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Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects
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 urin...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689277/ https://www.ncbi.nlm.nih.gov/pubmed/29974226 http://dx.doi.org/10.1007/s00394-018-1767-1 |
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author | 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. |
author_facet | 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. |
author_sort | Armbruster, M. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00394-018-1767-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6689277 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-66892772019-08-23 Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects 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. Eur J Nutr Original Contribution 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00394-018-1767-1) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2018-07-04 2019 /pmc/articles/PMC6689277/ /pubmed/29974226 http://dx.doi.org/10.1007/s00394-018-1767-1 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Contribution 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. Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
title | Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
title_full | Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
title_fullStr | Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
title_full_unstemmed | Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
title_short | Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
title_sort | metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects |
topic | Original Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689277/ https://www.ncbi.nlm.nih.gov/pubmed/29974226 http://dx.doi.org/10.1007/s00394-018-1767-1 |
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