Dr John Bowes explains his recent research from the CLUSTER consortium (which includes UK JIA Biologics Register data) looking at genetic markers predicting risk of uveitis. 

Juvenile idiopathic arthritis (JIA) can sometimes lead to a serious eye problem called uveitis (JIAU), which can cause vision loss if not treated early. Most cases of JIAU appear within four years of arthritis diagnosis. Around 13% of children with JIA develop uveitis, but the risk is much higher (up to 30%) in certain types of JIA. 

Clinical factors like age at arthritis onset, presence of certain antibodies (ANA), and JIA subtype can be used alongside screening guidelines to help doctors decide who is at risk of developing uveitis. However, these guidelines are not perfect and can be stressful for families because screening is frequent and sometimes difficult for young children. 

This study looked at genetic factors to see if they could improve predictions. Researchers analysed DNA from over 3,000 children with JIA, including nearly 600 who had uveitis. They focused on genes in the HLA region, which help control the immune system and found three genetic markers were strongly linked to uveitis risk. 

When researchers looked at the genetic markers, they found these added extra predictive power beyond the usual clinical factors (like age at arthritis onset, ANA antibody status, and JIA subtype). In other words, genetics gave new information that wasn’t already explained by those clinical factors and increased the accuracy of risk prediction. 

Why this matters: Including genetic markers in screening could help doctors identify children at highest risk earlier, reducing unnecessary checks for others and preventing severe eye damage. However, more research is needed before genetic testing becomes part of routine care. 

This highlights the importance of sample collection as part of the UK JIA Biologics Register. The development of prediction models that integrate genetics and clinical risk factors is only possible with access to these critical sample collections. 

You can read the full scientific paper here: https://pmc.ncbi.nlm.nih.gov/articles/PMC11605271/#_ci93_ 

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