Effects of high-fat diet feeding on Znt8-null mice: differences between β-cell and global knockout of Znt8
Bibliographic record
Abstract
Genomewide association studies have linked a polymorphism in the zinc transporter 8 (Znt8) gene to higher risk of developing type 2 diabetes. Znt8 is highly expressed in pancreatic β-cells where it is involved in the regulation of zinc transport into granules. However, Znt8 is also expressed in other tissues including α-cells, where its function is as yet unknown. Previous work demonstrated that mice lacking Znt8 globally were more susceptible to diet-induced obesity (Lemaire et al., Proc Natl Acad Sci USA 106: 14872-14877, 2009; Nicolson et al., Diabetes 58: 2070-2083, 2009). Therefore, the main goal of this study was to examine the physiological impact of β-cell-specific Znt8 deficiency in mice during high-fat high-calorie (HFHC) diet feeding. For these studies, we used β-cell-specific Znt8 knockout (Ins2Cre:Znt8loxP/loxP) and whole body Znt8 knockout (Cre-:Znt8(-/-)) mice placed on a HFHC diet for 16 wk. Ins2Cre:Znt8loxP/loxP mice on HFHC diet had similar body weights throughout the study but displayed impaired insulin biosynthesis and secretion and were glucose intolerant compared with littermate control Ins2Cre mice. In contrast, Cre-:Znt8(-/-) mice became remarkably obese, hyperglycemic, hyperinsulinemic, insulin resistant, and glucose intolerant compared with littermate control Cre- mice. These data show that β-cell Znt8 alone does not considerably aggravate weight gain and glucose intolerance during metabolic stress imposed by an HFHC diet. However, global loss of Znt8 is involved in exacerbating diet-induced obesity and resulting insulin resistance, and this may be due to the loss of Znt8 activity in a tissue other than the β-cell. Thus, our data suggest that Znt8 contributes to the risk of developing type 2 diabetes through β-cell- and non-β-cell-specific effects.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".