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.
“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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Self-citation
- 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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