Hypertrophic Cardiomyopathy: Understanding the Heterogeneity of Disease Expression and Outcomes

肥厚型心肌病：了解疾病表现和结果的异质性

基本信息

批准号：
10299353
负责人：
Carolyn Y Ho
金额：
$ 159.77万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10299353
关键词：
3-Dimensional Accounting Address Adverse event Affect Arrhythmia Atlases Atrial Fibrillation Automobile Driving Basic Science Cardiac Cardiomyopathies Cardiovascular Diseases Categories Cessation of life Classification Clinical Clinical Course of Disease Clinical Management Clinical Sciences Complex Computer Vision Systems Consequentialism DNA Dangerousness Data Data Set Diagnosis Disease Disease Resistance EFRAC Family Physicians Fibrosis Genes Genetic Genetic Predisposition to Disease Genetic Variation Genomics Genotype Geometry Goals Heart Heart Diseases Heart failure Heterogeneity Human Hypertrophic Cardiomyopathy Image Individual Left Ventricular Hypertrophy Longevity Machine Learning Magnetic Resonance Magnetic Resonance Imaging Modeling Molecular Molecular Motors Morphology Muscle Cells Mutation Natural History Outcome Pathogenicity Patient Care Patient-Focused Outcomes Patients Penetrance Performance Phenotype Predisposition Prevalence Registries Resolution SNP array Sarcomeres Severity of illness Specificity Standardization Statistical Data Interpretation Structure Symptoms Techniques Thick Variant adverse outcome automated analysis automated segmentation cardiac muscle disease case control clinical heterogeneity clinical risk cohort diagnostic accuracy digital disabling symptom disease heterogeneity disorder risk experience family management follow-up genetic analysis genetic makeup genetic testing genetic variant genome wide association study heart dimension/size heart function heart imaging heart rhythm high dimensionality improved insight machine learning algorithm new therapeutic target non-genetic novel novel marker patient population personalized diagnostics polygenic risk score predict clinical outcome predictive modeling premature preservation resilience risk prediction risk prediction model risk stratification secondary analysis segmentation algorithm success sudden cardiac death therapeutic target trait

项目摘要

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.

项目摘要肥厚性心肌病（HCM）是心肌的主要疾病，其特征是无法解释的左心室肥大（LVH），肌细胞混乱和纤维化。它是最普遍的通用心脏病，影响500人中约1人。 HCM一直是激烈的临床和基础科学研究的重点几十年来。这些努力为分子基础和临床过程提供了巨大的见解疾病 - 将肌节突变定义为最常见的遗传病因和表征表型光谱。额外的，先前的研究强调了HCM的巨大异质性。虽然许多患者的结果严重，包括心律不齐，晚期心力衰竭和突然的心脏死亡，许多其他人患有轻度疾病，伯恩伯恩症状低，寿命正常。而且，那里即使在具有相同基础的患者中，心脏形态和功能的多样性也令人震惊肌节突变。驱动这种明显异质性的因素对更好地表征疾病表达和临床结果的迫切需要。该建议旨在识别解释高度潜水员的基因型和表型特征 HCM的表现。这些目标将通过利用最近建立的肉瘤来解决人类心肌病注册中心（共享），包含9000多名HCM患者的数据，并应用 ART遗传，成像和统计分析。我们的目标是：（1）确定常见的遗传变异影响有或没有驱动肌节突变的HCM患者的疾病表达（肉瘤）和非核心HCM）。这些分析将询问背景遗传变异以检查个人的遗传化妆会影响其易感性或对疾病的抵抗。我们还将发展多基因风险评分评估常见遗传变异对疾病表达的累积影响。（2）表征通过使用机器学习技术影响疾病表达的表型因素确定来自常规性心脏磁的新颖，定量的高维成像特征共振（CMR）研究。然后，我们将这些功能纳入严格的预测模型中以改进临床风险分层。这种方法将使我们能够更深入地研究通过使用CMR成像可用的全部数字数据，从而绘制新的相关性在表型，疾病表现和临床结局之间。这些目标的成功完成将促进我们对为什么疾病经历在病人到患者之间如此不同的理解，提供新的洞悉机制和治疗靶标，并确定疾病严重程度的新型生物标志物。这些结果将通过提高HCM患者的临床管理来提高精确性和准确性诊断和风险分层。最后，获得的见解可能会为心脏病的更常见形式提供信息，例如心力衰竭和保留的射血分数，具有类似高度异质的表现。