Berry Polyphenols and Fibers Modulate Distinct Microbial Metabolic Functions and Gut Microbiota Enterotype-Like Clustering in Obese Mice
Bibliographic record
Abstract
Berries are rich in polyphenols and plant cell wall polysaccharides (fibres), including cellulose, hemicellulose, arabinans and arabino-xyloglucans rich pectin. Most of polyphenols and fibres are known to be poorly absorbed in the small intestine and reach the colon where they interact with the gut microbiota, conferring health benefits to the host. This study assessed the contribution of polyphenol-rich whole cranberry and blueberry fruit powders (CP and BP), and that of their fibrous fractions (CF and BF) on modulating the gut microbiota, the microbial functional profile and influencing metabolic disorders induced by high-fat high-sucrose (HFHS) diet for 8 weeks. Lean mice-associated taxa, including Akkermansia muciniphila, Dubosiella newyorkensis, and Angelakisella, were selectively induced by diet supplementation with polyphenol-rich CP and BP. Fibre-rich CF also triggered polyphenols-degrading families Coriobacteriaceae and Eggerthellaceae. Diet supplementation with polyphenol-rich CP, but not with its fibre-rich CF, reduced fat mass depots, body weight and energy efficiency in HFHS-fed mice. However, CF reduced liver triglycerides in HFHS-fed mice. Importantly, polyphenol-rich CP-diet normalized microbial functions to a level comparable to that of Chow-fed controls. Using multivariate association modelling, taxa and predicted functions distinguishing an obese phenotype from healthy controls and berry-treated mice were identified. The enterotype-like clustering analysis underlined the link between a long-term diet intake and the functional stratification of the gut microbiota. The supplementation of a HFHS-diet with polyphenol-rich CP drove mice gut microbiota from Firmicutes/Ruminococcus enterotype into an enterotype linked to healthier host status, that is Prevotella/Akkermansiaceae. This study highlights the prebiotic role of polyphenols, and their contribution to the compositional and functional modulation of the gut microbiota, counteracting obesity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".