![]() A Selection of PRS in the case–control study. Study design and flowchart for coronary artery disease (CAD). Furthermore, in secondary analysis, we extended our integrated method to analyze its predictive performance in non-European populations. Specifically, we analyzed UK Biobank data to test the hypothesis that integrated PRSs leveraging multiple newly developed PRS methods, and several genome-wide association study (GWAS) datasets, can improve risk prediction for CAD over the widely used PCE and thus provide improved clinical utility in European populations. In this manuscript, we investigate why different studies have reached different and controversial conclusions. On the other hand, several studies integrating PRSs into PCE to assess possible clinical utility have concluded that the current benefits of incorporating PRSs were minimal (although statistically significant) and were not considered clinically significant to warrant their use over current clinical used prediction models. Several studies have shown that PRSs can improve risk prediction accuracy for incident and prevalent CAD cases compared with individual conventional risk factors and combining risk prediction models (like PCE) with PRS improves the performance in terms of net reclassification improvement. However, population health utility of PRSs in CAD risk prediction is controversial. Recent advances in polygenic risk scores (PRSs) have sparked a great interest in enhancing disease risk prediction by using the information on millions of variants across the genome. Substantial advancements have been made over the past decades in identifying genetic variants associated with coronary artery disease (CAD). Because of the central role of accurate risk estimates in CVD prevention, improving accuracy beyond those already used in clinical practice like PCE could save lives by better identifying high-risk individuals. Current guidelines from the American College of Cardiology and American Heart Association suggest lipid-lowering treatments for individuals with greater than a 7.5% 10-year absolute risk of developing CVD based on pooled cohort equations (PCE). Risk estimates for CVD have become particularly important for disease prevention and clinical practice. ![]() These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.Ĭardiovascular disease (CVD) is a major cause of death worldwide. ConclusionsĪddition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and − 0.023 (95% CI, − 0.025 to − 0.022) for noncases. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634–0.646), 0.718 (95% CI, 0.713–0.723), and 0.753 (95% CI, 0.748–0.758), respectively. In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. A case–control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. MethodsĪn observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. ![]() Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations. ![]() The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial.
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