The impact of tea consumption on the risk of depression: A Mendelian randomization and Bayesian weighting algorithm study.

Abstract

The precise impact of tea consumption on the risk of depression remains unclear. This study aimed to explore the relationship between the consumption patterns of tea and the likelihood of depression onset, utilizing a two-sample Mendelian randomization (MR) methodology. We utilized available genome-wide association study (GWAS) datasets on tea intake and depressive disorders. To investigate the causal relationship between tea consumption and depression, we employed a set of two-sample Mendelian Randomization (MR) methods. These included the inverse-variance weighted (IVW) analysis, weighted median approach, and MR-Egger regression. Additionally, we utilized MR-PRESSO and the MR-Egger intercept test for the detection of pleiotropic effects. To ensure the robustness and consistency of our findings, a sensitivity analysis was carried out, applying the 'leave-one-out' strategy. The Bayesian weighted Mendelian randomization (BWMR) was employed to conduct additional testing on the obtained results. The study's outcomes revealed a causal association between increased tea intake and an increased risk of depression (Inverse-Variance Weighted Analysis: Odds Ratio [OR] = 1.029, 95% Confidence Interval [CI]: 1.003-1.055, p = 0.027). This was observed despite variations in instrumental variables and the nonexistence of horizontal pleiotropy. Furthermore, the robustness of our Mendelian Randomization investigation was affirmed through the implementation of the 'leave-one-out' method in our sensitivity analysis. The findings from BWMR were in line with those obtained from IVW (BWMR: OR=1.030, 95% CI: 1.003-1.057, p = 0.029). The results from this study indicate a substantial and positive causal link between the regularity of tea drinking and the risk of depression onset.