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Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
- Source :
- Medical Biophysics Publications, Nature Communications, Nature Communications, Vol 9, Iss 1, Pp 1-16 (2018), Nature Communications, Vol. 9, No 1 (2018) P. 4273, Nature Communications, 9:4273. Nature Publishing Group, Young, Alexandra L; Marinescu, Razvan V; Oxtoby, Neil P; Bocchetta, Martina; Yong, Keir; Firth, Nicholas C; et al.(2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.. Nature communications, 9(1), 4273. doi: 10.1038/s41467-018-05892-0. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/87b1x37r
- Publication Year :
- 2018
- Publisher :
- Scholarship@Western, 2018.
-
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.<br />Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.
- Subjects :
- Alzheimer Disease
Frontotemporal Dementia
Genotype
Humans
Models, Neurological
Neurodegenerative Diseases
Phenotype
Reproducibility of Results
Time Factors
Science
Neurodegenerative Diseases/classification/pathology
Inference
Disease
Computational biology
Biology
Frontotemporal Dementia/genetics/pathology
Article
03 medical and health sciences
0302 clinical medicine
Models
MD Multidisciplinary
medicine
Stage (cooking)
lcsh:Science
030304 developmental biology
0303 health sciences
Neurodegeneration
Genetic FTD Initiative
Temporal complexity
Alzheimer Disease/genetics/pathology
Alzheimer’s Disease Neuroimaging Initiative
medicine.disease
Precision medicine
3. Good health
ddc:618.97
Neurological
lcsh:Q
030217 neurology & neurosurgery
Frontotemporal dementia
Subjects
Details
- ISSN :
- 20411723
- Database :
- OpenAIRE
- Journal :
- Medical Biophysics Publications, Nature Communications, Nature Communications, Vol 9, Iss 1, Pp 1-16 (2018), Nature Communications, Vol. 9, No 1 (2018) P. 4273, Nature Communications, 9:4273. Nature Publishing Group, Young, Alexandra L; Marinescu, Razvan V; Oxtoby, Neil P; Bocchetta, Martina; Yong, Keir; Firth, Nicholas C; et al.(2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.. Nature communications, 9(1), 4273. doi: 10.1038/s41467-018-05892-0. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/87b1x37r
- Accession number :
- edsair.doi.dedup.....172bdfce50541185157d28e1b5345699