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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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Items 101–200 of 282
The Aging Process: A Metabolomics Perspective
Castro, A., Signini, É. D. F., de Oliveira, J. M., Leal, M. C. B. D. M., Rehder‐Santos, P., Millan-Mattos, J. C., Minatel, V., Pantoni, C. B. F., Oliveira, R. V., Catai, A. M., et al. (2022) · Molecules · doi:10.3390/molecules27248656 · Cited by 38
Hernandes, V. V., Đorđević, N., Hantikainen, E. M., Sigurdsson, B. B., Smarason, S., García-Larsen, V., Gögele, M., Caprioli, G., Bozzolan, I., Pramstaller, P. P., et al. (2022) · Metabolites · doi:10.3390/metabo12030205 · Cited by 33
Sources of Variability in Metabolite Measurements from Urinary Samples
Xiao, Q., Moore, S. C., Boca, S. M., Matthews, C. E., Rothman, N., Stolzenberg‐Solomon, R. Z., Sinha, R. R., Cross, A. J., & Sampson, J. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095749 · Cited by 37
Deployment-Associated Exposure Surveillance With High-Resolution Metabolomics
Walker, D. I., Mallon, T. M., Hopke, P. K., Uppal, K., Go, Y., Rohrbeck, P., Pennell, K. D., & Jones, D. P. (2016) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000000768 · Cited by 37
Metabolomics applied to diabetes−lessons from human population studies
Liggi, S., & Griffin, J. L. (2017) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2017.10.011 · Cited by 34
Metabolomic signatures of low birthweight: Pathways to insulin resistance and oxidative stress
Metrustry, S., Karhunen, V., Edwards, M. H., Menni, C., Geisendorfer, T., Huber, A., Reichel, C., Dennison, E. M., Cooper, C. C., Spector, T. D., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194316 · Cited by 35
Self-citationMetabolomic study of aging in mouse plasma by gas chromatography–mass spectrometry
Seo, C., Hwang, Y.-H., Kim, Y., Joo, B. S., Yee, S., Kim, C. M., & Paik, M. (2016) · Journal of Chromatography B · doi:10.1016/j.jchromb.2016.04.052 · Cited by 35
Application of AI in biological age prediction
Meng, D., Zhang, S., Huang, Y., Mao, K., & Han, J. J. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102777 · Cited by 35
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
Assessment of metabolic phenotypic variability in children’s urine using 1H NMR spectroscopy
Maître, L., Lau, C. E., Vizcaíno, E., Robinson, O., Casas, M., Siskos, A., Want, E. J., Athersuch, T. J., Slama, R., Vrijheid, M., et al. (2017) · Scientific Reports · doi:10.1038/srep46082 · Cited by 35
Dietary protein and amino acid intakes for mitigating sarcopenia in humans
He, W., Connolly, E. D., Cross, H. R., & Wu, G. (2024) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2024.2348549 · Cited by 39
Population Screening for Biological and Environmental Properties of the Human Metabolic Phenotype
Walker, D. I., Go, Y., Liu, K., Pennell, K. D., & Jones, D. P. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800344-2.00007-0 · Cited by 34
Carlsson, H., Abujrais, S., Herman, S., Khoonsari, P. E., Åkerfeldt, T., Svenningsson, A., Burman, J., & Kultima, K. (2020) · Metabolomics · doi:10.1007/s11306-020-1648-5 · Cited by 30
NMR-based metabonomic analysis of HUVEC cells during replicative senescence
Yi, S., Lin, K., Jiang, T., Shao, W., Huang, C., Jiang, B., Li, Q., & Lin, D. (2020) · Aging · doi:10.18632/aging.102834 · Cited by 35
Pallister, T., Haller, T., Thorand, B., Altmaier, E., Cassidy, A., Martin, T. C., Jennings, A., Mohney, R. P., Gieger, C., MacGregor, A. J., et al. (2016) · European Journal of Nutrition · doi:10.1007/s00394-016-1278-x · Cited by 33
Self-citationAmrock, L. G., & Deiner, S. G. (2014) · International Anesthesiology Clinics · doi:10.1097/aia.0000000000000026 · Cited by 33
Longitudinal profiling of metabolic ageing trends in two population cohorts of young adults
