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Metabolomic markers reveal novel pathways of ageing and early development in human populations
Open accessAbstract
BACKGROUND: Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age. METHODS: Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels. RESULTS: We identified a panel of 22 metabolites which combined are strongly correlated with age (R(2) = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10(-157)) and lung function (FEV1 beta = -0.04, SE = 0.008, P = 1.8 × 10(-8) adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = -0.01, SE = 0.002, P = 1.9 × 10(-6)) and birthweight (beta = -0.06, SE = 0.01, P = 2.5 × 10(-9)). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10(-6)). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = -0.20, SE = 0.04, P = 2.9 × 10(-8)). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes. CONCLUSIONS: Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.
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“State-of-the-art mass spectrometry and NMR methods can identify hundreds to thousands of metabolites in human plasma, and multiple studies have attempted to understand how they interact with ageing113–116.”
From Measuring biological age using omics data (2022) · cites this paper as [116]
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“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 [116]
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Cites
Methodology for Genetic Studies of Twins and Families
Neale, M. C., & Cardon, L. R. (1992) · doi:10.1007/978-94-015-8018-2 · Cited by 4,005
Effect of In Utero and Early-Life Conditions on Adult Health and Disease
Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008) · New England Journal of Medicine · doi:10.1056/nejmra0708473 · Cited by 3,898
Diagnosis of osteoporosis and assessment of fracture risk
Kanis, J. А. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08761-5 · Cited by 2,266
Changes in the normal maximal expiratory flow-volume curve with growth and aging.
Knudson, R. J., Lebowitz, M. D., Holberg, C. J., & Burrows, B. A. (1983) · PubMed · doi:10.1164/arrd.1983.127.6.725 · Cited by 1,913
Feldman, H. A., Longcope, C., Derby, C. A., Johannes, C. B., Araujo, A. B., Coviello, A. D., Bremner, W. J., & McKinlay, J. B. (2002) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.87.2.8201 · Cited by 1,861
Obesity, cigarette smoking, and telomere length in women
Valdes, A., Andrew, T., Gardner, J., Kimura, M., Oelsner, E., Cherkas, L., Aviv, A., & Spector, T. (2005) · The Lancet · doi:10.1016/s0140-6736(05)66630-5 · Cited by 1,363
Self-citationEvans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. W., & Milgram, K. E. (2009) · Analytical Chemistry · doi:10.1021/ac901536h · Cited by 1,317
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Self-citationValidation of a DNA methylation microarray for 450,000 CpG sites in the human genome
Sandoval, J., Heyn, H., Moran, S., Serra-Musach, J., Pujana, M. A., Bibikova, M., & Esteller, M. (2011) · Epigenetics · doi:10.4161/epi.6.6.16196 · Cited by 990
Herskind, A. M., McGue, M., Holm, N. V., Sørensen, T. I. A., Harvald, B., & Vaupel, J. W. (1996) · Human Genetics · doi:10.1007/bf02185763 · Cited by 879
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Jylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240
Broadhurst, D., Goodacre, R., Reinke, S. N., Kuligowski, J., Wilson, I. D., Lewis, M. R., & Dunn, W. B. (2018) · Metabolomics · doi:10.1007/s11306-018-1367-3 · Cited by 963
Biomarkers of aging for the identification and evaluation of longevity interventions
Moqri, M., Herzog, C., Poganik, J. R., Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.003 · Cited by 736
Sayed, N., Huang, Y., Nguyen, K., Krejciova-Rajaniemi, Z., Grawe, A. P., Gao, T., Tibshirani, R., Hastie, T., Alpert, A., Cui, L., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00082-y · Cited by 572
