The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Human serum metabolic profiles are age dependent
Open accessAbstract
Summary: Understanding the complexity of aging is of utmost importance. This can now be addressed by the novel and powerful approach of metabolomics. However, to date, only a few metabolic studies based on large samples are available. Here, we provide novel and specific information on age‐related metabolite concentration changes in human homeostasis. We report results from two population‐based studies: the KORA F4 study from Germany as a discovery cohort, with 1038 female and 1124 male participants (32–81 years), and the TwinsUK study as replication, with 724 female participants. Targeted metabolomics of fasting serum samples quantified 131 metabolites by FIA‐MS/MS. Among these, 71/34 metabolites were significantly associated with age in women/men (BMI adjusted). We further identified a set of 13 independent metabolites in women (with P values ranging from 4.6 × 10−04 to 7.8 × 10−42, α corr = 0.004). Eleven of these 13 metabolites were replicated in the TwinsUK study, including seven metabolite concentrations that increased with age (C0, C10:1, C12:1, C18:1, SM C16:1, SM C18:1, and PC aa C28:1), while histidine decreased. These results indicate that metabolic profiles are age dependent and might reflect different aging processes, such as incomplete mitochondrial fatty acid oxidation. The use of metabolomics will increase our understanding of aging networks and may lead to discoveries that help enhance healthy aging.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 [114]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,893
Franceschi, C., Capri, M., Monti, D., Giunta, S., Olivieri, F., Sevini, F., Panourgia, M. P., Invidia, L., Celani, L., Scurti, M., et al. (2007) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2006.11.016 · Cited by 2,087
Psychogios, N., Hau, D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., Sinelnikov, I. V., Krishnamurthy, R., Eisner, R., Gautam, B., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016957 · Cited by 1,779
KORA - A Research Platform for Population Based Health Research
Holle, R., Happich, M., Löwel, H., & Wichmann, H. ‐. (2005) · Das Gesundheitswesen · doi:10.1055/s-2005-858235 · Cited by 753
Self-citationDifferences between Human Plasma and Serum Metabolite Profiles
Yu, Z., Kastenmüller, G., He, Y., Belcredi, P., Möller, G., Prehn, C. P., Mendes, J., Wahl, S., Roemisch‐Margl, W., Ceglarek, U., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021230 · Cited by 523
Self-citationAnalysis of the Adult Human Plasma Metabolome
Lawton, K. A., Berger, A., Mitchell, M., Milgram, K. E., Evans, A. M., Guo, L., Hanson, R. W., Kalhan, S. C., Ryals, J. A., & Milburn, M. V. (2008) · Pharmacogenomics · doi:10.2217/14622416.9.4.383 · Cited by 490
Metabolism, Cell Surface Organization, and Disease
Dennis, J. W., Nabi, I. R., & Demetriou, M. (2009) · Cell · doi:10.1016/j.cell.2009.12.008 · Cited by 450
Andrew, T., Hart, D. J., Snieder, H., de Lange, M., Spector, T. D., & MacGregor, A. J. (2001) · Twin Research · doi:10.1375/1369052012803 · Cited by 440
Self-citationKORA-gen - Resource for Population Genetics, Controls and a Broad Spectrum of Disease Phenotypes
Wichmann, H. ‐., Gieger, C., & Illig, T. (2005) · Das Gesundheitswesen · doi:10.1055/s-2005-858226 · Cited by 417
Self-citationOutlier identification in high dimensions
Peter Filzmoser, A., Maronna, R. A., & Werner, M. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.05.018 · Cited by 401
Cited by
Sorted by citations
Inflammaging: a new immune–metabolic viewpoint for age-related diseases
Franceschi, C., Garagnani, P., Parini, P., Giuliani, C., & Santoro, A. (2018) · Nature Reviews Endocrinology · doi:10.1038/s41574-018-0059-4 · Cited by 3,586
