Increased milk protein content and whey-to-casein ratio in milk served with breakfast cereal reduce postprandial glycemia in healthy adults: An examination of mechanisms of action
Bibliographic record
Abstract
This study describes the effects on glycemic response and the underlying mechanisms of action of increasing the protein concentration and decreasing the casein-to-whey ratio in milk when consumed with a high glycemic breakfast cereal. Twelve healthy men and women, aged 18 to 30 yr and with a body mass index of 20 to 24.9 kg/m 2 , consumed (in random order) milk beverages (250 mL) containing either 3.1 or 9.3% protein and casein-to-whey ratios of either 80:20 or 40:60. We measured postprandial appetite, glucose, regulatory hormones, and stomach emptying rate over 200 min, as well as food intake at an ad libitum meal at 120 min. Although pre-meal appetite was suppressed to a greater extent with milk beverages that had high (9.3%) compared with regular (3.1%) protein content, food intake was similar among all 4 treatments. Pre-meal mean blood glucose was lower with beverages that had high rather than regular milk protein content, with the lowest glucose peaks after the high milk protein treatment with the 40:60 casein-to-whey ratio. Pre-meal insulin and C-peptide levels were not affected by milk protein content or casein-to-whey ratio, but pre-meal glucagon-like peptide 1 was higher after the treatment containing high milk protein and the 40:60 casein-to-whey ratio, and pre-meal cholecystokinin was higher after the treatments containing high milk protein content. Plasma paracetamol response was also lower after the treatments containing high compared with regular milk protein content. When consumed with carbohydrate, milk beverages with high protein content and (to a lesser extent) a decreased casein-to-whey ratio lowered postprandial glycemia through insulin-independent mechanisms, primarily associated with delayed stomach emptying.
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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 | 0.001 |
| 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) | 0.001 | 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".