The effect of dairy and nondairy beverages consumed with high glycemic cereal on subjective appetite, food intake, and postprandial glycemia in young adults
Bibliographic record
Abstract
The objective was to compare the effect of dairy and nondairy beverages when consumed with carbohydrate at breakfast on subjective appetite, food intake (FI), and postprandial glycemia (PPG) in healthy young adults. Twenty-six healthy males and females (13 males and 13 females; 23.0 ± 2.6 years; BMI: 22.3 ± 1.5 kg/m 2 ) participated in a randomized crossover study. They consumed nonisocaloric amounts (250 mL) of almond beverage, soy beverage, 1% fat milk, yogurt beverage, and water (control) with cereal and 120 min later, an ad libitum meal. Subjective appetite, PPG, and insulin were measured at baseline and at intervals before and after the meal at which FI was measured. Post-treatment blood glucose was lowest following soy beverage compared with all treatments but was not different from milk (p = 0.0002). There were no differences between any other treatments. However, over the first hour, PPG for all treatments was 27% lower compared with water (p < 0.0001). Milk and yogurt beverage led to the highest insulin concentrations post-treatment (p < 0.0001) but there were no differences between treatments postmeal. All treatments reduced appetite and led to lower FI at the meal compared with water, but FI was lower after milk compared with all treatments except yogurt beverage (p < 0.0001). Both dairy and nondairy beverages consumed with a high glycemic cereal at breakfast increased satiety and decreased FI compared with water with cereal. Despite higher carbohydrate content, all beverages led to similar or lower PPG than the water breakfast, but dairy beverages increased insulin more than nondairy beverages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".