Development of a Novel Risk Prediction Model for Rheumatoid Arthritis–Associated Interstitial Lung Disease (RA-ILD): A Longitudinal Study
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Objectives
Interstitial lung disease (ILD) is one of the most common and potentially most devastating extra-articular complication of rheumatoid arthritis (RA) and is associated with substantial morbidity and mortality. However, early identification tools remain limited. This study aimed to identify plasma protein biomarkers of RA-ILD and develop an interpretable machine learning model for risk prediction using data from the UK Biobank.
Methods
We evaluated the association between baseline RA and incident ILD risk using Cox proportional hazards models, followed by Mendelian randomization (MR) to assess causal relationship. We then analyzed 2,920 plasma proteins (Olink platform) from 781 RA patients. Proteins associated with ILD risk were identified and used to construct eight machine learning models, with performance assessed by ROC and decision curve analysis. The best-performing model was further interpreted using Shapley additive explanations (SHAP) to evaluate feature importance.
Results
RA patients had significantly higher ILD risk (HR: 4.425, 95% CI: 3.549-5.518). MR supported a causal association (OR: 1.227, 95% CI: 1.121-1.343). The CatBoost model showed the best performance, achieving an area under the curve (AUC) of 0.884 (95% CI: 0.773,0.996). The SHAP analysis identified LAG3, NPC2, and LAMP3 are the three most important plasma protein predictors of ILD development in patients with RA.
Conclusion
Plasma proteomics combined with machine learning may provide a promising approach for identifying biomarkers and predicting ILD risk in patients with RA. LAG3, NPC2, and LAMP3 may serve as candidate biomarkers for RA-ILD and warrant further validation.
