Clinical Trial Readiness In X-Linked Dystonia Parkinsonism: Assessment of Sensor-Based And Blood Biomarkers for Early Detection and Monitoring Disease Progression

项目总结 帕金森症是第二种最常见的运动障碍，影响着超过90万美国人及其患病率 正在上升，而第三常见的肌张力障碍困扰着25万美国人。X连锁肌张力障碍帕金森综合征 (XDP)是一种罕见的神经遗传性运动障碍，具有广泛的表型谱，从帕金森综合征到 与帕金森病(PD)难以区分的全身性肌张力障碍，类似于DYT1(一种遗传性肌张力障碍)， 或者是肌张力障碍和帕金森症的结合。由于X基因失活，女性XDP携带者有患帕金森症的风险 代表了一种可能的遗传风险因素。因此，XDP既是肌张力障碍的优秀模型，也是 帕金森症和洞察力可以同时告知表型和混合运动障碍，这是臭名昭著的 评估起来很有挑战性。提供足够的临床试验终点、独立于评分者的量化评估 迫切需要运动功能检查来识别可能出现明显临床症状之前的早期异常。 并跟踪疾病进展，考虑到依赖于评分者的临床评级量表的不可靠性。这个项目将 为斯蒂芬博士提供一套技能，使他能够评估潜在的疾病量化指标 使用运动传感器检测肌张力障碍和帕金森症的严重程度和进展，并将这些测量与 提出生化生物标记物，使用机器学习来定义最优参数。该项目旨在 使用纯表型(DYT1和PD)和 结合(XDP)：目标1)利用基于技术的评估作为更敏感和更准确的衡量标准 隔离肌张力障碍(DYT1)与合并帕金森症(XDP)与临床量表的比较；目标2)至 检查XDP、PD和DYT1中基于传感器的疾病进展监测超过1年的准确性 患者；以及目的3)分析2个建议的血液生物标记物(一个针对XDP和神经丝轻链， 神经退行性变的一般标志物)，并结合这些运动和血液标志物，使用机器 学习预测疾病表型/生物型和临床病程。这项研究是对NINDS的补充 罕见神经疾病的临床试验准备情况的目标，更广泛的目标是更好地理解这些 混合运动障碍背景下的常见表型，使用创新的技术和分析 方法：研究方法。该奖项的目标是为候选人成为一名完全独立的调查员做好准备。 在专家环境下对肌张力障碍、帕金森症和其他运动障碍进行定量评估 师徒关系。职业发展计划包括培训目标：1)学习可转移的动作分析技能， 2)学习机器学习技术以开发运动行为的预测模型，并将其结合起来 血液生物标记物数据；3)生物标记物科学和生物信息学的介绍。成功 该项目的完成将使Stephen博士处于一个独特的位置，为XDP的临床试验准备工作提供信息。 与其他肌张力障碍相关，并准备提交竞争R01申请，重点是量化 遗传性肌张力障碍、帕金森综合征和混合性运动障碍的运动分析和生物流体标记。

PROJECT SUMMARY Parkinsonism, the second most common movement disorder, affects over 900,000 Americans and its prevalence is rising, while dystonia, the third most common, afflicts 250,000 Americans. X-linked dystonia parkinsonism (XDP) is a rare neurogenetic movement disorder with a wide phenotypic spectrum ranging from a parkinsonism indistinguishable from Parkinson's disease (PD), generalized dystonia, similar to DYT1 (a hereditary dystonia), or combined dystonia and parkinsonism. Female XDP carriers are at risk for parkinsonism, given X-inactivation and represents a possible genetic risk factor. As such, XDP serves as an excellent model of both dystonia and parkinsonism, and insights can inform both phenotypes and mixed movement disorders, which are notoriously challenging to assess. To provide adequate clinical trial endpoints, rater-independent, quantitative assessments of motor function are urgently needed to identify early abnormalities which may predate overt clinical symptoms and track disease progression, given the unreliability of rater-dependent clinical rating scales. This project will provide Dr. Stephen with a skill set that will allow him to assess potential quantitative measures of disease severity and progression in dystonia and parkinsonism using motion sensors and compare these measures with proposed biochemical biomarkers, using machine learning to define optimal parameters. This project aims to address three key knowledge gaps in dystonia and parkinsonism, using pure phenotypes (DYT1 and PD) and in combination (XDP): Aim 1) to utilize technology-based evaluations as more sensitive and accurate measures of dystonia in isolation (DYT1) vs. in combination with parkinsonism (XDP) compared to clinical scales; Aim 2) to examine the accuracy of sensor-based monitoring of disease progression over 1 year in XDP, PD and DYT1 patients; and Aim 3) to analyze 2 proposed blood biomarkers (one specific to XDP, and neurofilament light chain, a general marker of neurodegeneration) in XDP and combine these motor and blood markers, using machine learning to predict disease phenotype/biotype and clinical course. This research complements the NINDS objective of clinical trial readiness in rare neurological disorders, with a wider goal of better understanding these common phenotypes in the context of a mixed movement disorder, using innovative technology and analysis methods. The goal of this award is to prepare the candidate to become a fully independent investigator in the quantitative assessment of dystonia, parkinsonism and other movement disorders, in the setting of expert mentorship. The career development plan includes training goals: 1) learning transferrable motion analysis skills, 2) learning machine learning techniques to develop predictive models of motor behaviors and combining these with blood biomarker data; and 3) an introduction to biomarker science and bioinformatics. Successful completion of this project will put Dr. Stephen in a unique position to inform clinical trial readiness efforts in XDP, relevant to other dystonia and to prepare for submission of a competitive R01 application focusing on quantitative movement analysis and biofluid markers in genetic dystonia, parkinsonism and mixed movement disorders.