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.

项目总结