Genetic cardiomyopathy is increasingly recognised and can lead to heart failure, arrhythmia and sudden cardiac death. Some gene positive patients have rapidly progressive disease with high rates of heart failure and cardiac transplantation, while others present with SCD. Other gene positive patients will never develop cardiomyopathy. At present, we cannot distinguish between these groups and rely on expensive and labour-intensive surveillance by electrocardiography, echocardiography and sometimes cardiac magnetic resonance imaging. This study will investigate existing and novel biomarkers (including blood, urine electrocardiographic and imaging) at various stages of disease in patients with a personal or family history of TTN, MYBPC3, LMNA, FLNC or DSP gene variant, which are known to cause cardiomyopathy.
There is a growing appreciation for the role that genetics play in the development of cardiomyopathy, which can lead to heart failure, arrhythmia and sudden cardiac death. Increased use of genetic testing has identified numerous gene variants, which cause cardiomyopathy with dilated, hypertrophic, restrictive, non-dilated left ventricular and arrhythmogenic right ventricular phenotypes described. Some gene variants cause a rapidly progressive cardiomyopathy with high rates of heart failure and cardiac transplantation, while others present with SCD, meaning that genotype-specific risk stratification and clinical surveillance is urgently needed. Some gene-positive individuals will never develop cardiomyopathy due to variable penetrance. At present, we cannot distinguish between these patients and therefore rely on expensive and labour-intensive surveillance by electrocardiography, echocardiography and sometimes cardiac magnetic resonance imaging. For every gene-positive affected individual with cardiomyopathy, cascade genetic testing will identify other gene-positive family members who are often asymptomatic and may not yet be affected. A blood or urine-based biomarker that identifies pre-clinical disease or cardiomyopathy would allow for more efficient monitoring of gene positive people and could replace multiple, repeated electrocardiograms, echocardiograms and cardiac magnetic resonance imaging scans. A biomarker that accurately identifies pre-clinical cardiomyopathy could enable targeted early treatment. A biomarker that predicts future disease progression would be of high clinical value.
Study Type
OBSERVATIONAL
Enrollment
750
This study will investigate existing and novel biomarkers (including blood, urine electrocardiographic and imaging) at various stages of disease in patients with a personal or family history of TTN, MYBPC3, LMNA, FLNC or DSP gene variant, which are known to cause cardiomyopathy. Cardiomyopathy will be defined per European Society of Cardiology cardiomyopathy guidelines and heart failure stage will be defined per American Heart Associate guidelines.
Queen Elizabeth University Hospital
Glasgow, United Kingdom
RECRUITINGBiomarker performance
Diagnostic performance of existing and novel biomarkers across the spectrum of disease in patients with pathogenic/ likely pathogenic TTN, MYBPC3, LMNA, FLNC or DSP gene variants.
Time frame: 3 years
Biomarker correlation
Correlation of biomarkers with cardiac imaging measures of inflammation and myocardial fibrosis in genetic cardiomyopathies.
Time frame: 3 years
Prediction of cardiomyopathy development
Investigation of biomarkers that can predict which patients (who are gene positive without cardiomyopathy) will develop cardiomyopathy, heart failure, arrhythmia, or die
Time frame: 3 years with long-term data linkage
Prediction of cardiomyopathy progression
Investigation of biomarkers that can predict which patients (who are gene positive with cardiomyopathy) will have progressive cardiomyopathy, heart failure, arrhythmia, or die.
Time frame: 3 years with long-term data linkage
Natural history of genetic cardiomyopathies
Investigate the natural history of genetic cardiomyopathy due to variants in TTN, MYBPC3, LMNA, FLNC and DSP genes.
Time frame: 3 years with long-term data linkage
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