohnuki, M., Tanabe, K., Sutou, K., Teramoto, I., Sawamura, Y., Narita, M., Nakamura, M., Tokunaga, Y., Nakamura, M., Watanabe, A., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1413299111 · Cited by 256 · see citing works
Pfeifer, G. P., Steigerwald, S. D., Hansen, R. S., Gartler, S. M., & Riggs, A. D. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.21.8252 · Cited by 255 · see citing works
DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy
Lin, Q., Weidner, C. I., Costa, I. G., Marioni, R. E., Ferreira, M. R. P., Deary, I. J., & Wagner, W. (2016) · Aging · doi:10.18632/aging.100908 · Cited by 247 · see citing works
Biological age is increased by stress and restored upon recovery
Poganik, J. R., Zhang, B., Baht, G. S., Tyshkovskiy, A., Deik, A., Kerepesi, C., Yim, S. H., Lu, A. T., Haghani, A., Gong, T., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.03.015 · Cited by 246 · see citing works
Hernando-Herraez, I., Evano, B., Stubbs, T., Commere, P.-H., Jan Bonder, M., Clark, S., Andrews, S., Tajbakhsh, S., & Reik, W. (2019) · Nature Communications · doi:10.1038/s41467-019-12293-4 · Cited by 236 · see citing works
Cytogerontology since 1881: A reappraisal of August Weismann and a review of modern progress
Kirkwood, T. B. L., & Cremer, T. J. (1982) · Human Genetics · doi:10.1007/bf00569695 · Cited by 236 · see citing works
WormBase in 2022—data, processes, and tools for analyzing Caenorhabditis elegans
Davis, P., Zarowiecki, M., Arnaboldi, V., Becerra, A., Cain, S., Chan, J., Chen, W. J., Cho, J.-H., Beltrame, E. D. V., Diamantakis, S., et al. (2022) · Genetics · doi:10.1093/genetics/iyac003 · Cited by 226 · see citing works
A mammalian methylation array for profiling methylation levels at conserved sequences
Arneson, A., Haghani, A., Thompson, M. J., Pellegrini, M. A., Kwon, S. B., Vu, H., Maciejewski, E., Yao, M., Li, C. Z., Lu, A. T.-H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28355-z · Cited by 219 · see citing works
Erratum to: DNA methylation age of human tissues and cell types
Horvath, S. (2015) · Genome biology · doi:10.1186/s13059-015-0649-6 · Cited by 216 · see citing works
Laird, C. D., Pleasant, N. D., Clark, A. D., Sneeden, J. L., Hassan, K. M. A., Manley, N. C., Vary, J. C., Morgan, T. M., Hansen, R. S., & Stöger, R. J. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2536758100 · Cited by 211 · see citing works
Senchuk, M. M., Dues, D. J., Schaar, C. E., Johnson, B. K., Madaj, Z., Bowman, M. J., Winn, M. E., & Raamsdonk, J. M. V. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007268 · Cited by 173 · see citing works
Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms
Slieker, R. C., van Iterson, M., Luijk, R., Beekman, M., Zhernakova, D. V., Moed, M. H., Mei, H., van Galen, M., Deelen, P., Bonder, M. J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1053-6 · Cited by 169 · see citing works
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
Meyer, D. H., & Schumacher, B. (2021) · Aging Cell · doi:10.1111/acel.13320 · Cited by 165 · see citing works
Self-citationDynamic and static maintenance of epigenetic memory in pluripotent and somatic cells
Shipony, Z., Mukamel, Z., Cohen, N. M., Landan, G., Chomsky, E., Zeliger, S. R., Fried, Y. C., Ainbinder, E., Friedman, N., & Tanay, A. (2014) · Nature · doi:10.1038/nature13458 · Cited by 159 · see citing works
Can aging be programmed? A critical literature review
Kowald, A., & Kirkwood, T. B. L. (2016) · Aging Cell · doi:10.1111/acel.12510 · Cited by 154 · see citing works
Genomes Optimize Reproduction: Aging as a Consequence of the Developmental Program
de Magalhães, J. P., & Church, G. M. (2005) · Physiology · doi:10.1152/physiol.00010.2005 · Cited by 152 · see citing works
Potential energy landscapes identify the information-theoretic nature of the epigenome
Jenkinson, G., Pujadas, E., Goutsias, J. I., & Feinberg, A. P. (2017) · Nature Genetics · doi:10.1038/ng.3811 · Cited by 152 · see citing works
Programmatic features of aging originating in development: aging mechanisms beyond molecular damage?
