Exploration of metabolite signatures using high-throughput mass spectrometry coupled with multivariate data analysis

Abstract

Disease impacts important metabolic pathways and the alteration of metabolites may serve as a potential biomarker for early-stage diagnosis. High-resolution mass spectrometry-based metabolomics have been used to discover new biomarker metabolites. Rheumatoid arthritis (RA) seriously affects the quality of life in patients, but its pathophysiology remains unclear. This study aimed to develop a high-throughput approach by screening potential biomarkers to facilitate the diagnosis using metabolomics. The alteration of the metabolic profile of RA was investigated in human urine samples based on high-resolution UPLC-QTOF/MS and multivariate statistical analysis. Furthermore, ingenuity pathway analysis (IPA) was performed for the bioinformatics analysis of the data. Variable importance for projection values was determined, and the t-test was conducted for selecting a biomarker panel for RA. Receiver operating characteristic analysis was used to evaluate diagnostic accuracy of metabolites. We found that the score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between RA and healthy groups. Five metabolites were identified as potential biomarkers for RA. The values of AUC, ranging from 0.819 to 0.993, indicated the potential capacity of these metabolites to distinguish RA patients and demonstrated that the differentially expressed metabolites might be a useful tool for the effective diagnosis of RA. The most significantly altered networks included FXR/RXR activation and bile acid biosynthesis. This study demonstrates that a high-resolution mass spectrometry-based metabolomics approach could provide crucial insight into the pathogenesis mechanism of RA.