Identification of potential lipid biomarkers for active pulmonary tuberculosis using ultra-high-performance liquid chromatography-tandem mass spectrometry
Identification of potential lipid biomarkers for active pulmonary tuberculosis using ultra-high-performance liquid chromatography-tandem mass spectrometry
复制标题
使用超高效液相色谱-串联质谱法鉴定活动性肺结核的潜在脂质生物标志物
DOI：
10.1177/1535370220968058
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发表时间：
2021-02-01
影响因子：
3.2
通讯作者：
Li, Ji-Cheng
中科院分区：
文献类型：
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作者：
Han, Yu-Shuai;Chen, Jia-Xi;Li, Ji-Cheng
活动性肺结核的早期诊断是控制结核病的关键。宿主脂质是结核分枝杆菌代谢的营养来源。本研究采用超高效液相色谱-串联质谱法对肺结核患者、肺癌患者、社区获得性肺炎患者和正常健康对照者的血脂进行了筛查。主成分分析，正交偏最小二乘判别分析，K-均值聚类算法分析，以确定不同丰度的脂质。在所有受试者中共筛选出22种差异脂质。肺结核患者血浆磷脂水平降低，而胆固醇酯水平升高。我们推测M.结核病可以调节TB患者的脂质代谢，并且可以促进宿主辅助的磷脂的细菌降解和胆固醇酯的积累。这可能与干酪样坏死的肺空洞形成有关。受试者工作特征曲线分析结果显示，磷脂酰胆碱（PC，12：0/22：2）、PC（16：0/18：2）、胆固醇酯（20：3）和鞘磷脂（d18：0/18：1）等4种脂质可作为结核病早期诊断的潜在生物标志物。通过使用逻辑回归分析并结合上述四种脂质拟合诊断模型，其灵敏度为92.9%，特异性为82.4%，曲线下面积（AUC）值为0.934（95%CI 0.873 - 0.971）。机器学习方法（10倍交叉验证）证明该模型具有良好的准确性（0.908 AUC，85.3%灵敏度和85.9%特异性）。本研究中鉴定的脂质可能作为结核病诊断的新生物标志物。我们的研究可能为了解结核病的发病机制奠定基础。
Early diagnosis of active pulmonary tuberculosis (TB) is the key to controlling the disease. Host lipids are nutrient sources for the metabolism of Mycobacterium tuberculosis. In this research work, we used ultra-high-performance liquid chromatography-tandem mass spectrometry to screen plasma lipids in TB patients, lung cancer patients, community-acquired pneumonia patients, and normal healthy controls. Principal component analysis, orthogonal partial least squares discriminant analysis, and K-means clustering algorithm analysis were used to identify lipids with differential abundance. A total of 22 differential lipids were filtered out among all subjects. The plasma phospholipid levels were decreased, while the cholesterol ester levels were increased in patients with TB. We speculate that the infection of M. tuberculosis may regulate the lipid metabolism of TB patients and may promote host-assisted bacterial degradation of phospholipids and accumulation of cholesterol esters. This may be related to the formation of lung cavities with caseous necrosis. The results of receiver operating characteristic curve analysis revealed four lipids such as phosphatidylcholine (PC, 12:0/22:2), PC (16:0/18:2), cholesteryl ester (20:3), and sphingomyelin (d18:0/18:1) as potential biomarkers for early diagnosis of TB. The diagnostic model was fitted by using logistic regression analysis and combining the above four lipids with a sensitivity of 92.9%, a specificity of 82.4%, and the area under the curve (AUC) value of 0.934 (95% CI 0.873 – 0.971). The machine learning method (10-fold cross-validation) demonstrated that the model had good accuracy (0.908 AUC, 85.3% sensitivity, and 85.9% specificity). The lipids identified in this study may serve as novel biomarkers in TB diagnosis. Our research may pave the foundation for understanding the pathogenesis of TB.