A whey protein supplement decreases post-prandial glycemia
Bibliographic record
Abstract
BACKGROUND: Incidence of diabetes, obesity and insulin resistance are associated with high glycemic load diets. Identifying food components that decrease post-prandial glycemia may be beneficial for developing low glycemic foods and supplements. This study explores the glycemic impact of adding escalating doses of a glycemic index lowering peptide fraction (GILP) from whey to a glucose drink. METHODS: Ten healthy subjects (3M, 7F, 44.4 +/- 9.3 years, BMI 33.6 +/- 4.8 kg/m2) participated in an acute randomised controlled study. Zero, 5, 10 and 20 g of protein from GILP were added to a 50 g glucose drink. The control (0 g of GILP) meal was repeated 2 times. Capillary blood samples were taken fasting (0 min) and at 15, 30, 45, 60, 90 and 120 minutes after the start of the meal and analyzed for blood glucose concentration. RESULTS: Increasing doses of GILP decreased the incremental areas under the curve in a dose dependant manner (Pearson's r = 0.48, p = 0.002). The incremental areas (iAUC) under the glucose curve for the 0, 5, 10, and 20 g of protein from GILP were 231 +/- 23, 212 +/- 23, 196 +/- 23, and 138 +/- 13 mmol.min/L respectively. The iAUC of the 20 g GILP was significantly different from control, 5 g GILP and 10 g GILP (p < 0.001). Average reduction in the glucose iAUC was 4.6 +/- 1.4 mmol.min/L per gram of ingested GILP. CONCLUSION: Addition of GILP to a oral glucose bolus reduces blood glucose iAUC in a dose dependent manner and averages 4.6 +/- 1.4 mmol.min/L per gram of GILP. These data are consistent with previous research on the effect of protein on the glycemic response of a meal.
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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) | 0.001 | 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 | 0.001 | 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".