Autoantibody status according to multiparametric assay accurately estimates connective tissue disease classification and identifies clinically relevant disease clusters

Abstract

Objective Assessment of circulating autoantibodies represents one of the earliest diagnostic procedures in patients with suspected connective tissue disease (CTD), providing important information for disease diagnosis, identification and prediction of potential clinical manifestations. The purpose of this study was to evaluate the ability of multiparametric assay to correctly classify patients with multiple CTDs and healthy controls (HC), independent of clinical features, and to evaluate whether serological status could identify clusters of patients with similar clinical features.Methods Patients with systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjogren’s syndrome (SjS), undifferentiated connective tissue disease (UCTD), idiopathic inflammatory myopathies (IIM) and HC were enrolled. Serum was tested for 29 autoantibodies. An XGBoost model, exclusively based on autoantibody titres was built and classification accuracy was evaluated. A hierarchical clustering model was subsequently developed and clinical/laboratory features compared among clusters.Results 908 subjects were enrolled. The classification model showed a mean accuracy of 60.84±4.05% and a mean area under the receiver operator characteristic curve of 88.99±2.50%, with significant discrepancies among groups. Cluster analysis identified four clusters (CL). CL1 included patients with typical features of SLE. CL2 included most patients with SjS, along with some SLE and UCTD patients with SjS-like features. CL4 included anti-Jo1 patients only. CL3 was the largest and most heterogeneous, including all the remaining subjects, overall characterised by low titre or lower-prevalence autoantibodies.Conclusion Extended multiparametric autoantibody assay allowed an accurate classification of CTD patients, independently of clinical features. Clustering according to autoantibody titres is able to identify clusters of CTD subjects with similar clinical features, independently of their final diagnosis.