Differences in the Central Anorectic Effects of Glucagon-Like Peptide-1 and Exendin-4 in Rats
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide (GLP)-1 is a regulatory peptide synthesized in the gut and the brain that plays an important role in the regulation of food intake. Both GLP-1 and exendin (Ex)-4, a long-acting GLP-1 receptor (GLP-1r) agonist, reduce food intake when administered intracerebroventricularly, whereas Ex4 is much more potent at suppressing food intake when given peripherally. It has generally been hypothesized that this difference is due to the relative pharmacokinetic profiles of GLP-1 and Ex4, but it is possible that the two peptides control feeding via distinct mechanisms. RESEARCH DESIGN AND METHODS: In this study, the anorectic effects of intracerebroventricular GLP-1 and Ex4, and the sensitivity of these effects to GLP-1r antagonism, were compared in rats. In addition, the GLP-1r dependence of the anorectic effect of intracerebroventricular Ex4 was assessed in GLP-1r(-/-) mice. RESULTS: Intracerebroventricular Ex4 was 100-fold more potent than GLP-1 at reducing food intake, and this effect was insensitive to GLP-1r antagonism. However, GLP-1r antagonists completely blocked the anorectic effect of intraperitoneal Ex4. Despite the insensitivity of intracerebroventricular Ex4 to GLP-1r antagonism, intracerebroventricular Ex4 failed to reduce food intake in GLP-1r(-/-) mice. CONCLUSIONS: These data suggest that although GLP-1rs are required for the actions of Ex4, there appear to be key differences in how GLP-1 and Ex4 interact with central nervous system GLP-1r and in how Ex4 interacts with GLP-1r in the brain versus the periphery. A better understanding of these unique differences may lead to expansion and/or improvement of GLP-1-based therapies for type 2 diabetes and obesity.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".