2026-07-21T11:48:45Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/143282025-04-01T12:25:12Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608
Repisalud
author
Rodriguez-Sanchez, Fernando
author
Rodriguez-Blazquez, Carmen
author
Bielza, Concha
author
Larrañaga, Pedro
author
Weintraub, Daniel
author
Martínez-Martín, Pablo
author
Rizos, Alexandra
author
Schrag, Anette
author
Chaudhuri, Kallol Ray
funder
Ministerio de Economía y Competitividad (España)
funder
Unión Europea. Comisión Europea. H2020
funder
Fundación BBVA
2022-05-09T10:13:10Z
2022-05-09T10:13:10Z
2021-12
Sci Rep. 2021 Dec 8;11(1):23645.
10.1038/s41598-021-03118-w
2045-2322
Scientific Reports
34880345
http://hdl.handle.net/20.500.12105/14328
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.
eng
Parkinson’s disease
Identifying Parkinson’s disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering
research article
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IdentifyingParkinsonsDiseasesSbtypes_2021.pdf
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IdentifyingParkinsonsDiseasesSbtypesSupplementaryInformation_2021.pdf
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IdentifyingParkinsonsDiseasesSbtypes_2021.pdf.txt
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IdentifyingParkinsonsDiseasesSbtypesSupplementaryInformation_2021.pdf.txt