Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data
Divya Ramamoorthy, Kristen Severson, Soumya Ghosh, Karen Sachs, Answer ALS, Emily G. Baxi, Alyssa N. Coyne, Elizabeth Mosmiller, Lindsey R. Hayes, Aianna Cerezo, Omar Ahmad, Promit Roy, Steven R. Zeiler, John W. Krakauer, Jonathan Li, Aneesh Donde, Nhan Huynh, Miriam Adam, Brook T. Wassie, Alex Lenail, Natasha Leanna Patel-Murray, Yogindra Raghav, Karen Sachs, Velina Kozareva, Stanislav Tsitkov, Tobias Ehrenberger, Julia Kaye, Leandro de Araújo Lima, Stacia K. Wyman, Edward Vertudes, Naufa Amirani, Krishna Kumar Raja, Reuben Thomas, Ryan G. Lim, Ricardo Miramontes, Jie Wu, Vineet Vaibhav, Andrea Matlock, Vidya Venkatraman, Ronald Holewenski, Niveda Sundararaman, Rakhi Pandey, Danica-Mae Manalo, Aaron P. Frank, Loren Ornelas, Lindsey Panther, Emilda Gomez, Erick Galvez, Daniel I. Pérez, Imara Meepe, Susan Lei, Louis Pinedo, Chunyan Liu, Ruby Moran, Dhruv Sareen, Barry Landin, Carla Agurto, Guillermo Cecchi, Raquel Norel, Sara Thrower, Sarah Luppino, Alanna Farrar, Lindsay Pothier, Hong Yu, Ervin Sinani, Prasha Vigneswaran, Alexander Sherman, S. Michelle Farr, Berhan Mandefro, Hannah Trost, Maria G. Bañuelos, Verónica Vázquez García, Michael Workman, Ritchie Ho, Robert H. Baloh, Jennifer Roggenbuck, Matthew B. Harms, Carolyn Prina, Sarah Heintzman, Stephen J. Kolb, Jennifer Stocksdale, Keona Q. Wang, Todd M. Morgan, Daragh Heitzman, Arish Jamil, Jennifer Jockel‐Balsarotti, Elizabeth Karanja, Jesse Markway, Molly McCallum, Timothy J. Miller, Ben Joslin, Deniz Alibazoglu, Senda Ajroud‐Driss, Jay C. Beavers, Mary Bellard, Elizabeth J. Bruce, Nicholas J. Maragakis, Merit Cudkowicz, James D. Berry, Terri G. Thompson
Abstract
The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.