CTSA Program Researchers Advance Heart Condition Study Through Precision Medicine and Digital Health

Translational Science Highlight

  • With NCATS support, researchers are advancing precision medicine and digital health to combat a dangerous heart condition through improved screening and diagnostic methods. This research is validating an approach that could be applied to many complex disorders.

Atrial fibrillation (AFib), a type of irregular heartbeat, puts patients at increased risk for stroke. AFib can be treated with medications or various procedures, and the risk of stroke can be reduced with blood thinners. One issue, however, is that many people do not know they have AFib until after a life-threatening event such as stroke occurs. Health professionals need better ways to identify and evaluate individuals at risk.

One solution is to use a person’s unique genetic characteristics to guide and tailor health screening, diagnosis and treatment decisions. This precision medicine approach for diagnosis and treatment is increasingly becoming a clinical reality. But progress has been limited for complex disorders like AFib that do not have a single genetic cause.

Researchers at the Scripps Translational Science Institute in San Diego, part of NCATS’ Clinical and Translational Science Awards (CTSA) Program, are working to address this challenge for common heart conditions with complex causes. These efforts can ultimately help advance precision medicine approaches to preventing and diagnosing other types of complex diseases.

Precision Medicine Meets Wearable Technology

As a cardiologist at the Scripps Clinic, Evan Muse, M.D., Ph.D., observed that some patients were being diagnosed with AFib only after they had survived a stroke: “There had to be a better way to identify these patients before devastating complications like stroke occurred.”

This is where precision medicine could help. Every person has a unique genome, or collection of genes. But some individuals have specific “genetic variants” in common that make them more likely to develop a disease or condition, such as AFib. Each variant can increase a person’s genetic risk for the disease, so researchers combine many known variants into an algorithm that calculates a person’s overall genetic risk score.

Evan Muse, M.D., Ph.D., giving a presentation.

Evan Muse, M.D., Ph.D., leads a discussion during a Journal Club meeting with trainees at the Scripps Translational Science Institute. (Scripps Translational Science Institute Photo)

Previous research on how well the score could predict a person’s risk of developing AFib centered on patient data that already existed. Muse and his colleagues wanted a real-time approach that would begin with tracking patients who had not yet been diagnosed with AFib and follow them over time. This type of prospective study provides important evidence for the test’s validity, as well as information to help health professionals determine whether they should use the test with their patients.

One challenge to conducting a prospective study was ensuring that Muse and his team did not miss anyone who had the condition. Typically, AFib is detected with a test in a doctor’s office or by having the person wear a portable device that monitors heart rhythm for 24 to 48 hours. But both approaches can miss people who only have brief episodes of AFib every now and then. Muse saw an opportunity to take a digital health approach by using a newer mobile heart monitor the size of a small bandage that stays on for up to two weeks, measuring and recording every heartbeat.

With CTSA Program interests in digital health and precision medicine, Muse received support to launch a study to evaluate more than 900 people who were at risk for AFib and had symptoms of the condition but had not been diagnosed. The study team determined the genetic risk score of the participants and evaluated them for AFib using the new wearable monitor.

Translating Risk into Empowerment

The genetic risk scores for study participants accurately predicted who would be diagnosed with AFib via digital monitoring. Participants with the highest scores were more than three times as likely to have AFib as those with the lowest scores, even after considering traditional risk factors such as smoking and age. The results were published in the March 2018 issue of PLOS Medicine.

Many people in the study would not have been diagnosed using the conventional approach to AFib screening. The genetic risk score could help doctors decide which patients would benefit most from more intense screening.

Muse also sees the risk score as a way to make patients more active partners in their health outcomes. Commercially available wearables, such as smartwatches, can already detect irregular heartbeats, so people who know they are at high risk could monitor their health in addition to working with their doctor.

“People shouldn’t feel doomed if they have a high risk score,” Muse said. “I want people to feel empowered. There are things you can do to reduce your risk.” For example, losing weight can help prevent or reduce the number of AFib episodes.

Training the Next Generation of Leaders

The AFib study was Muse’s first clinical trial as lead investigator. He credits his mentoring and training in the CTSA Clinical Research KL2 Scholar program for preparing him for the new role. The KL2 program provides project funding and guidance to early-stage clinical investigators to help them gain the skills to conduct multidisciplinary, translational and team-based research. This collaborative training helped Muse coordinate among 17 clinical sites for the AFib study — no small challenge for a first-time lead investigator.

“It really demonstrates the strengths of the CTSA Program at Scripps and the excellent team of leaders, who helped guide me at every step,” Muse said.

Muse said he will put his KL2-developed skills to further use as he continues to work to advance precision medicine and digital health. Currently, he is preparing to lead a pilot study in the NIH-led All of Us Research Program, an unprecedented precision medicine effort to gather data from more than 1 million U.S. participants to accelerate research and improve health.

Posted June 2018