Glycoprotein 130 Receptor Signaling Mediates α-Cell Dysfunction in a Rodent Model of Type 2 Diabetes
Bibliographic record
Abstract
Dysregulated glucagon secretion accompanies islet inflammation in type 2 diabetes. We recently discovered that interleukin (IL)-6 stimulates glucagon secretion from human and rodent islets. IL-6 family cytokines require the glycoprotein 130 (gp130) receptor to signal. In this study, we elucidated the effects of α-cell gp130 receptor signaling on glycemic control in type 2 diabetes. IL-6 family cytokines were elevated in islets in rodent models of this disease. gp130 receptor activation increased STAT3 phosphorylation in primary α-cells and stimulated glucagon secretion. Pancreatic α-cell gp130 knockout (αgp130KO) mice showed no differences in glycemic control, α-cell function, or α-cell mass. However, when subjected to streptozotocin plus high-fat diet to induce islet inflammation and pathophysiology modeling type 2 diabetes, αgp130KO mice had reduced fasting glycemia, improved glucose tolerance, reduced fasting insulin, and improved α-cell function. Hyperinsulinemic-euglycemic clamps revealed no differences in insulin sensitivity. We conclude that in a setting of islet inflammation and pathophysiology modeling type 2 diabetes, activation of α-cell gp130 receptor signaling has deleterious effects on α-cell function, promoting hyperglycemia. Antagonism of α-cell gp130 receptor signaling may be useful for the treatment of type 2 diabetes.
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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 | 0.000 |
| 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 | 0.001 | 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".