de Magalhães, J. P. (2012) · The FASEB Journal · doi:10.1096/fj.12-210872 · Cited by 147 · see citing works
Juvenile hormone regulation of longevity in the migratory monarch butterfly
Herman, W. S., & Tatar, M. (2001) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2001.1765 · Cited by 141 · see citing works
Genome-wide RNA polymerase stalling shapes the transcriptome during aging
Gyenis, Á., Chang, J., Demmers, J. J. P. G., Bruens, S. T., Barnhoorn, S., Brandt, R. M. C., Baar, M. P., Rašeta, M., Derks, K., Hoeijmakers, J. H. J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01279-6 · Cited by 140 · see citing works
Universal DNA methylation age across mammalian tissuesPreprint
Lu, A. T., Fei, Z., Haghani, A., Robeck, T. R., Zoller, J. A., Li, C., Lowe, R., Yan, Q. J., Zhang, J., Vu, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.18.426733 · Cited by 139 · see citing works
Simplified Assay for Epigenetic Age Estimation in Whole Blood of Adults
Vidal-Bralo, L., Lopez-Golan, Y., & Gonzalez, A. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00126 · Cited by 134 · see citing works
Connecting Variability in Global Transcription Rate to Mitochondrial Variability
das Neves, R. P., Jones, N. S., Andreu, L. F., Gupta, R., Enver, T., & Iborra, F. J. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000560 · Cited by 133 · see citing works
Systematic underestimation of the epigenetic clock and age acceleration in older subjects
Khoury, L. Y. E., Gorrie-Stone, T. J., Smart, M., Hughes, A., Bao, Y., Andrayas, A., Burrage, J., Hannon, E. J., Meena Kumari, B., Mill, J. S., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1810-4 · Cited by 133 · see citing works
The hyperfunction theory: An emerging paradigm for the biology of aging
Gems, D. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101557 · Cited by 129 · see citing works
DNA methylation networks underlying mammalian traits
Haghani, A., Li, C. Z., Robeck, T. R., Zhang, J., Lu, A. T., Ablaeva, J., Acosta-Rodríguez, V. A., Adams, D. M., Alagaili, A. N., Almunia, J., et al. (2023) · Science · doi:10.1126/science.abq5693 · Cited by 128 · see citing works
Single-cell genomics meets human genetics
Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124 · see citing works
Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging
Kerepesi, C., Zhang, B., Lee, S., Trapp, A., & Gladyshev, V. N. (2021) · Science Advances · doi:10.1126/sciadv.abg6082 · Cited by 123 · see citing works
Cellular reprogramming and epigenetic rejuvenation
Simpson, D. J., Olova, N. N., & Chandra, T. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01158-7 · Cited by 123 · see citing works
Methylation and epigenetic fidelity
Riggs, A. D., & Xiong, Z. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307781100 · Cited by 121 · see citing works
Staying true to yourself: mechanisms of DNA methylation maintenance in mammals
Petryk, N., Bultmann, S., Bartke, T., & Defossez, P. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1154 · Cited by 120 · see citing works
Aging is associated with a systemic length-associated transcriptome imbalance
Stoeger, T., Grant, R. A., McQuattie‐Pimentel, A. C., Anekalla, K. R., Liu, S. S., Navarro, H. T., Singer, B. D., Abdala‐Valencia, H., Schwake, M., Tétreault, M., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00317-6 · Cited by 120 · see citing works
Rangaraju, S., Solis, G. M., Thompson, R. C., Gomez-Amaro, R. L., Kurian, L., Encalada, S. E., Niculescu, A. B., Salomon, D. R., & Petrascheck, M. (2015) · eLife · doi:10.7554/elife.08833 · Cited by 111 · see citing works
Age-related diseases as vicious cycles
Belikov, A. V. (2018) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.002 · Cited by 92 · see citing works
Nature of epigenetic aging from a single-cell perspective
Tarkhov, A. E., Lindstrom-Vautrin, T., Zhang, S., Ying, K., Moqri, M., Zhang, B., Tyshkovskiy, A., Levy, O., & Gladyshev, V. N. (2024) · Nature Aging · doi:10.1038/s43587-024-00616-0 · Cited by 83 · see citing works
The DREAM complex functions as conserved master regulator of somatic DNA-repair capacities
Bujarrabal-Dueso, A., Sendtner, G., Meyer, D. H., Chatzinikolaou, G., Stratigi, K., Garinis, G. A., & Schumacher, B. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-00942-8 · Cited by 78 · see citing works
Self-citationThe Ground Zero of Organismal Life and Aging
Gladyshev, V. N. (2020) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2020.08.012 · Cited by 74 · see citing works
The Link Between Epigenetic Clocks for Aging and Senescence
Wagner, W. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00303 · Cited by 69 · see citing works
Ageing as a software design flaw
de Magalhães, J. P. (2023) · Genome biology · doi:10.1186/s13059-023-02888-y · Cited by 66 · see citing works
Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach
