OBJECTIVES: The clinical progression of juvenile idiopathic arthritis (JIA) is unpredictable. Knowing who will develop severe disease could facilitate rapid intensification of therapies. We use genetic variants conferring susceptibility to JIA to predict disease outcome measures.
METHODS: Seven hundred and thirteen JIA patients with genotype data and core outcome variables (COVs) at diagnosis (baseline) and 1 year follow up were identified from the Childhood Arthritis Prospective Study (CAPS). A weighted genetic risk score (GRS) was generated, including all SNPs previously associated with JIA susceptibility (p-value < 5x10-08). We used multivariable linear regression to test the GRS for association with COVS (limited joint count, active joint count, physician global assessment, parent/patient general evaluation, childhood health assessment questionnaire and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and ILAR category. The GRS was split into quintiles to identify high (quintile 5) and low (quintile 1) risk groups.
RESULTS: Patients in the high-risk group for the GRS had a younger age at presentation (median low risk 7.79, median high risk 3.51). No association was observed between the GRS and any outcome measures at 1 year follow up or baseline.
CONCLUSION: For the first time we have used all known JIA genetic susceptibility loci (p = <5x10-08) in a GRS to predict changes in disease outcome measured over time. Genetic susceptibility variants are poor predictors of changes in core outcome measures, it is likely that genetic factors predicting disease outcome are independent to those predicting susceptibility. The next step will be to conduct a genome-wide association analysis of JIA outcome.