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
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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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