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

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.

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

“…Targeted analyses contributed 57 a priori known metabolites, whereas from the non-targeted analyses approximately 20% of analytes could be identified. A full descriptive table of the metabolites that were identified and quantified is shown in Rist et al [19].…”

“…This section provides a short overview of the different analytical methods used. Details are available in the supplement of Rist et al [19] Non-targeted GC × GC-MS analysis of plasma and urine samples All 24-h urine and fasting plasma samples were analyzed by non-targeted GC × GC-MS using a Shimadzu GCMS QP2010 Ultra instrument equipped with a ZOEX ZX2 modulator according to the method established by Weinert et al [29]. With this method a wide range of metabolites can be detected, such as amines, amino acids, organic acids, sugars, sugar alcohols, other polyols etc.…”

“…All plasma and urine samples were analyzed by 1D-1 H-NMR spectroscopy as described in Rist et al [19,34]. Typically, metabolites that can be detected include organic acids, amino acids, amines, sugars, sugar alcohols, and others.…”

“…Metabolomics (blood, urine) can accurately distinguish age and sex differences in humans [12][13][14][15][16][17][18][19], but can only partially explain the components of body composition [20][21][22][23] such as LBM, muscle and fat mass. It remains unclear whether the discrimination of LBM is nothing more than the discrimination of sex, because most of the metabolites for sex seem to be identical to the metabolites discriminating LBM [24].…”

“…More than 1000 analytes from a wide range of chemical classes were obtained through the combination of non-targeted GC × GC-MS, different targeted GC-MS and LC-MS methods, as well as 1 H-NMR. In this cohort, we recently identified distinct metabolite patterns associated with age and sex [19]. Since sex has an impact on LBM, which is the major determinant of REE, we particularly considered these factors during data evaluation.…”