Whole and Fractionated Yellow Peas Fail to Elicit Changes in Body Composition in Hypercholesterolemic Men and Woman
Bibliographic record
Abstract
Few well‐controlled clinical studies have evaluated the health benefits and potential uses for pulses as a nutraceutical. Some evidence in animal models suggests that pulse consumption elicits a change in body composition. The purpose of this study was to evaluate the efficacy of pulse and pulse fractions to elicit a change in body fat in humans while keeping body weight constant. Hypercholesterolemic volunteers (N=17) were enrolled to participate in a triple crossover study. Subjects were randomized to receive 50 g/d white wheat flour (WWF), 11 g/d pea hull flour (PHF) or 50 g/d whole pea flour (WPF) for three 28‐day phases, separated by 28‐day washout periods. During the study periods, participants consumed a fixed diet under partial supervision, individually tailored to their caloric requirement. Dual X‐ray absorptiometry was used to determine end‐point and percent changes in lean (LBM) and fat (FBM) tissue between day 1 and 29 of each treatment phase. Neither whole nor fractionated pea flours elicit any change in body composition compared to control (LBM: WWF 48.18 ± 1.73 kg, PHF 49.70 ± 1.73 kg, WPF 49.79 ± 1.73 kg; FBM: WWF 33.64 ± 1.68 kg, PHF 33.91 ± 1.67 kg, WPF 33.28 ± 1.67 kg) (p>0.05). The present study suggests that previously reported effects of pulses on body composition are unlikely due to a direct phytochemical effect on cellular metabolism, rather due to reduced caloric intake. Funded by Pulse 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".