Background/Purpose: The severity of radiologic progression is variable between rheumatoid arthritis (RA) patients. Recently several genetic severity variants have been identified and replicated. This study determined the contribution of the established genetic severity factors to the explained variance in radiologic progression and evaluated whether genetic factors, in addition to traditional clinical and serological risk factors, improved the accuracy of predicting the severity of radiologic progression.

Methods: 417 early RA patients with yearly radiologic follow up were studied. The main outcome measure was the progression in Sharp-van der Heijde scores (SHSs) over six years. Genetic variants in the following genes were studied: HLA-DRB1, CD40, IL-15, DKK-1, IL2RA, GRZB, IL-4R, SPAG16, C5orf30, MMP-9 and OPG.  Linear regression analyses were used to determine the explained variance and the net improvement in reclassifications of prediction models without and with genetic risk factors. For studying reclassification, the continuous outcome was categorized based on the severity of radiologic progression in progression in SHSs over six years ≤6, 7-30 and >30 units, indicating mild, moderate and severe radiologic progression.

Results: Treatment effects and a combination of traditional risk factors explained 7.1% and 31.2% of the variance in progression in SHSs over six years. The genetic factors together explained 18.1%. When added to treatment effects and traditional factors, the genetic risk factors additionally explained 7.4% of the variance. Compared to a prediction model without genetic factors, a prediction model also including genetic factors yielded a net correct reclassification of 5.9% of the patients; now 62% of those patients were correctly classified.  Thus with a model including known traditional and genetic factors 38.1% of the patients were still not correctly classified. Evaluating the reclassifications per severity group, the net percentages of patients that were additionally correctly classified were 0%, 5.1% and 13.2% for the groups with mild, moderate and severe progression respectively. Hence, including genetic factors led to improved identification of patients with severe radiological progression in particular. Sensitivity analyses using imputation of missing radiographs yielded comparable results.

Conclusion: When added to a model consisting of traditional factors, genetic risk factors improved the predictive accuracy. Nonetheless, the predictive performance was still insufficient for use in clinical practice.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/predicting-the-severity-of-joint-damage-in-rheumatoid-arthritis-the-contribution-of-genetic-factors/