Wild blueberry proanthocyanidins shape distinct gut microbiota profile and influence glucose homeostasis and intestinal phenotypes in high-fat high-sucrose fed mice
Bibliographic record
Abstract
Blueberries are a rich source of polyphenols, widely studied for the prevention or attenuation of metabolic diseases. However, the health contribution and mechanisms of action of polyphenols depend on their type and structure. Here, we evaluated the effects of a wild blueberry polyphenolic extract (WBE) (Vaccinium angustifolium Aiton) on cardiometabolic parameters, gut microbiota composition and gut epithelium histology of high-fat high-sucrose (HFHS) diet-induced obese mice and determined which constitutive polyphenolic fractions (BPF) was responsible for the observed effects. To do so, the whole extract was separated in three fractions, F1) Anthocyanins and phenolic acids, F2) oligomeric proanthocyanidins (PACs), phenolic acids and flavonols (PACs degree of polymerization DP < 4), and F3) PACs polymers (PACs DP > 4) and supplied at their respective concentration in the whole extract. After 8 weeks, WBE reduced OGTT AUC by 18.3% compared to the HFHS treated rodents and the F3 fraction contributed the most to this effect. The anthocyanin rich F1 fraction did not reproduce this response. WBE and the BPF restored the colonic mucus layer. Particularly, the polymeric PACs-rich F3 fraction increased the mucin-secreting goblet cells number. WBE caused a significant 2-fold higher proportion of Adlercreutzia equolifaciens whereas oligomeric PACs-rich F2 fraction increased by 2.5-fold the proportion of Akkermansia muciniphila. This study reveals the key role of WBE PACs in modulating the gut microbiota and restoring colonic epithelial mucus layer, providing a suitable ecological niche for mucosa-associated symbiotic bacteria, which may be crucial in triggering health effects of blueberry polyphenols.
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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) | 0.000 | 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 | 0.001 |
| 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".