Computational Intelligence Group, Universidad Politécnica de Madrid, Madrid, Spain.
National Center of Epidemiology, Carlos III Institute of Health, Madrid, Spain.
Sci Rep. 2021 Dec 8;11(1):23645. doi: 10.1038/s41598-021-03118-w.
Identification of Parkinson's disease subtypes may help understand underlying disease mechanisms and provide personalized management. Although clustering methods have been previously used for subtyping, they have reported generic subtypes of limited relevance in real life practice because patients do not always fit into a single category. The aim of this study was to identify new subtypes assuming that patients could be grouped differently according to certain sets of related symptoms. To this purpose, a novel model-based multi-partition clustering method was applied on data from an international, multi-center, cross-sectional study of 402 Parkinson's disease patients. Both motor and non-motor symptoms were considered. As a result, eight sets of related symptoms were identified. Each of them provided a different way to group patients: impulse control issues, overall non-motor symptoms, presence of dyskinesias and pyschosis, fatigue, axial symptoms and motor fluctuations, autonomic dysfunction, depression, and excessive sweating. Each of these groups could be seen as a subtype of the disease. Significant differences between subtypes (P< 0.01) were found in sex, age, age of onset, disease duration, Hoehn & Yahr stage, and treatment. Independent confirmation of these results could have implications for the clinical management of Parkinson's disease patients.
帕金森病亚型的识别有助于了解潜在的疾病机制，并提供个性化的管理。虽然聚类方法以前曾用于亚组分析，但由于患者并不总是符合单一类别，因此它们报告的通用亚型在实际实践中相关性有限。本研究的目的是假设根据某些相关症状集，患者可以以不同的方式分组，从而确定新的亚型。为此，一种新的基于模型的多分区聚类方法应用于来自国际、多中心、横断面研究的 402 名帕金森病患者的数据。同时考虑了运动和非运动症状。结果确定了 8 组相关症状。每组症状都提供了一种不同的分组方式：冲动控制问题、整体非运动症状、是否存在异动症和精神症状、疲劳、轴性症状和运动波动、自主神经功能障碍、抑郁和过度出汗。这些组中的每一个都可以被视为疾病的一种亚型。在性别、年龄、发病年龄、病程、Hoehn & Yahr 分期和治疗方面，各亚型之间存在显著差异（P<0.01）。这些结果的独立证实可能对帕金森病患者的临床管理具有重要意义。
