Effect of hummus on postprandial glucose and insulin responses in healthy individuals (1039.6)
Bibliographic record
Abstract
Background: Pulses and chick peas are some of the lowest glycemic index (GI) foods and in clinical studies have been shown to improve glycemic control in type 2 diabetes. Hummus, composed of chick peas, vegetable oil and tahini, has a healthy macronutrient profile and is a popular and commonly consumed product. However few studies have assessed the potential health benefits of hummus consumption. The aim of present study was to determine the glycemic index of hummus and to assess the dose response effect of hummus on the post‐prandial blood glucose and insulin responses when consumed alone or when combined with a high carbohydrate food. Methods: 10 healthy participants consumed breakfast study meals, in random order, on eleven occasions over a 6‐10wk period: Study Meals: Phase 1: 1‐3. White Bread (WB) Control (1) (25g available carbohydrate [avCHO]) 4. Hummus (1 serving [28g]) (2.7g avCHO) 5. Hummus (4 servings [112g]) (10.8g avCHO) 6. Hummus (9 servings [259g]) (25g avCHO) Phase 2: 7,8. WB Control (2) (50g av CHO) 9. Hummus (1 serving [28g]) plus WB (50g avCHO) 10. Hummus (2 servings [56g]) plus WB (50g avCHO) 11. Hummus (4 servings [112g]) plus WB (50g avCHO) Postprandial glucose and insulin was measured over a 2hr period for the determination of glycemic index (GI), glycemic load (GL) and insulin index (II). Results: To be presented. Grant Funding Source : Sabra Dipping Company
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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) | 0.003 | 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".