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  1. Michele Hu1,3,
  2. Michael Lawton2,
  3. Fahd Baig1,3,
  4. Michal Rolinski1,3,
  5. Claudio Ruffmann1,3,
  6. Kannan Nithi3,
  7. Paul Tomlinson1,3,
  8. Konrad Szewczyk-Krolikowski1,3,
  9. Margaret May2,
  10. Yoav Ben-Shlomo2
  1. 1Oxford Parkinson's Disease Centre, Nuffield Department of Clinical Neurosciences, Oxford University
  2. 2School of Social and Community Medicine, University of Bristol
  3. 3Oxford University Hospitals NHS Trust


Background Conventional “top-down” classification approaches depend on a-priori assumptions about disease phenotype. “Bottom-up” data-driven statistical approaches in well-characterised, incident cohorts are relatively novel and may have specific advantages for disease stratification. The Discovery cohort is a population-based, prospective early PD cohort that aims to better understand the biology and identify prognostic markers through comprehensive motor/non-motor longitudinal assessment.

Methods 454 consecutive patients (UKPDBB probable PD diagnosis ≤3.5 years) were included in factor and k-means cluster analysis which discriminated between patients using motor and non-motor baseline assessments (MDS-UPDRS I-IV, H&Y, Timed walk, Flamingo test, Purdue Pegboard, Sniffin smell, Constipation and RBD Questionnaires, Epworth Sleep Scale, Leeds Anxiety and Depression Scale, MMSE, MoCA, verbal fluencies)

Results 5 patient clusters were identified: Group 1 (28%) were younger and had milder disease, Group 2 (19%) scored poorly on constipation and psychological well-being, Group 3 (18%) were tremor dominant, Group 4 (14%) had markedly poorer psychological well-being, Group 5 (21%) were driven by cognitive/ postural problems.

Conclusions This data-driven approach identified novel patient clusters (Groups 2, 4) characterised by constipation and poorer psychological well-being that would have been missed using traditional motor classification. Future work will assess whether group memberships predicts treatment-response and progression, including dementia and motoric decline.


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