Jones, M. J., Islam, S. A., Edgar, R. D., & Kobor, M. S. (2015) · Methods in molecular biology · doi:10.1007/7651_2015_262 · Cited by 62 · see citing works
Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs
Smits, A. H., Lindeboom, R. G. H., Perino, M., van Heeringen, S. J., Veenstra, G. J. C., & Vermeulen, M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku661 · Cited by 62 · see citing works
Porter, H. L., Brown, C. A., Roopnarinesingh, X., Giles, C. B., Georgescu, C., Freeman, W. M., & Wren, J. D. (2021) · Aging Cell · doi:10.1111/acel.13492 · Cited by 60 · see citing works
Clock Work: Deconstructing the Epigenetic Clock Signals in Aging, Disease, and ReprogrammingPreprint
Levine, M. E., Higgins‐Chen, A., Thrush, K., Minteer, C. J., & Niimi, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.13.480245 · Cited by 58 · see citing works
Ueber die Dauer des Lebens; ein Vortrag
Weismann, A. (1882) · G. Fischer eBooks · doi:10.5962/bhl.title.21312 · Cited by 58 · see citing works
Genetic loci and metabolic states associated with murine epigenetic aging
Mozhui, K., Lu, A. T., Li, C. Z., Haghani, A., Sandoval-Sierra, J. V., Wu, Y., Williams, R. W., & Horvath, S. (2022) · eLife · doi:10.7554/elife.75244 · Cited by 55 · see citing works
Epigenetic clocks and programmatic aging
Gems, D., Virk, R. S., & de Magalhães, J. P. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102546 · Cited by 50 · see citing works
Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear
Vershinina, O., Bacalini, M. G., Zaikin, A. A., Franceschi, C., & Ivanchenko, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-88504-0 · Cited by 49 · see citing works
Grandi, F. C., Rosser, J. M., Newkirk, S. J., Yin, J., Jiang, X., Xing, Z., Whitmore, L. S., Bashir, S., Ivics, Z., Izsvák, Z., et al. (2015) · Genome Research · doi:10.1101/gr.185132.114 · Cited by 48 · see citing works
Lack of evidence for increased transcriptional noise in aged tissues
Ibáñez-Solé, O., Ascensión, A. M., Araúzo-Bravo, M. J., & Izeta, A. (2022) · eLife · doi:10.7554/elife.80380 · Cited by 38 · see citing works
Vijg, J., & Kennedy, B. K. (2015) · Gerontology · doi:10.1159/000439348 · Cited by 37 · see citing works
An epigenetic clock controls aging
Mitteldorf, J. (2015) · Biogerontology · doi:10.1007/s10522-015-9617-5 · Cited by 37 · see citing works
Probabilistic inference of epigenetic age acceleration from cellular dynamics
Dabrowski, J., Yang, E. J., Crofts, S., Hillary, R. F., Simpson, D. J., McCartney, D. L., Marioni, R. E., Kirschner, K., Crespo, E. L., & Chandra, T. (2024) · Nature Aging · doi:10.1038/s43587-024-00700-5 · Cited by 33 · see citing works
Dynamic Crowding Regulates Transcription
Shim, A. R., Nap, R. J., Huang, K., Almassalha, L. M., Matusda, H., Backman, V., & Szleifer, I. G. (2019) · Biophysical Journal · doi:10.1016/j.bpj.2019.11.007 · Cited by 32 · see citing works
Ibáñez-Solé, O., Barrio, I., & Izeta, A. (2023) · iScience · doi:10.1016/j.isci.2023.106368 · Cited by 31 · see citing works
Demethylation and derepression of genomic retroelements in the skeletal muscles of aged mice
Min, B., Jeon, K., Park, J. S., & Kang, Y. (2019) · Aging Cell · doi:10.1111/acel.13042 · Cited by 28 · see citing works
DNA Methylation Networks Underlying Mammalian TraitsPreprint
Haghani, A., Lu, A. T., Li, C., Robeck, T. R., Belov, K., Breeze, C. E., Brooke, R. T., Clarke, S., Faulkes, C. G., Fei, Z., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.16.435708 · Cited by 26 · see citing works
Does Modulation of an Epigenetic Clock Define a Geroprotector?
Schork, N. J., Beaulieu‐Jones, B. K., Liang, W. S., Smalley, S. L., & Goetz, L. H. (2022) · Advances in Geriatric Medicine and Research · doi:10.20900/agmr20220002 · Cited by 24 · see citing works
Bertucci, E. M., Shealy, E. P., & Parrott, B. B. (2024) · Aging · doi:10.18632/aging.205503 · Cited by 22 · see citing works
Aging clocks, entropy, and the limits of age-reversalPreprint
Tarkhov, A. E., Denisov, K. A., & Fedichev, P. O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.06.479300 · Cited by 19 · see citing works
Could aging evolve as a pathogen control strategy?
Lidsky, P. V., & Andino, R. (2022) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2022.08.003 · Cited by 15 · see citing works
Development of a novel epigenetic clock resistant to changes in immune cell compositionPreprint
Tomusiak, A., Floro, A., Tiwari, R., Riley, R. R., Matsui, H., Andrews, N., Kasler, H. G., & Verdin, E. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.01.530561 · Cited by 15 · see citing works
Epigenetic fidelity in complex biological systems and implications for ageingPreprint
Duffield, T., Csuka, L., Akalan, A., Magdaleno, G. D. V., Senez, L., Palmer, D. H., & de Magalhães, J. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.29.538716 · Cited by 11 · see citing works
Is Aging an Inevitable Characteristic of Organic Life or an Evolutionary Adaptation?
