通过交叉诊断聚类分析确定的小脑疾病认知障碍亚型：一项德国多中心研究的结果
Subtypes of cognitive impairment in cerebellar disease identified by cross-diagnostic cluster-analysis: results from a German multicenter study.
作者信息
Liu Qi, Rubarth Kerstin, Faber Jennifer, Sulzer Patricia, Dogan Imis, Barkhoff Miriam, Minnerop Martina, Berlijn Adam M, Elben Saskia, Jacobi Heike, Aktories Julia-Elisabeth, Huvermann Dana M, Erdlenbruch Friedrich, Van der Veen Raquel, Müller Johanna, Nio Enzo, Frank Benedikt, Köhrmann Martin, Wondzinski Elke, Siebler Mario, Reetz Kathrin, Konczak Jürgen, Konietschke Frank, Klockgether Thomas, Synofzik Matthis, Röske Sandra, Timmann Dagmar, Thieme Andreas
机构信息
Department of Neurology, Essen University Hospital, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany.
Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany.
出版信息
J Neurol. 2024 Dec 21;272(1):83. doi: 10.1007/s00415-024-12831-1.
BACKGROUND
Cognitive and neuropsychiatric impairment, known as cerebellar cognitive affective syndrome (CCAS), may be present in cerebellar disorders. This study identified distinct CCAS subtypes in cerebellar patients using cluster analysis.
METHODS
The German CCAS-Scale (G-CCAS-S), a brief screening test for CCAS, was assessed in 205 cerebellar patients and 200 healthy controls. K-means cluster analysis was applied to G-CCAS-S data to identify cognitive clusters in patients. Demographic and clinical variables were used to characterize the clusters. Multiple linear regression quantified their relative contribution to cognitive performance. The ability of the G-CCAS-S to correctly distinguish between patients and controls was compared across the clusters.
RESULTS
Two clusters explained the variance of cognitive performance in patients' best. Cluster 1 (30%) exhibited severe impairment. Cluster 2 (70%) displayed milder dysfunction and overlapped substantially with that of healthy controls. Cluster 1 patients were on average older, less educated, showed more severe ataxia and more extracerebellar involvement than cluster 2 patients. The cluster assignment predicted cognitive performance even after adjusting for all other covariates. The G-CCAS-S demonstrated good discriminative ability for cluster 1, but not for cluster 2.
CONCLUSIONS
The variance of cognitive impairment in cerebellar disorders is best explained by one severely affected and one mildly affected cluster. Cognitive performance is not only predicted by demographic/clinical characteristics, but also by cluster assignment itself. This indicates that factors that have not been captured in this study likely have effects on cognitive cerebellar functions. Moreover, the CCAS-S appears to have a relative weakness in identifying patients with only mild cognitive deficits.
STUDY REGISTRATION
The study has prospectively been registered at the German Clinical Study Register ( https://www.drks.de ; DRKS-ID: DRKS00016854).
背景
认知和神经精神障碍，即小脑认知情感综合征（CCAS），可能存在于小脑疾病中。本研究通过聚类分析确定了小脑疾病患者中不同的CCAS亚型。
方法
对205例小脑疾病患者和200例健康对照者进行了德国CCAS量表（G-CCAS-S）评估，这是一种CCAS的简短筛查测试。采用K均值聚类分析对G-CCAS-S数据进行分析，以确定患者的认知聚类。使用人口统计学和临床变量对聚类进行特征描述。多元线性回归量化了它们对认知表现的相对贡献。比较了G-CCAS-S在各聚类中正确区分患者和对照的能力。
结果
两个聚类最能解释患者认知表现的差异。聚类1（30%）表现出严重损伤。聚类2（70%）表现出较轻的功能障碍，且与健康对照者有很大重叠。聚类1患者平均年龄更大、受教育程度更低，与聚类2患者相比，共济失调更严重，小脑外受累更多。即使在调整了所有其他协变量后，聚类分配仍能预测认知表现。G-CCAS-S对聚类1显示出良好的鉴别能力，但对聚类2则不然。
结论
小脑疾病中认知障碍的差异最好由一个严重受影响的聚类和一个轻度受影响的聚类来解释。认知表现不仅由人口统计学/临床特征预测，还由聚类分配本身预测。这表明本研究中未捕捉到的因素可能对小脑认知功能有影响。此外，CCAS-S在识别仅有轻度认知缺陷的患者方面似乎相对较弱。
研究注册
该研究已在德国临床研究注册中心（https://www.drks.de；DRKS-ID：DRKS00016854）进行了前瞻性注册。
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