Assessment of ALS Risk Factors from ALS TDI's Ongoing Natural History Study (n = 784)

项目摘要/摘要 此应用程序来自ALS治疗开发研究所(ALS TDI)的研究人员 进行纵向队列研究以研究肌营养不良患者的专业知识 约翰·霍普金斯大学在数据管理、统计学、 分析，和ALS患者的倡导。另外，我们还有一个顾问，一个患有肌萎缩侧索硬化症的人 世卫组织是地理信息软件、位置智能和地图绘制方面的专家。 在ALS TDI的精准医学计划(PMP)的保护伞下，我们有两个队列 共有800多名肌萎缩侧索硬化症患者入选。使用我们收集的安全、基于Web的门户 广泛的自然历史数据，如头部和颈部受伤，居住地理， 服兵役，还有更多。我们通过重新管理调查来随着时间的推移收集数据 每三个月问一次问题。 我们还收集有关临床数据类型的数据，包括： ·每月自行管理的肌萎缩侧索硬化功能评定量表(FRS) ·每月语音记录数据 ·基于加速计的月度活动数据 ·纵向血样数据--基因组测序、蛋白质组学、 转录学，全血细胞计数，临床化学代谢小组 我们打算对我们的自然历史数据进行全面分析，以确认潜在的 风险因素或确定新的风险因素。一旦识别或确认了潜在的风险因素，我们 然后计划进一步阐明这些因素如何影响ALS的各种特征，如临床 诊断、进展和结果。例如，我们有工具来探索关联 使用我们拥有的任何或所有数据在潜在风险因素和进展率之间进行比较 收集跟踪ALS功能评定量表(ALSFRS-r)、纵向语音记录数据、 和纵向活动数据。此外，我们可以将已确认的风险因素与数据进行比较 从我们的献血计划中获得DNA序列分析等数据。 肌萎缩侧索硬化症危险因素的进一步表征及其对 人类肌萎缩侧索硬化症的病理生理学可能为潜在的肌萎缩侧索硬化症的概念和设计提供信息 治疗。此外，识别ALS患者群体中的亚群可能会增加成功率 在临床试验中，不同的药物可能被给予特定的亚组。此外, 我们可以确定风险因素，例如有毒污染物，这些是潜在可以避免的， 从根本上预防个体肌萎缩侧索硬化的发展，改善整体健康 人口中的一部分。

Project Summary/Abstract This application is from investigators at the ALS Therapy Development Institute (ALS TDI) with expertise in the conduct of longitudinal cohort studies to study people living with amyotrophic lateral sclerosis and Johns Hopkins University with expertise in data management, statistics, analysis, and ALS patient advocacy. In addition, we have a consultant, a person living with ALS, who is a specialist in geographic information software, location intelligence and mapping. Under the umbrella of ALS TDI’s Precision Medicine Program (PMP) we have two cohorts totaling more than 800 people with ALS enrolled. Using a secure, web-based portal we collect extensive natural history data on topics such as head and neck injuries, residential geography, military service, and much more. We gather data over time by re-administering survey questions every three months. We also collect data on clinical data types including: · Monthly Self-Administered ALS Functional Rating Scale (FRS) · Monthly Voice Recording Data · Monthly Accelerometer Based Activity Data · Longitudinal Blood Sample Data – Genome Sequencing, Proteomics, Transcriptomics, Complete Blood Count, Clinical Chemistry Metabolic Panel We intend to perform a comprehensive analysis on our natural history data to confirm potential risk factors or identify new ones. Once potential risk factors are identified or confirmed, we then plan to further elucidate how these factors affect various features of ALS such as clinical diagnosis, progression, and outcomes. For example, we have the tools to explore associations between potential risk factors and progression rates using any or all of the data we have collected tracking the ALS functional rating scale (ALSFRS-r), longitudinal voice recording data, and longitudinal activity data. Further, we could compare confirmed risk factors to data acquired from our blood sample donation program such as DNA sequence analysis data. Further characterization of ALS risk factors and study of how they contribute to the pathophysiology of ALS in humans may inform the conception and design of potential ALS treatments. Also, identifying sub-sets within ALS patient populations may increase the success of clinical trials in which distinct drugs may be administered to specific sub-groups. In addition, we may identify risk factors, for example - toxic pollutants, that could be potentially avoided, essentially preventing the development of ALS in individuals and improving the general health of the population.