Comprehensive subtyping of Parkinson's disease patients with similarity fusion: a case study with BioFIND data

Parkinson's disease (PD) is a complex neurodegenerative disorder with diverse clinical manifestations. To better understand this disease, research has been done to categorize, or subtype, patients, using an array of criteria derived from clinical assessments and biospecimen analyses. In this study, using data from the BioFIND cohort, we aimed at identifying subtypes of moderate-toadvanced PD via comprehensively considering motor and non-motor manifestations. A total of 103 patients were included for analysis. Through the use of a patient-wise similarity matrix fusion technique and hierarchical agglomerative clustering analysis, three unique subtypes emerged from the clustering results. Subtype I, comprised of 60 patients (similar to 58.3%), was characterized by mild symptoms, both motor and non-motor. Subtype II, comprised of 20 (similar to 19.4%) patients, was characterized by an intermediate severity, with a high tremor score and mild non-motor symptoms. Subtype III, comprised of 23 (similar to 22.3%) patients, was characterized by more severe motor and non-motor symptoms. These subtypes show statistically significant differences when looking at motor (on and off medication) clinical features and non-motor clinical features, while there was no clear difference in demographics, biomarker levels, and genetic risk scores.

Automated summary

This study aimed to identify subtypes of moderate-to-advanced Parkinson's disease by comprehensively considering motor and non-motor manifestations. Three unique subtypes emerged from the clustering results, each characterized by different levels of symptom severity. These subtypes showed significant differences in motor and non-motor clinical features, providing important information for further research on the treatment and management of Parkinson's disease.