Journal List > J Rheum Dis > v.27(2) > 1144362

Lee and Song: The Uric Acid and Gout have No Direct Causality With Osteoarthritis: A Mendelian Randomization Study

Abstract

Objective

To examine whether uric acid level or gout is causally associated with the risk of osteoarthritis.

Methods

We performed a two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW), MR-Egger regression, and weighted median methods. We used the publicly available summary statistics datasets of uric acid level or gout genomewide association studies (GWASs) as the exposure, and a GWAS in 3,498 patients with osteoarthritis in the arcOGEN study and 11,009 controls of European ancestry as the outcome.

Results

Six single nucleotide polymorphisms (SNPs) from the GWAS data on uric acid level and 12 SNPs from the GWAS data on gout were selected as instrumental variables (IVs). The IVW analysis did not support a causal association between uric acid level or gout and risk of osteoarthritis (beta=–0.026, standard error [SE]=0.096, p=0.789; beta=–0.018, SE=0.025, p=0.482). MR-Egger regression revealed no causal association between uric acid level or gout and risk of osteoarthritis (beta=0.028, SE=0.142, p=0.852; beta=–0.056, SE=0.090, p=0.548). Similarly, no evidence of a casual association was provided by the weighted median approach (beta=0.004, SE=0.064, p=0.946; beta=–0.005, SE=0.025, p=0.843).

Conclusion

The results of MR analysis demonstrates that uric acid level and gout may be not causally associated with the increased risk of osteoarthritis. Considering MR study is not susceptible to bias from unmeasured confounders or reverse causation, the epidemiological evidence for an association between uric acid level or gout and a higher risk of osteoarthritis may be due to residual confounding or reverse causation rather than direct causality.

REFERENCES

1. Dieppe PA, Lohmander LS. Pathogenesis and management of pain in osteoarthritis. Lancet. 2005; 365:965–73.
crossref
2. Litwic A, Edwards MH, Dennison EM, Cooper C. Epidemiology and burden of osteoarthritis. Br Med Bull. 2013; 105:185–99.
crossref
3. Wortmann RL. Gout and hyperuricemia. Curr Opin Rheumatol. 2002; 14:281–6.
crossref
4. Terkeltaub RA. Clinical practice. Gout. N Engl J Med. 2003; 349:1647–55.
5. Ding X, Zeng C, Wei J, Li H, Yang T, Zhang Y, et al. The associations of serum uric acid level and hyperuricemia with knee osteoarthritis. Rheumatol Int. 2016; 36:567–73.
crossref
6. Howard RG, Samuels J, Gyftopoulos S, Krasnokutsky S, Leung J, Swearingen CJ, et al. Presence of gout is associated with increased prevalence and severity of knee osteoarthritis among older men: results of a pilot study. J Clin Rheumatol. 2015; 21:63–71.
7. Dalbeth N, Aati O, Kalluru R, Gamble GD, Horne A, Doyle AJ, et al. Relationship between structural joint damage and urate deposition in gout: a plain radiography and dual-energy CT study. Ann Rheum Dis. 2015; 74:1030–6.
crossref
8. Yokose C, Chen M, Berhanu A, Pillinger MH, Krasnokutsky S. Gout and osteoarthritis: associations, pathophysiology, and therapeutic implications. Curr Rheumatol Rep. 2016; 18:65.
crossref
9. 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
10. Kolz M, Johnson T, Sanna S, Teumer A, Vitart V, Perola M, et al. Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet. 2009; 5:e1000504.
crossref
11. Sulem P, Gudbjartsson DF, Walters GB, Helgadottir HT, Helgason A, Gudjonsson SA, et al. Identification of low-frequency variants associated with gout and serum uric acid levels. Nat Genet. 2011; 43:1127–30.
crossref
12. Matsuo H, Yamamoto K, Nakaoka H, Nakayama A, Sakiyama M, Chiba T, et al. Genomewide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes. Ann Rheum Dis. 2016; 75:652–9.
crossref
13. Nakayama A, Nakaoka H, Yamamoto K, Sakiyama M, Shaukat A, Toyoda Y, et al. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann Rheum Dis. 2017; 76:869–77.
crossref
14. arcOGEN Consortium;arcOGEN Collaborators. Zeggini E, Panoutsopoulou K, Southam L, Rayner NW, Day-Williams AG, Lopes MC, et al. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genomewide association study. Lancet. 2012; 380:815–23.
15. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37:658–65.
crossref
16. Hartwig FP, Davies NM, Hemani G, Davey Smith G. Two-sample Mendelian randomization: avoiding the down-sides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016; 45:1717–26.
crossref
17. 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
18. 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
19. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017; 32:377–89.
crossref
20. 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
21. 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; 16:78972.
crossref
22. Egger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ. 1997; 315:1533–7.
crossref
23. Neogi T, Krasnokutsky S, Pillinger MH. Urate and osteoarthritis: Evidence for a reciprocal relationship. Joint Bone Spine. 2019; 86:576–82.
crossref
24. Hill HA, Kleinbaum DG. Bias in observational studies. Wiley StatsRef. 2000; DOI: DOI: 10.1002/9781118445112.stat05111.
crossref
25. Lee YH, Bae SC, Song GG. Hepatitis B virus (HBV) reactivation in rheumatic patients with hepatitis core antigen (HBV occult carriers) undergoing anti-tumor necrosis factor therapy. Clin Exp Rheumatol. 2013; 31:118–21.
26. 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 D.C.: National Academies Press (US);2008.
27. VanderWeele TJ, Tchetgen Tchetgen EJ, Cornelis M, Kraft P. Methodological challenges in Mendelian randomization. Epidemiology. 2014; 25:427–35.
crossref
28. Thompson JR, Minelli C, Bowden J, Del Greco FM, Gill D, Jones EM, et al. Mendelian randomization incorporating uncertainty about pleiotropy. Stat Med. 2017; 36:4627–45.
crossref
29. Paaby AB, Rockman MV. The many faces of pleiotropy. Trends Genet. 2013; 29:66–73.
crossref
30. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004; 33:30–42.
crossref
31. 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
32. Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016; 35:1880–906.
crossref

