Determinants of accelerated metabolomic and epigenetic aging in a UK cohort

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

The Hallmarks of Aging

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 · see citing works

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 · 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

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 · 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

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 3,577 · see citing works

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 · see citing works

The Job Content Questionnaire (JCQ): An instrument for internationally comparative assessments of psychosocial job characteristics.

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 3,201 · see citing works

Protective and Damaging Effects of Mediators of Stress: Elaborating and Testing the Concepts of Allostasis and Allostatic Load

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 · see citing works

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 · see citing works

Declining NAD+ Induces a Pseudohypoxic State Disrupting Nuclear-Mitochondrial Communication during Aging

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 · see citing works

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 · see citing works

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 · see citing works

Polyamine transporter potABCD is required for virulence of encapsulated but not nonencapsulated Streptococcus pneumoniae

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 · see citing works

NAD ⁺ in aging, metabolism, and neurodegeneration

Verdin, E. (2015) · Science · doi:10.1126/science.aac4854 · Cited by 1,319 · see citing works

Socioeconomic status and the 25×25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women

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 · see citing works

Dimensional 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 · see citing works

Biological Age Predictors

Jylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240 · see citing works

Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease

van der Harst, P., & Verweij, N. (2018) · Circulation Research · doi:10.1161/circresaha.117.312086 · Cited by 1,141 · 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

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 · see citing works

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 · see citing works

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 931 · see citing works

Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray

Chen, Y.-A., Lemire, M., Choufani, S., Butcher, D., Grafodatskaya, D., Zanke, B., Gallinger, S., Hudson, T. J., & Weksberg, R. (2013) · Epigenetics · doi:10.4161/epi.23470 · Cited by 908 · see citing works

Modeling the Rate of Senescence: Can Estimated Biological Age Predict Mortality More Accurately Than Chronological Age?

Levine, M. E. (2012) · The Journals of Gerontology Series A · doi:10.1093/gerona/gls233 · Cited by 897 · see citing works

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Cardiovascular Epidemiology and Genetics

Soininen, P., Kangas, A. J., Würtz, P., Suna, T., & Ala‐Korpela, M. (2015) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000216 · Cited by 890 · see citing works

Development of a Robust and Repeatable UPLC−MS Method for the Long-Term Metabolomic Study of Human Serum

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 · see citing works

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 · see citing works

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 729 · see citing works

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 · see citing works

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 · see citing works

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

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 · see citing works

Brief screening instrument for post-traumatic stress disorder

Brewin, C. R., Rose, S., Andrews, B., Green, J., Tata, P. R., McEvedy, C., Turner, S. W., & Foa, E. B. (2002) · The British Journal of Psychiatry · doi:10.1017/s0007125000161896 · Cited by 631 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping

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 · see citing works

Of markets and rights: Discourse in the 2008 and 2013 Global Action Plan for the Prevention and Control of Noncommunicable Diseases

Yang, J. S. (2016) · Annals of Global Health · doi:10.1016/j.aogh.2016.04.293 · Cited by 535 · see citing works

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 · see citing works

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 · see citing works

Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing?

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 419 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 334 · see citing works

Vitamin D and Aging

Gallagher, J. C. (2013) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2013.02.004 · Cited by 308 · see citing works

Objective Set of Criteria for Optimization of Sample Preparation Procedures for Ultra-High Throughput Untargeted Blood Plasma Lipid Profiling by Ultra Performance Liquid Chromatography–Mass Spectrometry

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 303 · see citing works

Time and the Metrics of Aging

Ferrucci, L., Levine, M. E., Kuo, P.-L., & Simonsick, E. M. (2018) · Circulation Research · doi:10.1161/circresaha.118.312816 · Cited by 303 · see citing works

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 · see citing works

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 272 · see citing works

Social adversity and epigenetic aging: a multi-cohort study on socioeconomic differences in peripheral blood DNA methylation

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

MARK-AGE biomarkers of ageing

Bürkle, 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 228 · see citing works

Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

Parsons, H. M., Ludwig, C., Günther, U. L., & Viant, M. R. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-234 · Cited by 223 · see citing works

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 · see citing works

Metabolic syndrome and major depression

Marazziti, D., Rutigliano, G., Baroni, S., Landi, P., & Dell’Osso, L. (2013) · CNS Spectrums · doi:10.1017/s1092852913000667 · Cited by 212 · see citing works

Human 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 · see citing works

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 207 · see citing works

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

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 · see citing works

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 192 · see citing works

Measuring 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 · see citing works

Association of DNA Methylation-Based Biological Age With Health Risk Factors and Overall and Cause-Specific Mortality

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 181 · see citing works

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 · see citing works

Age‐associated micro RNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

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 165 · see citing works

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Joshi, 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 · see citing works

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. (2016) · The Journals of Gerontology Series A · doi:10.1093/gerona/glw191 · Cited by 159 · see citing works

Socioeconomic status, non-communicable disease risk factors, and walking speed in older adults: multi-cohort population based study

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 152 · see citing works

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

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 143 · see citing works

Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort

Castagné, 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

Fish 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 · see citing works

Vitamin E and genome stability.

