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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.
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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 [114]
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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
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Johnson, L. C., Martens, C. R., Santos‐Parker, J. R., Bassett, C. J., Strahler, T. R., Cruickshank-Quinn, C. I., Reisdorph, N. A., McQueen, M. B., & Seals, D. R. (2018) · Clinical Science · doi:10.1042/cs20180409 · Cited by 45
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Jones, J. W., Alloush, J., Sellamuthu, R., Chua, H. L., MacVittie, T. J., Orschell, C. M., & Kane, M. A. (2019) · Health Physics · doi:10.1097/hp.0000000000000961 · Cited by 43
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Schulte, E. C., Altmaier, E., Berger, H. S., Do, K. T., Kastenmüller, G., Wahl, S., Adamski, J., Peters, A., Krumsiek, J., Suhre, K., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147129 · Cited by 41
Self-citationWanichthanarak, K., Jeamsripong, S., Pornputtapong, N., & Khoomrung, S. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.04.009 · Cited by 42
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Xie, K., Qin, Q., Long, Z., Yang, Y., Peng, C., Xi, C., Li, L., Wu, Z., Daria, V., Zhao, Y., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.602887 · Cited by 41
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Almeida, I., Magalhães, S., & Nunes, A. (2021) · Biogerontology · doi:10.1007/s10522-021-09921-2 · Cited by 44
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Collino, S., Martin, F., Karagounis, L. G., Horcajada, M. N., Moco, S., Franceschi, C., Kussmann, M., & Offord, E. (2013) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2013.11.003 · Cited by 38
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 40
Shi, M., Han, S., Klier, K., Fobo, G., Montrone, C., Yu, S., Harada, M., Henning, A., Friedrich, N., Bahls, M., et al. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01862-z · Cited by 39
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Self-citationPontoizeau, C., Mouchiroud, L., Molin, L., Lamarche, A. M. D., Dallière, N., Toulhoat, P., Elena‐Herrmann, B., & Solari, F. (2014) · Journal of Proteome Research · doi:10.1021/pr5000686 · Cited by 40
Bub, A., Kriebel, A., Dörr, C., Bandt, S., Rist, M. J., Röth, A., Hummel, E., Kulling, S. E., Hoffmann, I., & Watzl, B. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5792 · Cited by 38
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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
Saito, K., Maekawa, K., Pappan, K. L., Urata, M., Ishikawa, M., Kumagai, Y., & Saito, Y. (2013) · Metabolomics · doi:10.1007/s11306-013-0591-0 · Cited by 36
Vukajlović, J. M., Drakulić, D., Pejić, S., Ilic, T. V., Stefanović, A., Petković, M., & Schiller, J. (2019) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.8595 · Cited by 37
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
Ward-Caviness, C. K., Breitner, S., Wolf, K., Cyrys, J., Kastenmüller, G., Wang‐Sattler, R., Schneider, A. E., & Peters, A. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw247 · Cited by 35
Self-citationEmerging Omics Approaches in Aging Research
Lorusso, J. S., Sviderskiy, O., & Labunskyy, V. M. (2017) · Antioxidants and Redox Signaling · doi:10.1089/ars.2017.7163 · Cited by 39
Ye, G., Ding, D., Gao, H., Chi, Y., Chen, J., Wu, Z., Lin, Y., & Dong, S. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.07.192 · Cited by 34
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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
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Adamski, J. (2016) · Diabetologia · doi:10.1007/s00125-016-4044-y · Cited by 32
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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
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Hoffman, J. M., Tran, V., Wachtman, L. M., Green, C. L., Jones, D. P., & Promislow, D. (2016) · Experimental Gerontology · doi:10.1016/j.exger.2016.01.007 · Cited by 29
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Jové, M., Ayala, V., Ramírez‐Núñez, O., Naudí, A., Cabré, R., Spickett, C. M., Portero‐Otín, M., & Pamplona, R. (2013) · Journal of Proteome Research · doi:10.1021/pr400064a · Cited by 29
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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
Cai, D., Zhao, Z., Zhao, L., Dong, Y., Wang, L., Zhao, S., & Li, Q. (2022) · Nutrients · doi:10.3390/nu14204420 · Cited by 25
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Wennberg, A. M. V., Schafer, M. J., LeBrasseur, N. K., Savica, R., Bui, H. H., Hagen, C. E., Hollman, J. H., Petersen, R. C., & Mielke, M. M. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx139 · Cited by 25
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Schäfer, N., Yu, Z., Wagener, A., Millrose, M. K., Reißmann, M., Bortfeldt, R. H., Dieterich, C., Adamski, J., Wang‐Sattler, R., Illig, T., et al. (2013) · Metabolomics · doi:10.1007/s11306-013-0590-1 · Cited by 24
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Kim, S., Cheon, H., Song, J.-C., Yun, S., Park, S. I., & Jeon, J. (2014) · Osong Public Health and Research Perspectives · doi:10.1016/j.phrp.2014.10.002 · Cited by 24
Bovo, S., Mazzoni, G., Calò, D. G., Galimberti, G., Fanelli, F., Mezzullo, M., Schiavo, G., Scotti, E., Manisi, A., Samorè, A. B., et al. (2015) · Journal of Animal Science · doi:10.2527/jas.2015-9528 · Cited by 24
Individual bioenergetic capacity as a potential source of resilience to Alzheimer’s disease
Arnold, M., Büyüközkan, M., Doraiswamy, P. M., Nho, K. T., Wu, T., Gudnason, V. G., Launer, L. J., Wang‐Sattler, R., Adamski, J., Initiative, T. A. D. N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57032-0 · Cited by 24
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Breast Cancer in the Elderly: Which Lessons Have We Learned?
