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Updated: Jun 30, 2025
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018
Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes
Ling-Xiao Cao1,2, Jin-Hui Yin1,2, Gang Du1,2,3
1China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Insights
Researchers identified three distinct Huntington's disease (HD) patient clusters using clinical data and biomarkers. These findings offer new insights into the complex pathophysiology of this neurodegenerative disorder.
Area of Science:
- Neuroscience
- Genetics
- Clinical Neurology
Background:
- Huntington's disease (HD) is a progressive neurodegenerative disorder with varied clinical presentations.
- Understanding distinct patient subgroups and their biomarkers is crucial for elucidating HD pathophysiology.
Purpose of the Study:
- To identify distinct clinical clusters within Huntington's disease (HD) patient cohorts.
- To identify specific biomarkers associated with each identified HD cluster.
- To enhance the understanding of HD pathophysiology through clinical and biomarker analysis.
Main Methods:
- Utilized data from the Enroll-HD program, recruiting 104 HD patients and 31 controls.
- Employed principal components analysis and k-means clustering to define HD patient groups.
- Analyzed plasma cytokine levels and brain structural imaging as potential biomarkers.
Main Results:
- Identified three distinct Huntington's disease (HD) clusters based on clinical manifestations.
- Cluster 1 showed severe symptoms (except chorea), reduced brain volume, and elevated IL-2 levels.
- Cluster 2 exhibited severe chorea and larger pallidum volume; Cluster 3 presented mild motor symptoms with psychiatric issues.
Conclusions:
- Successfully identified three Huntington's disease (HD) clusters characterized by distinct clinical features and biomarkers.
- These findings contribute to a deeper comprehension of the underlying pathophysiology of Huntington's disease.
- The identified clusters and biomarkers may aid in future personalized treatment strategies for HD.
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.
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