Causal relationship between PM2.5 exposure and population-level antidepressant medication use: A Mendelian...

Exposure to fine particulate matter (PM2.5) has been associated with an increased risk of depression; however, causal evidence regarding its influence on the clinical burden of depressive symptoms remains limited. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to assess whether genetically predicted chronic PM2.5 exposure is causally associated with the likelihood of antidepressant medication use. Antidepressant use, as derived from national prescription registers, serves as an objective, treatment-based proxy likely indicative of greater symptom burden, capturing clinically managed depression at the population level. Genome-wide association study (GWAS) summary statistics were utilized, involving data on PM2.5 exposure (n = 423,796 European participants) and antidepressant medication usage (131,176 cases, 104,642 controls). Forty-eight single-nucleotide polymorphisms (SNPs) associated with PM2.5 exposure (p < 5 × 10^-6) were selected as instrumental variables. Primary analyses employed the inverse-variance weighted (IVW) method, complemented by comprehensive sensitivity analyses. Our results revealed that genetically predicted PM2.5 exposure was associated with a higher prevalence of antidepressant use (IVW OR = 1.496; 95 % CI: 1.238-1.808; p < 0.001). Findings were consistent across multiple MR methods, including weighted median and simple mode analyses. Sensitivity analyses identified no evidence of directional pleiotropy or influential outlier SNPs. In conclusion, this Mendelian randomization study provides genetic evidence supporting a potential causal relationship between chronic PM2.5 exposure and an increased prevalence of antidepressant medication use, which may be indicative of greater depressive symptom burden at the population level. These findings suggest that reducing air pollution could be a valuable strategy for mitigating the global burden of depression.