Statistical analysis
WME
REGRESSION
We utilized three different statistical methods: inverse variance weighting(IVW), weighted median estimator (WME), and MR Egger regression. Specifically, IVW was the primary analysis method, while the other two methods supplemented our analysis. Heterogeneity was detected using Cochran's Q test. We evaluated the bias of gene pleiotropy using MR Egger intercept analysis. If the regression intercept is closer to 0, gene pleiotropy is less likely to have occurred. We also used a sensitivity analysis known as leave-one-out. We replicated the above method for the verification cohort. To ensure independent comparison based on MR, we established a machine learning model using the harmonized data. For the analysis, we employed the linear regression model in this test set. To examine the impact of effect size on the outcome of exposure factors, we randomly split the data into a training set (80%) and a test set (20%). We considered a All analyses were conducted using RStudio 2023.03.1, R 4.22, TwoSample MR, and the MR PRESSO package. In univariate MR analysis of blood pressure and OA, we considered a 