Causal relationship between diabetes and depression: A bidirectional Mendelian randomization study

Abstract

Objective: This study explores the causal relationship between diabetes and depression using a two-sample Mendelian Randomization (TSMR) method.

Methods: The study selected single nucleotide polymorphisms (SNPs) closely associated with diabetes and depression in European populations from the Genome-Wide Association Study (GWAS) database, to serve as instrumental variables (IVs). The main evaluation method was inverse variance weighted analysis (IVW), supplemented by verification using Weighted median, Weighted mode, and MR Egger methods. The Odds Ratio (OR) and 95 % Confidence Interval (CI) were used as the main evaluation indicators, along with sensitivity analysis.

Results: This study found a negative correlation between diabetes and depression, suggesting that diabetes may reduce the risk of depression [IVW(FE): OR: 0.901, 95 % CI: 0.823 to 0.987; P = 0.025 < 0.05]. This finding was further confirmed by the Weighted median [OR: 0.844, 95 % CI: 0.730 to 0.974; P = 0.021 < 0.05] and Weighted mode method [OR: 0.766, 95 % CI: 0.637 to 0.921; P = 0.006 < 0.05]. However, the reverse showed no causal relationship between depression and diabetes (P > 0.05). Sensitivity analysis found no pleiotropy, and there were no large influences from individual SNPs on the result's robustness; the results are stable and reliable.

Conclusion: For the first time, this study using TSMR analysis found a negative correlation between diabetes and the risk of depression onset in European populations, suggesting that diabetes might reduce the risk of depression. But as the mechanisms are still unclear, these findings warrant further study.

Keywords: Depression; Diabetes; Genome-wide association study; Relationship; Two-sample Mendelian randomization.