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.

项目概要 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，虽然这种差异背后的因素尚不清楚，但有所增加。 西班牙裔/拉丁裔的心血管疾病（CVD）风险可能是一个病理因素。 ADRD 的变化在临床症状变得明显之前几年就开始发生，干预措施最有可能 迫切需要开发最早检测 ADRD 的工具。 ADRD 的表现，尤其是西班牙裔，他们在较早的年龄就出现 ADRD 症状，但 与其他群体相比，在更晚期的疾病阶段被诊断出各种行为表现出变化。 ADRD 的临床前阶段，包括睡眠、步态速度和体力活动等。 移动技术现在提供了被动收集、跟踪和分析这些行为的新方法， 我们的初步工作表明，作为一个人在日常生活中的应用，不引人注意。 从智能手机和可穿戴设备被动收集数字健康数据的机器学习模型 区分患有和不患有轻度认知障碍的人，在主要非西班牙裔人群中准确率达到 85% 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）驱动的分析和洞察，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），识别 ADRD 风险的数字生物标志物，重点关注血管 扩大西班牙裔/拉丁裔对痴呆症的贡献。 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 使用集成无源移动传感技术绘制西班牙裔（N=300；年龄 50-70 岁）ADRD 风险特征 来自智能手机和腕戴式可穿戴设备的功能以及机器学习方法将带来 ADRD 风险。 由认知状态和心血管疾病风险负担（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，以进一步研究 ADRD 风险的分类目标 2 研究 ADRD 风险数字签名的性别差异。 我们还将研究社会文化因素（例如语言使用、文化适应）对这些特征的影响。 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 在有或没有 ADRD 风险的西班牙裔人群中进行了三年的研究 位于一家著名的研究机构内。 作为 ADRD 研究和工程创新的先锋，拟议的研究包括多学科 团队在这一前沿提案的各个方面都具有专业知识，并认识到社区的价值。 参与研究方法，我们与社区利益相关者合作，以确保我们的研究的相关性 我们的工作可以彻底改变 ADRD 的早期检测并减少 ADRD。 通过开发一种低负担、低成本的方法来识别西班牙裔 ADRD 风险，从而消除差异。