Postprandial Blood Glucose and Insulin Response in Healthy Adults When Lentils Replace High-Glycemic Index Food Ingredients in Muffins, Chilies and Soups
Bibliographic record
Abstract
Objectives: This study aimed to assess postprandial blood glucose response (PBGR), relative glycemic response (RGR) and insulin response when 25 g available carbohydrates (AC) is replaced with cooked lentils in the formulation of muffins, chilies and soups. Methods: In randomized, crossover studies, healthy adults consumed foods containing 25 g AC from green lentils, red lentils or a control (wheat muffin, n = 24; rice chili, n = 24; potato soup, n = 20). Blood collected at fasting and at 15, 30, 45, 60, 90 and 120 min was analyzed to derive the incremental area under the response curve (iAUC) for glucose, insulin, RGR and maximum concentration (CMAX). Treatment effects were assessed with repeated measures ANOVA. Results: A replacement of 25 g AC with green lentils significantly decreased glucose iAUC compared to chili and soup (p < 0.0001), but not muffin (p = 0.07) controls, while also eliciting a significantly lower insulin iAUC for all three foods (muffin p = 0.03; chili p = 0.0002; soup p < 0.0001). Red lentil foods significantly decreased glucose iAUC (muffin p = 0.02; chili p < 0.0001; soup p < 0.0001) compared to controls, with a significantly lower insulin iAUC for chili and soup (p < 0.0001) but not muffins (p = 0.09). The RGR for muffins, chilies and soups was 88, 58 and 61%, respectively, for green lentils, and 84, 48 and 49%, respectively, for red lentils. Conclusions: PBGR, insulin and RGR are decreased when lentils are incorporated into food products, providing credible evidence to promote carbohydrate replacement with lentil-based foods.
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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 | 0.000 |
| 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".