DNA methylation-based measures of accelerated biological ageing and the risk of dementia in the oldest-old: a study of the Lothian Birth Cohort 1921
Open accessAbstract
Background: Previous studies have demonstrated an association between DNA methylation-based measures of accelerated ageing and age-related health outcomes and mortality. As a disease closely associated with advancing age, we hypothesized that DNA methylation-based measures of accelerated ageing might be associated with risk for dementia. This study therefore aimed to examine the association between four recognised measures of age acceleration and subsequent dementia. Methods: Study subjects (n = 488) were members of the Lothian Birth Cohort 1921. Dementia case ascertainment used data from death certificates, electronic hospital records, and clinical reviews. Venous blood samples were taken at baseline, at age 79 years. DNA methylation and measures of epigenetic age were calculated in accordance with Horvath’s epigenetic clock tutorial, using the online calculator (https://dnamage.genetics.ucla.edu/). From these values, four measures of accelerated ageing were calculated: extrinsic epigenetic age acceleration (EEAA), intrinsic epigenetic age acceleration (IEAA), AgeAccelPheno and AgeAccelGrim. Competing risk regression models – with death as a competing risk – were performed to examine the association between each measure of accelerated ageing and incident dementia. APOE ɛ4 status, sex, age, smoking status, history of cardiovascular disease, cerebrovascular disease, hypertension, and diabetes were included as covariates. Results: None of the multivariate models revealed a positive association between increased epigenetic age acceleration and dementia risk. Across all included models, never-smoking increased risk for dementia (HR 1.69 [1.06, 2.71], p = 0.03), and having no APOE ɛ4 alleles reduced risk for dementia (HR 0.44 [0.29, 0.67], p < 0.001). Conclusions: The present study did not demonstrate any consistent association between DNA methylation-based measures of accelerated ageing and dementia in subjects aged over 79 years. Further, larger studies – including separate analyses of dementia subtypes – are required to further investigate the potential association between DNA methylation-based measures of accelerated ageing and dementia.
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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 [66]
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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
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
DNA methylation and its basic function
Moore, L. D., Le, T., & Fan, G. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2012.112 · Cited by 4,906 · 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
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
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-citationDNA 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-citationAlzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956 · see citing works
Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.86.1.130 · Cited by 809 · see citing works
Self-citationMethylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease
Lunnon, K., Smith, R., Hannon, E., De Jager, P. L., Srivastava, G., Volta, M., Troakes, C., Al-Sarraj, S., Burrage, J., Macdonald, R., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3782 · Cited by 574 · see citing works
Marioni, R. E., Shah, S. H., McRae, A. F., Ritchie, S. J., Terrera, G. M., Harris, S. E., Gibson, J., Redmond, P. H., Cox, S. R., Pattie, A., et al. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu277 · Cited by 559 · see citing works
Self-citationBrain age and other bodily ‘ages’: implications for neuropsychiatry
Cole, J. H., Marioni, R. E., Harris, S. E., & Deary, I. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0098-1 · Cited by 436 · see citing works
Self-citationCohort Profile: The Lothian Birth Cohorts of 1921 and 1936
Deary, I. J., Gow, A. J., Pattie, A., & Starr, J. M. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr197 · Cited by 401 · see citing works
Self-citationWain, L. V., Shrine, N. R. G., Miller, S., Jackson, V. E., Ntalla, I., Artigas, M. S., Billington, C. K., Kheirallah, A. K., Allen, R. J., Cook, J. P., et al. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(15)00283-0 · Cited by 384 · see citing works
Cigarette smoking and DNA methylation
Lee, K. W. K., & Pausová, Z. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00132 · Cited by 373 · see citing works
Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring
Horvath, S., Pirazzini, C., Bacalini, M. G., Gentilini, D., Di Blasio, A. M., Delledonne, M., Mari, D., Arosio, B., Monti, D., Passarino, G., et al. (2015) · Aging · doi:10.18632/aging.100861 · Cited by 316 · see citing works
Cohort Profile Update: The Lothian Birth Cohorts of 1921 and 1936
Taylor, A. M., Pattie, A., & Deary, I. J. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy022 · Cited by 235 · see citing works
Self-citationA lifetime of intelligence: Follow-up studies of the Scottish mental surveys of 1932 and 1947.
