Thinking about walking: Can digital phenotyping of mobility improve the prediction of Alzheimer's dementia and inform on the pathologies and proteins contributing to this association?

思考步行：移动的数字表型可以改善阿尔茨海默氏痴呆症的预测并提供有关导致这种关联的病理学和蛋白质的信息吗？

• 批准号: 10524888
• 负责人: ARON S BUCHMAN
• 金额: $ 58.27万
• 依托单位: RUSH UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524888
• 关键词: Accelerometer; Address; Adult; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Attention; Biological Markers; Brain Pathology; Brain region; Cessation of life; Cognition; Cognitive; Complement; Complex; Data; Dementia; Disease; Early Onset Alzheimer Disease; Elderly; Gait; Impaired cognition; Impairment; Individual; Lead; Measures; Memory; Motor; Movement; Participant; Pathologic; Pathology; Phenotype; Prefrontal Cortex; Prevention; Proteins; Proteome; Public Health; Research; Resources; Risk; Role; Sampling; Set protein; Subgroup; Testing; Thinking; Walking; Work; Wrist; base; clinical care; cognitive ability; cognitive testing; digital; executive function; improved; improved mobility; indexing; motor impairment; novel; pre-clinical; prevent; sensor

ABSTRACT In its earliest stage Alzheimer’s disease does not manifest cognitive impairment while dementia is a late manifestation. A biomarker to identify preclinical Alzheimer’s dementia is crucial for treatments aimed at its prevention. Alzheimer’s disease can also degrade non-cognitive functions like mobility that precedes and predicts cognitive impairment in many older adults. To use mobility as a biomarker, it is crucial to identify the metrics that best predict Alzheimer’s dementia and the mechanisms that account for this association. We must think to move. Mobility requires motor and cognitive abilities that derive from distinct brain regions. This may explain why mobility is an early predictor of dementia. Yet, motor testing usually only quantifies movement duration. So, the role of cognitive abilities in the association of mobility with Alzheimer’s dementia is unclear. Unobtrusive sensors can be used to assess cognitive and motor metrics crucial for mobility. This study will use novel digital mobility phenotyping to improve the prediction of Alzheimer’s disease dementia and identify brain pathologies and proteins that inform on this association. This study responds to NOT-AG-20-053 and will add new resources to those available from 1000 older adults in the Rush Memory and Aging Project (R01AG17917). To improve the prediction of Alzheimer’s dementia, we will add cognitive mobility metrics e.g., motor planning and attentional metrics to a single-testing session. To capture the varied cognitive demands during everyday mobility, we will also add new multi-day mobility metrics obtained from a wrist sensor. Motor planning is related to supplementary motor area (SMA) and task attention and executive function are regulated by dorsolateral prefrontal cortex (DLPFC). So, we focus on these regions to identify mechanisms shared by mobility and Alzheimer’s disease dementia. In 200 decedents with available brain pathologies, we will collect new proteome data from SMA to complement the available DLPFC proteome. Aim 1 will add new digital cognitive mobility metrics to motor metrics obtained from a single-testing session as well as novel multi-day mobility metrics to improve the prediction of Alzheimer’s dementia. Sensors yield large numbers of mobility metrics. Aim 1 will isolate individual metrics that predict Alzheimer’s dementia. Aim 2 will analyze these novel metrics with a second approach to identify different mobility subgroups that may have varied risks of Alzheimer’s dementia. To inform on the mechanisms underlying the association of mobility and Alzheimer’s dementia, Aim 3 will use brain pathologies to determine the pathologic bases for these mobility subgroups. Aim 4 will collect proteome from SMA and DLPFC to identify cortical proteins independently related to mobility subgroups when controlling for ADRD pathologies. From the set of proteins related to mobility, we will identify a subset that are also related to Alzheimer’s dementia. This study will inform on why mobility predicts Alzheimer’s dementia and optimize its use as a biomarker for preclinical Alzheimer’s disease. Targeting the proteins identified may catalyze new treatments for both immobility and Alzheimer’s dementia.

抽象的 在最早的阿尔茨海默氏病，痴呆症是一个晚期的认知障碍 表现形式。 预防。 预测许多老年人的认知障碍。 最能预测阿尔茨海默氏症的痴呆症的指标和解释这种关联的机制。 我们必须考虑移动。 这可能解释了为什么痴呆的早期预测因子。 运动持续时间。 不引人注目的传感器可用于评估流动性的认知和运动指标。 这项研究将使用新颖的数字移动表型来改善阿尔茨海默氏病的预测 痴呆并确定有关关联信息的脑病理和蛋白质。 这项研究对NOT-20-053做出了响应，并将针对1000名老年人的可用 在Rush Memory和老化项目中（R01AG17917）。 将添加认知流动性指标，例如，电动机计划和正式指标 捕获日常出行期间的认知需求，我们还将添加新的多日移动指标 从腕部传感器获得的电动机策划与推导运动区域（SMA）有关 和执行功能由背面的前额叶皮层（DLPFC）调节。 确定流动性和阿尔茨海默氏病痴呆症的机制。 大脑病理，我们将收集从SMA到CompFC蛋白质组的新蛋白质组数据。 AIM 1将为单个测试会话获得的运动指标添加新的数字认知流动性指标 以及新型的多日移动性指标，以改善阿尔茨海默氏症的痴呆症的预测 AIM 1的移动性指标将隔离预测阿尔茨海默氏症的单个指标 用第二种方法分析这些THEL指标，以识别可能具有的不同移动性亚组 阿尔茨海默氏症的痴呆症风险各异。 阿尔茨海默氏症的痴呆症，AIM 3将使用脑病理学来确定TESE活动性的病理基础 亚组。AIM4将从SMA和DLPFC收集蛋白质组，以独立识别皮质蛋白 与移动性亚组相关时，从与蛋白质相关的蛋白质中 流动性，我们将确定一个子集与阿尔茨海默氏症的痴呆症有关。 流动性可以预测阿尔茨海默氏症的痴呆症，并优化其作为临床前阿尔茨海默氏病的生物标志物的使用。 靶向鉴定的蛋白质可能会催化新的治疗方法，以促进新疗法和阿尔茨海默氏症的痴呆症。