Independent risk factors for simvastatin-related myopathy and relevance to different types of muscle symptom
Bibliographic record
Abstract
AIMS: Statins are widely used to prevent cardiovascular events, but little is known about the impact of different risk factors for statin-related myopathy or their relevance to reports of other types of muscle symptom. METHODS AND RESULTS: An observational analysis was undertaken of 171 clinically adjudicated cases of myopathy (defined as unexplained muscle pain or weakness with creatine kinase >10× upper limit of normal) and, separately, of 15 208 cases of other muscle symptoms among 58 390 individuals with vascular disease treated with simvastatin for a mean of 3.4 years. Cox proportional hazards models were used to identify independent predictors of myopathy. The rate of myopathy was low: 9 per 10 000 person-years of simvastatin therapy. Independent risk factors for myopathy included: simvastatin dose, ethnicity, sex, age, body mass index, medically treated diabetes, concomitant use of niacin-laropiprant, verapamil, beta-blockers, diltiazem and diuretics. In combination, these risk factors predicted more than a 30-fold risk difference between the top and bottom thirds of a myopathy risk score (hazard ratio : 34.35, 95% CI: 12.73-92.69, P across thirds = 9·1 × 10-48). However, despite the strong association with myopathy, this score was not associated with the other reported muscle symptoms (P across thirds = 0.93). Likewise, although SLCO1B1 genotype was associated with myopathy, it was not associated with other muscle symptoms. CONCLUSIONS: The absolute risk of simvastatin-related myopathy is low, but individuals at higher risk can be identified to help guide patient management. The lack of association of the myopathy risk score with other muscle symptoms reinforces randomized placebo-controlled evidence that statins do not cause the vast majority of reported muscle symptoms.
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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".