Progression Subtypes in Parkinson’s Disease: A Data-driven Multi-Cohort Analysis

Background The progression of Parkinson’s disease (PD) is heterogeneous across patients. This heterogeneity complicates patients counseling and inflates 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. Methods To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts. 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). These subtypes were then characterized across the three cohorts using clinical scores, DaTSCAN imaging and digital gait biomarkers. To assign patients to progression subtypes from baseline data, we developed predictive models and performed extensive cross-cohort validation. Results In each cohort, we identified a fast-progressing and a slow-progressing subtype. These subtypes were reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response and features extracted from DaTSCAN imaging and digital gait assessments. Predictive models achieved robust performance with ROC-AUC up to 0.79 for subtype identification. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on predictions from baseline can reduce the required cohort size by 43%. Conclusion Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts and can be predicted from baseline data. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. The predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement ERA PerMed EU-wide project DIGIPD (01KU2110), Federal Ministry of Education and Research of Germany (16DKWN1113A), French Government (ANR-10-IAIHU-06), French Ministry of Research (ANR-11-INBS-0006), Fondation d Entreprise EDF and the Luxembourg National Research Fund (FNR/NCER13/BM/11264123). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRBParis VI gave ethical approval for this work (RCB: 2014-A00725-42) Luxembourg National Ethics Board gave ethical approval for this work (CNER Ref: 201407/13) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The source code used for training LTJMM, VaDER and all relevant statistical analyses will be published at upon acceptance of the paper under the MIT license, thereby providing free access for anyone. As this study is a retrospective analysis, availability of the clinical data depends on the individual study groups (PPMI: [www.ppmi-info.org][1], ICEBERG: marie.vidailhet{at}psl.aphp.fr, LuxPARK: rejko.krueger{at}uni.lu). [1]: http://www.ppmi-info.org