Revisiting the bioavailability of flavan-3-ols in humans: A systematic review and comprehensive data analysis
Bibliographic record
Abstract
This systematic review summarizes findings from human studies investigating the different routes of absorption, metabolism, distribution and excretion (ADME) of dietary flavan-3-ols and their circulating metabolites in healthy subjects. Literature searches were performed in PubMed, Scopus and the Web of Science. Human intervention studies using single and/or multiple intake of flavan-3-ols from food, extracts, and pure compounds were included. Forty-nine human intervention studies met inclusion criteria. Up to 180 metabolites were quantified from blood and urine samples following intake of flavan-3-ols, mainly as phase 2 conjugates of microbial catabolites (n = 97), with phenyl-γ-valerolactones being the most representative ones (n = 34). Phase 2 conjugates of monomers and phenyl-γ-valerolactones, the main compounds in both plasma and urine, reached two peak plasma concentrations (Cmax) of 260 and 88 nmol/L at 1.8 and 5.3 h (Tmax) after flavan-3-ol intake. They contributed to the bioavailability of flavan-3-ols for over 20%. Mean bioavailability for flavan-3-ols was moderate (31 ± 23%, n bioavailability values = 20), and it seems to be scarcely affected by the amount of ingested compounds. While intra- and inter-source differences in flavan-3-ol bioavailability emerged, mean flavan-3-ol bioavailability was 82% (n = 1) and 63% (n = 2) after (−)-epicatechin and nut (hazelnuts, almonds) intake, respectively, followed by 25% after consumption of tea (n = 7), cocoa (n = 5), apples (n = 3) and grape (n = 2). This highlights the need to better clarify the metabolic yield with which monomer flavan-3-ols and proanthocyanidins are metabolized in humans. This work clarified in a comprehensive way for the first time the ADME of a (poly)phenol family, highlighting the pool of circulating compounds that might be determinants of the putative beneficial effects linked to flavan-3-ol intake. Lastly, methodological inputs for implementing well-designed human and experimental model studies were provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".