NMR-BASED METABOLOMIC ANALYSIS FOR THE DIAGNOSIS OF SYSTEMIC SCLEROSIS

초록

Background: Systemic sclerosis (SSc) is a complex multi-organ autoimmune disease that is caused by in?ammation, vasculopathy and ?brosis. 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 expectedtobeausefultoolfortheidenti?cationofbiomarkersandnewtherapeutic 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-?ve 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: R2Y=0.654, Q2=0.482)andorthogonalpartialleastsquaresdiscriminationanalysis(OPLS-DA: R2Y=0.83, Q2=0.674) (Figure 1). We identi?ed nine discriminatory metabolites (p<0.05): isopropanol, lactate, 2-oxoisocaproate, glucose, and formate were increasedandpyruvate,glutamate,methylguanidine,andmethanolweredecreased in SSc compared with those in the HCs. Using these metabolites for diagnosis of SSc, sensitivity was 96.36% and speci?city 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 identi?cation of potential diagnostic biomarkers of SSc.