Claycombe, K., & Meydani, S. (2001) · Mutation research · doi:10.1016/s0027-5107(01)00077-x · Cited by 113 · see citing works

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 95 · see citing works

The Airwave Health Monitoring Study of police officers and staff in Great Britain: Rationale, design and methods

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 · see citing works

Bayesian 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 77 · see citing works

Circadian Control of Fatty Acid Elongation by SIRT1 Protein-mediated Deacetylation of Acetyl-coenzyme A Synthetase 1

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 72 · see citing works

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 66 · see citing works

Telomere length increase after weight loss induced by bariatric surgery: results from a 10 year prospective study

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 · see citing works

Mortality risk associated with psychological distress and major depression: A population-based cohort study

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 · see citing works

Workflow for Integrated Processing of Multicohort Untargeted 1H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology

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 · see citing works

International 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 · see citing works

Dietary assessment of British police force employees: a description of diet record coding procedures and cross-sectional evaluation of dietary energy intake reporting (The Airwave Health Monitoring Study)

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 · see citing works

Telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: Do they measure the same thing?Preprint

Belsky, D. W., Moffitt, T. E., Cohen, A. A., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/071373 · Cited by 17 · see citing works

Genetically determined prospect to become long-lived is associated with less abdominal fat and in particular less abdominal visceral fat in men

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 · 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

A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking

Higgins‐Chen, A., Thrush, K., Wang, Y., Minteer, C. J., Kuo, P., 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 498 · see citing works

Biomarkers of aging

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 389 · see citing works

What is new in the exposome?

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 303 · see citing works

Associations 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 241 · see citing works

Metformin decelerates aging clock in male monkeys

Yang, Y., Lu, X., Liu, N., Ma, S., Zhang, H., Zhang, Z., Yang, K., Jiang, M., Zheng, Z., Qiao, Y., et al. (2024) · Cell · doi:10.1016/j.cell.2024.08.021 · Cited by 227 · see citing works

Biological factors influencing depression in later life: role of aging processes and treatment implications

Szymkowicz, S. M., Gerlach, A., Homiack, D., & Taylor, W. D. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02464-9 · Cited by 212 · see citing works

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Jendoubi, T. (2021) · Metabolites · doi:10.3390/metabo11030184 · Cited by 177 · see citing works

The metabolomics of human aging: Advances, challenges, and opportunities

Panyard, D. J., Yu, B., & Snyder, M. P. (2022) · Science Advances · doi:10.1126/sciadv.add6155 · Cited by 155 · see citing works

Determining a multimodal aging clock in a cohort of Chinese women

Li, J., Xiong, M., Fu, X.-H., Fan, Y., Dong, C., Sun, X., Zheng, F., Wang, S., Liu, L., Xu, M., et al. (2023) · Med · doi:10.1016/j.medj.2023.06.010 · Cited by 148 · see citing works

Nutrition Strategies Promoting Healthy Aging: From Improvement of Cardiovascular and Brain Health to Prevention of Age-Associated Diseases

Fekete, M., Szarvas, Z., Fazekas-Pongor, V., Feher, A., Csipo, T., Forrai, J., Dosa, N., Peterfi, A., Lehoczki, A., Tarantini, S., et al. (2022) · Nutrients · doi:10.3390/nu15010047 · Cited by 137 · see citing works

Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality

Li, X., Cao, X., Zhang, J., Fu, J., Mohedaner, M., Danzengzhuoga, Sun, X., Yang, G., Yang, Z., Kuo, C., et al. (2023) · Journal of the American Geriatrics Society · doi:10.1111/jgs.18611 · Cited by 114 · see citing works

Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106 · see citing works

Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research

Raffington, L., & Belsky, D. W. (2022) · Current Environmental Health Reports · doi:10.1007/s40572-022-00338-8 · Cited by 94 · see citing works

Targeting metabolic pathways for extension of lifespan and healthspan across multiple species

