Investigation of the Association of GATM Gene Polymorphisms with Statin-Induced Myopathy
Bibliographic record
Abstract
Statins are one of the mainstay medicines for treating hyperlipidemia. The effect of statins can extend beyond their direct role in cholesterol transport to anti-inflammatory and plaque stabilization in coronary artery disease (CAD). Statins are known to reduce low-density lipoproteins (LDL) by up to 55%, which can inadvertently reduce the risk of cardiac events in patients. Strong clinical profile notwithstanding, statins are notorious for their muscle-related adverse effects. Some variations in genes such as glycine amidinotransferase (GATM) have been implicated in this side effect; however, the reports are not all congruent. In the current study, a prospective cohort design was used to establish both drug efficacy and the incidence of muscle-rated adverse reactions in patients receiving atorvastatin and rosuvastatin. The muscle adverse effects were recorded using the “Statin Associated Muscle Symptoms-Clinical Index” (SAMS-CI) tool. Drug plasma levels were recorded using LC-MS, and GATM rs9806699 was genotype using direct Sanger sequencing. A binary logistic model was used to determine the association between genotypes and the incidence of muscle symptoms, whereas stepwise linear regression was used to determine the association between plasma concentration, genetics, and muscle symptoms. Among 130 enrolled patients, 97(74.62%) received rosuvastatin 10 mg (once daily), and 33 (25.38%) received atorvastatin 20 mg (once daily). A total of 26 patients reported adverse drug reactions according to the SAMS-CI. The genotype frequency of GATM rs9806699-GG was 45.74%, whereas the heterozygous genotype (AG) was 34(36.17%), and the AA genotype was 18.09%. There was no significant difference found between the plasma concentrations of both rosuvastatin and atorvastatin among GATM rs9806699 genotypes. Despite the high incidence of muscular adverse effects, there was no significant association between GATM genotypes and SAMS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".