Diagnostic Tools for Targeted Heart Failure Treatments

心力衰竭（HF）目前是一种全球性流行病，预计在未来会增加。 尽管有关键的需求，HF药物发现的努力正在下降，因为需要 大型和长期临床试验，以验证发病率和死亡率终点的疗效。一个最近 然而，FDA发布的指南草案澄清了对症状或身体功能的影响 对生存率或住院治疗没有有利影响的药物可能是批准药物的基础， 治疗心力衰竭开发启动诊断工具以监测 心血管系统，包括心肌功能，将支持HF药物的新趋势 发展每年进行的诊断性导管插入术超过100万次; 数据存储在电子健康记录中。分析这些数据可以提供一个 这是一个前所未有的机会，可以识别血液动力学功能变化的模式， 各种HF。我们的方法将结合联合收割机计算和机器学习方法， 实现这一目标。糖尿病（DM）在男性和女性分别有2X和4X， 心力衰竭（HF）事件的风险较高。最近的一项表型分组研究确定了一个亚组， HFpEF伴糖尿病患者的心血管死亡和住院风险最高， 其他各组本研究为HFpEF的靶向治疗提供了契机 通过开发诊断工具来识别候选的dHFpEF患者。在这里， 我们将测试诊断工具对HFpEF进行分层并在体外对其建模的可行性。 该研究的目的1是优化已经开发的诊断平台AI辅助， 系统生物学集成患者分层技术（AASIST），用于分析数据 通过经胸超声心动图（TTE）和右心导管插入术（RHC）收集 分类dHFpEF表型的目的。我们期望鉴定出几组dHFpEF 由心肌的机械特性限定（例如，升高的左心室 刚度）。 本研究的目的2是分析左室僵硬度和收缩力的相应参数， 体外使用工程心脏组织NuHeart，从DM患者的细胞中重建。我们 将用各种环境挑战培养NuHeart以模拟dHFpEF。尽管生活方式 风险（例如，吸烟、低体力活动）可能超过遗传的影响，我们假设 来自糖尿病患者的NuHeart易发生HFpEF表型 这取决于其培养条件。

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 A recently issued FDA draft guidance, however, clarified that the effect on symptoms or physical function without a favorable effect on survival or hospitalization could be a basis for approving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.