Predicting uveitis onset in children with juvenile idiopathic arthritis

PROJECT SUMMARY
Juvenile idiopathic arthritis-associated uveitis (JIA-U) is a chronic inflammatory eye disease that affects 20% of
children with JIA and can lead to sight-threatening complications. Regular ophthalmic screening of children
with JIA is crucial because early uveitis detection can prevent visual complications and influence therapeutic
management. Our long-term goal is to prevent sight-threatening ocular damage in children with JIA-U by
improving uveitis detection and treatment.
This investigation is a multi-site longitudinal translational study that is a collaborative effort between experts in
ophthalmology, rheumatology, proteomics, assay development, genetics, and biostatistics. The objectives of
this study are to: 1) Delineate a biomarker profile in tears that identifies children at increased risk for JIA-U, 2)
Identify genetic risk factors for early uveitis diagnosis among children with JIA, and 3) Develop a uveitis risk
estimator integrating demographics, clinical factors, JIA-U biomarkers, and genetic variations. We will enroll
250 children with recently diagnosed JIA, without clinical evidence of uveitis, and follow them prospectively.
Levels of biomarkers will be compared at serial time points in children with JIA-U and those who do not
develop uveitis to understand the window of detection. We will also conduct SNP-based genetic association
studies that include 750 children with JIA (no uveitis) and 750 with JIA-U to identify genetic variations that are
related to biomarker levels and also to genetic predilection to eye disease. Using an integrative approach that
considers clinical, biological, and genetic factors, we will generate a risk estimator for uveitis that will identify
children at elevated risk for eye disease. We expect that application of this new tool early in disease will allow
for early intervention and improved vision outcomes.

Grant Number: 5R01EY030521-05
NIH Institute/Center: NIH
Principal Investigator: Sheila Angeles-Han