Elucidating hereditary transthyretin-mediated heart failure risk using machine learning, polygenic risk and recall by genotype approaches in African ancestry individuals

利用机器学习、多基因风险和非洲血统个体基因型记忆方法阐明遗传性​​转甲状腺素蛋白介导的心力衰竭风险

• 批准号: 10348687
• 负责人: Ron Do
• 金额: $ 74.38万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10348687
• 关键词: Address; Adopted; African American population; African Caribbean; African ancestry; Age; American Medical Association; Amyloid; Amyloid Fibrils; Amyloid deposition; Cardiac; Cardiomyopathies; Cardiovascular system; Clinical Trials; Complex; Data; Deposition; Diagnosis; Diphosphates; Disease; Documentation; Echocardiography; Electronic Health Record; Environment; Future; Genes; Genetic; Genotype; Germ-Line Mutation; Goals; Grant; Health system; Healthcare Systems; Heart failure; Hispanic Americans; Hospitalization; Image; Image Analysis; Individual; Inherited; Journals; Knowledge; Lead; Learning; Link; Machine Learning; Magnetic Resonance; Mediating; Medicine; Methods; Minority Groups; Morbidity - disease rate; Mutation; Myocardial; Myocardium; Nuclear; Onset of illness; Patients; Penetrance; Pennsylvania; Phenotype; Population; Prealbumin; Quality of life; Recontacts; Research; Risk; Risk Factors; Scanning; Single Nucleotide Polymorphism; Structure; Sum; Supportive care; Technetium; Technetium 99m; Testing; Universities; Work; base; biobank; burden of illness; clinical care; clinical risk; data repository; disease diagnosis; ethnic minority; heart imaging; improved; innovation; insight; mortality; multi-ethnic; multimodal data; multimodality; polygenic risk score; population health; precision medicine; racial and ethnic; racial and ethnic disparities; racial minority; risk stratification; scale up; screening; targeted treatment; tool; trait; underserved minority

PROJECT SUMMARY / ABSTRACT Mutations in the Transthyretin (TTR) gene can lead to deposition of abnormal amyloid fibrils in the myocardium, resulting in hereditary transthyretin amyloid cardiomyopathy (hATTR-CM) and leading to heart failure. Targeted therapies for hATTR-CM have recently been developed and have shown to improve mortality and hospitalization. Recently, we led a study (Journal of American Medical Association, Dec 2019) that showed that the TTR V122I mutation, commonly observed in racial/ethnic minorities (4% in African Americans (AAs) and 1% in Hispanic Americans (HAs)), confers two-fold increased risk of heart failure. Despite this strong effect, only 11% of V122I carriers with heart failure were appropriately diagnosed with hATTR-CM, suggesting marked underdiagnosis and mis-diagnosis of the disease. We further showed subclinical evidence of echocardiographic derangements in young, asymptomatic V122I carriers, suggesting early signs can occur well before onset of disease. We propose to extend our prior work by addressing knowledge gaps which are necessary for targeted therapies to attain their full potential. These include: understanding the incomplete penetrance of V122I; identifying V122I carriers in large health care systems where genotyping is not common; and understanding subclinical disease burden. In Aim 1, we will examine the interplay between a polygenic risk score, which are comprised of millions of single nucleotide variants with small effects, and V122I, a monogenic mutation with a single strong effect, analyzed in conjunction with clinical risk factors on heart failure in in 6,609 AAs and 9,006 HAs in the BioMe biobank and 5,833 AAs in the Penn Medicine Biobank (PMBB). In Aim 2, we will apply machine learning tools to multi-modal electronic health record (EHR) data to identify V122I carriers in ~8 million patients from an electronic health record (EHR) data repository at Mount Sinai. In Aim 3, we will evaluate subclinical effects of amyloid deposition on cardiac structural/functional traits in young, asymptomatic V122I carriers by recalling V122I carriers for imaging evaluation including research-grade echocardiograms, cardiac magnetic resonance and technetium nuclear scanning. The proposal is innovative because we are utilizing two large diverse ancestry EHR-linked biobanks from academic health systems (BioMe at Mount Sinai, and PMBB at University of Pennsylvania), along with adopting cutting-edge methods including multi-ethnic polygenic risk scores, and machine learning approaches on multi-modal EHR data. We further propose patient recall based on genotypes and perform deep phenotyping using comprehensive heart imaging scans. This proposal has the potential to realize the potential of precision medicine for heart failure in racial/ethnic minorities by informing clinical care, population management, risk stratification and clinical trials.

项目概要/摘要 甲状腺素运载蛋白 (TTR) 基因的突变可导致异常淀粉样原纤维沉积在 心肌，导致遗传性运甲状腺素蛋白淀粉样心肌病 (hATTR-CM) 并导致心脏 失败。 hATTR-CM 的靶向疗法最近已被开发出来，并被证明可以降低死亡率 和住院治疗。 最近，我们领导的一项研究（《美国医学会杂志》，2019 年 12 月）表明，TTR V122I 突变，常见于少数种族/族裔（非裔美国人 (AA) 为 4%，美国黑人为 1%） 西班牙裔美国人（HA））导致心力衰竭的风险增加两倍。尽管效果如此强烈，但只有 11% 患有心力衰竭的 V122I 携带者通过 hATTR-CM 进行了适当诊断，表明显着 对疾病的诊断不足和误诊。我们进一步显示了亚临床证据 年轻无症状 V122I 携带者的超声心动图紊乱表明可能出现早期症状 早在疾病发作之前。 我们建议通过解决有针对性的知识差距来扩展我们之前的工作 疗法以充分发挥其潜力。其中包括：了解V122I的不完全外显率； 在基因分型不常见的大型医疗保健系统中识别 V122I 携带者；和理解 亚临床疾病负担。在目标 1 中，我们将检查多基因风险评分之间的相互作用，这些评分是 由数百万个影响较小的单核苷酸变体组成，V122I 是一种单基因突变，具有 单一强效应，结合 6,609 例 AA 和 9,006 例心力衰竭的临床危险因素进行分析 BioMe 生物库中包含 HA，Penn Medicine 生物库 (PMBB) 中包含 5,833 个 AA。在目标 2 中，我们将应用 机器学习工具与多模式电子健康记录 (EHR) 数据相结合，以识别约 8 年内的 V122I 携带者 来自西奈山电子健康记录 (EHR) 数据存储库的百万名患者。在目标 3 中，我们将 评估淀粉样蛋白沉积对年轻无症状心脏结构/功能特征的亚临床影响 V122I 携带者通过召回 V122I 携带者进行影像评估，包括研究级超声心动图， 心脏磁共振和锝核扫描。 该提案具有创新性，因为我们正在利用来自两个大型不同血统的 EHR 相关生物库 学术卫生系统（西奈山的 BioMe 和宾夕法尼亚大学的 PMBB），以及 采用尖端方法，包括多种族多基因风险评分和机器学习方法 多模式 EHR 数据。我们进一步提出基于基因型的患者回忆并进行深度 使用综合心脏成像扫描进行表型分析。 该提案有可能实现针对种族/族裔心力衰竭的精准医疗的潜力 通过向临床护理、人口管理、风险分层和临床试验提供信息来帮助少数群体。