Iron metabolism disorder and multiple sclerosis: a comprehensive analysis

Abstract

Background: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. Currently, the pathological mechanisms of MS are not fully understood, but research has suggested that iron metabolism disorder may be associated with the onset and clinical manifestations of MS.

Methods and materials: The study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS.

Results: This study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36)).

Conclusion: This study comprehensively explored the relationship between iron metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the treatment of MS.

Keywords: Mendelian randomization; bioinformatic analysis; causal relationship; iron metabolism; multiple sclerosis.