The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research
Abstract
The Geoscience paradigm suggests that targeting the aging process could delay or prevent the risk of multiple major age-related diseases, such as cancer, cardiovascular disease, Alzheimer’s disease, diabetes, and osteoporosis, to name only a few(1,2). These diseases have well-established clinical diagnostic and classification criteria, yet despite the fact that aging may be the key driver across these diverse pathologies, aging remains a latent concept with no agreed upon molecular definition. If our goal is to develop interventions to slow the increase in biological aging as a function of chronological time, and thus facilitate health promotion and disease prevention, we need to first come up with clinically valid measures of the underlying biological process and/or classification criteria for what it means to be biologically, rather than chronologically, “aged.” This collection of primary papers and reviews focuses on DNA methylation and its promise as a means to define biological age. As discussed in the primary research paper by Nelson et al. (3), developing biomarkers of aging would serve three important purposes: First, biomarkers can provide both diagnostic and prognostic insight that may inform medical and/or personal decisions; second, they may uncover mechanistic clues that aid basic research; and third, they have the potential to inform intervention strategies. As a result, the quest to develop both valid and reliable biomarkers of aging has been gaining steam over…
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Second-generation DNAm clocks are more predictive of morbidity and mortality than first-generation clocks and are proposed to have improved potential for testing impacts of interventions to slow aging.
“The second-generation clocks are more predictive of morbidity and mortality as compared with the first-generation clocks59 and are proposed to have improved potential for testing impacts of interventions to slow aging14.”
From Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial (2023) · cites this paper as [59]
Citations
Cites
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
Geroscience: Linking Aging to Chronic Disease
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., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.039 · Cited by 2,764
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
Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging
Seeman, T. E., McEwen, B. S., Rowe, J. W., & Singer, B. H. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.081072698 · Cited by 1,385 · cited 1×
“(15) sought to link three clocks—Horvath, Hannum, and Levine (also commonly referred to as the PhenoAge clock) (17)—to another biomarker often used in epidemiolocal and sociological studies to model aging, Allostatic Load (AL)(18,19).”
BackgroundBocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014821 · Cited by 1,012
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
A 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
Flagged first, then by citations
Research works printed but not linked: 12
- Sierra, 2016, The emergence of geroscience as an interdisciplinary approach to the enhancement of health span and life span, Cold Spring Harb Perspect Med, 6, a025163, 10.1101/cshperspect.a025163 · DOI
- Nelson, 2019, Biomarkers for aging identified in cross-sectional studies tend to be non-causative, J Gerontol A Biol Sci Med Sci, glz174, 10.1093/gerona/glz174 · DOI · Paper page
- Tanaka, 2018, Plasma proteomic signature of age in healthy humans, Aging Cell, 17, e12799, 10.1111/acel.12799 · DOI · Paper page
- Chen, 2015, Three-dimensional human facial morphologies as robust aging markers, Cell Res, 25, 574, 10.1038/cr.2015.36 · DOI · Paper page
- Horvath, 2018, DNA methylation-based biomarkers and the epigenetic clock theory of ageing, Nat Rev Genet, 19, 371, 10.1038/s41576-018-0004-3 · DOI · Paper page
- Hannum, 2013, Genome-wide methylation profiles reveal quantitative views of human aging rates, Mol Cell, 49, 359, 10.1016/j.molcel.2012.10.016 · DOI · Paper page
- Bressler, 2019, Epigenetic age acceleration and cognitive function in African American adults in midlife: the atherosclerosis risk in communities study, J Gerontol A Biol Sci Med Sci, glz245
- Ryan, 2019, A systematic review and meta-analysis of environmental, lifestyle, and health factors associated with DNA methylation age, J Gerontol A Biol Sci Med Sci, glz099
- McCrory, 2019, Epigenetic clocks and allostatic load reveal potential sex-specific drivers of biological aging, J Gerontol A Biol Sci Med Sci, glz241, 10.1093/gerona/glz241 · DOI
- Maddock, 2019, DNA methylation age and physical and cognitive aging, J Gerontol A Biol Sci Med Sci, glz246, 10.1093/gerona/glz246 · DOI
- Levine, 2018, An epigenetic biomarker of aging for lifespan and healthspan, Aging (Albany NY), 10, 573, 10.18632/aging.101414 · DOI · Paper page
- McEwen, 1998, Stress, adaptation, and disease. Allostasis and allostatic load, Ann N Y Acad Sci, 840, 33, 10.1111/j.1749-6632.1998.tb09546.x · DOI · Paper page
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McCrory, C., Fiorito, G., Hernandez, B., Polidoro, S., O’Halloran, A. M., Hever, A., Ni Cheallaigh, C., Lu, A. T., Horvath, S., Vineis, P., et al. (2021) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glaa286 · Cited by 464
Vaiserman, A., & Krasnienkov, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.630186 · Cited by 454
Waziry, R., Ryan, C. P., Corcoran, D. L., Huffman, K. M., Kobor, M. S., Kothari, M., Graf, G. H., Kraus, V. B., Kraus, W. E., Lin, D. T. S., et al. (2023) · Nature Aging · doi:10.1038/s43587-022-00357-y · Cited by 217
Crimmins, E. M., Thyagarajan, B., Levine, M. E., Weir, D. R., & Faul, J. (2021) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glab016 · Cited by 213
Self-citationBischoff-Ferrari, H. A., Gängler, S., Wieczorek, M., Belsky, D. W., Ryan, J., Kressig, R. W., Stähelin, H. B., Theiler, R., Dawson-Hughes, B., Rizzoli, R., et al. (2025) · Nature Aging · doi:10.1038/s43587-024-00793-y · Cited by 140
Metadata
- Authors
- Levine, M. E.
- Venue
- The Journals of Gerontology: Series A
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-29
- Volume
- 75
- Issue
- 3
- Pages
- 463-465
- ISSN
- 1079-5006
- 1758-535X
- Cited by
- 99
- References
- 7
- Language
- en
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Funders
- National Institutes of Health
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