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

• 批准号: 10469679
• 负责人: Carolyn Y Ho
• 金额: $ 159.08万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 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/10469679
• 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; Modeling; Molecular; Molecular Motors; Morphology; Muscle Cells; Mutation; Natural History; Outcome; Pathogenicity; Patient Care; Patient-Focused Outcomes; Patients; Penetrance; Performance; Persons; 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 magnetic resonance imaging; 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

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 肥厚型心肌病（HCM）是一种原发性心肌疾病，其特征在于： 原因不明的左心室肥大（LVH）、肌细胞紊乱和纤维化。这是最普遍的遗传 心脏病，影响约500人中的1人。HCM已成为临床和基础科学研究的热点 几十年这些努力提供了显着的见解的分子基础和临床过程中， 疾病-定义肌节突变作为最常见的遗传病因，并表征 表型谱此外，先前的研究已经强调了HCM的巨大异质性。虽然 许多患者有严重的后果，包括心律失常、晚期心力衰竭和突发性心脏病。 死亡，许多其他人经历轻微的疾病，症状负担低，寿命正常。而且 在心脏形态和功能方面存在着惊人的多样性，即使在具有相同基础疾病的患者中也是如此。 肌节突变驱动这种显著异质性的因素知之甚少，突出了 迫切需要更好地表征疾病表达和临床结果的决定因素。 该提案旨在确定基因型和表型特征，这些特征解释了高度多样性的 HCM的表现。这些目标将通过利用最近建立的Sarcomeric 人类心肌病登记处（SHaRe），包含超过9000例HCM患者的数据，并应用 最先进的基因成像和统计分析我们的目标是：（1）确定共同的遗传变异， 影响有和没有驱动肌节突变（肌节突变）的HCM患者的疾病表达 和非肌节HCM）。这些分析将询问背景遗传变异，以检查 个人的遗传构成影响他们对疾病的易感性或抵抗力。我们还将开发 多基因风险评分，以评估常见遗传变异对疾病表达的累积效应。（二） 利用机器学习技术表征影响疾病表达的表型因素 识别新的，定量的高维成像特征， 共振（CMR）研究。然后，我们将把这些特征纳入严格的预测模型，以提高 临床危险分层。这种方法将使我们能够更深入地了解 通过使用CMR成像提供的全系列数字数据， 表型、疾病表现和临床结果之间的关系。成功实现这些目标将 推进我们对为什么不同患者的疾病经历如此不同的理解，提供新的 深入了解机制和治疗靶点，并确定疾病严重程度的新生物标志物。这些 结果将通过提高HCM患者的精确度和准确性来影响HCM患者的临床管理。 诊断和风险分层。最后，获得的见解可能会为更常见的心脏病提供信息， 例如射血分数保留的心力衰竭，具有类似的高度异质性表现。