BackgroundThis study elucidated several plasma proteins that are causally linked to the risk of diabetic neuropathy (DN), offering novel insights into the protein-mediated DN pathogenesis and potential targets for therapeutic intervention.MethodsWe employed Mendelian randomization (MR) utilizing genome-wide association study (GWAS) data to evaluate the causal effects of 4,907 proteins on DN. We retrieved a high-throughput sequencing dataset (GSE148061, containing 53 DN patients and 53 healthy donors) from the Gene Expression Omnibus (GEO) database to perform differential gene analysis and functional enrichment analysis, aiming to clarify disease pathogenesis.
The MR findings were subsequently validated through Bayesian colocalization analysis and cluster identification. We utilized two machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF).
Further, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted on these key genes to uncover the underlying molecular mechanisms influencing DN. Finally, we constructed a DN mouse model to validate the diagnostic potential of these identified genes.ResultsWe identified seven plasma proteins significantly associated with DN.
Frontiers in Immunology published a clinical update in Infectious Disease on 27 Apr 2026.
The item focuses on Identification of biomarkers in diabetic neuropathy: a Mendelian randomization and bioinformatics analysis.
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