Rheumatoid Arthritis: A Polygenic Disease with Multiple Phenotypes

Full text

Rheumatoid arthritis (RA) is recognized as a multigene disorder with a number of genetic polymorphisms contributing to disease pathogenesis. Here, we propose that the diagnostic category of RA includes multiple subtypes of disease and that the different phenotypes of RA correlate to different genotypes. Support for this concept has come from a reappraisal of the clinical heterogeneity of RA and the observation that HLA-DRB1 polymorphisms are useful in describing genetic heterogeneity of RA phenotypes. A series of HLA-DRB1 genes has been identified as RA-associated and, in recent years, emphasis has been put on the sequence similarities of these alleles. An alternative view focuses on the amino acid variations found with different alleles being enriched in distinct subtypes of RA. Rheumatoid factor positive destructive joint disease is predominantly associated with the HLA-DRB1^*0401 allele while HLA-DRB1^*0404 and B1^*0101 predisposes for milder and often seronegative disease. Expression of disease-associated alleles on both haplotypes carries a high risk for extra-articular manifestations. Besides HLA gene polymorphisms, emergence of CD28-deficient CD4 T cells identifies RA patients with extra-articular manifestations. These cells undergo clonal expansion in vivo, produce high amounts of IFN-γ and exhibit autoreactivity. Concordance of monozygotic twins for the expression of CD4⁺CD28^- T cells suggests a role for genetic factors in the generation of these unusual T cells. Evidence for heterogeneity of the synovial component of RA comes from studies describing three distinct patterns of lymphoid organizations in the synovium. Each pattern of lymphoid organization correlates with a unique profile of tissue cytokines, demonstrating that several pathways of immune deviation modulate disease expression in RA. Defining RA variants has major implications on how the disease is studied, treated, and managed. Identifying combinations of RA risk genes that correlate with disease variants could become an important diagnostic tool.