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

Abstract

Purpose Differences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects. Methods We investigated 301 healthy male and female subjects (18-80 years) under standardized conditions in the crosssectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC-MS, GC-MS, LC-MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS. Results When evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately. Conclusions We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.

An NMR-Based Approach to Identify Urinary Metabolites Associated with Acute Physical Exercise and Cardiorespiratory Fitness in Healthy Humans—Results of the KarMeN Study

“…The second aim was to investigate if either the urinary metabolites at rest, post-exercise, or the ratio of the post-to pre-exercise metabolite concentrations are associated with the CRF, which was determined by measuring the VO 2peak . As the metabolomics data were obtained from a comparatively large and heterogeneous population, this study was particularly suitable to analyze in how far urinary metabolite pattern can account for the variation in the VO 2peak , when simultaneously considering covariates like age, sex, menopausal status, and LBM-all factors determining both the physical fitness [7,36] and the human metabolome [37][38][39][40][41]. A targeted nuclear magnetic resonance (NMR)-based approach was applied to quantify the pre-and post-exercise urinary levels of 47 metabolites.…”

Glyphosate and AMPA levels in human urine samples and their correlation with food consumption: results of the cross-sectional KarMeN study in Germany

“…The KarMeN study was conducted at the Max Rubner-Institut in Karlsruhe, Germany. The main aim was to characterize the blood plasma and urine metabolome of healthy women and men (range 18-80 years) by targeted and non-targeted metabolite profiling, and to assess the influence of sex, age, body composition, diet, and physical activity on the metabolite profiles of the participants (Armbruster et al 2018;Biniaminov et al 2018;Bub et al 2016;Krüger et al 2017;Mack et al 2018;Merz et al 2018;Rist et al 2017).…”

Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach

“…These models can be applied to build predictive equations aiming to capture the non-linearity between variables, resulting in better accuracy. These new models have also been applied to better predict resting energy expenditure (REE) [ 16 ].…”