Dietary Pea Fiber Supplementation Improves Glycemia and Induces Changes in the Composition of Gut Microbiota, Serum Short Chain Fatty Acid Profile and Expression of Mucins in Glucose Intolerant Rats
Bibliographic record
Abstract
Several studies have demonstrated the beneficial impact of dried peas and their components on glucose tolerance; however, the role of gut microbiota as a potential mediator is not fully examined. In this study, we investigated the effect of dietary supplementation with raw and cooked pea seed coats (PSC) on glucose tolerance, microbial composition of the gut, select markers of intestinal barrier function, and short chain fatty acid profile in glucose intolerant rats. Male Sprague Dawley rats were fed high fat diet (HFD) for six weeks to induce glucose intolerance, followed by four weeks of feeding PSC-supplemented diets. Cooked PSC improved glucose tolerance by approximately 30% (p < 0.05), and raw and cooked PSC diets reduced insulin response by 53% and 56% respectively (p < 0.05 and p < 0.01), compared to HFD (containing cellulose as the source of dietary fiber). 16S rRNA gene sequencing on fecal samples showed a significant shift in the overall microbial composition of PSC groups when compared to HFD and low fat diet (LFD) controls. At the family level, PSC increased the abundance of Lachnospiraceae and Prevotellaceae (p < 0.001), and decreased Porphyromonadaceae (p < 0.01) compared with HFD. This was accompanied by increased mRNA expression of mucin genes Muc1, Muc2, and Muc4 in ileal epithelium (p < 0.05). Serum levels of acetate and propionate increased with raw PSC diet (p < 0.01). These results indicate that supplementation of HFD with PSC fractions can improve glycemia and may have a protective role against HFD-induced alterations in gut microbiota and mucus layer.
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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 | 0.000 |
| 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".