Integrative Motor Activity Biomarker for the Risk of Alzheimer's Risk

阿尔茨海默病风险的综合运动活动生物标志物

• 批准号: 9804299
• 负责人: Kun Hu
• 金额: $ 358.47万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9804299
• 关键词: Accelerometer; Address; Adopted; Adverse effects; Affect; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid beta-Protein; Artificial Intelligence; Autopsy; Biological Markers; Biology; Brain; Brain Stem; Categories; Cerebrovascular Disorders; Cessation of life; Characteristics; Circadian Dysregulation; Clinical; Cognition; Complex; Cost of Illness; Data; Data Analytics; Dementia; Disease; Disease Progression; Early Diagnosis; Elderly; Enrollment; Failure; Fractals; Functional disorder; Genetic; Genetic Risk; Goals; Healthcare; Histopathology; Immunochemistry; Impaired cognition; Individual; Intervention; Knowledge; Lead; Link; Measurement; Measures; Memory; Modeling; Monitor; Motor; Motor Activity; Movement; Muscle; Nerve; Neural Network Simulation; Neurobehavioral Manifestations; Participant; Pathology; Pattern; Performance; Phase; Physical activity; Physiological; Physiology; Preventive Intervention; Public Health; Regulation; Rest; Risk; Risk Factors; Series; Sex Differences; Sleep; Sleep Fragmentations; Sleep disturbances; Spinal Cord; Structure; System; Techniques; Therapeutic; Time; aged; analytical tool; base; circadian; clinical Diagnosis; deep learning; deep neural network; dementia risk; effective intervention; genetic risk factor; genetic variant; genome wide association study; high risk; insight; longitudinal database; mild cognitive impairment; motor control; multimodality; neuropathology; non-genetic; novel; novel therapeutic intervention; pre-clinical; predictive marker; predictive tools; sex; tau aggregation; wearable device

Project Summary/Abstract Developing effective interventions for prevention and treatment of Alzheimer's disease (AD) requires early detection of the disease. With recent advances in wearable device and physiological data analytical tools, it is feasible to assess many physiological functions unobtrusively by monitoring spontaneous motor activity. The goal of this project is to develop an integrated, non-invasive biomarker for the risk of Alzheimer's dementia using motor activity recordings. Among many physiological functions derived from motor activity, reduced physical activity levels, sleep disturbances, circadian dysfunction, and perturbation in fractal physiological regulation appear to precede the cognitive symptoms of Alzheimer's disease (AD), and signify an elevated risk of developing Alzheimer's dementia. However, it is unknown whether these dysfunctions predict Alzheimer's risk independently or they are interconnected to amplify/diminish each other's adverse effect. For a better prediction of Alzheimer's dementia using motor activity, PI and his team propose to leverage the above physiological risk factors using a novel artificial intelligence technique. To achieve this, PI and his team will utilize the existing longitudinal database of the Memory and Aging Project at Rush Alzheimer's Disease Center, in which over 1,400 old participants have been enrolled since 2005 and have agreed to (i) undergo annual motor activity monitor and structured clinical examinations and (ii) donate brain, the entire spinal cord, and selected nerve and muscles at the time of death. The ambulatory motor activity recordings collected annually will be used to assess a series of constructs including (i) physical activity (level of physical activity, intensity of physical activity, and average daily inactivity duration), (ii) sleep characteristics (total sleep duration, sleep efficiency, and sleep fragmentation), (iii) circadian rhythmicitiy (normalized 24-h amplitude, acrophase of daily activity rhythm, interdaily stability, and intradaily variability), and (iv) fractal motor regulation (temporal correlations in motor activity fluctuations at small and large time scales). Using these physiological measures together with clinical diagnosis, cognition, genetics, and post-mortem histopathology, three aims will be addressed: 1) determine whether a deep learning based neural network model can construct an integrated biomarker from the above physiological measures for better prediction of the risk of Alzheimer's dementia and the risk of conversion from mild cognitive impairment to Alzheimer's dementia in a short time frame (i.e., 2 years); 2) determine whether the integrated biomarker modifies or interacts with the genetic effect on AD; and 3) determine how specifically the integrated biomarker reflects AD pathology at autopsy. Achieving the aims will result in the first integrated biomarker of motor activity that leverages multimodal, noninvasive measurements for a better prediction of Alzheimer's dementia. The results to be obtained may also lead to a better understanding of the complex biology and physiology of AD, which will potentially guide the seeking of disease modifying therapies or interventions.

项目总结/摘要 开发有效的干预措施预防和治疗阿尔茨海默病（AD）需要早期 疾病的检测。随着可穿戴设备和生理数据分析工具的最新进展， 通过监测自发运动活动，可以不引人注目地评估许多生理功能。的 该项目的目标是开发一种综合的、非侵入性的阿尔茨海默氏痴呆症风险生物标志物 使用运动活动记录。在许多源自运动活动的生理功能中， 身体活动水平，睡眠障碍，昼夜节律功能障碍，以及分形生理学的扰动 调节似乎先于阿尔茨海默病（AD）的认知症状，并意味着风险升高 患上老年痴呆症的几率然而，这些功能障碍是否预示着阿尔茨海默氏症， 风险独立存在，或者它们相互关联，以放大/减少彼此的不利影响。对美好 利用运动活动预测阿尔茨海默氏症痴呆症，PI和他的团队建议利用上述 生理风险因素使用一种新的人工智能技术。为了实现这一目标，PI和他的团队将 利用拉什阿尔茨海默病中心记忆和衰老项目现有的纵向数据库， 自2005年起，已有超过1，400名长者参加，并同意（i）每年接受 运动活动监测和结构化的临床检查，以及（ii）捐献大脑、整个脊髓，以及 选择的神经和肌肉每年收集的步行运动记录 将用于评估一系列结构，包括（i）身体活动（身体活动的水平， 身体活动和平均每日不活动持续时间），（ii）睡眠特征（总睡眠持续时间、睡眠 效率和睡眠片段），（iii）昼夜节律性（标准化的24小时振幅，每日峰值相位 活动节律、日间稳定性和日内变异性），以及（iv）分形运动调节（时间 在小的和大的时间尺度上运动活动波动的相关性）。通过这些生理学的测量 与临床诊断、认知、遗传学和死后组织病理学一起， 解决：1）确定基于深度学习的神经网络模型是否可以构建集成的 - 来自上述生理测量的生物标志物，用于更好地预测阿尔茨海默氏痴呆症的风险， 在短时间内从轻度认知障碍转化为阿尔茨海默氏痴呆的风险（即，2 2）确定整合的生物标志物是否改变对AD的遗传效应或与其相互作用;以及 3)确定综合生物标志物如何具体反映尸检时的AD病理学。实现目标 将产生第一个运动活动的综合生物标志物， 更好地预测阿尔茨海默氏痴呆症。获得的结果也可能导致 更好地了解AD的复杂生物学和生理学，这将潜在地指导寻求 疾病修饰疗法或干预。