Intracellular zinc in insulin secretion and action: a determinant of diabetes risk?
Bibliographic record
Abstract
Zinc is an important micronutrient, essential in the diet to avoid a variety of conditions associated with malnutrition such as diarrhoea and alopecia. Lowered circulating levels of zinc are also found in diabetes mellitus, a condition which affects one in twelve of the adult population and whose treatments consume approximately 10 % of healthcare budgets. Zn2+ ions are essential for a huge range of cellular functions and, in the specialised pancreatic β-cell, for the storage of insulin within the secretory granule. Correspondingly, genetic variants in the SLC30A8 gene, which encodes the diabetes-associated granule-resident Zn2+ transporter ZnT8, are associated with an altered risk of type 2 diabetes. Here, we focus on (i) recent advances in measuring free zinc concentrations dynamically in subcellular compartments, and (ii) studies dissecting the role of intracellular zinc in the control of glucose homeostasis in vitro and in vivo. We discuss the effects on insulin secretion and action of deleting or over-expressing Slc30a8 highly selectively in the pancreatic β-cell, and the role of zinc in insulin signalling. While modulated by genetic variability, healthy levels of dietary zinc, and hence normal cellular zinc homeostasis, are likely to play an important role in the proper release and action of insulin to maintain glucose homeostasis and lower diabetes risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".