Novel liquid chromatography-mass spectrometry for metabolite biomarkers of acute lung injury disease

Abstract

Sepsis-induced acute lung injury (ALI) remains a leading cause of death in intensive care units. Early detection is very important for improving ALI outcome. With the progress of the omics technologies, metabolomics has been recently used for biomarker identification. We aimed to identify the metabolomic biomarkers of ALI in a discovery cohort of patients in the Chinese Han population using UPLC/Q-TOF MS/MS and multivariate statistical analysis. Serum samples were collected from ICU patients with ALI. Orthogonal partial least-squares discriminant analyses were performed for the discrimination of ALI and healthy groups. Variable importance in projection values were calculated to identify potential biomarkers for ALI. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic accuracy of metabolites. Supervised multivariate analysis yielded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Four metabolites were identified as potential biomarkers for ALI, with higher potential for improving patient survival and quality of life. These potential metabolite candidates were individually validated in an additional independent cohort. The area under the curve (AUC) values ranging from 0.803 to 0.982 indicate the potential capacity of these metabolites to distinguish ALI patients. According to the ROC analysis, sphingosine was potentially the most specific biomarker for discriminating ALI from healthy controls, with an AUC of 0.994, demonstrating that global metabolite profiling by UPLC/MS might be a useful tool for the effective diagnosis and further understanding of ALI.