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 inflammation, vasculopathy and fibrosis. Clinical heterogeneity, unpredictable course, high mortality and resistance to treatment make physicians still troublesome . 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. Objective: To identify the biomarker candidates for the diagnosis of SSc using metabolomic analysis. Methods: Fifty-two SSc patients (46 females (88.5%); mean age 59.30 ± 11.44 years; disease duration 6.75 ± 4.23 years; 11 diffuse cutaneous SSc, 41 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=0.773, Q2=0.581) and orthogonal partial least squares discrimination analysis (OPLS-DA: R2Y=0.773, Q2=0.624) (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. Conclusion: 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.