A prospective multiethnic HFpEF cohort from California's Central Valley

摘要 这是一项新的U 01申请，旨在为前瞻性多种族HFpEF队列建立临床中心（CC） 来自加州中央谷的样本进行深入的表型分析。调查小组成员多样 多学科，并与医学院，工程学院和 农业与环境科学学院。调查小组高度合作， 以前参与涉及电子数据的大型研究网络的良好记录， 异构来源，包括EHR、移动的医疗技术和直接发送给参与者的邮件/网络 门户加州大学戴维斯分校心力衰竭网络拥有全国最多样化的患者人群之一， 来自加州中央谷拉丁裔人口的大量代表。我们的网络拥有广泛的 catchman地区的病人数量大幅增长。拟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， 不同异质性HFpEF人群中代谢综合征和炎症的变化。一 这项研究的一个独特之处是使用功能连接组学分析来直接利用 HFpEF人群的异质性。事实上，正是异质性提供了必要的数据 需要推导出互连来测试关键网络驱动程序，这将提供最终的见解， 治疗的潜在分子靶点以及HFpEF亚型的无偏倚分类。最后， 功能连接组学分析使得能够模块化地“插入”新数据集（例如，微生物），来源于 来自HeartShare Network中其他CC的深度表型分析。因此，我们提出的研究 有望在技术上和概念上对分子生物学的整体理解做出贡献。 HFpEF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.