AB0181 Glutamate as A Metabolic Biomarker for Monitoring Disease Activity of Systemic Lupus Erythematosus. (10th June 2014)

• Record Type: Journal Article
• Authors: Saegusa, J.; Irino, Y.; Yoshida, M.; Tanaka, S.; Kogata, Y.; Kageyama, G.; Tsuji, G.; Kawano, S.; Kumagai, S.; Morinobu, A.
• Is Part Of: Annals of the rheumatic diseases. Volume 73:Supplement 2(2014)
• Page Start: 863
• Page End: 863
• Publication Date: 2014-06-10
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2014-eular.2294
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: "Omics" technologies, which include genomics, transcriptomics, proteomics, and metabolomics, are based on the comprehensive biochemical and molecular characterization of a biological sample. Among these, metabolomics has recently developed rapidly. Metabolomics, or metabolome analysis, is the comprehensive identification and quantification of all the low-molecular-weight metabolites in a biological sample. Because metabolism closely influences the organism's phenotype, the characteristics of a disease are thought to more closely reflect alterations in the levels of metabolites than changes in gene or protein expression. Therefore, the examination of alterations in metabolite levels should help elucidate the physiological and pathological characteristics of diseases in more detail. Besides, cell metabolism is known to have a tremendous impact on the function of various immune cells. Objectives: To identify novel metabolic biomarkers for diagnosing and monitoring systemic lupus erythematosus (SLE) by using gas chromatography/mass spectrometry (GC/MS). Methods: Serum samples were obtained in the morning from fasting patients with SLE (n=26), rheumatoid arthritis (RA) (n=32), and healthy volunteers (n=26). Serum metabolite profiling was performed by GC/MS. The metabolic profiles of the patient and control groups were compared using multivariate statistical analysis. Results: The levels of 25 metabolites were significantly different in SLE patients compared to healthy controls. The two-dimensional (2D) plot of the principal component analysis (PCA) scores showed a distinct clustering of the two groups. The corresponding 2D-PCA loadings plot and the 2D-scores plot for partial least squares-discriminant analysis (PLS-DA) loadings plot revealed that variations in the levels of glutamate, urea, tyrosine, phosphate and glycerol greatly contributed to the observed separation of the metabolite profiles of the SLE patients and healthy controls. In addition, there was a large difference in serum metabolite levels between SLE and RA, and a number of metabolites showed opposite changes between them. Furthermore, of 25 metabolites that were significantly changed in SLE, the serum level of glutamate was significantly correlated with the SLE disease activity index (SLEDAI) score and serum levels of C4 (Figure). Conclusions: The pathogenesis of SLE may be accompanied by variations in the serum levels of low-molecular-weight metabolites, which supports the potential for using GC/MS-based metabolomics as a diagnostic and monitoring tool for SLE. Disclosure of Interest: None declared DOI: 10.1136/annrheumdis-2014-eular.2294