Thévenot, E., Roux, A., Xu, Y., Ezan, É., & Junot, C. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00354 · Cited by 1,802
Jylhävä, J., Pedersen, N. L., & Hägg, S. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.03.046 · Cited by 1,240
Novel biomarkers for pre-diabetes identified by metabolomics
Wang‐Sattler, R., Yu, Z., Herder, C., Messias, A. C., Floegel, A., He, Y., Heim, K., Campillos, M., Holzapfel, C., Thorand, B., et al. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.43 · Cited by 701
Self-citationSayed, 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
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 402
Tryptophan Metabolism in Inflammaging: From Biomarker to Therapeutic Target
Sorgdrager, F. J. H., Naudé, P. J. W., Kema, I. P., Nollen, E. A. A., & Deyn, P. P. D. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02565 · Cited by 381
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
Quality assurance procedures for mass spectrometry untargeted metabolomics. a review
Dudzik, D., Barbas-Bernardos, C., Garcı́a, A., & Barbas, C. (2017) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2017.07.044 · Cited by 361
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
Self-citationMetabolomic markers reveal novel pathways of ageing and early development in human populations
Menni, C., Kastenmüller, G., Petersen, A. K., Bell, J. T., Psatha, M., Tsai, P.-C., Gieger, C., Schulz, H., Erte, I., John, S., et al. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt094 · Cited by 282
Self-citationA metabolic view on menopause and ageing
Auro, K. M., Joensuu, A., Fischer, K., Kettunen, J., Salo, P. P., Mattsson, H., Niironen, M., Kaprio, J. A., Eriksson, J. G., Lehtimäki, T. J., et al. (2014) · Nature Communications · doi:10.1038/ncomms5708 · Cited by 274
Siskos, A. P., Jain, P., Römisch-Margl, W., Bennett, M., Achaintre, D., Asad, Y., Marney, L., Richardson, L., Koulman, A., Griffin, J. L., et al. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b02930 · Cited by 269
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
Alzheimer’s pathogenic mechanisms and underlying sex difference
Zhu, D., Montagne, A., & Zhao, Z. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03830-w · Cited by 275
Determinants of accelerated metabolomic and epigenetic aging in a UK cohort
Robinson, O., Chadeau Hyam, M., Karaman, I., Climaco Pinto, R., Ala-Korpela, M., Handakas, E., Fiorito, G., Gao, H., Heard, A., Jarvelin, M., et al. (2020) · Aging Cell · doi:10.1111/acel.13149 · Cited by 205
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
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
Longitudinal 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
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
Human plasma stability during handling and storage: impact on NMR metabolomics
Pinto, J., Domingues, M. R., Galhano, E. M. B., Pita, C., Almeida, M. D. C., Carreira, I. M., & Gil, A. M. (2014) · The Analyst · doi:10.1039/c3an02188b · Cited by 171
Blue Journal Conference. Aging and Susceptibility to Lung Disease
Thannickal, V. J., Murthy, M., Balch, W. E., Chandel, N. S., Meiners, S., Eickelberg, O., Selman, M., Pardo, A., White, E. S., Levy, B. D., et al. (2015) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201410-1876pp · Cited by 165
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
Distinct metabolomic signatures are associated with longevity in humans
Cheng, S. C., Larson, M. G., McCabe, E. L., Murabito, J. M., Rhee, E. P., Ho, J. E., Jacques, P. F., Ghorbani, A., Magnusson, M., Souza, A. L., et al. (2015) · Nature Communications · doi:10.1038/ncomms7791 · Cited by 163
Lu, Y., Wang, Y., Ong, C. N., Subramaniam, T., Choi, H., Yuan, J., Koh, W., & Pan, A. (2016) · Diabetologia · doi:10.1007/s00125-016-4069-2 · Cited by 161
