Regulation of glucagon secretion by zinc: lessons from the β cell‐specific Znt8 knockout mouse model
Bibliographic record
Abstract
In type-2 diabetes, hyperglucagonaemia aggravates elevated blood glucose levels. Relative to our knowledge of the β-cell and insulin secretion, there remains a limited understanding of glucagon secretion in α-cells. Regulation of glucagon may be dependent on a combination of factors, which include direct glucose sensing by the α-cell, innervations from the autonomic nervous system and potential 'paracrine' actions by hormones and factors that are released by adjacent endocrine cells within the islets. The list of potential 'paracrine' regulators within the islet includes insulin, somatostatin, γ-aminobutyric acid, glutamate and zinc. Zinc crystallises with insulin in β-cells and is co-secreted with insulin. In the scientific literature, the effect of exogeneous zinc on glucagon secretion has been debated. Here, we confirm that an increase in exogeneous zinc does inhibit glucagon secretion. To determine if there are physiological effects of zinc on glucagon secretion we used a β-cell-specific ZnT8 knockout (Znt8BKO) mouse model. Znt8BKO mice, despite showing lower granular zinc content in β-cells, showed no changes in fasted plasma glucagon levels and glucose regulated glucagon secretion. These findings suggest that zinc secreted from β-cell does not regulate glucagon secretion.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".