J Rheum Dis.  2019 Apr;26(2):131-136. 10.4078/jrd.2019.26.2.131.

Causal Association between Rheumatoid Arthritis with the Increased Risk of Type 2 Diabetes: A Mendelian Randomization Analysis

• Lee YH
• Song GG

• KMID: 2442024
• DOI: http://doi.org/10.4078/jrd.2019.26.2.131

Abstract

OBJECTIVE
This study aimed to examine whether rheumatoid arthritis (RA) is causally associated with type 2 diabetes (T2D).
METHODS
We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics datasets from a genome-wide association studies (GWAS) meta-analysis of 5,539 autoantibody-positive individuals with RA and 20,169 controls of European descent, and a GWAS dataset of 10,247 individuals with T2D and 53,924 controls, overwhelmingly of European descent as outcomes.
RESULTS
We selected 10 single-nucleotide polymorphisms from GWAS data on RA as instrumental variables to improve the inference. The IVW method supported a causal association between RA and T2D (β=0.044, standard error [SE]=0.022, p=0.047). The MR-Egger analysis showed a causal association between RA and T2D (β=0.093, SE=0.033, p=0.023). In addition, the weighted median approach supported a causal association between RA and T2D (β=0.056, SE=0.025, p=0.028). The association between RA and T2D was consistently observed using IVW, MR Egger, and weighted median methods. Cochran's Q test indicated no evidence of heterogeneity between instrumental variable estimates based on individual variants and MR-Egger regression revealed that directional pleiotropy was unlikely to have biased the results (intercept=−0.030; p=0.101).
CONCLUSION
MR analysis supports that RA may be causally associated with an increased risk of T2D.

Keyword

Rheumatoid arthritis; Type 2 diabetes; Mendelian randomization

MeSH Terms

Arthritis, Rheumatoid*
Bias (Epidemiology)
Dataset
Genome-Wide Association Study
Mendelian Randomization Analysis*
Methods
Population Characteristics
Random Allocation

Figure

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  Figure 1. Forest plot of the causal effects of rheumatoid arthri-tis-associated single-nucleotide polymorphisms on type 2 diabetes. MR: Mendelian randomization, IVW: inverse-variance weighted.

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  Figure 2. Scatter plots of the genetic associations of rheumatoid arthritis against those of type 2 diabetes. The slopes of each line represent the causal association for each method. Blue line represents the inverse-variance weighted estimate, green line represents the weighted median estimate, and dark blue line represents the MR-Egger estimate. SNP: single-nucleotide polymorphism.

Reference

1. Edwards CJ, Cooper C. Early environmental factors and rheumatoid arthritis. Clin Exp Immunol. 2006; 143:1–5.

2. Lee YH, Bae SC, Choi SJ, Ji JD, Song GG. Genomewide pathway analysis of genomewide association studies on systemic lupus erythematosus and rheumatoid arthritis. Mol Biol Rep. 2012; 39:10627–35.

3. Prentki M, Nolan CJ. Islet beta cell failure in type 2 diabetes. J Clin Invest. 2006; 116:1802–12.

4. Avina-Zubieta JA, Thomas J, Sadatsafavi M, Lehman AJ, Lacaille D. Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis. 2012; 71:1524–9.

5. Shah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M, Gale CP, et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1· 9 million people. Lancet Diabetes Endocrinol. 2015; 3:105–13.

6. Nicolau J, Lequerré T, Bacquet H, Vittecoq O. Rheumatoid arthritis, insulin resistance, and diabetes. Joint Bone Spine. 2017; 84:411–6.

7. Solomon DH, Love TJ, Canning C, Schneeweiss S. Risk of diabetes among patients with rheumatoid arthritis, psoriatic arthritis and psoriasis. Ann Rheum Dis. 2010; 69:2114–7.

8. Su CC, Chen IeC, Young FN, Lian IeB. Risk of diabetes in patients with rheumatoid arthritis: a 12-year retrospective cohort study. J Rheumatol. 2013; 40:1513–8.

9. Simard JF, Mittleman MA. Prevalent rheumatoid arthritis and diabetes among NHANES III participants aged 60 and older. J Rheumatol. 2007; 34:469–73.

10. Ozen G, Pedro S, Holmqvist ME, Avery M, Wolfe F, Michaud K. Risk of diabetes mellitus associated with disease-modifying antirheumatic drugs and statins in rheumatoid arthritis. Ann Rheum Dis. 2017; 76:848–54.

11. Jiang P, Li H, Li X. Diabetes mellitus risk factors in rheumatoid arthritis: a systematic review and meta-analysis. Clin Exp Rheumatol. 2015; 33:115–21.

12. Burgess S, Daniel RM, Butterworth AS, Thompson SG. EPIC-InterAct Consortium. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015; 44:484–95.

13. Lawlor DA. Commentary: two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol. 2016; 45:908–15.

14. Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, et al. Genomewide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet. 2010; 42:508–14.

15. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012; 44:981–90.

16. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37:658–65.

17. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014; 506:376–81.

18. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013; 178:1177–84.

19. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015; 44:512–25.

20. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017; 32:377–89.

21. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016; 40:304–14.

22. Hemani G, Zheng J, Wade KH, Laurin C, Elsworth B, Burgess S, et al. MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. bioRxiv. 2016; DOI: DOI: 10.1101/078972.

23. Egger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ. 1997; 315:1533–7.

24. Popa C, Netea MG, van Riel PL, van der Meer JW, Stalenhoef AF. The role of TNF-alpha in chronic inflammatory conditions, intermediary metabolism, and cardiovascular risk. J Lipid Res. 2007; 48:751–62.

25. Song GG, Bae SC, Lee YH. Association between vitamin D intake and the risk of rheumatoid arthritis: a meta-analysis. Clin Rheumatol. 2012; 31:1733–9.

26. Viswanathan M, Berkman ND, Dryden DM, Hartling L. Assessing risk of bias and confounding in observational studies of interventions or exposures: further development of the RTI item bank. Rockville (MD): Agency for Healthcare Research and Quality (US);. 2013; Aug. Report No.: 13-EHC106-EF 2013.

27. Smith GD, Ebrahim S. Mendelian randomization: genetic variants as instruments for strengthening causal inference in observational studies. Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial surveys. Washington (DC): National Academies Press (US);2008.

28. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004; 33:30–42.

29. Swerdlow DI, Kuchenbaecker KB, Shah S, Sofat R, Holmes MV, White J, et al. Selecting instruments for Mendelian randomization in the wake of genomewide association studies. Int J Epidemiol. 2016; 45:1600–16.