Association between gut microbiota dysbiosis and age-related macular degeneration progression: A bioinformatics approach

Abstract

Gut microbiota dysbiosis has been linked to the progression of age-related macular degeneration, though the underlying molecular mechanisms remain unclear. This study is the first to systematically link gutMGene-derived genes to AMD pathogenesis using a multi-algorithm machine learning approach. Using the gutMGene database, we identified gut microbiota-related genes and analyzed the GSE29801 dataset for differential expression. Our enrichment analysis revealed unique insights into the involvement of gut microbiota-related genes in inflammatory, immune response, and metabolic pathways in age-related macular degeneration. Machine learning algorithms (LASSO, Random Forest, XGBoost) identified five consistent biomarker genes: CXCL10, FADS3, GHRL, APOE, and VEGFA. A nomogram was developed to predict AMD risk, showing moderate-to-high predictive accuracy with area under the curve of 0.719 (GSE29801) and 0.933 (GSE99248). Gene set variation analysis indicated upregulation of inflammatory and immune pathways and downregulation of lipid metabolism pathways in age-related macular degeneration. Single-gene set enrichment analysis further underscored the roles of diagnostic genes in immune response and metabolic regulation. This study contributes novel evidence that gut microbiota dysbiosis influences AMD progression through systemic inflammatory and metabolic pathways, and highlights potential therapeutic targets.