Lentils Do Not Affect Satiety or Food Intake When Substituted for Wheat Flour in a Muffin Matrix in Healthy Adults
Bibliographic record
Abstract
Increasing overweight and obesity rates demand new prevention and management strategies, which may include satiating functional foods due to their potential to decrease appetite and food intake. Lentils have potential to increase satiety due to their unique nutritional profile, and may be an excellent functional food candidate with potential to assist with body weight management. While lentils have been previously investigated for their effects on satiety with mixed results, the effects of replacing commonly consumed carbohydrates with lentils within food matrices on satiety remain largely unknown. The objective of this study was to determine the effects of replacing wheat flour with two varieties of lentils within a muffin matrix on satiety and food intake. Healthy adults (n = 24, age of 25.4 ± 0.9 years, BMI of 23.2 ± 0.5 kg/m2) completed a randomized crossover study in which they consumed muffins made with wheat flour or in which the wheat flour was substituted with green or red lentils, separated by washout periods of at least 7 days. Subjective appetite sensations were measured using visual analog scales (VAS) from 0 to 180 minutes, food intake was measured at an ad libitum test meal at 180 minutes, and 24-hour energy intake was measured using weighed food records. Results showed that substituting wheat flour with either green or red lentils within the muffins had no significant effects on subjective appetite, food intake, or 24-hour energy intake. These data show that substituting wheat flour with green or red lentils within a food matrix such as a muffin may not increase satiety, and may not translate to a decrease in subsequent food intake. (Supported by Agriculture and Agri-Food Canada-Pulse Canada's Growing Forward 2 Program and the Pulse Science Cluster.)
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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) | 0.000 | 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".