Turner, N. H., Zafarana, E., Becheri, D., Mottino, G., & Biganzoli, L. (2013) · Future Oncology · doi:10.2217/fon.13.140 · Cited by 23
Li, J., Gan‐Schreier, H., Zhu, X., Wang, W., Tuma‐Kellner, S., Liebisch, G., Stremmel, W., & Chamulitrat, W. (2017) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2017.09.001 · Cited by 23
Saito, K., Ikeda, M., Kojima, Y., Hosoi, H., Saito, Y., & Kondo, S. (2018) · Cancer Chemotherapy and Pharmacology · doi:10.1007/s00280-018-3655-z · Cited by 23
Cozma, C., Hovakimyan, M., Iurascu, M. I., Makhseed, N., Selim, L., Alhashem, A. M., Ben‐Omran, T., Mahmoud, I. G. E. D., Menabawy, N. M. A., Al‐Mureikhi, M., et al. (2019) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-019-1183-5 · Cited by 22
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Kang, J., Guo, X., Peng, H., Deng, Y., Lai, J., Tang, L., Aoieong, C., Tou, T., Tsai, T., & Liu, X. (2023) · International Urology and Nephrology · doi:10.1007/s11255-023-03779-8 · Cited by 24
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
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Yusri, K., Kumar, S., Fong, S., Gruber, J., & Sorrentino, V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126793 · Cited by 23
Sorgdrager, F. J. H., van der Ley, C. P., van Faassen, M. H. J. R., Calus, E., Nollen, E. A. A., Kema, I. P., Dam, D. V., & Deyn, P. D. (2020) · International Journal of Tryptophan Research · doi:10.1177/1178646920972657 · Cited by 19
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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
Ren, M., Lin, D. Z., Liu, Z. P., Sun, K., Wang, C., Lao, G. J., Fan, Y., Wang, X. Y., Liu, J., Du, J., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.745214 · Cited by 20
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Tu, W. B., Christofk, H. R., & Plath, K. (2023) · Development · doi:10.1242/dev.199961 · Cited by 23
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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
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O’Connor, J., Herrera, G., Martínez‐Romero, A., de Oyanguren, F. S., Díaz, L., Carvalho‐Gomes, Â., Balaguer, S., & Callaghan, R. C. (2014) · Immunology Letters · doi:10.1016/j.imlet.2014.09.009 · Cited by 24
Conway, J., Certo, M., Lord, J. M., Mauro, C., & Duggal, N. A. (2021) · British Journal of Pharmacology · doi:10.1111/bph.15671 · Cited by 22
Cesare, F. D., Luchinat, C., Tenori, L., & Saccenti, E. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab335 · Cited by 19
Lazado, C. C., Stiller, K. T., Reiten, B. K. M., Osório, J., Kolarevic, J., & Johansen, L. (2021) · Aquatic Toxicology · doi:10.1016/j.aquatox.2021.105935 · Cited by 19
Reece, A. S., & Hulse, G. K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192416721 · Cited by 19
Onwuka, S., Bravo-Merodio, L., Gkoutos, G. V., & Acharjee, A. (2024) · iScience · doi:10.1016/j.isci.2024.110298 · Cited by 20
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-citationEffects of Aging and Methionine Restriction on Rat Kidney Metabolome
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Barwon Infant Study Investigator Team, Ellul, S., Ponsonby, A., Carlin, J. B., Collier, F., Mansell, T., Vuillermin, P., Burgner, D. P., & Saffery, R. (2020) · Pediatric Research · doi:10.1038/s41390-020-0762-4 · Cited by 18
Familial Resemblance for Serum Metabolite Concentrations
Draisma, H. H. M., Beekman, M., Pool, R., van Ommen, G. B., Vaarhorst, A. A. M., de Craen, A. J. M., Willemsen, G. A. H. M., Slagboom, P. E., & Boomsma, D. I. (2013) · Twin Research and Human Genetics · doi:10.1017/thg.2013.59 · Cited by 17
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Stratmann, B., Richter, K., Wang, R., Yu, Z., Xu, T., Prehn, C. P., Adamski, J., Illig, T., Tschoepe, D., & Wang‐Sattler, R. (2017) · International Journal of Endocrinology · doi:10.1155/2017/7938216 · Cited by 16
Self-citationBongaerts, M., Bonte, R., Demirdas, S., Jacobs, E. H., Oussoren, E., van der Ploeg, A., Wagenmakers, M. A. E. M., Hofstra, R. M. W., Blom, H. J., Reinders, M. J. T., et al. (2020) · Metabolites · doi:10.3390/metabo11010008 · Cited by 17
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
Metabotypes of response to bariatric surgery independent of the magnitude of weight loss
