The relationship between the presence of anti-cyclic citrullinated peptide antibodies and clinical phenotype in very early rheumatoid arthritis

<p>Abstract</p> <p>Background</p> <p>Anti-cyclic citrullinated peptide (anti-CCP) antibodies are highly specific for RA, but are not detectable in all RA patients. The aim of this study was to establish whether the clinical phenotypes of anti-CCP positive and negative disease are distinct at the earliest clinically apparent phase of disease.</p> <p>Methods</p> <p>Patients were recruited from the Birmingham early inflammatory arthritis clinic. Participants were included in the current study if they presented within 3 months of symptom onset and fulfilled 1987 ACR criteria for RA at some point during an 18 month follow-up. Data were collected on demographic variables, joint symptoms and tender (n = 68) and swollen (n = 66) joint counts. CRP, ESR, rheumatoid factor and anti-CCP2 status were measured.</p> <p>Results</p> <p>92 patients were included (48 anti-CCP positive). The anti-CCP positive and negative groups were comparable in terms of demographic variables, inflammatory markers, joint counts and 1987 ACR classification criteria, except that more anti-CCP positive patients were rheumatoid factor positive (83.3% vs. 11.4%, p < 0.01). There was no significant difference in the pattern of joint involvement, except for an increased prevalence of knee joint swelling in anti-CCP positive patients (42.9% vs. 22.2%, p = 0.03).</p> <p>Conclusions</p> <p>Patients with and without anti-CCP antibodies present in a similar way, even within three months of clinically apparent disease that eventually develops into RA.</p

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models

Genetics of rheumatoid arthritis: what have we learned?

Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting 0.5–1% of the population worldwide. The disease has a heterogeneous character, including clinical subsets of anti-citrullinated protein antibody (ACPA)-positive and APCA-negative disease. Although the pathogenesis of RA is poorly understood, progress has been made in identifying genetic factors that contribute to the disease. The most important genetic risk factor for RA is found in the human leukocyte antigen (HLA) locus. In particular, the HLA molecules carrying the amino acid sequence QKRAA, QRRAA, or RRRAA at positions 70–74 of the DRβ1 chain are associated with the disease. The HLA molecules carrying these “shared epitope” sequences only predispose for ACPA-positive disease. More than two decades after the discovery of HLA-DRB1 as a genetic risk factor, the second genetic risk factor for RA was identified in 2003. The introduction of new techniques, such as methods to perform genome-wide association has led to the identification of more than 20 additional genetic risk factors within the last 4 years, with most of these factors being located near genes implicated in immunological pathways. These findings underscore the role of the immune system in RA pathogenesis and may provide valuable insight into the specific pathways that cause RA