Pancreatic β cell–selective zinc transporter 8 insufficiency accelerates diabetes associated with islet amyloidosis
Bibliographic record
Abstract
GWAS have shown that the common R325W variant of SLC30A8 (ZnT8) increases the risk of type 2 diabetes (T2D). However, ZnT8 haploinsufficiency is protective against T2D in humans, counterintuitive to earlier work in humans and mouse models. Therefore, whether decreasing ZnT8 activity is beneficial or detrimental to β cell function, especially under conditions of metabolic stress, remains unknown. In order to examine whether the existence of human islet amyloid polypeptide (hIAPP), a coresident of the insulin granule, affects the role of ZnT8 in regulating β cell function, hIAPP-expressing transgenics were generated with reduced ZnT8 (ZnT8B+/- hIAPP) or null ZnT8 (ZnT8B-/- hIAPP) expression specifically in β cells. We showed that ZnT8B-/- hIAPP mice on a high-fat diet had intensified amyloid deposition and further impaired glucose tolerance and insulin secretion compared with control, ZnT8B-/-, and hIAPP mice. This can in part be attributed to impaired glucose sensing and islet cell synchronicity. Importantly, ZnT8B+/- hIAPP mice were also glucose intolerant and had reduced insulin secretion and increased amyloid aggregation compared with controls. These data suggest that loss of or reduced ZnT8 activity in β cells heightened the toxicity induced by hIAPP, leading to impaired β cell function and glucose homeostasis associated with metabolic stress.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".