Recent Advances in the Diagnosis and Management of Gestational Diabetes Mellitus: A Narrative Review

Abstract

Abstract Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and is associated with adverse short- and long-term health outcomes in both mothers and offspring. Its increasing global prevalence and substantial heterogeneity in pathogenesis and clinical presentation underscore the limitations of conventional glucose-centered approaches to risk assessment and management. Although the oral glucose tolerance test (OGTT) remains the established diagnostic standard, its relatively late diagnostic window and limited ability to predict disease development have stimulated interest in novel strategies for early risk stratification and individualized care. Importantly, emerging approaches are not yet sufficiently validated to replace the OGTT in routine clinical practice. This narrative review summarizes recent advances in GDM diagnosis and management, focusing on emerging biomarkers, multi-omics technologies, artificial intelligence (AI), and precision medicine. We examine the potential roles of inflammatory biomarkers, adipokines, metabolomic signatures, gut microbiota, and genomic and transcriptomic profiles in the early prediction of GDM, as well as AI-based models integrating multidimensional clinical and molecular data. Advances in precision nutrition, continuous glucose monitoring, pharmacological optimization, digital health technologies, and microbiota-targeted interventions are also discussed. Particular attention is given to the distinction between clinically established strategies, promising approaches supported by emerging evidence, and exploratory technologies that remain investigational. Despite substantial progress, important challenges remain, including heterogeneity in diagnostic criteria, limited reproducibility and clinical translation of multi-omics findings, insufficient external validation of AI models, and barriers to the implementation of individualized management. Key evidence gaps include the need for prospective and external validation, cost-effectiveness assessments, regulatory evaluation, and real-world implementation studies. Precision medicine in GDM should therefore be viewed as an evolving framework that integrates molecular, metabolic, clinical, and environmental information to improve risk stratification and guide individualized management rather than as a replacement for established diagnostic standards. Future GDM care is likely to move toward integrated precision medicine models combining multi-omics profiling, AI-assisted decision support, continuous metabolic monitoring, and personalized interventions, with the ultimate goal of improving maternal and offspring health across the life course.