Parkhitko, A. A., Filine, E., Mohr, S. E., Moskalev, A., & Perrimon, N. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101188 · Cited by 90 · see citing works

Decoding aging clocks: New insights from metabolomics

Huang, H., Chen, Y., Xu, W., Cao, L., Qian, K., Bischof, E. Y., Kennedy, B. K., & Pu, J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.11.007 · Cited by 91 · see citing works

Aging 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

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Fong, S., Pabis, K., Latumalea, D., Dugersuren, N., Unfried, M., Tolwinski, N. S., Kennedy, B. K., & Gruber, J. (2024) · Nature Aging · doi:10.1038/s43587-024-00646-8 · Cited by 86 · see citing works

Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets

Wu, L., Xie, X., Liang, T., Ma, J., Yang, L., Yang, J., Li, L., Xi, Y., Li, H., Zhang, J., et al. (2021) · Biomolecules · doi:10.3390/biom12010039 · Cited by 82 · see citing works

Human age reversal: Fact or fiction?

Johnson, A. A., English, B. W., Shokhirev, M. N., Sinclair, D. A., & Cuellar, T. L. (2022) · Aging Cell · doi:10.1111/acel.13664 · Cited by 81 · see citing works

Senescence-resistant human mesenchymal progenitor cells counter aging in primates

Lei, J., Xin, Z., Liu, N., Ning, T., Jing, Y., Qiao, Y., He, Z., Jiang, M., Yang, Y., Zhang, Z., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.021 · Cited by 82 · see citing works

Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms

Mutz, J., Iniesta, R., & Lewis, C. M. (2024) · Science Advances · doi:10.1126/sciadv.adp3743 · Cited by 77 · see citing works

Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine

Prattichizzo, F., Frigé, C., Pellegrini, V., Scisciola, L., Santoro, A., Monti, D., Rippo, M. R., Ivanchenko, M., Olivieri, F., & Franceschi, C. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102253 · Cited by 75 · see citing works

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

Luo, Q., Dwaraka, V. B., Chen, Q., Tong, H., Zhu, T., Seale, K., Raffaele, J. M., Zheng, S., Mendez, T. L., Chen, Y., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01211-5 · Cited by 61 · see citing works

Inflammation and Epigenetic Aging Are Largely Independent Markers of Biological Aging and Mortality

Cribb, L., Hodge, A. M., Yu, C., Li, S. X., English, D. R., Makalic, E., Southey, M. C., Milne, R. L., Giles, G. G., & Dugué, P. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac147 · Cited by 60 · see citing works

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Chen, R., Wang, Y., Zhang, S., Bulloch, G., Zhang, J., Liao, H., Shang, X., Clark, M., Peng, Q., Ge, Z., et al. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.50 · Cited by 59 · see citing works

Association of Markers of Inflammation, the Kynurenine Pathway and B Vitamins with Age and Mortality, and a Signature of Inflammaging

Dugué, P., Hodge, A. M., Ulvik, A., Ueland, P. M., Midttun, Ø., Rinaldi, S., MacInnis, R. J., Li, S. X., Meyer, K., Navionis, A., et al. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab163 · Cited by 57 · see citing works

Metabolite signatures of chronological age, aging, survival, and longevity

Paola Sebastiani, B., Monti, S., Lustgarten, M. S., Song, Z., Ellis, D., Tian, Q., Schwaiger-Haber, M., Stancliffe, E., Leshchyk, A., Short, M. I., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114913 · Cited by 56 · see citing works

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

Wang, Y., Grant, O. A., Zhai, X., McDonald-Maier, K. D., & Schalkwyk, L. C. (2023) · GeroScience · doi:10.1007/s11357-023-00871-w · Cited by 52 · see citing works

Association of lifestyle with mortality and the mediating role of aging among older adults in China

Jin, S., Li, C., Cao, X., Chen, C., Ye, Z., & Liu, Z. (2021) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2021.104559 · Cited by 50 · see citing works

Measuring healthy ageing: current and future tools

Silva, N., Rajado, A. T., Esteves, F., Brito, D. V. C., Apolónio, J. D., Roberto, V. P., Binnie, A., Araújo, I. M., Nóbrega, C., Bragança, J., et al. (2023) · Biogerontology · doi:10.1007/s10522-023-10041-2 · Cited by 50 · see citing works

Circulating cell-free DNA in health and disease — the relationship to health behaviours, ageing phenotypes and metabolomics

