Longitudinal Changes in Gait and Ankle Function in Youth with Charcot-Marie-Tooth Disease

项目概要/摘要 腓骨肌萎缩症（CMT）是最常见的遗传性周围神经病变，但仍然是一种罕见的疾病。 这种疾病在美国影响了大约15万人。CMT导致远端肌肉无力， 挛缩，影响行走能力。行走能力的逐渐丧失是导致 CMT患者的生活质量降低。由于疾病进展具有高度异质性， 来预测一个人的步态将如何随着时间的推移而变化。这项研究的长期目标是了解 CMT青年患者步态期间踝关节功能变化的自然史能够预测疾病 个别患者的进展。目前的项目旨在1）获得后续（纵向）步态分析 既往步态分析研究的患者数据; 2）识别患者和临床特征，包括 功能性步态表型，其预测步态功能随时间的恶化模式。我们假设 步行能力的变化可以通过考虑基于踝关节的功能步态表型来预测 除了临床特征如CMT类型、性别、年龄、活动度和 实力我们以前研究了CMT中步态与年龄之间的关系，但这一研究是有限的 通过使用主要是横截面数据。从一个小样本的纵向 评估表明，可能存在一些变化模式，而这些变化模式没有被跨部门数据所反映。 本研究将收集既往接受过步态分析的CMT患者的随访步态分析数据 研究以创建更大的纵向数据集。这些纵向数据将用于确定是否 功能性步态表型可以改善对青年和年轻成年人未来行走能力的预测， CMT。更好地理解并能够预测个体患者预期的步态变化， 重要的是，既要改善对个别患者的护理，也要能够评估新兴的影响， 在未来的临床试验中。

PROJECT SUMMARY / ABSTRACT Charcot-Marie-Tooth disease (CMT) is the most commonly inherited peripheral neuropathy but is still a rare disease affecting approximately 150,000 people in the United States. CMT causes distal muscle weakness and contracture, impairing the ability to walk. Progressive loss of walking ability is the most significant contributor to reduced quality of life in persons with CMT. Because disease progression is highly heterogeneous, it is difficult to predict how an individual’s gait will change over time. The long-term goal of this research is to understand the natural history of changes in ankle function during gait in youth with CMT to be able to predict disease progression for individual patients. The current project aims to 1) obtain follow-up (longitudinal) gait analysis data on patients with prior gait analysis studies and 2) identify patient and clinical characteristics, including functional gait phenotypes, that predict patterns of deterioration of gait function over time. We hypothesize that changes in walking ability can be predicted by considering functional gait phenotypes based on ankle kinematics and kinetics in addition to clinical characteristics like CMT type, sex, age, range of motion, and strength. We have previously studied the association between gait and age in CMT, but this research is limited by the use of primarily cross-sectional data. Preliminary results from a small sample of longitudinal assessments suggest that there may be patterns of change that are not captured by the cross-sectional data. This study will collect follow-up gait analysis data on patients with CMT who have had prior gait analysis studies to create a larger longitudinal data set. These longitudinal data will be used to determine whether functional gait phenotypes can improve the prediction of future walking ability in youth and young adults with CMT. Better understanding and being able to predict the changes in gait expected for individual patients is important both to improve care for individual patients and also to be able to evaluate the effects of emerging treatments in future clinical trials.