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Reevaluation of the interaction between HLA-DRB1 shared epitope alleles, PTPN22, and smoking in determining susceptibility to autoantibody-positive and autoantibody-negative rheumatoid arthritis in a large UK Caucasian population.

Authors
Journal
Arthritis & Rheumatism
0004-3591
Publisher
Wiley Blackwell (John Wiley & Sons)
Publication Date
Disciplines
  • Biology
  • Mathematics

Abstract

OBJECTIVE: To define interactions between the HLA-DRB1 shared epitope (SE), PTPN22, and smoking in cyclic citrullinated peptide (CCP) antibody- and rheumatoid factor (RF)-positive and -negative rheumatoid arthritis (RA). METHODS: Data on approximately 5,000 RA patients and approximately 3,700 healthy controls recruited from 6 centers in the UK were analyzed; not all centers had both genotype data and smoking data available for study. The magnitude of association was assessed in autoantibody-positive and -negative subgroups. The effect of smoking on antibody status among cases was assessed following adjustment for year of birth and center, using Mantel-Haenszel analysis. Analyses of the combined effects of PTPN22, HLA-DRB1 SE, and smoking were performed using additive and multiplicative models of interaction within a logistic regression framework. RESULTS: The combined effects of PTPN22, HLA-DRB1 SE, and smoking were defined, with no evidence of departure from a multiplicative model. Within the case population, all 3 factors were independently associated with the generation of CCP antibodies (odds ratio [OR] 11.1, P < 0.0001), whereas only HLA-DRB1 SE and smoking were independently associated with RF production (OR 4.4, P < 0.0001). There was some evidence of increasing likelihood of antibody positivity with heavier smoking. Finally, we demonstrated that smoking was associated with the generation of both CCP and RF antibodies (OR 1.7, P = 0.0001). CONCLUSION: PTPN22 appears to be primarily associated with anticitrulline autoimmunity, whereas HLA-DRB1 SE is independently associated with RF. This study has confirmed associations of specific gene-environment combinations with a substantially increased risk of developing RA. Further work is needed to determine how these data can be used to inform clinical practice.

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