Acute effects of pea protein and hull fibre alone and combined on blood glucose, appetite, and food intake in healthy young men – a randomized crossover trial
Bibliographic record
Abstract
Whether pulse components can be used as value-added ingredients in foods formulated for blood glucose (BG) and food intake (FI) control requires investigation. The objective of this study was to examine of the effects of pea components on FI at an ad libitum meal, as well as appetite and BG responses before and after the meal. In a repeated-measures crossover trial, men (n = 15) randomly consumed (i) pea hull fibre (7 g), (ii) pea protein (10 g), (iii) pea protein (10 g) plus hull fibre (7 g), (iv) yellow peas (406 g), and (v) control. Pea hull fibre and protein were served with tomato sauce and noodles, while yellow peas were served with tomato sauce. Control was noodles and tomato sauce. FI was measured at a pizza meal (135 min). Appetite and BG were measured pre-pizza (0-135 min) and post-pizza (155-215 min). Protein plus fibre and yellow peas led to lower pre-pizza BG area under the curve compared with fibre and control. At 30 min, BG was lower after protein plus fibre and yellow peas compared with fibre and control, whereas at 45 and 75 min, protein plus fibre and yellow peas led to lower BG compared with fibre (p < 0.05). Following the pizza meal (155 min), yellow peas led to lower BG compared with fibre (p < 0.05). No differences were observed in FI or appetite. This trial supports the use of pea components as value-added ingredients in foods designed to improve glycemic control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".