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PLoS One. 2017 Jun 12;12(6):e0178982. doi: 10.1371/journal.pone.0178982. eCollection 2017.

A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease.

Author information

1
GNS Healthcare, Cambridge, Massachusetts, United States of America.
2
Novartis, Cambridge, Massachusetts, United States of America.
3
Alexion Pharmaceuticals, Cambridge, Massachusetts, United States of America.
4
University of Rochester, Rochester, New York, United States of America.
5
Morton and Gloria Movement Disorders Clinic and the Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital and the University of Toronto, Toronto, Ontario, Canada.
6
Institute for Neurodegenerative Disorders, New Haven, Connecticut, United States of America.
7
Georgetown University, Washington, DC, United States of America.
8
National Institute on Aging, NIH, Bethesda, Maryland, United States of America.
9
University of San Francisco & San Francisco Veterans Affairs Medical Center, San Francisco, California, United States of America.
10
Biogen Idec, Cambridge, Massachusetts, United States of America.
11
Voyager Therapeutics, Cambridge, Massachusetts, United States of America.

Abstract

BACKGROUND:

There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research.

OBJECTIVE:

To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD.

METHODS:

Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III.

RESULTS:

The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson's Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study.

CONCLUSIONS:

Baseline function near the time of Parkinson's disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson's disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.

PMID:
28604798
PMCID:
PMC5467836
DOI:
10.1371/journal.pone.0178982
[Indexed for MEDLINE]
Free PMC Article

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