Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome

Background: Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual’s genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS. Methods: We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen. Results: We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P =2.87×10 −8 ) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082–1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497–1.565]). These findings were replicated in FinnGen (interaction P =1.38×10 −6 ). Conclusions: In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.