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Abstract
Our published paper investigates strategies for reducing the risk of coronary artery and cardiovascular disease, and asks whether the addition of genetic information improves the performance of established tools for calculating disease risk. Our study finds that it does, using data from UK Biobank both to build a better disease risk model and (in a separate subset of UK Biobank participants) evaluate its effectiveness.
What is new about this study? - We combined the largest available data with established and novel methods to construct the most predictive polygenic risk score (PRS) to date for coronary artery disease. - Our integrated risk tool, which combines our PRS and the established Pooled Cohort Equations (PCE) risk tool, has a significantly improved predictive performance (NRI = 5 7%, 95% CI 4 4 7 0) against PCE alone. - This superior performance is enhanced when individuals are stratified into age-by-sex subgroups: all the subgroup NRIs are larger than the overall NRI, ranging from 7 7% 17 3%, with the largest improvement in younger middle-aged men (40-55yo, NRI 17 3% against PCE).
What are the clinical implications? - Future iterations of cardiovascular risk prediction tools would benefit from the addition of PRS. - The improved accuracy of risk estimation in 40-55yo men could motivate early prevention strategies.