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Characterization of Parkinson's Disease Subtypes and Related Attributes.

Publication ,  Journal Article
Shakya, S; Prevett, J; Hu, X; Xiao, R
Published in: Frontiers in neurology
January 2022

Parkinson's disease is a progressive neurodegenerative disease with complex, heterogeneous motor and non-motor symptoms. The current evidence shows that there is still a marked heterogeneity in the subtyping of Parkinson's disease using both clinical and data-driven approaches. Another challenge posed in PD subtyping is the reproducibility of previously identified PD subtypes. These issues require additional results to confirm previous findings and help reconcile discrepancies, as well as establish a standardized application of cluster analysis to facilitate comparison and reproducibility of identified PD subtypes. Our study aimed to address this gap by investigating subtypes of Parkinson's disease using comprehensive clinical (motor and non-motor features) data retrieved from 408 de novo Parkinson's disease patients with the complete clinical data in the Parkinson's Progressive Marker Initiative database. A standardized k-means cluster analysis approach was developed by taking into consideration of common practice and recommendations from previous studies. All data analysis codes were made available online to promote data comparison and validation of reproducibility across research groups. We identified two distinct PD subtypes, termed the severe motor-non-motor subtype (SMNS) and the mild motor- non-motor subtype (MMNS). SMNS experienced symptom onset at an older age and manifested more intense motor and non-motor symptoms than MMNS, who experienced symptom onset at a younger age and manifested milder forms of Parkinson's symptoms. The SPECT imaging makers supported clinical findings such that the severe motor-non-motor subtype showed lower binding values than the mild motor- non-motor subtype, indicating more significant neural damage at the nigral pathway. In addition, SMNS and MMNS show distinct motor (ANCOVA test: F = 47.35, p< 0.001) and cognitive functioning (F = 33.93, p< 0.001) progression trends. Such contrast between SMNS and MMNS in both motor and cognitive functioning can be consistently observed up to 3 years following the baseline visit, demonstrating the potential prognostic value of identified PD subtypes.

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Published In

Frontiers in neurology

DOI

EISSN

1664-2295

ISSN

1664-2295

Publication Date

January 2022

Volume

13

Start / End Page

810038

Related Subject Headings

  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1701 Psychology
  • 1109 Neurosciences
  • 1103 Clinical Sciences
 

Citation

APA
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ICMJE
MLA
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Shakya, S., Prevett, J., Hu, X., & Xiao, R. (2022). Characterization of Parkinson's Disease Subtypes and Related Attributes. Frontiers in Neurology, 13, 810038. https://doi.org/10.3389/fneur.2022.810038
Shakya, Shamatree, Julia Prevett, Xiao Hu, and Ran Xiao. “Characterization of Parkinson's Disease Subtypes and Related Attributes.Frontiers in Neurology 13 (January 2022): 810038. https://doi.org/10.3389/fneur.2022.810038.
Shakya S, Prevett J, Hu X, Xiao R. Characterization of Parkinson's Disease Subtypes and Related Attributes. Frontiers in neurology. 2022 Jan;13:810038.
Shakya, Shamatree, et al. “Characterization of Parkinson's Disease Subtypes and Related Attributes.Frontiers in Neurology, vol. 13, Jan. 2022, p. 810038. Epmc, doi:10.3389/fneur.2022.810038.
Shakya S, Prevett J, Hu X, Xiao R. Characterization of Parkinson's Disease Subtypes and Related Attributes. Frontiers in neurology. 2022 Jan;13:810038.

Published In

Frontiers in neurology

DOI

EISSN

1664-2295

ISSN

1664-2295

Publication Date

January 2022

Volume

13

Start / End Page

810038

Related Subject Headings

  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1701 Psychology
  • 1109 Neurosciences
  • 1103 Clinical Sciences