Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism

Abstract

Background: Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.

Methods: Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.

Results: There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.

Conclusions: These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.

Keywords: crossover; dietary intervention; dietary patterns; glycemic load; inflammation; insulin resistance; metabolomics; whole grains.

Copyright © American Society for Nutrition 2019.

Figures

FIGURE 1

Metabolic pathway analysis comparing a low-glycemic whole-grain dietary pattern to a diet high in refined grains and added sugars conducted using CAMERA. The x-axis shows the pathway size and the y-axis shows the negative logarithm (in base 10) of FDR-adjusted P-values from pathway enrichment analysis. In addition to a KEGG pathway (hsa00380) 2 data-driven metabolic pathways (NET06 and COR10) were enriched at <10% FDR. Metabolites in enriched pathways are shown in Figure 2. CAMERA, correlation-adjusted mean rank gene set analysis; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes.

FIGURE 2

Metabolites in enriched pathways (at 10% FDR). The color of each node corresponds to the test statistic from univariate analysis, as depicted in the color bar. In total, 4 metabolites are included in more than 1 enriched pathway. FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes.