SAT0199 Nmr-Based Metabolomic Analysis for The Diagnosis of Systemic Sclerosis. (15th July 2016)

• Record Type: Journal Article
• Authors: Jung, K.H.; Oh, S.; Park, W.; Lim, M.J.; Kwon, S.; Park, S.
• Is Part Of: Annals of the rheumatic diseases. Volume 75(2016)Supplement 2
• Page Start: 740
• Page End: 740
• Publication Date: 2016-07-15
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2016-eular.3126
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Systemic sclerosis (SSc) is a complex multi-organ autoimmune disease that is caused by inflammation, vasculopathy and fibrosis. Clinical heterogeneity, unpredictable course, high mortality and resistance to treatment make physicians still frustrated. Recently, there are unmet needs of useful biomarkers for diagnosis, disease activity and severity of SSc. Metabolomics is expected to be a useful tool for the identification of biomarkers and new therapeutic targets. Several researchers have applied metabolomics to autoimmune diseases such as systemic lupus erythematosus, and rheumatoid arthritis. Objectives: To identify the biomarker candidates for the diagnosis of SSc using metabolomic analysis. Methods: Fifty-five patients (48 females (87%); mean age 59.30 ± 11.44 years; disease duration 6.92 ± 4.36 years; 11 diffuse cutaneous SSc, 44 limited cutaneous SSc) and thirty age, gender matched healthy controls (HCs) were enrolled. Serum samples after 8 h of fasting were stored at -80° and analysed using nuclear magnetic resonance (NMR)-based metabolomics. Results: Multivariate analysis showed metabolic differences between SSc and HCs using partial least squares discrimination analysis (PLS-DA: R 2 Y=0.654, Q 2 =0.482) and orthogonal partial least squares discrimination analysis (OPLS-DA: R 2 Y=0.83, Q 2 =0.674) (Figure 1 ). We identified nine discriminatory metabolites ( p <0.05): isopropanol, lactate, 2-oxoisocaproate, glucose, and formate were increased and pyruvate, glutamate, methylguanidine, and methanol were decreased in SSc compared with those in the HCs. Using these metabolites for diagnosis of SSc, sensitivity was 96.36% and specificity was 80% by Leave-one-out analysis. Conclusions: There are considerable differences in the serum metabolomic characteristics between SSc and HCs. We expect that metabolomic analysis can be a useful tool for identification of potential diagnostic biomarkers of SSc. Disclosure of Interest: None declared