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Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery
Joshua Elliott1, Barbara Bodinier1, Tom A Bond1
1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Insights
Adding a polygenic risk score to coronary artery disease (CAD) prediction models offers a modest improvement in accuracy. Further research is needed before widespread clinical use of this genetic risk information.
Area of Science:
- Cardiovascular Disease Epidemiology
- Genetics and Genomics
- Predictive Modeling
Background:
- The clinical utility of polygenic risk scores (PRS) for coronary artery disease (CAD) alongside existing risk prediction models remains unclear.
- Established models, such as pooled cohort equations, provide a baseline for risk assessment.
Purpose of the Study:
- To determine if a PRS for CAD enhances risk prediction accuracy beyond current pooled cohort equations.
- To evaluate the incremental predictive value of PRS in a large, diverse population.
Main Methods:
- An observational study utilized UK Biobank data from 2006-2010, including a case-control sample for PRS optimization and a larger cohort for predictive accuracy evaluation.
- The study assessed discrimination (C statistic), calibration, and reclassification of incident CAD risk using PRS, pooled cohort equations, and their combination.
- A risk threshold of 7.5% was applied for reclassification analysis.
Main Results:
- Combining PRS with pooled cohort equations yielded a C statistic of 0.78, a modest improvement over pooled cohort equations alone (0.76).
- The addition of PRS resulted in a 4.0% net reclassification improvement for incident CAD at a 7.5% risk threshold.
- While statistically significant, the improvement in predictive accuracy and risk stratification was modest and applied to a small proportion of individuals.
Conclusions:
- Incorporating a PRS for CAD into pooled cohort equations offers a statistically significant but modest enhancement in predicting incident CAD.
- The current evidence suggests limited clinical impact for widespread implementation, warranting further investigation.
- The study highlights the potential of genetic information but emphasizes the need for careful evaluation before clinical adoption.
Importance:
The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain.
Objective:
To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations.
Design, Setting, And Participants:
Observational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD.
Exposures:
Polygenic risk score for CAD, pooled cohort equations, and both combined.
Main Outcomes And Measures:
CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed.
Results:
In the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women [58.2%]) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% [95% CI, 3.1% to 4.9%]).
Conclusions And Relevance:
The addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.
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