Integrating multi-omics and digital phenotyping for precision care in gestational diabetes

Gestational diabetes mellitus (GDM) is a prevalent and heterogeneous pregnancy complication that elevates cardiometabolic risk for both mother and child. Conventional diagnostic and treatment approaches remain “one-size-fits-all,” neglecting the inter-individual biological variation that drives risk and treatment response. Multi-omic profiling—including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and the microbiome—provides complementary mechanistic windows into susceptibility, pathophysiology, and therapeutic responsiveness. When these molecular layers are combined with digital phenotyping, particularly continuous glucose monitoring (CGM) and the derived metric time-in-range (TIR), they provide an integrated framework for identifying biologically coherent subtypes and guiding individualized intervention. Recent studies demonstrate that linking omic signatures to CGM features improves prediction of post-prandial glucose excursions and clarifies mechanisms underlying glycemic variability. TIR has emerged as a pragmatic, reproducible endpoint that reflects both maternal metabolic control and neonatal outcomes, positioning it as a bridge between molecular discovery and clinical relevance. This narrative review synthesizes current evidence across omic domains, examines approaches for integrating molecular and digital data, and outlines a translational roadmap for validation and implementation. By integrating continuous physiological monitoring with molecular data, precision care for GDM can evolve from population-based management toward proactive, data-driven strategies that improve outcomes for mothers and offspring.