Abstract

BackgroundEicosapentaenoic acid (EPA) and α-lipoic acid (α-LA) have been investigated for their beneficial effects on obesity and cardiovascular risk factors. In the current research, the goal was to evaluate metabolomic changes following the dietary supplementation of these two lipids, alone or combined in healthy overweight/obese sedentary women following an energy-restricted diet. For this purpose, an untargeted metabolomics approach was conducted on urine samples using liquid chromatography coupled with time of flight mass spectrometry (HPLC-TOF-MS).MethodsThis is a short-term double blind placebo-controlled study with a parallel nutritional design that lasted 10 weeks. Participants were assigned to one of the 4 experimental groups [Control, EPA (1.3 g/d), α-LA (0.3 g/d) and EPA+α-LA (1.3 g/d + 0.3 g/d)]. All intervention groups followed an energy-restricted diet of 30% less than total energy expenditure. Clinically relevant biochemical measurements were analyzed. Urine samples (24 h) were collected at baseline and after 10 weeks. Untargeted metabolomic analysis on urine samples was carried out, and principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed for the pattern recognition and characteristic metabolites identification.ResultsUrine samples were scattered in the PCA scores plots in response to the supplementation with α-LA. Totally, 28 putative discriminant metabolites in positive ionization, and 6 in negative ionization were identified among groups clearly differentiated according to the α-LA administration. Remarkably is the presence of an ascorbate intermediate metabolite (one of the isomers of trihydroxy-dioxohexanoate, or dihydroxy–oxohexanedionate) in the groups supplemented with α-LA. This fact might be associated with antioxidant properties of both α-LA and ascorbic acid. Correlations between phenotypical parameters and putative metabolites of provided additional information on whether there is a direct or inverse relationship between them. Especially interesting are the negative correlation between ascorbate intermediate metabolite and asymmetric dimethylarginine (ADMA) and the positive one between superoxide dismutase (SOD) and α-LA supplementation.ConclusionsThis metabolomic approach supports that the beneficial effects of α-LA administration on body weight reduction may be partly explained by the antioxidant properties of this organosulfur carboxylic acid mediated by isomers of trihydroxy-dioxohexanoate, or dihydroxy–oxohexanedionate.Trial registrationClinicaltrials.gov NCT01138774.

Highlights

  • Eicosapentaenoic acid (EPA) and α-lipoic acid (α-LA) have been investigated for their beneficial effects on obesity and cardiovascular risk factors

  • Targeted metabolomic is a powerful tool to quantify of known metabolites with similar chemical structures [2], while untargeted metabolomic involves the use of nuclear magnetic resonance (NMR) or mass spectrometry (MS) for the simultaneous measurement of as many known and unknown metabolites as possible in a biological specimen [3]

  • Urinary metabolomic profile HPLC-TOF-MS method allowed the detection of 4.752 features in the electrospray ionization (ESI)+ mode and 4.713 features in the ESI- mode

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Summary

Introduction

Eicosapentaenoic acid (EPA) and α-lipoic acid (α-LA) have been investigated for their beneficial effects on obesity and cardiovascular risk factors. Targeted metabolomic is a powerful tool to quantify of known metabolites with similar chemical structures (e.g. amino acids, acylcarnitines, organic acids, etc.) [2], while untargeted metabolomic involves the use of nuclear magnetic resonance (NMR) or mass spectrometry (MS) for the simultaneous measurement of as many known and unknown metabolites as possible in a biological specimen [3]. This latter approach is generally used to compare biological samples or clinical states and to report on differences between these situations in order to assess the complete set of metabolites – metabolome [4]. Preliminary results on metabolomic biomarkers related to the different dietary patterns could help to better understand the inter-individual differences in cardiovascular risk and nutritional responses for further applications in precision nutrition [7]

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