Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference


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210 Scopus citations


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.

Original languageEnglish
Article number4273
JournalNature Communications
Issue number1
StatePublished - Dec 1 2018

Bibliographical note

Funding Information:
A.L.Y. is supported by a Doctoral Prize Fellowship from the EPSRC. N.P.O. is supported by the Biomarkers Across Neurodegenerative Diseases programme, which is funded by The Michael J. Fox Foundation for Parkinson’s Research, the Alzheimer’s Association, Alzheimer’s Research UK and the Weston Brain Institute. R.V.M. is supported by the EPSRC Centre For Doctoral Training in Medical Imaging with grant EP/L016478/1. D.L.T. is supported by the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575). K.D. is supported by an Alzheimer’s Society PhD Studentship. J.B.R. is supported by the Wellcome Trust (103838). G.G.F. was supported by Associazione Italiana Ricerca Alzheimer ONLUS (AIRAlzh Onlus)-COOP Italia. J.D.W. is supported by the Alzheimer's Society, Alzheimer's Research UK and the NIHR UCLH Biomedical Research Centre. S.C. acknowledges the support of the NIHR Queen Square Dementia BRU, ARUK (ART-SRF2010-3), ESRC/NIHR (ES/L001810/1) and EPSRC (EP/M006093/1). J.M.S. acknowledges the support of the NIHR Queen Square Dementia BRU, the NIHR UCL/H Biomedical Research Centre, Wolfson Foundation, EPSRC (EP/J020990/1), MRC (MR/L023784/1), ARUK (ARUK-Network 2012-6-ICE; ARUK-PG2017-1946; ARUK-PG2017-1946), Brain Research Trust (UCC14191) and European Union’s Horizon 2020 research and innovation programme (Grant 666992). J.D.R. is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration. The Dementia Research Centre is supported by Alzheimer’s Research UK, Brain Research Trust and The Wolfson Foundation. This work is supported by the NIHR Queen Square Dementia Biomedical Research Unit and the NIHR UCL/H Biomedical Research Centre. This work is supported by EPSRC grants EP/J020990/01 and EP/M020533/1 and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 666992 (EuroPOND: http://www.europond.eu). This work was also supported by the MRC UK GENFI grant (MR/M023664/1). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.;Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sitesin Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Publisher Copyright:
© 2018, The Author(s).

ASJC Scopus subject areas

  • Physics and Astronomy (all)
  • Chemistry (all)
  • Biochemistry, Genetics and Molecular Biology (all)


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