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Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis.
Hähnel, Tom; Raschka, Tamara; Sapienza, Stefano; Klucken, Jochen; Glaab, Enrico; Corvol, Jean-Christophe; Falkenburger, Björn H; Fröhlich, Holger.
Afiliación
  • Hähnel T; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany. tom.haehnel@scai-extern.fraunhofer.de.
  • Raschka T; Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. tom.haehnel@scai-extern.fraunhofer.de.
  • Sapienza S; Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
  • Klucken J; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany.
  • Glaab E; Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Corvol JC; Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
  • Falkenburger BH; Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Fröhlich H; Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
NPJ Parkinsons Dis ; 10(1): 95, 2024 May 02.
Article en En | MEDLINE | ID: mdl-38698004
ABSTRACT
The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Parkinsons Dis Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Parkinsons Dis Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos