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

BACKGROUND: Sjogren'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. METHODS: Three 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. RESULTS: The 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. CONCLUSIONS: Our 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.