Traditional Risk Models Predict Heart Disease as Well as Genetic Test

Cardiovascular factors typically evaluated in an annual physical, including blood pressure, cholesterol levels, diabetes, and smoking status, are just as accurate in predicting who will develop coronary heart disease (CHD) as an advanced genetic test, a study published in JAMA revealed.

CHD is the leading cause of death worldwide, killing an estimated 3.8 million men and 3.4 million women in the US each year, researchers said. Identifying those at increased risk for CHD could help patients avoid adverse events, such as heart attacks, through lifestyle changes and preventive treatments like cholesterol-lowering statins.

To help predict high-risk patients, the American College of Cardiology and the American Heart Association partnered to develop a risk calculator known as the 2013 ACC/AHA Pooled Cohort Equations based on traditional cardiovascular risk factors.

However, researchers noted that many individuals calculated to be low risk with this tool still develop CHD, and only a minority of those calculated to be high risk end up having heart attacks or other cardiac events.

Previous studies have examined the use of DNA to predict CHD risk more accurately, but it’s unclear whether DNA associations with cardiac events that have already happened could translate into predictive value for future events. Additionally, researchers haven’t compared the predictive value of DNA to that of traditional risk factors.

A team from UT Southwestern Medical Center used data from two long-term studies that follow heart health in thousands of individuals: The Atherosclerosis Risk in Communities (ARIC) study and the Multi-Ethnic Study of Atherosclerosis (MESA). Because the polygenic risk calculator was developed using individuals of European descent, researchers included only this population in their own analysis.

The group extracted data on traditional CHD risk factors and genetics from 7,306 individuals ages 45-79. Researchers put this data through both the ACC/AHA tool and the polygenic risk calculator for participants at baseline, then examined how these scores compared with which individuals experienced cardiac events over an average of 15 years.

The results showed a strong association between polygenic risk scores and CHD. Participants who scored highest on this calculator at baseline were most likely to experience cardiac events over the follow-up period. However, researchers found that these results were about the same using the ACC/AHA calculator.

The polygenic risk calculator reclassified about five percent of individuals to either a higher or lower risk category, but many of these classifications didn’t match who developed CHD or not.

The study showed that a polygenic risk score didn’t provide much more information than the ACC/AHA tool to help clinicians more accurately predict CHD.

"Genetics is an important determinant of familial diseases and a key tool for understanding human biology, and the idea that genetics may also be important for predicting common diseases has been a source of excitement over the past several years. But as an everyday clinical tool for predicting cardiovascular risk, human genetics isn't there yet," said Thomas J. Wang, M.D., the Donald W. Seldin Distinguished Chair in Internal Medicine at UT Southwestern.

"We should not lose sight of traditional risk factors for assessing risk of cardiovascular disease, counseling about that risk, and strategizing on reducing it."

A recent study funded by the National Institute on Minority Health and Health Disparities further illustrates these findings. The research demonstrated that a simple predictive risk model could help forecast stroke risk in adult patients who experience migraine with aura. The team extracted five risk factors for stroke to develop the model, including diabetes, age greater than 65 years, heart rate variability, high blood pressure, and gender.

“People who have migraine with aura are at increased risk for an ischemic stroke,” said Souvik Sen, MD, MPH, co-author of the stroke prediction study, and professor and chair of the neurology department at the University of South Carolina School of Medicine in Columbia, South Carolina.

“With our new risk-prediction tool, we could start identifying those at higher risk, treat their risk factors and lower their risk of stroke.”

While DNA and genomics have emerged as a promising way to assess people’s risk of disease, the UT Southwestern study indicates that more research is necessary in these areas before they can become the standard risk prediction method in healthcare.