Oat β‐glucan increases postprandial cholecystokinin levels, decreases insulin response and extends subjective satiety in overweight subjects
Bibliographic record
Abstract
This study recorded acute biochemical and subjective measures of satiety, followed by energy intake from a subsequent meal, after varying doses of beta-glucan in extruded breakfast cereals. Molecular weight, solubility and viscosity of beta-glucan products were determined. Seven male and seven female subjects (BMI 25-36 kg/m) consumed five breakfasts (different doses of beta-glucan sourced from two different technological processes) and dietary intake was measured after four hours. Blood was collected to measure glucose, insulin, ghrelin and cholecystokinin, and visual analogue scales measured subjective satiety. Molecular weight, solubility and viscosity indicated products were likely to increase luminal viscosity. beta-Glucan was found to decrease insulin secretion over 2 h (RMANOVA, p = 0.011) in a dose responsive manner from 2.16 to 5.68 g per serving (p = 0.007). Cholecystokinin levels increased linearly over the same range of beta-glucan concentrations (p = 0.002) in women. Subjective satiety was increased at a beta-glucan dose of 2.2 g (p = 0.039). Subsequent meal intake decreased by greater than 400 kJ with higher beta-glucan dose (>5 g). beta-Glucan improves satiety and release of cholecystokinin is likely to be part of the mechanism. Products with different sources of beta-glucan provide similar benefits but each product requires individual testing.
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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".