Paracrine control of α-cell glucagon exocytosis is compromised in human type-2 diabetes
Bibliographic record
Abstract
Abstract Glucagon is secreted from pancreatic α-cells to activate gluconeogenesis and other pathways that raise blood glucose during hypoglycemia. Glucose-dependent regulation of glucagon secretion involves both α-cell-intrinsic mechanisms and paracrine control through insulin and somatostatin. In type-2 diabetes (T2D) inadequately high glucagon levels contribute to hyperglycemia. To understand these disease-associated changes at the cellular level, and to isolate intrinsic and paracrine effects, we analyzed glucagon granule exocytosis and membrane excitability in isolated α-cells from 56 non-diabetic (ND) and 15 T2D human donors. High resolution imaging showed that glucagon granule exocytosis had a U-shaped sensitivity to glucose, with the slowest rate around 7 mM glucose, and accelerated rates at <5 and >10 mM glucose. Exocytosis was reduced in T2D α-cells, but their glucose sensitivity remained intact and there were no changes in voltage-dependent ion currents or granule trafficking. Instead, α-cells from T2D donors were markedly insensitive to somatostatin and insulin, which rapidly inhibited exocytosis and electrical activity in ND cells. Thus, intrinsic mechanisms do not inhibit glucagon secretion at hyperglycemia, and elevated glucagon levels in human T2D reflect an insensitivity of α-cells to paracrine inhibition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".