Glucagon-Like Peptide-1 Is Involved in the Thermic Effects of Dietary Proteins in Male Rodents
Bibliographic record
Abstract
Protein intake potently increases body temperature and energy expenditure, but the underlying mechanism thereof remains incompletely understood. Simultaneously, protein intake potently stimulates glucagon-like peptide-1 (GLP-1) secretion. Here, we examined the involvement of GLP-1 in the thermic effects of dietary proteins in rodents by measuring rectal temperature and energy expenditure and modulating GLP-1 signaling. Rectal temperature of rats or mice fasted for 4 or 5 hours were measured using a thermocouple thermometer before and after an oral administration of nutrients. Oxygen consumption after oral protein administration was also measured in rats. Rectal temperature measurements in rats confirmed an increase in core body temperature after refeeding, and the thermic effect of the oral administration of protein was greater than that of a representative carbohydrate or lipid. Among the five dietary proteins examined (casein, whey, rice, egg, and soy), soy protein had the highest thermic effect. The thermic effect of soy protein was also demonstrated by increased oxygen consumption. Studies using a nonselective β-adrenergic receptor antagonist and thermal camera suggested that brown adipose tissue did not contribute to soy protein-induced increase in rectal temperature. Furthermore, the thermic effect of soy protein was completely abolished by antagonism and knockout of the GLP-1 receptor, yet potentiated via augmentation of intact GLP-1 levels through inhibition of dipeptidyl peptidase-4 activity. These results indicate that GLP-1 signaling is essential for the thermic effects of dietary proteins in rats and mice, and extend the metabolic actions of GLP-1 ensuing from nutrient ingestion to encompass the thermic response to ingested protein.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".