<i>CKM</i> and <i>LILRB5</i> Are Associated With Serum Levels of Creatine Kinase
Bibliographic record
Abstract
BACKGROUND: Statins (HMG-CoA reductase inhibitors) are the most prescribed class of lipid-lowering drugs for the treatment and prevention of cardiovascular disease. Creatine kinase (CK) is a commonly used biomarker to assist in the diagnosis of statin-induced myotoxicity but the normal range of CK concentrations is wide, which limits its use as a diagnostic biomarker. METHODS AND RESULTS: We conducted a genome-wide association study of serum CK levels in 3412 statin users. Patients were recruited in Quebec, Canada, and genotyped on Illumina Human610-Quad and an iSelect panel enriched for lipid homeostasis, hypertension, and drug metabolism genes. We found a strong association signal between serum levels of CK and the muscle CK (CKM) gene (rs11559024: P=3.69×10(-16); R(2)=0.02) and with the leukocyte immunoglobulin-like receptor subfamily B member 5 (LILRB5) gene (rs2361797: P=1.96×10(-10); R(2)=0.01). Genetic variants in those 2 genes were independently associated with CK levels in statin users. Results were successfully replicated in 5330 participants from the Montreal Heart Institute Biobank in statin users for CKM (rs11559024: P=4.32×10(-16); R(2)=0.02) and LILRB5 (rs12975366 P=4.45×10(-10); R(2)=0.01) and statin nonusers (P=4.08×10(-7), R(2)=0.01; P=3.17×10(-9), R(2)=0.02, respectively). CONCLUSIONS: This is the first genome-wide study to report on the underlying genetic determinants of CK variation in a population of statin users. We found statistically significant association for variants in the CKM and LILRB5 genes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".