Mäkinen, V., Karsikas, M., Kettunen, J., Lehtimäki, T. J., Kähönen, M. A. P., Viikari, J. S. A., Perola, M., Salomaa, V. V., Järvelin, M., Raitakari, O. T., et al. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac062 · Cited by 30
Belyi, A. A., Alekseev, A. A., Fedintsev, A., Balybin, S. N., Proshkina, E., Shaposhnikov, M. V., & Moskalev, A. (2020) · Antioxidants · doi:10.3390/antiox9121239 · Cited by 31
Ball, H. C., levari-Shariati, S., Cooper, L. N., & Aliani, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196154 · Cited by 32
Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing
Wilkinson, D. J., Blanco, G. R., Dunn, W. B., Phillips, B. E., Williams, J. P., Greenhaff, P. L., Smith, K., Gallagher, I. J., & Atherton, P. J. (2020) · Aging · doi:10.18632/aging.103513 · Cited by 29
Impact of acute stress on murine metabolomics and metabolic flux
Lee, W. D., Liang, L., AbuSalim, J. E., Jankowski, C. S. R., Samarah, L. Z., Neinast, M. D., & Rabinowitz, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2301215120 · Cited by 27
Mukherjee, P. K., Funchain, P., Retuerto, M., Jurevic, R. J., Fowler, N., Burkey, B. B., Eng, C., & Ghannoum, M. A. (2016) · BBA Clinical · doi:10.1016/j.bbacli.2016.12.001 · Cited by 29
Metabolic View on Human Healthspan: A Lipidome-Wide Association Study
Carrard, J., Gallart‐Ayala, H., Infanger, D., Teav, T., Wagner, J., Knaier, R., Colledge, F., Streese, L., Königstein, K., Hinrichs, T., et al. (2021) · Metabolites · doi:10.3390/metabo11050287 · Cited by 28
Lung transcriptomic clock predicts premature aging in cigarette smoke-exposed mice
Choukrallah, M.-A., Hoeng, J., Peitsch, M. C., & Martin, F. (2020) · BMC Genomics · doi:10.1186/s12864-020-6712-z · Cited by 27
Ji, X., Chen, M., Zhao, M., Song, Y., Lin, Y., Yin, H., & Zhao, L. (2020) · Journal of Functional Foods · doi:10.1016/j.jff.2020.104333 · Cited by 27
Metabolomics of Cerebrospinal Fluid from Healthy Subjects Reveal Metabolites Associated with Ageing
Carlsson, H., Rollborn, N., Herman, S., Freyhult, E., Svenningsson, A., Burman, J., & Kultima, K. (2021) · Metabolites · doi:10.3390/metabo11020126 · Cited by 27
Signini, É. D. F., Castro, A., Rehder‐Santos, P., Millan-Mattos, J. C., de Oliveira, J. M., Minatel, V., Pantoni, C. B. F., Selistre-de-Araújo, H. S., Fabrizzi, F., Porta, A., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25747-5 · Cited by 26
An epidemiological introduction to human metabolomic investigations
Joshi, A. D., Rahnavard, A., Kachroo, P., Mendez, K. M., Lawrence, W. R., Julián‐Serrano, S., Hua, X., Fuller, H., Sinnott-Armstrong, N., Tabung, F. K., et al. (2023) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2023.06.006 · Cited by 26
C-Mannosylation: Modification on Tryptophan in Cellular Proteins
Ihara, Y., Inai, Y., Ikezaki, M., Matsui, I.-S. L., Manabe, S., & Ito, Y. (2014) · Springer eBooks · doi:10.1007/978-4-431-54841-6_67 · Cited by 25
Yengo, L., Arredouani, A., Marre, M., Roussel, R., Vaxillaire, M., Falchi, M., Haoudi, A., Tichet, J., Balkau, B., Bonnefond, A., et al. (2016) · Molecular Metabolism · doi:10.1016/j.molmet.2016.08.011 · Cited by 25
Untargeted Metabolomic Analysis of Human Milk from Mothers of Preterm Infants
Nolan, L. S., Lewis, A. N., Gong, Q., Sollome, J. J., DeWitt, O., Williams, R. D., & Good, M. L. (2021) · Nutrients · doi:10.3390/nu13103604 · Cited by 24
Chou, C., Mohanty, S., Kang, H. A., Kong, L., Ávila-Pacheco, J., Joshi, S. R., Ueda, I., Devine, L., Raddassi, K., Pierce, K. A., et al. (2022) · Aging Cell · doi:10.1111/acel.13682 · Cited by 23
Seo, S.-H., Na, C.-S., Park, S.-E., Kim, E.-J., Kim, W.-S., Park, C., Oh, S., You, Y., Lee, M., Cho, K., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2226915 · Cited by 25
Reprogramming cellular senescence and aging clocks for advanced cancer immunotherapy
