Endothelial lipase genetic polymorphisms and the lipid-lowering response in patients with coronary artery disease on rosuvastatin
Bibliographic record
Abstract
BACKGROUND: Endothelial lipase (EL) plays an important role in the regulation of lipid metabolism by reducing the high density lipoprotein cholesterol (HDL-C) levels and inducing the macrophages to take up native low density lipoprotein cholesterol (LDL-C). Our purpose was to investigate the impact of EL genetic polymorphisms on the lipid-lowering effects of rosuvastatin in Chinese coronary artery disease (CAD) patients. METHODS: One hundred twenty-one unrelated CAD patients, who underwent the treatment with rosuvastatin (10mg/day) for four to eight weeks, were enrolled in this study. Before and after treatment, serum lipids levels were measured. Genotypes of EL 2037T/C and 2237 G/A polymorphisms were detected by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. RESULTS: Patients with EL 2037C allele (CC + CT) had significantly lower LDL-C levels than those with TT genotype (CC + CT: 2.60 ± 0.74 mmol/l; TT: 2.90 ± 0.87 mmol/l; P = 0.047), before rosuvastatin treatment. No significant differences between baseline lipid levels and the EL 2237G/A genotypes were observed. After treatment with rosuvastatin, total cholesterol (TC), high triglyceride (TG) and LDL-C levels decreased from baseline, on average, by 23.09 % (4.59 ± 0.96 mmol/l to 3.47 ± 0.83 mmol/l), 6.36 % (2.01 ± 1.18 mmol/l to 1.68 ± 1.16 mmol/l), 32.48 % (2.77 ± 0.83 mmol/l to 1.79 ± 0.62 mmol/l), respectively (all P < 0.05) in all patients. While changes in HDL-C levels did not reach statistical significance. No significant effects of EL 2037T/C or 2237G/A polymorphism were observed on lipid-lowering effects of rosuvastatin. CONCLUSIONS: EL 2037T/C and 2237 G/A polymorphisms might not affect the lipid-owing effects of rosuvastatin in Chinese CAD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".