GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality
Open accessAbstract
The aging process is characterized by the presence of high interindividual variation between individuals of the same chronical age prompting a search for biomarkers that capture this heterogeneity. Epigenetic clocks measure changes in DNA methylation levels at specific CpG sites that are highly correlated with calendar age. The discrepancy resulting from the regression of DNA methylation age on calendar age is hypothesized to represent a measure of biological aging with a positive/negative residual signifying age acceleration (AA)/deceleration, respectively. The present study examines the associations of 4 epigenetic clocks—Horvath, Hannum, PhenoAge, GrimAge—with a wide range of clinical phenotypes (walking speed, grip strength, Fried frailty, polypharmacy, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), Sustained Attention Reaction Time, 2-choice reaction time), and with all-cause mortality at up to 10-year follow-up, in a sample of 490 participants in the Irish Longitudinal Study on Ageing (TILDA). HorvathAA and HannumAA were not predictive of health; PhenoAgeAA was associated with 4/9 outcomes (walking speed, frailty MOCA, MMSE) in minimally adjusted models, but not when adjusted for other social and lifestyle factors. GrimAgeAA by contrast was associated with 8/9 outcomes (all except grip strength) in minimally adjusted models, and remained a significant predictor of walking speed, .polypharmacy, frailty, and mortality in fully adjusted models. Results indicate that the GrimAge clock represents a step-improvement in the predictive utility of the epigenetic clocks for identifying age-related decline in an array of clinical phenotypes promising to advance precision medicine.
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.
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?
The DNAm GrimAge clock shows consistently stronger associations with various measures of age-related dysfunction, including heart disease, frailty, and cognitive and physical decline, compared to other clocks.
“The DNAm GrimAge clock shows consistently stronger associations with various measures of age-related dysfunction, including heart disease, frailty, and cognitive and physical decline, compared to other clocks47,48,50,64–72.”
From Measuring biological age using omics data (2022) · cites this paper as [68]
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
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2005.53221.x · Cited by 25,553 · see citing works
Frailty in Older Adults: Evidence for a Phenotype
Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., Seeman, T., Tracy, R., Kop, W. J., Burke, G., et al. (2001) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/56.3.m146 · Cited by 24,907 · see citing works
International physical activity questionnaire: 12-country reliability and validity
CRAIG, C. L., MARSHALL, A. L., SJ??STR??M, M., BAUMAN, A. E., BOOTH, M. L., AINSWORTH, B. E., PRATT, M., EKELUND, U., YNGVE, A., SALLIS, J. F., et al. (2003) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000078924.61453.fb · Cited by 21,619 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · 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
Self-citationGenome-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
Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/59.3.m255 · Cited by 4,032 · 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
Self-citationDNA 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
Self-citationDetecting Alcoholism: The CAGE Questionnaire
Ewing, J. A. (1984) · JAMA · doi:10.1001/jama.1984.03350140051025 · Cited by 3,005 · 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
Self-citationThe Impact of Age on Cognition
Murman, D. (2015) · Seminars in Hearing · doi:10.1055/s-0035-1555115 · Cited by 1,619 · see citing works
DNA methylation age of blood predicts all-cause mortality in later life
Marioni, R. E., Shah, S., McRae, A. F., Chen, B. H., Colicino, E., Harris, S. E., Gibson, J., Henders, A. K., Redmond, P., Cox, S. R., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0584-6 · Cited by 1,261 · see citing works
Self-citationJylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240 · see citing works
DNA methylation-based measures of biological age: meta-analysis predicting time to death
Chen, B. H., Marioni, R. E., Colicino, E., Peters, M. J., Ward-Caviness, C. K., Tsai, P.-C., Roetker, N. S., Just, A. C., Demerath, E. W., Guan, W., et al. (2016) · Aging · doi:10.18632/aging.101020 · Cited by 1,126 · see citing works
Self-citationZhang, 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
Reversal 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 · see citing works
Self-citationRoalf, D. R., Moberg, P. J., Xie, S. X., Wolk, D. A., Moelter, S. T., & Arnold, S. E. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.10.001 · Cited by 396 · see citing works
Cohort Profile: The Irish Longitudinal Study on Ageing
Kearney, P. M., Cronin, H., O’Regan, C., Kamiya, Y., Savva, G. M., Whelan, B., & Kenny, R. A. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr116 · Cited by 351 · see citing works
Self-citationDesign and Methodology of The Irish Longitudinal Study on Ageing
Whelan, B. J., & Savva, G. M. (2013) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12199 · Cited by 319 · see citing works
Fiorito, G., Polidoro, S., Dugué, P., Mika Kivimaki, B., Ponzi, E., Matullo, G., Guarrera, S., Assumma, M. B., Georgiadis, P., Kyrtopoulos, S. Α., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16391-5 · Cited by 262 · see citing works
Self-citationDietary and Lifestyle Factors of DNA Methylation
Lim, U., & Song, M. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-612-8_23 · Cited by 206 · see citing works
Kenny, R. A., Coen, R. F., Frewen, J., Donoghue, O. A., Cronin, H., & Savva, G. M. (2013) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12195 · Cited by 205 · see citing works
Self-citationAre processing speed tasks biomarkers of cognitive aging?
