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.

摘要 在其早期阶段，阿尔茨海默病并不表现出认知障碍，而痴呆症是一种晚期疾病。 表现。识别临床前阿尔茨海默氏痴呆症的生物标志物对于针对其 预防阿尔茨海默氏病也会降低非认知功能，如运动能力， 预测了许多老年人的认知障碍。为了使用流动性作为生物标志物，关键是要确定 最能预测阿尔茨海默氏症的指标以及解释这种关联的机制。 我们必须考虑行动。移动性需要来自不同大脑区域的运动和认知能力。 这可能解释了为什么移动性是痴呆症的早期预测因素。然而，运动测试通常只量化 运动持续时间。因此，认知能力在活动能力与阿尔茨海默氏痴呆症之间的关系中的作用是 不清楚非侵入式传感器可用于评估对移动性至关重要的认知和运动指标。 这项研究将使用新的数字移动表型来改善阿尔茨海默病的预测 并确定大脑病理和蛋白质，告知这种关联。 本研究响应了NOT-AG-20-053，并将为1000名老年人的可用资源添加新资源 Rush Memory and Aging Project（R01AG17917）为了提高阿尔茨海默氏痴呆症的预测，我们 将增加认知移动性度量，运动规划和注意力指标到一个单一的测试会话。到 为了捕捉日常移动中的各种认知需求，我们还将增加新的多日移动指标 从手腕传感器获得。运动规划与辅助运动区（SMA）和任务注意有关 执行功能受背外侧前额叶皮层（DLPFC）的调节。所以，我们关注这些区域 以确定活动性和阿尔茨海默病痴呆症的共同机制。在200名死者中， 我们将从SMA中收集新的蛋白质组数据，以补充现有的DLPFC蛋白质组。 Aim 1将在从单次测试会话获得的运动指标中添加新的数字认知移动性指标， 以及新的多日活动指标，以改善阿尔茨海默氏痴呆症的预测。传感器产量大 移动性指标的数量。目标1将分离预测阿尔茨海默氏症痴呆症的个体指标。目标2将 用第二种方法来分析这些新的度量， 患阿尔茨海默氏症的风险通报将流动性和就业机会联系起来的机制， 阿尔茨海默氏痴呆症，目标3将使用大脑病理学来确定这些流动性的病理基础 分组。目的4收集SMA和DLPFC的蛋白质组，独立鉴定皮层蛋白质 在控制ADRD病理时与移动性亚组相关。从一组蛋白质， 移动性，我们将确定一个子集，也与阿尔茨海默氏症痴呆症。这项研究将说明为什么 流动性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。 靶向已鉴定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治疗方法。