Genomic Risk Score for Coronary Artery Disease
Study Questions:
What is the potential utility of a coronary artery disease (CAD) genomic risk score for primary prevention screening?
Methods:
A new genomic risk score for CAD was generated using data from previous large-scale genetic association studies (metaGRS) encompassing 1.7 million genetic variants. This score was then tested alone and with standard risk factors in 22,242 CAD cases and 460,387 noncases from the UK Biobank.
Results:
A 1 standard deviation increase in the metaGRS was associated with a hazard ratio of 1.7 for CAD (95% confidence interval, 1.68-1.73). A metaGRS in the top 20% was associated with a hazard ratio of 4.17 compared to a score in the lowest 20%, while this hazard ratio was 2.83 for subjects on lipid-lowering or antihypertensive therapy. A 10% CAD risk was reached at 48 years of age in men with a metaGRS in the top 20% with >2 risk factors. The metaGRS predicted CAD better than any of the six conventional risk factors (diabetes mellitus, smoking, hypertension, body mass index, family history, high cholesterol).
Conclusions:
This newly developed genomic score demonstrates the potential for early genomic screening to complement conventional CAD risk prediction strategies.
Perspective:
Repeated risk factor assessment for stratification of CAD risk has been instrumental in the primary prevention of cardiovascular disease (CVD) complications. The concept of a genetic risk score, reflective of lifelong exposure to genetic risk factors, has great merit. However, previous attempts to improve upon traditional risk factor assessments have met with limited success for a variety of reasons. The metaGRS developed in the current study enhances risk prediction by using vast amounts of data from large previous genetic association studies and then validating the score using the UK Biobank. Although further validation of this metaGRS against other risk scores and more diverse populations will be useful, it appears that incorporation of genetic risk scores into clinical practice for CVD prevention strategies will soon be feasible.
Keywords: Antihypertensive Agents, Body Mass Index, Coronary Artery Disease, Cholesterol, Diabetes Mellitus, Dyslipidemias, Genetics, Genomics, Hypertension, Lipids, Primary Prevention, Risk Factors, Smoking
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