Comprehensive Genetic and Pathway Analysis of Gestational Diabetes: A Multidimensional Bioinformatics Approach

ABSTRACT: Gestational diabetes mellitus (GDM) is a complex metabolic disorder with significant health implications for both mother and fetus, yet its molecular underpinnings remain incompletely defined. This study employed an integrative bioinformatics approach to elucidate the genetic architecture and molecular pathways involved in GDM, using a curated set of 30 GDM-associated genes from the DisGeNET database. Comprehensive analyses, including Gene Ontology, pathway enrichment, transcriptional regulation, tissue expression, metabolite interaction, and drug association studies, were conducted using R version 4.4.2 with stringent statistical controls (adjusted p < 0.05). The results revealed strong enrichment in vitamin B12 and folate metabolism pathways, implicating a critical nutritional-genetic interface. Key genes such as IL6, INSR, LEP, TNF, and CRP were linked to metabolic and inflammatory regulation, while pathways related to adipogenesis, leptin-insulin signaling, and non-alcoholic fatty liver disease emerged as central networks. Hormonal metabolites and potential therapeutic agents, including statins and anti-inflammatory drugs, were identified, and transcriptional analyses highlighted complex regulatory mechanisms. Tissue-specific findings emphasized the systemic nature of GDM, with liver, adipose, and pancreatic involvement. Collectively, this study provides a multidimensional view of GDM pathogenesis and identifies candidate biomarkers and therapeutic targets, laying the groundwork for future functional validation and precision medicine strategies.