Whey Proteins in the Regulation of Food Intake and Satiety
Bibliographic record
Abstract
Whey protein has potential as a functional food component to contribute to the regulation of body weight by providing satiety signals that affect both short-term and long-term food intake regulation. Because whey is an inexpensive source of high nutritional quality protein, the utilization of whey as a physiologically functional food ingredient for weight management is of current interest. At present, the role of individual whey proteins and peptides in contributing to food intake regulation has not been fully defined. However, Whey protein reduces short-term food intake relative to placebo, carbohydrate and other proteins. Whey protein affects satiation and satiety by the actions of: (1) whey protein fractions per se; (2) bioactive peptides; (3) amino-acids released after digestion; (4) combined action of whey protein and/or peptides and/or amino acids with other milk constituents. Whey ingestion activates many components of the food intake regulatory system. Whey protein is insulinotropic, and whey-born peptides affect the renin-angiotensin system. Therefore whey protein has potential as physiologically functional food component for persons with obesity and its co-morbidities (hypertension, type II diabetes, hyper- and dislipidemia). It remains unclear, however, if the favourable effects of whey on food intake, subjective satiety and intake regulatory mechanisms in humans are obtained from usual serving sizes of dairy products. The effects described have been observed in short-term experiments and when whey is consumed in much higher amounts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".