The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Determinants of accelerated metabolomic and epigenetic aging in a UK cohort
Open accessPublished version of a preprint
Abstract
Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample ( N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration ( N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.
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
Figures
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?
The cited source says what the claim attributes to it.
In the citing paper
“Robinson et al.120 additionally assessed mass spectrometry metabolomic ageing clocks for the enrichment of biological pathways and identified enrichments for the metabolism of several vitamins, amino acids and xenobiotics.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“Metabolomic age acceleration correlated with triglyceride levels, obesity, heavy drinking, diabetes mellitus, depressive symptoms, depression, anxiety and post-traumatic stress disorder.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“Furthermore, metabolomic clocks have demonstrated lower age prediction accuracy than other omics data types despite training on extremely large sample sizes, and effect sizes for ageing traits have been modest113,116–121.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“and Robinson et al.’s metabolomic age gap had no correlation with the Horvath, Hannum and PhenoAge clocks in the same cohort, yet there was some overlap in the correlation between age gap and phenotypes tested120.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“This study compared multiple untargeted metabolomic methods for developing ageing clocks”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The Hospital Anxiety and Depression Scale
Zigmond, A. S., & Snaith, R. P. (1983) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.1983.tb09716.x · Cited by 47,139
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
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
Depression and quality of life in older persons: A review
Sivertsen, H., Bjørkløf, G. H., Engedal, K., Selbæk, G., & Helvik, A.-S. (2015) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000437299 · Cited by 14,152
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
Adherence to a Mediterranean diet and survival in a Greek population
Trichopoulou, A., Costacou, T., Bamia, C., & Trichopoulos, D. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa025039 · Cited by 4,397
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
Karasek, R. A., Brisson, C., Kawakami, N., Houtman, I. L. D., Bongers, P. M., & Amick, B. C. (1998) · Journal of Occupational Health Psychology · doi:10.1037//1076-8998.3.4.322 · Cited by 4,196
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
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis
Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., Wishart, D. S., & Xia, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky310 · Cited by 3,637
HMDB 4.0: the human metabolome database for 2018
Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1089 · Cited by 3,438
McEWEN, B. S., & SEEMAN, T. (1999) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1999.tb08103.x · Cited by 1,824
Adherence to a DASH-Style Diet and Risk of Coronary Heart Disease and Stroke in Women
Fung, T. T. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.7.713 · Cited by 1,567
Chen, Y. A., Lemire, M., Choufani, S., Butcher, D. T., Grafodatskaya, D., Zanke, B. W., Gallinger, S., Hudson, T. J., & Weksberg, R. (2013) · Epigenetics · doi:10.4161/epi.23470 · Cited by 1,547
Gomes, A. P., Price, N. L., Ling, A. J. Y., Moslehi, J. J., Montgomery, M. K., Rajman, L., White, J. P., Teodoro, J. S., Wrann, C. D., Hubbard, B. P., et al. (2013) · Cell · doi:10.1016/j.cell.2013.11.037 · Cited by 1,482
Facing up to the global challenges of ageing
Partridge, L., Deelen, J., & Slagboom, P. E. (2018) · Nature · doi:10.1038/s41586-018-0457-8 · Cited by 1,475
Reactive oxygen species act through p38 MAPK to limit the lifespan of hematopoietic stem cells
Ito, K., Hirao, A., Arai, F., Takubo, K., Matsuoka, S., Miyamoto, K., Ohmura, M., Naka, K., Hosokawa, K., Ikeda, Y., et al. (2006) · Nature Medicine · doi:10.1038/nm1388 · Cited by 1,362
Pipkins, H. R., Bradshaw, J. L., Keller, L. E., Swiatlo, E., & McDaniel, L. S. (2017) · PLOS ONE · doi:10.1371/journal.pone.0179159 · Cited by 1,338
NAD + in aging, metabolism, and neurodegeneration
Verdin, E. (2015) · Science · doi:10.1126/science.aac4854 · Cited by 1,319
