Whole-grain pasta reduces appetite and meal-induced thermogenesis acutely: a pilot study
Bibliographic record
Abstract
In epidemiological studies, the intake of foods rich in dietary fiber is associated with a reduced risk of developing overweight and type 2 diabetes. This work aims to identify acute strategies to regulate appetite and improve glucose control by using different pasta meals. Hence, 4 different isocaloric lunch meals, consisting of (i) refined-grain pasta (RG+T), (ii) whole-grain pasta (WG+T), (iii) lemon juice-supplemented refined-grain pasta (LRG+T), and (iv) refined-grain pasta with legumes (RG+L), were administered to 8 healthy participants in a crossover design. On the test days, participants underwent baseline measurements, including appetite sensation, blood sample, and resting energy expenditure (EE), after which the test lunch was served. Subjective appetite was assessed and a blood sample was taken each hour for 240 min, and postprandial EE was measured for 3 h. In repeated-measures analysis of covariance (ANCOVA), postprandial fullness (p = 0.001) increased and hunger (p = 0.038) decreased. WG+T had a lower EE than did both LGR+T (p = 0.02) and RG+L (p < 0.001). Likewise, meal-induced thermogenesis was lower for WG+T compared with RG+L (58 ± 81 kJ vs 248 ± 188 kJ; p < 0.05). Plasma glucose (p = 0.001) was lower for RG+T, and triacylglycerols (p = 0.02) increased for LRG+T; however, insulin, C-peptide, and ghrelin were comparable in all other meals. In conclusion, our study indicates that acute consumption of whole-grain pasta may promote fullness and reduce hunger, lowering postprandial thermogenesis, and adding lemon juice to the pasta or legumes does not appear to affect appetite. However, none of pasta meal alterations improved the postprandial metabolic profile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".