The role of intestinal microbiota and microRNAs in the anti-inflammatory effects of cranberry: from pre-clinical to clinical studies
Bibliographic record
Abstract
Cranberries have known anti-inflammatory properties, which extend their benefits in the context of several chronic diseases. These benefits highly rely on the polyphenol profile of cranberries, one of few foods rich in A-type proanthocyanidin (PAC). A-type PAC comprises flavan-3-ol subunits with an additional interflavan ether bond in the conformational structure of the molecule, separating them from the more commonly found B-type PAC. PACs with a degree of polymerization higher than three are known to reach the colon intact, where they can be catabolyzed by the gut microbiota and biotransformed into lower molecular weight organic acids that are available for host absorption. Gut microbiota-derived metabolites have garnered much attention in the past decade as mediators of the health effects of parent compounds. Though, the mechanisms underlying this phenomenon remain underexplored. In this review, we highlight emerging evidence that postulates that polyphenols, including ones derived from cranberries, and their metabolites could exert anti-inflammatory effects by modulating host microRNAs. Our review first describes the chemical structure of cranberry PACs and a pathway for how they are biotransformed by the gut microbiota. We then provide a brief overview of the benefits of microbial metabolites of cranberry in the intestinal tract, at homeostasis and in inflammatory conditions. Finally, we discuss the role of microRNAs in intestinal health and in response to cranberry PAC and how they could be used as targets for the maintenance of intestinal homeostasis. Most of this research is pre-clinical and we recognize that conducting clinical trials in this context has been hampered by the lack of reliable biomarkers. Our review discusses the use of miRNA as biomarkers in this context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".