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dc.contributor.authorRodriguez-Sanchez, Fernando
dc.contributor.authorRodriguez-Blazquez, Carmen 
dc.contributor.authorBielza, Concha
dc.contributor.authorLarrañaga, Pedro
dc.contributor.authorWeintraub, Daniel
dc.contributor.authorMartinez-Martin, Pablo 
dc.contributor.authorRizos, Alexandra
dc.contributor.authorSchrag, Anette
dc.contributor.authorChaudhuri, K. Ray
dc.date.accessioned2022-05-09T10:13:10Z
dc.date.available2022-05-09T10:13:10Z
dc.date.issued2021-12
dc.identifier.citationSci Rep. 2021 Dec 8;11(1):23645.es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/14328
dc.description.abstractIdentification 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.es_ES
dc.description.sponsorshipF.R.-S., C.B., and P.L. are supported in part by the Spanish Ministry of Economy and Competitiveness through the PID2019-109247GB-I00 project, by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), and by the BBVA Foundation (2019 Call) through the ”Score-based nonstationary temporal Bayesian networks. Applications in climate and neuroscience” project.es_ES
dc.language.isoenges_ES
dc.publisherNature Publishing Group es_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectParkinson’s diseasees_ES
dc.titleIdentifying Parkinson’s disease subtypes with motor and non-motor symptoms via model-based multi-partition clusteringes_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID34880345es_ES
dc.format.volume11es_ES
dc.format.number1es_ES
dc.format.page23645es_ES
dc.identifier.doi10.1038/s41598-021-03118-wes_ES
dc.contributor.funderMinisterio de Economíay Competitividad (España) es_ES
dc.contributor.funderUnión Europea. Comisión Europea. H2020es_ES
dc.contributor.funderFundación BBVAes_ES
dc.description.peerreviewedes_ES
dc.identifier.e-issn2045-2322es_ES
dc.relation.publisherversionhttps://doi.org/10.1038/s41598-021-03118-wes_ES
dc.identifier.journalScientific Reportses_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/945539/EUes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PID2019-109247GB-I00es_ES


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