Kananen, L., Hurme, M., Bürkle, A., Moreno‐Villanueva, M., Bernhardt, J. H., Debacq‐Chainiaux, F., Grubeck‐Loebenstein, B., Malavolta, M., Basso, A., Piacenza, F., et al. (2022) · GeroScience · doi:10.1007/s11357-022-00590-8 · Cited by 48 · see citing works

Multi-omics approaches to human biological age estimation

Solovev, I. A., Shaposhnikov, M. V., & Moskalev, A. (2019) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2019.111192 · Cited by 48 · see citing works

A Metabolomic Aging Clock Using Human Cerebrospinal Fluid

Hwangbo, N., Zhang, X., Raftery, D., Gu, H., Hu, S., Montine, T. J., Quinn, J. F., Chung, K. A., Hiller, A., Wang, D., et al. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab212 · Cited by 48 · see citing works

Assessing the rate of aging to monitor aging itself

Xia, X., Wang, Y., Yu, Z., Chen, J., & Han, J. J. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101350 · Cited by 46 · see citing works

Unveiling the distribution of chemical constituents at different body parts and maturity stages of Ganoderma lingzhi by combining metabolomics with desorption electrospray ionization mass spectrometry imaging (DESI)

Xia, J., He, X., Yang, W., Song, H., Yang, Z., Zhang, G., Yang, Z., Chen, H., Liang, Z., Kollie, L., et al. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2023.137737 · Cited by 45 · see citing works

The ticking of aging clocks

Han, J. J. (2023) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2023.09.007 · Cited by 45 · see citing works

Mendelian randomization supports causality between overweight status and accelerated aging

Chen, Z., Chen, Z., & Jin, X. (2023) · Aging Cell · doi:10.1111/acel.13899 · Cited by 44 · see citing works

The metabolome as a biomarker of aging in Drosophila melanogaster

Zhao, X., Golic, F. T., Harrison, B. R., Manoj, M., Hoffman, E. V., Simon, N., Johnson, R. S., MacCoss, M. J., McIntyre, L. M., & Promislow, D. (2022) · Aging Cell · doi:10.1111/acel.13548 · Cited by 43 · see citing works

Does healthy lifestyle attenuate the detrimental effects of urinary polycyclic aromatic hydrocarbons on phenotypic aging? An analysis from NHANES 2001–2010

Yang, Z., Pu, F., Cao, X., Li, X., Sun, S., Zhang, J., Chen, C., Han, L., Yang, Y., Wang, W., et al. (2022) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2022.113542 · Cited by 42 · see citing works

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

Halama, A., Zaghlool, S. B., Thareja, G., Kader, S. A., Al‐Muftah, W., Mook-Kanamori, M. J., Sarwath, H., Mohamoud, Y. A., Stephan, N., Ameling, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51134-x · Cited by 41 · see citing works

Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood

Emwas, A.-H. M., Zacharias, H. U., Alborghetti, M. R., Gowda, G. A. N., Raftery, D., McKay, R. T., Chang, C., Saccenti, E., Gronwald, W., Schuchardt, S., et al. (2025) · Metabolomics · doi:10.1007/s11306-025-02259-7 · Cited by 41 · see citing works

DNA methylation age as a biomarker for cancer

Lau, C. E., & Robinson, O. (2021) · International Journal of Cancer · doi:10.1002/ijc.33451 · Cited by 40 · see citing works

Large‐Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC–MS measurements

Lassen, J. K., Wang, T., Nielsen, K. L., Hasselstrøm, J. B., Johannsen, M., & Villesen, P. (2023) · Aging Cell · doi:10.1111/acel.13813 · Cited by 39 · see citing works

Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases

Yu, Z., Zhou, Y., Mao, K., Pang, B., Wang, K., Jin, T., Zheng, H., Zhai, H., Wang, Y., Xu, X., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.05.012 · Cited by 39 · see citing works

Association between the dietary index for gut microbiota and diabetes: the mediating role of phenotypic age and body mass index

Huang, Y., Liu, X., Lin, C., Chen, X., Li, Y., Huang, Y., Wang, Y., & Liu, X. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1519346 · Cited by 39 · see citing works

Epigenetic clocks and research implications of the lack of data on whom they have been developed: a review of reported and missing sociodemographic characteristics

Watkins, S., Testa, C., Chen, J. T., Vivo, I. D., Simpkin, A. J., Tilling, K. M., Roux, A. V. D., Smith, G. D., Waterman, P. D., Suderman, M. J., et al. (2023) · Current Zoology · doi:10.1093/eep/dvad005 · Cited by 38 · see citing works

