Separating Smoking-Related Diseases Using NMR-Based Metabolomics of Exhaled Breath Condensate
Separating Smoking-Related Diseases Using NMR-Based Metabolomics of Exhaled Breath Condensate
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DOI：
10.1021/pr301171p
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发表时间：
2013-03-01
影响因子：
4.4
通讯作者：
Motta, Andrea
中科院分区：
文献类型：
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作者：
de Laurentiis, Guglielmo;Paris, Debora;Motta, Andrea
基于核磁共振 (NMR) 的代谢组学可将肺部疾病患者的呼出气冷凝物 (EBC) 特征与健康受试者的呼出气冷凝物特征区分开来。在这里，我们展示了基于核磁共振的代谢组学在区分暴露于相同危险因素（即吸烟相关疾病中的吸烟习惯）的患者方面的区分能力。通过核磁共振波谱法、主成分分析 (PCA) 和潜在结构判别分析 (PLS-DA) 投影，对来自一组当前吸烟者（无慢性阻塞性肺疾病 (COPD，以下简称 HS)、COPD 吸烟者和患有肺朗格汉斯细胞组织细胞增多症 (PLCH) 的受试者）的 50 个重复 EBC 样本进行分析。 EBC 光谱的聚类是疾病特异性 COPD，而 PLCH 样品呈现出与 HS 不同的特征，显示乙酸盐增加和 1-甲基咪唑减少。 2-丙醇和异丁酸酯的相反行为表征了相对于 PLCH 的 COPD（COPD 高/低，PLCH 低/高）。 2 分量和 3 分量 PLS-DA 模型均显示出 96% 的交叉验证准确度，R-2 和 Q(2) 值分别在 0.97-0.87 和 0.91-0.78 范围内，R-2 = 0.87 和 Q(2) = 038，表明每个模型 (R-2) 都能很好地解释数据变化，具有良好的预测性 (Q(2))。 EBC 的 NMR 谱可将 COPD 和 PLCH 患者与 HS 患者及其之间区分开来，并为每一类患者提供明确的代谢特征。 EBC 谱的特异性表明，疾病本身驱动代谢分离压倒了由于吸烟习惯而导致的“共同背景”。即使存在很强的共同因素，EBC-NMR 研究也为评估气道疾病的演变提供了强大的工具。
Nuclear magnetic resonance (NMR)-based metabolomics separates exhaled breath condensate (EBC) profiles of patients affected by pulmonary disease from those of healthy subjects. Here we show the discriminatory ability of NMR-based metabolomics in separating patients exposed to the same risk factor, namely, smoking habit in smoking related diseases. Fifty duplicated EBC samples from a cohort of current smokers without chronic obstructive pulmonary disease (COPD, henceforth HS), COPD smokers, and subjects with established pulmonary Langerhans cell histiocytosis (PLCH) were analyzed by means of NMR spectroscopy followed by principal component analysis (PCA) and projection to latent structures discriminant analysis (PLS-DA). Clusterization of EBC spectra was disease specific COPD and PLCH samples present a profile different from that of HS, showing acetate increase and 1-methylimidazole reduction. An inverse behavior of 2-propanol and isobutyrate characterized COPD with respect to PLCH (high/low in COPD, low/high in PLCH). Both the 2-component and the 3-component PLS-DA models showed a 96% cross validated accuracy, presenting R-2 and Q(2) values in the ranges of 0.97-0.87 and 0.91-0.78, respectively, and R-2 = 0.87 and Q(2) = 038, indicating that data variation is well explained by each model (R-2), with a good predictivity (Q(2)). NMR spectra of EBC discriminate COPD and PLCH patients from HS and between them, with well-defined metabolic profiles for each class. The specificity of EBC profiles suggests that disease itself drives metabolic separation overwhelming the "common background" due to smoking habit EBC-NMR investigation offers a powerful tool for assessing the evolution of airway diseases even in the presence of a strong common factor.