J Rheum Dis 2020; 27(2): 88-95  
The Uric Acid and Gout have No Direct Causality With Osteoarthritis: A Mendelian Randomization Study
Young Ho Lee, M.D., Ph.D., Gwan Gyu Song, M.D., Ph.D.
Department of Rheumatology, Korea University College of Medicine, Seoul, Korea
Correspondence to: Young Ho Lee http://orcid.org/0000-0003-4213-1909
Department of Rheumatology, Korea University Anam Hospital, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Korea.
E-mail:lyhcgh@korea.ac.kr
Received: August 20, 2019; Revised: October 24, 2019; Accepted: November 15, 2019; Published online: April 1, 2020.
© Korean College of Rheumatology. All rights reserved.

This is a open Access article, which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 genome-wide 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.
Keywords: Uric acid, Gout, Osteoarthritis, Mendelian randomization analysis
INTRODUCTION

Osteoarthritis is the most common disease of joints, characterized by a progressive degeneration of articular cartilage, joint pain, and immobility [1]. It is a major cause of pain and disability in elderly people, and its prevalence continues to increase worldwide [2]. The health burden of osteoarthritis is growing with increase in the average longevity worldwide. Therefore, a comprehensive understanding of the risk factors associated with the occurrence of osteoarthritis is needed. Hyperuricemia is a condition in which the serum urate level exceeds the limit of urate solubility (6.8 mg/dL), which reflects supersaturation of the extracellular fluid with urate, and predisposes the body to gout [3]. Gout is an inflammatory disorder characterized by hyperuricemia and effects of urate crystal deposition, including episodic gout flares, gouty arthropathy, tophi, and urolithiasis [3]. Hyperuricemia leads to the deposition of monosodium urate (MSU) crystals in tissues, and increase in the levels of hyperuricemia is correlated with increasing incidences of gouty arthritis [4].

Epidemiological studies have demonstrated that higher serum uric acid levels and gout are associated with an increased risk of osteoarthritis, suggesting uric acid and gout as possible risk factors for osteoarthritis [5,6]. The relationship among hyperuricemia, gout, and osteoarthritis may be explained by the finding that MSU crystal deposition on/in cartilage may create local mechanical and/or inflammatory damage, promoting osteoarthritis development, and that uric acid transporters are present on human articular chondrocytes, suggesting that these cells may internalize uric acid with potential pro-oxidant effects [7]. However, the shared risk factors due to obesity and aging represent a common feature among hyperuricemia, gout and osteoarthritis [8]. Hyperuricemia and gout may predispose joints to osteoarthritis, and osteoarthritis-caused cartilage damage may promote MSU deposition; further these conditions may trigger or result in shared inflammatory cascades. Observational studies are prone to biases, such as reverse causation interpretations and residual confounding, preventing a clear understanding of the effects of uric acid level and gout on osteoarthritis.

Mendelian randomization (MR) is a technique that uses genetic variants as instrumental variables (IVs) to assess whether an observational association between a risk factor and an outcome is consistent with a causal effect [9]. However, to date, MR has not been used to explore the causal effects of uric acid level and gout on the risk of osteoarthritis. The aim of this study was to examine, using a two-sample MR analysis, whether uric acid level and gout are causally associated with the risk of osteoarthritis.

MATERIALS AND METHODS

Data sources and selection of genetic variants

We searched NHGRI-EBI GWAS (https://www.ebi.ac.uk/gwas), a comprehensive catalog of reported associations from published GWAS studies. We used publicly available summary statistics datasets from uric acid level or gout GWASs. For the exposure dataset, we used publicly available summary statistics datasets from a meta-analysis of GWASs on uric acid levels from 14 studies with a total 28,141 participants of European descent [10], and three GWASs on gout (European: n=968 cases and 15,506 controls [11]; Japanese: n=945 cases and 1,213 controls, followed by 1,048 cases and 1,334 controls [12]), and replicated the results using a dataset from a GWAS with 1,396 cases and 1,268 controls from Caucasian and New Zealand Polynesian populations [13]. We used summary statistics from a GWAS based on 3,498 patients with osteoarthritis of the knee and hip in the arcOGEN study and 11,009 controls from the UK or European descent, as an outcome [14]. A two-sample MR study uses genetic variants associated with uric acid level or gout as IVs to improve the inference. We obtained summary statistics (beta coefficients and standard errors [SE]) for single-nucleotide polymorphisms (SNPs) associated with uric acid level and gout as IVs from uric acid level and gout GWASs, and utilized the summary data of SNPs from osteoarthritis GWAS as an outcome. The ‘top hits’ from a GWAS can be used to define genetic instruments for exposures in MR analyses. The selection criteria for IVs were as follows; 1) Association p-values of SNPs with exposure should be less than a threshold of p<2.04e-05 in GWAS dataset of exposure. 2) The SNPs from exposure dataset also should be present in GWAS dataset of outcome.

