Substituting Wheat Flour with Lentils in a Muffin Matrix Reduces Postprandial Blood Glucose in Healthy Adults
Bibliographic record
Abstract
Consumption of functional foods is an effective strategy for consumers to acquire health benefits of foods that are otherwise difficult to incorporate into their diets. Lentils, a type of pulse, are abundant in nutrients and can improve postprandial blood glucose response (PBGR) when they replace a portion of a starchy food in a mixed meal; however, they are poorly consumed in North America. Lentil-containing functional foods can improve diet quality of North Americans but their effect on postprandial blood glucose is unknown. Healthy adults (n = 24: 26.9 ± 1.3 years old, BMI of 23.9 ± 0.4 kg/m2) participated in this randomized crossover clinical trial, in which they completed three 3-hour study visits separated by washout periods of 3 – 7 days. Muffins containing 25 g of available carbohydrate from lentils (small green and split red), which replaced a portion of wheat flour, were compared to a wheat muffin control. Fasting and postprandial (15, 30, 45, 60, 90, 120 min) blood samples were collected by finger prick for analysis of blood glucose. Red lentil muffins significantly reduced blood glucose incremental area under the curve (iAUC) (136.6 ± 13.2 mmol/L.min; p = 0.02) compared to wheat muffins (169.0 ± 12.8 mmol/L.min), with no differences in peak blood glucose. Although not significant, green lentil muffin also reduced blood glucose iAUC (142.6 ± 14.9 mmol/L.min; p = 0.07). These results show that lentils can reduce PBGR when incorporated into a food product, but this effect may depend on lentil cultivar. These data support the use of functional foods to impart health benefits in a palatable and convenient way. (Funded by AAFC Growing Forward 2 and Pulse Science Cluster; NCT02426606).
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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) | 0.001 | 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".