Predictors and Biomarkers for VCID versus Cognitive Recovery after Stroke Thrombectomy in Rural Appalachia

血管认知障碍和痴呆(VCID)存在于25%-30%的中风患者中，而且很难 在中风时预测这些患者将是谁。应对阿尔茨海默氏症的国家计划和 NHLBI-NINDS VCID研讨会呼吁增加对VCID预测的研究，特别是在服务不足的情况下 人口。这项拟议的研究将确定农村、医疗服务不足的患者认知恢复的预测因素 急诊大血管闭塞(ELVO)卒中机械取栓后。这些预测者将针对 将从强化康复中受益的患者。我们的新生物样本登记簿被用来识别 取栓术中采集的系统血液中的蛋白质作为认知功能的独立预测因子 卒中后出院时的表现和90天的随访。假设这些蛋白质将提供 对包括痴呆症在内的认知损害的递增预测有效性，高于临床 特征、健康的社会决定因素和传统的ADRD血浆生物标记物。拟议的研究将 前瞻性纳入225例血栓摘除时采集体动脉血的患者并分析 蛋白质表达。使用为服务不足的农村参与者量身定做的保留策略，神经认知测试数据 并将在3个月至2年后采集静脉血进行纵向蛋白表达 血栓切除术。该研究地点位于美国中风带，有一些医疗服务最匮乏的农村地区 全国各县将为NIH优先关注健康差距做出贡献。具体目标1：测试 假设患者家乡乡村生活水平的提高预示着中风后认知结果的恶化， 无论是尖锐的还是长期的。广义估计方程模型将检验主县的主要效应 乡村和其他社会健康决定因素对认知状态的影响(正常，认知障碍，无痴呆， 痴呆)，时间对跨时点认知表现的主要影响，以及乡土性和 跨时间点的认知状态的时间。家族性、合并症和血栓切除时间之间的关系 血液蛋白表达将作为辅助分析。具体目标2：检验ADRD的假设 血栓摘除时全身血中生物标志物是急性卒中后认知功能的生物标志物 两年后出现损害和慢性VCID。将对血液样本进行已知蛋白质组的评估 UKY生物标记中心的认知生物标记。具体目标3：检验小说的假设 血栓摘除时全身血液中的生物标记物预测包括痴呆在内的慢性VCID 并高于ADRD标记的影响。将对血栓切除时间的血液进行蛋白质组学分析 检测标本中炎症相关蛋白的表达。递阶广义估计 方程模型将评估随时间增加的认知状态预测的增量改进 这些加法标记。分析将使用套索方法和分割样本交叉验证来创建 基于ADRD和预测认知结果的新型生物标记物的预测模型。

Vascular cognitive impairment and dementia (VCID) is present in 25-30% of stroke patients, and it is difficult to predict at the time of stroke who those patients will be. The National Plan to Address Alzheimer’s Disease and the NHLBI-NINDS VCID Workshop called for increased research on VCID prediction, especially in underserved populations. The proposed research will identify predictors of cognitive recovery in rural, underserved patients after mechanical thrombectomy for emergent large vessel occlusion (ELVO) stroke. These predictors will target patients that would benefit from intensive rehabilitation. Our novel biologic sample registry was used to identify proteins in systemic blood samples taken during thrombectomy as independent predictors of cognitive performance at post-stroke discharge and 90-day follow-up. The hypothesis is that these proteins will provide incremental predictive validity for cognitive impairment including dementia, over and above clinical characteristics, social determinants of health, and traditional ADRD plasma biomarkers. The proposed study will prospectively enroll 225 patients collecting systemic arterial blood at the time of thrombectomy and analyze protein expression. Using a retention strategy tailored to underserved rural participants, neurocognitive test data and venous blood samples for longitudinal protein expression will be collected from 3 months to 2 years post- thrombectomy. The study site, located in the US Stroke Belt with some of the most medically underserved rural counties in the nation, will contribute to the NIH priority focus on health disparities. Specific Aim 1: Test the hypothesis that increased patient home county rurality predicts worse cognitive outcomes post-stroke, both acutely and chronically. Generalized estimating equation models will test the main effect of home county rurality and other social determinants of health on cognitive status (normal, cognitive impairment no dementia, dementia), the main effect of time on cognitive performance across time points, and the interaction of rurality and time on cognitive status across time points. Associations among rurality, comorbidities, and time-of-thrombectomy blood protein expression will be auxiliary analyses. Specific Aim 2: Test the hypothesis that ADRD biomarkers in systemic blood at time of thrombectomy are biomarkers for acute post-stroke cognitive impairment and chronic VCID two years later. Blood samples will be assessed for known proteomic biomarkers for cognition at the UKY Biomarker Center. Specific Aim 3: Test the hypothesis that novel biomarkers in systemic blood at time of thrombectomy predict chronic VCID including dementia, over and above the effects of ADRD markers. Proteomic analysis will be conducted on time-of-thrombectomy blood samples to measure the expression of inflammatory-related proteins. Hierarchical generalized estimating equation models will assess the incremental improvement in prediction of cognitive status over time added by these addition markers. Analyses will use LASSO methods and split-sample cross-validation to create a predictive model based on both ADRD and novel biomarkers that predict cognitive outcomes.