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
Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites
Long, T., Hicks, M. A., Yu, H., Biggs, W., Kirkness, E. F., Menni, C., Zierer, J., Small, K. S., Mangino, M., Messier, H., et al. (2017) · Nature Genetics · doi:10.1038/ng.3809 · Cited by 423
Self-citationMenni, C., Fauman, E., Erte, I., Perry, J. R. B., Kastenmüller, G., Shin, S.-Y., Petersen, A.-K., Hyde, C., Psatha, M., Ward, K. J., et al. (2013) · Diabetes · doi:10.2337/db13-0570 · Cited by 416
Self-citationDeep biomarkers of human aging: Application of deep neural networks to biomarker development
Putin, E., Mamoshina, P., Aliper, A. M., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C. R., Vijg, J., & Zhavoronkov, A. A. (2016) · Aging · doi:10.18632/aging.100968 · Cited by 378
Metabolomics for clinical use and research in chronic kidney disease
Hocher, B., & Adamski, J. (2017) · Nature Reviews Nephrology · doi:10.1038/nrneph.2017.30 · Cited by 339
Pallister, T., Jackson, M. A., Martin, T. C., Zierer, J., Jennings, A., Mohney, R. P., MacGregor, A. J., Steves, C. J., Cassidy, A., Spector, T. D., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13722-4 · Cited by 315
Self-citationMolecular phenotyping of a UK population: defining the human serum metabolome
Dunn, W. B., Lin, W., Broadhurst, D., Begley, P., Brown, M., Zelena, E., Vaughan, A. A., Halsall, A., Harding, N., Knowles, J. D., et al. (2014) · Metabolomics · doi:10.1007/s11306-014-0707-1 · Cited by 261
Menni, C., Zierer, J., Pallister, T., Jackson, M. A., Long, T., Mohney, R. P., Steves, C. J., Spector, T. D., & Valdes, A. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-10382-2 · Cited by 237
Self-citationBhargava, P., Smith, M. D., Mische, L., Harrington, E., Fitzgerald, K. C., Martin, K. A., Kim, S., Reyes, A. A., Cardona, J. G., Volsko, C. M., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci129401 · Cited by 243
James, E. L., Michalek, R. D., Pitiyage, G. N., de Castro, A. M., Vignola, K. S., Jones, J., Mohney, R. P., Karoly, E. D., Prime, S. S., & Parkinson, E. K. (2015) · Journal of Proteome Research · doi:10.1021/pr501221g · Cited by 231
Self-citationRist, 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
Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome
Arnold, M., Nho, K., Kueider-Paisley, A., Massaro, T., Huynh, K., Brauner, B., MahmoudianDehkordi, S., Louie, G., Moseley, M. A., Thompson, J. W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14959-w · Cited by 202
Self-citationLongitudinal plasma metabolomics of aging and sex
Darst, B. F., Koscik, R. L., Hogan, K. J., Johnson, S. C., & Engelman, C. D. (2019) · Aging · doi:10.18632/aging.101837 · Cited by 198
Petersen, A.-K., Zeilinger, S., Kastenmüller, G., Römisch-Margl, W., Brugger, M., Peters, A., Meisinger, C., Strauch, K., Hengstenberg, C., Pagel, P., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddt430 · Cited by 183
Self-citationGut-Microbiota-Metabolite Axis in Early Renal Function Decline
Barrios, C., Beaumont, M., Pallister, T., Villar, J., Goodrich, J. K., Clark, A., Pascual, J., Ley, R. E., Spector, T. D., Bell, J. T., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0134311 · Cited by 175
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
Cheah, I. K.-M., Feng, L., Tang, R. M. Y., Lim, K. H. C., & Halliwell, B. B. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2016.07.074 · Cited by 171
Plasma metabolomic profiles enhance precision medicine for volunteers of normal health
Guo, L., Milburn, M. V., Ryals, J. A., Lonergan, S. C., Mitchell, M. W., Wulff, J. E., Alexander, D. C., Evans, A. M., Bridgewater, B., Miller, L. A. D., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508425112 · Cited by 176
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 159
Metabolite changes in blood predict the onset of tuberculosis