Trabado, S., Al‐Salameh, A., Croixmarie, V., Masson, P., Corruble, E., Fève, B., Colle, R., Ripoll, L., Walther, B., Boursier-Neyret, C., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173615 · Cited by 157
Determinants of the urinary and serum metabolome in children from six European populations
Lau, C. E., Siskos, A., Maître, L., Robinson, O., Athersuch, T. J., Want, E. J., Urquiza, J., Casas, M., Vafeiadi, M., Roumeliotaki, T., et al. (2018) · BMC Medicine · doi:10.1186/s12916-018-1190-8 · Cited by 153
Paradoxical changes in innate immunity in aging: recent progress and new directions
Montgomery, R. R., & Shaw, A. C. (2015) · Journal of Leukocyte Biology · doi:10.1189/jlb.5mr0315-104r · Cited by 147
Sex differences in the human metabolome
Costanzo, M., Caterino, M., Sotgiu, G., Ruoppolo, M., Franconi, F., & Campesi, I. (2022) · Biology of Sex Differences · doi:10.1186/s13293-022-00440-4 · Cited by 148
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-citationXu, T., Holzapfel, C., Dong, X., Bader, E., Yu, Z., Prehn, C. P., Perstorfer, K., Jaremek, M., Roemisch‐Margl, W., Rathmann, W., et al. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-60 · Cited by 140
Self-citationEffects of Metformin on Metabolite Profiles and LDL Cholesterol in Patients With Type 2 Diabetes
Xu, T., Brandmaier, S., Messias, A. C., Herder, C., Draisma, H. H. M., Demirkan, A., Yu, Z., Ried, J. S., Haller, T., Heier, M., et al. (2015) · Diabetes Care · doi:10.2337/dc15-0658 · Cited by 141
Self-citationAmino acids in the regulation of aging and aging-related diseases
Canfield, C.-A. E., & Bradshaw, P. C. (2019) · Translational Medicine of Aging · doi:10.1016/j.tma.2019.09.001 · Cited by 138
Hoffman, J. M., Soltow, Q. A., Li, S., Sidik, A., Jones, D. P., & Promislow, D. (2014) · Aging Cell · doi:10.1111/acel.12215 · Cited by 124
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-citationEmerging Insights into the Metabolic Alterations in Aging Using Metabolomics
Srivastava, S. (2019) · Metabolites · doi:10.3390/metabo9120301 · Cited by 124
Klavins, K., Koal, T., Dallmann, G., Marksteiner, J., Kemmler, G., & Humpel, C. (2015) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2015.05.003 · Cited by 111
Parkhitko, A. A., Binari, R. C., Zhang, N., Asara, J. M., Demontis, F., & Perrimon, N. (2016) · Genes & Development · doi:10.1101/gad.282277.116 · Cited by 110
Alcohol-induced metabolomic differences in humans
Jaremek, M., Yu, Z., Mangino, M., Mittelstraß, K., Prehn, C. P., Singmann, P., Xu, T., Dahmen, N., Weinberger, K. M., Suhre, K., et al. (2013) · Translational Psychiatry · doi:10.1038/tp.2013.55 · Cited by 103
Self-citationKim, M. G., Nevado‐Holgado, A. J., Whiley, L., Snowden, S. G., Soininen, H. S., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B. J., Thambisetty, M., et al. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160645 · Cited by 98
Deelen, J., Beekman, M., Capri, M., Franceschi, C., & Slagboom, P. E. (2013) · BioEssays · doi:10.1002/bies.201200148 · Cited by 94
Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
Long, N. P., Nghi, T. D., Kang, Y. P., Anh, N. H., Kim, H. M., Park, S. K., & Kwon, S. W. (2020) · Metabolites · doi:10.3390/metabo10020051 · 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
Di-(2-Ethylhexyl)-Phthalate (DEHP) Causes Impaired Adipocyte Function and Alters Serum Metabolites
Klöting, N., Hesselbarth, N., Gericke, M. T., Kunath, A., Biemann, R., Chakaroun, R., Kosacka, J., Kovacs, P. L., Kern, M., Stümvoll, M., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143190 · Cited by 86
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
Mielke, M. M., Haughey, N. J., Han, D., An, Y., Bandaru, V. V. R., Lyketsos, C. G., Ferrucci, L., & Resnick, S. M. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160925 · Cited by 87
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
Metabolomics Signatures of Aging: Recent Advances
Adav, S. S., & Wang, Y. (2021) · Aging and Disease · doi:10.14336/ad.2020.0909 · Cited by 95
Mielke, M. M., Bandaru, V. V. R., Han, D., An, Y., Resnick, S. M., Ferrucci, L., & Haughey, N. J. (2015) · Aging Cell · doi:10.1111/acel.12369 · Cited by 85
Vouk, K., Ribič-Pucelj, M., Adamski, J., & Rižner, T. L. (2016) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2016.02.023 · Cited by 84
Self-citationA metabolic signature predicts biological age in mice
Tomás‐Loba, A., de Jesus, B. B., Mato, J. M., & Blasco, M. A. (2012) · Aging Cell · doi:10.1111/acel.12025 · Cited by 83
Systems biology strategies to study lipidomes in health and disease
Hyötyläinen, T., & Orešič, M. (2014) · Progress in Lipid Research · doi:10.1016/j.plipres.2014.06.001 · Cited by 83
Monocytes present age-related changes in phospholipid concentration and decreased energy metabolism
Saare, M., Tserel, L., Haljasmägi, L., Taalberg, E., Peet, N., Eimre, M., Vetik, R., Kingo, K., Saks, K., Tamm, R., et al. (2020) · Aging Cell · doi:10.1111/acel.13127 · Cited by 79
Mutz, J., Iniesta, R., & Lewis, C. M. (2024) · Science Advances · doi:10.1126/sciadv.adp3743 · Cited by 77
Mielke, M. M., Bandaru, V. V. R., Han, D., An, Y., Resnick, S. M., Ferrucci, L., & Haughey, N. J. (2014) · Aging Cell · doi:10.1111/acel.12275 · Cited by 78
Fecal and urinary NMR-based metabolomics unveil an aging signature in mice
Calvani, R., Brasili, E., Praticò, G., Capuani, G., Tomassini, A., Marini, F., Sciubba, F., Finamore, A., Roselli, M., Marzetti, E., et al. (2013) · Experimental Gerontology · doi:10.1016/j.exger.2013.10.010 · Cited by 76
Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics
Saigusa, D., Matsukawa, N., Hishinuma, E., & Koshiba, S. (2020) · Drug Metabolism and Pharmacokinetics · doi:10.1016/j.dmpk.2020.11.008 · Cited by 79
Integrative metabolomics science in Alzheimer’s disease: Relevance and future perspectives
Lista, S., González‐Domínguez, R., López‐Ortiz, S., González‐Domínguez, Á., Menéndez, H., Martín-Hernández, J., Lucía, A., Emanuele, E., Centonze, D., Imbimbo, B. P., et al. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101987 · Cited by 76
Wang, F., Tessier, A., Liang, L., Wittenbecher, C., Haslam, D. E., Fernández-Duval, G., Eliassen, A. H., Rexrode, K. M., Tobias, D. K., Li, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41515-z · Cited by 72
Plasma acylcarnitine levels increase with healthy aging
Jarrell, Z. R., Smith, M. R., Hu, X. Y., Orr, M. L., Liu, K., Quyyumi, A. A., Jones, D. P., & Go, Y. (2020) · Aging · doi:10.18632/aging.103462 · Cited by 76
Multivariate analysis of NMR‐based metabolomic data
Debik, J., Sangermani, M., Wang, F., Madssen, T. S., & Giskeødegård, G. F. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4638 · Cited by 77
Voge, N. V., Perera, R. M., Mahapatra, S., Gresh, L., Balmaseda, Á., Loroño-Pino, M. A., Hopf-Jannasch, A. S., Belisle, J. T., Harris, E., Blair, C. D., et al. (2016) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0004449 · Cited by 69
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
Adam, J., Brandmaier, S., Leonhardt, J., Scheerer, M. F., Mohney, R. P., Xu, T., Bi, J., Rotter, M., Troll, M., Chi, S., et al. (2016) · Diabetes · doi:10.2337/db16-0512 · Cited by 68
Self-citationRole of kinase-coupled TRP channels in mineral homeostasis
Chubanov, V., Mittermeier, L., & Gudermann, T. W. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.11.003 · Cited by 64
Tayyari, F., Gowda, G. A. N., Olopade, O. I., Berg, R., Yang, H. H., Lee, M. P., Ngwa, W. F., Mittal, S. K., Raftery, D., & Mohammed, S. I. (2018) · Oncotarget · doi:10.18632/oncotarget.24433 · Cited by 63
Xie, S. Z.-J., Kaufmann, K. B., Wang, W., Chan‐Seng‐Yue, M., Gan, O. I., Laurenti, E., García‐Prat, L., Takayanagi, S., Ng, S., Xu, C., et al. (2020) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-20-0155 · Cited by 58
