Dietary choline and l-carnitine are biotransformed by the fecal microbiota into TMA, the intestinal precursor of TMAO, and its formation could be influenced by (poly)phenol-rich foods.

Cranberries are a rich source of poly(phenols), mainly monomeric and oligomeric flavan-3-ols. The metabolism of their main colonic compounds has been assessed.

Purpose Extensive inter-individual variability exists in the production of flavan-3-ol metabolites. Preliminary metabolic phenotypes (metabotypes) have been defined, but there is no consensus on the existence of metabotypes associated with the catabolism of catechins and proanthocyanidins. This study aims at elucidating the presence of different metabotypes in the urinary excretion of main flavan-3-ol colonic metabolites after consumption of cranberry products and at assessing the impact of the statistical technique used for metabotyping. Methods Data on urinary concentrations of phenyl-γ-valerolactones and 3-(hydroxyphenyl)propanoic acid derivatives from two human interventions has been used. Different multivariate statistics, principal component analysis (PCA), cluster analysis, and partial least square-discriminant analysis (PLS-DA), have been considered. Results Data pre-treatment plays a major role on resulting PCA models. Cluster analysis based on k-means and a final consensus algorithm lead to quantitative-based models, while the expectation–maximization algorithm and clustering according to principal component scores yield metabotypes characterized by quali-quantitative differences in the excretion of colonic metabolites. PLS-DA, together with univariate analyses, has served to validate the urinary metabotypes in the production of flavan-3-ol metabolites and to confirm the robustness of the methodological approach. Conclusions This work proposes a methodological workflow for metabotype definition and highlights the importance of data pre-treatment and clustering methods on the final outcomes for a given dataset. It represents an additional step toward the understanding of the inter-individual variability in flavan-3-ol metabolism. Trial registration The acute study was registered at clinicaltrials.gov as NCT02517775, August 7, 2015; the chronic study was registered at clinicaltrials.gov as NCT02764749, May 6, 2016.

Legumes are a well-known source of phytochemicals and are commonly believed to have similar composition between different genera. To date, there are no studies evaluating changes in legumes to discover those compounds that help to discriminate for food quality and authenticity. The aim of this work was to characterize and make a comparative analysis of the composition of bioactive compounds between Cicer arietinum L. (chickpea), Lens culinaris L. (lentil) and Phaseolus vulgaris L. (white bean) through an LC-MS-Orbitrap metabolomic approach to establish which compounds discriminate between the three studied legumes. Untargeted metabolomic analysis was carried out by LC-MS-Orbitrap from extracts of freeze-dried legumes prepared from pre-cooked canned legumes. The metabolomic data treatment and statistical analysis were realized by using MAIT R's package, and final identification and characterization was done using MSn experiments. Fold-change evaluation was made through Metaboanalyst 4.0. Results showed 43 identified and characterized compounds displaying differences between the three legumes. Polyphenols, mainly flavonol and flavanol compounds, were the main group with 30 identified compounds, followed by α-galactosides (n=5). Fatty acyls, prenol lipids, a nucleoside and organic compounds were also characterized. The fold-change analysis showed flavanols as the wider class of discriminative compounds of lentils compared to the other legumes; prenol lipids and eucomic acids were the most discriminative compounds of beans versus other legumes and several phenolic acids (such as primeveroside salycilic), kaempferol derivatives, coumesterol and α-galactosides were the most discriminative compounds of chickpeas. This study highlights the applicability of metabolomics for evaluating which are the characteristic compounds of the different legumes. In addition, it describes the future application of metabolomics as tool for the quality control of foods and authentication of different kinds of legumes.