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 them provided a different way to group patients: impulse control issues, overall non-motor symptoms, presence of dyskinesias and pyschosis, fatigue, axial symptoms and motor fluctuations, autonomic dysfunction, depression, and excessive sweating. Each of these groups could be seen as a subtype of the disease. Significant differences between subtypes (P< 0.01) were found in sex, age, age of onset, disease duration, Hoehn & Yahr stage, and treatment. Independent confirmation of these results could have implications for the clinical management of Parkinson’s disease patients.
Highlights
Identification of Parkinson’s disease subtypes may help understand underlying disease mechanisms and provide personalized management
Several issues have been raised about data-driven Parkinson’s disease (PD) subtypes, such as the low number in the samples, their lack of internal homogeneity, and their difficulty to reproduce meaningful data in real life and external validity[13,14]
The analysis was carried out on data gathered from the first validation study of the Movement Disorder Society Non-Motor Rating Scale (MDS-NMS), an international, multi-center, cross-sectional study that included PD English-speaking patients from England and the United S tates[21]
Summary
Identification of Parkinson’s disease subtypes may help understand underlying disease mechanisms and provide personalized management. 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. Significant differences between subtypes (P< 0.01) were found in sex, age, age of onset, disease duration, Hoehn & Yahr stage, and treatment Independent confirmation of these results could have implications for the clinical management of Parkinson’s disease patients. Several issues have been raised about data-driven PD subtypes, such as the low number in the samples, their lack of internal homogeneity, and their difficulty to reproduce meaningful data in real life and external validity[13,14] We believe that these issues may be a consequence of using single-partition clustering methods. The analysis of these subtypes and their associations may provide more accurate insights about the considered symptoms, as well as their relationship with socio-demographic and clinical information of the patients
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