Cooking enhances beneficial effects of pea seed coat consumption on glucose tolerance, incretin, and pancreatic hormones in high-fat-diet–fed rats
Bibliographic record
Abstract
Pulses, including dried peas, are nutrient- and fibre-rich foods that improve glucose control in diabetic subjects compared with other fibre sources. We hypothesized feeding cooked pea seed coats to insulin-resistant rats would improve glucose tolerance by modifying gut responses to glucose and reducing stress on pancreatic islets. Glucose intolerance induced in male Sprague-Dawley rats with high-fat diet (HFD; 10% cellulose as fibre) was followed by 3 weeks of HFD with fibre (10%) provided by cellulose, raw-pea seed coat (RP), or cooked-pea seed coat (CP). A fourth group consumed low-fat diet with 10% cellulose. Oral and intraperitoneal glucose tolerance tests (oGTT, ipGTT) were done. CP rats had 30% and 50% lower glucose and insulin responses in oGTT, respectively, compared with the HFD group (P < 0.05) but ipGTT was not different. Plasma islet and incretin hormone concentrations were measured. α- and β-cell areas in the pancreas and density of K- and L-cells in jejunum and ileum were quantified. Jejunal expression of hexose transporters was measured. CP feeding increased fasting glucagon-like peptide 1 and glucose-stimulated gastric inhibitory polypeptide responses (P < 0.05), but K- and L-cells densities were comparable to HFD, as was abundance of SGLT1 and GLUT2 mRNA. No significant difference in β-cell area between diet groups was observed. α-cell area was significantly smaller in CP compared with RP rats (P < 0.05). Overall, our results demonstrate that CP feeding can reverse adverse effects of HFD on glucose homeostasis and is associated with enhanced incretin secretion and reduced α-cell abundance.
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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 | 0.000 | 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) | 0.002 | 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".