Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis

Research output: Contribution to journalResearch articleContributedpeer-review


  • Tom Hähnel - , Department of Neurology, Fraunhofer Institute for Algorithms and Scientific Computing (Author)
  • Tamara Raschka - , Bonn-Aachen International Center for Information Technology, Fraunhofer Institute for Algorithms and Scientific Computing (Author)
  • Stefano Sapienza - , Luxembourg Institute of Health (Author)
  • Jochen Klucken - , Center Hospitalier de Luxembourg, University of Luxembourg, Luxembourg Institute of Health (Author)
  • Enrico Glaab - , University of Luxembourg (Author)
  • Jean-Christophe Corvol - , Sorbonne Université, Assistance publique – Hôpitaux de Paris, Pitié-Salpêtrière Hospital, INSERM - Institut national de la santé et de la recherche médicale (Author)
  • Björn H Falkenburger - , Department of Neurology, German Center for Neurodegenerative Diseases (DZNE) (Author)
  • Holger Fröhlich - , Bonn-Aachen International Center for Information Technology, Fraunhofer Institute for Algorithms and Scientific Computing (Author)


The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.


Original languageEnglish
Article number95
Number of pages13
JournalNPJ Parkinson's disease
Issue number1
Publication statusPublished - 2 May 2024

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ORCID /0000-0002-2387-526X/work/159171560
Scopus 85191966161


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