Abstract 4143289: Contemporary and Genomic Cardiovascular Risk Factors Have Explanatory Power Similar to Traditional Risk Factors for Incident Myocardial Infarction
Bibliographic record
Abstract
Background: Modifiable cardiovascular risk factors (CRF) have been proposed to be responsible for 80-90% of the risk for incident coronary artery disease (CAD). However, these studies were conducted prior to the era of preventive medications, novel biomarkers, and genetic risk scores. The relative contributions of traditional and contemporary CRF in light of secular trends in worsening cardiometabolic health globally have not been assessed. Hypothesis: Including genetic risk scores and contemporary biomarkers will enhance discrimination and explainability of myocardial infarction (MI) incidence prediction. Methods: The UKBiobank was used to identify traditional CRF (hypertension, diabetes, dyslipidemia, smoking, waist-to-hip ratio (WHR), diet, exercise, alcohol intake and socioeconomic deprivation), and contemporary/genetic CRF (lipoprotein(a), high-sensitivity C-reactive protein [hsCRP], familial hypercholesterolemia [FH] variants, and polygenic risk score for CAD [PRS CAD ]). Incident MI was defined as first-time MI diagnosis or coronary revascularization. Base model discrimination was assessed using C-statistics from Cox proportional hazards models. Percent contribution of each risk factor was calculated by explanatory power lost via Nagelkerke R 2 after removal of the CRF from the full model. Population attributable risks (PAR) were additionally assessed for each model and CRF individually. Results: Over a median [IQR] follow-up of 11.0 [9.6, 12.5] years, 17409/299707 (5.8%) of participants developed incident CAD. C-statistics sequentially increased from base model to traditional CRF to contemporary/genetic CRF model with PAR of 84.3% (95% CI 82.4%-86.5%) ( Table 1 ). Among CRFs, hypertension (C 0.74, R 2 loss 15.2%, PAR 32.5%) and PRS CAD (C 0.72, R 2 loss 12.4%, PAR 38.4%) most strongly explained MI incidence by all 3 indices. Based on discriminability, ApoB:ApoA1 ratio (C 0.71, R 2 loss 3.4%), presence of diabetes (C 0.71, R 2 loss 2.2%), and log(hsCRP) (C 0.71, R 2 loss 1.82%) were subsequently prioritized. PAR analyses included prevalence in prioritization where WHR, presence of diabetes, and log(lipoprotein(a)) levels rose higher. Conclusions: The addition of genetic risk factors and contemporary biomarkers to explanatory models for CAD shows previously underappreciated importance of contemporary CRFs such as PRS, hsCRP, and lipoprotein(a) alongside traditional CRFs such as hypertension, dyslipidemia and presence of diabetes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".