From Genome to Clinic: Gut Microbiota's Influence on Hyperuricemia Risk Unveiled by Mendelian Randomization

To assess whether the gut microbiota and HUA could be causally related, we used summary statistics from the MiBioGen and FinnGen consortia to conduct a two-sample MR analysis. All of the analyses were carried out via two-sample MR analysis using R (version 4.3.1) and the R package for MR (version 0.5.7) . The R2 was calculated as follows: R2 =[2×Beta2×MAF×(1-MAF)]/[(2×Beta2×MAF×(1-MAF)+SE2×2×N×MAF×(1-MAF)]. When evaluating how strongly IVs and exposure relate to one another, we computed the F statistic using the formula F=R2×(N-1-k)/[(1-R2)×k], where R2 is the proportion of phenotypic variation described by SNPs and k is the number of SNPs included in the instrument . When the F statistic's threshold was more than 10, it was deemed statistically significant, meaning that weak instrumental bias had no impact on the causal relationship .Using the IVW method, we computed the MR estimate based on multiple IVs, with significance determined by the magnitude of the P value . Heterogeneity was examined using Cochran's Q test (considered significant at P<0.05). Furthermore, we utilized MR‒Egger, the weighted median, and MR-PRESSO to verify the stability of the IVW results .MR‒Egger evaluates whether genetic variation exhibits pleiotropic influences on outcomes divergent from zero on average (directional pleiotropy) and offers reliable estimates of causal impacts under the weaker assumption known as the InSIDE assumption . The WM method provides precise estimates of causality. Outliers were detected and corrected after removing abnormal IVs by using the MR-PRESSO test . The outcomes derived from MR sensitivity analysis methods demonstrated robust concordance with the IVW approach (P< 0.05). Finally, a reverse magnetic resonance study was conducted to examine the causative relationship between the intestinal microbiota and HUA.

为评估肠道菌群与高尿酸血症（Hyperuricemia, HUA）是否存在因果关联，我们采用来自MiBioGen联盟与FinnGen联盟的汇总统计数据开展两样本孟德尔随机化（two-sample Mendelian Randomization, MR）分析。所有分析均通过R软件（版本4.3.1）及MR分析专用R包（版本0.5.7）完成。

R²的计算方式如下：
$R^2 = frac{2 imeseta^2 imes ext{MAF} imes(1- ext{MAF})}{2 imeseta^2 imes ext{MAF} imes(1- ext{MAF}) + ext{SE}^2 imes2 imes N imes ext{MAF} imes(1- ext{MAF})}$
其中$eta$为效应值，$ ext{MAF}$为次要等位基因频率（Minor Allele Frequency），$ ext{SE}$为标准误，$N$为研究样本量。

在评估工具变量（Instrumental Variable, IV）与暴露因素间的关联强度时，我们采用如下公式计算F统计量：
$F = frac{R^2 imes(N-1-k)}{(1-R^2) imes k}$
其中$R^2$为单核苷酸多态性（Single Nucleotide Polymorphism, SNP）所解释的表型变异比例，$k$为纳入工具变量的SNP数量。当F统计量大于10时，认为结果具有统计学意义，此时弱工具变量偏倚不会对因果关联推断产生干扰。

我们采用逆方差加权（Inverse Variance Weighted, IVW）法基于多组工具变量计算MR效应估计值，显著性由P值大小判定。采用科克伦Q检验（Cochran's Q test）评估效应异质性（P<0.05时判定为存在异质性）。此外，我们运用MR-Egger回归、加权中位数法及MR-PRESSO检验验证IVW结果的稳定性：MR-Egger回归可检测遗传变异是否存在平均偏离零值的定向多效性，并在InSIDE假设（即工具变量仅通过暴露因素影响结局，不存在直接多效性）这一较弱假设下提供可靠的因果效应估计；加权中位数法可获得精准的因果效应估计；MR-PRESSO检验可检出并校正异常工具变量，通过移除离群IVs修正分析结果。

孟德尔随机化敏感性分析所得结果与IVW法结果呈现稳健的一致性（P<0.05）。最后，我们开展反向孟德尔随机化分析，以验证肠道菌群与高尿酸血症之间的因果关联方向是否存在反转。