The Association between Genetic Polymorphisms and Simvastatin-Induced Myopathy: A Narrative Synthesis of Evidence
Bibliographic record
Abstract
BACKGROUND AND STUDY AIM: Genetic polymorphisms may play a role in muscular injury associated with simvastatin, but results were inconclusive. This study aimed to summarize evidence from the literature investigating the effects of genetic polymorphism on simvastatin-induced myopathy. METHODS: Studies regarding the association between genetic polymorphisms and simvastatin-induced myopathy were retrieved through electronic databases from February 1, 1990 to March 15, 2018. Two authors independently extracted data, including PMID, author, publication year, country, race, age, population characteristics, drugs, definition of case and control, gene, allele, SNP position, Hardy-Weinberg equilibrium, number of genotypes (case and control), minor allele frequency of cases and controls, association, study type and the Newcastle-Ottawa scale. Due to high heterogeneity in study design and outcome measurements among the included articles, a narrative synthesis of the evidence was conducted. RESULTS: A total of 10 association studies were identified in this study, including SLCO1B1, ABCB1, GATM, HTR3B, HTR7, RYR2 and HLA-DRB1. The evidence linking myopathy to rs4149056 in SLCO1B1 is of high quality, and this association has been reproduced in randomized trials and clinical practice-based cohorts. As for other candidate genetic markers, the evidences are limited or controversial, and additional well-designed studies with larger sample sizes, are required to further elucidate this association. CONCLUSION: SLCO1B1 genotype is a useful biomarker for predicting an increased risk of simvastatin-induced myopathy.
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".