Major depressive disorder and chronic gastritis: A bidirectional two-sample Mendelian randomization study

• Published in: Journal of psychosomatic research 2023-10, Vol.173, p.111458-111458, Article 111458
• Main Authors: Li, Junlin, Chen, Xiuyun, Yin, Mingyu, Lan, Xin, Xie, Lei, Huang, Wenguan, Luo, Minyi, Ai, Ying, He, Jinyang
• Format: Article
• Language: English
• Subjects: Chronic gastritis; Major depressive disorder; Mendelian randomization
• ISSN: 0022-3999; 1879-1360
• DOI: 10.1016/j.jpsychores.2023.111458

Observational studies have posited a strong correlation between chronic gastritis (CG) and major depressive disorder (MDD), but the nature of this association remains uncertain, owing to the challenges of establishing the temporal sequence. The present study sought to elucidate the elusive relationship between CG and MDD by employing a bidirectional two-sample Mendelian randomization (MR) approach. We extracted instrumental variants for MDD and CG from published genome-wide association study data, focusing on individuals of primarily European descent. A comprehensive suite of MR estimations and sensitivity analyses was performed to ensure the robustness of the findings. Each outcome database was analyzed separately in both directions. For MDD and CG, 221 and 5 genetic variants, respectively, were selectively extracted as instrumental variants. The results suggest that MDD is causally associated with an elevated risk of CG (IVW: 23andMe, OR = 1.33; 95% CI = 1.15–1.54; p = 1.06 × 10–4); conversely, no strong evidence was found to corroborate that CG exerts a causal effect on the incidence of MDD (IVW: OR = 1.01; 95% CI = 0.95–1.07; p = 0.68). These findings provide novel insights into the causal relationship between CG and MDD, which may have implications for clinical decision-making in patients with MDD and CG. •MDD is linked to higher CG risk, but no strong evidence supports CG causing MDD.•Novel insights on CG-MDD causal relationship aid clinical decision-making.•Robust findings achieved through extensive MR estimations and sensitivity analyses.