Stringhini, S., Carmeli, C., Jokela, M., Avendaño, M., Muennig, P., Guida, F., Ricceri, F., d'Errico, A., Barros, H., Bochud, M., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(16)32380-7 · Cited by 1,319
Self-citationDimensional assessment of chronic fatigue syndrome
Vercoulen, J. H. M. M., Swanink, C. M. A., Fennis, J. F. M., Galama, J. M. D., van der Meer, J. W. M., & Bleijenberg, G. (1994) · Journal of Psychosomatic Research · doi:10.1016/0022-3999(94)90099-x · Cited by 1,306
Jylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240
van der Harst, P., & Verweij, N. (2018) · Circulation Research · doi:10.1161/circresaha.117.312086 · Cited by 1,141
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
Quantification of biological aging in young adults
Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., Schaefer, J. D., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506264112 · Cited by 1,012
International Physical Activity Questionnaire
Fogelholm, M., Malmberg, J. J., Suni, J. H., Santtila, M., Kyröläinen, H., Mäntysaari, M., & Oja, P. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000194075.16960.20 · Cited by 977
Predicting Network Activity from High Throughput Metabolomics
Li, S., Park, Y. H., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., Jones, D. P., & Pulendran, B. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003123 · Cited by 955
Soininen, P., Kangas, A. J., Würtz, P., Suna, T., & Ala‐Korpela, M. (2015) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000216 · Cited by 917
Self-citationLevine, M. E. (2012) · The Journals of Gerontology Series A · doi:10.1093/gerona/gls233 · Cited by 897
Zelena, E., Dunn, W. B., Broadhurst, D., Francis‐McIntyre, S., Carroll, K. M., Begley, P., O’Hagan, S., Knowles, J. D., Halsall, A., HUSERMET Consortium, et al. (2009) · Analytical Chemistry · doi:10.1021/ac8019366 · Cited by 815
Metabolic Control of Longevity
López-Otín, C., Galluzzi, L., Freije, J. M. P., Madeo, F., & Kroemer, G. (2016) · Cell · doi:10.1016/j.cell.2016.07.031 · Cited by 753
An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease
Horvath, S., Gurven, M., Levine, M. E., Trumble, B. C., Kaplan, H. S., Allayee, H., Ritz, B. R., Chen, B. H., Lu, A. T.-H., Rickabaugh, T. M., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1030-0 · Cited by 749
The transcriptional landscape of age in human peripheral blood
Peters, M. J., Joehanes, R., Pilling, L. C., Schurmann, C., Conneely, K. N., Powell, J., Reinmaa, E., Sutphin, G. L., Zhernakova, A., Schramm, K., et al. (2015) · Nature Communications · doi:10.1038/ncomms9570 · Cited by 725
Defective Mitophagy in XPA via PARP-1 Hyperactivation and NAD+/SIRT1 Reduction
Fang, E. F., Scheibye-Knudsen, M., Brace, L. E., Kassahun, H., SenGupta, T., Nilsen, H., Mitchell, J. R., Croteau, D. L., & Bohr, V. A. (2014) · Cell · doi:10.1016/j.cell.2014.03.026 · Cited by 711
Berndt, S. I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M. F., Justice, A. E., Monda, K. L., Croteau-Chonka, D. C., Day, F. R., et al. (2013) · Nature Genetics · doi:10.1038/ng.2606 · Cited by 646
Brief screening instrument for post-traumatic stress disorder
Brewin, C. R., Rose, S., Andrews, B., Green, J., Tata, P., McEvedy, C., Turner, S., & Foa, E. B. (2002) · British Journal of Psychiatry · doi:10.1192/bjp.181.2.158 · Cited by 623
SIRT1 Mediates Central Circadian Control in the SCN by a Mechanism that Decays with Aging
Chang, H.-C., & Guarente, L. (2013) · Cell · doi:10.1016/j.cell.2013.05.027 · Cited by 615
GWAS on family history of Alzheimer’s disease
Marioni, R. E., Harris, S. E., Zhang, Q., McRae, A. F., Hagenaars, S. P., Hill, W. D., Davies, G., Ritchie, C. W., Gale, C. R., Starr, J. M., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0150-6 · Cited by 564
Dona, A. C., Jiménez, B., Schäfer, H., Humpfer, E., Spraul, M., Lewis, M. R., Pearce, J. T. M., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2014) · Analytical Chemistry · doi:10.1021/ac5025039 · Cited by 540
Self-citationYang, J. S. (2016) · Annals of Global Health · doi:10.1016/j.aogh.2016.04.293 · Cited by 535
A systematic review of leukocyte telomere length and age in adults
Müezzinler, A., Zaineddin, A. K., & Brenner, H. (2013) · Ageing Research Reviews · doi:10.1016/j.arr.2013.01.003 · Cited by 474
Genome-wide association study identifies 112 new loci for body mass index in the Japanese population
Akiyama, M., Okada, Y., Kanai, M., Takahashi, A., Momozawa, Y., Ikeda, M., Iwata, N., Ikegawa, S., Hirata, M., Matsuda, K., et al. (2017) · Nature Genetics · doi:10.1038/ng.3951 · Cited by 455
Belsky, D. W., Moffitt, T. E., Cohen, A. A., Corcoran, D. L., Levine, M. E., Prinz, J. A., Schaefer, J. D., Sugden, K., Williams, B. S., Poulton, R., et al. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx346 · Cited by 436
