Department of Neurology, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto, Japan.
Mov Disord. 2012 Jun;27(7):851-7. doi: 10.1002/mds.24994. Epub 2012 Jun 1.
The differential diagnosis of Parkinson's disease and multiple system atrophy can be challenging, especially in the early stages of the diseases. We developed a proteomic profiling strategy for parkinsonian diseases using mass spectrometry analysis for magnetic-bead-based enrichment of cerebrospinal fluid peptides/proteins and subsequent multivariate statistical analysis. Cerebrospinal fluid was obtained from 37 patients diagnosed with Parkinson's disease, 32 patients diagnosed with multiple system atrophy, and 26 patients diagnosed with other neurological diseases as controls. The samples were from the first cohort and the second cohort. Cerebrospinal fluid peptides/proteins were purified with C8 magnetic beads, and spectra were obtained by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Principal component analysis and support vector machine methods are used to reduce dimension of the data and select features to classify diseases. Cerebrospinal fluid proteomic profiles of Parkinson's disease, multiple system atrophy, and control were differentiated from each other by principal component analysis. By building a support vector machine classifier, 3 groups were classified effectively with good cross-validation accuracy. The model accuracy was well preserved for both cases, training by the first cohort and validated by the second cohort and vice versa. Receiver operating characteristics proved that the peak of m/z 6250 was the most important to differentiate multiple system atrophy from Parkinson's disease, especially in the early stages of the disease. A proteomic pattern classification method can increase the accuracy of clinical diagnosis of Parkinson's disease and multiple system atrophy, especially in the early stages.
帕金森病和多系统萎缩的鉴别诊断具有挑战性，特别是在疾病的早期阶段。我们使用基于质谱分析的磁珠富集脑脊液肽/蛋白和随后的多元统计分析，开发了一种用于帕金森病的蛋白质组谱分析策略。从 37 名被诊断为帕金森病的患者、32 名被诊断为多系统萎缩的患者和 26 名被诊断为其他神经疾病的患者中获得了脑脊液样本。这些样本来自第一队列和第二队列。用 C8 磁珠纯化脑脊液肽/蛋白，通过基质辅助激光解吸电离飞行时间质谱获得光谱。主成分分析和支持向量机方法用于降低数据维度并选择特征来分类疾病。通过主成分分析，帕金森病、多系统萎缩和对照组的脑脊液蛋白质组图谱相互区分。通过构建支持向量机分类器，有效地对 3 组进行分类，具有良好的交叉验证准确性。该模型的准确性在第一队列和第二队列的训练和验证中都得到了很好的保留，反之亦然。受试者工作特征曲线证明，m/z 6250 的峰值对区分多系统萎缩和帕金森病最为重要，尤其是在疾病的早期阶段。蛋白质组模式分类方法可以提高帕金森病和多系统萎缩的临床诊断准确性，尤其是在疾病的早期阶段。
