Vanessa L Kronzer | PubMed Health

PubMed2025

Polygenic risk scores for rheumatoid arthritis and idiopathic pulmonary fibrosis and associations with RA, interstitial lung abnormalities, and quantitative interstitial abnormalities among smokers.

OBJECTIVE: Genome-wide association studies (GWAS) facilitate construction of polygenic risk scores (PRSs) for rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). We investigated associations of RA and IPF PRSs with RA and high-resolution chest computed tomography (HRCT) parenchymal lung abnormalities. METHODS: Participants in COPDGene, a prospective multicenter cohort of current/former smokers, had chest HRCT at study enrollment. Using genome-wide genotyping, RA and IPF PRSs were constructed using GWAS summary statistics. HRCT imaging underwent visual inspection for interstitial lung abnormalities (ILA) and quantitative CT (QCT) analysis using a machine-learning algorithm that quantified percentage of normal lung, interstitial abnormalities, and emphysema. RA was identified through self-report and DMARD use. We investigated associations of RA and IPF PRSs with RA, ILA, and QCT features using multivariable logistic and linear regression. RESULTS: We analyzed 9,230 COPDGene participants (mean age 59.6 years, 46.4 % female, 67.2 % non-Hispanic White, 32.8 % Black/African American). In non-Hispanic White participants, RA PRS was associated with RA diagnosis (OR 1.32 per unit, 95 %CI 1.18-1.49) but not ILA or QCT features. Among non-Hispanic White participants, IPF PRS was associated with ILA (OR 1.88 per unit, 95 %CI 1.52-2.32) and quantitative interstitial abnormalities (adjusted β=+0.50 % per unit, p = 7.3 × 10-8) but not RA. There were no statistically significant associations among Black/African American participants. CONCLUSIONS: RA and IPF PRSs were associated with their intended phenotypes among non-Hispanic White participants but performed poorly among Black/African American participants. PRS may have future application to risk stratify for RA diagnosis among patients with ILD or for ILD among patients with RA.

Humans↗

PubMed2025

Quantifying and improving rheumatoid arthritis algorithm performance in biobank settings.

OBJECTIVE: To quantify and improve the performance of standard rheumatoid arthritis (RA) algorithms in a biobank setting. METHODS: This retrospective cohort study within the Mayo Clinic (MC) Biobank and MC Tapestry Study identified RA cases by presence of at least two RA codes OR positive anti-cyclic citrullinated peptide antibodies (CCP) plus disease-modifying anti-rheumatic drug (DMARD) prescription as of 7/18/2022. Rheumatology physicians manually verified all RA cases using RA criteria and/or rheumatology physician diagnosis plus DMARD use. All other biobank participants served as non-RA controls. We defined seropositivity as rheumatoid factor and/or anti-CCP positivity. We assessed rules-based and Electronic Medical Records and Genomics (eMERGE) RA algorithms using positive predictive value (PPV). Finally, we developed a novel RA algorithm using a LASSO-based machine learning approach with five-fold cross validation. RESULTS: We identified 1,316 confirmed RA cases (968 MC Biobank, 348 Tapestry, 70 % seropositive) and 82,123 non-RA controls (mean age 65, 61 % female). The PPV of 3 RA codes was 43 %, codes plus DMARD was 54 %, and codes plus DMARD plus seropositivity was 85 %. The PPV of eMERGE was 77 %. Available in the MC Biobank, self-reported RA (PPV 10 %) only minimally improved algorithm performance (PPV from 83 % to 85 %), whereas family history of RA (PPV 3 %) worsened performance. At 90 % PPV, the novel RA algorithm incorporating key variables such as anti-CCP and DMARD use increased sensitivity by 4-11 % compared to eMERGE. CONCLUSION: Rules-based and eMERGE RA algorithms had worse performance in biobank than administrative settings. Our novel RA algorithm outperformed these standard algorithms.

Humans↗