ArticlePloS one2017
A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Motor-Derived Digital Biomarkers for Identifying Low-MoCA Status in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Article
- Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson's Disease Model.Biomimetics (Basel, Switzerland) · 2023Article
- Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs.Frontiers in aging neuroscience · 2023Review
- Genetic polymorphisms of bone marrow stromal cell antigen-1 (BST-1/CD157): implications for immune/inflammatory dysfunction in neuropsychiatric disorders.Frontiers in immunology · 2023Review
- Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development.Frontiers in cardiovascular medicine · 2022Article
- Genetic cooperativity in multi-layer networks implicates cell survival and senescence in the striatum of Huntington's disease mice synchronous to symptoms.Bioinformatics (Oxford, England) · 2020Article
- Article
- Evolution of diagnostic criteria and assessments for Parkinson's disease mild cognitive impairment.Movement disorders : official journal of the Movement Disorder Society · 2018Review
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Authors and funding
15 authors.
Funding
Abstract
backgroundThere are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research.
objectiveTo identify predictors of long-term motor and cognitive outcomes and rate of progression in PD.
methodsNewly 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.
resultsThe 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.
conclusionsBaseline 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.
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