Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Alzheimer Disease; Frontotemporal Dementia; Genotype; Humans; Models, Neurological; Neurodegenerative Diseases; Phenotype; Reproducibility of Results; Time Factors
Aging
Time Factors
Genotype
Science
Neurodegenerative Diseases/classification/pathology
EMC OR-01
Models, Neurological
610
Neurodegenerative
Alzheimer's Disease
Frontotemporal Dementia/genetics/pathology
Article
12. Responsible consumption
03 medical and health sciences
0302 clinical medicine
Models
Alzheimer Disease
Health Sciences
Acquired Cognitive Impairment
Genetics
Machine Learning and Artificial Intelligence
2.1 Biological and endogenous factors
Humans
Alzheimer Disease; Frontotemporal Dementia; Genotype; Humans; Models, Neurological; Neurodegenerative Diseases; Phenotype; Reproducibility of Results; Time Factors; Chemistry (all); Biochemistry, Genetics and Molecular Biology (all); Physics and Astronomy (all)
Precision Medicine
Q
Neurosciences
Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD)
Reproducibility of Results
Genetic FTD Initiative
Alzheimer Disease/genetics/pathology
Neurodegenerative Diseases
Alzheimer’s Disease Neuroimaging Initiative
Biological Sciences
info:eu-repo/classification/ddc/618.97
Brain Disorders
4.1 Discovery and preclinical testing of markers and technologies
3. Good health
Good Health and Well Being
Phenotype
Frontotemporal Dementia
Neurological
ddc:618.97
Dementia
Biochemistry and Cell Biology
DOI:
10.1038/s41467-018-05892-0
Publication Date:
2018-10-09T14:18:41Z
AUTHORS (380)
ABSTRACT
AbstractThe 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.
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CITATIONS (379)
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