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. 2024 Jul 1;31(7):1058-1071.
doi: 10.5551/jat.64623. Epub 2024 Feb 23.

Polygenic Risk Scores in Predicting Coronary Artery Disease in Symptomatic Patients. A Validation Study

Iida Kujala  1 , Jagadish Vangipurapu  2 , Teemu Maaniitty  1   3 , Antti Saraste  1   4 , Juha Kere  5   6 , Juhani Knuuti  1   3
Affiliations

Polygenic Risk Scores in Predicting Coronary Artery Disease in Symptomatic Patients. A Validation Study

Iida Kujala et al. J Atheroscler Thromb. .

Abstract

Aim: Clinical risk scores for coronary artery disease (CAD) are used in clinical practice to select patients for diagnostic testing and therapy. Several studies have proposed that polygenic risk scores (PRSs) can improve the prediction of CAD, but the scores need to be validated in clinical populations with accurately characterized phenotypes. We assessed the predictive power of the three most promising PRSs for the prediction of coronary atherosclerosis and obstructive CAD.

Methods: This study was conducted on 943 symptomatic patients with suspected CAD for whom the phenotype was accurately characterized using anatomic and functional imaging. Previously published genome-wide polygenic scores were generated to compare a genetic model based on PRSs with a model based on clinical data. The test and PRS cohorts were predominantly Caucasian of northern European ancestry.

Results: All three PRSs predicted coronary atherosclerosis and obstructive CAD statistically significantly. The predictive accuracy of the models combining clinical data and different PRSs varied between 0.778 and 0.805 in terms of the area under the receiver operating characteristic (AUROC), being close to the model including only clinical variables (AUROC 0.769). The difference between the clinical model and combined clinical + PRS model was not significant for PRS1 (p=0.627) and PRS3 (p=0.061). Only PRS2 slightly improved the predictive power of the model (p=0.04). The likelihood ratios showed the very weak diagnostic power of all PRSs.

Conclusion: The addition of PRSs to conventional risk factors did not clinically significantly improve the predictive accuracy for either coronary atherosclerosis or obstructive CAD, showing that current PRSs are not justified for routine clinical use in CAD.

Keywords: Coronary artery disease; Coronary atherosclerosis; Polygenic risk score; Risk factors.

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Figures

Supplemental Fig.1. ROC curves
Supplemental Fig.1. ROC curves
Receiver operating characteristic (ROC) curves of four PRS in predicting atherosclerosis.
Fig.1. ROC curves of PRSs
Fig.1. ROC curves of PRSs
Receiver operating characteristic (ROC) curves of three PRSs in predicting obstructive CAD. AUC = Area under the curve, PRS = Polygenic risk score.
Fig.2. ROC curves of PRSs with clinical risk factors
Fig.2. ROC curves of PRSs with clinical risk factors
Receiver operating characteristic (ROC) curves of clinical data and PRS in predicting obstructive CAD. AUC = Area under the curve, PRS = Polygenic risk score.
Fig.3. Distribution charts of PRSs per standard deviation (SD)
Fig.3. Distribution charts of PRSs per standard deviation (SD)
Distribution charts demonstrating the distribution of PRSs per SD for each of the three PRSs in patients with (in green) and without (in blue) obstructive CAD. PRS = Polygenic risk score
Fig.4. Distribution of PRSs in deciles
Fig.4. Distribution of PRSs in deciles
The distribution of PRSs (in deciles) in participants with (in green) and without (in blue) obstructive CAD. PRS = Polygenic risk score
Supplemental Fig.2. Distribution in deciles
Supplemental Fig.2. Distribution in deciles
Distribution charts per SD. Distribution charts demonstrating distribution of PRS per SD for each four PRS separately in patients with (in green) and without (in blue) atherosclerosis.
Supplemental Fig.3. Distribution in deciles.
Supplemental Fig.3. Distribution in deciles.
Distribution of PRS (in deciles) in participants with (in green) and without (in blue) atherosclerosis.
See this image and copyright information in PMC

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