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

识别西班牙裔/拉丁裔中阿尔茨海默病和相关痴呆症风险的数字表型

• 批准号: 10665003
• 负责人: Maria Marquine
• 金额: $ 77.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665003
• 关键词: Acculturation; Address; Administrative Supplement; Age; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Apolipoprotein E; Artificial Intelligence; Behavior; Behavior assessment; Behavioral; Biological Markers; Blood; California; Cellular Phone; Classification; Clinical; Cognitive; Cohort Studies; Communities; Coupled; Data; Data Collection; Detection; Development; Diabetes Mellitus; Diagnosis; Digital biomarker; Disparity; Early Diagnosis; Early Intervention; Early identification; Electronics; Engineering; Ensure; Environment; Environmental Risk Factor; Funding; Gait speed; Health Care Costs; Health Disparities Research; Heterogeneity; Hispanic; Hispanic Populations; Hypertension; Incidence; Institution; Intervention; Language; Latina Population; Latino Population; Life; Light; Link; Methods; National Institute on Minority Health and Health Disparities; Neurocognitive; Neuropsychological Tests; Not Hispanic or Latino; Obesity; Participant; Pathologic; Pathologic Processes; Persons; Phenotype; Physical activity; Plasma; Population; Populations at Risk; Prevention; Reduce health disparities; Research; Risk; Risk Factors; Sampling; Science; Sex Differences; Sleep; Smoking; Socioeconomic Status; Source; Stream; Symptoms; Technology; Test Result; Time; Underserved Population; United States; Vascular Dementia; Woman; Work; Wrist; advanced disease; aged; apolipoprotein E-4; bilingualism; cardiovascular disorder risk; clinical care; community engaged research; cost; dementia risk; digital; digital health; handheld mobile device; health data; hispanic community; hypercholesterolemia; innovation; insight; literacy; machine learning method; machine learning model; men; mild cognitive impairment; mobile computing; mobile sensing; multidisciplinary; multimodality; multiple data sources; neurofilament; novel; pre-clinical; secondary analysis; sensor; sex; sociocultural determinant; tau-1; tool; wearable device

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.

项目摘要 西班牙裔/拉丁美洲人患阿尔茨海默氏病和相关痴呆症（ADRD）的风险增加 与非西班牙裔白人相比。尽管这种差异的因素尚不清楚，但增加了 西班牙裔/拉丁裔的心血管疾病（CVD）风险可能是一个因素。病理 ADRD的变化在临床症状成为证据之前的几年开始，干预措施最有可能 在ADRD的最早阶段赋予收益。迫切需要开发工具来检测最早的 ADRD的表现，尤其是在西班牙裔人中，他们在早期出现ADRD的症状，但是 在更晚期的疾病阶段被诊断出来。各种行为显示 ADRD的临床前阶段，包括睡眠，获得速度和体育锻炼等。最近的创新 现在，在移动技术中，提供了新颖的方法来被动地收集，跟踪和分析这些行为 当一个人从事日常生活时，毫不客气地。我们的初步工作表明了 机器学习模型可从智能手机和可穿戴设备中被动收集的数字健康数据 具有和没有轻度认知障碍的差异化人员，主要非西班牙裔精度为85％ 白色样本。在NIA健康差异研究框架的指导下，我们建议利用艺术 情报（AI）功率分析和见解，再加上消费电子中的便利传感器 （智能手机，腕上戴可穿戴设备），以识别ADRD风险的数字生物标志物，重点是血管 在西班牙裔/拉丁美洲人中对痴呆症的贡献。增强现有的西班牙裔/拉丁美洲人的队列研究 拟议的研究居住在南加州，有三个主要目的：AIM 1涉及确定数字 使用集成的被动移动传感器 功能，源自智能手机和腕上戴的可穿戴设备以及机器学习方法。 ADRD风险将 由认知状况和CVD风险伯恩定义（糖尿病，高胆固醇血症，高血压，肥胖症， 吸烟），还将结合载脂蛋白E（APOE-ε4）和基于等离子体的AD生物标志物 ADRD风险的分类。 AIM 2研究ADRD风险的数字签名中的性别差异和AIM 3 检查社会文化因素（例如语言使用，培养）对这些签名的影响。我们也会 调查数字数据特征的变化是否预测了纵向神经认知的变化 在有和没有ADRD风险的西班牙裔人群中三年。安置在著名的研究机构中 在ADRD研究与工程创新的先锋队，拟议的研究包括多学科 在此尖端提案的各个方面具有专业知识的团队。认识一个社区的价值 - 参与研究方法，我们与社区利益相关者合作，以确保我们的相关性 研究西班牙裔社区。我们的工作可能会彻底改变对ADRD的早期发现并减少ADRD 通过开发低燃烧，低成本的方法来识别西班牙裔风险的差异。