Deary, I. J., Whalley, L. J., & Starr, J. M. (2009) · American Psychological Association eBooks · doi:10.1037/11857-000 · Cited by 179 · see citing works
Self-citationRyan, J. L., Wrigglesworth, J., Loong, J., Fransquet, P., & Woods, R. L. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz099 · Cited by 172 · see citing works
The APOE Gene is Differentially Methylated in Alzheimer’s Disease
Foraker, J. E., Millard, S. P., Leong, L., Thomson, Z., Chen, S., Keene, C. D., Bekris, L. M., & Yu, C. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-143060 · Cited by 145 · see citing works
McCartney, D. L., Stevenson, A. J., Walker, R. M., Gibson, J., Morris, S. W., Campbell, A., Murray, A. D., Whalley, H. C., Porteous, D. J., McIntosh, A. M., et al. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.05.006 · Cited by 134 · see citing works
Self-citationThe epigenetic landscape of Alzheimer's disease
Lord, J., & Cruchaga, C. (2014) · Nature Neuroscience · doi:10.1038/nn.3792 · Cited by 122 · see citing works
Wen, K., Milić, J., El‐Khodor, B. F., Dhana, K., Nano, J., Pulido, T., Kraja, B., Začiragić, A., Bramer, W. M., Troup, J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167201 · Cited by 122 · see citing works
Methylation‐based estimated biological age and cardiovascular disease
Lind, L., Ingelsson, E., Sundström, J., Siegbahn, A., & Lampa, E. (2017) · European Journal of Clinical Investigation · doi:10.1111/eci.12872 · Cited by 112 · see citing works
Maintained memory in aging is associated with young epigenetic age
Degerman, S., Josefsson, M., Adolfsson, A. N., Wennstedt, S., Landfors, M., Haider, Z., Pudas, S., Hultdin, M., Nyberg, L. H., & Adolfsson, R. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.02.009 · Cited by 96 · see citing works
Blood DNA methylation as a potential biomarker of dementia: A systematic review
Fransquet, P. D., Lacaze, P., Saffery, R., McNeil, J., Woods, R., & Ryan, J. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.10.002 · Cited by 96 · see citing works
Scottish Council for Research in Education, 1928-1993
Morris, J. G. (1994) · ERA · Cited by 88 · see citing works
Ischemic stroke patients are biologically older than their chronological age
Soriano‐Tárraga, C., Steinhauer, E. G., Mola-Caminal, M., Vivanco‐Hidalgo, R. M., Ois, Á., Rodríguez-Campello, A., Cuadrado‐Godia, E., Sayols-Baixeras, S., Elosúa, R., Roquer, J., et al. (2016) · Aging · doi:10.18632/aging.101028 · Cited by 75 · see citing works
Healthy cognitive ageing in the Lothian Birth Cohort studies: marginal gains not magic bullet
Corley, J., Cox, S. R., & Deary, I. J. (2017) · Psychological Medicine · doi:10.1017/s0033291717001489 · Cited by 68 · see citing works
Self-citationChildhood Cognitive Ability and Incident Dementia
Russ, T. C., Hannah, J., Batty, G. D., Booth, C. C., Deary, I. J., & Starr, J. M. (2017) · Epidemiology · doi:10.1097/ede.0000000000000626 · Cited by 57 · see citing works
Self-citationGenotype effects contribute to variation in longitudinal methylome patterns in older people
Zhang, Q., Marioni, R. E., Robinson, M. R., Higham, J., Sproul, D., Wray, N. R., Deary, I. J., McRae, A. F., & Visscher, P. M. (2018) · Genome Medicine · doi:10.1186/s13073-018-0585-7 · Cited by 47 · see citing works
Self-citationSibbett, R., Russ, T. C., Deary, I. J., & Starr, J. M. (2017) · BMC Psychiatry · doi:10.1186/s12888-017-1366-3 · Cited by 38 · see citing works
Self-citationPhysical fitness and dementia risk in the very old: a study of the Lothian Birth Cohort 1921
Sibbett, R., Russ, T. C., Allerhand, M. H., Deary, I. J., & Starr, J. M. (2018) · BMC Psychiatry · doi:10.1186/s12888-018-1851-3 · Cited by 28 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [18]Scottish Council for Research in Education. The Intelligence of Scottish Children: A National Survey of an Age-Group. London: University of London Press; 1933.
Cited by
Sorted by citations
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
Brain age and other bodily ‘ages’: implications for neuropsychiatry
Cole, J. H., Marioni, R. E., Harris, S. E., & Deary, I. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0098-1 · Cited by 436 · see citing works
Self-citationAging biomarkers and the brain
Higgins‐Chen, A., Thrush, K., & Levine, M. E. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.01.003 · Cited by 86 · see citing works
Analysis of Epigenetic Age Acceleration and Healthy Longevity Among Older US Women
Jain, P., Binder, A. M., Chen, B. H., Parada, H., Gallo, L. C., Alcaraz, J. E., Horvath, S., Bhatti, P., Whitsel, E. A., Jordahl, K. M., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.23285 · Cited by 75 · see citing works
Epigenetic aging as a biomarker of dementia and related outcomes: a systematic review
Zhou, A., Wu, Z., Phyo, A. Z. Z., Torres, D. S., Vishwanath, S., & Ryan, J. L. (2022) · Epigenomics · doi:10.2217/epi-2022-0209 · Cited by 58 · see citing works
Galow, A.-M., & Peleg, S. (2022) · Cells · doi:10.3390/cells11030468 · Cited by 58 · see citing works
Shadyab, A. H., McEvoy, L. K., Horvath, S., Whitsel, E. A., Rapp, S. R., Espeland, M. A., Resnick, S. M., Manson, J. E., Chen, J., Chen, B. H., et al. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab245 · Cited by 40 · see citing works
Accelerated Epigenetic Aging in Peripheral Blood does not Predict Dementia Risk
Fransquet, P., Lacaze, P. A., Saffery, R., Shah, R. C., Vryer, R., Murray, A. M., Woods, R. L., & Ryan, J. L. (2021) · Current Alzheimer Research · doi:10.2174/1567205018666210823100721 · Cited by 30 · see citing works
Biological Age Acceleration and Motoric Cognitive Risk Syndrome
Sathyan, S., Ayers, E. I., Adhikari, D., Gao, T., Milman, S., Barzilai, N., & Verghese, J. (2023) · Annals of Neurology · doi:10.1002/ana.26624 · Cited by 24 · see citing works
Lynch, M. E., Arpawong, T. E., & Beam, C. R. (2023) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbad128 · Cited by 24 · see citing works
Ageing and Obesity Shared Patterns: From Molecular Pathogenesis to Epigenetics
Ghanemi, A., Yoshioka, M., & St‐Amand, J. (2021) · Diseases · doi:10.3390/diseases9040087 · Cited by 24 · see citing works
Pan, W., Han, Y. Y., Hu, H., & He, Y. (2022) · BMC Nephrology · doi:10.1186/s12882-022-02920-6 · Cited by 27 · see citing works
McMurran, C. E., Wang, Y., Mak, J. K. L., Karlsson, I., Tang, B., Ploner, A., Pedersen, N. L., & Hägg, S. (2023) · Brain · doi:10.1093/brain/awad252 · Cited by 22 · see citing works
Jain, P., Binder, A. M., Chen, B. H., Parada, H., Gallo, L. C., Alcaraz, J. E., Horvath, S., Bhatti, P., Whitsel, E. A., Jordahl, K. M., et al. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac190 · Cited by 20 · see citing works
Ye, Z., Lang, H., Xie, Z., Duan, S., Peng, B., Chen, X., Fang, Y., & Xin, J. (2024) · BMC Medicine · doi:10.1186/s12916-024-03640-4 · Cited by 19 · see citing works
Liu, H., Lutz, M. W., Luo, S., & Initiative, A. D. N. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac138 · Cited by 17 · see citing works
Hong, C., Yang, S., Wang, Q., Zhang, S., Wu, W., Chen, J., Zhong, D., Li, M., Li, L., Li, J., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.563051 · Cited by 15 · see citing works
Vyas, C. M., Sadreyev, R. I., Gatchel, J. R., Kang, J. H., Reynolds, C. F., Mischoulon, D., Chang, G., Hazra, A., Manson, J. E., Blacker, D., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230093 · Cited by 13 · see citing works
Hackenhaar, F. S., Study, T. A. I. B. A. L., Josefsson, M., Adolfsson, A. N., Landfors, M., Kauppi, K., Porter, T., Milicic, L., Laws, S. M., Hultdin, M., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230039 · Cited by 10 · see citing works
Epigenetic age acceleration predicts subject‐specific white matter degeneration in the human brain
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High-dimensional generalized propensity score with application to omics data
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Assessing brain and biological aging trajectories associated with Alzheimer’s disease
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Blostein, F., Bakulski, K. M., Fu, M., Wang, H., Zawistowski, M., & Ware, E. B. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251333707 · Cited by 7 · see citing works
