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Defining and predicting transdiagnostic categories of neurodegenerative disease

Nature Biomedical Engineering volume 4, pages 787–800 (2020)Cite this article

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Abstract

The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusions, neuritic plaques, inclusions of the transcriptional repressor TDP-43, angiopathy, neuron loss and gliosis. We also show that membership to the six transdiagnostic disease clusters, which explains more variance in cognitive phenotypes than can be explained by individual diagnoses, can be accurately predicted from scores of the Mini-Mental Status Exam, protein levels in cerebrospinal fluid, and genotype at the APOE and MAPT loci, via cross-validated multiple logistic regression. This combination of unsupervised and supervised data-driven tools provides a framework that could be used to identify latent disease subtypes in other areas of medicine.

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Fig. 1: Schematic of data processing.
Fig. 2: Unsupervised clustering of copathology groups disease entities into proteinopathy families.
Fig. 3: Comparison of ADNC and Lewy body copathology clusters.
Fig. 4: Comparison of MoCA scores between clusters.
Fig. 5: Disease clusters capture the relationship between cognitive measures and pathology scores.
Fig. 6: Comparison of CSF protein levels between disease clusters.
Fig. 7: Prevalence of Alzheimer’s disease risk alleles differs across disease clusters.
Fig. 8: Identifying disease labels from initial testing of CSF protein.

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Data availability

Source data for all figures and pathology scores for the 895 patients analysed here are available from figshare at https://doi.org/10.6084/m9.figshare.12519488.v1. The raw patient data are available from the authors, subject to approval from the Institutional Review Board of the University of Pennsylvania. For data requests, please visit https://www.med.upenn.edu/cndr/biosamples-brainbank.html and complete a Biosample Request Form. Source data are provided with this paper.

Code availability

All analysis code is available at https://github.com/ejcorn/neuropathcluster.

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Acknowledgements

D.S.B. and E.J.C. acknowledge support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the ISI Foundation, the Paul Allen Foundation, the Army Research Laboratory (W911NF-10-2-0022), the Army Research Office (Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474, DCIST-W911NF-17-2-0181), the Office of Naval Research, the National Institute of Mental Health (2-R01-DC-009209-11, R01-MH112847, R01-MH107235, R21-M MH-106799), the National Institute of Child Health and Human Development (1R01HD086888-01), National Institute of Neurological Disorders and Stroke (R01 NS099348) and the National Science Foundation (BCS-1441502, BCS-1430087, NSF PHY-1554488 and BCS-1631550). E.J.C. also acknowledges support from the National Institute of Mental Health (F30 MH118871-01). D.J.I. acknowledges the National Institute of Neurological Disorders and Stroke (R01-NS109260). J.Q.T., V.M.-Y.L. and E.B.L. thank members of the Center for Neurodegenerative Disease Research who contributed to the studies reviewed here. J.Q.T., V.M.-Y.L. and E.B.L. also thank the patients and their families for brain donation. J.Q.T., V.M.-Y.L. and E.B.L. acknowledge funding support from AG10124, AG17586, AG62418 and the Woods Foundation. The authors thank D. Wolk for helpful comments on the manuscript during the review process. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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Authors and Affiliations

  1. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Eli J. Cornblath

  2. Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Eli J. Cornblath & Danielle S. Bassett

  3. Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    John L. Robinson, Virginia M.-Y. Lee, John Q. Trojanowski & Danielle S. Bassett

  4. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    David J. Irwin

  5. Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Edward B. Lee

  6. Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  7. Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  8. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  9. Santa Fe Institute, Santa Fe, NM, USA

    Danielle S. Bassett

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Contributions

E.J.C. performed all analyses. E.J.C. and D.S.B. designed all analyses and wrote the manuscript. J.Q.T., E.B.L., J.L.R., and V.M.-Y.L. oversaw data collection. All authors edited the manuscript and interpreted data.

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Correspondence to Danielle S. Bassett.

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Cornblath, E.J., Robinson, J.L., Irwin, D.J. et al. Defining and predicting transdiagnostic categories of neurodegenerative disease. Nat Biomed Eng 4, 787–800 (2020). https://doi.org/10.1038/s41551-020-0593-y

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