6533b825fe1ef96bd12827e6

RESEARCH PRODUCT

Plasma Metabolomics Profiles are Associated with the Amount and Source of Protein Intake: A Metabolomics Approach within the PREDIMED Study.

subject

description

SCOPE: The plasma metabolomics profiles of protein intake has been rarely investigated. We aimed to identify the distinct plasma metabolomics profiles associated with overall intakes of protein as well as with intakes from animal and plant protein sources. METHODS AND RESULTS: Cross-sectional analysis using data from 1,833 participants at high risk of cardiovascular disease. Plasma metabolomics analysis was performed using LC-MS. Associations between 385 identified metabolites and the intake of total, animal protein (AP) and plant protein (PP), and plant-to-animal ratio (PR) were assessed using elastic net continuous regression analyses. A double 10-cross-validation (CV) procedure was used and Pearson correlations coefficients between multi-metabolite weighted models and reported protein intake in each pair of training-validation datasets were calculated. A wide set of metabolites was consistently associated with each protein source evaluated. These metabolites mainly consisted of amino acids and their derivatives, acylcarnitines, different organic acids and lipid species. Few metabolites overlapped among protein sources (i.e. C14:0 SM, C20:4 carnitine, GABA and allantoin) but none of them towards the same direction. Regarding AP and PP approaches, C20:4 carnitine and dimethylglycine were positively associated with PP but negatively associated with AP. However, allantoin, C14:0 SM, C38:7 PE plasmalogen, GABA, metronidazole and trigonelline (N-methylnicotinate) behaved contrary. Ten-CV Pearson correlations coefficients between self-reported protein intake and plasma metabolomics profiles ranged from 0.21 for PR to 0.32 for total protein. CONCLUSIONS: Different sets of metabolites were associated with total, animal and plant protein intake. Further studies are needed to assess the contribution of these metabolites in protein biomarkers’ discovery and prediction of cardiometabolic alterations.