Halama, A., Riesen, N., Möller, G., de Angelis, M. H., & Adamski, J. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12117 · Cited by 61
Self-citationLoo, R. L., Lodge, S., Kimhofer, T., Bong, S.-H., Begum, S., Whiley, L., Gray, N., Lindon, J. C., Nitschke, P., Lawler, N. G., et al. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00537 · Cited by 66
Romano, F., Meoni, G., Manavella, V., Baima, G., Tenori, L., Cacciatore, S., & Aimetti, M. (2018) · Journal of Periodontology · doi:10.1002/jper.18-0097 · Cited by 61
A lipidome landscape of aging in mice
Tsugawa, H., Ishihara, T., Ogasa, K., Iwanami, S., Hori, A., Takahashi, M., Yamada, Y., Satoh‐Takayama, N., Ohno, H., Minoda, A., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00610-6 · Cited by 64
Review: Current trends, challenges, and success stories in adjuvant research
Singleton, K. L., Joffe, A. R., & Leitner, W. W. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1105655 · Cited by 59
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
Debik, J., Euceda, L. R., Lundgren, S., Gythfeldt, H. V. D. L., Garred, Ø., Borgen, E., Engebraaten, O., Bathen, T. F., & Giskeødegård, G. F. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00316 · Cited by 57
Fages, A., Ferrari, P., Monni, S., Dossus, L., Floegel, A., Mode, N. A., Johansson, M., Travis, R. C., Bamia, C., Sánchez-Pérez, M.-J., et al. (2014) · Metabolomics · doi:10.1007/s11306-014-0647-9 · Cited by 57
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
Whole Blood Metabolomics in Aging Research
Kondoh, H., Kameda, M., & Yanagida, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms22010175 · Cited by 56
Comorbidity-associated glutamine deficiency is a predisposition to severe COVID-19
Matsuyama, T., Yoshinaga, S. K., Shibue, K., & Mak, T. W. (2021) · Cell Death and Differentiation · doi:10.1038/s41418-021-00892-y · Cited by 54
Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status
Sébédio, J. (2017) · Advances in food and nutrition research · doi:10.1016/bs.afnr.2017.01.001 · Cited by 52
Downregulation of the tyrosine degradation pathway extends Drosophila lifespan
Parkhitko, A. A., Ramesh, D., Wang, L., Leshchiner, D. R., Filine, E., Binari, R. C., Olsen, A. L., Asara, J. M., Cracan, V., Rabinowitz, J. D., et al. (2020) · eLife · doi:10.7554/elife.58053 · Cited by 52
Garvey, S. M., Dugle, J. E., Kennedy, A. D., McDunn, J. E., Kline, W. O., Guo, L., Guttridge, D. C., Pereira, S. L., & Edens, N. K. (2014) · Biogerontology · doi:10.1007/s10522-014-9492-5 · Cited by 51
Discovering and validating between-subject variations in plasma lipids in healthy subjects
Begum, H., Li, B., Shui, G., Cazenave‐Gassiot, A., Soong, R. C. T., Ong, R. T., Little, P., Teo, Y., & Wenk, M. R. (2016) · Scientific Reports · doi:10.1038/srep19139 · Cited by 50
Global metabolic profiling to model biological processes of aging in twins
Bunning, B., Contrepois, K., Lee‐McMullen, B., Dhondalay, G. K., Zhang, W., Tupa, D., Raeber, O., Desai, M., Nadeau, K. C., Snyder, M. P., et al. (2019) · Aging Cell · doi:10.1111/acel.13073 · Cited by 52
Fei, F., Lee, K. M., McCarry, B. E., & Bowdish, D. M. E. (2016) · Scientific Reports · doi:10.1038/srep22637 · Cited by 50
Metabolic phenotyping for monitoring ovarian cancer patients
Ke, C., Li, A., Hou, Y., Sun, M., Yang, K. G., Cheng, J., Wang, J., Ge, T., Zhang, F., Li, Q., et al. (2016) · Scientific Reports · doi:10.1038/srep23334 · Cited by 49
Yu, S., Xia, H., Guo, Y., Qian, X., Zou, X., Yang, H., Yin, M., & Liu, H. (2020) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2020.112746 · Cited by 50
The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology
Rockel, J., & Kapoor, M. (2018) · Metabolites · doi:10.3390/metabo8040092 · Cited by 43
Ageing Investigation Using Two-Time-Point Metabolomics Data from KORA and CARLA Studies
