ArticleArthritis care & research2026
Validation of a Genetic Risk Score Combined With Clinical Variables for Predicting Pulmonary Fibrosis in Early Rheumatoid Arthritis.
Article in Arthritis care & research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivePulmonary fibrosis (PF) is a severe extra-articular manifestation of rheumatoid arthritis (RA). This study aimed to externally validate a genetic risk score (GRS) and a combined risk score (CRS) for predicting the risk of RA-associated PF in an independent cohort of patients with early RA.
methodsThis study used an inception cohort of 1,118 patients diagnosed with RA from northern Sweden between 1996 and 2016. Clinical data were systematically collected, and genotyping was performed for 12 single-nucleotide polymorphisms (SNPs) associated with idiopathic PF. Statistical analyses, including logistic regression and area under the curve (AUC) assessments, were conducted to evaluate the performance of the GRS and in combination with clinical data as the CRS in predicting RA-PF development.
resultsOf the 1,115 patients with complete data, 60 (5.6%) were diagnosed with PF. PF was significantly associated with age, rheumatoid factor positivity, disease activity, and MUC5B (rs35705950) and FAM13A(rs2609255) SNPs. The GRS demonstrated a significant association with RA-PF (odds ratio 2.6, 95% confidence interval 1.6-4.5), whereas the CRS exhibited superior performance (AUC 0.75, P < 0.001) compared to the GRS alone (AUC 0.62). The combined risk score outperformed the GRS in discriminating RA-PF, indicating its potential utility in clinical practice.
conclusionThis study provides external validation of the Veterans Affairs Rheumatoid Arthritis Registry interstitial lung disease GRS (VARA-ILD-GRS) and the VARA-ILD-CRS in an RA cohort, demonstrating their generalizability and effectiveness in identifying individuals at high risk for RA-ILD. The findings support the integration of genetic and clinical data in risk stratification models, which could significantly improve screening strategies for patients with RA at risk of developing PF.
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