Discovering lipid phenotypic changes of sepsis-induced lung injury using high-throughput lipidomic analysis. Issue 44 (18th April 2016)

• Record Type: Journal Article
• Authors: Liang, Qun; Liu, Han; Jiang, Yan; Xing, Haitao; Zhang, Tianyu; Zhang, Ai-Hua
• Is Part Of: RSC advances. Volume 6:Issue 44(2016)
• Page Start: 38233
• Page End: 38237
• Publication Date: 2016-04-18
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c6ra03979k
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : The aim of this study was to use lipidomics to identify lipid molecules that could predict patients with sepsis-induced lung injury. Abstract : Sepsis-induced lung injury (SLI) with high mortality and morbidity remains a leading cause of death in intensive care. There is an urgent need for the identification of SLI biomarkers for effective diagnosis. Studies have suggested the promising use of blood lipids as functional intermediate phenotypes in medical research. Lipid metabolism is critical in disease development, and lipidomics is the comprehensive analysis of molecular lipids. Lipid profiling by mass spectrometry may improve SLI risk prediction. The aim of this study was to use lipidomics to identify lipid molecules that could predict SLI patients. We performed a comprehensive and untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from SLI patients and controls, and multivariate analysis methods were used to identify the lipids associated with SLI status. The serum samples were collected from SLI patients in the ICU. Lipid metabolic profiles were evaluated and compared between SLI and healthy cases. Variable importance in projection values were obtained to identify potential lipid biomarkers. A receiver operating characteristic curve was used to evaluate the power of the diagnostic biomarkers. Multivariate analysis revealed a good predictive model to distinguish their metabolic patterns. Seven lipid signatures were identified as potential biomarkers for SLI patients. These candidate lipids were validated in an additional independent cohort. According to the ROC analysis, the values of area under the curve (AUC) range from 0.813 to 0.997, indicating their potential power to distinguish between SLI and healthy cases. These results supported the concept that mass spectrometry-based lipid phenotype profiling might be a useful tool for the effective diagnosis of SLI.