Yang, H., Liu, D., Qiu, L., Wang, R., Zhang, C., Yu, D., Zhong, Q., Yuki, N., Song, Z., Zhu, Τ., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02459-8 · Cited by 30
Jasbi, P., Nikolich‐Žugich, J., Patterson, J. S., Knox, K. S., Jin, Y., Weinstock, G. M., Smith, P., Twigg, H. L., & Gu, H. (2023) · GeroScience · doi:10.1007/s11357-023-00823-4 · Cited by 24
Ala‐Korpela, M., & Smith, G. D. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw305 · Cited by 22
Metabolic Profiling of Cognitive Aging in Midlife
Huo, Z., Rana, B. K., Elman, J. A., Dong, R., Engelman, C. D., Johnson, S. C., Lyons, M. J., Franz, C. E., Kremen, W. S., & Zhao, J. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.555850 · Cited by 20
Cao, Z., Wu, T., Fang, Y., Sun, F., Ding, H., Zhao, L., & Shi, L. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1406234 · Cited by 23
Hoffman, J. M., Poonawalla, A., Icyuz, M., Swindell, W. R., Wilson, L. S., Barnes, S., & Sun, L. Y. (2020) · Aging · doi:10.18632/aging.102822 · Cited by 22
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
Aging markers in human urine: A comprehensive, non‐targeted LC‐MS study
Teruya, T., Goga, H., & Yanagida, M. (2020) · FASEB BioAdvances · doi:10.1096/fba.2020-00047 · Cited by 21
Connecting the epigenome, metabolome and proteome for a deeper understanding of disease
Suhre, K., & Zaghlool, S. B. (2021) · Journal of Internal Medicine · doi:10.1111/joim.13306 · Cited by 23
Self-citationMetabolome Profiling in Aging Studies
Balashova, E. E., Maslov, D. L., Trifonova, O. P., Lokhov, P. G., & Archakov, A. I. (2022) · Biology · doi:10.3390/biology11111570 · Cited by 22
Pandey, R. S., Arnold, M., Batra, R., Krumsiek, J., Kotredes, K. P., Garceau, D. T., Williams, H. M., Sasner, M., Howell, G. R., Kaddurah‐Daouk, R., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13851 · Cited by 22
Yusri, K., Kumar, S., Fong, S., Gruber, J., & Sorrentino, V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126793 · Cited by 23
Metabolic footprint of aging and obesity in red blood cells
Domingo-Ortí, I., Lamas‐Domingo, R., Ciudin, A., Hernández, C., Herance, J. R., Palomino‐Schätzlein, M., & Pineda‐Lucena, A. (2021) · Aging · doi:10.18632/aging.202693 · Cited by 22
Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours
Kadyrov, M., Whiley, L., Brown, B. M., Erickson, K. I., & Holmes, E. C. (2022) · Metabolites · doi:10.3390/metabo12090822 · Cited by 25
Yang, Q., Gao, S., Lin, J., Lyu, K., Wu, Z., Chen, Y., Qiu, Y., Zhao, Y., Wang, W., Lin, T., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04966-7 · Cited by 20
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 23
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
Implications of metabolism on multi‐systems healthy aging across the lifespan
Yao, S., Colangelo, L. A., Perry, A., Marron, M. M., Yaffe, K. S., Sedaghat, S., Lima, J. A. C., Tian, Q., Clish, C. B., Newman, A. B., et al. (2024) · Aging Cell · doi:10.1111/acel.14090 · Cited by 19
Molecular pathways associated with blood pressure and hexadecanedioate levels
Menni, C., Metrustry, S., Ehret, G., Dominiczak, A. F., Chowienczyk, P., Spector, T. D., Padmanabhan, S., & Valdes, A. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175479 · Cited by 20
Self-citationNierenberg, J. L., He, J., Li, C., Gu, X., Shi, M., Razavi, A. C., Mi, X., Li, S., Bazzano, L., Anderson, A. H., et al. (2020) · Aging · doi:10.18632/aging.103362 · Cited by 21
Bermingham, K. M., Brennan, L., Segurado, R., Barron, R. E., Gibney, E. R., Ryan, M. F., Gibney, M. J., & O’Sullivan, A. M. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00319 · Cited by 18
Reece, A. S., & Hulse, G. K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192416721 · Cited by 19
The induction of the fibroblast extracellular senescence metabolome is a dynamic process
