Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study
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Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18-80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms. Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy. The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.
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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 [113]
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 modest.
“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 [113]
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- Authors
- Rist, M. J., Roth, A., Frommherz, L., Weinert, C. H., Krüger, R., Merz, B., Bunzel, D., Mack, C., Egert, B., Bub, A., Görling, B., Tzvetkova, P., Luy, B., Hoffmann, I., Kulling, S. E., & Watzl, B.
- Affiliations
- Max Rubner Institut (DE)
- Karlsruhe Institute of Technology (DE)
- Corresponding author
- Rist, Manuela J.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-08-16
- Volume
- 12
- Issue
- 8
- Pages
- e0183228
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5558977
- OpenAlex
- W2749646273
- Cited by
- 202
- References
- 69
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 22 · 2023: 25 · 2022: 25 · 2021: 26 · 2020: 31 · 2019: 24 · 2018: 14 · 2017: 2
- FWCI
- 7.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- Repository KITopen (Karlsruhe Institute of Technology) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- OpenAgrar (submittedVersion, cc-by)
- KITopen (cc-by)
- Funders
- German Federal Ministry of Food and Agriculture (BMEL)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Keywords
- sex prediction, age prediction, plasma metabolites, urinary metabolites, sex differences, menopause, creatinine, branched-chain amino acids, sarcosine, choline, sedoheptulose, metabolic signatures, healthy adults
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Choline, Chromatography, Liquid, Cross-Sectional Studies, Female, Heptoses, Humans, Male, Gas Chromatography-Mass Spectrometry, Middle Aged, Magnetic Resonance Spectroscopy, Sex Factors, Metabolomics, Metabolome, Young Adult
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 ›
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