Development and internal validation of a clinical and genetic risk score for rheumatoid arthritis-associated interstitial lung disease

Author:

Wheeler Austin M12,Baker Joshua F3ORCID,Riley Thomas3,Yang Yangyuna2,Roul Punyasha2,Wysham Katherine D4,Cannon Grant W5ORCID,Kunkel Gary5,Kerr Gail6,Ascherman Dana P7,Monach Paul8,Reimold Andreas9,Poole Jill A2,Merriman Tony R10ORCID,Mikuls Ted R12,England Bryant R12ORCID

Affiliation:

1. VA Nebraska-Western Iowa Health Care System , Omaha, NE, USA

2. University of Nebraska Medical Center , Omaha, NE, USA

3. University of Pennsylvania & Corporal Michael J. Crescenz VA Medical Center , Philadelphia, PA, USA

4. VA Puget Sound Health Care System & University of Washington , Seattle, WA, USA

5. VA Salt Lake City & University of Utah , Salt Lake City, UT, USA

6. Washington DC VA, Howard University, & Georgetown University , Washington, DC, USA

7. Pittsburgh VA & University of Pittsburgh , Pittsburgh, PA, USA

8. Boston VA , Boston, MA, USA

9. Dallas VA & University of Texas Southwestern , Dallas, TX, USA

10. University of Alabama at Birmingham , Birmingham, AL, USA

Abstract

Abstract Objective Although clinical and genetic risk factors have been identified for rheumatoid arthritis-associated interstitial lung disease (RA-ILD), there are no current tools allowing for risk stratification. We sought to develop and validate an ILD risk model in a large, multicentre, prospective RA cohort. Methods Participants in the Veterans Affairs RA (VARA) registry were genotyped for 12 single nucleotide polymorphisms (SNPs) associated with idiopathic pulmonary fibrosis. ILD was validated through systematic record review. A genetic risk score (GRS) was computed from minor alleles weighted by effect size with ILD, using backward selection. The GRS was combined with clinical risk factors within a logistic regression model. Internal validation was completed using bootstrapping, and model performance was assessed by the area under the receiver operating curve (AUC). Results Of 2386 participants (89% male, mean age 69.5 years), 9.4% had ILD. Following backward selection, five SNPs contributed to the GRS. The GRS and clinical factors outperformed clinical factors alone in discriminating ILD (AUC 0.675 vs 0.635, P < 0.001). The shrinkage-corrected performance for combined and clinical-only models was 0.667 (95% CI 0.628, 0.712) and 0.623 (95% CI 0.584, 0.651), respectively. Twenty percent of the cohort had a combined risk score below a cut-point with >90% sensitivity. Conclusion A clinical and genetic risk model discriminated ILD in a large, multicentre RA cohort better than a clinical-only model, excluding 20% of the cohort from low-yield testing. These results demonstrate the potential utility of a GRS in RA-ILD and support further investigation into individualized risk stratification and screening.

Funder

VA CSR&D

National Institutes of Health

U.S. Department of Defence

Rheumatology Research Foundation

CSR&D Merit Award

RR&D Merit Award

National Institute of Occupational Safety and Health

Publisher

Oxford University Press (OUP)

Subject

Pharmacology (medical),Rheumatology

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