The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Putting epigenetic biomarkers to the test for clinical trials
Abstract
Reliable biomarkers are needed to test the effectiveness of interventions intended to improve health and extend lifespan.
Overview
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
Verification
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Until this study, none of the methods proposed to quantify biological aging had been tested in a randomized controlled trial of a geroscience-based intervention.
“However, until this study, none had been tested in a randomized controlled trial of a geroscience-based intervention49.”
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 [49]
The paper’s Abstract describes testing DunedinPoAm in a cohort from the CALERIE caloric-restriction trial. It does not establish that no biological-aging method had previously been tested in a randomized trial; it also discusses earlier small intervention studies, and its References list a 2019 randomized trial examining epigenetic aging. The copy does not establish whether that earlier trial qualifies as a geroscience-based intervention. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe PhenoAge and GrimAge clocks were developed to predict mortality risk at a single timepoint in mixed-age and older adults.
“The PhenoAge and GrimAge clocks were developed to predict mortality risk at a single timepoint in mixed-age and older adults.”
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 [49]
The Figure 1 caption (section labeled Abstract; no page given) calls GrimAge a mortality estimator and PhenoAge a health and lifespan biomarker. The discussion contrasts earlier biomarkers calibrated to endpoints such as death with DunedinPoAm’s measure of change over time. It does not specify that both clocks were developed to predict mortality risk at a single timepoint in mixed-age and older adults. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe GrimAge clock was based on analysis of eight plasma protein markers, smoking pack years, age, sex and mortality data from the Framingham Heart Study Offspring and Gen3 Cohorts.
“The GrimAge clock was based on analysis of eight plasma protein markers, smoking pack years, age, sex and mortality data from the Framingham Heart Study Offspring and Gen3 Cohorts (n = 2,751 participants aged 24–92 yr)47–49.”
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 [49]
The Figure 1 caption (Abstract; no page given) identifies GrimAge as a mortality estimator. Our copy does not describe its inputs, the Framingham cohorts, participant count, or age range, so those details are not supported by this source. (LLM, conf=0.83)
Experimental — accuracy not yet measuredNew measurements that summarize biological changes occurring with aging have potential to overcome the challenge that human aging takes decades to cause disease; measurements that both predict future disease, disability and mortality and can detect changes in aging processes over short timescales have potential to function as surrogate endpoints for intervention effects on healthy lifespan.
“New measurements that summarize biological changes occurring with aging have potential to overcome this challenge; measurements to quantify biological aging that both predict future disease, disability and mortality and can detect changes in aging processes over short timescales have potential to function as surrogate endpoints for intervention effects on healthy lifespan38,49.”
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 [49]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
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
Metformin as a tool to target aging
Barzilai, N., Crandall, J. P., Kritchevsky, S. B., & Espeland, M. A. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.05.011 · Cited by 1,096 · cited 1×
“Clinical trials are underway to test the geroscience hypothesis in humans (Barzilai et al., 2016).”
BackgroundSelf-citationReversal of epigenetic aging and immunosenescent trends in humans.
Fahy, G. M., Brooke, R. T., Watson, J. P., Good, Z., Vasanawala, S. S., Maecker, H., Leipold, M. D., Lin, D. T. S., Kobor, M. S., & Horvath, S. (2019) · Aging Cell · doi:10.1111/acel.13028 · Cited by 477
Flagged first, then by citations
Research works printed but not linked: 7
- Belsky, 2015, Quantification of biological aging in young adults, PNAS, 112, E4104, 10.1073/pnas.1506264112 · DOI · Paper page
- Belsky, 2020, Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm, eLife, 9, 10.7554/eLife.54870 · DOI · Paper page
- Chen, 2019, Effects of vitamin D3 supplementation on epigenetic aging in overweight and obese african americans with suboptimal vitamin D status: a randomized clinical trial, The Journals of Gerontology: Series A, 74, 91, 10.1093/gerona/gly223 · DOI · Paper page
- Hannum, 2013, Genome-wide methylation profiles reveal quantitative views of human aging rates, Molecular Cell, 49, 359, 10.1016/j.molcel.2012.10.016 · DOI · Paper page
- Horvath, 2013, DNA methylation age of human tissues and cell types, Genome Biology, 14, 10.1186/gb-2013-14-10-r115 · DOI · Paper page
- Justice, 2018, A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME biomarkers workgroup, GeroScience, 40, 419, 10.1007/s11357-018-0042-y · DOI · Paper page
- Levine, 2018, An epigenetic biomarker of aging for lifespan and healthspan, Aging, 10, 573, 10.18632/aging.101414 · DOI · Paper page
Cited by
Sorted by citations
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
Proteomics in aging research: A roadmap to clinical, translational research
Moaddel, R., Ubaida‐Mohien, C., Tanaka, T., Lyashkov, A., Basisty, N., Schilling, B., Semba, R. D., Franceschi, C., Gorospe, M., & Ferrucci, L. (2021) · Aging Cell · doi:10.1111/acel.13325 · Cited by 139
Whitman, E. T., Elliott, M. L., Knodt, A. R., Abraham, W. C., Anderson, T. J., Cutfield, N. J., Hogan, S., Ireland, D., Melzer, T. R., Ramrakha, S., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00897-z · Cited by 37
Metadata
- Authors
- Justice, J. N., & Kritchevsky, S. B.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2020-06-09
- Volume
- 9
- ISSN
- 2050-084X
- PMC
- PMC7282804
- Cited by
- 35
- References
- 4
- Language
- en
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
