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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.

Cardiorespiratory fitness (CRF) represents a strong predictor of all-cause mortality and is strongly influenced by regular physical activity (PA). However, the biological mechanisms involved in the body's adaptation to PA remain to be fully elucidated. The aim of this study was to systematically examine the relationship between CRF and plasma metabolite patterns in 252 healthy adults from the cross-sectional Karlsruhe Metabolomics and Nutrition (KarMeN) study. CRF was determined by measuring the peak oxygen uptake during incremental exercise. Fasting plasma samples were analyzed by nuclear magnetic resonance spectroscopy and mass spectrometry coupled to one- or two-dimensional gas chromatography or liquid chromatography. Based on this multi-platform metabolomics approach, 427 plasma analytes were detected. Bi- and multivariate association analyses, adjusted for age and menopausal status, showed that CRF was linked to specific sets of metabolites primarily indicative of lipid metabolism. However, CRF-related metabolite patterns largely differed between sexes. While several phosphatidylcholines were linked to CRF in females, single lyso-phosphatidylcholines and sphingomyelins were associated with CRF in males. When controlling for further assessed clinical and phenotypical parameters, sex-specific CRF tended to be correlated with a smaller number of metabolites linked to lipid, amino acid, or xenobiotics-related metabolism. Interestingly, sex-specific CRF explanation models could be improved when including selected plasma analytes in addition to clinical and phenotypical variables. In summary, this study revealed sex-related differences in CRF-associated plasma metabolite patterns and proved known associations between CRF and risk factors for cardiometabolic diseases such as fat mass, visceral adipose tissue mass, or blood triglycerides in metabolically healthy individuals. Our findings indicate that covariates like sex and, especially, body composition have to be considered when studying blood metabolic markers related to CRF. [ABSTRACT FROM AUTHOR]

Health has been defined as the capability of the organism to adapt to challenges. In this study, we tested to what extent comprehensively phenotyped individuals reveal differences in metabolic responses to a standardized mixed meal tolerance test (MMTT) and how these responses change when individuals experience moderate weight loss. Metabolome analysis was used in 70 healthy individuals. with profiling of ~300 plasma metabolites during an MMTT over 8 h. Multivariate analysis of plasma markers of fatty acid catabolism identified 2 distinct metabotype clusters (A and B). Individuals from metabotype B showed slower glucose clearance, had increased intra-abdominal adipose tissue mass and higher hepatic lipid levels when compared with individuals from metabotype A. An NMR-based urine analysis revealed that these individuals also to have a less healthy dietary pattern. After a weight loss of ~5.6 kg over 12 wk, only the subjects from metabotype B showed positive changes in the glycemic response during the MMTT and in markers of metabolic diseases. Our study in healthy individuals demonstrates that more comprehensive phenotyping can reveal discrete metabotypes with different outcomes in a dietary intervention and that markers of lipid catabolism in plasma could allow early detection of the metabolic syndrome. [ABSTRACT FROM AUTHOR]