Comparative analysis of progression milestones across Parkinson's disease clinical, pathological, and data-driven subtypes: a 10-year follow-up.

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Parkinson's disease (PD) heterogeneity complicates disease-modification clinical trial design and lead to ambiguous results, necessitating robust subtyping frameworks to identify rapid progressors. This study compared progression milestones across previously defined clinical (tremor-dominant [TD], postural instability/gait difficulty [PIGD], indeterminate), pathological (brain-first, body-first), and data-driven (diffuse malignant [DM], intermediate [IM], mild motor-predominant [MMP]) PD subtypes. Using data from the Parkinson's Progression Markers Initiative (PPMI), we analyzed milestone attainment across six functional domains and performed Cox proportional hazards regression. Randomized controlled trial sample size simulations evaluated the impact of subtyping on trial efficiency. Data-driven subtypes exhibited the highest progression rates, with DM patients attaining 63.0% of milestones, surpassing PIGD (55.6%) and body-first (54.0%) subtypes. Cox proportional hazards regression confirmed that DM patients had the highest hazard of progression compared to MMP, after adjusting for age at enrollment. Trial power simulations demonstrated that enrolling DM patients could reduce sample size requirements by approximately 50% using standard trial durations compared to unstratified PD cohorts. This analysis showed that data-driven subtyping, particularly the identification of the DM subtype, offers a promising strategy to optimize disease-modification trials in PD by capturing patients who meet progression milestones earlier.

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