Role of single serving form of dairy on satiety and postprandial glycaemia in young and older healthy adults
Bibliographic record
Abstract
Dairy proteins reduce appetite and improve postprandial glycaemic response in adults. However, there are no reports of dairy in amounts usually consumed on satiety and postprandial glycaemia in either young or older adults. In a randomized crossover design, 30 healthy young adults (age: 23.5 ± 0.5 years; body mass index (BMI): 21.8 ± 0.4 kg/m2) and 30 healthy/overweight older adults (age: 65.2 ± 0.5 years; BMI: 24.7 ± 0.6 kg/m2) consumed 1 serving (according to manufacturers’ labels) of skim milk (0.1% milk fat (MF)), whole milk (3.25% MF), plain Greek yogurt (2% MF), cheddar cheese (31% MF), and water (energy-free control) after a 12-h fast. Subjective appetite was measured every 15–30 min over 3 h. Blood glucose and insulin were measured at baseline and every 15–30 min over 2 h. All dairy treatments reduced post-treatment subjective appetite area under the curve (AUC) over 3 h by 8%–17% more than water. Greek yogurt reduced appetite 3-h AUC more than skim and whole milk by 9% and 7%, respectively (p < 0.0001). Post-treatment blood glucose 2-h AUC was 42% lower in young compared with older adults (p = 0.003). It was also 52%–78% lower after cheese compared with milks and yogurt (p < 0.0001). Post-treatment insulin AUC after cheese was only 10%–15% of that after milks and Greek yogurt (p < 0.0001). We conclude that single servings of dairy differ in effect on postprandial satiety and glycaemia and merit consideration in management of metabolic syndrome.
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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 | 0.001 |
| 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 | 0.001 | 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".