Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes

AimsHuntington'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.MethodsFollowing 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.ResultsThree 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.ConclusionWe have identified three HD clusters via clinical manifestations with distinct biomarkers. Our data shed light on better understanding about the pathophysiology of HD. Three clusters of patients with Huntington's disease (HD) were identified through principal components analysis (PCA). Cluster 1 demonstrated the most severe motor and nonmotor symptoms, the longest CAG repeats, the lowest whole brain volume, and higher plasma levels of IL-2. Cluster 2 was characterized with moderate motor symptoms, the most severe chorea, and the largest pallidum volume. Cluster 3 had the most benign motor symptoms but moderate psychiatric problems. image