Deary, I. J., Johnson, W., & Starr, J. M. (2010) · Psychology and Aging · doi:10.1037/a0017750 · Cited by 168 · see citing works
An epigenetic predictor of death captures multi-modal measures of brain health
Hillary, R. F., Stevenson, A. J., Cox, S. R., McCartney, D. L., Harris, S. E., Seeboth, A., Higham, J., Sproul, D., Taylor, A. M., Redmond, P., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-019-0616-9 · Cited by 139 · see citing works
Self-citationCastagné, R., Garès, V., Karimi, M., Chadeau‐Hyam, M., Vineis, P., Delpierre, C., & Kelly‐Irving, M. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0364-1 · Cited by 133 · see citing works
Self-citationHealth and Aging: Development of The Irish Longitudinal Study on Ageing Health Assessment
Cronin, H., O’Regan, C. M., Finucane, C., Kearney, P. M., & Kenny, R. A. (2013) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12197 · Cited by 119 · see citing works
Self-citationRichardson, K., Kenny, R. A., Peklar, J., & Bennett, K. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.02.016 · Cited by 106 · see citing works
Self-citationAssessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research
Levine, M. E. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa021 · Cited by 102 · see citing works
McCrory, C., Fiorito, G., Cheallaigh, C. N., Polidoro, S., Karisola, P., Alenius, H. T., Layte, R., Seeman, T. E., Vineis, P., & Kenny, R. A. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.02.018 · Cited by 97 · see citing works
Self-citationDNA methylation age and physical and cognitive ageing
Maddock, J., Fernandez, J. C., Wong, A., Cooper, R., Richards, M., Ong, K. K., Ploubidis, G. B., Goodman, A. T., Kuh, D. J., Bell, J. T., et al. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz246 · Cited by 82 · see citing works
Drieling, R. L., LaCroix, A. Z., Beresford, S. A. A., Boudreau, D. M., Kooperberg, C. L., & Heckbert, S. R. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv446 · Cited by 70 · see citing works
Epigenetic Clocks and Allostatic Load Reveal Potential Sex-Specific Drivers of Biological Aging
McCrory, C., Fiorito, G., McLoughlin, S., Polidoro, S., Cheallaigh, C. N., Bourke, N. M., Karisola, P., Alenius, H. T., Vineis, P., Layte, R., et al. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz241 · Cited by 62 · see citing works
Self-citationDevelopments in molecular epidemiology of aging
Hägg, S., Belsky, D. W., & Cohen, A. A. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180173 · Cited by 56 · see citing works
Circulating markers of ageing and allostatic load: A slow train coming
Shiels, P., Stenvinkel, P., Kooman, J., & McGuinness, D. (2016) · Practical Laboratory Medicine · doi:10.1016/j.plabm.2016.04.002 · Cited by 55 · see citing works
Zannas, A. S. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12535 · Cited by 27 · see citing works
DNA Methylome Marks of Exposure to Particulate Matter at Three Time Points in Early Life
Plusquin, M., Chadeau‐Hyam, M., Ghantous, A., Alfano, R., Bustamante, M., Chatzi, L., Cuenin, C., Gulliver, J., Herceg, Z., Kogevinas, M., et al. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.7b06447 · Cited by 27 · see citing works
Self-citationFrom John Snow to omics: the long journey of environmental epidemiology
Vineis, P. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0398-4 · Cited by 22 · see citing works
Self-citationDNA Methylation–Based Measures of Biological Aging
Dugué, P., Li, S., Hopper, J. L., & Milne, R. L. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812215-0.00003-0 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Organ aging signatures in the plasma proteome track health and disease
Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634 · see citing works
Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
Fiorito, G., Caini, S., Palli, D., Bendinelli, B., Saieva, C., Ermini, I., Valentini, V., Assedi, M., Rizzolo, P., Ambrogetti, D., et al. (2021) · Aging Cell · doi:10.1111/acel.13439 · Cited by 202 · see citing works
Self-citationPsychological and biological resilience modulates the effects of stress on epigenetic aging
Harvanek, Z. M., Fogelman, N., Xu, K., & Sinha, R. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01735-7 · Cited by 123 · see citing works
Verschoor, C. P., Lin, D. T. S., Kobor, M. S., Mian, O., Ma, J., Pare, G., & Ybazeta, G. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01150-1 · Cited by 58 · see citing works
Centering Age Inequality: Developing a Sociology-of-Age Framework
Barrett, A. E. (2022) · Annual Review of Sociology · doi:10.1146/annurev-soc-083121-043741 · Cited by 41 · see citing works
Sillanpää, E., Heikkinen, A., Kankaanpää, A., Paavilainen, A., Kujala, U. M., Tammelin, T. H., Kovanen, V., Sipilä, S., Pietiläinen, K. H., Kaprio, J., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01094-6 · Cited by 43 · see citing works
Clement, J., Yan, Q., Agrawal, M., Coronado, R. E., Sturges, J. A., Horvath, M., Lu, A. T., Brooke, R. T., & Horvath, S. (2022) · Aging Cell · doi:10.1111/acel.13696 · Cited by 34 · see citing works
Self-citationGaare, J. J., Brügger, K., Nido, G. S., & Tzoulis, C. (2023) · Movement Disorders · doi:10.1002/mds.29574 · Cited by 12 · see citing works
Sorlí, J. V., de la Cámara, E., Fernández-Carrión, R., Asensio, E. M., Portolés, O., Ortega-Azorín, C., Pérez-Fidalgo, A., Villamil, L. V., Fitó, M., Barragán, R., et al. (2024) · Nutrients · doi:10.3390/nu17010104 · Cited by 9 · see citing works
- Authors
- McCrory, C., Fiorito, G., Hernandez, B., Polidoro, S., O’Halloran, A. M., Hever, A., Ni Cheallaigh, C., Lu, A. T., Horvath, S., Vineis, P., & Kenny, R. A.
- Affiliations
- Trinity College Dublin (IE)
- University of Sassari (IT)
- Department of Medical Gerontology, Trinity College Dublin, Ireland
- Italian institute for Genomic Medicine (IT)
- Department of Clinical Medicine, St. James Hospital, Dublin, Ireland
- University of California, Los Angeles (US)
- MRC Centre for Environment and Health (GB)
- Corresponding author
- McCrory, Cathal
- ORCID
- Venue
- The Journals of Gerontology: Series A
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-19
- Volume
- 76
- Issue
- 5
- Pages
- 741-749
- ISSN
- 1079-5006 (ISSN-L)
- 1758-535X
- PMC
- PMC8087266
- OpenAlex
- W3106296722
- Cited by
- 464
- References
- 44
- Citations by year
- 2026: 69 · 2025: 116 · 2024: 84 · 2023: 90 · 2022: 72 · 2021: 33
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- The Journals of Gerontology Series A (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Funders
- Health Research Board (HRB) of Ireland (EIA-2017-012)
- LIFEPATH (633666)
- Irish Government
- Atlantic Philanthropies
- Irish Life plc
- NIH
- NIA (IU01AG060908)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Keywords
- GrimAge, epigenetic clocks, DNA methylation age, accelerated aging, biological aging, all-cause mortality, frailty, walking speed, polypharmacy, cognitive decline, Horvath clock, Hannum clock, PhenoAge clock, TILDA, aging biomarkers
- MeSH terms
- Walking Speed, Frailty, Aging, Female, Genetic Markers, Humans, Longitudinal Studies, Male, Middle Aged, Mortality, Neuropsychological Tests, Phenotype, Hand Strength, DNA Methylation, Polypharmacy, Epigenesis, Genetic
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