Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it