NEW YORK (GenomeWeb News) – Incorporating genetic information into heart attack risk prediction models based on traditional risk factors can help to more accurately classify a subset of individuals, according to a team of Mayo Clinic researchers.
In a study done through the National Human Genome Research Institute-funded Electronic Medical Records & Genomics, or eMERGE, Network, the investigators brought together information on traditional heart attack factors from medical records with data on 11 heart attack risk SNPs for nearly 1,300 individuals.
Their findings, presented at the American Heart Association Scientific Sessions meeting last night, indicate that this genetic information refined heart attack risk classifications for almost a third of those evaluated.
"This study tells us that genetic information may be helpful in screening people for their risk for having a heart attack," Mayo Clinic cardiologist Iftikhar Kullo, who is leading the study, said in a statement.
Heart attack risk is typically determined from a set of risk factors such as age, cholesterol levels, blood pressure, smoking behavior, and more. But such factors, which are brought together in a Framingham Risk Score for predicting heart risk over a decade, don't always classify individuals accurately.
"The method we have been using for decades to predict heart attack risk is not ideal," Kullo said. "[M]any people thought to be at low risk experience a heart attack."
In an effort to find ways to refine heart attack risk profiles, Kullo and his colleagues evaluated Framingham Risk Scores for 1,262 individuals with no history of heart disease based on their medical record data.
They also genotyped the individuals at 11 SNPs thought to be associated with heart disease using DNA isolated from the individuals' blood samples and compared the predictive value of genetic data alone with Framingham Risk Score predictions and models that included both Framingham Risk Score and SNP information.
By incorporating the SNP information, the researchers reported, they were able to reclassify 50 of the 197 individuals from the low-risk group into an intermediate-risk group and move 86 of 397 individuals in the intermediate risk group up to a higher risk ("intermediate-high") group.
Similarly, the team found that 54 of the 430 individuals considered intermediate-high risk belonged in the high risk category.
On the other hand, 77 intermediate risk, 79 intermediate-high risk, and 39 of 238 high risk individuals were bumped down to a lower risk category when their SNP data was added to their heart attack risk profiles.
If the findings pan out in future clinical studies, the researchers said, it may be possible to provide more accurate heart attack risk information to patients — particularly those who fall into intermediate risk categories based on traditional risk factor data.
Previous research evaluating half a dozen protein biomarkers for cardiovascular disease found only modest improvements in risk prediction when these markers were combined with traditional risk factor information.