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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts

Anant Dadu, Vipul K. Satone, Rachneet Kaur, Sayed Hadi Hashemi, Hampton Leonard, Hirotaka Iwaki, View ORCID ProfileMary B. Makarious, Kimberley Billingsley, Sara Bandres-Ciga, Lana J. Sargent, Alastair J. Noyce, View ORCID ProfileAli Daneshmand, Cornelis Blauwendraat, Ken Marek, Sonja W. Scholz, Andrew B. Singleton, Mike A. Nalls, Roy H. Campbell, Faraz Faghri
doi: https://doi.org/10.1101/2022.08.04.502846
Anant Dadu
1Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
5Data Tecnica International, Washington, DC, 20812, USA
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Vipul K. Satone
2Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
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Rachneet Kaur
2Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
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Sayed Hadi Hashemi
1Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
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Hampton Leonard
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
5Data Tecnica International, Washington, DC, 20812, USA
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Hirotaka Iwaki
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
5Data Tecnica International, Washington, DC, 20812, USA
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Mary B. Makarious
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
10Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
11UCL Movement Disorders Centre, University College London, London, UK
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  • ORCID record for Mary B. Makarious
Kimberley Billingsley
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
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Sara Bandres-Ciga
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
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Lana J. Sargent
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
6School of Nursing, Virginia Commonwealth University, Richmond, VA, 23298, USA
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Alastair J. Noyce
11UCL Movement Disorders Centre, University College London, London, UK
12Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London and Department of Neurology, Royal London Hospital, London, UK
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Ali Daneshmand
7Department of Neurology, Boston Medical Center, Boston University School of Medicine, Boston, MA, 02118, USA
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Cornelis Blauwendraat
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
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Ken Marek
13InviCRO LLC, Boston, Massachusetts
14Molecular Neuroimaging, A Division of InviCRO, New Haven, Connecticut
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Sonja W. Scholz
8Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
9Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Andrew B. Singleton
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
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Mike A. Nalls
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
5Data Tecnica International, Washington, DC, 20812, USA
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Roy H. Campbell
1Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
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Faraz Faghri
3Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, 20892, USA
4Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA
5Data Tecnica International, Washington, DC, 20812, USA
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  • For correspondence: faraz.faghri@nih.gov
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Abstract

Background The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need.

Methods and Findings We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson’s Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01 for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner.

Conclusions Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

Competing Interest Statement

There is a conflict of interest AD, HL, HI, MAN and FF and declare that they are consultants employed by Data Tecnica International, whose participation in this is part of a consulting agreement between the US National Institutes of Health and said company.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.
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Posted August 06, 2022.
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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts
Anant Dadu, Vipul K. Satone, Rachneet Kaur, Sayed Hadi Hashemi, Hampton Leonard, Hirotaka Iwaki, Mary B. Makarious, Kimberley Billingsley, Sara Bandres-Ciga, Lana J. Sargent, Alastair J. Noyce, Ali Daneshmand, Cornelis Blauwendraat, Ken Marek, Sonja W. Scholz, Andrew B. Singleton, Mike A. Nalls, Roy H. Campbell, Faraz Faghri
bioRxiv 2022.08.04.502846; doi: https://doi.org/10.1101/2022.08.04.502846
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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts
Anant Dadu, Vipul K. Satone, Rachneet Kaur, Sayed Hadi Hashemi, Hampton Leonard, Hirotaka Iwaki, Mary B. Makarious, Kimberley Billingsley, Sara Bandres-Ciga, Lana J. Sargent, Alastair J. Noyce, Ali Daneshmand, Cornelis Blauwendraat, Ken Marek, Sonja W. Scholz, Andrew B. Singleton, Mike A. Nalls, Roy H. Campbell, Faraz Faghri
bioRxiv 2022.08.04.502846; doi: https://doi.org/10.1101/2022.08.04.502846

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