The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Differential Responses of Dynamic and Entropic Aging Factors to Longevity Interventions
Abstract
Aging across most species, including mice and humans, is characterized by an exponential acceleration of mortality rates. In search for the molecular basis of this phenomenon, we analyzed DNA methylation (DNAm) changes in aging mice. Utilizing principal component analysis (PCA) on DNAm profiles, we identified a primary aging signature with an exponential age dependency, closely reflecting the Gompertz law’s description of mortality acceleration. This signature is the manifestation of the dynamic instability in the organism’s state that drives the aging process in mice. It aligns closely with regression-based aging clocks and responds to interventions such as caloric restriction and parabiosis. Additionally, we identified a linear DNAm signature, indicative of a global demethylation level. Through single-cell DNAm (scDNAm) data from aging animals, we demonstrate that this signature captures the exponential expansion of the state space volume spanned by individual cells within an aging organism, and thus quantifying linearly increasing configuration entropy, likely an irreversible process. Consistent with this interpretation, we found that neither caloric restriction (CR) nor parabiosis significantly impacts the entropic feature, reinforcing its link to irreversible damage.
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A one-dimensional representation of the complex biochemistry and phenotypes of aging is supported by analyses of aging data.
“The possibility of such a one-dimensional representation of the complex biochemistry and phenotypes of aging21–25 is supported by analyses of aging data26–30.”
From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [26]
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Rapamycin fed late in life extends lifespan in genetically heterogeneous mice
Harrison, D. E., Strong, R., Sharp, Z. D., Nelson, J. F., Astle, C. M., Flurkey, K., Nadon, N. L., Wilkinson, J. E., Frenkel, K., Carter, C. S., et al. (2009) · Nature · doi:10.1038/nature08221 · Cited by 3,766 · cited 1×
“24 per month, as detailed in our parameter estimates in [9], using mortality data from healthy controls in [20].”
BackgroundUsing 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
Validation of biomarkers of aging
Moqri, M., Herzog, C., Poganik, J. R., Ying, K., Justice, J. N., Belsky, D. W., Higgins-Chen, A. T., Chen, B. H., Cohen, A. A., Fuellen, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02784-9 · Cited by 361
Self-citationA whole lifespan mouse multi-tissue DNA methylation clock
Meer, M. V., Podolskiy, D. I., Tyshkovskiy, A., & Gladyshev, V. N. (2018) · eLife · doi:10.7554/elife.40675 · Cited by 176
Pyrkov, T. V., Avchaciov, K., Tarkhov, A. E., Menshikov, L. I., Gudkov, A. V., & Fedichev, P. O. (2021) · Nature Communications · doi:10.1038/s41467-021-23014-1 · Cited by 139
Self-citationAging clocks based on accumulating stochastic variation
Meyer, D. H., & Schumacher, B. (2024) · Nature Aging · doi:10.1038/s43587-024-00619-x · Cited by 128
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Research works printed but not linked: 17
- 10.1016/j.cell.2023.08.003 · DOI · Paper page
- 10.1038/s41576-018-0004-3 · DOI · Paper page
- 10.18632/aging.101684 · DOI · Paper page
- 2018, An epigenetic biomarker of aging for lifespan and healthspan, Aging (albany NY), 10, 573
- 10.1111/j.1467-9868.2005.00503.x · DOI · Paper page
- 10.1186/1471-2105-13-1 · DOI
- 2022, Unsupervised learning of aging principles from longitudinal data, Nature Communications, 13, 6529, 10.1038/s41467-022-34051-9 · DOI · Paper page
- N. M. Cohen , A. Lifshitz , R. Jaschek , E. Rinott , R. Balicer , L. I. Shlush , G. I. Barbash , and A. Tanay , Longitudinal machine learning uncouples healthy aging factors from chronic disease risks, Nature Aging, 1 (2023). · DOI
- A. E. Tarkhov , K. A. Denisov , and P. O. Fedichev , Aging clocks, entropy, and the limits of age-reversal, BioRxiv, 2022 (2022). · DOI
- M. J. Bonder , S. J. Clark , F. Krueger , S. Luo , J. A. de Sousa , A. M. Hashtroud , T. M. Stubbs , A.-K. Stark , S. Rulands , O. Stegle , et al., Single cell dna methylation ageing in mouse blood, bioRxiv, 2023 (2023). · DOI
- 2015, Critical dynamics of gene networks is a mechanism behind ageing and gompertz law, arXiv preprint
- J. G. Fox , S. Barthold , M. Davisson , C. E. Newcomer , F. W. Quimby , and A. Smith , The Mouse in biomedical research: diseases, Vol. 2 (Elsevier, 2006).
- 10.1111/acel.12738 · DOI
- T. M. Cover , Elements of information theory (John Wiley & Sons, 1999).
- 10.1016/j.cmet.2023.03.015 · DOI · Paper page
- 2023, Fail-tests of dna methylation clocks, and development of a noise barometer for measuring epigenetic pressure of aging and disease, Aging (Albany NY), 15, 8552
- A. E. Tarkhov , T. Lindstrom-Vautrin , S. Zhang , K. Ying , M. Moqri , B. Zhang , and V. N. Gladyshev , Nature of epigenetic aging from a single-cell perspective, bioRxiv, 2022 (2022). · DOI
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A damage accumulation model identifies distinct aging regimes across species
Raz, N., Yang, Y., Pridham, G., Shenhar, B., Frenkel, S., Levy, T., Tlusty, T., Mayo, A., & Alon, U. (2026) · Nature Aging · doi:10.1038/s43587-026-01138-7 · Cited by 7
Metadata
- Authors
- Perevoshchikova, K., & Fedichev, P. O.
- Publisher
- openRxiv
- Published
- 2024-02-28
- Cited by
- 6
- References
- 6
- Type
- Preprint
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