Defective Peroxisomal Proliferators Activated Receptor Gamma Activity Due to Dominant-Negative Mutation Synergizes with Hypertension to Accelerate Cardiac Fibrosis in Mice
Bibliographic record
Abstract
AIMS: Humans with inactivating mutations in peroxisomal proliferators activated receptor gamma (PPARgamma) typically develop a complex metabolic syndrome characterized by insulin resistance, diabetes, lipodystrophy, hypertension, and dyslipidaemia which is likely to increase their cardiovascular risk. Despite evidence that the activation of PPARgamma may prevent cardiac fibrosis and hypertrophy, recent evidence has suggested that pharmacological activation of PPARgamma causes increased cardiovascular mortality. In this study, we investigated the effects of defective PPARgamma function on the development of cardiac fibrosis and hypertrophy in a murine model carrying a human dominant-negative mutation in PPARgamma. METHODS AND RESULTS: Mice with a dominant-negative point mutation in PPARgamma (P465L) and their wild-type (WT) littermates were treated with either subcutaneous angiotensin II (AngII) infusion or saline for 2 weeks. Heterozygous P465L and WT mice developed a similar increase in systolic blood pressure, but the mutant mice developed significantly more severe cardiac fibrosis to AngII that correlated with increased expression of profibrotic genes. Both groups similarly increased the heart weight to body weight ratio compared with saline-treated controls. There were no differences in fibrosis between saline-treated WT and P465L mice. CONCLUSION: These results show synergistic pathogenic effects between the presence of defective PPARgamma and AngII-induced hypertension and suggest that patients with PPARgamma mutation and hypertension may need more aggressive therapeutic measures to reduce the risk of accelerated cardiac fibrosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".