Association between self-reported race and ethnicity and myositis-specific autoantibodies in a diverse cohort of patients with inflammatory myopathy

Myositis-specific autoantibodies (MSAs) are highly specific biomarkers for idiopathic inflammatory myopathies (IIMs). We investigated whether self-reported race and ethnicity were associated with the presence of specific MSAs. Charts of patients with IIM seen at 3 large healthcare systems in the same US city were reviewed. Demographic data and MSA test results were abstracted. Associations between race and ethnicity and presence of MSAs were analyzed using bivariate analysis and further characterized using separate unadjusted and adjusted logistic regression models. One hundred twenty-one subjects were included (19% Asian, 10% Black or African American, 27% Latinx or Hispanic, 36% non-Hispanic White, and 7% Other). In a bivariate analysis, anti-Jo-1 and anti-MDA5 autoantibodies were associated with race and ethnicity (p = 0.03 and 0.02, respectively). Black or African American subjects had increased odds of a positive anti-Jo-1 result compared to non-Hispanic White subjects on unadjusted logistic regression analysis (OR 8.61, 95% CI 1.61-46.07), although after adjustment for age and gender this finding was not significant. Subjects categorized as Other had increased odds of a positive anti-MDA5 result compared to non-Hispanic White subjects on both unadjusted (OR 55.0, 95% CI 2.02-1493) and adjusted analyses (OR 44.8, 95% CI 1.55-1298). Anti-Jo-1 and anti-MDA5 autoantibodies were significantly associated with race and ethnicity on bivariate analysis. Black or African American subjects had increased odds of positive anti-Jo-1 autoantibody on unadjusted, but not adjusted, logistic regression analysis. Subjects characterized as Other had increased odds of positive anti-MDA5 autoantibody, although confidence intervals were wide. Key Points • Association found between MSAs and race and ethnicity in diverse US cohort • Anti-Jo-1 and anti-MDA5 associated with race and ethnicity in bivariate analyses.