Sex-specific survival bias and interaction modeling in coronary artery disease risk prediction

BackgroundThe 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score is the standard approach to predict risk of incident cardiovascular events and recently, addition of CAD polygenic scores (PGSCAD) have been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. ObjectivesThis study performed an in-depth evaluation of age and sex effects in genetic CAD risk prediction. MethodsThe population-based Norwegian HUNT2 cohort of 51,036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372,410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards and Harrells concordance index, sensitivity, and specificity were compared. ResultsInclusion of age and sex interactions of PGSCAD to the prediction models increased C-index and sensitivity likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. The two-step approach identified a total of 82.6% of incident CAD cases (74.1% by ASCVD risk score and an additional 8.5% by the PGSCAD interaction model). ConclusionThese findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age and sex-interactions terms with polygenic scores to optimize detection of individuals at high-risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information. CONDENSED ABSTRACTThis study used two large population-based longitudinal datasets to evaluate genetic prediction of CAD including age and sex interactions. The model fit and sensitivity of the prediction models increased when including age and sex interaction of PGSCAD to the prediction models likely countering the observed survival bias in the baseline. The sensitivity for females was lower than for males in all models including genetic information. Our results highlight the importance and complexity of genetic risk and suggest including age and sex interactions with polygenic scores to identify more high-risk individuals for preventive interventions.