Cannabinoid receptor 1 antagonist treatment induces glucagon release and shows an additive therapeutic effect with GLP-1 agonist in diet-induced obese mice
Bibliographic record
Abstract
Cannabinoid 1 (CB1) receptor antagonists reduce body weight and improve insulin sensitivity. Preclinical data indicates that an acute dose of CB1 antagonist rimonabant causes an increase in blood glucose. A stable analog of glucagon-like peptide 1 (GLP-1), exendin-4 improves glucose-stimulated insulin secretion in pancreas, and reduces appetite through activation of GLP-1 receptors in the central nervous system and liver. We hypothesized that the insulin secretagogue effect of GLP-1 agonist exendin-4 may synergize with the insulin-sensitizing action of rimonabant. Intraperitoneal as well as intracerebroventricular administration of rimonabant increased serum glucose upon glucose challenge in overnight fasted, diet-induced obese C57 mice, with concomitant rise in serum glucagon levels. Exendin-4 reversed the acute hyperglycemia induced by rimonabant. The combination of exendin-4 and rimonabant showed an additive effect in the food intake, and sustained body weight reduction upon repeated dosing. The acute efficacy of both the compounds was additive for inducing nausea-like symptoms in conditioned aversion test in mice, whereas exendin-4 treatment antagonized the effect of rimonabant on forced swim test upon chronic dosing. Thus, the addition of exendin-4 to rimonabant produces greater reduction in food intake owing to increased aversion, but reduces the other central nervous system side effects of rimonabant. The hyperglucagonemia induced by rimonabant is partially responsible for enhancing the antiobesity effect of exendin-4.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".