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?

摘要 阿尔茨海默病早期不表现为认知障碍，而痴呆症则是晚期的 显化。识别临床前阿尔茨海默病的生物标记物对于针对其治疗至关重要 预防。阿尔茨海默病还会降低非认知功能，如先于和 可以预测许多老年人的认知障碍。要使用移动性作为生物标记物，关键是要确定 最好地预测阿尔茨海默氏症的指标以及解释这种联系的机制。 我们必须动动脑筋。机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活动能力是痴呆症的早期预测指标。然而，运动测试通常只是量化 移动持续时间。因此，认知能力在行动能力与阿尔茨海默氏症的联系中所起的作用是 不清楚。不显眼的传感器可以用来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 痴呆症，并确定大脑的病理和蛋白质提供信息的这种联系。 这项研究回应了NOT-AG-20-053，并将为1000名老年人提供的资源增加新的资源 在快速记忆和衰老项目(R01AG17917)中。为了改善对阿尔茨海默氏症的预测，我们 将认知移动性指标(例如，运动规划和注意力指标)添加到单一测试会话。至 捕捉日常移动性中的各种认知需求，我们还将添加新的多天移动性指标 从手腕传感器获取的。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质(DLPFC)调节。因此，我们专注于这些地区 以确定机动性和阿尔茨海默病痴呆的共同机制。在200名死者中有可用的 对于脑病理，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。 AIM 1将把新的数字认知移动性指标添加到从单一测试会话获得的运动指标中，如下所示 以及新的多天活动指标，以改善阿尔茨海默氏症的预测。传感器的产量很大 移动性指标的数量。目标1将分离预测阿尔茨海默氏症的个人指标。目标2将 使用第二种方法分析这些新指标，以识别可能具有 阿尔茨海默氏症的各种风险。让我们了解机动性和 阿尔茨海默氏症，AIM 3将使用大脑病理来确定这些活动的病理基础 子组。AIM 4将从SMA和DLPFC收集蛋白质组，以独立鉴定皮质蛋白质 在控制ADRD病理时与活动亚组相关。从一组与蛋白质相关的蛋白质中 移动性，我们将确定也与阿尔茨海默氏症相关的子集。这项研究将告诉我们为什么 机动性可以预测阿尔茨海默氏症，并优化其作为临床前阿尔茨海默病的生物标志物的使用。 靶向识别的蛋白质可能会催化治疗行动不便和阿尔茨海默氏症的新方法。

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.