Data-driven stratification of Parkinson’s disease patients based on the progression of motor and cognitive disease markers

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder with a complex set of motor and non-motor symptoms and a diverse disease progression. Subtyping PD patients is required for personalized therapies but stratification approaches based on intermediate phenotypes such as clinical assessment scores lack reproducibility and stability, which is at least partially due to the broad spectrum of methods that can be applied during different steps of data processing. We propose a novel approach that considers the progression of detailed clinical assessment scores in different domains over a period of five years. Furthermore, we confirm the robustness of our subtypes with comparisons to subtypes that emerge when using different data pre-processing or another clustering algorithm. Three subtypes were found with differentiable symptoms: The subtype has the fastest progression and is most severely affected in daily life, closely followed by the subtype. The subtype, in contrast, is characterized by moderate progression. These subtypes emerge from their progression pattern rather than from a snapshot during one time point. Hence we advocate for stratification approaches for PD subtyping that take longitudinal data over several years into account.

Keywords

• parkinson's disease (pd)
• ppmi
• stratification
• subtypes
• biomarker
• machine learning
• clustering