Pulse grain consumption and obesity: effects on energy expenditure, substrate oxidation, body composition, fat deposition and satiety
Bibliographic record
Abstract
Pulses have been identified as important components of a healthy diet. Assessment of pulse grains' nutritional composition alongside data from available preclinical and clinical trials suggests that pulses can modulate biological processes that lead to obesity. Components of pulse grains, including pulse-derived fibre and resistant starch, have been shown to alter energy expenditure, substrate trafficking and fat oxidation as well as visceral adipose deposition. Although mechanistic studies are scarce, studies have indicated that fibres found in pulses can have an impact on the expression of genes that modulate metabolism. Arginine and glutamine may produce thermogenic effects as major components of pulse grain proteins. Finally, evidence suggests that pulse-derived fibres, trypsin inhibitors and lectins may reduce food intake by inducing satiety via facilitating and prolonging cholecystokinin secretion. Nonetheless, the aforementioned data remain controversial and associations between dietary pulse grains and energy intake require further study. Given the available evidence, it can be concluded that pulses could be useful as functional foods and food ingredients that combat obesity.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".