Glucagon-like Peptide-1 Receptor Signaling Modulates β Cell Apoptosis
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) stimulates insulin secretion and augments beta cell mass via activation of beta cell proliferation and islet neogenesis. We examined whether GLP-1 receptor signaling modifies the cellular susceptibility to apoptosis. Mice administered streptozotocin (STZ), an agent known to induce beta cell apoptosis, exhibit sustained improvement in glycemic control and increased levels of plasma insulin with concomitant administration of the GLP-1 agonist exendin-4 (Ex-4). Blood glucose remained significantly lower for weeks after cessation of exendin-4. STZ induced beta cell apoptosis, which was significantly reduced by co-administration of Ex-4. Conversely, mice with a targeted disruption of the GLP-1 receptor gene exhibited increased beta cell apoptosis after STZ administration. Exendin-4 directly reduced cytokine-induced apoptosis in purified rat beta cells exposed to interleukin 1beta, tumor necrosis fator alpha, and interferon gamma in vitro. Furthermore, Ex-4-treated BHK-GLP-1R cells exhibited significantly increased cell viability, reduced caspase activity, and decreased cleavage of beta-catenin after treatment with cycloheximide in vitro. These findings demonstrate that GLP-1 receptor signaling directly modifies the susceptibility to apoptotic injury, and provides a new potential mechanism linking GLP-1 receptor activation to preservation or enhancement of beta cell mass 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) | 0.000 | 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".