Using anti-malondialdehyde-modified peptide adduct autoantibodies in serum of taiwanese women to diagnose primary Sjogren’s syndrome

Abstract

BackgroundSjogren’s syndrome (SS) is a systemic autoimmune disease featured with a dry mouth and dry eyes. Several autoantibodies, including anti-SSA, anti-SSB, antinuclear antibodies can be detected in patients with SS. Oxidation-specific epitopes (OSEs) can be formed from malondialdehyde (MDA)-modified protein adducts and trigger chronic inflammation. In this study, our purposes were used serum levels of anti-MDA-modified peptide adducts autoantibodies to evaluate predictive performance by machine learning algorithms in primary Sjögren's syndrome (pSS) and assess the association between pSS and healthy controls. MethodsThree novel MDA-modified peptide adducts, including immunoglobulin (Ig) gamma heavy chain 1 (IGHG1)102–131, complement factor H (CFAH)1045–1062, and Ig heavy constant alpha 1 (IGHA1)307–327 were identified and validated. Serum levels of protein, MDA-modified protein adducts, MDA, and autoantibodies recognizing unmodified peptides and MDA-modified peptide adducts were measured. Statistically significance in correlations and odds ratios (ORs) were estimated. ResultsThe random forest classifier utilized autoantibodies combination composed of IgM anti-IGHG1102-131, IgM anti-IGHG1102-131 MDA and IgM anti-IGHA1307-327 achieved predictive performance as an accuracy of 88.0%, a sensitivity of 93.7%, and a specificity of 84.4% which may be as potential diagnostic biomarkers to differentiate patients with pSS from rheumatoid arthritis (RA), and secondary SS in RA and HCs. ConclusionsOur findings imply that low levels of IgA anti-IGHG1102-131 MDA (OR = 2.646), IgA anti-IGHG1102-131 (OR = 2.408), IgA anti-CFAH1045-1062 (OR = 2.571), and IgA anti-IGHA1307-327 (OR = 2.905) may denote developing risks of pSS, respectively.