Whole and fractionated yellow pea flours modulate insulin, glucose, oxygen consumption, and the caecal microbiome in Golden Syrian hamsters
Bibliographic record
Abstract
The objective was to evaluate the effects of whole and fractionated yellow peas on circulating lipids, glucose and insulin levels, energy expenditure, and body composition, as well as to assess their prebiotic actions in Golden Syrian hamsters. Forty-five hamsters consumed a hypercholesterolemic diet for 28 days, then were randomly assigned to 1 of 3 groups: control (CON), whole pea flour (WPF), and fractionated pea flour (hulls only) (FPF). WPF and FPF were incorporated into the diets, replacing 10% of the cornstarch. WPF and FPF feeding produced negligible effects on circulating cholesterol and triglyceride levels. However, both WPF (56.76 ± 9.22 pmol·L⁻¹, p = 0.002) and FPF (89.27 ± 19.82 pmol·L⁻¹, p = 0.032) reduced circulating insulin levels compared with the CON group (131.70 ± 17.70 pmol·L⁻¹). Moreover, FPF decreased (p = 0.03) circulating glucose levels (6.26 ± 0.51 mmol·L⁻¹) compared with CON (8.27 ± 0.81 mmol·L⁻¹). Energy expenditure analysis revealed that hamsters consuming WPF demonstrated a higher (p = 0.036) oxygen consumption (2.00 ± 0.31 mL O₂·g⁻¹ lean body mass) vs. the CON group (1.56 ± 0.089 mL O₂·g⁻¹ lean body mass). Analysis of caecal digesta showed that WPF produced shifts in the abundance of microbial taxa with the most predominant changes occurring within the phylum Firmicutes. Yellow peas and their constituents should be investigated as future functional food ingredients that help prevent and manage lifestyle-related diseases such as diabetes and 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) | 0.000 | 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 | 0.000 |
| 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".