Predictive Accuracy of a Polygenic Risk Score–Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease

ABSTRACTBackgroundSeveral risk factors for coronary artery disease (CAD) have been described, some of which are genetically determined. The use of a polygenic risk score (PRS) could improve CAD risk assessment, but predictive accuracy according to age and sex is not well established.MethodsA PRSCAD including the weighted effects of >1.14 million SNPs associated with CAD was calculated in UK Biobank (n=408,422), using LDPred. Cox regressions were performed, stratified by age quartiles and sex, for incident MI and mortality, with a median follow-up of 11.0 years. Improvement in risk prediction of MI was assessed by comparing PRSCAD to the pooled cohort equation with categorical net reclassification index using a 2% threshold (NRI0.02) and continuous NRI (NRI>0).ResultsFrom 7,746 incident MI cases and 393,725 controls, hazard ratio (HR) for MI reached 1.53 (95% CI [1.49-1.56], p=2.69e-296) per standard deviation (SD) increase of PRSCAD. PRSCAD was significantly associated with MI in both sexes, with a stronger association in men (interaction p=0.002), particularly in those aged between 40-51 years (HR=2.00, 95% CI [1.86-2.16], p=1.93e-72). This group showed the highest reclassification improvement, mainly driven by the up-classification of cases (NRI0.02=0.199, 95% CI [0.157-0.248] and NRI>0=0.602, 95% CI [0.525-0.683]). From 23,982 deaths, HR for mortality was 1.08 (95% CI [1.06-1.09], p=5.46e-30) per SD increase of PRSCAD, with a stronger association in men (interaction p=1.60e-6).ConclusionOur PRSCAD predicts MI incidence and all-cause mortality, especially in men aged between 40-51 years. PRS could optimize the identification and management of individuals at risk for CAD.

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Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.121.003452 ◽

2021 ◽

Author(s):

Hasanga D. Manikpurage ◽

Aida Eslami ◽

Nicolas Perrot ◽

Zhonglin Li ◽

Christian Couture ◽

...

Keyword(s):

Myocardial Infarction ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Risk Score ◽

Predictive Accuracy ◽

Hazard Ratio ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Artery Disease ◽

Genetically Determined

Background: Several risk factors for coronary artery disease (CAD) have been described, some of which are genetically determined. The use of a polygenic risk score (PRS) could improve CAD risk assessment, but predictive accuracy according to age and sex is not well established. Methods: A PRS CAD including the weighted effects of >1.14 million SNPs associated with CAD was calculated in UK Biobank (n=408 422), using LDpred. Cox regressions were performed, stratified by age quartiles and sex, for incident myocardial infarction (MI) and mortality, with a median follow-up of 11.0 years. Improvement in risk prediction of MI was assessed by comparing PRS CAD to the pooled cohort equation with categorical net reclassification index using a 2% threshold (NRI 0.02 ) and continuous NRI (NRI >0 ). Results: From 7746 incident MI cases and 393 725 controls, hazard ratio for MI reached 1.53 (95% CI, 1.49–1.56; P =2.69×10 −296 ) per SD increase of PRS CAD . PRS CAD was significantly associated with MI in both sexes, with a stronger association in men (interaction P =0.002), particularly in those aged between 40 and 51 years (hazard ratio, 2.00 [95% CI, 1.86–2.16], P =1.93×10 −72 ). This group showed the highest reclassification improvement, mainly driven by the up-classification of cases (NRI 0.02 , 0.199 [95% CI, 0.157–0.248] and NRI >0 , 0.602 [95% CI, 0.525–0.683]). From 23 982 deaths, hazard ratio for mortality was 1.08 (95% CI, 1.06–1.09; P =5.46×10 −30 ) per SD increase of PRS CAD , with a stronger association in men (interaction P =1.60×10 −6 ). Conclusions: Our PRS CAD predicts MI incidence and all-cause mortality, especially in men aged between 40 and 51 years. PRS could optimize the identification and management of individuals at risk for CAD.

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