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 在早期阶段，阿尔茨海默病不会表现出认知障碍，而痴呆则是晚期阶段。 识别临床前阿尔茨海默病的生物标志物对于针对其的治疗至关重要。 阿尔茨海默病还会降低非认知功能，例如先天的活动能力。 预测许多老年人的认知障碍 要使用活动能力作为生物标志物，识别出认知障碍至关重要。 最能预测阿尔茨海默氏痴呆症的指标以及解释这种关联的机制。 我们必须思考才能移动，这需要来自不同大脑区域的运动和认知能力。 这也许可以解释为什么活动能力是痴呆症的早期预测因素，但运动测试通常只能量化。 因此，认知能力在运动与阿尔茨海默氏痴呆之间的关系中的作用是 不显眼的传感器可用于评估对移动性至关重要的认知和运动指标。 这项研究将使用新型数字移动表型来改善阿尔茨海默病的预测 痴呆症并识别与这种关联有关的大脑病理学和蛋白质。 这项研究响应 NOT-AG-20-053 并将为 1000 名老年人提供的资源添加新资源 在 Rush 记忆与衰老项目 (R01AG17917) 中，为了改善阿尔茨海默氏痴呆症的预测，我们 将在单个测试会话中添加认知移动指标，例如运动计划和注意力指标。 为了捕捉日常出行期间的认知变化需求，我们还将添加新的多日出行指标 从手腕传感器获得的运动规划与辅助运动区（SMA）和任务注意力有关。 和执行功能由背外侧前额叶皮层（DLPFC）调节，因此，我们重点关注这些区域。 确定 200 名死者的行动能力与阿尔茨海默病痴呆症共有的机制。 脑病理学方面，我们将从 SMA 收集新的蛋白质组数据，以补充现有的 DLPFC 蛋白质组。 目标 1 将在单次测试中获得的运动指标中添加新的数字认知移动指标，如下所示 以及用于改善阿尔茨海默氏痴呆症预测的新颖的多日活动指标传感器产生了巨大的成果。 目标 1 将分离出预测阿尔茨海默氏痴呆症的各个指标。 使用第二种方法分析这些新颖的指标，以确定可能具有的不同移动子组 了解阿尔茨海默氏症痴呆的各种风险。 阿尔茨海默氏痴呆症，Aim 3 将利用大脑病理学来确定这些活动的病理基础 目标 4 将从 SMA 和 DLPFC 收集蛋白质组，以独立识别皮质蛋白质。 控制 ADRD 病理时与流动性亚组相关。 移动性，我们将确定一个也与阿尔茨海默氏痴呆症相关的子集，这项研究将解释其中的原因。 流动性可以预测阿尔茨海默氏痴呆症，并优化其作为临床前阿尔茨海默氏病生物标志物的用途。 针对已识别的蛋白质可能会催化针对不动和阿尔茨海默氏痴呆症的新疗法。