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

Abstract

AbstractBackgroundThe 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.MethodsTo 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.ResultsIn 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%.ConclusionOur 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.