Using a multi-stranded, computer-based approach encompassing genetic and chemical information, clinical data, and the medical literature can help improve outcomes for individuals with inherited cardiomyopathy, according to an international group of researchers.
Many cardiovascular conditions have a genetic basis and inherited cardiomyopathy, where abnormal changes in the heart muscle can cause arrythmia and sudden cardiac death, is one such example. Around 1 in 500 people, have inherited cardiomyopathies, making it the most common form of genetic heart disease.
The recent collapse of Danish soccer star Christian Eriksen, likely due to such a cardiac condition, highlights the importance of early diagnosis and preventative measures for these individuals. However, while a lot is now known about the genetics of inherited arrythmias and cardiomyopathies, it is a complex area and good communication between medical geneticists, cardiologists and primary care doctors can be key to good patient outcomes.
Collating available information without help can also be difficult for clinicians. “A major hurdle in adequate clinical and genetic care is that information is so dispersed across the literature. This increases the risk of misinterpretation, and leads to suboptimal care,” Job Verdonschot, a researcher at the Maastricht University Medical Center who has a focus on this area, but was not involved in the current study, told Clinical OMICs.
Rameen Shakur is currently based at Massachusetts Institute of Technology and was previously a Wellcome fellow at the University of Cambridge and Wellcome Sanger Institute. He led the current study, which is published in the journal npj Genomic Medicine, which was a collaboration between researchers from the Wellcome Sanger Institute, University of Cambridge, Massachusetts Institute of Technology and Lund University.
“To better assimilate genomic data into real world clinical options for the millions of patients with inherited cardiovascular diseases and their families, we require a more integrated appreciation of genetic, physical, bio-chemical and clinical data,” Shakur explained in a press statement.
There is a lot of variation between individuals with cardiomyopathy in terms of disease prognosis and suitability of treatments, which can make accurate diagnosis and outcome prediction difficult for clinicians who often employ a “wait and see” approach.
To try and improve outcomes for patients, Shakur and colleagues created a computer-based model to better define the cardiomyopathy prognosis associated with different genetic variants impacting the function of cardiac troponin T, a protein that forms part of a complex of similar proteins that regulate and control cardiac muscle contraction. The model included genomic data and biological and chemical information about troponin function and impact on cardiomyopathy outcome.
Shakur and team also analyzed data from almost 1000 individuals included in around 100 earlier studies to help improve the accuracy of clinical outcome prediction for individuals with these mutations and to look for possible drug targets for treating these patients.
They found that there were ‘hotspots’ of variation in the troponin T gene TNNT2 associated with higher or lower risk for carriers. For example, variation at regions 90–130 was linked to increased risk for sudden cardiac death and variation located at regions 131–179 with heart failure death and transplantation. In contrast, variation at locations 1–89 and 200–288, was linked to lower risk outcomes.
The researchers now want to assess if new drugs to target these hotspots could be developed. “The patient process in inherited cardiovascular disease, unlike oncology, has sort of lagged behind in actually getting to grips with developing personalized therapeutics,” Shakur told Clinical OMICs. He explained that he and his colleagues are currently working on translating their findings into developing new more personalized therapeutics for individuals with these genetic variants.
In areas such as cardiology and oncology, where large amounts of clinical and genetic data need to be analyzed, adopting a computer-based approach, often using artificial intelligence approaches such as machine learning, can make diagnosis, outcome prediction and treatment more effective and efficient.
Verdonschot agrees that a multi-stranded and collaborative approach is key to improving patient outcomes. “What is desperately needed is that working groups from professional societies, or project teams get together and produce a synthesis of all that is known to guide choices about the best personal strategies for patients. These can then be evaluated in the context of clinical trials, or observational studies.”
Shakur thinks one way to improve collaboration and communication is to provide cardiologists with better training in genetics and genomics-based approaches and to continue to provide funding to academic projects with a focus on cardiovascular genetics.
He also thinks improved patient knowledge will help drive precision cardiology forward. “There’s a whole new generation of patients now who understand their condition. And I think it will be an interesting few years moving forward, because now patients are more in tune with what’s going on.”
“This study is the next step in integrating precision cardiology into clinical care, and working more closely with clinical genetics colleagues and patients with their families, bridging the gap between research and day to day treatment decisions. This research has allowed us to also open the door to potential new therapies, which we hope to introduce soon.”