Multiparametric autoantibody analysis: a new paradigm for the diagnosis of connective tissue diseases

Abstract Background In patients affected by connective tissue diseases (CTDs), the identification of wide autoantibody profiles may prove useful in early diagnosis, in the evaluation of prognosis (risk stratification), and in predicting response to therapy. The aim of the present study was to evaluate the utility of multiparametric autoantibody analysis performed by a new fully automated particle-based multi-analyte technology (PMAT) digital system in a large multicenter cohort of CTD patients and controls. Methods Serum samples from 787 patients with CTD (166 systemic lupus erythematosus; 133 systemic sclerosis; 279 Sjögren’s syndrome; 106 idiopathic inflammatory myopathies; 103 undifferentiated CTD), 339 patients with other disorders (disease controls) (118 infectious diseases, 110 organ-specific autoimmune diseases, 111 other rheumatic diseases), and 121 healthy subjects were collected in 13 rheumatologic centers of the FIRMA group. Sera were analyzed with the Aptiva-PMAT instrument (Inova Diagnostics) for a panel of 29 autoantibodies. Results Multiparametric logistic regression showed that enlarged antibody profiles have a higher diagnostic efficiency than that of individual antibodies or of antibodies that constitute classification criteria for a given disease and that probability of disease increases with multiple positive autoantibodies. Conclusions This is the first study that analyzes the clinical and diagnostic impact of autoantibody profiling in CTD. The results obtained with the new Aptiva-PMAT method may open interesting perspectives in the diagnosis and sub-classification of patients with autoimmune rheumatic diseases.