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Genetic factors associated with disease severity in rheumatoid arthritis

University of British Columbia

Rheumatoid arthritis (RA) is a chronic, debilitating disease characterized by inflammatory thickening of articular soft tissue, with extension of the synovium over the articular cartilage, cartilaginous destruction and subchondral erosion leading to progressive joint deformity and immobility. RA currently affects over half a million subjects in Canada. More than 1,300 patients die annually from gastric bleeding secondary to the use of NSAIDs. The clinical manifestations of RA encompass a spectrum ranging from mild to severe disease, with irreversible joint damage and disability. Our current understanding does not allow for the accurate prediction of the severity in RA. Yet, increasing evidence suggests that early intervention with immunosuppressive drugs may mitigate the relentless progression of this disease and prevent irreversible joint damage. The main objective of this study was to explore selected immune response genes potentially involved in disease severity using patient populations stratified according to disease severity. PCR-RFLP and fragment length analyses of microsatellites were used to study the effect of genetic polymorphisms of nine genes on chromosomes 2, 4, 6, 12 and 22, which included HLA-DRβ1, HLA-DQβ1, HLA-DPβ1, TNF, IL-1β, IL-IRN, IL-2, IL-2Rβ, and IFNγ. HLA-DRβ1 alleles containing the QKRAA or QRRAA epitope, HLA-DQβ1*302, HLA-DPβ1 *401 and the 126bp allele of IFNγ were identified as high risk alleles, which demonstrate a significant association with the severe RA. In contrast the HLA-DRβ1 alleles containing the DERRA epitope and HLDRβ1*8, HLA-DQβ 1*402/502/603/604, HLA-DPβ1*201/501/1001 and the 122bp allele of IFNγ were significantly decreased in frequency in the population with severe RA compared to those with mild disease, suggesting a potential protective effect of these alleles on the progression of the disease. The distribution of genetic polymorphism for all other gene markers were either similar between populations or had a statistically insignificant value in the prediction of the severity of RA. The combination of HLA class II and IFNγ high-risk alleles yielded a more specific prediction of disease severity. A logistic regression model was designed which incorporated demographic and clinical data, HLA class II and IFNγ gene polymorphisms in order to predict the severity of disease. The model was able to discriminate between mild and severe RA patients with an odds ratio of greater than 447, Chi-square=28.83, p<0.0001 and an overall accuracy of over 90%.

Thesis/Dissertation

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2009-07-15

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http://hdl.handle.net/2429/10814

Experimental Medicine

University of British Columbia

UBCV

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