Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men
Bibliographic record
Abstract
Background: Several risk factors for coronary artery disease (CAD) have been described, some of which are genetically determined. The use of a polygenic risk score (PRS) could improve CAD risk assessment, but predictive accuracy according to age and sex is not well established. Methods: A PRS CAD including the weighted effects of >1.14 million single nucleotide polymorphisms associated with CAD was calculated in UK Biobank (n=408 422), using LDpred. Cox regressions were performed, stratified by age quartiles and sex, for incident myocardial infarction (MI) and mortality, with a median follow-up of 11.0 years. Improvement in risk prediction of MI was assessed by comparing PRS CAD to the pooled cohort equation with categorical net reclassification index using a 2% threshold (NRI 0.02 ) and continuous NRI (NRI >0 ). Results: From 7746 incident MI cases and 393 725 controls, hazard ratio for MI reached 1.53 (95% CI, 1.49–1.56; P =2.69×10 −296 ) per SD increase of PRS CAD . PRS CAD was significantly associated with MI in both sexes, with a stronger association in men (interaction P =0.002), particularly in those aged between 40 and 51 years (hazard ratio, 2.00 [95% CI, 1.86–2.16], P =1.93×10 −72 ). This group showed the highest reclassification improvement, mainly driven by the up-classification of cases (NRI 0.02 , 0.199 [95% CI, 0.157–0.248] and NRI >0 , 0.602 [95% CI, 0.525–0.683]). From 23 982 deaths, hazard ratio for mortality was 1.08 (95% CI, 1.06–1.09; P =5.46×10 −30 ) per SD increase of PRS CAD , with a stronger association in men (interaction P =1.60×10 −6 ). Conclusions: Our PRS CAD predicts MI incidence and all-cause mortality, especially in men aged between 40 and 51 years. PRS could optimize the identification and management of individuals at risk for CAD.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".