Statistical analysis for Mendelian randomization

MR analysis requires genetic variants to be related to, but not potential confounders of, an exposure [15]. First, we assessed the independent association of SNPs with uric acid level or gout. Second, we examined the association between each SNP and the risk of osteoarthritis. Third, we combined these findings to estimate the uncompounded causal association between uric acid level or gout and osteoarthritis risk using MR analysis. We performed two-sample MR, which is a method used to estimate the causal effect of an exposure (uric acid level and gout) on outcomes (osteoarthritis) using summary statistics from different GWASs [16], to assess the causal relationships between uric acid level or gout and osteoarthritis risk.

The IVW method uses a meta-analysis approach to combine the Wald ratio estimates of the causal effect obtained from different SNPs, and provides a consistent estimate of the causal effect of the exposure on the outcome when each of the genetic variants satisfies the assumptions of an instrumental variable [17]. Although the inclusion of multiple variants in an MR analysis results in increased statistical power, it has the potential to include pleiotropic genetic variants that are not valid IVs [16]. To explore and adjust for pleiotropy (association of genetic variants with more than one variable), the weighted median and MR-Egger regression methods were used. MR-Egger regression analysis tests and accounts for the presence of unbalanced pleiotropy by introducing a parameter for this bias, through incorporation of summary data estimates of causal effects from multiple individual variants, making it robust to invalid instruments [18]. MR-Egger method performs a weighted linear regression of the gene-outcome coefficients on the gene-exposure coefficients [18]. The slope of this regression represents the causal effect estimate, and the intercept can be interpreted as an estimate of the average horizontal pleiotropic effect across the genetic variants [19]. The weighted median estimator provides a consistent estimate of the causal effect, even when up to 50% of the information contributing to the analysis comes from genetic variants that are invalid IVs [20]. The weighted median estimator has the advantage of retaining greater precision in the estimates than those from the MR-Egger analysis [20]. Tests were considered statistically significant at p<0.05. All MR analyses were performed on MR Base platform [21].

Heterogeneity and sensitivity test

We assessed heterogeneities among SNPs using Cochran’s Q-statistics and funnel plots [22]. We also performed a “leave one out” analysis to investigate the possibility that the causal association was driven by a single SNP.

RESULTS

Studies included in the meta-analysis

1) Instrumental variables for Mendelian randomization

We selected six SNPs as IVs from the GWAS data on uric acid level and 12 SNPs from the GWAS data on gout as IVs to improve the inference (Table 1, Figure 1). Two of the IVs (rs2231142, rs734553) were associated with both uric acid level and gout (Table 1). The selected IVs were associated with uric acid level and gout at genome-wide significance, except for rs10791821, 11733284, and rs734553 in gout (Table 1).

Table 1 . Instrumental SNPs from uric acid level (A) or gout (B) and osteoarthritis GWASs.

A. Uric acid levels

Instrumental SNPEffect alleleGeneExposure (uric acid)Outcome (osteoarthritis)


BetaSEp-valueCase (n)Control (n)BetaSEp-value
rs12356193ASLC16A90.0800.0141.00×10−83,49811,0090.0100.0290.657
rs17300741ASLC22A110.0600.0087.00×10−143,49811,009−0.0200.0220.259
rs2231142TABCG20.1400.0221.00×10−103,49811,009−0.0940.0340.006
rs734553TSLC2A90.4000.0131.00×10−1923,49811,0090.0100.0260.679
rs742132ASCGN0.0500.0099.00×10−93,49811,0090.0300.0210.169
rs780094TGCKR0.0500.0081.00×10−93,49811,009−0.0300.0200.190

