Lipid mediators of inflammation as novel plasma biomarkers to identify patients with bacteremia

• Published in: The Journal of infection Vol. 70; no. 5; pp. 433 - 444
• Main Authors: To, Kelvin K.W, Lee, Kim-Chung, Wong, Samson S.Y, Lo, Ka-Ching, Lui, Yin-Ming, Jahan, Akhee S, Wu, Andrea L, Ke, Yi-Hong, Law, Chun-Yiu, Sze, Kong-Hung, Lau, Susanna K.P, Woo, Patrick C.Y, Lam, Ching-Wan, Yuen, Kwok-Yung
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-05-2015
• Subjects: Adult; Aged; Aged, 80 and over; Bacteremia; Bacteremia - blood; Bacteremia - diagnosis; Biomarker; Biomarkers - blood; Carnitine - analogs & derivatives; Carnitine - blood; Chromatography, High Pressure Liquid; Fatty Acids - blood; Female; Humans; Infectious Disease; Inflammation Mediators - isolation & purification; Lipid; Lipids - blood; Male; Metabolomic; Metabolomics; Middle Aged; Organic acid; Plasma; Prognosis; ROC Curve; Sphingolipids - blood; Metabolomic; Plasma; Biomarker; Organic acid; Bacteremia; Lipid
• ISSN: 0163-4453; 1532-2742
• DOI: 10.1016/j.jinf.2015.02.011

Summary Objectives Rapid diagnostic tests for bacteremia are important for early treatment to improve clinical outcome. We sought to identify plasma biomarkers that can identify patients with bacteremia using an untargeted global metabolomic analysis. Methods Plasma metabolomic profiles were analyzed for 145 adult patients with (cases) and without (controls) bacteremia using ultra-high-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). All metabolites were compared between cases and controls using a 2-tier filtering approach, and each metabolite underwent receiver operating characteristic (ROC) curve analysis. Individual metabolites that distinguish between cases and controls were characterized. Subgroup analysis was performed to identify metabolites with prognostic significance. Results After 2-tier filtering, 128 molecular features were identified to be potential biomarkers that could distinguish cases from controls. Five metabolites had an area under the ROC curve (AUC) of >0.8 in ROC curve analysis, including a sphingolipid, an acylcarnitine, a fatty acid ester, and 2 glycerophosphocholines. These metabolites could distinguish cases from controls in the unsupervised hierarchical clustering analysis. Subgroup analysis of bacteremic patients showed that the level of trans-2,3,4-trimethoxycinnamate was lower in fatal than non-fatal cases. Conclusions Plasma lipid mediators of inflammation can distinguish bacteremia cases from non-bacteremia controls. These biomarkers may be used as targets for rapid test in clinical practice.