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

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

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.

MeSH terms

  • Aged
  • Alleles
  • Bayes Theorem*
  • Cognition*
  • Computer Simulation
  • Disease Progression
  • Female
  • Follow-Up Studies
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical
  • Models, Theoretical*
  • Motor Activity*
  • Parkinson Disease / diagnosis
  • Parkinson Disease / etiology
  • Parkinson Disease / physiopathology*
  • Parkinson Disease / psychology*
  • Polymorphism, Single Nucleotide
  • Prospective Studies
  • Reproducibility of Results
  • Severity of Illness Index

Grant support

This work was supported by National Institute of Neurological Disorders and Stroke (5U01NS050095); U.S. Department of Defense (DOD) Neurotoxin Exposure Treatment Parkinson's Research Program (W23RRYX7022N606); Michael J. Fox Foundation for Parkinson's Research; Parkinson's Disease Foundation; The analysis was funded by Biogen Idec, Inc, Cambridge, MA. The funder provided support in the form of salaries for Bernard Ravina (at Biogen at the time) and Ajay Verma, and, indirectly via a commercial agreement with GNS Healthcare to provide analytical services, for Boris Hayete, Bruce Church, Paul McDonagh (at GNS at the time), Karl Runge, Iya Khalil, Jason Laramie (at GNS at the time), and Diane Wuest. Biogen Idec did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors with other affiliations were supported by their respective institutions and grants. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.