Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference Academic Article uri icon

Overview

MeSH Major

  • Alzheimer Disease
  • Apolipoproteins E
  • Ferritins
  • Gene Expression Regulation

abstract

  • © 2018, The Author(s). 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.

authors

publication date

  • December 2018

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC6189176

Digital Object Identifier (DOI)

  • 10.1038/s41467-018-05892-0

PubMed ID

  • 30323170

Additional Document Info

start page

  • 4273

volume

  • 9

number

  • 1