Map of epigenetic age acceleration: A worldwide analysis

Yusipov, I., Kalyakulina, A., Trukhanov, A., Franceschi, C., & Ivanchenko, M. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102418 · Cited by 37 · see citing works

Structural inequality linked to brain volume and network dynamics in aging and dementia across the Americas

Legaz, A., Altschuler, F., Gonzalez-Gomez, R., Hernández, H., Baez, S., Migeot, J., Fittipaldi, S., Medel, V., Maito, M. A., Godoy, M. E., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00781-2 · Cited by 36 · see citing works

Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake

Playdon, M. C., Tinker, L. F., Prentice, R. L., Loftfield, E., Hayden, K. M., Horn, L. V., Sampson, J. N., Stolzenberg‐Solomon, R. Z., Lampe, J. W., Neuhouser, M. L., et al. (2023) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2023.10.016 · Cited by 35 · see citing works

Urinary peptidomic profiles to address age-related disabilities: a prospective population study

Martens, D. S., Thijs, L., Latosińska, A., Trenson, S., Siwy, J., Zhang, Z.-Y., Wang, C., Beige, J., Vlahou, A., Janssens, S. P., et al. (2021) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(21)00226-9 · Cited by 35 · see citing works

NMR metabolomic modeling of age and lifespan: A multicohort analysis

Lau, C. E., Manou, M., Markozannes, G., Ala‐Korpela, M., Ben‐Shlomo, Y., Chaturvedi, N., Engmann, J., Gentry‐Maharaj, A., Herzig, K., Hingorani, A. D., et al. (2024) · Aging Cell · doi:10.1111/acel.14164 · Cited by 34 · see citing works

A Novel Metabolomic Aging Clock Predicting Health Outcomes and Its Genetic and Modifiable Factors

Jia, X., Fan, J., Wu, X., Cao, X., Ma, L., Abdelrahman, Z., Zhao, F., Zhu, H., Bizzarri, D., van den Akker, E. B., et al. (2024) · Advanced Science · doi:10.1002/advs.202406670 · Cited by 34 · see citing works

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution

Tong, H., Guo, X., Jacques, M., Luo, Q., Eynon, N., & Teschendorff, A. E. (2024) · Aging · doi:10.18632/aging.206184 · Cited by 33 · see citing works

Approach to Obesity in the Older Population

Buch, A., Marcus, Y., Shefer, G., Zimmet, P., & Stern, N. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab359 · Cited by 31 · see citing works

Metabolomics in aging research: aging markers from organs

Fang, W., Chen, S., Jin, X., Liu, S., Cao, X., & Liu, B. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1198794 · Cited by 30 · see citing works

Postmortem metabolomics as a high-throughput cause-of-death screening tool for human death investigations

Ward, L. J., Kling, S., Engvall, G., Söderberg, C., Kugelberg, F. C., Gréen, H., & Elmsjö, A. (2024) · iScience · doi:10.1016/j.isci.2024.109794 · Cited by 29 · see citing works

An improved epigenetic counter to track mitotic age in normal and precancerous tissues

Zhu, T., Tong, H., Du, Z., Beck, S., & Teschendorff, A. E. (2024) · Nature Communications · doi:10.1038/s41467-024-48649-8 · Cited by 27 · see citing works

Utilization of Host and Microbiome Features in Determination of Biological Aging

Ratiner, K., Abdeen, S. K., Goldenberg, K., & Elinav, E. (2022) · Microorganisms · doi:10.3390/microorganisms10030668 · Cited by 27 · see citing works

Work-related stress and well-being in association with epigenetic age acceleration: A Northern Finland Birth Cohort 1966 Study

Sterrantino, A. F., Fiorito, G., d’Errico, A., Robinson, O., Virtanen, M., Ala‐Mursula, L., Järvelin, M., Ronkainen, J., & Vineis, P. (2022) · Aging · doi:10.18632/aging.203872 · Cited by 26 · see citing works

Pathway‐based integration of multi‐omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers

Garcia‐Segura, M. E., Durainayagam, B. R., Liggi, S., Graça, G., Jiménez, B., Dehghan, A., Tzoulaki, I., Karaman, İ., Elliott, P., & Griffin, J. L. (2022) · Journal of Neurochemistry · doi:10.1111/jnc.15719 · Cited by 25 · see citing works

Blood DNA methylation signature of diet quality and association with cardiometabolic traits

