Pharmacogenetics and Rheumatoid Arthritis: A Clinical Application

Similar variations in response associated with genetic polymorphism have been reported for other RA therapies, including variation in toxicity to azathioprine, allopurinol hypersensitivity, and variability of tacrolimus pharmacokinetics.22 

The use of genetic polymorphism as a predictive tool in clinical practice offers an important strategy to identify those who are likely to benefit from treatment. It has important economic and health outcomes implications as well, such as avoiding exposure to unnecessary adverse events or expensive treatment in those who are unlikely to respond. Indeed, variability in response to expensive anti-TNF-α agents has prompted efforts to identify biomarkers that are predictive of response. 

This has included multiple genome-wide association studies intended to provide unbiased scans for variants associated with response, although studies to date have been inconclusive, with very few examples of clinically useful pharmacogenetic biomarkers.22 

However, the potential of germ-line genetic variation as a biomarker of treatment response offers distinct advantages over conventional biomarkers because germ-line genetic variation is stable throughout a person’s lifespan such that genetic variants can be measured well ahead of clinical need using relatively inexpensive analytic assays. In fact, large-scale preemptive genotyping is already in progress across multiple healthcare systems. 

Nongenetic biomarkers, such as gene-expression signatures, can show very strong correlation with clinical response to drug treatment, and combining genetic and nongenetic measurements is a promising path forward in the effort to develop predictive biomarkers. This may provide the best predictive signature of treatment outcome.22

The involvement of environmental risk factors suggests that behavior may account for a substantial proportion of RA risk, and these factors can be modified to change RA risk and outcomes. Risk models based on family history and epidemiologic and genetic factors show that seropositive and seronegative RA are highly discriminatory. Therefore, assessing epidemiologic and genetic factors among individuals with positive family history of RA may identify those suitable for RA prevention strategies.23 

Personalized risk education may be an important method to encourage those at increased risk for RA to change behaviors and potentially modify their risk. The Personalized Risk Estimator for Rheumatoid Arthritis (PRE-RA) family study risk assessment and education study was designed to assess willingness to change RA-related behaviors.24,25 

The investigators hypothesized that participants who received personalized RA risk tools and health education would be more willing to change RA risk behaviors compared with participants who received standard RA information. This study is important because it provides a rationale for RA prevention efforts in the clinical setting among individuals with genetic predisposition to the disease.

Summary and Clinical Applicability

Compelling evidence suggests a strong genetic association in the risk for RA, which is modified by environmental factors including smoking, alcohol consumption, sex, and age. Genetic polymorphism has been used to explain the variation in RA treatment response. Genotyping patients according to their genetic markers may have important clinical application in pretreatment predictions of treatment outcome. Determining the clinical utility of genotyping, however, requires more extensive research and large-scale multicenter studies. The influence of environmental factors in modifying genetic risk for RA suggests that personalized risk assessment and patient education to encourage modification of risk factors may reduce the risk for RA disease and improve outcomes.

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1. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 2016 May 3. [Epub ahead of print]

2. Frisell T, Hellgren K, Alfredsson L, Raychaudhuri S, Klareskog L, Askling J. Familial aggregation of arthritis-related diseases in seropositive and seronegative rheumatoid arthritis: a register-based case-control study in Sweden. Ann Rheum Dis. 2016;75(1):183-189.

3. de Vries R. Genetics of rheumatoid arthritis: time for a change! Curr Opin Rheumatol. 2011;23(3):227-232.

4. Frisell T, Saevarsdottir S, Askling J. Family history of rheumatoid arthritis: an old concept with new developments. Nat Rev Rheumatol. 2016;12(6):335-343.  

5. Felson DT, Klareskog L. The genetics of rheumatoid arthritis: new insights and implications. JAMA. 2015;313(16):1623-1624.

6. Rodríguez-Elías AK, Maldonado-Murillo K, López-Mendoza LF, Ramírez-Bello J. Genetics and genomics in rheumatoid arthritis (RA): an update [in Spanish]. Gac Med Mex. 2016;152(2):218-227.

7. Rantapää-Dahlqvist S, de Jong BA, Berglin E, et al. Antibodies against cyclic citrullinated peptide and IgA rheumatoid factor predict the development of rheumatoid arthritis. Arthritis Rheum. 2003;48(10):2741-2749.

8. Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376-381.

9. Fernando MM, Stevens CR, Walsh EC, et al. Defining the role of the MHC in autoimmunity: a review and pooled analysis. PLoS Genet. 2008;4(4):e1000024.

10. Di Giuseppe D, Discacciati A, Orsini N, Wolk A. Cigarette smoking and risk of rheumatoid arthritis: a dose-response meta-analysis. Arthritis Res Ther. 2014;16(2):R61.

11. Wesley A, Bengtsson C, Elkan AC, Klareskog L, Alfredsson L, Wedrén S; Epidemiological Investigation of Rheumatoid Arthritis Study Group. Association between body mass index and anti-citrullinated protein antibody-positive and anti-citrullinated protein antibody-negative rheumatoid arthritis: results from a population-based case-control study. Arthritis Care Res (Hoboken). 2013;65(1):107-112.

12. Sparks JA, Chang SC, Deane KD, et al. Associations of smoking and age with inflammatory joint signs among first-degree relatives without rheumatoid arthritis: Results from the Studies of the Etiology of RA. Arthritis Rheumatol. 2016 Feb 11. [Epub ahead of print]

13. Di Giuseppe D, Wallin A, Bottai M, Askling J, Wolk A. Long-term intake of dietary long-chain n-3 polyunsaturated fatty acids and risk of rheumatoid arthritis: a prospective cohort study of women. Ann Rheum Dis. 2014;73(11):1949-1953.

14. Di Giuseppe D, Bottai M, Askling J, Wolk A. Physical activity and risk of rheumatoid arthritis in women: a population-based prospective study. Arthritis Res Ther. 2015;17:40.

15. Frisell T, Holmqvist M, Källberg H, Klareskog L, Alfredsson L, Askling J. Familial risks and heritability of rheumatoid arthritis: role of rheumatoid factor/anti-citrullinated protein antibody status, number and type of affected relatives, sex, and age. Arthritis Rheum. 2013;65(11):2773-2782.

16. Jiang X, Frisell T, Askling J, et al. To what extent is the familial risk of rheumatoid arthritis explained by established rheumatoid arthritis risk factors? Arthritis Rheumatol. 2015;67(2):352-362.

17. Källberg H, Ding B, Padyukov L, et al. Smoking is a major preventable risk factor for rheumatoid arthritis: estimations of risks after various exposures to cigarette smoke. Ann Rheum Dis. 2011;70(3):508-511.

18. Sparks JA, Chen CY, Hiraki LT, Malspeis S, Costenbader KH, Karlson EW. Contributions of familial rheumatoid arthritis or lupus and environmental factors to risk of rheumatoid arthritis in women: a prospective cohort study. Arthritis Care Res (Hoboken). 2014;66(10):1438-1446.

19. Stidham RW, Lee TC, Higgins PD, et al. Systematic review with network meta-analysis: the efficacy of anti-TNF agents for the treatment of Crohn’s disease. Aliment Pharmacol Ther. 2014;39(12):1349-1362.

20. Zink A, Strangfeld A, Schneider M, et al. Effectiveness of tumor necrosis factor inhibitors in rheumatoid arthritis in an observational cohort study: comparison of patients according to their eligibility for major randomized clinical trials. Arthritis Rheum. 2006;54(11):3399-3407.

21. Lima A, Bernardes M, Azevedo R, Medeiros R, Seabra V. Pharmacogenomics of methotrexate membrane transport pathway: can clinical response to methotrexate in rheumatoid arthritis be predicted? Int J Mol Sci. 2015;16(6):13760-13780.

22. Maranville JC, Di Rienzo A. Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases. Pharmacogenomics. 2014;15(15):1931-1940.

23. Sparks JA, Chen CY, Jiang X, et al. Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history. Ann Rheum Dis. 2015;74(8):1522-1529.

24. Personalized Risk Estimator for Rheumatoid Arthritis Family Study (PRE-RA). Updated September 25, 2015. Accessed May 20, 2016.

25. Sparks JA, Iversen MD, Miller Kroouze R, et al. Personalized Risk Estimator for Rheumatoid Arthritis (PRE-RA) family study: rationale and design for a randomized controlled trial evaluating rheumatoid arthritis risk education to first-degree relatives. Contemp Clin Trials. 2014;39(1):145-157.