The longitudinal study of the Northern Finland birth cohort of 1966
Rantakallio, P. T. (1988) · Paediatric and Perinatal Epidemiology · doi:10.1111/j.1365-3016.1988.tb00180.x · Cited by 373
Individual variability in human blood metabolites identifies age-related differences
Chaleckis, R., Murakami, I., Takada, J., Kondoh, H., & Yanagida, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1603023113 · Cited by 373
DNA damage and ageing: new-age ideas for an age-old problem
Garinis, G. A., van der Horst, G. T. J., Vijg, J., & H.J. Hoeijmakers, J. (2008) · Nature Cell Biology · doi:10.1038/ncb1108-1241 · Cited by 366
Human serum metabolic profiles are age dependent
Yu, Z., Zhai, G., Singmann, P., He, Y., Xu, T., Prehn, C., Römisch‐Margl, W., Lattka, E., Gieger, C., Soranzo, N., et al. (2012) · Aging Cell · doi:10.1111/j.1474-9726.2012.00865.x · Cited by 340
Gallagher, J. C. (2013) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2013.02.004 · Cited by 308
Sarafian, M. H., Gaudin, M., Lewis, M. R., Martin, F., Holmes, E. C., Nicholson, J. K., & Dumas, M. (2014) · Analytical Chemistry · doi:10.1021/ac500317c · Cited by 306
Self-citationFerrucci, L., Levine, M. E., Kuo, P.-L., & Simonsick, E. M. (2018) · Circulation Research · doi:10.1161/circresaha.118.312816 · Cited by 303
Metabolic control of methylation and acetylation
Su, X., Wellen, K. E., & Rabinowitz, J. D. (2015) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2015.10.030 · Cited by 296
Socio-economic status, cortisol and allostatic load: a review of the literature
Dowd, J. B., Simanek, A. M., & Aiello, A. E. (2009) · International Journal of Epidemiology · doi:10.1093/ije/dyp277 · Cited by 285
A metabolic view on menopause and ageing
Auro, K. M., Joensuu, A., Fischer, K., Kettunen, J., Salo, P. P., Mattsson, H., Niironen, M., Kaprio, J. A., Eriksson, J. G., Lehtimäki, T. J., et al. (2014) · Nature Communications · doi:10.1038/ncomms5708 · Cited by 274
Self-citationFiorito, 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
Self-citationBürkle, A., Moreno‐Villanueva, M., Bernhard, J., Blasco, M. A., Zondag, G. C. M., Hoeijmakers, J. H. J., Toussaint, O., Grubeck‐Loebenstein, B., Mocchegiani, E., Collino, S., et al. (2015) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2015.03.006 · Cited by 239
Parsons, H. M., Ludwig, C., Günther, U. L., & Viant, M. R. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-234 · Cited by 223
Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis
Fiorito, G., the BIOS Consortium, the Lifepath consortium, McCrory, C., Robinson, O., Carmeli, C., Rosales, C. O., Zhang, Y., Colicino, E., Dugué, P.-A., et al. (2019) · Aging · doi:10.18632/aging.101900 · Cited by 219
Self-citationMetabolic syndrome and major depression
Marazziti, D., Rutigliano, G., Baroni, S., Landi, P., & Dell’Osso, L. (2013) · CNS Spectrums · doi:10.1017/s1092852913000667 · Cited by 216
Urinary metabolic signatures of human adiposity
Elliott, P., Posma, J. M., Chan, Q. M., García‐Pérez, I., Wijeyesekera, A., Bictash, M., Ebbels, T. M. D., Ueshima, H., Zhao, L., Horn, L. V., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa5680 · Cited by 211
Self-citationHuman longevity: 25 genetic loci associated in 389,166 UK biobank participants
Pilling, L. C., Kuo, C.-L., Sicinski, K., Tamosauskaite, J., Kuchel, G. A., Harries, L. W., Herd, P., Wallace, R., Ferrucci, L., & Melzer, D. (2017) · Aging · doi:10.18632/aging.101334 · Cited by 211
Rist, M. J., Roth, A., Frommherz, L., Weinert, C. H., Krüger, R., Merz, B., Bunzel, D., Mack, C., Egert, B., Bub, A., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0183228 · Cited by 202
A genome-wide scan for common variants affecting the rate of age-related cognitive decline
Jager, P. L. D., Shulman, J. M., Chibnik, L. B., Keenan, B. T., Raj, T., Wilson, R. S., Yu, L., Leurgans, S. E., Tran, D., Aubin, C., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.09.033 · Cited by 196
Dugué, P., Bassett, J. K., Joo, J. E., Baglietto, L., Jung, C.-H., Wong, E. M., Fiorito, G., Schmidt, D. F., Makalic, E., Li, S., et al. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx291 · Cited by 182
Self-citationMeasuring Biological Age via Metabonomics: The Metabolic Age Score
Hertel, J., Friedrich, N., Wittfeld, K., Pietzner, M., Budde, K., Van der Auwera, S., Lohmann, T., Teumer, A., Völzke, H., Nauck, M., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00561 · Cited by 181
Increased mortality among people with anxiety disorders: total population study.