Lidsky, P. V., Yuan, J.-J., Rulison, J. M., & Andino-Pavlovsky, R. (2022) · Biochemistry (Moscow) · doi:10.1134/s0006297922120021 · Cited by 10 · see citing works
Flagged first, then by citations
4 of the 110 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Epigenetic ageing clocks: statistical methods and emerging computational challenges
Teschendorff, A. E., & Horvath, S. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00807-w · Cited by 220 · see citing works
Quantifying the stochastic component of epigenetic aging
Tong, H., Dwaraka, V. B., Chen, Q., Luo, Q., Lasky-Su, J. A., Smith, R. C., & Teschendorff, A. E. (2024) · Nature Aging · doi:10.1038/s43587-024-00600-8 · Cited by 75 · see citing works
Development of an epigenetic clock resistant to changes in immune cell composition
Tomusiak, A., Floro, A., Tiwari, R., Riley, R. R., Matsui, H., Andrews, N., Kasler, H. G., & Verdin, E. M. (2024) · Communications Biology · doi:10.1038/s42003-024-06609-4 · Cited by 73 · see citing works
Biomarkers of aging: from molecules and surrogates to physiology and function
Furrer, R., & Handschin, C. (2025) · Physiological Reviews · doi:10.1152/physrev.00045.2024 · Cited by 62 · see citing works
Lyu, Y.-X., Fu, Q., Wilczok, D., Ying, K., King, A., Antebi, A., Vojta, A., Stolzing, A., Moskalev, A., Georgievskaya, A., et al. (2024) · Aging · doi:10.18632/aging.206135 · Cited by 46 · see citing works
Self-citationLiesenfelder, S., Mabrouk, M. H. E., Iliescu, J., Baranda, M. V., Mizi, A., Perez-Correa, J.-F., Wessiepe, M., Papantonis, A., & Wagner, W. (2025) · Nature Aging · doi:10.1038/s43587-025-00841-1 · Cited by 39 · see citing works
Probabilistic inference of epigenetic age acceleration from cellular dynamics
Dabrowski, J., Yang, E. J., Crofts, S., Hillary, R. F., Simpson, D. J., McCartney, D. L., Marioni, R. E., Kirschner, K., Crespo, E. L., & Chandra, T. (2024) · Nature Aging · doi:10.1038/s43587-024-00700-5 · Cited by 33 · see citing works
Heritability of intrinsic human life span is about 50% when confounding factors are addressed
Shenhar, B., Pridham, G., Oliveira, T. L. D., Yang, Y., Raz, N., Deelen, J., Hägg, S., & Alon, U. (2026) · Science · doi:10.1126/science.adz1187 · Cited by 41 · see citing works
The use and misuse of ‘biological aging’ in health research
Ikram, M. A. (2024) · Nature Medicine · doi:10.1038/s41591-024-03297-9 · Cited by 29 · see citing works
Targeting DNA damage in ageing: towards supercharging DNA repair
Bujarrabal-Dueso, A., Garinis, G. A., Robbins, P. D., Vijg, J., & Schumacher, B. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01212-6 · Cited by 30 · see citing works
Self-citationSmurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature
Zane, F., Bouzid, H., Marmol, S. S., Brazane, M., Besse, S., Molina, J. L., Cansell, C., Aprahamian, F., Durand, S., Ayache, J., et al. (2023) · Aging Cell · doi:10.1111/acel.13946 · Cited by 27 · see citing works
Longitudinal changes in epigenetic clocks predict survival in the InCHIANTI cohort
Kuo, P., Moore, A. Z., Tanaka, T., Belsky, D. W., Lu, A. T.-H., Horvath, S., Bandinelli, S., & Ferrucci, L. (2026) · Nature Aging · doi:10.1038/s43587-026-01066-6 · Cited by 21 · see citing works
Improved resilience and proteostasis mediate longevity upon DAF-2 degradation in old age
Molière, A., Park, J. Y. C., Goyala, A., Vayndorf, E. M., Zhang, B., Hsiung, K. C., Jung, Y., Kwon, S., Statzer, C., Meyer, D. H., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01232-x · Cited by 19 · see citing works
Self-citationThe energy resistance principle
Picard, M. A., & Murugan, N. J. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.09.002 · Cited by 17 · see citing works
OMICmAge quantifies biological age by integrating multi-omics with electronic medical records
Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Armstrong, J., Sehgal, R., Argentieri, M. A., Richmond, A., Aparicio, A., Mendez, K. M., Chen, Y., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01073-7 · Cited by 18 · see citing works
Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects
Kriukov, D. V., Kuzmina, E., Efimov, E., Dylov, D. V., & Khrameeva, E. E. (2024) · Aging Cell · doi:10.1111/acel.14283 · Cited by 19 · see citing works
Epigenetic networks coordinate DNA methylation across the genome
Wagner, W. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.06.037 · Cited by 20 · see citing works
Aging by the clock and yet without a program
