Polygenic risk score for coronary artery disease across the spectrum of atherosclerotic disease
Bibliographic record
Abstract
AIMS: Coronary artery disease (CAD) polygenic risk scores (PRS) enhance risk stratification, but it is unknown whether the degree varies across the spectrum of atherosclerotic cardiovascular disease (ASCVD). We compared the association of a CAD PRS and coronary events in patients with ASCVD and a prior ischemic event, ASCVD without event, and without overt ASCVD. METHODS: Genotyped patients from 6 multinational cardiovascular trials were categorized into low (bottom 20%), intermediate (middle 60%), and high (top 20%) genetic risk using a genome-wide CAD PRS, then grouped by ASCVD status. The primary endpoint was any major coronary event, a composite of death from coronary disease, myocardial infarction, or coronary revascularization. RESULTS: 59,905 participants (mean age, 66 years; 71% male) were included; 47,456 (79%) had established ASCVD. Compared with low genetic risk, major coronary events were more frequent in high (HR, 2.06; 95%CI, 1.88-2.24; p<0.001) and intermediate (HR, 1.57; 95%CI, 1.45-1.70; p<0.001) genetic risk. Genetic risk was more strongly associated with major coronary events in patients without overt ASCVD (HR between high vs. low genetic risk, 4.63) than patients with ASCVD without (HR, 1.73) or with an ischemic event (HR, 1.63) (Pinteraction<0.001). Absolute risk difference between high and low genetic risk was comparable across ASCVD categories (5.0-7.0% difference at 3 years). CONCLUSION: A CAD PRS was associated with incident major coronary events in all ASCVD categories. Although genetics provided the strongest relative association in patients without established ASCVD, the absolute risk gradient was comparable for patients with and without ASCVD.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".