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Related Experiment Video

Updated: May 24, 2025

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

Peripheral Biomarker Signatures in Rheumatoid Arthritis-Associated Interstitial Lung Disease.

Austin M Wheeler¹, Joshua F Baker², Thomas R Riley²

¹VA Nebraska-Western Iowa Health Care System and University of Nebraska Medical Center, Omaha.

Arthritis & Rheumatology (Hoboken, N.J.)

|March 3, 2025

Summary

This summary is machine-generated.

Peripheral blood biomarkers can help identify rheumatoid arthritis-associated interstitial lung disease (RA-ILD) in patients with rheumatoid arthritis (RA). These biomarkers improve risk stratification beyond current clinical and genetic factors.

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Area of Science:

Rheumatology
Pulmonology
Biomarker Discovery

Background:

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a serious complication of rheumatoid arthritis (RA), leading to significant morbidity and mortality.
Current methods for identifying patients at high risk for RA-ILD are insufficient, necessitating improved risk stratification tools.

Purpose of the Study:

To identify unique peripheral blood biomarker signatures in RA patients that can enhance the discrimination of RA-ILD.
To evaluate the utility of these biomarker signatures in improving RA-ILD risk stratification beyond established clinical and genetic factors.

Main Methods:

A cross-sectional study involving 2,001 RA patients from the Veterans Affairs Rheumatoid Arthritis Registry.
Measurement of autoantibodies, proinflammatory cytokines/chemokines, adipokines, alarmins, and matrix metalloproteinases in serum/plasma.
Principal component analysis to derive biomarker signatures, followed by logistic regression to assess predictive models including clinical, genetic (MUC5B rs35705950), and biomarker data.

Main Results:

Of 2,001 RA participants, 6.4% had RA-ILD. Eight principal components (PCs) derived from biomarker signatures were significantly associated with RA-ILD after adjusting for clinical factors.
These PCs encompassed innate/allergic responses, autoantibodies, adipokines, alarmins, tissue remodeling, and neutrophil chemotaxis.
Models incorporating PCs (AUC 0.739) and PCs with MUC5B (AUC 0.751) demonstrated superior performance in predicting RA-ILD compared to clinical risk factors alone (AUC 0.630).

Conclusions:

Peripheral blood biomarker signatures are demonstrably associated with RA-ILD.
These biomarkers significantly improve the identification and risk stratification of RA-ILD beyond existing clinical risk factors.
The findings suggest that diverse biological pathways contribute to the pathogenesis of RA-ILD.