Rethinking the Mechanism of the Health Benefits of Proanthocyanidins: Absorption, Metabolism, and Interaction with Gut Microbiota
Bibliographic record
Abstract
Proanthocyanidins, as the oligomers or polymers of flavan-3-ol, are widely discovered in plants such as fruits, vegetables, cereals, nuts, and leaves, presenting a major part of dietary polyphenols. Although proanthocyanidins exert several types of bioactivities, such as antioxidant, antimicrobial, cardioprotective, and neuroprotective activity, their exact mechanisms remain unclear. Due to the complexity of the structure of proanthocyanidins, such as their various monomers, different linkages and isomers, investigation of their bioavailability and metabolism is limited, which further hinders the explanation of their bioactivities. Since the large molecular weight and degree of polymerization limit the bioavailability of proanthocyanidins, the major effective site of proanthocyanidins is proposed to be in the gut. Many studies have revealed the effects of proanthocyanidins from different sources on changing the composition of gut microbiota based on in vitro and in vivo models and the bioactivities of their metabolites. However, the metabolic routes of proanthocyanidins by gut microbiota and their mutual interactions are still sparse. Thus, this review summarizes the chemistry, absorption, and metabolic pathways of proanthocyanidins ranging from monomers to polymers, as well as the mutual interactions between proanthocyanidins and gut microbiota, in order to better understand how proanthocyanidins exert their health-promoting functions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".