Prevention of Cardiac Hypertrophy by Atorvastatin in a Transgenic Rabbit Model of Human Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Cardiac hypertrophy, a major determinant of morbidity and mortality in hypertrophic cardiomyopathy (HCM), is considered a secondary phenotype and potentially preventable. To test this hypothesis, we screened 30 5- to 6-month-old beta-myosin heavy chain Q403 transgenic rabbits by echocardiography and selected 26 without cardiac hypertrophy. We randomized the transgenic rabbits to treatment with atorvastatin (2.5 mg/Kg/d), known to block hypertrophic signaling or a placebo. We included 15 nontransgenic rabbits as controls. Cardiac phenotype was analyzed serially before, 6 and 12 months after randomization. Serum total cholesterol levels were reduced by 49% with atorvastatin administration. Left-ventricular mass, wall thickness; myocyte size, myocardial levels of molecular markers of hypertrophy, lipid peroxides, and oxidized mitochondrial DNA; and the number of terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling (TUNEL)-positive myocytes were increased significantly in the placebo but not in the atorvastatin group. Myocardium catalase mRNA levels were decreased by 5-fold in the placebo but were normal in the atorvastatin group. Catalase protein level and activity were not significantly changed. Levels of membrane-bound Ras and phospho-p44/42 mitogen-activated-protein kinase (MAPK) were increased in the placebo group (approximately 2.5 fold) but were reduced in the atorvastatin group. Levels of GTP- and membrane-bound RhoA and Rac1, phospho-p38, and phospho-c-Jun NH2-terminal kinases were unchanged. Thus, atorvastatin prevented development of cardiac hypertrophy; determined at organ, cellular, and molecular levels, partly through reducing active Ras and p44/42 MAPK. The results indicate potential beneficial effects of atorvastatin in prevention of cardiac hypertrophy, a major determinant of morbidity in all forms of cardiovascular diseases, and beckon clinical studies in humans with HCM.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".