Distinguishing true from false idiopathic pulmonary fibrosis: Progress in biomarkers for early identification of rheumatoid arthritis

BACKGROUND: Patients initially diagnosed with idiopathic pulmonary fibrosis (IPF) have an underlying risk of being in a preclinical phase of rheumatoid arthritis (RA), but effective biomarkers for early identification of this transition are lacking. OBJECTIVE: This review aims to comprehensively summarize predictive markers for future RA in patients meeting IPF diagnostic criteria, supporting early risk stratification and pre-arthritic intervention. METHODS: Using "idiopathic pulmonary fibrosis", "rheumatoid arthritis", "predictive markers", "markers", "clinical research", "in vitro experiments", "mechanism", and their combinations as keywords, a structured literature search was conducted in the PubMed database for relevant literature from 2001 to 2025. RESULTS: The propensity for RA to develop in patients initially meeting IPF diagnostic criteria has a multi-dimensional basis: genetic susceptibility provides the background; immune dysregulation mediates the dissemination of autoimmunity from a pulmonary origin to systemic involvement; inflammatory markers reflect disease activity; environmental exposure acts as an external trigger. Based on these mechanisms, four predictive marker types were identified: genetic susceptibility, immune activation, inflammatory injury, and environmental exposure. However, current studies are mainly cross-sectional and retrospective, lacking prospective validation of predictive efficacy; combined application strategies, clinical value and microbiome-based indicators all require further clinical verification. CONCLUSION: This review identified four early-warning markers-genetic susceptibility, immune activation, inflammatory injury, and environmental exposure-that may signal future RA in patients initially presenting with IPF. These findings support shifting from passive diagnosis to active screening for earlier intervention. Future research should focus on multi-dimensional prediction models, prospective cohort studies, and ultimately achieving early identification and intervention for this hidden RA-prone population.