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: 10298104
• 负责人: Maria Marquine
• 金额: $ 80.51万
• 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/10298104
• 关键词: Acculturation; Address; Administrative Supplement; Age; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Apolipoprotein E; Artificial Intelligence; Behavior; Behavioral; Biological Markers; Blood; California; Cellular Phone; Classification; Clinical; Cognitive; Cohort Studies; Communities; Coupled; Data; Data Collection; Detection; Development; Diabetes Mellitus; Diagnosis; Early Diagnosis; Early Intervention; Early identification; Electronics; Engineering; Ensure; Environment; Environmental Risk Factor; Funding; Gait speed; Gold; Health Care Costs; Heterogeneity; Hispanics; Hypertension; Incidence; Institution; Intervention; Language; Latino; Life; Light; Link; Machine Learning; Methods; Modeling; Neurocognitive; Neuropsychological Tests; Not Hispanic or Latino; Obesity; Participant; Pathologic; Pathologic Processes; Persons; Phenotype; Physical activity; Plasma; Population; Prevention; 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; base; bilingualism; cardiovascular disorder risk; clinical care; community engaged research; cost; dementia risk; digital; digital health; handheld mobile device; health data; health disparity; hypercholesterolemia; innovation; insight; literacy; machine learning method; men; mild cognitive impairment; mobile computing; multidisciplinary; multimodality; multiple data sources; neurofilament; novel; pre-clinical; secondary analysis; sensor; sex; social culture; tau-1; tool

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/拉丁美洲人的发展风险增加，发展阿尔茨海默氏病和相关痴呆症（ADRD） 与非西班牙裔白人相比。 西班牙裔/拉丁裔的心血管疾病（CVD）可能是病理学因素。 ADRD的变化在临床症状变得明显和最有可能的国际性之前开始了几年 在ADRD的最早阶段授予收益。 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。 ADRD的临床前阶段，包括睡眠，步态速度和体育锻炼等 在移动技术中，现在提供了不动说的新颖方法来被动地收集，跟踪和分析这些行为 毫不客气地，作为我们的日常生活的一种遗物。 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 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 情报（AI）功率分析和见解，再加上容易获得的消费电子传感器 （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。 居住在南加州，支撑研究Hree主要目的：AIM 1涉及确定数字 使用集成的被动移动传感的西班牙裔（n = 300; 50-70岁）中ADRD风险的签名 功能，源自智能手机和破旧的可穿戴设备以及机器学习方法。 由认知状况和CVD风险负担定义（糖尿病，高胆固醇血症，高血压，肥胖症， 吸烟）和Willoporate载脂蛋白E（APOE-ε4）和基于等离子体的AD生物标志物 ADD风险的分类2。 研究社会文化因素（例如，语言使用，裁员）对这些签名的影响 调查数字数据特征的变化是否预测了纵向神经认知的跨度 在西班牙裔的一部分中，有三年的风险。 在ADRD研究与工程创新的先锋队，支撑研究包括一个多学科 在此Cuttts构造边缘提案的各个方面具有专业知识的团队。 参与研究方法，我们与社区利益相关者合作，以确保或 向西班牙裔社区学习。 通过开发低负担，低成本的方法来识别西班牙裔风险的差异。