Meyer, D. H., Maklakov, A. A., & Schumacher, B. (2025) · Nature Aging · doi:10.1038/s43587-025-00975-2 · Cited by 20 · see citing works
Self-citationMeasuring biological age: Insights from omics studies
Kočar, E., Šket, R., Vasle, A. H., Avguštin, G., Benedik, E., Seljak, B. K., Simić, P., Martinko, A., Morrison, S. A., Sorić, M., et al. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102988 · Cited by 21 · see citing works
Development of a novel epigenetic clock resistant to changes in immune cell compositionPreprint
Tomusiak, A., Floro, A., Tiwari, R., Riley, R. R., Matsui, H., Andrews, N., Kasler, H. G., & Verdin, E. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.01.530561 · Cited by 15 · see citing works
Predicting healthspan and disease risks through biological age
Li, G., Cheng, L., Wong, I. N., Yin, Y., Chen, J., Liu, L., & Zhang, K. (2025) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2025.10.006 · Cited by 16 · see citing works
Age-associated changes in transcriptional elongation and their effects on homeostasis
Papantonis, A., Antebi, A., Partridge, L., & Beyer, A. (2024) · Trends in Cell Biology · doi:10.1016/j.tcb.2024.11.005 · Cited by 13 · see citing works
Human Brain Cell‐Type‐Specific Aging Clocks Based on Single‐Nuclei Transcriptomics
Muralidharan, C., Zakar‐Polyák, E., Adami, A., Abbas, A. A., Sharma, Y., Garza, R., Johansson, J., Atacho, D. A. M., Renner, É., Palkovits, M., et al. (2025) · Advanced Science · doi:10.1002/advs.202506109 · Cited by 14 · see citing works
Metabolomic-based aging clocks
de Opakua, A. I., Conde, R., de Diego, Á., Bizkarguenaga, M., Embade, N., Lu, S. C.-L., Mato, J. M., & Millet, Ó. (2025) · npj Metabolic Health and Disease · doi:10.1038/s44324-025-00078-x · Cited by 12 · see citing works
Cummings, S. R., Hong, N., & Cohen, A. A. (2025) · Aging Cell · doi:10.1111/acel.70292 · Cited by 12 · see citing works
Systemic epigenetic dysregulation as a driver of ageing and a therapeutic target
Yücel, A. D., & Gladyshev, V. N. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-026-00958-0 · Cited by 12 · see citing works
Aging Clocks, Entropy, and the Challenge of Age Reversal
Tarkhov, A. E., Denisov, K. A., & Fedichev, P. O. (2024) · doi:10.59368/agingbio.20240031 · Cited by 30 · see citing works
DNA methylation entropy is a biomarker for aging
Chan, J., Rubbi, L., & Pellegrini, M. A. (2025) · Aging · doi:10.18632/aging.206220 · Cited by 12 · see citing works
Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish
Costa, E. K., Chen, J., Guldner, I. H., Mboning, L., Schmahl, N., Tsenter, A., Nagvekar, R., Wu, M.-R., Losada, P. M., Bouchard, L.-S., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01074-6 · Cited by 18 · see citing works
Too old for healthy aging? Exploring age limits of longevity treatments
Chaudhari, P., & Ermolaeva, M. (2024) · npj Metabolic Health and Disease · doi:10.1038/s44324-024-00040-3 · Cited by 11 · see citing works
Litichevskiy, L., Considine, M., Gill, J., Shandar, V., Cox, T. O., Descamps, H. C., Wright, K. M., Amses, K. R., Dohnalová, L., Liou, M. J., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-025-01963-3 · Cited by 15 · see citing works
Do we actually need aging clocks?
Kriukov, D. V., Efimov, E., Gelfand, M. S., Moskalev, A., & Khrameeva, E. E. (2025) · npj Aging · doi:10.1038/s41514-025-00312-2 · Cited by 13 · see citing works
Designing Epigenetic Clocks for Wildlife Research
Newediuk, L., Richardson, E. S., Bohart, A. M., Roberto‐Charron, A., Garroway, C. J., & Jones, M. J. (2025) · Molecular Ecology Resources · doi:10.1111/1755-0998.14120 · Cited by 13 · see citing works
Fan, X., Qian, Q., Li, W., Liu, T., Zeng, C., Jia, P., Lin, H., Gao, X., Li, J., Xia, M., et al. (2025) · Genome Research · doi:10.1101/gr.280155.124 · Cited by 10 · see citing works
CT Biomarkers for Phenotypic Biological Aging: Emerging Concepts and Advantages
Lee, M. H., Garrett, J. W., Liu, D., & Pickhardt, P. J. (2025) · Radiographics · doi:10.1148/rg.250007 · Cited by 9 · see citing works
Křepelová, A., Rasa, S. M. M., Annunziata, F., Lu, J. N., Giannuzzi, C., Omrani, O., Wyart, E., Porporato, P. E., Ansari, I., Bilenko, D., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-01021-x · Cited by 9 · see citing works
Gallrein, C., Meyer, D. H., Woitzat, Y., Ramirez-Ramirez, V., Vuong-Brender, T. T. K., Kirstein, J., & Schumacher, B. (2026) · Nature Aging · doi:10.1038/s43587-026-01067-5 · Cited by 12 · see citing works
Self-citationBiomarkers for aging of blood – how transferable are they between mice and humans?
