Researchers use DNA analysis to diagnose subtypes of heart disease

The human heart is a complicated, complex organ, and like a machine that starts sputtering, its function deteriorates for a variety of reasons. Cardiomyopathy — any disease of the heart muscle that makes it less efficient at pumping blood — can be caused by blockages, thickening of the muscles, or enlargement of the heart’s chambers, among other things. For clinicians, differentiating and correctly diagnosing these types of diseases can be difficult.

Now, a multidisciplinary team of clinicians and researchers at UT Southwestern has shown that certain subtypes of cardiomyopathy can be accurately diagnosed by analyzing how the DNA molecules inside heart cells organized in chromatin— dense structure of DNA. Chromatin changes affect which genes are active in the cell and thus can affect heart function.

“Being able to better diagnose cardiomyopathies has implications not only for guiding treatment but also for informing patients about their prognosis,” said cardiologist Nikhil Munshi, MD, PhD, associate professor of internal medicine at UT Southwestern and co-senior author of the new work, published in c Circulation.

When someone has symptoms of heart disease, such as shortness of breath, dizziness, or swelling in the legs and feet, doctors usually examine the heart with tools such as an echocardiogram to determine how the heart muscles and valves are working. If they suspect a blockage, they’ll go for a coronary angiogram, in which a dye helps visualize how blood flows through the heart. The results help determine what type of treatment—from drugs to heart stents—is best for any given patient.

However, in about a quarter of cases, cardiologists cannot pinpoint the underlying cause of cardiomyopathy. Moreover, autopsies of patients with heart disease have shown that doctors often misdiagnose subtypes of cardiomyopathy.

Dr. Munshi, in collaboration with UT Southwestern surgeons, transplant cardiologists and Gary Hohn, Ph.D., assistant professor at the Cecil H. and Ida Green Center for Reproductive Biology, analyzed cells from the left ventricle of 15 patients with cardiomyopathy and six healthy controls. Because heart biopsies are not usually performed in cardiomyopathy, the researchers used samples from patients who had undergone heart transplants or myectomies (surgical removal of muscle tissue), as well as samples from healthy donor hearts.

They used a machine learning approach analyze thousands of chromatin sites in each patient’s cells and identify differences between patients with three subtypes of cardiomyopathy –hypertrophic cardiomyopathy (caused by thickening of the walls of the left ventricle), ischemic cardiomyopathy (caused by blockage of the coronary arteries) and non-ischemic cardiomyopathy (caused by enlargement of the left ventricle without major structural changes). The machine learning program was able to recognize different chromatin signatures in each group of patients.

“Chromatin is like a unique fingerprint of a cell’s state,” said Dr. Hon. “This was a proof-of-principle study to show that we can actually train an algorithm to distinguish these fingerprints between groups of patients.”

To test the effectiveness of the program, the researchers used it on three new samples of patients who were not included in the original sample. The program correctly identified each patient’s type of cardiomyopathy and showed that chromatin structures changed after cardiomyopathy treatment.

Because heart biopsy is not currently the standard of care for patients with cardiomyopathy, there is no direct route to using the new data in the clinic, the researchers said. However, if chromatin samples allow for a significant improvement in diagnosis cardiomyopathythis may encourage the use of biopsy.

“Heart biopsies have become very safe, so if we can prove there’s a really good reason to start doing more routine biopsies for treatment, they could become more routine,” Dr. Munshi said.