Metabolomic Changes Upon Conjugated Linoleic Acid Supplementation and Predictions of Body Composition Responsiveness

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Abstract

Context

Conjugated linoleic acid (CLA) may optimize body composition, yet mechanisms underlining its benefits are not clear in humans.

Objective

We aimed to reveal the CLA-induced changes in the plasma metabolome associated with body composition improvement and the predictive performance of baseline metabolome on intervention responsiveness.

Methods

Plasma metabolome from overnight fasted samples at pre- and post-intervention of 65 participants in a 12-week randomized, placebo-controlled trial (3.2 g/day CLA vs 3.2 g/day sunflower oil) were analyzed using untargeted LC-MS metabolomics. Mixed linear model and machine learning were applied to assess differential metabolites between treatments, and to identify optimal panel (based on baseline conventional variables vs metabolites) predicting responders of CLA-derived body composition improvement (increased muscle variables or decreased adiposity variables) based on dual-energy x-ray absorptiometry.

Results

Compared with placebo, CLA altered 57 metabolites (P < 0.10) enriched in lipids/lipid-like molecules including glycerophospholipids (n = 7), fatty acyls (n = 6), and sphingolipids (n = 3). CLA-upregulated cholic acid (or downregulated aminopyrrolnitrin) was inversely correlated with changes in muscle and adiposity variables. Inter-individual variability in response to CLA-derived body composition change. The areas under the curves of optimal metabolite panels were higher than those of optimal conventional panels in predicting favorable response of waist circumference (0.93 [0.82-1.00] vs 0.64 [0.43-0.85]), visceral adiposity index (0.95 [0.88-1.00] vs 0.58 [0.35-0.80]), total fat mass (0.94 [0.86-1.00] vs 0.69 [0.51-0.88]) and appendicular fat mass (0.97 [0.92-1.00] vs 0.73 [0.55-0.91]) upon CLA supplementation (all FDR P < 0.05).

Conclusion

Post-intervention metabolite alterations were identified, involving in lipid/energy metabolism, associated with body composition changes. Baseline metabolite profiling enhanced the prediction accuracy for responsiveness of CLA-induced body composition benefits.

Related collections

Most cited references 39

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin, Natacha Turck, Alexandre Hainard … (2013)

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Health Effects of Overweight and Obesity in 195 Countries over 25 Years.

Ashkan Afshin, Mohammad Forouzanfar, Marissa Reitsma … (2018)

Background While the rising pandemic of obesity has received significant attention in many countries, the effect of this attention on trends and the disease burden of obesity remains uncertain. Methods We analyzed data from 67.8 million individuals to assess the trends in obesity and overweight prevalence among children and adults between 1980 and 2015. Using the Global Burden of Disease study data and methods, we also quantified the burden of disease related to high body mass index (BMI), by age, sex, cause, and BMI level in 195 countries between 1990 and 2015. Results In 2015, obesity affected 107.7 million (98.7-118.4) children and 603.7 million (588.2- 619.8) adults worldwide. Obesity prevalence has doubled since 1980 in more than 70 countries and continuously increased in most other countries. Although the prevalence of obesity among children has been lower than adults, the rate of increase in childhood obesity in many countries was greater than the rate of increase in adult obesity. High BMI accounted for 4.0 million (2.7- 5.3) deaths globally, nearly 40% of which occurred among non-obese. More than two-thirds of deaths related to high BMI were due to cardiovascular disease. The disease burden of high BMI has increased since 1990; however, the rate of this increase has been attenuated due to decreases in underlying cardiovascular disease death rates. Conclusions The rapid increase in prevalence and disease burden of elevated BMI highlights the need for continued focus on surveillance of BMI and identification, implementation, and evaluation of evidence-based interventions to address this problem.

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MissForest--non-parametric missing value imputation for mixed-type data.

Daniel J Stekhoven, Peter Bühlmann (2012)

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch

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Author and article information

Contributors

Xin Liu: (View ORCID Profile)

Lin Shi: (View ORCID Profile)

Journal

Title: The Journal of Clinical Endocrinology & Metabolism

Publisher: The Endocrine Society

ISSN (Print): 0021-972X

ISSN (Electronic): 1945-7197

Publication date Created: September 01 2022

Publication date Created: August 18 2022

Publication date Created: June 15 2022

Publication date Other: September 01 2022

Publication date (Print): August 18 2022

Publication date (Electronic): June 15 2022

Volume: 107

Issue: 9

Pages: 2606-2615

Article

DOI: 10.1210/clinem/dgac367

SO-VID: 061c17ae-5103-4bab-987c-efb686b44a49

License:

https://academic.oup.com/pages/standard-publication-reuse-rights

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