Fornage, M., Tarraf, W., Daviglus, M. L., DeCarli, C. S., González, K. A., Isasi, C. R., Kuwayama, S., Lamar, M., Levin, B. E., Parada, H., et al. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-01941-w · Cited by 5 · see citing works
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Yuan, S., Liu, Q., Huang, X., Xu, Z., Bai, Z., Qin, L., Shen, Q., Tang, E., Wu, J., Yang, K., et al. (2025) · Medicine Plus · doi:10.1016/j.medp.2025.100111 · Cited by 3 · see citing works
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Choi, B.-Y., Ryoo, S.-W., Son, S.-Y., Lee, J.-H., Min, K., & Min, J. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26041478 · Cited by 3 · see citing works
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Kuznetsov, N. V., Statsenko, Y., & Ljubisavljevic, M. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26041738 · Cited by 3 · see citing works
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Eissman, J. M., Ma, Y., Qiao, M., Reyes‐Dumeyer, D., Piriz, A. L., Lee, A., Lantigua, R., Medrano, M. Z., Mejía, D. R., Honig, L. S., et al. (2025) · medRxiv · doi:10.1101/2025.05.22.25328181 · Cited by 2 · see citing works
DNA Methylation Clocks in Age-related Disease
Fransquet, P., Wrigglesworth, J., & Ryan, J. L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91909-8.00011-6 · Cited by 1 · see citing works
The evidence for epigenetic age acceleration in neurological disease: A systematic review
Smith, R. D., Manavi, D., Quigley, N., Fard, A. R., Toumbas, G., McMurran, C. E., Kotter, M. R., & Mowforth, O. D. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103201 · Cited by 1 · see citing works
Liu, G., Hong, C., Huang, Y., Guan, B., Liu, Y., Liu, Z., & Luo, Y. (2025) · Global Transitions · doi:10.1016/j.glt.2025.08.001 · Cited by 1 · see citing works
Fornage, M., Tarraf, W., Xia, R., Ordonez, A., Sofer, T., Márquez, F., Thyagarajan, B., Talavera, G. A., Gallo, L. C., DeCarli, C. S., et al. (2025) · Aging · doi:10.18632/aging.206317 · Cited by 1 · see citing works
Hackenhaar, F. S., Josefsson, M., Adolfsson, A. N., Landfors, M., Kauppi, K., Porter, T., Milicic, L., Laws, S. M., Hultdin, M., Adolfsson, R., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2276882/v2 · Cited by 1 · see citing works
Weißenburg, A. M., Junge, M. P., Homann, J., Dobričić, V., Vetter, V. M., Lindenberger, U., Lill, C. M., Demuth, I., Düzel, S., & Bertram, L. (2026) · medRxiv · doi:10.64898/2026.03.23.26349074 · Cited by 1 · see citing works
Hackenhaar, F. S., Josefsson, M., Adolfsson, A. N., Landfors, M., Kauppi, K., Porter, T., Milicic, L., Laws, S. M., Hultdin, M., Adolfsson, R., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2276882/v1
Hackenhaar, F. S., Josefsson, M., Adolfsson, A. N., Landfors, M., Kauppi, K., Porter, T., Milicic, L., Laws, S. M., Hultdin, M., Adolfsson, R., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2276882/v3
Hackenhaar, F. S., Josefsson, M., Adolfsson, A. N., Landfors, M., Kauppi, K., Porter, T., Milicic, L., Laws, S. M., Hultdin, M., Adolfsson, R., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2276882/v4
Pan, W., Han, Y., Hu, H., & He, Y. (2022) · Figshare · doi:10.6084/m9.figshare.20581017
- Authors
- Sibbett, R. A., Altschul, D. M., Marioni, R. E., Deary, I. J., Starr, J. M., & Russ, T. C.
- Affiliations
- Alzheimer Scotland (GB)
- University of Edinburgh (GB)
- Corresponding author
- Sibbett, Ruth A.
- Venue
- BMC Psychiatry
- Published
- 2020-02-28
- Volume
- 20
- Issue
- 1
- Pages
- 91-91
- ISSN
- 1471-244X (ISSN-L)
- PMC
- PMC7048023
- OpenAlex
- W3012576542
- Cited by
- 41
- References
- 38
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 2 · 2023: 8 · 2022: 12 · 2021: 6 · 2018: 1
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Age UK (disconnected mind grant)
- Biotechnology and Biological Sciences Research Council (15/SAG09977, MR/K026992/1)
- Scottish Office Home and Health Department (CZB/4/505, ETM/55, CZH/4/213, CZG/3/2/79)
- Medical Research Council (MR/K026992/1, Centenary grant)
- royal society (wolfson rsearch merit award)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- DNA methylation, epigenetic age, dementia risk, oldest old, Lothian Birth Cohort 1921, APOE ε4, smoking, competing risk regression
- MeSH terms
- Aged, Aged, 80 and over, Aging, Dementia, Humans, DNA Methylation, Epigenesis, Genetic, Epigenomics
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 ›
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