Identifying illness parameters in fatiguing syndromes using classical projection methods
Identifying illness parameters in fatiguing syndromes using classical projection methods
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DOI：
10.2217/14622416.7.3.407
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发表时间：
2006-04-01
期刊：
影响因子：
2.1
通讯作者：
Unger, ER
中科院分区：
文献类型：
--
作者：
Broderick, G;Craddock, RC;Unger, ER
目的：检验多变量投影方法在识别临床和基因表达数据的常见变化模式方面的潜力，这些数据可以捕捉患有不明原因的疲劳和非疲劳的对照组参与者的疾病状态。方法：对111名女性受试者的数据进行分析。共有59项指标描述疾病，包括多维疲劳量表(MFI)、医疗结局简表36(SF-36)、疾病控制和预防中心(CDC)症状清单和认知反应。用偏最小二乘法构造了两个特征空间：一个是基于外周血单个核细胞基因表达的症状空间，另一个是基于117个临床变量的特征空间。采用乘法散射校正和分位数归一化方法对基因芯片数据进行趋势去除和范围调整。使用样本之间的平均皮尔逊相关性来评估微阵列质量。Benjamini-Hochberg多项检测标准用于鉴定显著表达的探针。结果：59个症状中的一个共同趋势构成了整个组中不疲倦受试者的分离。这种分离由两个共调节模式支持，代表总的微阵列变异的10%。在39个主要贡献者中，17个探针涉及基本的细胞过程，涉及细胞信号、离子转运和免疫系统功能。最有影响力的基因是Sestrin 1(SESN1)，支持最近的证据表明氧化应激参与慢性疲劳综合征(CFS)。临床特征空间中的主要变量描述了睡眠期间的心率变异性(HRV)。钾和游离甲状腺素(T4)也占主导地位。结论：将多种症状、基因或临床变量组合成综合特征，比单独使用最有影响力的变量更能区分疾病状态。虽然确切的机制尚不清楚，但结果表明，氧化应激、免疫系统功能障碍和钾失衡之间存在共同的联系，导致交感迷走神经平衡受损，强烈反映在异常的HRV中。
Objectives: To examine the potential of multivariate projection methods in identifying common patterns of change in clinical and gene expression data that capture the illness state of subjects with unexplained fatigue and nonfatigued control participants. Methods: Data for 111 female subjects was examined. A total of 59 indicators, including multidimensional fatigue inventory (MFI), medical outcome Short Form 36 (SF-36), Centers for Disease Control and Prevention (CDC) symptom inventory and cognitive response described illness. Partial least squares (PLS) was used to construct two feature spaces: one describing the symptom space from gene expression in peripheral blood mononuclear cells (PBMC) and one based on 117 clinical variables. Multiplicative scatter correction followed by quantile normalization was applied for trend removal and range adjustment of microarray data. Microarray quality was assessed using mean Pearson correlation between samples. Benjamini-Hochberg multiple testing criteria served to identify significantly expressed probes. Results: A single common trend in 59 symptom constructs isolates of nonfatigued subjects from the overall group. This segregation is supported by two co-regulation patterns representing 10% of the overall microarray variation. Of the 39 principal contributors, the 17 probes annotated related to basic cellular processes involved in cell signaling, ion transport and immune system function. The single most influential gene was sestrin 1 (SESN1), supporting recent evidence of oxidative stress involvement in chronic fatigue syndrome (CFS). Dominant variables in the clinical feature space described heart rate variability (HRV) during sleep. Potassium and free thyroxine (T4) also figure prominently. Conclusion: Combining multiple symptom, gene or clinical variables into composite features provides better discrimination of the illness state than even the most influential variable used alone. Although the exact mechanism is unclear, results suggest a common link between oxidative stress, immune system dysfunction and potassium imbalance in CFS patients leading to impaired sympatho-vagal balance strongly reflected in abnormal HRV.