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

• Published in: European journal of nutrition 2019-09, Vol.58 (6), p.2207-2217
• Main Authors: Armbruster, M., Rist, M., Seifert, S., Frommherz, L., Weinert, C., Mack, C., Roth, A., Merz, B., Bunzel, D., Krüger, R., Kulling, S., Watzl, B., Bub, A.
• Format: Article
• Language: English
• Subjects: Absorptiometry; Absorptiometry, Photon; Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Artificial intelligence; Basal Metabolism - physiology; Body Composition - physiology; Body mass; Calorimetry; Chemistry; Chemistry and Materials Science; Cross-Sectional Studies; Data processing; Dual energy X-ray absorptiometry; Energy expenditure; Female; Healthy Volunteers; Humans; Lean body mass; Learning algorithms; Male; Metabolites; Metabolome - physiology; Metabolomics; Middle Aged; NMR; Nuclear magnetic resonance; Nutrition; Obesity; Original Contribution; Reference Values; Sex differences; Sex Factors; Urine; Women; Young Adult
• ISSN: 1436-6207; 1436-6215
• DOI: 10.1007/s00394-018-1767-1

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 cross-sectional 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.