The effect of a high fat and high carbohydrate meal on satiety hormone response and subjective hunger in humans
Bibliographic record
Abstract
The effect of macronutrient content on postprandial hormonal release and subjective hunger was investigated in 10 male subjects. In random order, subjects either continued an overnight fast or were given a meal high in carbohydrate (CHO) or fat Blood samples and subjective hunger ratings were recorded 30 minutes preprandial and at 30, 90, and 210 minutes postprandial. Following the CHO meal, postprandial levels of insulin, C‐peptide, and amylin were significantly greater (all: P<0.001) and GLP‐1 had a non‐significant increase when compared to fat. Following the fat meal, glucagon and GLP‐1 increased significantly compared to the CHO meal (both: P<0.001). Whilst hormonal responses were different following the CHO and fat meal, postprandial subjective hunger ratings did not significantly differ. In both meal protocols, “fullness” was associated with plasma GLP‐1 (r = 0.84, P<0.01), glucagon (r = −0.80, P<0.01), and C‐peptide (r = −0.65, P<0.05). “Hunger” was related to GLP‐1 (r = − 0.84, P<0.01) and C‐peptide (r = 0.77, P<0.01), whereas “desire to eat” was solely correlated to GLP‐1 (r = −0.85, P<0.01)). In conclusion, although a single meal high in CHO or fat produced differing postprandial hormonal responses, subjective hunger was not affected. It appears that there are varying degrees of hormonal action that can influence subjective hunger, suggesting that when different macronutrients are consumed distinct combinations of hormones are released. However, these varying hormonal combinations seem to affect subjective hunger similarly. Funded by AHFMR and CIHR.
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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".