TY - JOUR
T1 - Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data
AU - Answer ALS
AU - Pooled Resource Open-Access ALS Clinical Trials Consortium
AU - ALS/MND Natural History Consortium
AU - Ramamoorthy, Divya
AU - Severson, Kristen
AU - Ghosh, Soumya
AU - Sachs, Karen
AU - Baxi, Emily G.
AU - Coyne, Alyssa N.
AU - Mosmiller, Elizabeth
AU - Hayes, Lindsey
AU - Cerezo, Aianna
AU - Ahmad, Omar
AU - Roy, Promit
AU - Zeiler, Steven
AU - Krakauer, John W.
AU - Li, Jonathan
AU - Donde, Aneesh
AU - Huynh, Nhan
AU - Adam, Miriam
AU - Wassie, Brook T.
AU - Lenail, Alex
AU - Patel-Murray, Natasha Leanna
AU - Raghav, Yogindra
AU - Sachs, Karen
AU - Kozareva, Velina
AU - Tsitkov, Stanislav
AU - Ehrenberger, Tobias
AU - Kaye, Julia A.
AU - Lima, Leandro
AU - Wyman, Stacia
AU - Vertudes, Edward
AU - Amirani, Naufa
AU - Raja, Krishna
AU - Thomas, Reuben
AU - Lim, Ryan G.
AU - Miramontes, Ricardo
AU - Wu, Jie
AU - Vaibhav, Vineet
AU - Matlock, Andrea
AU - Venkatraman, Vidya
AU - Holewenski, Ronald
AU - Sundararaman, Niveda
AU - Pandey, Rakhi
AU - Manalo, Danica Mae
AU - Frank, Aaron
AU - Ornelas, Loren
AU - Panther, Lindsey
AU - Gomez, Emilda
AU - Galvez, Erick
AU - Perez, Daniel
AU - Meepe, Imara
AU - Lei, Susan
AU - Pinedo, Louis
AU - Liu, Chunyan
AU - Moran, Ruby
AU - Sareen, Dhruv
AU - Landin, Barry
AU - Agurto, Carla
AU - Cecchi, Guillermo
AU - Norel, Raquel
AU - Thrower, Sara
AU - Luppino, Sarah
AU - Farrar, Alanna
AU - Pothier, Lindsay
AU - Yu, Hong
AU - Sinani, Ervin
AU - Vigneswaran, Prasha
AU - Sherman, Alexander V.
AU - Farr, S. Michelle
AU - Mandefro, Berhan
AU - Trost, Hannah
AU - Banuelos, Maria G.
AU - Garcia, Veronica
AU - Workman, Michael
AU - Ho, Richie
AU - Baloh, Robert
AU - Roggenbuck, Jennifer
AU - Harms, Matthew B.
AU - Prina, Carolyn
AU - Heintzman, Sarah
AU - Kolb, Stephen
AU - Stocksdale, Jennifer
AU - Wang, Keona
AU - Morgan, Todd
AU - Heitzman, Daragh
AU - Jamil, Arish
AU - Jockel-Balsarotti, Jennifer
AU - Karanja, Elizabeth
AU - Markway, Jesse
AU - McCallum, Molly
AU - Miller, Tim
AU - Joslin, Ben
AU - Alibazoglu, Deniz
AU - Ajroud-Driss, Senda
AU - Beavers, Jay C.
AU - Bellard, Mary
AU - Bruce, Elizabeth
AU - Maragakis, Nicholas
AU - Cudkowicz, Merit E.
AU - Berry, James
AU - Thompson, Terri
AU - Finkbeiner, Steven
AU - Thompson, Leslie M.
AU - Van Eyk, Jennifer E.
AU - Svendsen, Clive N.
AU - Rothstein, Jeffrey D.
AU - Glass, Jonathan D.
AU - Fournier, Christina N.
AU - Sherman, Alexander
AU - Lunetta, Christian
AU - Walk, David
AU - Hayat, Ghazala
AU - Wymer, James
AU - Gwathmey, Kelly
AU - Olney, Nicholas
AU - Ajroud-Driss, Senda
AU - Heiman-Patterson, Terry
AU - Arcila-Londono, Ximena
AU - Faulconer, Kenneth
AU - Sanani, Ervin
AU - Berger, Alex
AU - Mirochnick, Julia
AU - Herrington, Todd M.
AU - Berry, James D.
AU - Ng, Kenney
AU - Fraenkel, Ernest
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85137680244&partnerID=8YFLogxK
U2 - 10.1038/s43588-022-00299-w
DO - 10.1038/s43588-022-00299-w
M3 - Article
AN - SCOPUS:85137680244
SN - 2662-8457
VL - 2
SP - 605
EP - 616
JO - Nature Computational Science
JF - Nature Computational Science
IS - 9
ER -