Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering.

ABSTRACT: Identification of Parkinson's disease subtypes may help understand underlying disease mechanisms and provide personalized management. Although clustering methods have been previously used for subtyping, they have reported generic subtypes of limited relevance in real life practice because patients do not always fit into a single category. The aim of this study was to identify new subtypes assuming that patients could be grouped differently according to certain sets of related symptoms. To this purpose, a novel model-based multi-partition clustering method was applied on data from an international, multi-center, cross-sectional study of 402 Parkinson's disease patients. Both motor and non-motor symptoms were considered. As a result, eight sets of related symptoms were identified. Each of th

SUBMITTER: Rodriguez-Sanchez F 

PROVIDER: S-EPMC8654994 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature