OP0193 EVALUATION OF THE CAUSAL EFFECTS BETWEEN GOUT AND HYPERTENSION: A MENDELIAN RANDOMIZATION STUDY

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 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). 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