Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.
Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.
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DOI：
10.3233/jpd-140523
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Hu MT
中科院分区：
文献类型：
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作者：
Lawton M;Baig F;Rolinski M;Ruffman C;Nithi K;May MT;Ben-Shlomo Y;Hu MT
背景：帕金森氏症在表现时有一系列的临床特征，这些特征可能代表了该疾病的亚型。然而，如何最好地对患者进行分组并没有被广泛接受的共识。目的：利用数据驱动的方法，在一个特征明确、以人群为基础的发病率队列中，揭示帕金森病表型的任何异质性。方法：使用广泛的运动、认知和非运动指标对769例连续患者进行评估，平均病程为1.3年。采用链式方程法对缺失数据进行了多次插值处理。我们使用探索性因子分析和验证性因子分析来确定合适的域，以包括在我们的聚类分析中。k -均值聚类分析的因素得分和所有变量不加载到一个因素被用来确定表型亚组。结果：我们的因子分析发现了三个重要的因素特征：心理健康特征；无震颤运动特征，如姿态和刚性；认知特征。我们随后的5个聚类模型确定了以下群体的特征：(1)轻度运动和非运动疾病（25.4%），(2)不良姿势和认知（23.3%），(3)严重震颤（20.8%），(4)不良心理健康，RBD和睡眠（18.9%），(5)严重运动和非运动疾病伴不良心理健康（11.7%）。结论：我们的方法确定了几个主要由多巴胺能抵抗特征（RBD，认知和姿势受损，心理健康不良）驱动的帕金森病表型亚组，除了多巴胺能反应的运动特征外，这些亚组对研究早期帕金森病的病因，进展和药物反应可能很重要。
Background: Within Parkinson’s there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients. Objective: Use a data-driven approach to unravel any heterogeneity in the Parkinson’s phenotype in a well-characterised, population-based incidence cohort. Methods: 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups. Results: Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%). Conclusion: Our approach identified several Parkinson’s phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson’s.