James, E. N., Bennett, M. H., & Parkinson, E. K. (2018) · Scientific Reports · doi:10.1038/s41598-018-29809-5 · Cited by 17
van der Spek, A., Broer, L., Draisma, H. H. M., Pool, R., Albrecht, E., Beekman, M., Mangino, M., Raag, M., Nyholt, D. R., Dharuri, H. K., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-47282-6 · Cited by 18
Self-citationAge-Dependent Changes in Plasma Amino Acids Contribute to Alterations in Glycoxidation Products
Foroumandi, E., Alizadeh, M., & Kheirouri, S. (2018) · Journal of Medical Biochemistry · doi:10.1515/jomb-2017-0065 · Cited by 17
The Kidney–Gut–Muscle Axis in End-Stage Renal Disease is Similarly Represented in Older Adults
Lustgarten, M. S. (2019) · Nutrients · doi:10.3390/nu12010106 · Cited by 17
Direct Regulation of TLR5 Expression by Caveolin-1
Lim, J. S., Nguyen, K. C. T., Han, J. M., Jang, I., Fabian, C., & Cho, K. A. (2015) · Molecules and Cells · doi:10.14348/molcells.2015.0213 · Cited by 17
Twin metabolomics: the key to unlocking complex phenotypes in nutrition research
Barron, R. E., Bermingham, K. M., Brennan, L., Gibney, E. R., Gibney, M. J., Ryan, M. F., & O’Sullivan, A. M. (2016) · Nutrition Research · doi:10.1016/j.nutres.2016.01.010 · Cited by 17
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Rashidi, A., Shanley, R., Holtan, S. G., MacMillan, M. L., Blazar, B. R., Khoruts, A., & Weisdorf, D. J. (2018) · Transplantation and Cellular Therapy · doi:10.1016/j.bbmt.2018.06.036 · Cited by 17
Caballero, F. F., Struijk, E. A., Lana, A., Soto, A. B., Rodríguez‐Artalejo, F., & López‐García, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-82912-y · Cited by 16
Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid
Peters, K., Herman, S., Khoonsari, P. E., Burman, J., Neumann, S., & Kultima, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-97491-1 · Cited by 16
Ala‐Korpela, M., Lehtimäki, T. J., Kähönen, M. A. P., Viikari, J. S. A., Perola, M., Salomaa, V. V., Kettunen, J., Raitakari, O. T., & Mäkinen, V. (2023) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgad032 · Cited by 14
Using mass spectrometry imaging to visualize age-related subcellular disruption
Hogan, K. A., Zeidler, J. D., Beasley, H. K., Alsaadi, A. I., Alshaheeb, A. A., Chang, Y., Tian, H., Hinton, A. O., Jr., & McReynolds, M. R. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.906606 · Cited by 16
Tryptophan metabolism is differently regulated between large and small dogs
Hoffman, J. M., Kiklevich, J. V., Austad, M., Tran, V., Jones, D. P., Royal, A. B., Henry, C. J., & Austad, S. N. (2019) · GeroScience · doi:10.1007/s11357-019-00114-x · Cited by 15
Prescott, J. N., Keyser, A. J., Litwin, P., Dunbar, M. D., McClelland, R. L., Ruple, A., Ernst, H., Butler, B. L., Kauffman, M., Avery, A. C., et al. (2025) · GeroScience · doi:10.1007/s11357-025-01571-3 · Cited by 17
Zhang, X., Adebayo, A. S., Wang, D., Raza, Y. N., Tomlinson, M., Dooley, H. C., Bowyer, R. C. E., Small, K. S., Steves, C. J., Spector, T. D., et al. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4754 · Cited by 14
Self-citationAnti‐ageing effect of red ginseng revealed by urinary metabonomics using RRLC‐Q‐TOF‐MS
Sun, J., Jiao, C., Ma, Y., Chen, J., Wu, W., & Liu, S. (2018) · Phytochemical Analysis · doi:10.1002/pca.2758 · Cited by 14
A novel assay for detection and quantification of C-mannosyl tryptophan in normal or diabetic mice
Sakurai, S., Inai, Y., Minakata, S., Manabe, S., Ito, Y., & Ihara, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-41278-y · Cited by 14
Zhang, Z., Wang, J., Yu, B., Sun, Y., Chen, Y., Lu, Y., Wang, N., & Xia, F. (2024) · Journal of Endocrinological Investigation · doi:10.1007/s40618-024-02436-w · Cited by 14
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 15
Proteomics and metabolomics: can they solve some mysteries of the newborn?
