ABSTRACT
OBJECTIVES: Juvenile idiopathic arthritis (JIA)-associated uveitis (JIAU) is a serious JIA comorbidity that can result in vision impairment. This study aimed to identify genetic risk factors, within the major histocompatibility complex , for JIAU and evaluate their contribution for improving risk classification when combined with clinical risk factors. METHODS: Data on single nucleotide polymorphisms, amino acids and classical human leukocyte antigen (HLA) alleles were available for 2,497 JIA patients without uveitis and 579 JIAU patients (female=2060, male=1015). Analysis was restricted to patients with inferred European ancestry. Forward conditional logistic regression identified genetic markers exceeding a Bonferroni corrected significance (6x10-6). Multivariable logistic regression estimated the effects of clinical and genetic risk factors and a likelihood ratio test calculated the improvement in model fit when adding genetic factors. Uveitis risk classification performance of a model integrating genetic and clinical risk factors was estimated using area under the receiver operator characteristic curve and compared to a model of clinical risk factors alone. RESULTS: Three genetic risk factors were identified mapping to HLA-DRB1, HLA-DPB1 and HLA-A. These markers were statistically independent from clinical risk factors and significantly improved the fit of a model when included with clinical risk factors (P = 3.3x10-23). The addition of genetic markers improved the classification of JIAU compared to a model of clinical risk factors alone (AUC 0.75 vs. 0.71). CONCLUSIONS: Integration of a genetic and clinical risk prediction model outperforms a model based solely on clinical risk factors. Future JIAU risk prediction models should include genetic risk factors.
ABSTRACT
OBJECTIVES: The clinical progression of 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: A total of 713 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 single nucleotide polymorphisms (SNPs) previously associated with JIA susceptibility (P-value < 5×10-08). 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 HAQ and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and International League of Associations of Rheumatology (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=<5×10-08) 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.
