Leveraging the Electronic Health Record and Integrating Social and Biological Data to Expand Dementia Research in Understudied Populations in Los Angeles County

项目摘要 该提案要求UH2/UH3资助建立一个研究阿尔茨海默病的合作研究计划 (AD)通过洛杉矶的UCLA卫生系统， 县该项目由一个在AD/ADRD临床和神经生物学方面具有专业知识的多学科团队领导 (PI Vossel，Co-I Chang）;社会和环境（PI Mayeda）;社会文化（Co-I Díaz-Santos，Adrissi）;以及 基因组（Co-I Chang，Deters）研究。该计划是基于在玛丽S。伊斯顿老年痴呆症中心 研究和护理（主任沃塞尔），其中有强大的关系，与大洛杉矶。社区和持续 教育和外联活动。AD和ADRD包括具有一系列环境，社会， 基因组和临床机制。为了提高我们对AD/ADRD异质性的理解，个体 所有群体，包括传统上未得到充分研究的群体，必须加以研究。加州大学洛杉矶分校的卫生系统服务于 美国最大的西班牙裔/拉丁裔（HL）、黑人和亚裔美国人/太平洋岛民（AAPI）人口之一， 美国的这些研究不足的人群的传统AD/ADRD招募具有挑战性。 利用电子健康记录（EHR）工具招募未充分研究的人群并进行常规分析 收集的EHR数据将降低进入门槛。我们的目标是招募HL、Black和AAPI AD/ADRD 通过电子健康记录工具以及与初级保健诊所和社区的合作伙伴关系，接下来，我们将评估 EHR工具和伙伴关系的招聘效率，其次是EHR与社会决定因素的增强 健康（SDOH），遗传，血液生物标志物，神经成像和神经生理学数据，以研究 AD/ADRD在这些研究不足的人群中。我们的长期目标是开发和扩展与EHR相关的 洛杉矶的AD/ADRD研究基础设施县通过加州大学洛杉矶分校健康的网络网站，允许整合 SDOH，神经生物学和基因组数据。我们将改善招募和保留未充分研究的ADRD 在研究人群中招募160名HL、160名黑人和100名AAPI AD/ADRD个体和对照。在 在初步工作中，我们的团队已经将痴呆筛查整合到EHR中，以改善AD和ADRD的诊断 在初级保健和研究基因组，社会和环境的风险因素，AD和ADRD从EHR。 在UH2阶段，我们将1）在HL、Black和AAPI AD/ADRD参与者招募中使用EHR工具，2） 让HL、Black和AAPI社区合作伙伴参与进来，以改善研究招募和设计，以及3）分享临床， 社会和基因组数据。在UH3阶段，我们将评估痴呆症 HL、黑人和AAPI个体中AD和ADRD招募的筛查和EHR工具，5）识别AD 来自健康的社会决定因素、血液生物标志物、神经生理学和合并症因素的内表型 在HL、Black和AAPI个体中，以及6）使用多基因预测HL、Black和AAPI个体中的AD 风险、SDOH和合并症。这一创新计划将支持我们的目标是发现个性化的风险 这些因素将产生早期的行为干预和精确的治疗。

PROJECT SUMMARY This proposal requests UH2/UH3 funding to build a collaborative research program to study Alzheimer’s disease (AD) and AD related dementias (ADRD) in diverse populations through the UCLA Health System in Los Angeles County. This program is led by a multi-disciplinary team with expertise in AD/ADRD clinical and neurobiological (PI Vossel, Co-I Chang); social and environmental (PI Mayeda); sociocultural (Co-I Díaz-Santos, Adrissi); and genomic (Co-I Chang, Deters) research. The program is based in the Mary S. Easton Center for Alzheimer’s Research and Care (Director Vossel), which has robust ties with greater L.A. communities and ongoing education and outreach activities. AD and ADRD comprise syndromes with a spectrum of environmental, social, genomic, and clinical mechanisms. To improve our understanding of the heterogeneity of AD/ADRD, individuals from all groups, including traditionally understudied groups, must be studied. The UCLA Health System serves one of the largest Hispanic/Latinx (HL), Black, and Asian American/Pacific Islander (AAPI) populations in the United States. Traditional AD/ADRD recruitment of these understudied populations has been challenging. Leveraging electronic health records (EHR) tools to recruit understudied populations and analyzing routinely collected EHR data would lower the barrier to entry. Our objective is to recruit HL, Black, and AAPI AD/ADRD cohorts via EHR tools and partnerships with primary care clinics and communities. Next, we will evaluate recruitment efficiency of EHR tools and partnerships, followed by augmentation of EHR with social determinants of health (SDOH), genetic, blood biomarker, neuroimaging, and neurophysiology data to study mechanisms of AD/ADRD in these understudied populations. Our long-term goal is to develop and scale an EHR-linked AD/ADRD research infrastructure in L.A. County through UCLA Health’s network sites, allowing integration of SDOH, neurobiological and genomic data. We will improve recruitment and retention of understudied ADRD populations in research by enrolling 160 HL, 160 Black, and 100 AAPI AD/ADRD individuals and controls. In preliminary work, our team has integrated dementia screening in the EHR to improve AD and ADRD diagnosis in primary care and studied genomic, social, and environmental risk factors of AD and ADRD from the EHR. During the UH2 phase we will 1) utilize EHR tools in HL, Black, and AAPI AD/ADRD participant recruitment, 2) engage HL, Black, and AAPI community partners to improve study recruitment and design, and 3) share clinical, social, and genomic data on NIA-supported infrastructures. During the UH3 phase we will 4) evaluate dementia screening and EHR tools on AD and ADRD recruitment in HL, Black, and AAPI individuals, 5) identify AD endophenotypes from social determinants of health, blood biomarker, neurophysiologic, and comorbidity factors in HL, Black and AAPI individuals, and 6) predict AD among HL, Black, and AAPI individuals using polygenic risk, SDOH, and comorbidities. This innovative program will support our goal is to discover personalized risk factors that will yield early behavioral interventions and precise therapies.