Identifying Digital Phenotypes of Risk for Alzheimer's Disease and Related Dementias Among Hispanics/Latinos

项目摘要 西班牙裔/拉丁裔患阿尔茨海默病和相关痴呆症（ADRD）的风险增加 与非西班牙裔白人相比。虽然造成这种差异的因素还没有得到很好的理解，但增加了 西班牙裔/拉丁美洲人的心血管疾病（CVD）风险可能是一个促成因素。病理 ADRD的变化在临床症状变得明显之前开始年就开始了，干预最有可能 在ADRD的早期阶段提供益处。迫切需要开发工具，以最早发现 ADRD的表现，特别是在西班牙裔，谁在早期的ADRD症状，但 在更先进的疾病阶段诊断比其他群体。各种各样的行为显示了 ADRD的临床前阶段，包括睡眠、步态速度和身体活动等。最近的创新 在移动的技术中，现在提供了被动地收集、跟踪和分析这些行为的新方法， 不引人注意地，作为一个人从事他们的日常生活。我们的初步工作表明， 从智能手机和可穿戴设备被动收集的数字健康数据的机器学习模型 在主要为非西班牙裔的患者中，有轻度认知障碍和无轻度认知障碍的患者的准确率为85%， 白色样品。在NIA健康差异研究框架的指导下，我们建议利用人工 智能（AI）驱动的分析和见解，加上消费电子产品中现成的传感器 （智能手机、腕戴式可穿戴设备），以识别ADRD风险的数字生物标志物，重点关注血管 在西班牙裔/拉丁裔中，痴呆症的贡献。增强现有的西班牙裔/拉丁裔队列研究 居住在南加州，拟议的研究有三个主要目标：目标1涉及确定数字 使用集成无源移动的感知的西班牙裔（N=300;年龄50-70岁）ADRD风险特征 功能，来自智能手机和腕戴式可穿戴设备，以及机器学习方法。ADRD风险将 通过认知状态和CVD风险负担（糖尿病、高胆固醇血症、高血压、肥胖， 吸烟），还将纳入载脂蛋白E（APOE-ε4）和基于血浆的AD生物标志物， ADRD风险分类。目的2调查ADRD风险数字签名的性别差异，目的3 研究社会文化因素的影响（例如，语言使用，文化适应）。我们还将 研究数字数据特征的变化是否可以预测一段时间内的纵向神经认知变化。 在有和没有ADRD风险的西班牙裔亚组中进行了三年的研究。位于著名的研究机构内 作为ADRD研究和工程创新的先锋，拟议的研究包括多学科的 我们的团队在这一尖端提案的各个方面都具有专业知识。认识到社区的价值- 参与研究的方法，我们已经与社区利益相关者合作，以确保我们的相关性 学习西班牙语社区。我们的工作可能会彻底改变ADRD的早期检测， 通过开发一种低负担，低成本的方法来确定西班牙裔人中的ADRD风险，从而消除差异。

PROJECT SUMMARY Hispanics/Latinos are at increased risk for developing Alzheimer’s disease and related dementias (ADRD) compared to non-Hispanic Whites. While factors underlying this disparity are not well understood, increased cardiovascular disease (CVD) risk among Hispanics/Latinos is likely to be a contributory factor. Pathological changes of ADRD begin years before clinical symptoms become evident and interventions are most likely to confer benefit in the earliest stages of ADRD. There is a pressing need to develop tools to detect the earliest manifestations of ADRD, particularly in Hispanics, who develop symptoms of ADRD at earlier ages, yet are diagnosed at more advanced disease stages than other groups. A variety of behaviors show changes in the preclinical stages of ADRD, including sleep, gait speed, and physical activity, among others. Recent innovations in mobile technology now offer novel ways to collect, track, and analyze these behaviors passively and unobtrusively, as a person engages in their daily life. Our preliminary work demonstrated that the application of machine learning models to passively-collected digital health data from smartphones and wearables differentiated persons with and without mild cognitive impairment with 85% accuracy in a primarily non-Hispanic White sample. Guided by the NIA Health Disparities Research Framework, we propose to leverage artificial intelligence (AI)-powered analytics and insights, coupled with readily available sensors in consumer electronics (smartphones, wrist-worn wearables), to identify digital biomarkers of ADRD risk, with a focus on vascular contributions to dementia, among Hispanics/Latinos. Augmenting an existing cohort study of Hispanics/Latinos residing in Southern California, the proposed study has three principal aims: Aim 1 involves determining digital signatures of ADRD risk among Hispanics (N=300; aged 50-70 years) using integrated passive mobile sensing features, derived from smartphones and wrist-worn wearables, and machine learning methods. ADRD risk will be defined by cognitive status and CVD risk burden (diabetes, hypercholesterolemia, hypertension, obesity, smoking) and will also incorporate apolipoprotein E (APOE-ε4) and plasma-based AD biomarkers for further classification of ADRD risk. Aim 2 investigates sex differences in digital signatures of ADRD risk and Aim 3 examines the impact of sociocultural factors (e.g., language use, acculturation) on these signatures. We will also investigate whether changes in digital data features predict longitudinal neurocognitive change over a span of three years in a subset of Hispanics with and without ADRD risk. Housed within a renowned research institution at the vanguard of ADRD research and engineering innovations, the proposed study includes a multidisciplinary team with expertise across all aspects of this cutting-edge proposal. Recognizing the value of a community- engaged research approach, we have partnered with community stakeholders to ensure the relevance of our study to the Hispanic community. Our work could revolutionize early detection of ADRD and reduce ADRD disparities by developing a low burden, low-cost approach to identify ADRD risk among Hispanics.