Identification of potential lipid biomarkers for active pulmonary tuberculosis using ultra-high-performance liquid chromatography-tandem mass spectrometry. Issue 4 (February 2021)

• Record Type: Journal Article
• Authors: Han, Yu-Shuai; Chen, Jia-Xi; Li, Zhi-Bin; Chen, Jing; Yi, Wen-Jing; Huang, Huai; Wei, Li-Liang; Jiang, Ting-Ting; Li, Ji-Cheng
• Is Part Of: Experimental biology and medicine. Volume 246:Issue 4(2021)
• Page Start: 387
• Page End: 399
• Publication Date: 2021-02
• Subjects: Pulmonary tuberculosis -- plasma -- lipidomics -- potential biomarkers -- phospholipids -- cholesterol esters; Physiology -- Periodicals; Biology, Experimental -- Periodicals; Medicine, Experimental -- Periodicals; 610.72
• DOI: 10.1177/1535370220968058
• Languages: English
• ISSNs: 1535-3702

Abstract

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.