AB0123 Exploring the metabolomic profile of synovial fluid to reflect the disease activity in rheumatoid arthritis
Authors: J.K. Ahn, J.-W. Hwang, E.-M. Koh, H.-S. Cha, H. Jeong, Y. Eun, J. Lee, H. Kim
Journal: Annals of the Rheumatic Diseases (2018)

<h3>Background</h3> Rheumatoid arthritis (RA) is a chronic, inflammatory disease that mainly affects the synovial joints. Metabolomics, which is defined as the comprehensive analysis of the small-molecule metabolites in a biological system, is a rapidly developing approach in biomarker research. <h3>Objectives</h3> This study was to determine whether there is variation in the synovial fluid (SF) metabolome in RA patients according to the degree of disease activity using gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS), in order to gain more insight into the pathologic metabolic alterations in RA. <h3>Methods</h3> SF samples were analysed in 47 patients with active RA, divided into moderately active- (2.8&lt;DAS28 ESR&lt;5.1, n=35, male 28.6%, mean age 55.1 years) and highly active group (DAS28-ESR≥5.1, n=12, male 41.7%, mean age 59.1 years). SF metabolite profiles of RA based on DAS28-ESR value were performed using GC/TOF MS, with univariate analysis such as student t-test and correlation analysis followed by multivariate analysis such as partial least-squares regression-discriminant analysis (PLS-DA) and pathway analyses such as metabolite set enrichment analysis. <h3>Results</h3> There was no differences in demographic data and medications use between two groups. A total of 123 metabolites were identified from samples. These 123 identified metabolites were classified into various chemical classes, such as amino acids (21% of identified metabolites), organic acids (20%), sugar and sugar alcohols (19%), fatty acids (15%), amines (11%), and phosphates (6%). The SF metabolite profiles obtained from GC/TOF-MS analysis can distinguish moderately active group from highly active group. The variation values of the PLS-DA model are <i>R<sup>2</sup>X</i> of 0.171, <i>R<sup>2</sup>Y</i> of 0.849, and <i>Q<sup>2</sup></i> of 0.504, respectively, indicating strong explanation and prediction capabilities of the model. To find potential metabolic biomarkers reflecting the disease activity of RA, variable importance on projection (VIP) values of all the metabolites from the PLS-DA model were determined. Also, other statistical criteria including correlation coefficient and <i>P</i> value were assessed. Twelve potential metabolites reflecting the disease activity of RA are found (2-hydroxyvalerate, fucose, tryptophan, indole-3-lactate, isothreonate, thymine, phenylalanine, lactose, arabitol, and mannose-6-phosphate, citrate, oxoproline). In pathway analysis, we found six unique pathways, namely, fructose and mannose degradation, phenylalanine and tyrosine metabolism, citric acid cycle, galactose metabolism, tryptophan metabolism and pyrimidine metabolism that were significantly associated with disease activity in RA. <h3>Conclusions</h3> Synovial metabolite profiles are robustly altered along the disease activity of RA. Metabolomic approaches based on GC/TOF-MS could provide valuable information on the underlying pathogenic mechanisms of RA activity, and a novel perspective on the search of new biomarkers and drug targets. <h3>Disclosure of Interest</h3> None declared