Parkinson's disease subtypes in the Oxford Parkinson disease centre (OPDC) discovery cohort

Michael Lawton, Fahd Baig, Michal Rolinski, Claudio Ruffman, Kannan Nithi, Margaret T May, Yoav Ben-Shlomo, Michele T M Hu*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

78 Citations (Scopus)
376 Downloads (Pure)


BACKGROUND: Within Parkinson's there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients.

OBJECTIVE: Use a data-driven approach to unravel any heterogeneity in the Parkinson's phenotype in a well-characterised, population-based incidence cohort.

METHODS: 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups.

RESULTS: Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%).

CONCLUSION: Our approach identified several Parkinson's phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson's.

Original languageEnglish
Pages (from-to)269-279
Number of pages11
JournalJournal of Parkinson's Disease
Issue number2
Publication statusPublished - 1 Jun 2015


  • Adult
  • Aged
  • Aged, 80 and over
  • Cluster Analysis
  • Cohort Studies
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neuropsychological Tests
  • Parkinson Disease
  • Phenotype


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