Weiner, J. M., Maertzdorf, J., Sutherland, J. S., Duffy, F. J., Thompson, E. G., Suliman, S., McEwen, G. K., Thiel, B., Parida, S. K., Żyła, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07635-7 · Cited by 163
Self-citationAntiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances
Jiang, Q., Liu, J., Huang, S., Wang, X.-Y., Chen, X., Liu, G.-H., Ye, K., Song, W., Masters, C. L., Wang, J., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02145-7 · Cited by 165
Pietzner, M., Kaul, A., Henning, A., Kastenmüller, G., Artati, A., Lerch, M. M., Adamski, J., Nauck, M. A., & Friedrich, N. (2017) · BMC Medicine · doi:10.1186/s12916-017-0974-6 · Cited by 152
Self-citationKelly, R. S., Heiden, M. G. V., Giovannucci, E. L., & Mucci, L. A. (2016) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-15-1223 · Cited by 150
Circulating Proteomic Signatures of Chronological Age
Menni, C., Kiddle, S. J., Mangino, M., Viñuela, A., Psatha, M., Steves, C., Sattlecker, M., Buil, A., Newhouse, S., Nelson, S., et al. (2015) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glu121 · Cited by 144
Self-citationLewis, 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
Integration of ‘omics’ data in aging research: from biomarkers to systems biology
Zierer, J., Menni, C., Kastenmüller, G., & Spector, T. D. (2015) · Aging Cell · doi:10.1111/acel.12386 · Cited by 144
Self-citationThe implication of frailty on preoperative risk assessment
Amrock, L. G., & Deiner, S. G. (2014) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000000065 · Cited by 139
Urine metabolome profiling of immune-mediated inflammatory diseases
Alonso, A., Julià, A., Vinaixa, M., Domènech, E., Fernández‐Nebro, A., Cañete, J. D. D., Ferrándiz, C., Tornero, J., Gisbert, J. P., Nos, P., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0681-8 · Cited by 132
Omics technologies and the study of human ageing
Valdes, A. M., Glass, D., & Spector, T. D. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3553 · Cited by 123
Self-citationCassol, E., Misra, V., Dutta, A., Morgello, S., & Gabuzda, D. H. (2014) · AIDS · doi:10.1097/qad.0000000000000303 · Cited by 127
Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics
Srivastava, S. (2019) · Metabolites · doi:10.3390/metabo9120301 · Cited by 124
Pallister, T., Jennings, A., Mohney, R. P., Yarand, D., Mangino, M., Cassidy, A., MacGregor, A. J., Spector, T. D., & Menni, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158568 · Cited by 119
Self-citationMenni, C., Graham, D., Kastenmüller, G., Alharbi, N. H. J., Alsanosi, S., McBride, M., Mangino, M., Titcombe, P., Shin, S.-Y., Psatha, M., et al. (2015) · Hypertension · doi:10.1161/hypertensionaha.115.05544 · Cited by 111
Self-citationvan den Akker, E. B., Trompet, S., Barkey Wolf, J. J. H., Beekman, M., Suchiman, H. E. D., Deelen, J., Asselbergs, F. W., Boersma, E., Cats, D., Elders, P. M., et al. (2020) · Circulation: Genomic and Precision Medicine · doi:10.1161/circgen.119.002610 · Cited by 109
Jennings, A., MacGregor, A. J., Pallister, T., Spector, T. D., & Cassidy, A. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.08.307 · Cited by 98
Self-citationFood Preference Patterns in a UK Twin Cohort
Pallister, T., Sharafi, M., Lachance, G., Pirastu, N., Mohney, R. P., MacGregor, A. J., Feskens, E. J. M., Duffy, V. B., Spector, T. D., & Menni, C. (2015) · Twin Research and Human Genetics · doi:10.1017/thg.2015.69 · Cited by 90
Self-citationApplying the exposome concept in birth cohort research: a review of statistical approaches
Santos, S., Maître, L., Warembourg, C., Agier, L., Richiardi, L., Basagaña, X., & Vrijheid, M. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-020-00625-4 · Cited by 95
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 93
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 91
Human Aging Is a Metabolome-related Matter of Gender
Jové, M., Maté, I., Naudí, A., Mota‐Martorell, N., Portero‐Otín, M., la Fuente, M. D., & Pamplona, R. (2015) · The Journals of Gerontology Series A · doi:10.1093/gerona/glv074 · Cited by 86
Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches
Kudryashova, K. S., Burka, K., Kulaga, A. Y., Vorobyeva, N. S., & Kennedy, B. K. (2020) · PROTEOMICS · doi:10.1002/pmic.201900408 · Cited by 95
Pallister, T., Jackson, M. A., Martin, T. C., Glastonbury, C. A., Jennings, A., Beaumont, M., Mohney, R. P., Small, K. S., MacGregor, A. J., Steves, C. J., et al. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.70 · Cited by 87
Self-citationAge and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects
Vignoli, A., Tenori, L., Luchinat, C., & Saccenti, E. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00404 · Cited by 89
In Pursuit of Healthy Aging: Effects of Nutrition on Brain Function
Melzer, T. M., Manosso, L. M., Yau, S., Gil‐Mohapel, J., & Brocardo, P. S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095026 · Cited by 97
Mutz, J., Iniesta, R., & Lewis, C. M. (2024) · Science Advances · doi:10.1126/sciadv.adp3743 · Cited by 77
The effects of age and dietary restriction on the tissue‐specific metabolome of D rosophila
Laye, M. J., Tran, V., Jones, D. P., Kapahi, P., & Promislow, D. (2015) · Aging Cell · doi:10.1111/acel.12358 · Cited by 78
Developmental aspects of a life course approach to healthy ageing
Hanson, M. A., Cooper, C. C., Sayer, A. A., Eendebak, R. J. A. H., Clough, G. F., & Beard, J. (2015) · The Journal of Physiology · doi:10.1113/jp270579 · Cited by 85
metabolic profiling of Parkinson's disease and mild cognitive impairment
Burté, F., Houghton, D. J., Lowes, H., Pyle, A., Nesbitt, S., Yarnall, A. J., Yu‐Wai‐Man, P., Burn, D. J., Santibanez‐Koref, M., & Hudson, G. (2017) · Movement Disorders · doi:10.1002/mds.26992 · Cited by 74
Yousri, N. A., Fakhro, K. A., Robay, A., Rodríguez-Flores, J. L., Mohney, R. P., Zeriri, H., Odeh, T., Kader, S. A., Al‐Dous, E., Thareja, G., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-01972-9 · Cited by 73
Self-citationChan, M. S., Arnold, M., Offer, A. R., Hammami, I., Mafham, M. M., Armitage, J., Perera, R., & Parish, S. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab069 · Cited by 73
Reference intervals for plasma-free amino acid in a Japanese population
Yamamoto, H., Kondo, K., Tanaka, T., Muramatsu, T., Yoshida, H., Imaizumi, A., Nagao, K., Noguchi, Y., & Miyano, H. (2015) · Annals of Clinical Biochemistry International Journal of Laboratory Medicine · doi:10.1177/0004563215583360 · Cited by 74
Aging and cardiovascular diseases: the role of gene-diet interactions
Corella, D., & Ordovás, J. M. (2014) · Ageing Research Reviews · doi:10.1016/j.arr.2014.08.002 · Cited by 83
Ellul, S., Wake, M. A., Clifford, S. A., Lange, K., Würtz, P., Juonala, M., Dwyer, T., Carlin, J. B., Burgner, D. P., & Saffery, R. (2019) · BMJ Open · doi:10.1136/bmjopen-2017-020900 · Cited by 69
Herman, S., Åkerfeldt, T., Spjuth, O., Burman, J., & Kultima, K. (2019) · Cells · doi:10.3390/cells8020084 · Cited by 65
Large-scale non-targeted metabolomic profiling in three human population-based studies
Ganna, A., Fall, T., Salihović, S., Lee, W., Broeckling, C. D., Kumar, J., Hägg, S., Stenemo, M., Magnusson, P. K. E., Prenni, J. E., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0893-5 · Cited by 68
Herman, S., Khoonsari, P. E., Tolf, A., Steinmetz, J. L., Zetterberg, H., Åkerfeldt, T., Jakobsson, P., Larsson, A., Spjuth, O., Burman, J., et al. (2018) · Theranostics · doi:10.7150/thno.26249 · Cited by 64
A Metabolome-Wide Study of Dry Eye Disease Reveals Serum Androgens as Biomarkers
Vehof, J., Hysi, P. G., & Hammond, C. J. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2016.12.011 · Cited by 66
Zierer, J., Pallister, T., Tsai, P.-C., Krumsiek, J., Bell, J. T., Lauc, G., Spector, T. D., Menni, C., & Kastenmüller, G. (2016) · Scientific Reports · doi:10.1038/srep37646 · Cited by 63
Self-citationKoh, L. Y., & Hwang, N. C. (2018) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2018.02.032 · Cited by 65
