Effects of cultured dairy and nondairy products added to breakfast cereals on blood glucose control, satiation, satiety, and short-term food intake in young women
Bibliographic record
Abstract
Breakfast cereals are often consumed with dairy products or nondairy alternatives; however, the effect of the combination on blood glucose and food intake control is not well investigated. In a randomized, crossover study, 24 healthy women (age: 22.7 ± 2.5 years; body mass index: 22.1 ± 1.5 kg/m2) consumed, to satiation, 1 of 3 treatments: Greek yogurt with granola (150 kcal, 9.2 g protein, 2.6 g fat, 2.0 g dietary fibre, and 21.5 g available carbohydrate/100 g); cultured coconut product with granola (146 kcal, 3.2 g protein, 3.2 g fat, 5.6 g dietary fibre, and 21.9 g available carbohydrate/100 g); or water control. The data were analyzed with repeated-measures ANOVA. The 2 h blood glucose iAUC was 52% lower after the dairy compared with nondairy treatment (P < 0.0001). While there were no differences in food intake between the caloric treatments consumed to satiation, protein intake was 3 times higher and fibre intake was 4 times lower after the dairy compared with nondairy treatment. Both caloric treatments resulted in similar suppression of ad libitum food intake at 2 h (P < 0.003) and subjective appetite over 2 h (P < 0.0001) compared with water. The cumulative food intake over 2 h was lower after water (P < 0.05). The 1.8-fold increase in postprandial insulin after dairy compared with nondairy treatment may explain the reduction in blood glucose without an increase in subsequent energy intake. Novelty Blood glucose in young females is lower after a breakfast with granola in a high-protein cultured dairy than when in a high-fibre nondairy cultured product. Subjective appetite over 2 h and food intake 2 h later was similarly lower after both breakfasts but cumulative intake was higher compared with breakfast skipping.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".