Evaluation of the Performance of the RECODe Equation with the Addition of Polygenic Risk Scores for Adverse Cardiovascular Outcomes in Individuals with Type II Diabetes.

Early identification of individuals with T2D who are at greatest risk of cardiovascular complications can lead to targeted intensive risk-factor modification with the aim of altering the natural history of the disease.Current approaches to risk prediction tailored to individuals with diabetes are exemplified by the RECODe algorithms which predict both individual and composite CVD outcomes among individuals with T2D.We sought to determine if the addition of a polygenic risk score to current clinical risk models improve predictive modeling of adverse cardiovascular events in individuals with type II diabetes.We demonstrate that although PRS associate with CVD outcomes independent of traditional risk factors among individuals with T2D, the addition of PRS to traditional, validated models does not specifically improve the predictive performance as compared to the base model.RECODe demonstrated modest discrimination potential at baseline (AUC = 0.66). As such, the lack of improved risk prediction may reflect the performance of the RECODe equation in our cohort as opposed to lack of PRS utility.Current performance of clinical risk models appears modest. Although PRS doesn't meaningfully improve performance, there is still substantial opportunity to improve risk prediction.