Study on the Role of Gut Microbiota Metabolites in Postmenopausal Osteoporosis Based on Network Pharmacology

Background Postmenopausal osteoporosis (PMOP) is a prevalent skeletal disorder characterized by reduced bone mass and increased fracture risk, driven by estrogen deficiency and disrupted bone remodeling. Emerging evidence suggests that gut microbiota and their metabolites influence bone homeostasis, yet their pharmacological mechanisms remain underexplored. This study employs network pharmacology to investigate the molecular interactions and pathways through which gut microbiota metabolites modulate PMOP pathogenesis. Methods Gut microbiota metabolites and associated target genes were retrieved from the gutMGene v2.0 database, with potential targets predicted using the SEA and SwissTargetPrediction databases. PMOP-related targets were sourced from GeneCards, DisGeNET, and OMIM databases. Core targets were identified through protein-protein interaction (PPI) network analysis, followed by Gene Ontology (GO) and KEGG pathway enrichment analyses. A microbiota-substrate-metabolite-target (MSMT) network was constructed to map interactions. Results A total of 48 overlapping targets between gut microbiota metabolites and PMOP were identified, with IL6, TNF, TP53, AKT1, and IL1B emerging as hub genes. The MSMT network revealed associations with 31 metabolites, including flavonoids (e.g., kaempferol, luteolin, diosmetin), 23 substrates, and 65 microbial taxa, such as Akkermansia muciniphila. KEGG analysis highlighted enrichment in PI3K-Akt, MAPK, IL-17, TNF, and osteoclast differentiation pathways, indicating a regulatory network involving inflammation and bone remodeling. Conclusion Gut microbiota metabolites influence PMOP through key targets (IL6, TNF, TP53, AKT1, IL1B) and pathways, forming an inflammation-bone remodeling axis. These findings suggest that targeting this network, particularly through modulation of microbial metabolites, offers a promising therapeutic strategy for PMOP. Further validation is needed to translate these insights into clinical applications.