OP0193 EVALUATION OF THE CAUSAL EFFECTS BETWEEN GOUT AND HYPERTENSION: A MENDELIAN RANDOMIZATION STUDY
Authors: Y. J. Chang, L. C. Wang, C. K. Chen, C. I. Hsieh, C. F. Kuo
Journal: Annals of the Rheumatic Diseases (2021)

Background: Gout is the most common inflammatory arthritis worldwide associated with comorbidities that may impair well-being and reduce longevity. Epidemiological evidence generally supports that gout patients are at high risk of hypertension. However, the causality between gout and hypertension is uncertain since confounding and other types of bias are difficult to contain in the observational study. Objectives: To test the causal link between gout and hypertension using a Mendelian Randomization (MR) analysis. Methods: A mendelian randomization analysis was conducted using individual patient data from the Taiwan Biobank featured 2452 individuals with gout and 66527 controls. We selected 12 SNPs as instrumental variables (IVs) with p-values &lt; 5 × 10 −8 and the linkage disequilibrium (LD) R 2 value less than 0.8. We conducted traditional MR analysis using the inversed weighted variance (IVW) and median methods with different settings as the primary analysis. Further IV assumption-free methods, the MR-Egger methods [1], Causal Analysis Using Summary Effect Estimates (CAUSE) model [2], and structural equation modeling (SEM) [3,4] were also performed as a sensitivity analysis. Results: The prevalence of hypertension was 0.15% (n = 9549) in the cohort. Table 1 shows causal effect estimates between gout and hypertension using different methods. The average causal effect β is estimated at 0.09 and the corresponding odds ratio (OR) at 1.09 using traditional methods across different settings. Similar estimates were observed in the MR-Egger method, SEM model, and the CAUSE model, demonstrating the robustness of the causal association between gout and hypertension considering pleiotropic effects (Table 1). Furthermore, the model fit of the hypothesized SEM model is excellent with a comparative fit index of 0.978 and Tucker-Lewis index of 0.968. The SEM model explains at least 32.70% variance of hypertension and 32.6% variance of gout (Figure 1). Table 1. Estimate the causal effect of MR analysis and sensitivity analysis Method Causal effect estimate β 95% lower bound 95% upper bound p-value Primary analysis IVW with fixed effect, first order 0.0900 0.0656 a 0.1145 a &lt;10 -5 IVW with fixed effect, second order 0.0895 0.0647 a 0.1143 a &lt;10 -5 IVW with random effect, first order 0.0900 0.0656 a 0.1145 a &lt;10 -5 IVW with random effect, second order 0.0895 0.0647 a 0.1143 a &lt;10 -5 Median method, simple 0.0989 0.0645 a 0.1332 a &lt;10 -5 Median method, weighted 0.0902 0.0583 a 0.1220 a &lt;10 -5 Median method, penalized 0.0902 0.0583 a 0.1220 a &lt;10 -5 Sensitivity analysis MR-Egger 0.0786 0.0132 a 0.1440 a 0.0183 SEM 0.0519 0.0481 a 0.0697 a &lt;10 -5 CAUSE model 0.0764 0.0176 b 0.1349 b &lt;10 -5 * a represents the 95% confidence interval; b represents the 95% credible interval. ** For the random effect model, if the estimated residual standard error is less than 1, then the MendelianRandomization package will automatically set the value of residual standard error into 1. Figure 1. Pathway analysis for SEM study assessing the relationships between gout and hypertension. Conclusion: These results strongly suggest that the association between gout and hypertension has a causal basis. References: [1]Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015 Apr; 44(2):512-525. [2]Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020 Jul; 52(7):740-747. [3]Streiner DL. Building a better model: an introduction to structural equation modelling. Can J Psychiatry. 2006 Apr; 51(5):317-324. [4]Stephen B, Rhian MD, Adam SB, Simon GT, and the EPIC-InterAct. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015 Apr; 44(2): 484-495. Disclosure of Interests: None declared