A genomic risk score was better than conventional risk factors at predicting incident CAD, according to new findings.
The researchers used a meta-analytic approach to combine large-scale, genome-wide and targeted genetic association data to develop a genomic risk score for CAD (metaGRS) incorporating 1.7 million genetic variants.
The score was tested by itself and in comparison with conventional risk factors in 22,242 patients with CAD and 460,387 participants without CAD from the U.K. Biobank (mean age, 57 years; 54% women).
Michael Inouye, PhD, from the Cambridge Baker Systems Genomics Initiative in Melbourne, Australia, and Cambridge, United Kingdom, and colleagues found that association between increase in metaGRS score and development of CAD was larger than any previously published, externally tested genetic risk score. For every 1-standard deviation increase in metaGRS score, the HR for CAD was 1.71 (95% CI, 1.68-1.73).
Compared with the bottom 20% of individuals in metaGRS score, those in the top 20% had a fourfold increased risk for CAD (HR = 4.17; 95% CI, 3.97-4.38), according to the researchers. When that analysis was limited to those on lipid-lowering or antihypertensive medications, the HR was 2.83 (95% CI, 2.61-3.07).
Compared with any of six conventional risk factors — smoking, diabetes, hypertension, BMI, self-reported high cholesterol and family history — the metaGRS score had a higher C-index for incident CAD (0.623; 95% CI, 0.615-0.631); the second-best C-index was for self-reported high cholesterol (0.594; 95% CI, 0.587-0.601).
Combining the metaGRS score with the six conventional risk factors produced a C-index (0.696; 95% CI, 0.688-0.703) that was 2.6% greater than the C-index for the six conventional risk factors combined (0.67; 95% CI, 0.663-0.678).
Men in the top 20% of metaGRS score who had more than two conventional risk factors reached 10% of cumulative risk for CAD by age 48 years, Inouye and colleagues wrote.
The findings “substantially advance the concept of using genomic information to help stratify individuals for CAD risk in general populations, an approach that leverages the fixed nature of germline DNA over the life course to anticipate different lifelong trajectories of CAD risk,” the researchers wrote.
In a related editorial, Pradeep Natarajan, MD, MMSc, from the Cardiovascular Research Center and the Center for Genomic Medicine at Brigham and Women’s Hospital, the department of medicine at Harvard Medical School and the Program in Medical and Population Genetics at the Broad Institute of Harvard and MIT, wrote that the findings “show that incorporation of CHD polygenic risk with clinical risk factors can improve risk prediction and may help identify individuals who are candidates for earlier preventive therapies. Additionally, this single genetic test (currently < $100) only needs to be performed once, and this framework can be applied to calculate polygenic risk for virtually any trait.” – by Erik Swain
Disclosures: Inouye and Natarajan report no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.