RFA-TS-23-001: Assessment of ALS Risk Factors from ALS TDI's Ongoing Natural History Study (n = 784)

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.