Multi-omic markers of cardiac function and structure within the pressure overloaded heart

项目总结/摘要 这项拟议的研究是出于主动脉瓣狭窄（AS）的公共卫生重要性，AS是一种常见的 与发病率和死亡率相关的瓣膜性心脏病。一旦出现症状， 如果不治疗，1年时的死亡率为50%。一旦心脏代偿机制出现临床症状 失败，表明需要主动脉瓣置换术（AVR），但由于AS主要影响老年人， 症状往往被错误地归因于共病条件，高龄，或两者-导致延迟 治疗虽然最终接受AVR的大多数患者的症状和体征都有所改善， 存活率，近一半的患者在AVR后的第一年内死亡或未能获得症状和健康状况 他们进行AVR的改进。我们的研究表明，不可逆的心脏重塑和 与延迟治疗相关的损伤导致了这些不良的临床结果，突出了未满足的需求， 客观和敏感的措施，以告知有关AVR时机的临床决策。我们的长期目标是 开发和实施基于组学的精准医学方法，用于识别严重AS患者 有不可逆心脏重塑和损伤风险的患者，他们将从早期AVR中受益，我们的中心 假设反映心脏结构和功能维度的多组学特征将识别 不可逆的心脏重构，因此预测AVR的临床反应。我们之前的工作和 初步研究为我们的假设提供了强有力的支持，并表明我们的经验 多学科团队是唯一有资格完成拟议研究的团队。我们已经确定了代谢组学 与心脏功能和结构测量密切相关并预测AVR后死亡率的特征， 以及与心脏功能相关的蛋白质组特征，其区分严重AS和与 mortality.在这个建议中，我们采用了纵向系统生物学方法，利用了最新的 蛋白质组学和代谢组学（多组学）科学：发现，测试和交叉验证多组学特征， 严重AS患者的心脏功能和结构（目的1）;纵向多组学特征 AVR后心脏结构和功能变化的特征和相关性（目标2）;并评价 多组学特征预测AVR反应的准确性（目标3）。我们的方法将使我们能够 确定严重AS患者不可逆心脏重塑和损伤的多组学特征。这些 数据将支持开发新的基于精确医学的策略，用于识别 将受益于早期临床干预，以降低AVR后的死亡率并最大限度地提高健康水平。

PROJECT SUMMARY/ABSTRACT This proposed study is motivated by the public health importance of aortic stenosis (AS), a common form of valvular heart disease that is associated with substantial morbidity and mortality. Once symptomatic, survival is a dismal 50% at 1-year if left untreated. Clinical symptoms develop once cardiac compensatory mechanisms fail, indicating the need for aortic valve replacement (AVR), but because AS primarily affects the elderly, symptoms are often incorrectly attributed to comorbid conditions, advanced age, or both—resulting in delayed treatment. While most patients that eventually undergo AVR experience improvements in symptoms and survival, nearly half of patients die within the first year after AVR or fail to reap the symptom and health status improvements for which they underwent AVR. Our research suggests that irreversible cardiac remodeling and injury related to delayed treatment contribute to these poor clinical outcomes, highlighting the unmet need for objective and sensitive measures to inform clinical decisions regarding the timing of AVR. Our long-term goal is to develop and implement an omics-based precision medicine approach for identifying patients with severe AS at-risk for irreversible cardiac remodeling and injury who would benefit from earlier AVR, and our central hypothesis is that multi-omic signatures reflective of dimensions of cardiac structure and function will identify irreversible cardiac remodeling and therefore predict the clinical response to AVR. Our prior work and preliminary studies provide strong support for our hypothesis and demonstrate that our experienced multidisciplinary team is uniquely qualified to complete the proposed study. We have identified metabolomic signatures that relate strongly to measures of cardiac function and structure and predict mortality after AVR, and also proteomic signatures relating to cardiac function that differentiate severe AS and associate with mortality. Within this proposal, we adopt a longitudinal systems biology approach that leverages the latest in proteomic and metabolomic (multi-omic) sciences to: discover, test, and cross-validate multi-omic signatures of cardiac function and structure in patients with severe AS (Aim 1); characterize longitudinal multi-omic signatures and associations with changes in cardiac structure and function after AVR (Aim 2); and evaluate the accuracy with which multi-omic signatures predict response to AVR (Aim 3). Our approach will enable us to identify multi-omic signatures of irreversible cardiac remodeling and injury in patients with severe AS. These data will support the development of new precision medicine-based strategies for identifying patients who would benefit from earlier clinical intervention in an effort to reduce mortality and maximize health after AVR.