Figure 1.
Forrest plot of the causal effects of uric acid level (A) or gout (B)-associated SNPs on osteoarthritis. SNP: single nucleotide polymorphism, MR: Mendelian randomization, IVW: inverse-variance weighted, Na: not available.
jrd-27-88f1.tif
Figure 2.
Scatter plots of genetic associations of uric acid level (A) or gout (B) against the genetic associations of osteoarthritis. The slopes of each line represent the causal association for each method. Blue line represents the IVW estimate, green line represents the weighted median estimate, and dark blue line represents the MR-Egger estimate. IVW: inverse-variance weighted, SNP: single nucleotide polymorphism, MR: Mendelian randomization, Na: not available.
jrd-27-88f2.tif
Figure 3.
Funnel plot to assess the heterogeneity of the causal effects of uric acid level (A) or gout (B)-associated SNPs on osteoarthritis. Blue line represents the IVW estimate, and dark blue line represents the MR-Egger estimate. SNP: single nucleotide polymorphism, IVW: inverse-variance weighted, MR: Mendelian randomization, SE: standard error, β: beta coefficient.
jrd-27-88f3.tif
Table 1.
Instrumental SNPs from uric acid level (A) or gout (B) and osteoarthritis GWASs
A. Uric acid levels
Instrumental SNP Effect allele Gene Exposure (uric acid) Outcome (osteoarthritis)
Beta SE p-value Case (n) Control (n) Beta SE p-value
rs12356193 A SLC16A9 0.080 0.014 1.00×10−8 3,498 11,009 0.010 0.029 0.657
rs17300741 A SLC22A11 0.060 0.008 7.00×10−14 3,498 11,009 −0.020 0.022 0.259
rs2231142 T ABCG2 0.140 0.022 1.00×10−10 3,498 11,009 −0.094 0.034 0.006
rs734553 T SLC2A9 0.400 0.013 1.00×10−192 3,498 11,009 0.010 0.026 0.679
rs742132 A SCGN 0.050 0.009 9.00×10−9 3,498 11,009 0.030 0.021 0.169
rs780094 T GCKR 0.050 0.008 1.00×10−9 3,498 11,009 −0.030 0.020 0.190
B. Gout
Instrumental SNP Effect allele Gene Exposure (gout) Outcome (osteoarthritis)
Beta SE p-value Case (n) Control (n) Beta SE p-value
rs1014290 T SLC2A9 0.451 0.042 7.00×10−26 3,498 11,009 0.041 0.024 0.075
rs10791821 G MAP3K11 0.451 0.084 1.00×10−7 3,498 11,009 0.000 0.026 0.973
rs1165176 G SLC17A1 0.351 0.057 1.00×10−9 3,498 11,009 0.010 0.021 0.717
rs11733284 A NIPAL1 0.215 0.045 9.00×10−7 3,498 11,009 −0.041 0.024 0.099
rs11758351 G HIST1H2BF 0.336 0.058 2.00×10−8 3,498 11,009 0.049 0.031 0.088
rs1260326 T GCKR 0.307 0.043 2.00×10−12 3,498 11,009 −0.041 0.024 0.089
rs2231142 T ABCG2 0.513 0.075 3.00×10−12 3,498 11,009 −0.094 0.034 0.006
rs2728125 C ABCG2 0.713 0.046 7.00×10−54 3,498 11,009 −0.073 0.036 0.043
rs3114020 C ABCG2 0.637 0.051 9.00×10−35 3,498 11,009 −0.030 0.021 0.193
rs4073582 G CNIH-2 0.507 0.086 6.00×10−9 3,498 11,009 0.020 0.024 0.259
rs4766566 T CUX2 0.412 0.046 4.00×10−20 3,498 11,009 0.030 0.026 0.201
rs734553 T SLC2A9 0.329 0.065 2.00×10−7 3,498 11,009 0.010 0.026 0.652

