Whole and fractionated yellow pea flour consumption alters post‐ prandial glucose response and insulin parameters, but not lipids or glucose levels, in overweight or obese, hypercholesterolemic humans
Bibliographic record
Abstract
Yellow pea flour is a low fat, high fiber food; however, underlying mechanisms regarding health benefits of whole (WPF) and fractionated (FPF) yellow pea flours remain understudied. In a controlled feeding crossover study, 23 hypercholesterolemic, overweight and obese men and women received for 28 d two muffins/d containing WPF, FPF or white wheat flour (WF) as control. Treatments were separated by a 28 d washout. Two WPF muffins contained 50 g of whole yellow peas, with the amount of FPF in FPF treatments equaling the amount of pea‐derived fiber in WPF treatments. WPF and FPF consumption failed to affect fasting total cholesterol, LDL‐C, HDL‐C, and glucose. Subjects with a BMI between 25 and 34.9 receiving FPF tended ( p=0.075 ) to have reduced post‐prandial glucose responses compared to WF. Insulin levels for WPF (31.8 ±6.5 pmol/ml, p=0.02) and FPF (32.0±6.5 pmol/ml, p=0.026 ) were lower compared to WF (37.9±6.5 pmol/ml). Insulin homeostasis model‐assessment showed that consumption of WPF ( p=0.019 ) and FPF ( p=0.012 ) increased insulin sensitivity 20% compared to WF. Results suggest that at energy balance, WPF and FPF do not alter fasting lipid and glucose levels and exert minimal effects on post‐prandial glucose response in hypercholesterolemic men and woman with elevated BMIs. Conversely, 50 g/d WPF and FPF consumption improves fasting insulin levels and sensitivity and thus provide dietary health benefits. Supported by Pulse Innovation Project, Canada
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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".