Meier, S. M., Mattheisen, M., Mors, O., Mortensen, P. B., Laursen, T. M., & Penninx, B. W. (2016) · British Journal of Psychiatry · doi:10.1192/bjp.bp.115.171975 · Cited by 181
Huan, T., Chen, G., Liu, C., Bhattacharya, A., Rong, J., Chen, B. H., Seshadri, S. S., Tanrıverdi, K., Freedman, J. E., Larson, M. G., et al. (2017) · Aging Cell · doi:10.1111/acel.12687 · Cited by 171
Stringhini, S., Carmeli, C., Jokela, M. M., Avendaño, M., McCrory, C., d’Errico, A., Bochud, M., Barros, H., Costa, G., Chadeau‐Hyam, M., et al. (2018) · BMJ · doi:10.1136/bmj.k1046 · Cited by 164
Self-citationJoshi, P. K., Pirastu, N., Kentistou, K. A., Fischer, K., Hofer, E., Schraut, K. E., Clark, D. W., Nutile, T., Barnes, C. L. K., Timmers, P. R. H. J., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00934-5 · Cited by 161
Lewis, M. R., Pearce, J. T. M., Spagou, K., Green, M., Dona, A. C., Yuen, A. H. Y., David, M., Berry, D. J., Chappell, K. E., der Sluis, V. H.-V., et al. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01481 · Cited by 152
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
Self-citationFish consumption and all-cause mortality: a meta-analysis of cohort studies
Zhao, L.-G., Sun, J.-W., Yang, Y., Ma, X., Wang, Y.-Y., & Xiang, Y.-B. (2016) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2015.72 · Cited by 132
The Longitudinal Study of Aging in Human Young Adults: Knowledge Gaps and Research Agenda
Moffitt, T. E., Belsky, D. W., Danese, A., Poulton, R., & Caspi, A. (2017) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glw191 · Cited by 117
Vitamin E and genome stability.
Claycombe, K., & Meydani, S. (2001) · Mutation research · doi:10.1016/s0027-5107(01)00077-x · Cited by 113
Traumatic Stress and Accelerated Cellular Aging: From Epigenetics to Cardiometabolic Disease
Wolf, E. J., & Morrison, F. G. (2017) · Current Psychiatry Reports · doi:10.1007/s11920-017-0823-5 · Cited by 99
Elliott, P., Vergnaud, A., Singh, D., Neasham, D., Spear, J., & Heard, A. (2014) · Environmental Research · doi:10.1016/j.envres.2014.07.025 · Cited by 88
Self-citationBayesian association scan reveals loci associated with human lifespan and linked biomarkers
McDaid, A. F., Joshi, P. K., Porcu, E., Komljenović, A., Li, H., Sorrentino, V., Litovchenko, M., Bevers, R. P. J., Rüeger, S., Reymond, A., et al. (2017) · Nature Communications · doi:10.1038/ncomms15842 · Cited by 79
Sahar, S., Masubuchi, S., Eckel‐Mahan, K. L., Vollmer, S., Galla, L., Ceglia, N., Masri, S., Barth, T. K., Grimaldi, B., Oluyemi, O. I., et al. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.537191 · Cited by 74
Biological Age, Not Chronological Age, Is Associated with Late-Life Depression
Brown, P. J., Wall, M. M., Chen, C., Levine, M. E., Yaffe, K. S., Roose, S. P., & Rutherford, B. R. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx162 · Cited by 68
Laimer, M. W., Melmer, A., Lamina, C., Raschenberger, J., Adamovski, P., Engl, J., Ress, C., Tschoner, A., Gelsinger, C., Mair, L., et al. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.238 · Cited by 62
Chiu, M., Vigod, S. N., Rahman, F., Wilton, A. S., Lebenbaum, M., & Kurdyak, P. A. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.02.075 · Cited by 45
Karaman, İ., Ferreira, D. L. S., Boulangé, C. L., Kaluarachchi, M. R., Herrington, D. M., Dona, A. C., Castagné, R., Moayyeri, A., Lehne, B. C., Loh, M. C. S., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00125 · Cited by 43
Self-citationInternational Physical Activity Questionnaire
