Clinical conditions and their impact on utility of genetic scores for prediction of acute coronary syndrome

Aims Early prediction of acute coronary syndrome (ACS) is a major goal for prevention of coronary heart disease (CHD). 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 and Results We explored the association between 405 clinical conditions diagnosed before baseline and 9,080 incident cases of ACS in 387,832 individuals from the UK Biobank. We identified 80 conventional (e.g., stable angina pectoris (SAP), type 2 diabetes mellitus) and unconventional (e.g., diaphragmatic hernia, inguinal hernia) associations with ACS. Results were replicated in 6,430 incident cases of ACS in 177,876 individuals from FinnGen. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of SAP yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction p-value=2.87×10 −8 ) risk for ACS in individuals with SAP (HR=1.163 [95% CI: 1.082–1.251]) compared to individuals without SAP (HR=1.531 [95% CI: 1.497–1.565]). These findings were replicated in FinnGen (interaction p-value=1.38×10 −6 ). Conclusion 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 CHD. PS for ACS may be more appropriate for asymptomatic individuals than symptomatic individuals with clinical suspicion for CHD.