Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study Article Swipe
Manuela J. Rist
,
Alexander Röth
,
Lara Frommherz
,
Christoph H. Weinert
,
Ralf Krüger
,
Benedikt Merz
,
Diana Bunzel
,
Carina Mack
,
Björn Egert
,
Achim Bub
,
Benjamin Görling
,
Pavleta Tzvetkova
,
Burkhard Luy
,
Ingrid Hoffmann
,
Sabine E. Kulling
,
Bernhard Watzl
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0183228
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0183228
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. n 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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0183228
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183228&type=printable
- OA Status
- gold
- Cited By
- 202
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2749646273
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- OpenAlex ID
-
https://openalex.org/W2749646273Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0183228Digital Object Identifier
- Title
-
Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-08-16Full publication date if available
- Authors
-
Manuela J. Rist, Alexander Röth, Lara Frommherz, Christoph H. Weinert, Ralf Krüger, Benedikt Merz, Diana Bunzel, Carina Mack, Björn Egert, Achim Bub, Benjamin Görling, Pavleta Tzvetkova, Burkhard Luy, Ingrid Hoffmann, Sabine E. Kulling, Bernhard WatzlList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0183228Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183228&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183228&type=printableDirect OA link when available
- Concepts
-
Metabolomics, Metabolome, Metabolite, Urine, Analyte, Sarcosine, Chemistry, Physiology, Bioinformatics, Biology, Medicine, Internal medicine, Chromatography, Biochemistry, Amino acid, GlycineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
202Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 25, 2023: 29, 2022: 24, 2021: 28Per-year citation counts (last 5 years)
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70Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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