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Journal List > J Rheum Dis > v.26(2) > 1122081

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

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 genomewide 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 (in-tercept=−0.030; p=0.101).

Conclusion

MR analysis supports that RA may be causally associated with an increased risk of T2D.

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REFERENCES

1. Edwards CJ, Cooper C. Early environmental factors and rheumatoid arthritis. Clin Exp Immunol. 2006; 143:1–5.
crossref
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.
crossref
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.
crossref
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.
crossref
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.
crossref
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.
crossref
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.
crossref
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.
crossref
13. Lawlor DA. Commentary: two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol. 2016; 45:908–15.
crossref
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.
crossref
16. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37:658–65.
crossref
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.
crossref
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.
crossref
20. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017; 32:377–89.
crossref
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.
crossref
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.
crossref
23. Egger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ. 1997; 315:1533–7.
crossref
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.
crossref
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.
crossref
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.
crossref
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jrd-26-131f1.tif
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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jrd-26-131f2.tif
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.
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Table 1.
Instrumental single-nucleotide polymorphisms associated with RA and T2D GWASs
Instrumental SNP Gene Chromosome loci Effect allele Exposure (RA) Outcome (T2D)
Beta SE p-value Beta SE p-value
rs10040327 IL6ST 5 A −0.288 0.044 6.65E-11 −0.037 0.039 0.344
rs3087243 CTLA4 2 A −0.139 0.023 2.24E-08 0.014 0.023 0.534
rs396458 CYP21A2 6 G −0.528 0.035 9.15E-50 0.043 0.033 0.195
rs460568 C6orf120 6 T 0.262 0.031 3.81E-16 0.027 0.032 0.391
rs4810485 CD40 20 T −0.163 0.030 5.69E-09 0.016 0.026 0.538
rs6679677 DCLRE1B 1 A 0.663 0.038 4.39E-70 −0.007 0.036 0.855
rs6920220 LOC102723649 6 A 0.199 0.029 2.49E-12 −0.008 0.028 0.777
rs9268145 C6orf10 6 G 1.058 0.027 <5.00E-08 0.089 0.028 0.001
rs9277412 HLA-DPB1 6 T −0.357 0.029 1.80E-39 0.014 0.025 0.564
rs9784876 TAP2 6 A 0.668 0.047 2.40E-46 0.067 0.042 0.110

RA: rheumatoid arthritis, T2D: type 2 diabetes, GWAS: genomewide association study, SNP: single-nucleotide polymorphism, Beta: beta coefficient, SE: standard error, IL6ST: interleukin 6 signal transducer, CTLA4: cytotoxic T-lymphocyte-associated protein 4, CYP21A2: cytochrome P450 family 21 subfamily A member 2, C6orf120: chromosome 6 open reading frame 120, CD40: cluster of differentiation 40, DCLRE1B: DNA crosslink repair 1B, LOC102723649: uncharacterized LOC102723649, C6orf10: chromosome 6 open reading frame 10, HLA-DPB1: major histocompatibility complex, Class II, DP Beta 1, TAP2: transporter 2, ATP binding cassette subfamily B member.

Table 2.
MR estimates from each method used to determine the causal effect of rheumatoid arthritis on the risk of type 2 diabetes
MR method Number of SNPs Beta coefficient Standard error Association p-value Cochran Q-statistic Heterogeneity p-value
Inverse variance weighted 10 0.044 0.022 0.047 12.14 0.206
MR-Egger 10 0.093 0.033 0.023 8.50 0.387
Weighted median 10 0.056 0.025 0.028 NA NA

MR: Mendelian randomization, SNP: single-nucleotide polymorphism, NA: not available.

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