Identifying the clusters within nonmotor manifestations in early Parkinson's disease by using unsupervised cluster analysis.
Identifying the clusters within nonmotor manifestations in early Parkinson's disease by using unsupervised cluster analysis.
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DOI：
10.1371/journal.pone.0091906
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Jeon BS
中科院分区：
文献类型：
--
作者：
Yang HJ;Kim YE;Yun JY;Kim HJ;Jeon BS
帕金森病 (PD) 的经典分类和数据驱动分类主要基于运动症状，很少关注非运动表现的聚类。收集了 56 名 3 年内发病的 PD 患者的人口统计学、运动和非运动特征的临床数据，包括韩国版嗅探棒 (KVSS) 测试结果以及对非运动特征筛查问卷的回答。使用无监督层次聚类分析（HCA）对非运动亚组进行分类。除了无人监督的 HCA 之外，我们还进行了横断面分析，将 KVSS 嗅觉测试的表现与患者的其他非运动表现进行比较。根据 KVSS 测试，49 名患者 (87.5%) 患有嗅觉减退。 HCA 建议所有 PD 患者使用三个非运动簇，对于新发 PD 患者使用两个非运动簇，但没有对相关性进行先验假设。在横断面分析中，梦境行为在嗅觉评分较低的患者中更为普遍，这意味着嗅觉功能受损（对于所有帕金森病患者，P = 0.029；对于新发帕金森病患者，P = 0.046）。我们通过使用无监督的层次聚类提出早期 PD 中存在不同的非运动表现集群。据我们所知，这项研究首次报告了根据疾病早期多种非运动表现的无监督 HCA 识别非运动亚组。
Classical and data-driven classifications of Parkinson's disease (PD) are based primarily on motor symptoms, with little attention being paid to the clustering of nonmotor manifestations. Clinical data on demographic, motor and nonmotor features, including the Korean version of the sniffin' stick (KVSS) test results, and responses to the screening questionnaire of the nonmotor features were collected from 56 PD patients with disease onset within 3 years. Nonmotor subgroups were classified using unsupervised hierarchical cluster analysis (HCA). In addition to unsupervised HCA, we performed a cross-sectional analysis comparing the performance on the KVSS olfactory test with other nonmotor manifestations of the patients. Forty-nine patients (87.5%) had hyposmia based on the KVSS test. HCA suggested three nonmotor clusters for all PD patients and two nonmotor clusters in de novo PD patients, without a priori assumptions about the relatedness. In the cross-sectional analysis, dream-enactment behavior was more prevalent in patients with lower olfactory scores, implying impaired olfactory function (P = 0.029 for all PD patients; P = 0.046 for de novo PD patients). We propose the existence of different clusters of nonmotor manifestations in early PD by using unsupervised hierarchical clustering. To our knowledge, this study is the first to report the identification of nonmotor subgroups based on unsupervised HCA of multiple nonmotor manifestations in the early stage of the disease.
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