Short term supplementation with cranberry extract modulates gut microbiota in human and displays a bifidogenic effect
Bibliographic record
Abstract
Cranberry is associated with multiple health benefits, which are mostly attributed to its high content of (poly)phenols, particularly flavan-3-ols. However, clinical trials attempting to demonstrate these positive effects have yielded heterogeneous results, partly due to the high inter-individual variability associated with gut microbiota interaction with these molecules. In fact, several studies have demonstrated the ability of these molecules to modulate the gut microbiota in animal and in vitro models, but there is a scarcity of information in human subjects. In addition, it has been recently reported that cranberry also contains high concentrations of oligosaccharides, which could contribute to its bioactivity. Hence, the aim of this study was to fully characterize the (poly)phenolic and oligosaccharidic contents of a commercially available cranberry extract and evaluate its capacity to positively modulate the gut microbiota of 28 human subjects. After only four days, the (poly)phenols and oligosaccharides-rich cranberry extract, induced a strong bifidogenic effect, along with an increase in the abundance of several butyrate-producing bacteria, such as Clostridium and Anaerobutyricum. Plasmatic and fecal short-chain fatty acids profiles were also altered by the cranberry extract with a decrease in acetate ratio and an increase in butyrate ratio. Finally, to characterize the inter-individual variability, we stratified the participants according to the alterations observed in the fecal microbiota following supplementation. Interestingly, individuals having a microbiota characterized by the presence of Prevotella benefited from an increase in Faecalibacterium with the cranberry extract supplementation.
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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) | 0.000 | 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) | 0.001 | 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".