UCI Health receives $3.1 million NIH grant to study predictors of heart disease

Using blood, genetic and imaging tests to identify who is at risk

August 06, 2015

UCI Health cardiologist Shaista Malik, MD, PhD, MPH, has received a five-year, $3.1 million grant from that National Heart, Lung and Blood Institute of the National Institutes for Health to study how blood, genetic and imaging tests can predict who will develop heart disease. Malik is medical director of the UCI Health Preventive Cardiology Program and director of the Susan Samueli Institute for Integrative Medicine.

“The goal of this study is to lay the foundation for precision medicine by determining the relative effect of genetic markers, advanced tests of inflammation and metabolic factors such as blood pressure, blood sugar and cholesterol that predict coronary heart disease,” Malik said. “The results can be used to help establish new methods of identifying who is at greatest risk and may benefit from early treatment that could help them prevent heart disease.”

Malik said that intervention based on identifying a patient’s risk factors is the basis of preventive cardiology, and that developing more effective ways to predict who will have a heart attack or die from heart disease is very important in prevention.

The study is called SBHW-PREDICT (South Bay Heart Watch – PRoteomics, gEnetics, and Directed Imaging using CT) and will analyze serum and cell samples collected 20 years ago for the South Bay Heart Watch study to determine whether using imaging markers, a biomarker profile, or a combination of the two, is more effective than traditional risk factors in predicting coronary heart disease events, particularly in those with either a family history of, or a higher genetic predisposition for coronary heart disease.

“Precision or personalized medicine is based on genetic information as well as biomarkers used to tailor treatment to the patient,” Malik said. “We will examine more than 200 genetic markers for cardiovascular disease in this study. The results will help devise a framework for classifying those at highest risk for heart disease and then tailor treatment for specific risk factor levels, such as high cholesterol, for each individual.”

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