Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.
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Authors
Bocchetta, Martina
Yong, Keir
Firth, Nicholas C
Dick, Katrina M
van Swieten, John
Borroni, Barbara
Galimberti, Daniela
Masellis, Mario
Tartaglia, Maria Carmela
Graff, Caroline
Tagliavini, Fabrizio
Frisoni, Giovanni B
Finger, Elizabeth
de Mendonça, Alexandre
Sorbi, Sandro
Warren, Jason D
Crutch, Sebastian
Fox, Nick C
Ourselin, Sebastien
Rohrer, Jonathan D
Genetic FTD Initiative (GENFI)
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Publication Date
2018-10-15Journal Title
Nat Commun
ISSN
2041-1723
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Pages
4273
Language
eng
Type
Article
Physical Medium
Electronic
Metadata
Show full item recordCitation
Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., Cash, D. M., et al. (2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.. Nat Commun, 9 (1), 4273. https://doi.org/10.1038/s41467-018-05892-0
Abstract
The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique-Subtype and Stage Inference (SuStaIn)-able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10-4) or temporal stage (p = 3.96 × 10-5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
Keywords
Genetic FTD Initiative (GENFI), Alzheimer’s Disease Neuroimaging Initiative (ADNI), Humans, Alzheimer Disease, Neurodegenerative Diseases, Reproducibility of Results, Genotype, Phenotype, Models, Neurological, Time Factors, Frontotemporal Dementia
Sponsorship
Wellcome Trust (103838/Z/14/Z)
Medical Research Council (MR/J009482/1)
Medical Research Council (MR/M009041/1)
Medical Research Council (MC_U105597119)
Medical Research Council (MR/M024873/1)
Medical Research Council (MC_UU_00005/12)
Identifiers
External DOI: https://doi.org/10.1038/s41467-018-05892-0
This record's URL: https://www.repository.cam.ac.uk/handle/1810/286277
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