Characterizing Microbial Markers Predictive for ALS Onset and Progression
Abstract
In this study, we will combine a rich database of patient clinical data, longitudinal collection of microbiome samples, state-of-the-art multiomic analyses, and advanced Bayesian ML methods to identify the microbial biomarkers predictive of ALS. This approach will provide a platform for identifying distinct microbial signatures linked to ALS risk and disease progression, elucidating potential solutions for treating ALS patients to slow down disease progression as well as for preventing ALS in high-risk deployed military personnel. We hypothesize that certain microbial species, or metabolites from the gut microbiome contribute to systemic re-conditioning and dysbiosis, thus leading to increased risk and more rapid progression of ALS. We will enroll 100 ALS patients and two different controls for each patient, a spouse/partner control and a control that is sex, age, and geographical location matched and will conduct comprehensive multiomics analyses, including fecal microbiome metabolomics and metagenomics. We will then utilize advanced ML approaches to identify the microbial markers most predictive of ALS risk and progression.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2024
- Accession Number
- AD1229691
Entities
People
- Catherine Lomen-hoerth
- Crystal Jaing
- Lorene M Nelson
Organizations
- Lawrence Livermore National Laboratory
- Stanford University
- University of California, San Francisco