A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury
A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury
复制标题
DOI：
10.1093/bioinformatics/btq254
复制
发表时间：
2010-07-15
期刊：
影响因子：
5.8
通讯作者：
Gerszten, Robert E.
中科院分区：
文献类型：
--
作者：
Baumgartner, Christian;Lewis, Gregory D.;Gerszten, Robert E.
动机：在心血管疾病中发现新的和意想不到的生物标志物是一个高度数据驱动的过程，需要现代代谢物分析技术、生物信息学和生物统计学的补充力量。早期心肌损伤的临床生物标志物缺乏。进行了一项前瞻性生物标志物队列研究，以识别、分类和描述计划性心肌梗死（PMI）和自发性（SMI）心肌梗死的早期代谢生物标志物的动力学模式。我们应用靶向质谱（MS）为基础的代谢产物分析平台，从仔细的表型患者进行酒精室间隔消融术的肥厚性梗阻性心肌病作为人类模型的PMI的连续血液样本。SMI患者和接受导管插入术但未诱导心肌梗死的患者作为阳性和阴性对照，以评估PMI中鉴定的标志物的普遍性。为了识别串联质谱数据中具有高预测值的代谢物，我们引入了一种新的特征选择方法，用于将代谢特征分类为三类弱预测因子、中等预测因子和强预测因子，其可以容易地应用于配对和未配对的样本。我们的范例优于标准的零假设显著性检验和其他流行的方法的功能选择的受试者工作曲线下的面积和产品的灵敏度和特异性。我们的研究结果强调，这种新方法能够识别，分类和验证参与心肌损伤相关途径的多种代谢物水平的变化，早在PMI后10分钟。
Motivation: The discovery of new and unexpected biomarkers in cardiovascular disease is a highly data-driven process that requires the complementary power of modern metabolite profiling technologies, bioinformatics and biostatistics. Clinical biomarkers of early myocardial injury are lacking. A prospective biomarker cohort study was carried out to identify, categorize and profile kinetic patterns of early metabolic biomarkers of planned myocardial infarction (PMI) and spontaneous (SMI) myocardial infarction. We applied a targeted mass spectrometry (MS)-based metabolite profiling platform to serial blood samples drawn from carefully phenotyped patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy serving as a human model of PMI. Patients with SMI and patients undergoing catheterization without induction of myocardial infarction served as positive and negative controls to assess generalizability of markers identified in PMI.Results: To identify metabolites of high predictive value in tandem mass spectrometry data, we introduced a new feature selection method for the categorization of metabolic signatures into three classes of weak, moderate and strong predictors, which can be easily applied to both paired and unpaired samples. Our paradigm outperformed standard null-hypothesis significance testing and other popular methods for feature selection in terms of the area under the receiver operating curve and the product of sensitivity and specificity. Our results emphasize that this new method was able to identify, classify and validate alterations of levels in multiple metabolites participating in pathways associated with myocardial injury as early as 10 min after PMI.