Chak, C. M., Lacruz, M. E., Adam, J., Brandmaier, S., Covic, M., Huang, J., Meisinger, C., Tiller, D., Prehn, C. P., Adamski, J., et al. (2019) · Metabolites · doi:10.3390/metabo9030044 · Cited by 49
Self-citationGender- and Age-Associated Differences in Serum Metabolite Profiles among Japanese Populations
Saito, K., Maekawa, K., Kinchen, J. M., Tanaka, R., Kumagai, Y., & Saito, Y. (2016) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.b16-00226 · Cited by 46
Saw, W., Tantoso, E., Begum, H., Zhou, L., Zou, R., He, C., Chan, S. L., Tan, L. W.-L., Wong, L.-P., Xu, W., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00413-x · Cited by 44
Metabolomics-based profiles predictive of low bone mass in menopausal women
Miyamoto, T. K., Hirayama, A., Sato, Y., Koboyashi, T., Katsuyama, E., Kanagawa, H., Fujie, A., Morita, M., Watanabe, R., Tando, T., et al. (2018) · Bone Reports · doi:10.1016/j.bonr.2018.06.004 · Cited by 46
A genetic model of methionine restriction extends Drosophila health- and lifespan
Parkhitko, A. A., Wang, L., Filine, E., Jouandin, P., Leshchiner, D. R., Binari, R. C., Asara, J. M., Rabinowitz, J. D., & Perrimon, N. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110387118 · Cited by 44
Metabolomics-Based Identification of Metabolic Dysfunction in Frailty
Westbrook, R., Zhang, C., Yang, H., Tian, J., Guo, S., Xue, Q., Walston, J. D., Le, A., & Abadir, P. M. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab315 · Cited by 49
Geijsen, A. J. M. R., van Roekel, E. H., van Duijnhoven, F. J. B., Achaintre, D., Bachleitner‐Hofmann, T., Baierl, A., Bergmann, M. M., Böhm, J., Bours, M. J. L., Brenner, H., et al. (2019) · International Journal of Cancer · doi:10.1002/ijc.32666 · Cited by 46
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
Panyard, D. J., McKetney, J., Deming, Y. K., Morrow, A. R., Ennis, G. E., Jonaitis, E. M., Hulle, C. A. V., Yang, C., Sung, Y. J., Ali, M., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13130 · Cited by 48
Plasma Metabolic Profiles in Women are Menopause Dependent
Ke, C., Hou, Y., Zhang, H., Yang, K. G., Wang, J., Guo, B., Zhang, F., Li, H., Zhou, X., Li, Y., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141743 · Cited by 44
Self-citation
Metadata
- Authors
- Yu, Z., Zhai, G., Singmann, P., He, Y., Xu, T., Prehn, C., Römisch‐Margl, W., Lattka, E., Gieger, C., Soranzo, N., Heinrich, J., Standl, M., Thiering, E., Mittelstraß, K., Wichmann, H., Peters, A., Suhre, K., Li, Y., Adamski, J., Spector, T. D., Illig, T., & Wang‐Sattler, R.
- Affiliations
- China University of Mining and Technology (CN)
- Memorial University of Newfoundland (CA)
- Chinese Academy of Sciences (CN)
- Helmholtz Munich (DE)
- King's College London (GB)
- Harvard University (US)
- Corresponding author
- Wang‐Sattler, Rui
- ORCID
- Venue
- Aging Cell
- Publisher
- Wiley
- Published
- 2012-08-27
- Volume
- 11
- Issue
- 6
- Pages
- 960-967
- ISSN
- 1474-9718 (ISSN-L)
- 1474-9726
- PMC
- PMC3533791
- OpenAlex
- W2121544545
- Cited by
- 340
- References
- 48
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 34 · 2023: 30 · 2022: 40 · 2021: 31 · 2020: 26 · 2019: 22 · 2018: 18 · 2017: 17 · 2016: 25 · 2015: 19 · 2014: 18 · 2013: 18 · 2012: 2
- FWCI
- 7.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Aging Cell (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Wellcome Trust
- National Institute for Health and Care Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Keywords
- aging, age-related metabolic changes, serum metabolites, fatty acid oxidation, sphingomyelin, phosphatidylcholine, histidine, healthy aging, sex differences, TwinsUK
- MeSH terms
- Adult, Age Factors, Aged, Aged, 80 and over, Aging, Fatty Acids, Female, Humans, Male, Middle Aged, Oxidation-Reduction, Sex Factors, Biomarkers, Body Mass Index, Multivariate Analysis, Flow Injection Analysis, Tandem Mass Spectrometry, Metabolomics, Metabolome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
