Abstract 4143289: Contemporary and Genomic Cardiovascular Risk Factors Have Explanatory Power Similar to Traditional Risk Factors for Incident Myocardial Infarction

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.