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.