Causal associations between biomarkers and depression: A Mendelian randomization study

Depression is one of the most prevalent and debilitating mental health conditions worldwide. Biomarkers have received considerable attention in depression research, to provide breakthroughs in early diagnosis, pathophysiological understanding, and personalized treatment. We employed Mendelian randomization (MR) methods to examine the causal connection between biomarkers and genetic predisposition to depression. Leveraging a substantial cohort from publicly accessible genome-wide association study datasets of European populations, we analyzed MR, utilizing the inverse variance weighting model as the primary approach. Additionally, we evaluated heterogeneity and horizontal pleiotropy. Our MR analysis revealed significant associations between biomarkers and depression. The inverse variance weighting model indicated that urinary potassium (odds ratio [OR] = 0.670, P = .012, β = −0.4), peak expiratory flow (OR = 0.886, P = .011, β = −0.143), cholesterol levels (OR = 0.935, P = .00508, β = −0.0674), and direct low-density lipoprotein levels (OR = 0.925, P = .00303, β = −0.0782) were associated with a reduced risk of depression. Conversely, triglyceride levels (OR = 1.05, P = .0246, β = 0.0489) were associated with a higher risk of developing depression. This study found that urinary potassium, peak expiratory flow, cholesterol levels, and direct low-density lipoprotein levels were associated with a reduced risk of depression. In contrast, triglyceride levels were correlated with a heightened risk of depression.