SNP: single nucleotide polymorphism, GWAS: genomewide association study, Beta: beta coefficient, SE: standard error, SLC16A9: Solute Carrier Family 16 Member 9, SLC22A11: Solute Carrier Family 22 Member 11, ABCG2: ATP Binding Cassette Subfamily G Member 2 (Junior Blood Group), SLC2A9: Solute Carrier Family 2 Member 9, SCGN: Secretagogin, EF-Hand Calcium Binding Protein, GCKR: Glucokinase Regulator, MAP3K11: Mitogen-Activated Protein Kinase Kinase Kinase 11, SLC17A1: Solute Carrier Family 17 Member 1, NIPAL1: NIPA-Like Domain Containing 1, HIST1H2BF: Histone Cluster 1 H2B Family Member F, CNIH-2: Cornichon Family AMPA Receptor Auxiliary Protein 2, CUX2: Cut-Like Homeobox 2.

Table 2.
The MR estimates of the causal effect of uric acid level (A) and gout (B) on osteoarthritis risk, derived using different methods
A. Uric acid level
MR method Number of SNP Beta SE Association p-value Cochran Q statistic Heterogeneity p-value
Inverse variance weighted 6 −0.026 0.096 0.789 13.02 0.232
MR-Egger 6 0.028 0.142 0.852 12.09 0.166
Weighted median 6 0.004 0.064 0.946 na na
B. Gout
MR method Number of SNP Beta SE Association p-value Cochran Q statistic Heterogeneity p-value
Inverse variance weighted 12 −0.018 0.025 0.482 26.53 0.005
MR-Egger 12 −0.056 0.090 0.548 26.03 0.004
Weighted median 12 −0.005 0.025 0.834 na na

MR: Mendelian randomization, SNP: single nucleotide polymorphism, Beta: beta coefficient, SE: standard error, na: not available.

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