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Cited by 203Metabolomics and Mass Spectrometry StudiesHealth, Environment, Cognitive AgingAdvanced Chemical Sensor Technologies

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Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

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Rist, Manuela J., Roth, Alexander, Frommherz, Lara, Weinert, Christoph H., Krüger, Ralf, Merz, Benedikt, Bunzel, Diana, Mack, Carina, Egert, Björn, Bub, Achim, Görling, Benjamin, Tzvetkova, Pavleta, Luy, Burkhard, Hoffmann, Ingrid, Kulling, Sabine E., Watzl, Bernhard·2017·PLOS ONE
DOI 10.1371/journal.pone.0183228PMC PMC5558977
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Abstract

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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Crossref · 10.1371/journal.pone.0183228

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Fig 1 Metabolite patterns for the prediction of sex. Top 25 metabolites important for the correct prediction of sex of the KarMeN study participants in all algorithms applied on plasma (A) and 24 h urine (B) metabolite profiles. Positive and negative weights favor female and male class, respectively. Patterns are shown for linear SVM (blue bars) and glmnet (red bars) only, since PLS only yields positive values. Metabolites are sorted according to “mean rank” of all three algorithms. Analytical methods from which metabolites stem are denoted in parentheses, with CB, clinical biochemistry; GC, GC-MS; GC×GC, GC×GC-MS; LC, LC-MS; NMR, nuclear magnetic resonance. * Tentatively identified using the NIST2011 library solely based on mass spectral similarity. ** Identified using the FIEHN library based on mass spectral similarity and retention index. *** Signal possibly includes other metabolites. Abbreviations: U, unknown; 3-OH-3-MBA, 3-hydroxy-3-methylbutyric acid; 4-DTA, 4-deoxythreonic acid; α-KGA, α-ketoglutaric acid; 2-HPAA, 2-hydroxyphenylacetic acid.
Fig 1 Metabolite patterns for the prediction of sex. Top 25 metabolites important for the correct prediction of sex of the KarMeN study participants in all algorithms applied on plasma (A) and 24 h urine (B) metabolite profiles. Positive and negative weights favor female and male class, respectively. Patterns are shown for linear SVM (blue bars) and glmnet (red bars) only, since PLS only yields positive values. Metabolites are sorted according to “mean rank” of all three algorithms. Analytical methods from which metabolites stem are denoted in parentheses, with CB, clinical biochemistry; GC, GC-MS; GC×GC, GC×GC-MS; LC, LC-MS; NMR, nuclear magnetic resonance. * Tentatively identified using the NIST2011 library solely based on mass spectral similarity. ** Identified using the FIEHN library based on mass spectral similarity and retention index. *** Signal possibly includes other metabolites. Abbreviations: U, unknown; 3-OH-3-MBA, 3-hydroxy-3-methylbutyric acid; 4-DTA, 4-deoxythreonic acid; α-KGA, α-ketoglutaric acid; 2-HPAA, 2-hydroxyphenylacetic acid.CC BY · PubMed Central
Fig 2 Metabolite patterns for the prediction of age in men. Top 25 metabolites important for the correct prediction of age of the male KarMeN study participants in all algorithms applied on plasma (A) and 24 h urine (B) metabolite profiles. Positive and negative weights favor older and younger age, respectively. Patterns are shown for linear SVM (blue bars) and glmnet (red bars) only, since PLS only yields positive values. Metabolites are sorted according to “mean rank” of all three algorithms. Analytical methods from which metabolites stem are denoted in parentheses, with CB, clinical biochemistry; GC, GC-MS; GC×GC, GC×GC-MS; LC, LC-MS; NMR, nuclear magnetic resonance. * Tentatively identified using the NIST2011 library solely based on mass spectral similarity. ** Identified using the FIEHN library based on mass spectral similarity and retention index. *** Signal possibly includes other metabolites. Abbreviations: U, unknown; PO4, phosphate; Met, L-methionine; 1,5-AS, 1,5-anhydro-D-sorbitol; Phe, phenylalanine; 4-HMA, 4-hydroxymandelic acid; N-AAA, N-acetylaspartic acid; 2,5-FDCA, 2,5-furandicarboxylic acid; ism, isomer; 3-AIBA, 3-aminoisobutyric acid.
Fig 2 Metabolite patterns for the prediction of age in men. Top 25 metabolites important for the correct prediction of age of the male KarMeN study participants in all algorithms applied on plasma (A) and 24 h urine (B) metabolite profiles. Positive and negative weights favor older and younger age, respectively. Patterns are shown for linear SVM (blue bars) and glmnet (red bars) only, since PLS only yields positive values. Metabolites are sorted according to “mean rank” of all three algorithms. Analytical methods from which metabolites stem are denoted in parentheses, with CB, clinical biochemistry; GC, GC-MS; GC×GC, GC×GC-MS; LC, LC-MS; NMR, nuclear magnetic resonance. * Tentatively identified using the NIST2011 library solely based on mass spectral similarity. ** Identified using the FIEHN library based on mass spectral similarity and retention index. *** Signal possibly includes other metabolites. Abbreviations: U, unknown; PO4, phosphate; Met, L-methionine; 1,5-AS, 1,5-anhydro-D-sorbitol; Phe, phenylalanine; 4-HMA, 4-hydroxymandelic acid; N-AAA, N-acetylaspartic acid; 2,5-FDCA, 2,5-furandicarboxylic acid; ism, isomer; 3-AIBA, 3-aminoisobutyric acid.CC BY · PubMed Central

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Metadata

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)
DOI
10.1371/journal.pone.0183228
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
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

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