Elimination of glucagon-like peptide 1R signaling does not modify weight gain and islet adaptation in mice with combined disruption of leptin and GLP-1 action.
Bibliographic record
Abstract
Leptin and glucagon-like peptide 1 (GLP-1) exhibit opposing actions in the endocrine pancreas. GLP-1 stimulates insulin biosynthesis, secretion, and islet growth, whereas leptin inhibits glucose-dependent insulin secretion and insulin gene transcription. In contrast, GLP-1 and leptin actions overlap in the central nervous system, where leptin has been shown to activate GLP-1 circuits that inhibit food intake. To determine the physiological importance of GLP-1 receptor (GLP-1R)-leptin interactions, we studied islet function and feeding behavior in ob/ob:GLP-1R(-/-) mice. Although GLP-1R actions are thought to be essential for glucose-dependent insulin secretion, the levels of fasting glucose, glycemic excursion after glucose loading, glucose-stimulated insulin, and pancreatic insulin RNA content were similar in ob/ob:GLP-1R(+/+) versus ob/ob:GLP-1R(-/-) mice. Despite evidence linking GLP-1R signaling to the regulation of islet neogenesis and proliferation, ob/ob:GLP-1R(-/-) mice exhibited significantly increased islet numbers and area and an increase in the number of large islets compared with GLP-1R(+/+) or (-/-) mice (P < -0.01 to 0.05). Similarly, growth rates and both shortand long-term control of food intake were comparable in ob/ob:GLP-1R(+/+) versus ob/ob:GLP-1R4(-/-) mice. Furthermore, leptin produced a similar inhibition of food intake in GLP-1R(-/-), ob/ob:GLP-1R(+/+), and ob/ob:GLP1R4(-/-) mice. These findings illustrate that although leptin and GLP-1 actions overlap in the brain and endocrine pancreas, disruption of GLP-1 signaling does not modify the response to leptin or the phenotype of leptin deficiency in the ob/ob mouse, as assessed by long-term control of body weight or the adaptive beta-cell response to insulin resistance in vivo.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 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".