Mehta, S. P., Jarvis, A., Standifer, D., & Warnimont, C. (2018) · Critical Reviews in Physical and Rehabilitation Medicine · doi:10.1615/critrevphysrehabilmed.2018026180 · Cited by 40
Gibson, R., Eriksen, R., Lamb, K., McMeel, Y., Vergnaud, A., Spear, J., Aresu, M., Chan, Q. M., Elliott, P., & Frost, G. S. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-012927 · Cited by 38
Self-citationBelsky, D. W., Moffitt, T. E., Cohen, A. A., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/071373 · Cited by 17
Sala, M. L., Röell, B., van der Bijl, N., van der Grond, J., de Craen, A. J. M., Slagboom, P. E., van der Geest, R. J., Roos, A., & Kroft, L. J. M. (2015) · Age and Ageing · doi:10.1093/ageing/afv063 · Cited by 13
Research works printed but not linked: 2
- 10.1017/S0007125000161896 · DOI · Paper page
- World Health Organisation, 2013, Global action plan for the prevention and control of noncommunicable diseases 2013–2020
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
Higgins-Chen, A. T., Thrush, K. L., Wang, Y., Minteer, C. J., Kuo, P.-L., Wang, M., Niimi, P., Sturm, G., Lin, J., Moore, A. Z., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00248-2 · Cited by 502
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 402
Vineis, P., Robinson, O., Chadeau‐Hyam, M., Dehghan, A., Mudway, I., & Dagnino, S. (2020) · Environment International · doi:10.1016/j.envint.2020.105887 · Cited by 324
Self-citationAssociations between alcohol consumption and gray and white matter volumes in the UK Biobank
Daviet, R., Aydogan, G., Jagannathan, K., Spilka, N. H., Koellinger, P., Kranzler, H. R., Nave, G., & Wetherill, R. R. (2022) · Nature Communications · doi:10.1038/s41467-022-28735-5 · Cited by 250
Metadata
- Authors
- Robinson, O., Chadeau Hyam, M., Karaman, I., Climaco Pinto, R., Ala-Korpela, M., Handakas, E., Fiorito, G., Gao, H., Heard, A., Jarvelin, M., Lewis, M., Pazoki, R., Polidoro, S., Tzoulaki, I., Wielscher, M., Elliott, P., & Vineis, P.
- Affiliations
- MRC Centre for Environment and Health (GB)
- MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK
- Computational Medicine, Faculty of Medicine University of Oulu and Biocenter Oulu Oulu Finland
- University of Sassari (IT)
- Imperial College London (GB)
- Italian institute for Genomic Medicine (IT)
- Corresponding author
- Robinson, Oliver
- ORCID
- Venue
- Aging Cell
- Publisher
- Wiley
- Published
- 2020-05-03
- Volume
- 19
- Issue
- 6
- Pages
- e13149-e13149
- ISSN
- 1474-9718 (ISSN-L)
- 1474-9726
- PMC
- PMC7294785
- OpenAlex
- W3021249782
- Cited by
- 205
- References
- 87
- Citations by year
- 2026: 42 · 2025: 30 · 2024: 49 · 2023: 33 · 2022: 23 · 2021: 18 · 2020: 9
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Aging Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Horizon 2020 Framework Programme (633666, 633595, 733206)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- 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
- Keywords
- biological aging, DNA methylation age, accelerated aging, untargeted metabolomics, lifestyle risk factors, alcohol consumption, depression, obesity, diabetes mellitus, hypertension, socioeconomic status, epigenetic clocks, cross-cohort validation
- MeSH terms
- Adult, Aged, Aging, Female, United Kingdom, Humans, Male, Middle Aged, Cohort Studies, DNA Methylation, Metabolomics, Young Adult, 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 ›
Are you an author, or do you see a problem with this page? Request a review
