A genomic risk prediction tool developed by researchers in Australia and the U.K. has achieved greater risk discrimination than its predecessors while identifying patients at the highest and lowest likelihood of developing coronary artery disease (CAD), according to a study published ahead of print in the Journal of the American College of Cardiology.
University of Cambridge researcher Michael Inouye, PhD, and colleagues said they created the tool in an effort to expand physicians’ ability to predict the trajectory of CAD both in the general population and in high-risk heart patients.
“Although family history has long been identified as a risk factor for CAD, elucidation of the genetic architecture of CAD has advanced substantially only during the past decade with the advent of genome-wide association studies,” Inouye, also director of the Cambridge Baker Systems Genomics Initiative at Cambridge, and co-authors wrote in JACC. “Results from these assumption-free surveys across the genome have laid foundations for developing genomic risk scores (GRS) in the estimation of an individual’s underlying genomic risk.”
Genomic risk prediction using germline DNA offers clinicians an opportunity to screen for CAD even before birth, but past GRS systems have failed to use full genome-wide variation, have been unable to provide precise effect size estimates and have lacked external testing in diverse, large-scale cohorts.
The authors said their novel development, a risk score for CAD they call metaGRS, is a “more powerful and generalizable” option. The team crafted the tool from genetic data consisting of 1.7 million genetic variants, then externally tested the GRS in 22,242 CAD patients and 460,387 non-CAD patients from the U.K. Biobank. The system was tested on its own, the authors wrote, as well as in combination with other traditional CAD risk factors like smoking, diabetes, BMI, hypertension and high cholesterol.
The researchers found their metaGRS was able to achieve greater risk discrimination than past models based on single nucleotide polymorphisms (SNPs).
“The hazard ratio for CAD was 1.71 per standard deviation increase in metaGRS—an association larger than any other externally testing genetic risk score previously published,” they said.
The metaGRS stratified patients based on different life course trajectories of CAD risk, meaning those in the top quintile of metaGRS distribution had a hazard ratio of 4.17 compared to those in the bottom 20 percent. The corresponding hazard ratio for individuals taking lipid-lowering or antihypertensive medications was 2.83, suggesting the predictive ability of metaGRS is independent of conventional risk factors.
“As our data have suggested that higher genetic risk can at least partly be attenuated by lipid-lowering and/or antihypertensive therapies, it implies that individuals at high genetic risk may gain the most from early initiation of these therapies and, therefore, constitute a subpopulation for which primary prevention may be particularly cost-effective,” Inouye and his colleagues wrote. “However, as our results have suggested that the metaGRS predicts CAD risk even among individuals taking CAD therapies at baseline, it also underscores the need to develop new therapies to address residual disease risk.”
The authors said the data generated by metaGRS indicated who was at a high risk of premature CAD, as well as those whose symptoms would likely never evolve to the point of requiring intervention. Now, they’re looking at how they can integrate the system into clinical workflow.
“Although applied health studies will be needed to evaluate properly the clinical utility of CAD genomic risk scores, elements of potential clinical implementation can now be foreseen,” Inouye and his team said. “For example, genome-wide array genotyping has a one-time cost (approximately $50 at current prices) and can be used to calculate updated genomic risk scores for CAD as further, more powerful association data emerge.”