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

Manuela J Rist, Alexander Roth, 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 and Bernhard Watzl

PLOS ONE, 2017, vol. 12, issue 8, 1-21

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.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183228 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 83228&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0183228

DOI: 10.1371/journal.pone.0183228

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().