It is unclear how vegetarian dietary patterns influence plasma metabolites involved in biological processes regulating chronic diseases. We sought to identify plasma metabolic profiles distinguishing vegans (avoiding meat, eggs, dairy) from non-vegetarians (consuming ≥28 g/day red meat) of the Adventist Health Study-2 cohort using global metabolomics profiling with ultra-performance liquid chromatography mass spectrometry (UPLC-MS/MS). Differences in abundance of metabolites or biochemical subclasses were analyzed using linear regression models, adjusting for surrogate and confounding variables, with cross-validation to simulate results from an independent sample. Random forest was used as a learning tool for classification, and principal component analysis was used to identify clusters of related metabolites. Differences in covariate-adjusted metabolite abundance were identified in over 60% of metabolites (586/930), after adjustment for false discovery. The vast majority of differentially abundant metabolites or metabolite subclasses showed lower abundance in vegans, including xanthine, histidine, branched fatty acids, acetylated peptides, ceramides, and long-chain acylcarnitines, among others. Many of these metabolite subclasses have roles in insulin dysregulation, cardiometabolic phenotypes, and inflammation. Analysis of metabolic profiles in vegans and non-vegetarians revealed vast differences in these two dietary groups, reflecting differences in consumption of animal and plant products. These metabolites serve as biomarkers of food intake, many with potential pathophysiological consequences for cardiometabolic diseases.
Keywords: biomarkers; cohort; dietary pattern; false discovery; linear regression; metabolomics; vegetarian.