Early Recognition of Progressive Lung Fibrosis in Systemic Rheumatic Diseases

Connective tissue diseases (CTDs) cover a broad range of systemic rheumatic disorders characterized by abnormal immune activation, chronic inflammatory response, and fibrosis of internal organs. The most prevalent is interstitial lung disease (ILD), a severe pulmonary complication seen in 10 to 50% of CTDs and a major determinant of disability and death. Prevalence and clinical course of CTD-ILDs vary widely and seem to be independent of treatment. Current screening and prognosis prediction strategies based on clinical variables and auto-antibodies are inadequate, and disease biomarkers are lacking. The research project aims to identify biomarkers of ILD involvement in CTD patients by characterizing the proteome and transcriptome of extracellular vesicles (EVs) isolated from serum. This will be integrated with high-resolution computed tomography (HRCT) using artificial intelligence (AI)-based imaging assessment. These novel biomarkers are expected to address some current limitations of standard laboratory biomarkers and conventional HRCT imaging. The investigator will involve a total of 200 CTD patients divided into two equal groups: those with ILD and those without. Serum EVs will be extracted from patient sera and characterized based on proteome and transcriptome content using mass spectrometry analysis and next-generation RNA-sequencing. The investigator will compare CTD patients with and without ILD, and progressive and non-progressive ILD patients according to OMERACT (Outcome Measures in Rheumatology) initiative criteria during a 12-month follow-up. HRCT features analyzed by a commercially available deep learning AI software will also be compared among CTD-ILD patients based on the occurrence of progression during follow-up. An advanced approach combining EVs analysis in serum and AI algorithms of HRCT images, and functional fibrosis assessment in vivo, could enhance our understanding of CTD-ILDs pathogenesis. The proposal aims to investigate for the first time the EVs proteomic and transcriptomic profile in serum of patients with CTDs to identify possible biomarkers of lung involvement. The integration of circulating EVs biomarkers with clinical phenotype and with advanced imaging technologies will provide novel diagnostic algorithms that early identify patients with lung involvement in CTD and patients at risk of pulmonary progression.