title: Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.
abstract: 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%).
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Lawton
authors.affiliation: Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK.
authors.forename: Fahd
authors.initials: F
authors.lastname: Baig
authors.affiliation: Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK.
authors.forename: Michal
authors.initials: M
authors.lastname: Rolinski
authors.affiliation: Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK.
authors.forename: Claudio
authors.initials: C
authors.lastname: Ruffman
authors.affiliation: Department of Clinical Neurology, John Radcliffe Hospital, Oxford, UK.
authors.forename: Kannan
authors.initials: K
authors.lastname: Nithi
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: Margaret T
authors.initials: MT
authors.lastname: May
authors.affiliation: School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: Yoav
authors.initials: Y
authors.lastname: Ben-Shlomo
authors.affiliation: Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK.|Department of Clinical Neurology, John Radcliffe Hospital, Oxford, UK.
authors.forename: Michele T M
authors.initials: MT
authors.lastname: Hu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests; D010300:Parkinson Disease / Q000145:classification* / Q000150:complications / Q000175:diagnosis*; D010641:Phenotype
keywords: Cohort studies; Parkinson’s disease; cluster analysis; factor analysis