The Effect of Whey Protein on Post‐Meal Blood Glucose and Insulin
Bibliographic record
Abstract
Dairy protein ingestion is associated with satiety and the reduction of blood glucose (BG) in response to carbohydrate. The objective of this experiment was to examine the effect of whey protein on pre‐ and post‐meal glucose and insulin. Healthy normal weight adult males (n = 10) and females (n = 8) were provided whey protein (5, 10, 20 and 40 g) and hydrolyzed whey protein (10 g) in 300 ml of water or water alone 30 min prior to a pizza meal (12 kcal/kg body weight). Insulin and BG were measured by finger prick at baseline and at intervals pre‐and post‐meal for 170 min. Pre‐ (0‐30 min) and post‐ (30‐170 min) meal areas under the curve (AUCs) for BG and insulin were calculated. Sex (p <0.05) and whey protein (p <0.001) affected BG and insulin responses. Females had higher post‐meal BG and insulin AUCs than males (n =18, p < 0.05). Whey protein (10 g to 40 g) increased pre‐meal insulin and BG but reduced post‐meal BG and insulin AUCs (n = 18, p <0.05). The lower post‐meal BG AUC after whey protein was not associated with a higher insulin AUC compared with the control suggesting that whey protein treatments improved post‐meal insulin sensitivity. We concluded that the ingestion of whey protein prior to a meal resulted in lower post‐meal BG and insulin and reduced the amount of insulin required for the post‐meal glucose response. Grant Funding Source Natural Sciences and Engineering Research Council of Canada (NSERC) and Kraft Ins.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".