Hypertrophic Cardiomyopathy: Understanding the Heterogeneity of Disease Expression and Outcomes

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PROJECT SUMMARY Hypertrophic cardiomyopathy (HCM) is a primary disorder of the myocardium that is characterized by unexplained left ventricular hypertrophy (LVH), myocyte disarray, and fibrosis. It is the most prevalent genetic heart disorder, affecting ~1 in 500 people. HCM has been the focus of intense clinical and basic science study for decades. These efforts have provided remarkable insights into the molecular basis and clinical course of disease-- defining sarcomere mutations as the most common genetic etiology and characterizing the phenotypic spectrum. Additionally, prior studies have underscored the great heterogeneity of HCM. Although many patients have serious outcomes, including arrhythmias, advanced heart failure, and sudden cardiac death, many others experience mild disease with low symptom burden and normal longevity. Moreover, there is striking diversity in cardiac morphology and function, even amongst patients with identical underlying sarcomere mutations. The factors that drive such marked heterogeneity are poorly understood, highlighting the critical need to better characterize determinants of disease expression and clinical outcomes. This proposal seeks to identify genotypic and phenotypic features that account for the highly diverse manifestations of HCM. These goals will be addressed by leveraging the recently established Sarcomeric Human Cardiomyopathy Registry (SHaRe), containing data on over 9000 HCM patients, and applying state-of- the-art genetic, imaging, and statistical analyses. Our aims are: (1) To identify common genetic variation that impacts disease expression in HCM patients both with and without a driving sarcomere mutation (sarcomeric and non-sarcomeric HCM). These analyses will interrogate background genetic variation to examine how an individual’s genetic make-up influences their susceptibility or resistance to disease. We will also develop polygenic risk scores to assess the cumulative effect of common genetic variants on disease expression. (2) To characterize phenotypic factors that influence disease expression by utilizing machine-learning techniques to identify novel, quantitative high-dimensional imaging features from routinely-performed cardiac magnetic resonance (CMR) studies. We will then incorporate these features into rigorous prediction models to improve clinical risk stratification. This approach will allow us to look more deeply into the structure and function of the heart by using the full array of digital data available from CMR imaging, thereby drawing new correlations between phenotype, disease manifestations, and clinical outcomes. Successful completion of these aims will advance our understanding of why disease experience can be so different from patient to patient, provide new insights into mechanism and therapeutic targets, and identify novel biomarkers of disease severity. These results will impact clinical management of patients with HCM by improving the precision and accuracy of diagnosis and risk stratification. Finally, insights gained will likely inform more common forms of heart disease, such as heart failure with preserved ejection fraction, with similarly highly heterogeneous manifestations.