Regulation of glucagon secretion by glucose: paracrine, intrinsic or both?
Bibliographic record
Abstract
Glucagon secretion is regulated by glucose but the mechanisms involved remain hotly debated. Both intrinsic (within the α-cell itself) and paracrine (mediated by factors released β- and/or δ-cells) have been postulated. Glucagon secretion is maximally suppressed by glucose concentrations that do not affect insulin and somatostatin secretion, a finding that highlights the significance of intrinsic regulation of glucagon secretion. Experiments on islets from mice lacking functional ATP-sensitive potassium channels (K(ATP)-channels) indicate that these channels are critical to the α-cell's capacity to sense changes in extracellular glucose. Here, we review recent data on the intrinsic and paracrine regulation of glucagon secretion in human pancreatic islets. We propose that glucose-induced closure of the K(ATP)-channels, via membrane depolarization, culminates in reduced electrical activity and glucagon secretion by voltage-dependent inactivation of the ion channels involved in action potential firing. We further demonstrate that glucagon secretion measured in islets isolated from donors with type-2 diabetes is reduced at low glucose and that glucose stimulates rather than inhibits secretion in these islets. We finally discuss the relative significance of paracrine and intrinsic regulation in the fed and fasted states and propose a unifying model for the regulation of glucagon secretion that incorporates both modes of control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".