MRI and CSF Biomarkers of White Matter Injury in VCID

摘要/摘要 血管性认知障碍痴呆（VCID）是一种异质性疾病，是导致痴呆的重要原因 痴呆症。该提案是对 RFA 的回应，旨在识别生物标志物以将患者分为亚组 用于治疗试验。尽管对多种生物标志物的了解很多，但我们的研究还存在重大差距。 了解在合作研究中使用的最佳方案，这是本提案的目的。皮质下 缺血性血管病 (SIVD) 是进行性小血管病 (SVD) 形式，最适合 治疗试验。 MRI 模式和脑脊液生化研究提供了最有前途的生物标志物。白色 物质损伤是SIVD的标志，而MRI是显示进行性变化的最佳方法。 脑脊液的生化研究显示白蛋白、基质金属蛋白酶 (MMP) 的炎症生物标志物 和细胞因子。在一项正在进行的 SIVD 临床研究中，我们的小组通过 MRI 确定了微观结构研究 脑脊液中 MMP 的生化研究作为两个最有前途的生物标志物。这两个阶段， 里程碑驱动的提案是确定最佳的微观结构和生化生物标志物，以识别 SIVD 亚组并用作进展的替代标志物。在第一个U2阶段，最优方法 测量 CSF MMP 将确定患者分类和最佳 MRI 生物标志物以显示 进展将被确定。外观正常的白质 (NAWM)，这是一个具有正常 FLAIR 的区域 信号，通常具有异常的扩散信号，表明组织处于危险之中（前驱期）。假设 CSF MRI生物标志物可用于对合并CSF的SIVD患者进行初步分类 和 MRI 用于分类和作为预测疾病进展的替代标志物 用于患者治疗决策。共有三个具体目标：1）证明 FLAIR 图像定义的白质高信号 (WMH) 可以根据生物标志物进行预测 根据多壳、高 b 值扩散 MRI (dMRI) 计算； 2）识别功能性大脑连接， 预测认知能力下降（执行力和执行能力）的结构性大脑连接和灰质萎缩生物标志物 记忆功能）在 VCID 受试者中持续两到三年；以及，3) 比较 MMP 使用两种新方法通过酶谱法进行测量，包括基于 ELISA 的方法和 基于免疫捕获和荧光肽裂解的活性测定，以优化生化 研究。该提案将填补有关用于合作研究的最佳生物标志物的知识空白。 主要目标是完善用于患者选择的生物标志物集，这将在 U2 中完成 通过将新墨西哥大学 (UNM) 现有队列的规模从 100 名患者增加到 200 名患者， 并进行 CSF 研究以进行患者分类和纵向 dMRI 研究以定义替代 用于 U3 期临床试验结果测量的标记物。长期目标是拥有 计划在第五年进行未来治疗试验时生物标记物已到位。

Summary/Abstract Vascular cognitive impairment dementia (VCID) is a heterogeneous disease that is an important cause of dementia. This proposal is in response to a RFA to identify biomarkers to separate patients into subgroups for treatment trials. Although much is known about multiple biomarkers individually, there is a major gap in our understanding of the optimal ones to use in collaborative studies, which is the aim of this proposal. Subcortical ischemic vascular disease (SIVD) is the progressive small vessel disease (SVD) form that is optimal for treatment trials. MRI modalities and CSF biochemical studies provide the most promising biomarkers. White matter damage is the hallmark of SIVD, and MRI is the optimal method to show the progressive changes. Biochemical studies of CSF show the inflammatory biomarkers of albumin, matrix metaloproteinases (MMPs) and cytokines. In an on-going clinical study of SIVD, our group has identified microstructural studies with MRI and biochemical studies of MMPs in the CSF as the two most promising biomarkers. This two-phase, milestone-driven proposal is to identify the optimal microstructural and biochemical biomarkers to both identify the SIVD subgroup and to use as surrogate markers of progression. In the first U2 phase, the optimal method to measure CSF MMPs will be determined for patient classification and the optimal MRI biomarkers to show progression will be determined. Normal-appearing white matter (NAWM), which is a region with normal FLAIR signal, often has abnormal diffusion signals, indicating tissue at risk (prodromal). The hypothesis is that CSF and MRI biomarkers can be used for classification of SIVD patients with CSF for primarily classification and MRI for both classification and as a surrogate marker for predicting disease progression that can be used for patient treatment decisions. There are three specific aims: 1) to demonstrate that the growth of white matter hyperintensities (WMHs) as defined by FLAIR images can be predicted based on biomarkers calculated from multi-shell, high b-value diffusion MRI (dMRI); 2) to identify functional brain connectivity, structural brain connectivity and gray matter atrophy biomarkers that predict cognitive decline (executive and memory function) in VCID subjects over a period of two to three years; and, 3) to compare MMP measurements made with zymography with two novel methods, including an ELISA-based method and an activity assay based on immunocapture and fluorescent peptide cleavage in order to optimize the biochemical studies. This proposal will fill a gap in knowledge as to the optimal biomarkers to use for collaborative studies. The major aims are related to refining the set of biomarkers for patient selection, which will be done in the U2 phase by increasing the size of the existing University of New Mexico (UNM) cohort from 100 to 200 patients, and to perform the CSF studies for patient classification and the dMRI studies longitudinally to define surrogate markers to use for outcome measures in clinical trials in the U3 phase. The long-term goal is to have the biomarkers in place by the time the future treatment trials are planned in the fifth year.