Tharmapalan, V., & Wagner, W. (2024) · Experimental Hematology · doi:10.1016/j.exphem.2024.104600 · Cited by 9 · see citing works
Hubert, D. L., Arnold, K. R., Greenspan, Z. S., Pupo, A., Robinson, R. D., Chavarin, V. V., Barter, T. T., Djukovic, D., Raftery, D., Vue, Z., et al. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf082 · Cited by 8 · see citing works
DoliClock: a lipid-based aging clock reveals accelerated aging in neurological disorders
Latumalea, D., Unfried, M., Barardo, D., Gruber, J., & Kennedy, B. K. (2025) · Aging · doi:10.18632/aging.206266 · Cited by 8 · see citing works
Machine learning-guided deconvolution of plasma protein levels
Pietzner, M., Beuchel, C. F., Demircan, K., Anton, J. H., Zeng, W., Römisch‐Margl, W., Yasmeen, S., Uluvar, B., Zoodsma, M., Koprulu, M., et al. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00158-6 · Cited by 8 · see citing works
Lee, J., Han, M., Wang, K., Butler, L. R., & Sinclair, D. A. (2026) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2026.101442 · Cited by 7 · see citing works
Cell-type-specific transposon demethylation and TAD remodeling in aging mouse brain
Zeng, Q., Wang, W., Tian, W., Klein, A., Bartlett, A., Liu, H., Nery, J. R., Castanon, R., Osteen, J. K., Johnson, N. D., et al. (2026) · Cell · doi:10.1016/j.cell.2026.02.015 · Cited by 12 · see citing works
Insights into the therapeutic strategies for aging and aging-associated diseases
Dong, R., Wu, Q., Kan, J., Fu, C., Sorrentino, V., Chow, A., Lei, Y., Wu, D., Xu, Z., Du, J., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02662-z · Cited by 9 · see citing works
Hänggi, N. V., Neubauer, J., Marti, Y., Banemann, R., Kulstein, G., Courts, C., Gosch, A., Hadrys, T., Haas, C., & Dørum, G. (2025) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2025.103282 · Cited by 9 · see citing works
Time is encoded by methylation changes at clustered CpG sites
Ochana, B.-L., Nudelman, D., Cohen, D., Peretz, A., Piyanzin, S., Gal-Rosenberg, O., Horn, A., Loyfer, N., Varshavsky, M., Raisch, R., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115958 · Cited by 11 · see citing works
Probing epigenetic clocks as a rational markers of biological age using blood cell countsPreprint
Jonkman, T. H., van Zwet, E. W., & Heijmans, B. T. (2025) · medRxiv · doi:10.1101/2025.05.12.25327213 · Cited by 7 · see citing works
Martínez-Enguita, D., Hillerton, T., Åkesson, J., Kling, D., Lerm, M., & Gustafsson, M. (2025) · Frontiers in Aging · doi:10.3389/fragi.2024.1526146 · Cited by 8 · see citing works
Avelar, R. A., Palmer, D. H., Kulaga, A. Y., & Fuellen, G. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102737 · Cited by 8 · see citing works
Methylation clocks for evaluation of anti-aging interventions
Mitteldorf, J. (2025) · Aging · doi:10.18632/aging.206245 · Cited by 6 · see citing works
Epigenetic clock and lifespan prediction in the short-lived killifish Nothobranchius furzeriPreprint
Giannuzzi, C., Baumgart, M., Neri, F., & Cellerino, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.07.606986 · Cited by 6 · see citing works
Histological aging signatures enable tissue-specific disease prediction from bloodPreprint
Abila, E., Buljan, I., Zheng, Y., Veres, T., Weng, Z., Nackenhorst, M. C., Hulla, W., Tolkach, Y., Wöehrer, A., & Rendeiro, A. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.14.618081 · Cited by 8 · see citing works
Methylation Data Analysis and Interpretation
Zhu, Y., Mao, W., Hosseini, R., & Chikina, M. D. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-120924-091033 · Cited by 5 · see citing works
Li, J., Lu, X., Tong, T., Zhou, X., Zhang, B., Sun, X., Zhao, B., Xu, G., Yang, J., Fan, Y., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01131-0 · Cited by 5 · see citing works
A glossary for social-to-biological research
Robertson, T., Benzeval, M., Betzer, M., Booker, C. L., Castagné, R., Delpierre, C., Gallagher, I. J., Kelly‐Irving, M., McCrory, C., Morris, T., et al. (2024) · Longitudinal and Life Course Studies · doi:10.1332/17579597y2024d000000032 · Cited by 5 · see citing works
Mei, X., Kabir, H., Conboy, M. J., & Conboy, I. M. (2025) · GeroScience · doi:10.1007/s11357-025-01750-2 · Cited by 5 · see citing works
Differential Responses of Dynamic and Entropic Aging Factors to Longevity InterventionsPreprint
Perevoshchikova, K., & Fedichev, P. O. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.25.581928 · Cited by 5 · see citing works
Heritability of human lifespan is about 50% when confounding factors are addressedPreprint
Shenhar, B., Pridham, G., Oliveira, T. L. D., Yang, Y., Raz, N., Deelen, J., Hägg, S., & Alon, U. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.20.649385 · Cited by 5 · see citing works
A Minimal Model Explains Aging Regimes and Guides Intervention StrategiesPreprint
Fedichev, P. O., & Gruber, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.25.671954 · Cited by 5 · see citing works
Targeting Genome Stability to Mitigate Human Aging and Disease
Toiber, D., & Schumacher, B. (2025) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-042624-105942 · Cited by 4 · see citing works