Domínguez-Barragán, J., Fernández‐Sanlés, A., Hernáez, Á., Llauradó-Pont, J., Marrugat, J., Robinson, O., Tzoulaki, I., Elosúa, R., & Lassale, C. M. (2023) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad317 · Cited by 24 · see citing works

Small immunological clocks identified by deep learning and gradient boosting

Kalyakulina, A., Yusipov, I., Kondakova, E., Bacalini, M. G., Franceschi, C., Vedunova, M. V., & Ivanchenko, M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1177611 · Cited by 22 · see citing works

Structural inequality and temporal brain dynamics across diverse samples

Baez, S., Hernandez, H., Moguilner, S., Cuadros, J., Santamaria‐Garcia, H., Medel, V., Migeot, J., Cruzat, J., Valdes‐Sosa, P. A., Lopera, F., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.70032 · Cited by 21 · see citing works

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Yusri, K., Kumar, S., Fong, S., Gruber, J., & Sorrentino, V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126793 · Cited by 21 · see citing works

Associations of four biological age markers with child development: A multi-omic analysis in the European HELIX cohort

Robinson, O., Lau, C. E., Joo, S., Andrusaityte, S., Borras, E., de Prado-Bert, P., Chatzi, L., Keun, H. C., Grazuleviciene, R., Gutzkow, K. B., et al. (2023) · eLife · doi:10.7554/elife.85104 · Cited by 20 · see citing works

Nutrient regulation of development and cell fate decisions

Tu, W. B., Christofk, H. R., & Plath, K. (2023) · Development · doi:10.1242/dev.199961 · Cited by 20 · see citing works

Metabolomic age and risk of 50 chronic diseases in community‐dwelling adults: A prospective cohort study

Shang, X., Liu, J., Zhu, Z., Zhang, X., Huang, Y., Liu, S., Wang, W., Zhang, X., Ma, S., Tang, S., et al. (2024) · Aging Cell · doi:10.1111/acel.14125 · Cited by 20 · see citing works

Association of ultra-processed food-related metabolites with selected biochemical markers in the UK Biobank

Kityo, A., Choi, B., Lee, J.-E., Kim, C., & Lee, S. (2025) · Nutrition Journal · doi:10.1186/s12937-025-01077-w · Cited by 20 · see citing works

The X-Age Project to construct a Chinese aging clock

Li, J., Jiang, M., Wang, Q., Zheng, Z., Shen, J., Li, J., Xiong, M., Zheng, Y., Lu, X., Cai, Y., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00935-w · Cited by 18 · see citing works

Association between work characteristics and epigenetic age acceleration: cross-sectional results from UK – Understanding Society study

Sterrantino, A. F., Fiorito, G., d’Errico, A., Virtanen, M., Ala‐Mursula, L., Järvelin, M., Vineis, P., & Robinson, O. (2022) · Aging · doi:10.18632/aging.204327 · Cited by 18 · see citing works

Systematic estimation of biological age of in vitro cell culture systems by an age-associated marker panel

Hartmann, C., Herling, L., Hartmann, A., Köckritz, V., Fuellen, G., Walter, M., & Hermann, A. (2023) · Frontiers in Aging · doi:10.3389/fragi.2023.1129107 · Cited by 18 · see citing works

How do metabolic processes age: Evidence from human metabolomic studies

Robinson, O., & Lau, C. E. (2023) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2023.102360 · Cited by 18 · see citing works

CellBiAge: Improved single-cell age classification using data binarization

Yu, D., Li, M., Linghu, G., Hu, Y., Hajdarovic, K. H., Wang, A., Singh, R., & Webb, A. E. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113500 · Cited by 17 · see citing works

A new monocyte epigenetic clock reveals nonlinear effects of alcohol consumption on biological aging in three independent cohorts (N = 2242)

Liang, X., Sinha, R., Justice, A. C., Cohen, M. H., Aouizerat, B. E., & Xu, K. (2022) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14803 · Cited by 17 · see citing works

LipidClock: A Lipid-Based Predictor of Biological Age

Unfried, M., Ng, L. F., Cazenave‐Gassiot, A., Batchu, K. C., Kennedy, B. K., Wenk, M. R., Tolwinski, N. S., & Gruber, J. (2022) · Frontiers in Aging · doi:10.3389/fragi.2022.828239 · Cited by 16 · see citing works

Applicability of epigenetic age models to next-generation methylation arrays

Garma, L. D., & Quintela-Fandiño, M. (2024) · Genome Medicine · doi:10.1186/s13073-024-01387-4 · Cited by 16 · see citing works

Methylation status of VTRNA2-1 / nc886 is stable across populations, monozygotic twin pairs and in majority of tissues

Marttila, S., Tamminen, H., Rajić, S., Mishra, P. P., Lehtimäki, T. J., Raitakari, O. T., Kähönen, M. A. P., Kananen, L., Jylhävä, J., Hägg, S., et al. (2022) · Epigenomics · doi:10.2217/epi-2022-0228 · Cited by 16 · see citing works

A geroscience approach for Parkinson’s disease: Conceptual framework and design of PROPAG-AGEING project

Pirazzini, C., Azevedo, T., Baldelli, L., Bartoletti‐Stella, A., Buonaura, G. C., Molin, A. D., Dimitri, G. M., Doykov, I., Gómez‐Garre, P., Hägg, S., et al. (2020) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2020.111426 · Cited by 15 · see citing works

Metabolomic Changes Are Predictive of Aging in Laying Hens

Bendikov-Bar, I., Malitsky, S., Itkin, M., Rusal, M., & Sagi, D. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab135 · Cited by 14 · see citing works

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

Wang, T., Beyene, H. B., Yi, C., Cinel, M. A., Mellett, N. A., Olshansky, G., Meikle, T. G., Wu, J. Q., Dakic, A., Watts, G. F., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105199 · Cited by 14 · see citing works

Multi‐omic analysis of biological aging biomarkers in long‐term calorie restriction and endurance exercise practitioners: A cross‐sectional study

Fiorito, G., Tosti, V., Polidoro, S., Bertozzi, B., Veronese, N., Cava, E., Spelta, F., Piccio, L. M., Early, D. S., Raftery, D., et al. (2024) · Aging Cell · doi:10.1111/acel.14442 · Cited by 14 · see citing works

Association of clinical biomarker-based biological age and aging trajectory with cardiovascular disease and all-cause mortality in Chinese adults: a population-based cohort study

Dai, Q., Sun, H., Yang, X., Chen, S., Zhang, X., Yin, Z., Zhao, X., Wu, S., Cao, Z., Wu, Y., et al. (2025) · BMC Public Health · doi:10.1186/s12889-025-22114-7 · Cited by 13 · see citing works

Mass spectrometry-based metabolomics for an in-depth questioning of human health

Alvès, S., Paris, A., & Rathahao, E. (2020) · Advances in clinical chemistry · doi:10.1016/bs.acc.2020.02.009 · Cited by 13 · see citing works

Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank

You, G., Wang, K., Shen, R., Chen, X., Jiang, J., Sun, Y., Wu, D., Xu, J., Huang, K., & Yao, C. (2025) · Metabolism · doi:10.1016/j.metabol.2025.156467 · Cited by 12 · see citing works

Nutrition, DNA Methylation and Obesity Across Life Stages and Generations

Meir, A. Y., Yun, H., Stampfer, M. J., Liang, L., & Hu, F. B. (2023) · Epigenomics · doi:10.2217/epi-2023-0172 · Cited by 12 · see citing works

There Are Multiple Clocks That Time Us: Cross-Sectional and Longitudinal Associations Among 14 Alternative Indicators of Age and Aging

Drewelies, J., Homann, J., Vetter, V. M., Düzel, S., Kühn, S., Deecke, L., Steinhagen‐Thiessen, E., Jawinski, P., Markett, S. A., Lindenberger, U., et al. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae244 · Cited by 12 · see citing works

Epigenetic clocks and inflammaging: pitfalls caused by ignoring cell-type heterogeneity

Guo, X., & Teschendorff, A. E. (2025) · GeroScience · doi:10.1007/s11357-025-01677-8 · Cited by 12 · see citing works

Urine Untargeted Metabolomic Profiling Is Associated with the Dietary Pattern of Successful Aging among Malaysian Elderly

Fakhruddin, N. N. I. N. M., Shahar, S., Ismail, I. S., Azam, A. A., & Rajab, N. F. (2020) · Nutrients · doi:10.3390/nu12102900 · Cited by 12 · see citing works

The duration of lithium use and biological ageing: telomere length, frailty, metabolomic age and all-cause mortality

Mutz, J., Wong, W. L. E., Powell, T. R., Young, A. H., Dawe, G. S., & Lewis, C. M. (2024) · GeroScience · doi:10.1007/s11357-024-01142-y · Cited by 11 · see citing works

Unite to predict

Kothari, M., & Belsky, D. W. (2021) · eLife · doi:10.7554/elife.66223 · Cited by 11 · see citing works

Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis

Singh, A., Kinnebrew, G. H., Hsu, P.-C., Weng, D. Y., Song, M., Reisinger, S. A., McElroy, J. P., Keller-Hamilton, B. L., Ferketich, A. K., Freudenheim, J. L., et al. (2023) · Metabolites · doi:10.3390/metabo13080899 · Cited by 10 · see citing works

Carnosine alleviates oxidative stress to prevent cellular senescence by regulating Nrf2/HO-1 pathway: a promising anti-aging strategy for oral mucosa

He, H., Lv, C., Xie, Y., Li, W., Ling, Z., Cheng, B., & Tao, X. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1559584 · Cited by 10 · see citing works

Protein Catabolites as Blood‐Based Biomarkers of Aging Physiology: Findings From the Dog Aging Project

Harrison, B. R., Partida‐Aguilar, M., Marye, A., Djukovic, D., Kauffman, M., Dunbar, M. D., Mariner, B. L., McCoy, B. M., Algavi, Y. M., Muller, E., et al. (2025) · Aging Cell · doi:10.1111/acel.70226 · Cited by 10 · see citing works

Associations of depression and anxiety symptoms in childhood and adolescence with epigenetic aging

Ingram, S. J., Vazquez, A. Y., Klump, K. L., Hyde, L. W., Burt, S. A., & Clark, S. L. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.02.044 · Cited by 10 · see citing works

Measuring biological age using metabolomics

Robinson, O., & Lau, C. E. (2020) · Aging · doi:10.18632/aging.104216 · Cited by 10 · see citing works

Sebacic Acid as a Potential Age-Related Biomarker of Liver Aging: Evidence Linking Mice and Human

Huang, C., Lee, W., Huang, Y., Tsai, T., Chen, L., & Lin, C. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad121 · Cited by 9 · see citing works

Understanding Aging through the Lense of Gut Microbiome

Ira, R., Adwani, J., Krishnan, A. O., Subramanian, G., Yadav, S., Shukla, S., Rao, S., & Prakash, T. (2024) · Exploratory Research and Hypothesis in Medicine · doi:10.14218/erhm.2024.00008 · Cited by 9 · see citing works

Network modeling and topology of aging

Li, F., Yang, D., Wu, S., Xue, C., Sang, M., Liu, X., Che, J., Wu, J., Gragnoli, C., Griffin, C. H., et al. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.10.006 · Cited by 9 · see citing works

Effects of accelerated biological age on depressive symptoms in a causal reasoning framework

Gao, S., Deng, H., Wen, S., & Wang, Y. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.07.019 · Cited by 9 · see citing works

Metabolomic aging and its association with depression in a UK cohort

Robinson, O., Chadeau‐Hyam, M., Karaman, İ., Pinto, R. C., Elliott, P., & Vineis, P. (2018) · Revue d Épidémiologie et de Santé Publique · doi:10.1016/j.respe.2018.05.122

Assessing Metabolic Ageing via DNA Methylation Surrogate Markers: A Multicohort Study in Britain, Ireland and the USA

Xu, K., Hernández, B., Arpawong, T. E., Camuzeaux, S., Chekmeneva, E., Crimmins, E. M., Elliott, P., Fiorito, G., Jiménez, B., Kenny, R. A., et al. (2025) · Aging Cell · doi:10.1111/acel.14484 · Cited by 5 · see citing works

Heterogeneous metabolomic aging across the same age and prediction of health outcomePreprint

Jia, X., Fan, J., Wu, X., Cao, X., Ma, L., Abdelrahman, Z., Bizzarri, D., van den Akker, E. B., Slagboom, P. E., Deelen, J., et al. (2024) · medRxiv · doi:10.1101/2024.04.22.24306156

Assessment of ageing using global mass-spectrometry based metabolomics: A cross-cohort longitudinal study in the UK and IrelandPreprint

Lau, C. C., Chekmeneva, E., Pinto, R. C., O’Halloran, A. M., Chu, D. K. H., Dehghan, A., Tzoulaki, I., Elliott, P., Kenny, R. A., McCrory, C., et al. (2026) · medRxiv · doi:10.64898/2026.02.09.26345895 · Cited by 1 · see citing works

A metabolomic clock of population ageing: cross-cohort validation and associations with frailty and cognitive function

Lau, C. E., Chekmeneva, E., Pinto, R. C., O’Halloran, A. M., Chu, D. K. H., Dehghan, A., Tzoulaki, I., Elliott, P., Kenny, R. A., McCrory, C., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02412-7 · Cited by 1 · see citing works