Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome.
Circulation. Genomic and precision medicine
Background - Acute coronary syndrome (ACS) is a clinically significant presentation 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 - 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. Results were replicated in 6,430 incident cases of ACS in 177,876 individuals from FinnGen. Results - 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. 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). 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 CHD. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for CHD.
View details for DOI 10.1161/CIRCGEN.120.003283
View details for PubMedID 34232692