Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches

Pedro Mena, Claudia Favari, Animesh Acharjee, Saisakul Chernbumroong, Letizia Bresciani, Claudio Curti, Furio Brighenti, Christian Heiss, Ana Rodriguez-Mateos, Daniele Del Rio, Pedro Mena, Claudia Favari, Animesh Acharjee, Saisakul Chernbumroong, Letizia Bresciani, Claudio Curti, Furio Brighenti, Christian Heiss, Ana Rodriguez-Mateos, Daniele Del Rio

Abstract

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.

Keywords: Flavan-3-ols; Inter-individual variation; Metabotypes; Phenolic metabolites; Phenyl-γ-valerolactones.

Conflict of interest statement

PM, ARM, and DDR received a research grant from the Cranberry Institute. The rest of the authors declare no conflict of interest.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Score (A, C, E) and loading (B, D, F) plots resulting after PCA analysis on non-transformed, centered data for individual metabolites (A, B), non-transformed, centered and unit variance scaled data for individual metabolites (C, D), non-transformed, centered data for sums of metabolites belonging to the same aglycone compound (E, F)
Fig. 2
Fig. 2
Two-classes cluster plot resulted from the application of different clustering methods on the datasets with individual metabolites (A) and with sums of metabolites belonging to the same aglycone compound (B)
Fig. 3
Fig. 3
A-E PLS-DA models (score and loading plots) considering individual metabolites and the clusters obtained from different clustering methods: (A) final consensus –– FC ––, (B) k-means –– Kmeans ––, (C) expectation–maximization — EM — and PC score-based models for 2 (D) or 3 (E) groups
Fig. 4
Fig. 4
Mean urinary excretion (µmol) over 24 h of sums of metabolites belonging to the same aglycone compound (3ʹOH-PVLs, sum of conjugates from the aglycone 5-(3′-hydroxyphenyl)-γ-valerolactone; 4ʹOH-PVLs, 5-(4ʹ-hydroxyphenyl)-γ-valerolactone; 3ʹ,4ʹdiOH-PVLs, 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone; HPPs, 3-(hydroxyphenyl)propanoic acid), calculated both before and after cluster analysis (“Individual” and “Sums”, respectively). Clustering has been performed on the basis of: Final Consensus (first column), k-means (second column), expectation–maximization algorithm (third column), PC score forming 2 groups (fourth column), PC score forming 3 groups (fifth column). Different letters indicate statistically significant differences (p 

Fig. 5

Inter-individual variability in phase II…

Fig. 5

Inter-individual variability in phase II metabolism illustrated by the sulfate (SULF)/glucuronide (GLUC) ratio…

Fig. 5
Inter-individual variability in phase II metabolism illustrated by the sulfate (SULF)/glucuronide (GLUC) ratio of the sums of respective conjugated metabolites (A) and of 5-(3′,4′-dihydroxyphenyl)-γ-valerolactones (dOH-PVL) (B) in urine samples. (C) Relationship between the sulfate/glucuronide ratio of all the metabolites and the sulfate/glucuronide ratio of 5-(3′,4′-dihydroxyphenyl)-γ-valerolactones
Fig. 5
Fig. 5
Inter-individual variability in phase II metabolism illustrated by the sulfate (SULF)/glucuronide (GLUC) ratio of the sums of respective conjugated metabolites (A) and of 5-(3′,4′-dihydroxyphenyl)-γ-valerolactones (dOH-PVL) (B) in urine samples. (C) Relationship between the sulfate/glucuronide ratio of all the metabolites and the sulfate/glucuronide ratio of 5-(3′,4′-dihydroxyphenyl)-γ-valerolactones

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