# Causal Relationship Between Depression and Traumatic Brain Injury: A Two‐Sample Mendelian Randomization Analysis

## TL;DR

This study finds that depression increases the risk of traumatic brain injury and vice versa, using genetic data to support a bidirectional causal relationship.

## Contribution

The study provides novel evidence of a bidirectional causal link between depression and TBI using Mendelian randomization.

## Key findings

- Depression increases the risk of traumatic brain injury (OR = 1.137).
- Traumatic brain injury increases the risk of depression (OR = 1.083).
- Causal relationships were robust across multiple statistical models and sensitivity analyses.

## Abstract

Traumatic brain injury (TBI) and depression are major global health burdens, yet their bidirectional causal relationship remains unclear.

To explore the causal relationship between depression and TBI, and to clarify whether depression is one of the potential risk factors for TBI and whether TBI is one of the pathogenic factors for depression.

This bidirectional two‐sample Mendelian randomization (MR) analysis investigated causal relationships between depression (n = 170,756) and TBI (n = 3193) using genome‐wide association study (GWAS) summary statistics. Genetic instruments were selected as single nucleotide polymorphisms (SNPs) significantly associated with exposures (depression/TBI) and outcomes (TBI/depression) at genome‐wide significance (P < 5 × 10⁻⁶). The inverse variance weighted (IVW) method under fixed‐effects and multiplicative random‐effects models served as the primary analytical approach, with Cochran's Q test evaluating SNP heterogeneity. To address horizontal pleiotropy, MR‐Egger regression and MR‐PRESSO(MR Pleiotropy RESidual Sum and Outlier)outlier correction were applied. Sensitivity analyses included weighted median, penalized weighted median, maximum likelihood estimation, and leave‐one‐out validation to ensure robustness. All analyses were conducted using the TwoSampleMR package in R (v4.3.2), with effect estimates reported as odds ratios (OR) and 95% confidence intervals (CI).

MR analyses revealed bidirectional causal relationships between depression and TBI. In forward analyses, depression increased TBI risk across multiple IVW frameworks (fixed‐effects IVW: OR = 1.137, 95% CI = 1.019–1.271, P = 0.022; multiplicative random‐effects IVW: OR = 1.137, 95% CI = 1.014–1.277, P = 0.028), corroborated by maximum likelihood estimation (OR = 1.137, 95% CI = 1.017–1.274, P = 0.024). Reverse analyses demonstrated TBI's causal effect on depression through IVW models (fixed‐effects: OR = 1.083, 95% CI = 1.036–1.131, P < 0.001; multiplicative random‐effects: OR = 1.083, 95% CI = 1.043–1.124,P < 0.001) and penalized weighted median methods (OR = 1.079, 95% CI = 1.018–1.145, P = 0.011). Robustness was confirmed by null heterogeneity (Cochran's Q: forward P = 0.209, reverse P = 0.596) and absence of horizontal pleiotropy (MR‐PRESSO: forward P = 0.218, reverse P = 0.672; MR‐Egger intercepts: forward P = 0.661, reverse P = 0.874). All effect estimates remained stable in sensitivity analyses, supporting unconfounded causal inference.

Our MR analyses robustly demonstrate bidirectional causality: depression is a risk factor for TBI (OR = 1.137, 95% CI = 1.019–1.271), and TBI subsequently increases depression risk (OR = 1.083, 95% CI = 1.036–1.131), advocating integrated clinical monitoring.

Causal Link: Depression → TBI via MR Analysis