Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes
Abstract Aims Huntington's disease (HD) is a progressive neurodegenerative disorder with heterogeneous clinical manifestations. Identifying distinct clinical clusters and their relevant biomarkers could elucidate the underlying disease pathophysiology. Methods Following the Enroll‐HD program in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-03-01
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| Series: | Brain and Behavior |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/brb3.3469 |
| _version_ | 1827308781817561088 |
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| author | Ling‐Xiao Cao Jin‐Hui Yin Gang Du Qing Yang Yue Huang |
| author_facet | Ling‐Xiao Cao Jin‐Hui Yin Gang Du Qing Yang Yue Huang |
| author_sort | Ling‐Xiao Cao |
| collection | DOAJ |
| description | Abstract Aims Huntington's disease (HD) is a progressive neurodegenerative disorder with heterogeneous clinical manifestations. Identifying distinct clinical clusters and their relevant biomarkers could elucidate the underlying disease pathophysiology. Methods Following the Enroll‐HD program initiated in 2018.09, we have recruited 104 HD patients (including 21 premanifest) and 31 health controls at Beijing Tiantan Hospital. Principal components analysis and k‐means cluster analysis were performed to determine HD clusters. Chi‐square test, one‐way ANOVA, and covariance were used to identify features among these clusters. Furthermore, plasma cytokines levels and brain structural imaging were used as biomarkers to delineate the clinical features of each cluster. Results Three clusters were identified. Cluster 1 demonstrated the most severe motor and nonmotor symptoms except for chorea, the lowest whole brain volume, the plasma levels of IL‐2 were higher and significantly associated with cluster 1. Cluster 2 was characterized with the most severe chorea and the largest pallidum volume. Cluster 3 had the most benign motor symptoms but moderate psychiatric problems. Conclusion We have identified three HD clusters via clinical manifestations with distinct biomarkers. Our data shed light on better understanding about the pathophysiology of HD. |
| first_indexed | 2024-04-24T19:16:52Z |
| format | Article |
| id | doaj.art-e59e6834a840414c982646d859236045 |
| institution | Directory Open Access Journal |
| issn | 2162-3279 |
| language | English |
| last_indexed | 2024-04-24T19:16:52Z |
| publishDate | 2024-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj.art-e59e6834a840414c982646d8592360452024-03-26T05:06:45ZengWileyBrain and Behavior2162-32792024-03-01143n/an/a10.1002/brb3.3469Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changesLing‐Xiao Cao0Jin‐Hui Yin1Gang Du2Qing Yang3Yue Huang4China National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital, Capital Medical University Beijing ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital, Capital Medical University Beijing ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital, Capital Medical University Beijing ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital, Capital Medical University Beijing ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital, Capital Medical University Beijing ChinaAbstract Aims Huntington's disease (HD) is a progressive neurodegenerative disorder with heterogeneous clinical manifestations. Identifying distinct clinical clusters and their relevant biomarkers could elucidate the underlying disease pathophysiology. Methods Following the Enroll‐HD program initiated in 2018.09, we have recruited 104 HD patients (including 21 premanifest) and 31 health controls at Beijing Tiantan Hospital. Principal components analysis and k‐means cluster analysis were performed to determine HD clusters. Chi‐square test, one‐way ANOVA, and covariance were used to identify features among these clusters. Furthermore, plasma cytokines levels and brain structural imaging were used as biomarkers to delineate the clinical features of each cluster. Results Three clusters were identified. Cluster 1 demonstrated the most severe motor and nonmotor symptoms except for chorea, the lowest whole brain volume, the plasma levels of IL‐2 were higher and significantly associated with cluster 1. Cluster 2 was characterized with the most severe chorea and the largest pallidum volume. Cluster 3 had the most benign motor symptoms but moderate psychiatric problems. Conclusion We have identified three HD clusters via clinical manifestations with distinct biomarkers. Our data shed light on better understanding about the pathophysiology of HD.https://doi.org/10.1002/brb3.3469cytokinesHuntington's diseaseneuroimagingsubtype |
| spellingShingle | Ling‐Xiao Cao Jin‐Hui Yin Gang Du Qing Yang Yue Huang Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes Brain and Behavior cytokines Huntington's disease neuroimaging subtype |
| title | Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes |
| title_full | Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes |
| title_fullStr | Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes |
| title_full_unstemmed | Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes |
| title_short | Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes |
| title_sort | identifying and verifying huntington s disease subtypes clinical features neuroimaging and cytokine changes |
| topic | cytokines Huntington's disease neuroimaging subtype |
| url | https://doi.org/10.1002/brb3.3469 |
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