Moayyeri, A., Cheung, C., Tan, K. C. B., Morris, J. A., Cerani, A., Mohney, R. P., Richards, J. B., Hammond, C. J., Spector, T. D., & Menni, C. (2017) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3358 · Cited by 61
Self-citationHu, X.-Y., Li, Y., Li, L.-Q., Zheng, Y., Lv, J.-H., Huang, S., Zhang, W., Liu, L., Zhao, L., Liu, Z., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-019974 · Cited by 61
Paglia, G., Fabiola Del Greco, M., Sigurdsson, B. B., Rainer, J., Volani, C., Hicks, A. A., Pramstaller, P. P., & Smarason, S. (2018) · Clinica Chimica Acta · doi:10.1016/j.cca.2018.08.014 · Cited by 61
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 63
Metabolomic study of carotid–femoral pulse-wave velocity in women
Menni, C., Mangino, M., Cecelja, M., Psatha, M., Brosnan, M. J., Trimmer, J. K., Mohney, R. P., Chowienczyk, P., Padmanabhan, S., Spector, T. D., et al. (2014) · Journal of Hypertension · doi:10.1097/hjh.0000000000000467 · Cited by 59
Self-citationMetabolite signatures of chronological age, aging, survival, and longevity
Sebastiani, P., 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 58
Serum metabolomics in animal models and human disease
James, E. L., & Parkinson, E. K. (2015) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0000000000000200 · Cited by 59
Livshits, G., Malkin, I., Bowyer, R. C. E., Verdi, S., Bell, J. T., Menni, C., Williams, F. M. K., & Steves, C. J. (2018) · Pain · doi:10.1097/j.pain.0000000000001364 · Cited by 57
Self-citationCirculating glucuronic acid predicts healthspan and longevity in humans and mice
Ho, A., Sinick, J., Esko, T., Fischer, K., Menni, C., Zierer, J., Matey-Hernandez, M. L., Fortney, K., & Morgen, E. K. (2019) · Aging · doi:10.18632/aging.102281 · Cited by 58
Self-citationMacdonald-Dunlop, E., Taba, N., Klarić, L., Frkatović-Hodžić, A., Walker, R. M., Hayward, C., Esko, T., Haley, C., Fischer, K., Wilson, J. F., et al. (2022) · Aging · doi:10.18632/aging.203847 · Cited by 60
Huang, J., Weinstein, S. J., Moore, S. C., Derkach, A., Hua, X., Liao, L. M., Gu, F., Mondul, A. M., Sampson, J. N., & Albanes, D. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy017 · Cited by 52
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Metadata
- Authors
- Menni, C., Kastenmüller, G., Petersen, A. K., Bell, J. T., Psatha, M., Tsai, P.-C., Gieger, C., Schulz, H., Erte, I., John, S., Brosnan, M. J., Wilson, S. G., Tsaprouni, L., Lim, E. M., Stuckey, B., Deloukas, P., Mohney, R., Suhre, K., Spector, T. D., & Valdes, A. M.
- Affiliations
- University of Nottingham (GB)
- Corresponding author
- Valdes, Ana M
- ORCID
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-29
- Volume
- 42
- Issue
- 4
- Pages
- 1111-1119
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC3781000
- OpenAlex
- W2109880669
- Cited by
- 282
- References
- 34
- Citations by year
- 2026: 14 · 2025: 21 · 2024: 27 · 2023: 17 · 2022: 23 · 2021: 26 · 2020: 28 · 2019: 20 · 2018: 22 · 2017: 18 · 2016: 25 · 2015: 24 · 2014: 12 · 2013: 5
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- BCU Open Access Repository (Birmingham City University) (acceptedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Site cant be reached (submittedVersion)
- Funders
- Wellcome Trust (098051)
- European Commission (277849)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Birth, Development, and Health
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- human aging, metabolomic markers, birth weight, monozygotic twin discordance, DNA methylation, CpG sites, lung function, bone mineral density, early development, epigenetics
- MeSH terms
- Adolescent, Adult, Aged, Aging, Birth Weight, Forced Expiratory Volume, Humans, Middle Aged, Phenotype, Tryptophan, Twins, Monozygotic, Vital Capacity, Biomarkers, Bone Density, DNA Methylation, Metabolomics, Young Adult
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