Self-citationProteomic aging clocks in epidemiological studies: advances, applications and prospects
Xiao, H., Lau, C. E., Dehghan, A., & Robinson, O. (2026) · Nature Aging · doi:10.1038/s43587-026-01118-x · Cited by 4 · see citing works
Quantifying stochasticity in the aging DNA methylome
Bell, C. G. (2024) · Nature Aging · doi:10.1038/s43587-024-00634-y · Cited by 4 · see citing works
Epigenetics and aging: relevance for sleep medicine
Cortese, R. (2024) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000001109 · Cited by 4 · see citing works
Senolytic compounds reduce epigenetic age of blood samples in vitro
Tharmapalan, V., Sarvaas, M. D. M., Bleichert, M., Wessiepe, M., & Wagner, W. (2025) · npj Aging · doi:10.1038/s41514-025-00199-z · Cited by 4 · see citing works
The stochasticity of biological aging
Meyer, D. H., & Schumacher, B. (2025) · Nature Medicine · doi:10.1038/s41591-025-03687-7 · Cited by 4 · see citing works
Self-citationWang, H., Xiao, F., Zhao, L., Su, Q., Xia, T., Yang, L.-Q., Ma, S., & Kong, Q.-P. (2025) · Genome Medicine · doi:10.1186/s13073-025-01533-6 · Cited by 3 · see citing works
Metabolomic signatures of extreme old age: findings from the New England Centenarian Study
Monti, S., Lustgarten, M. S., Huang, Z., Song, Z., Li, M., Ellis, D., Tian, Q., Ferrucci, L., Rappaport, N., Andersen, S. L., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02174-2 · Cited by 3 · see citing works
The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi‐Omics Integration
Liu, L., Lai, Y., Tian, C., Huang, Y., Hao, J., Zhao, Y., Chen, D., Wu, T., Zhou, D., Zheng, X., et al. (2026) · Aging Cell · doi:10.1111/acel.70579 · Cited by 3 · see citing works
Epigenetic information loss is a common feature of multiple diseases and aging
Sagy, N., Chang, C., Gal, M., & Bar, D. Z. (2025) · GeroScience · doi:10.1007/s11357-025-01767-7 · Cited by 4 · see citing works
Age Deceleration and Reversal Gene Patterns in Dauer Diapause
Totska, K., Barata, J. C. V. V., Sandt, W., Meyer, D. H., & Schumacher, B. (2025) · Aging Cell · doi:10.1111/acel.70253 · Cited by 3 · see citing works
Self-citationImproving our understanding of the biology of aging: findings from the Age-It Research Program
Chiti, F., Conti, F., Corda, D., Giorgino, F., Graziani, A., Passarino, G., Sandri, M., & di Fagagna, F. D. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf197 · Cited by 4 · see citing works
Aging as Entropy: A Quantifiable Framework
Hong, N., & Cohen, A. A. (2025) · Endocrinology and Metabolism · doi:10.3803/enm.2025.2719 · Cited by 3 · see citing works
Garma, L. D., Pernas, S., Fullana, B., Vethencourt, A., Baz, D. V., García-Manrique, T., Campelo, R. G., Reboredo, C., Terrasa, J., Perelló, A., et al. (2026) · Epigenetics · doi:10.1080/15592294.2026.2714582 · Cited by 2 · see citing works
Beyond genes and environment: mapping biological stochasticity in aging
Hruby, A. J., Garcia, G., Thorwald, M. A., Finch, C. E., Johnson, J., & Higuchi‐Sanabria, R. (2025) · GeroScience · doi:10.1007/s11357-025-01673-y · Cited by 2 · see citing works
Green, D. C., Reynard, L. N., Henstock, J. R., Reppe, S., Gautvik, K. M., Peffers, M. J., Shanley, D. P., Clegg, P., & Canty‐Laird, E. G. (2025) · Aging Cell · doi:10.1111/acel.70149 · Cited by 2 · see citing works
A sex-adjusted 7-biomarker clinical aging clock for translational preventative medicine
Meyer, D. H., Mejia, G., Molière, A., Wangprapa, P., Khunlertkit, T., Teekakirikul, P., Ewald, C. Y., & Viecelli, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-27478-9 · Cited by 2 · see citing works
Self-citationEpigenetic program of ontogenesis and hyperfunction theory: reinterpreting the mechanisms of aging
Salnikov, L. (2026) · Frontiers in Aging · doi:10.3389/fragi.2026.1735269 · Cited by 2 · see citing works
Complex genotype–phenotype relationships in neurodevelopmental disorders
Dvir, E., Meshorer, E., & Shifman, S. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.03.005 · Cited by 3 · see citing works
dos Santos, G. A., Castro, J. P., Guardia, G. D. A., dos Santos, F. F., Birbrair, A., & Galante, P. A. F. (2026) · Aging Cell · doi:10.1111/acel.70696 · Cited by 3 · see citing works
Caloric restriction modulates genome-wide somatic mutation in mice
Grońska-Pęski, M., Acosta-Rodríguez, V. A., Srinivasa, A., Garrido, A., Picciotto, S., Green, C. B., Takahashi, J. S., Shoag, J. E., & Evrony, G. D. (2026) · Cell · doi:10.1016/j.cell.2026.08.013 · Cited by 2 · see citing works
Dynamic and reversible transcriptomic age shifts induced by COVID-19 in Korean whole blood
An, K., Kwon, Y., Bhak, J., Ryu, H., Jeon, S., Nam, D., & Bhak, J. (2025) · Aging · doi:10.18632/aging.206270 · Cited by 4 · see citing works
Molecular Basis of Sperm Methylome Response to Aging and Stress
Arowolo, O. K., Zhu, J., Nowak, K., Pilsner, J. R., & Suvorov, A. (2026) · Biology · doi:10.3390/biology15060504 · Cited by 2 · see citing works
Predicting murine age across tissues and cell types using single cell transcriptome dataPreprint
Neumann, J. F., Leote, A. C., Liersch, M., & Beyer, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512922 · Cited by 2 · see citing works
