Overexpression of PPARγ Specifically in Pancreatic β-Cells Exacerbates Obesity-Induced Glucose Intolerance, Reduces β-Cell Mass, and Alters Islet Lipid Metabolism in Male Mice
Bibliographic record
Abstract
The contribution of peroxisomal proliferator-activated receptor (PPAR)-γ agonism in pancreatic β-cells to the antidiabetic actions of thiazolidinediones has not been clearly elucidated. Genetic models of pancreatic β-cell PPARγ ablation have revealed a potential role for PPARγ in β-cell expansion in obesity but a limited role in normal β-cell physiology. Here we overexpressed PPARγ1 or PPARγ2 specifically in pancreatic β-cells of mice subjected to high-fat feeding using an associated adenovirus (β-PPARγ1-HFD and β-PPARγ2-HFD mice). We show β-cell-specific PPARγ1 or PPARγ2 overexpression in diet-induced obese mice exacerbated obesity-induced glucose intolerance with decreased β-cell mass, increased islet cell apoptosis, and decreased plasma insulin compared with obese control mice (β-eGFP-HFD mice). Analysis of islet lipid composition in β-PPARγ2-HFD mice revealed no significant changes in islet triglyceride content and an increase in only one of eight ceramide species measured. Interestingly β-PPARγ2-HFD islets had significantly lower levels of lysophosphatidylcholines, lipid species shown to enhance insulin secretion in β-cells. Gene expression profiling revealed increased expression of uncoupling protein 2 and genes involved in fatty acid transport and β-oxidation. In summary, transgenic overexpression of PPARγ in β-cells in diet-induced obesity negatively impacts whole-animal carbohydrate metabolism associated with altered islet lipid content, increased expression of β-oxidative genes, and reduced β-cell mass.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".