Genetic Determinants of Myocardial Infarction Risk in Familial Hypercholesterolemia
Bibliographic record
Abstract
Background Familial hypercholesterolemia (FH) is an inherited condition of elevated serum low-density lipoprotein (LDL) cholesterol leading to premature coronary heart disease. We evaluated whether FH mutations are independently associated with the development of myocardial infarction (MI), after adjusting for LDL cholesterol level and clinical risk factors. Methods In 182 unrelated patients from different families referred with clinically suspected FH, targeted next-generation DNA sequencing was performed on 73 lipid-related genes and 178 single nucleotide polymorphisms, at 300-times mean read depth, to identify monogenic mutations and high-risk single nucleotide polymorphisms. Results Pathogenic FH mutations were identified in 27% of patients. Patients with mutations, compared with those without, were 12 years younger when referred to the lipid clinic ( P < 0.001) and had higher baseline and post-treatment LDL cholesterol by 1.11 mmol/L ( P < 0.001) and 0.62 mmol/L ( P = 0.01), respectively. The hazard ratio for premature MI with respect to having an FH mutation, controlling for sex, hypertension, body mass index, diabetes, LDL cholesterol, and smoking, was 4.51 ( P = 0.002). Conclusion FH is a genetically diverse condition. FH mutations are independently associated with higher risk of premature MI in patients referred for hypercholesterolemia. Therefore, genotyping could guide cardiovascular risk stratification in the personalized treatment of FH.
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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 | 0.002 |
| 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) | 0.001 | 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".