Page 81 - Recognizing axial spondyloarthritis - Janneke de Winter

ANTI-CD74 ANTIBODIES IN EARLY AXIAL SPA
of Hannover developed the ELISAs to detect anti-CD74 IgG and IgA antibodies in cooperation with AESKU Diagnostics (Wendelsheim, Germany), as described earlier by Baerlecken et al (8). The originally described ELISAs were improved for the current study since the old (peptide-based) test only worked with sera that had been frozen for a long time. The tests were performed according to the manufacturer’s protocol. Anti-CD74 IgG is expressed as optical density (OD), anti- CD74 IgA is expressed as OD (AS vs. healthy controls) or U/mL (in the SPACE cohort, at that time standard sera were available).
We also measured the amount of total serum IgA, since earlier studies showed that total serum IgA is elevated in axSpA (12,13).
Data analysis
A χ2 test and Mann-Whitney U test were used for categorical and continuous data, respectively. Categorical data are presented as numbers (%), continuous data are presented as the mean (SD) or as median (interquartile range, IQR) as appropriate. Statistical tests were 2-sided, and p-values less than 0.05 were considered significant. Only the available data were analyzed. Linear correlation between two continuous variables was calculated by calculating Pearson’s correlation coefficient (r). Receiver Operating Characteristic (ROC) analysis and the maximum value of the Youden index (sensitivity + specificity – 1) (14) was used to evaluate the predictive value of anti-CD74 IgG and IgA antibodies and to calculate the best possible cut-off. These cut-off values were used to calculate the positive predictive value (PPV) and negative predictive value (NPV) and positive and negative likelihood ratios (LR+ and LR-, respectively) of anti-CD74 IgG and IgA antibodies in discriminating between axSpA and CBP patients.
We explored which characteristics among axSpA patients were associated with higher anti-CD74 IgA levels by logistic regression. We started with a set of predetermined candidate variables: HLA-B27, disease duration, sacro-iliitis on X ray or MRI, peripheral (arthritis, dactylitis and heel enthesitis) and extra- articular (uveitis, psoriasis and IBD) disease manifestations and used backward elimination to create the final model.
We evaluated the association between anti-CD74 IgA and total IgA by univariate and multivariate logistic regression by both forward selection and backward elimination. Besides total IgA and anti-CD74 IgA we added potential confounding variables: HLA-B27, disease duration, sacro-iliitis on X ray or MRI, peripheral (arthritis, dactylitis and heel enthesitis) and extra-articular (uveitis, psoriasis and IBD) disease manifestations. Since the interpretation of the regression analysis might be influenced by collinearity between anti-CD74 and total IgA, we first assessed the amount of collinearity by calculating the Pearson’s correlation coefficient, variance inflating factor (VIF) and tolerance (R2). Values of Pearson’s correlation coefficient >0.7, a VIF of more than 5 and a tolerance of less than 0.20 were considered problematic for interpreting the regression model (15).
We used IBM SPSS Statistics 24 for al analyses.
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