Using genetic instruments to predict the causal relationship between air pollution and mental disorders: A multi-model Mendelian randomization analysis.

BACKGROUND

Air pollution has become a significant environmental health issue. Previous studies have shown that air pollution increases the risk of mental disorders through various pathways, such as inflammation, oxidative stress, and disruptions in neurotransmitter systems. However, the potential causal relationship between air pollution and mental disorders remains understudied. Understanding the causal relationship between gene prediction of air pollution and mental disorders can provide new insights for improving public health interventions and policies.

METHODS

A Mendelian randomization study design was employed. Exposures include PM2.5, PM2.5-10, PM10, NOx, and NO2. Outcomes include depression, anxiety disorders, schizophrenia, bipolar disorder, obsessive-compulsive disorder, and post-traumatic stress disorder. In this study, in addition to using the four common Mendelian randomization analysis methods (i.e. IVW, MR Egger, Weighted median, Weighted mode), we also employed Bayesian Weighted Mendelian Random, MR-RAPS, and cML as supplementary methods. Sensitivity analyses were conducted to examine pleiotropy and heterogeneity.

RESULTS

Our analysis uncovered a causal relationship between PM2.5 absorbance and depression (β = 0.0361, se = 0.0166; p = 0.0298). This finding was further validated with Firth correction and SPA correction. There is no causal link between other air pollutants and depression. Moreover, no evidence supports a link between air pollution and other mental disorders. Sensitivity analysis suggests the robustness of the results.

CONCLUSION

Mendelian randomization studies provide evidence for a causal relationship between PM2.5 absorbance and depression. This result should be interpreted with caution. Further research is needed to corroborate the complex relationship between air pollution and mental disorders.