Metabolomics and nutrient intake reveal metabolite-nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study.

Abstract

Background: Despite advances in metabolomics, the complex relationship between metabolites and nutrient intake in metabolic syndrome (MetS) remains poorly understood in the Korean population.

Objective: This study aimed to characterize the metabolomic profiles and nutrient intake associated with MetS and to examine their relationships in the Ansan-Ansung cohort of the Korean Genome and Epidemiology Study (KoGES).

Methods: Data from 2,306 middle-aged adults (1,109 men and 1,197 women) in the KoGES Ansan-Ansung cohort were analyzed. Plasma metabolites were measured using liquid chromatography-mass spectrometry, identifying 135 metabolites. Nutrient intake was assessed using a validated semi-quantitative food frequency questionnaire covering 23 nutrients. MetS-associated metabolites and nutrients were identified using the Wilcoxon rank-sum test, logistic regression, partial least squares-discriminant analysis, and group least absolute shrinkage and selection operator analysis. Pathway enrichment analysis identified key metabolic pathways, and fixed-effects models were applied to assess metabolite-nutrient relationships based on MetS status.

Results: Eleven metabolites, including hexose (FC = 0.95, P = 7.04 × 10^-54), alanine, and branched-chain amino acids, and three nutrients including fat, retinol, and cholesterol, were significantly associated with MetS (FC range = 0.87-0.93; all P < 0.05). Pathway analysis highlighted disruptions in arginine biosynthesis and arginine-proline metabolism. The MetS group exhibited six unique metabolite-nutrient pairs that were not observed in the non-MetS group, including 'isoleucine-fat,' 'isoleucine-P,' 'proline-fat,' 'leucine-fat,' 'leucine-P,' and 'valerylcarnitine-niacin.' Notably, dysregulated metabolism of branched-chain amino acids, such as isoleucine and leucine, has been implicated in oxidative stress. Importantly, the stochastic gradient descent classifier achieved the best predictive performance among the eight machine learning models (area under the curve, AUC = 0.84), highlighting the robustness of classification based on metabolite data. However, the absence of external validation limits the generalizability of these findings.

Conclusions: This comprehensive metabolomic analysis of the KoGES Ansan-Ansung cohort revealed distinct metabolic profiles and nutrient intake patterns associated with MetS, highlighting altered metabolite-nutrient relationships and disrupted metabolic pathways. These findings provide new insights into potential associations between metabolic phenotypes and dietary intake, which may help inform individualized dietary approaches related to MetS, such as branched-chain amino acids-restricted diets (valine, isoleucine, leucine), reduced intake of hexose-rich carbohydrates, and modulation of niacin-rich protein sources according to individual metabolic profiles.