Interactions between the gut microbiome, dietary restriction, and aging in genetically diverse micePreprint
Litichevskiy, L., Considine, M., Gill, J., Shandar, V., Cox, T. O., Descamps, H. C., Wright, K. M., Amses, K. R., Dohnalová, L., Liou, M. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568137 · Cited by 3 · see citing works
Malaiwong, N., Dahse, A.-K., Lin, L.-C., Das, R., Porta-de-la-Riva, M., Scholz, N., & Krieg, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.04.621798 · Cited by 2 · see citing works
Cerebrovascular ageing: how zebrafish can contribute to solving the puzzle
Malkinson, G., & Henriques, C. M. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1548242 · Cited by 1 · see citing works
Mammadova, J., Richards, A. C., Torriente, A. G., Adler, E. R., Cruz, R. J., Palfi, S., Lee, D. H., Sam, C., Al‐Jumayli, M., Berglund, A., et al. (2025) · Cardio-Oncology · doi:10.1186/s40959-025-00340-7 · Cited by 1 · see citing works
Epigenetic clocks as quantitative readouts of epigenetic drift
Pérez, R. F., Tejedor, J. R., Fernández, A. F., & Fraga, M. F. (2026) · Nature Aging · doi:10.1038/s43587-026-01152-9 · Cited by 1 · see citing works
Multi-omics profiling reveals systemic rejuvenation of the aged kidney through senolytic therapy
Chen, S., Zhang, C., Cang, P., Guo, W., Xiao, L., He, Y., Su, Q., Ouyang, S., Zha, J., Maimaitirexiati, G., et al. (2026) · npj Regenerative Medicine · doi:10.1038/s41536-026-00490-x · Cited by 1 · see citing works
Jang, S., Kim, S., Kang, D., Hong, C., Bok, E., Lee, S., Choe, Y., & Kim, J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2528874123 · Cited by 1 · see citing works
Responsiveness of epigenetic aging biomarkers to longevity interventions in humans
Sehgal, R., Borrus, D. S., Armstrong, J., González, J. T., Kasamoto, J., Markov, Y., Priyanka, A., Smith, R. C., Carreras-Gallo, N., Lasky-Su, J. A., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04562-9 · Cited by 30 · see citing works
Epigenetic Aging in Brain Tissue of the Self‐Fertilizing Vertebrate, Kryptolebias marmoratus
Bélik, J., & Silvestre, F. (2026) · Ecology and Evolution · doi:10.1002/ece3.73881 · Cited by 1 · see citing works
Cost-effective solutions for high-throughput enzymatic DNA methylation sequencingPreprint
Longtin, A., Watowich, M. M., Sadoughi, B., Petersen, R. M., Brosnan, S. F., Buetow, K. H., Cai, Q., Gurven, M., Highland, H. M., Huang, Y.-T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.09.612068 · Cited by 1 · see citing works
Collective dynamics of DNA methylation during ageingPreprint
Hashtroud, A. M., Bonder, M. J., Stegle, O., von Meyenn, F., Reik, W., & Rulands, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.15.628564 · Cited by 1 · see citing works
Epigenetic entropy, social disparity, and health and lifespan in the Women’s Health InitiativePreprint
Mozhui, K., Starlard‐Davenport, A., Sun, Y., Shadyab, A. H., Casanova, R., Thomas, F., Wallace, R. B., Fowke, J. H., & Johnson, K. (2025) · medRxiv · doi:10.1101/2025.02.21.25322696 · Cited by 1 · see citing works
Immune cell composition is an important contributor to epigenetic age variation
Marttila, S. (2025) · Epigenomics · doi:10.1080/17501911.2025.2476391 · Cited by 1 · see citing works
Cell-Type-Specific Transposable Element Demethylation and TAD Remodeling in the Aging Mouse BrainPreprint
Zeng, Q., Tian, W., Klein, A., Bartlett, A., Liu, H., Nery, J. R., Castanon, R., Osteen, J. K., Johnson, N. D., Wang, W., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.21.648266 · Cited by 1 · see citing works
Reversible Transcriptomic Age Shifts from Physiological Stress in Whole BloodPreprint
An, K., Kwon, Y., Bhak, J., Ryu, H., Jeon, S., Nam, D., & Bhak, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.08.611853
Fisher Information and the Dynamics of Multicellular AgingPreprint
Hale, Z. F., Cánez, G. A., & Michaels, T. C. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.24.634675
- Authors
- Meyer, D. H., & Schumacher, B.
- Affiliations
- University of Cologne (DE)
- Corresponding author
- Meyer, David H.
- Schumacher, Björn
- Venue
- Nature Aging
- Published
- 2024-05-09
- Volume
- 4
- Issue
- 6
- Pages
- 871-885
- ISSN
- 2662-8465 (ISSN-L)
- PMC
- PMC11186771
- OpenAlex
- W4396761662
- Cited by
- 128
- References
- 110
- Citations by year
- 2026: 43 · 2025: 60 · 2024: 21 · 2023: 3 · 2022: 1
- FWCI
- 14.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Aging (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- EC | Horizon 2020 Framework Programme (H2020-MSCA-ITN-2018 (Healthage and ADDRESS ITNs))
- John Templeton Foundation (61734)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Keywords
- aging clocks, stochastic variability, biological age, chronological age, DNA methylation, transcriptomics, programmed aging, heterochronic parabiosis, caloric restriction, partial reprogramming, smoking, age-related diseases
- MeSH terms
- Aging, Animals, Biological Clocks, Computer Simulation, Humans, Male, Models, Biological, Parabiosis, Smoking, Stochastic Processes, DNA Methylation, Caloric Restriction, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
