Differential responses of circulating amylin to high‐fat <i> vs.</i> high‐carbohydrate meal in healthy men
Bibliographic record
Abstract
OBJECTIVE: The success of an amylin analogue in weight loss trials has generated interest in amylin as a physiological satiety signal. Little is known about how plasma amylin responds to macronutrients. This study examined the effects of a high-carbohydrate meal (CHO), a high-fat meal (FAT) or a continued fast (FAST) on amylin concentrations and correlations among other satiety hormones and measures of appetite. DESIGN/PATIENTS: In a randomized, crossover design, 10 healthy males consumed a meal high in carbohydrate or fat or continued fasted. MEASUREMENTS: Blood samples and subjective hunger scores were obtained at baseline and 30, 90 and 210 min postprandial. RESULTS: After CHO, amylin, insulin and C-peptide were greater and des-acyl ghrelin lower compared to FAT (P < 0.001). Area under the curve (AUC) was greater for amylin and insulin and lower for des-acyl ghrelin following CHO. Subjective satiety and fullness were higher for CHO and FAT than FAST at 30 and 90 min but only for CHO at 210 min (P < 0.01). Hunger and desire to eat were lower for CHO and FAT than FAST at 30 and 90 min but only for CHO at 210 min (P < 0.005). Amylin was negatively correlated to hunger, desire to eat, and nausea and positively related to satiety and insulin. Des-acyl ghrelin was negatively associated with C-peptide, insulin and GLP-1 and satiety. CONCLUSIONS: CHO enhances amylin and suppresses des-acyl ghrelin to a greater extent than FAT in healthy men. The mechanisms responsible for these changes and their implications in the physiology of satiety remain to be elucidated.
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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".