A prospective multiethnic HFpEF cohort from California's Central Valley

摘要 这是一项新的U01申请，旨在为预期的多种族HFpEF队列建立临床中心(CC) 从加州中央山谷来进行深入的表型分析。调查团队是多元化的， 多学科，并与医学院、工程学院和 农业与环境科学学院。调查团队高度合作，并拥有 以前参与大型研究网络的良好记录，涉及来自 不同来源，包括电子病历、移动保健技术和直接面向参与者的邮件/网络 传送门。加州大学戴维斯分校心力衰竭网络拥有全国最多元化的患者群体之一， 来自加州中央山谷的大量拉丁裔人口。我们的网络有广泛的 病人数量显著增长的捕鱼区。提议的深度表型分析将需要 加州大学戴维斯分校新开发的全身PET扫描的优势，以破译器官特异性的作用 不同和异质的HFpEF人群中代谢综合征和炎症的变化。一 拟议研究的独特之处在于使用功能连接学分析来直接开发 HFpEF群体的异质性。事实上，正是这种异质性提供了必要的数据 需要派生互连以测试关键网络驱动程序，这将提供对 治疗的潜在分子靶点以及对HFpEF亚型的公正分类。最后， 功能连接学分析实现了新数据集(例如微生物群)的模块化“插件”，源自 来自心脏分享网络中其他CC的深入表型分析。因此，我们建议的研究 承诺在技术和概念上对分子的整体理解做出贡献 HFpEF中异质性疾病的潜在机制。

ABSTRACT This is a new U01 application to establish a Clinical Center (CC) for a prospective multiethnic HFpEF cohort from California central valley for deep phenotyping analyses. The investigative team is diverse, multidisciplinary, and complementary with investigators from School of Medicine, College of Engineering and College of Agricultural and Environmental Sciences. The investigative team is highly collaborative and has strong track records of previous participation in large-scale research networks involving electronic data from heterogeneous sources, including EHRs, mobile health technologies, and direct-to-participant mailings/web portals. UC Davis Heart Failure Network has one of the most diverse patient population in the country, with a large representation of Latino population from California Central Valley. Our network has an extensive catchman area with a momentous growth in patient volume. The proposed deep phenotyping will take advantage of the newly developed total-body PET scan at UC Davis to decipher the roles of organ-specific variations in metabolic syndrome and inflammation in a diverse and heterogenous HFpEF population. One unique feature of the proposed study is the use of functional connectomics analyses to directly exploit the heterogeneity in the HFpEF population. Indeed, it is the heterogeneity that provides the necessary data needed to derive the interconnections to test the critical network drivers, that will provide eventual insights into potential molecular targets for therapy as well as the unbiased classifications of subtypes of HFpEF. Finally, functional connectomics analyses enable modular “plugin” of new datasets (e.g., microbiomes), derived from deep phenotyping analyses from other CCs in the HeartShare Network. Therefore, our proposed study promises to contribute both technically and conceptually to the overall understanding of the molecular mechanisms underlying the heterogenous disease in HFpEF.