B. Gout

Instrumental SNPEffect alleleGeneExposure (gout)Outcome (osteoarthritis)


BetaSEp-valueCase (n)Control (n)BetaSEp-value

rs1014290TSLC2A90.4510.0427.00×10−263,49811,0090.0410.0240.075
rs10791821GMAP3K110.4510.0841.00×10−73,49811,0090.0000.0260.973
rs1165176GSLC17A10.3510.0571.00×10−93,49811,0090.0100.0210.717
rs11733284ANIPAL10.2150.0459.00×10−73,49811,009−0.0410.0240.099
rs11758351GHIST1H2BF0.3360.0582.00×10−83,49811,0090.0490.0310.088
rs1260326TGCKR0.3070.0432.00×10−123,49811,009−0.0410.0240.089
rs2231142TABCG20.5130.0753.00×10−123,49811,009−0.0940.0340.006
rs2728125CABCG20.7130.0467.00×10−543,49811,009−0.0730.0360.043
rs3114020CABCG20.6370.0519.00×10−353,49811,009−0.0300.0210.193
rs4073582GCNIH-20.5070.0866.00×10−93,49811,0090.0200.0240.259
rs4766566TCUX20.4120.0464.00×10−203,49811,0090.0300.0260.201
rs734553TSLC2A90.3290.0652.00×10−73,49811,0090.0100.0260.652

SNP: single nucleotide polymorphism, GWAS: genome-wide 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..


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.

2) Mendelian randomization results

The IVW method yielded no evidence to support a causal association between uric acid level or gout and the risk of osteoarthritis (beta=−0.026, SE=0.096, p=0.789; beta=−0.018, SE=0.025, p=0.482) (Table 2, Figures 1, 2). In the MR-Egger test, the intercept represents the average pleiotropic effect across the genetic variants, i.e., the average direct effect of a variant on the outcome. If the intercept differs from zero, it serves as an evidence of directional pleiotropy. MR-Egger regression revealed that directional pleiotropy was unlikely to be biasing uric acid level (intercept=−0.013, p=0.610) or uric acid results (intercept=0.018, p=0.668), while yielding no causal association between uric acid level or gout and the risk of osteoarthritis (beta=0.028, SE=0.142, p=0.852; beta=−0.056, SE=0.090, p=0.548) (Table 2, Figures 1, 2). In addition, the weighted median approach provided no evidence of a causal association between uric acid level or gout and the risk of osteoarthritis (beta=0.004, SE=0.064, p=0.946; beta=−0.005, SE=0.025, p=0.843) (Table 2, Figures 1, 2). We performed subgroup analysis using exposure dataset of European. However, the MR result from European did not change significantly the overall MR result.

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 methodNumber of SNPBetaSEAssociation p-valueCochran Q statisticHeterogeneity p-value
Inverse variance weighted6−0.0260.0960.78913.020.232
MR-Egger60.0280.1420.85212.090.166
Weighted median60.0040.0640.946nana

B. Gout

MR methodNumber of SNPBetaSEAssociation p-valueCochran Q statisticHeterogeneity p-value

Inverse variance weighted12−0.0180.0250.48226.530.005
MR-Egger12−0.0560.0900.54826.030.004
Weighted median12−0.0050.0250.834nana

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


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.

3) Heterogeneity and sensitivity test

Cochran’s Q-test indicated no evidence of heterogeneity between IV estimates based on the individual variants of uric acid level, but not gout (Table 2). The funnel test yielded symmetry, indicating no evidence of heterogeneity in the MR analyses of uric acid level and gout (Figure 3). Results from the “leave one out” analysis demonstrated that no single SNP was driving the IVW point estimate.

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.
CONCLUSION

In conclusion, the results of our 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. Well-designed epidemiological and MR studies using more variants that explain a greater proportion of uric acid level or gout are warranted to confirm or rule out its causal relationship with osteoarthritis.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Y.H.L. was involved in conception and design of study, acquisition of data, analysis and interpretation of data, drafting the manuscript, and revising the manuscript. G.G.S. was involved in conception and design of study, analysis and interpretation of data, and drafting the manuscript.

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