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

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.