Objectives: Observational studies had investigated the association of iron metabolism with anxiety disorders. The conclusions were inconsistent and not available to reveal the causal or reverse-causal association due to the confounding. In this study we estimated the potential causal effect of iron homeostasis markers on anxiety disorders using two-sample Mendelian randomization (MR) analysis.
Methods: Summary data of single nucleotide polymorphisms (SNPs) associated with four iron-related biomarkers were extracted from a recent report about analysis of three genome-wide association study (GWAS), the sample size of which ranged from 131471 to 246139 individuals. The corresponding data for anxiety disorders were from Finngen database (20992 cases and 197800 controls). The analyses were mainly based on inverse variance weighted (IVW) method. In addition, the heterogeneity and pleiotropy of the results were assessed by Cochran's Q test and MR-Egger regression.
Results: Basing on IVW method, genetically predicted serum iron level, ferritin and transferrin had negative effects on anxiety disorders. The odd ratios (OR) of anxiety disorders per 1 standard deviation (SD) unit increment in iron status biomarkers were 0.922 (95% confidence interval (CI) 0.862-0.986; p = 0.018) for serum iron level, 0.873 (95% CI 0.790-0.964; p = 0.008) for log-transformed ferritin and 0.917 (95% CI 0.867-0.969; p = 0.002) for transferrin saturation. But no statical significance was found in the association of 1 SD unit increased total iron-binding capacity (TIBC) with anxiety disorders (OR 1.080; 95% CI 0.988-1.180; p = 0.091). The analyses were supported by pleiotropy test which suggested no pleiotropic bias.
Conclusion: Our results indicated that genetically determined iron status biomarkers causally linked to the risk of anxiety disorders, providing valuable insights into the genetic research and clinical intervention of anxiety disorders.
Copyright: © 2024 Yin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.