Buonocore, G., Mussap, M., & Fanos, V. (2013) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.3109/14767058.2013.832579 · Cited by 12
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Khan, M. S., Hu, Q., Okeibunor, K., Ma, L., & Bopassa, J. C. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1641323 · Cited by 12
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Mirzaei, S., DeVon, H. A., Cantor, R. M., Cupido, A. J., Pan, C., Ha, S. M., Silva, L. F., Hilser, J. R., Hartiala, J., Allayee, H., et al. (2024) · Metabolites · doi:10.3390/metabo14030174 · Cited by 10
Bonomo, R., Cavaletti, G., & Skene, D. J. (2020) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2020.1782746 · Cited by 10
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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
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Luo, J., Wang, H., Moyer, D. C., Guo, Z., Robinson, J. L., Gustafsson, J., Anton, M., Chen, Y., Kerkhoven, E. J., Nielsen, J., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516511123 · Cited by 13
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da Costa, J. P., Rocha‐Santos, T., & Duarte, A. C. (2015) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2015.09.011 · Cited by 9
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Derwall, M., & Coburn, M. (2019) · Der Anaesthesist · doi:10.1007/s00101-019-00699-0 · Cited by 9
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Lindon, J. C., & Wilson, I. D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800344-2.00002-1 · Cited by 9
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Hoffman, J. M., Ross, C. N., Tran, V. T., Promislow, D., Tardif, S. D., & Jones, D. P. (2018) · American Journal of Primatology · doi:10.1002/ajp.22944 · Cited by 9
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Wang, Z., & Yang, B. (2024) · Engineering · doi:10.1016/j.eng.2024.09.010 · Cited by 9
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Hantikainen, E. M., Weichenberger, C. X., Đorđević, N., Hernandes, V. V., Foco, L., Gögele, M., Melotti, R., Pattaro, C., Ralser, M., Amari, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-75627-3 · Cited by 8
Prospective association between plasma amino acids and healthy aging in older adults
González‐Beltrán, D., Yévenes‐Briones, H., Lana, A., Cárdenas‐Valladolid, J., Salinero-Fort, M. Á., Rodríguez‐Artalejo, F., López‐García, E., & Caballero, F. F. (2025) · Journal of Internal Medicine · doi:10.1111/joim.20105 · Cited by 8
Yamamoto, H., Suzuki, M., Matsuta, R., Sasaki, K., Kang, M.-I., Kami, K., Tatara, Y., Itoh, K., & Nakaji, S. (2021) · Metabolites · doi:10.3390/metabo11050314 · Cited by 8
The HuMet Repository: Watching human metabolism at work
Weinisch, P., Raffler, J., Römisch‐Margl, W., Arnold, M., Mohney, R. P., Rist, M. J., Prehn, C. P., Skurk, T., Hauner, H., Daniel, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114416 · Cited by 8
Self-citationAmino Acid Profile of Synovial Fluid Following Intra-articular Ankle Fracture
Leimer, E. M., Tanenbaum, L. M., Nettles, D. L., Bell, R. D., Easley, M. E., Setton, L. A., & Adams, S. B. (2018) · Foot & Ankle International · doi:10.1177/1071100718786163 · Cited by 8
Shen, C., Mo, H., Dunn, D. M., & Watkins, B. A. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.766711 · Cited by 8
Mangino, M. (2014) · Proceedings of The Nutrition Society · doi:10.1017/s0029665114000640 · Cited by 9
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Neal, R. E., Chen, J., Webb, C., Stocke, K. S., Gambrell, C., Greene, R. M., & Pisano, M. (2016) · Reproductive Toxicology · doi:10.1016/j.reprotox.2016.06.009 · Cited by 8
CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status
Dong, R., Darst, B. F., Deming, Y. K., Ma, Y., Lu, Q., Zetterberg, H., Blennow, K., Carlsson, C. M., Johnson, S. C., Asthana, S., et al. (2021) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12167 · Cited by 8
Mussap, M., Puddu, M., & Fanos, V. (2023) · Current Medicinal Chemistry · doi:10.2174/0929867330666230509110108 · Cited by 8
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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