Diagnostic Tools for Targeted Heart Failure Treatments

心力衰竭(HF)目前是一种全球性的流行病，预计未来还会增加。 尽管存在严重需求，但由于以下方面的要求，高频药物的研发工作正在减少 大型和长期的临床试验，以验证对发病率和死亡率终点的疗效。A最近 然而，FDA发布的指南草案澄清了对症状或身体功能的影响 如果没有对生存或住院的有利影响，可能成为批准药物治疗的基础 治疗心力衰竭。开发Start诊断工具以监控的功能属性 心血管系统，包括心肌功能，将支持心力衰竭药物的新趋势 发展。每年&gt；100万次诊断性插管； 数据存储在电子健康记录中。分析这些数据可以提供 史无前例的机会来确定血液动力学中的功能变化模式 各种手足口病。我们的方法将结合计算和机器学习方法来 实现这一目标。男性和女性的糖尿病(DM)分别是2X和4X， 发生心力衰竭(HF)事件的风险更高。最近的一项表型分组研究发现， 患有糖尿病的HFpEF患者心血管死亡和住院风险最高 其他团体。这项研究带来了开发针对HFpEF的靶向治疗的机会 与糖尿病(DHFpEF)通过开发诊断工具来识别候选dHFpEF患者。这里, 我们将测试诊断工具对HFpEF分层的可行性，并在体外对其进行建模。 这项研究的目标1是优化已经开发的人工智能辅助的诊断平台， 系统生物学集成患者分层技术(AASIST)，用于分析数据 经胸超声心动图(TTE)和右心导管术(RHC)收集 目的：对dHFpEF表型进行分类。我们希望确定几个dHFpEF组 由其心肌的机械特性(例如，左心室抬高)定义 刚度)。 本研究的目的二是分析左心室硬度和收缩性能的相关参数。 使用从糖尿病患者细胞中重组的工程化心脏组织NuHeart进行体外实验。我们 将在各种环境挑战下培养NuHeart以模拟dHFpEF。虽然生活方式 我们推测，风险(例如吸烟、低体力活动)可能会超过遗传影响 来自糖尿病患者的NuHeart易患HFpEF表型 这取决于它的文化条件。

Heart failure (HF) is a global epidemic at present and is projected to increase in the future. Despite the critical needs, HF drug discovery efforts are declining because of the requirement of large and long clinical trials to validate efficacy in morbidity and mortality endpoints. A recently issued FDA draft guidance, however, clarified that the effect on symptoms or physical function without a favorable effect on survival or hospitalization could be a basis for approving drugs to treat heart failure. Developing start diagnostic tools to monitor functional properties of cardiovascular systems, including myocardium function, will support a new trend of HF drug development. Annually >1 million diagnostic catheterizations have been performed; numerous data are stored in the electronic health record. Analyzing those data could provide an unprecedented opportunity to identify patterns of functional changes in the hemodynamics of various HFs. Our approach will combine computational and machine learning methods to achieve this goal. Diabetes mellitus (DM) in men and women have a 2X and 4X, respectively, higher risk of heart failure (HF) incident. A recent phenogrouping study identified a subgroup of HFpEF with diabetes having the highest risk of cardiovascular death and hospitalization among other groups. This study brought up an opportunity to develop a targeted therapy for HFpEF with DM (dHFpEF) by developing diagnostic tools to identify candidate dHFpEF patients. Here, we will test the feasibility of diagnostic tools to stratify HFpEF and model it in vitro. Aim 1 of the study is to optimize an already developed diagnostic platform, AI-Assisted, Systems-biology Integrated patient Stratification Technology (AASIST), to analyze data collected by trans-thoracic echocardiography (TTE) and right heart catheterization (RHC) for the purpose of classifying dHFpEF phenotypes. We expect to identify a few groups of dHFpEF defined by their mechanical properties of the myocardium (e.g., elevated left ventricular stiffness). Aim 2 of this study is to analyze corresponding parameters of LV stiffness and contractility in vitro using engineered heart tissues, NuHeart, reconstituted derived from DM patients’ cells. We will culture NuHeart with various environmental challenges to model dHFpEF. While lifestyle risks (e.g., smoking, low physical activities) may outweigh genetic influences, we hypothesize that NuHeart derived from a diabetic patient is susceptible to develop HFpEF phenotype depending on its culture conditions.