Palau‐Rodriguez, M., Tulipani, S., Marco‐Ramell, A., Miñarro, A., Jáuregui, O., Sánchez‐Pla, Á., Ramos‐Molina, B., Tinahones, F. J., & Andrés‐Lacueva, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198214 · Cited by 16
Metabolism of sleep and aging: Bridging the gap using metabolomics
Sengupta, A., & Weljie, A. M. (2018) · Nutrition and Healthy Aging · doi:10.3233/nha-180043 · 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
Liu, X., Zhang, M., Liu, X., Sun, H., Guo, Z., Tang, X., Wang, Z., Li, J., He, L., Zhang, W., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01507 · Cited by 15
Fat metabolism is associated with telomere length in six population-based studies
van der Spek, A., Karamujić‐Čomić, H., Pool, R., Bot, M., Beekman, M., Garmaeva, S., Arp, P., Henkelman, S., Liu, J., Alves, A. C., et al. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab281 · Cited by 16
Deciphering hallmark processes of aging from interaction networks
Smita, S., Lange, F., Wolkenhauer, O., & Köhling, R. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.07.017 · Cited by 15
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Lan, W., Xiao, X., Nian, J., Wang, Z., Zhang, X., Wu, Y., Zhang, D., Chen, J., Bao, W., Li, C., et al. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae221 · Cited by 15
Blood metabolic and physiological profiles of Bama miniature pigs at different growth stages
Mo, J., Lu, Y., Xing, T., Xu, D., Zhang, K., Zhang, S., Wang, Y., Yan, G., Lan, G., & Liang, J. (2022) · Porcine Health Management · doi:10.1186/s40813-022-00278-7 · Cited by 14
Petrick, L. M., Guan, H., Page, G. P., Dolios, G., Niedzwiecki, M. M., Wright, R. O., & Wright, R. J. (2024) · Environment International · doi:10.1016/j.envint.2024.108663 · Cited by 14
Metabolic changes associated with PFAS exposure in firefighters: A pilot study
Rotander, A., Gómez, M. J., Mueller, J. F., Toms, L.-M. L., & Hyötyläinen, T. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176004 · Cited by 13
Collino, S., Martin, F., Montoliu, I., Barger, J. L., Silva, L. D., Prolla, T. A., Weindruch, R. H., & Kochhar, S. (2013) · Metabolites · doi:10.3390/metabo3040881 · Cited by 14
Chronic exercise improves hepatic acylcarnitine handling
Hernández‐Saavedra, D., Hinkley, J. M., Baer, L. A., Pinckard, K. M., Vidal, P., Nirengi, S., Brennan, A. M., Chen, E. Y.-S., Narain, N. R., Bussberg, V., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109083 · Cited by 13
Differential metabolomics networks analysis of menopausal status
Cui, X., Yu, X., Sun, G., Hu, T., Likhodii, S. S., Zhang, J., Randell, E. W., Gao, X., Fan, Z., & Zhang, W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222353 · Cited by 13
Nelson, A. E., & Arbeeva, L. S. (2022) · The Journal of Rheumatology · doi:10.3899/jrheum.220326 · Cited by 15
Palau‐Rodriguez, M., Tulipani, S., Marco‐Ramell, A., Miñarro, A., Jáuregui, O., González‐Domínguez, R., Sánchez‐Pla, Á., Ramos‐Molina, B., Tinahones, F. J., & Andrés‐Lacueva, C. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00144 · Cited by 14
Ghafouri, B., Thordeman, K., Hadjikani, R., Nord, A. B., Gerdle, B. U. C., & Bäckryd, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-26405-6 · Cited by 13
Wu, S., Liu, X., Yang, H., Ma, W., & Qin, Z. (2023) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2023.08.2194 · Cited by 13
Velho, T. R., Pinto, F. R., Ferreira, R., Pereira, R. M., Duarte, A. M., Harada, M., Willmann, K. L., Pedroso, D. C. S., Paixão, T., Guerra, N. C., et al. (2025) · Intensive Care Medicine · doi:10.1007/s00134-024-07770-4 · Cited by 12
Self-citationFoerster, J., Hyötyläinen, T., Orešič, M., Nygrén, H., & Boeing, H. (2015) · Metabolism · doi:10.1016/j.metabol.2015.07.004 · Cited by 14
Plasma B Vitamers: Population Epidemiology and Parent-Child Concordance in Children and Adults
Andraos, S., Jones, B., Wall, C. R., Thorstensen, E. B., Kussmann, M., Cameron‐Smith, D., Lange, K., Clifford, S. A., Saffery, R., Burgner, D. P., et al. (2021) · Nutrients · doi:10.3390/nu13030821 · Cited by 12
Radford‐Smith, D. E., Selvaraj, E. A., Peters, R., Orrell, M., Bolon, J., Anthony, D. C., Pavlides, M., Lynch, K. D., Geremia, A., Bailey, A. A., et al. (2022) · Liver International · doi:10.1111/liv.15192 · Cited by 12
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
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