Appraisal of multiple polygenic risk scores to estimate the risk of myocardial infarction and coronary artery lesions
Bibliographic record
Abstract
Polygenic risk scores (PRS) could help to identify individuals with a high genetic risk profile for coronary artery disease (CAD). We aimed to evaluate the association between previously reported PRS and myocardial infarction (MI) as well as the extent and recurrence of coronary artery lesions. We validated previously reported CAD-PRS and 6 cardiovascular (CV) risk factors PRS (systolic blood pressure [SBP], type 2 diabetes [T2D], body-mass index [BMI], low-density lipoprotein cholesterol [LDL], triglycerides [TG], and lipoprotein-[a][Lp(a)]) in individuals of European ancestry from two Canadian population-based cohorts, the Canadian Longitudinal Study on Aging (CLSA, N = 24,599) and CARTaGENE (N = 26,806). Using a stepwise model, we determined an optimal combination of PRS to identify MI. We tested the selected PRS for association with the severity and recurrence of atherosclerotic CAD evaluated by coronary angiography in patients undergoing cardiac surgery (QUEBEC-ANGIO, N = 4108). We show that the CAD-PRS most strongly associated with MI has odds ratios per standard deviation increment of 1.75 [1.64–1.86] (P = 1.57E-70) in CLSA and 1.87 [1.73–2.03] (P = 3.06E-53) in CARTaGENE. In CLSA, the optimal model includes CAD-PRS, SBP-PRS, BMI-PRS, LDL-PRS, TG-PRS and Lp(a)-PRS. Adding these PRS increases modestly yet significantly the discriminative capacity when compared to traditional risk factors (difference of AUC = 0.025 [0.019–0.031] in CLSA, 0.018 [0.012–0.024] in CARTaGENE). In QUEBEC-ANGIO, the CAD-PRS is gradually and significantly associated with the extent and recurrence of CAD. Screening multiple validated PRS may significantly improve genetic risk estimation of MI as well as the extent and recurrence of coronary artery lesions. Scores using common genetic (DNA) variations that can be measured in a blood sample have been developed to predict the risk of many diseases, including coronary heart disease (leading to heart attacks). In this study, we combined many of these scores to identify individuals who had a heart attack. We show that adding scores to known risk factors significantly improves prediction. We also show that some of these scores are associated with the level of obstruction in heart vessels measured during a specialized procedure. The use of these scores may improve the prediction of the risk of heart attack and obstruction of heart vessels. Manikpurage et al. evaluate the association between existing polygenic risk scores (PRS) and myocardial infarction (MI) as well as the extent of coronary artery lesions at coronary angiography. The combination of several PRS could improve risk estimation of MI, extent and recurrence of atherosclerotic coronary lesions.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".