Dataset Information

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 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.

SUBMITTER: Armbruster M

PROVIDER: S-EPMC6689277 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Armbruster M M Rist M M Seifert S S Frommherz L L Weinert C C Mack C C Roth A A Merz B B Bunzel D D Krüger R R Kulling S S Watzl B B Bub A A

European journal of nutrition 20180704 6

<h4>Purpose</h4>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.<h4>Methods</h4>We investigated 301 healthy male and female subjects (18-80 years) under standardized conditions i ...[more]

PMID: 29974226

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Project description:Background and Aims:The unique demographic and dietary characteristics of modern Arabic population require development of a new predictive equation for the estimation of resting energy expenditure (REE). This study presented new equations characteristic to Saudi population. Methods:A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18-57?±?14 years). REE was measured (REEm) by indirect calorimetry (IC) and predicted (REEp) using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new set of equations to predict REE of men and women was developed. Accuracy of the new main equations was further tested in an external sample of 48 subjects (men?=?50%). Results:Using a number of parameters (bias, underprediction, overprediction, and % accurate prediction), our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5% and an accurate prediction (?55%) at the individual level. Based on these findings, a new prediction equation (hereafter referred to as the Almajwal-Abulmeaty (AA) equation) was developed using this study's data, after a rigorous stepwise regression analysis using the following formula: REE?=?3832.955?+?AdjWt (kg)?×?48.037?-?Ht (cm)?×?30.642?+?gender?×?141.268?-?age (years)?×?4.525 [AdjWt is Adjusted body weight?=?(Wt?-?IBW)/4?+?IBW. IBW is Ideal body weight; for men IBW?=?(Ht(cm)?-?152.4)?×?1.0714)?+?45.36 and for women IBW?=?(Ht(cm)-152.4)?×?0.8928)?+?45.36]. The regression model accounted for approximately 70% of the variance in REEm (R 2?=?0.702). Conclusion:Previous equations likely over- or underpredicted REE. Therefore, the new predictive AA equations developed in this study are recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass indexes. Future research is also required for further clinical and cross-validation of these new equations.

Project description:Background/objectivesThe aim of this study was to compare resting energy expenditure (REE) measured (MREE) by indirect calorimetry (IC) and REE predicted (PREE) from established predictive equations in a large sample of obese Caucasian adults.Subjects/methodsWe evaluated 1851 obese patients (body mass index (BMI)>30 kg m-2) aged between 18a and 65 years. Data were obtained by comparing MREE with PREE, derived from different equations, within and between normal weight and obese groups. The mean differences between PREE and MREE as well as the accuracy prediction within ±10% level were investigated in the whole sample and in three subgroups, classified by BMI (Group 1=30-39.9 kg m-2; Group 2=40-49.9 kg m-2; Group 3>50 kg m-2).ResultsWe observed that FAO, Henry and Muller3 (body composition (BC)) equations provided good mean PREE-MREE (bias -0.7, -0.3 and 0.9%; root mean standard error (RMSE) 273, 263 and 269 kcal per day, respectively); HB and Henry equations were more accurate individually (57 and 56.9%). Only the Muller1 (BC) equation gave the lowest PREE-MREE difference (bias -1.7%; RMSE 228 kcal per day) in females, while Johnstone and De Lorenzo equations were the most accurate (55.1 and 54.8%). When the sample was split into three subgroups according to BMI, no differences were found in males; however, the majority of the equations included in this study failed to estimate REE in severely obese females (BMI>40 kg m-2). Overall, prediction accuracy was low (~55%) for all predictive equations, regardless of BMI.ConclusionsDifferent established equations can be used for estimating REE at the population level in both sexes. However, the accuracy was very low for all predictive equations used, particularly among females and when BMI was high, limiting their use in clinical practice. Our findings suggest that the validation of new predictive equations would improve the accuracy of REE prediction, especially for severely obese subjects (BMI>40 kg m-2).

Project description:The aim of this study was to investigate the effect of galacto-oligosaccharides, lactulose, apple fiber and sugar beet pectin on the composition and activity of human colonic microbiota of lean and obese healthy subjects using an in vitro model of the proximal colon: TIM-2. Substrate fermentation was assessed by measuring the production of short-chain and branched-chain fatty acids, lactate and ammonia and by studying the composition of the bacterial communities over time. The results suggest that energy harvest (in terms of metabolites) of lean and obese microbiotas is different and may depend on the fermentable substrate. For galacto-oligosaccharides and lactulose, the cumulative amount of short-chain fatty acids plus lactate produced in TIM-2 was lower in the fermentation experiments with the lean microbiota (123 and 155 mmol, respectively) compared to the obese (162 and 173 mmol, respectively). This was reversed for the pectin and the fiber. The absolute amount produced of short-chain fatty acids including lactate was higher after 72 h in the fermentation experiments with apple fiber-L (108 mmol) than with apple fiber-O (92 mmol). Sugar beet-L was also higher (130 mmol) compared to sugar beet-O (103 mmol). Galacto-oligosaccharides and lactulose boosted the balance of health-promoting over toxic metabolites produced by the microbiota from obese subjects. Firmicutes were more predominant in the inoculum prepared from feces of obese subjects compared to lean subjects. The average abundance at time zero was 92% and 74%, respectively. On the other hand, Bacteroidetes were more dominant in the microbiota prepared with homogenates from lean subjects with an average abundance of 22% compared with the microbiota prepared with homogenates from obese subjects (3.6%). This study brings evidence that different fermentable carbohydrates are fermented differently by lean and obese microbiotas, which contributes to the understanding of the role of diet and the microbiota in tackling obesity.

Dataset Information

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

Purpose

Methods

Results

Conclusions

Publications

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

Similar Datasets

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