Characterization of Parkinson's Disease Subtypes and Related Attributes
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 cha...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.810038/full |
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| author | Shamatree Shakya Julia Prevett Xiao Hu Xiao Hu Xiao Hu Ran Xiao |
| author_facet | Shamatree Shakya Julia Prevett Xiao Hu Xiao Hu Xiao Hu Ran Xiao |
| author_sort | Shamatree Shakya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:33:53Z |
| format | Article |
| id | doaj.art-265c9873c52546ef951761d6371ac07f |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-13T18:33:53Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-265c9873c52546ef951761d6371ac07f2022-12-22T02:34:57ZengFrontiers Media S.A.Frontiers in Neurology1664-22952022-05-011310.3389/fneur.2022.810038810038Characterization of Parkinson's Disease Subtypes and Related AttributesShamatree Shakya0Julia Prevett1Xiao Hu2Xiao Hu3Xiao Hu4Ran Xiao5School of Nursing, Duke University, Durham, NC, United StatesSchool of Nursing, Duke University, Durham, NC, United StatesSchool of Nursing, Emory University, Atlanta, GA, United StatesDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United StatesDepartment of Computer Science, College of Arts and Sciences, Emory University, Atlanta, GA, United StatesSchool of Nursing, Duke University, Durham, NC, United StatesParkinson'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.https://www.frontiersin.org/articles/10.3389/fneur.2022.810038/fullParkinson's diseasesymptomssubtypescluster analysisdata driven approach |
| spellingShingle | Shamatree Shakya Julia Prevett Xiao Hu Xiao Hu Xiao Hu Ran Xiao Characterization of Parkinson's Disease Subtypes and Related Attributes Frontiers in Neurology Parkinson's disease symptoms subtypes cluster analysis data driven approach |
| title | Characterization of Parkinson's Disease Subtypes and Related Attributes |
| title_full | Characterization of Parkinson's Disease Subtypes and Related Attributes |
| title_fullStr | Characterization of Parkinson's Disease Subtypes and Related Attributes |
| title_full_unstemmed | Characterization of Parkinson's Disease Subtypes and Related Attributes |
| title_short | Characterization of Parkinson's Disease Subtypes and Related Attributes |
| title_sort | characterization of parkinson s disease subtypes and related attributes |
| topic | Parkinson's disease symptoms subtypes cluster analysis data driven approach |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2022.810038/full |
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