Mining transcriptomic data for gestational diabetes mellitus: What public datasets reveal

Abstract

Gestational diabetes mellitus (GDM) affects up to 14% of pregnancies globally and remains a major threat to maternal and fetal health, contributing to complications such as preeclampsia, macrosomia, and long-term metabolic disorders. As GDM involves dynamic immune-metabolic changes at the maternal-fetal interface, characterizing its gene expression landscape across relevant tissues is essential to identify biomarkers, therapeutic targets, and disease mechanisms. Over the past several years, numerous transcriptomic studies and publicly available datasets have been generated to uncover molecular mechanisms of GDM across diverse tissues and experimental models. Despite their potential, these resources remain underutilized, with many datasets yet to be fully mined for their biological and clinical insights. This review provides an overview of methodological advances in gene expression profiling relevant to GDM and highlights key tissues and models used. We discuss limitations of current datasets such as the scarcity of single-cell data and metadata inconsistencies and propose future directions, including integrative and cell-type-resolved transcriptomic studies. By consolidating and contextualizing current resources, this article underscores the importance of deeper data reuse and provides a concise roadmap for future research on the pathogenesis of GDM.

Keywords: Gestational diabetes mellitus; Gene expression; RNA-sequencing; DNA microarray; Molecular mechanism

Core Tip: Gestational diabetes mellitus (GDM) affects up to 14% of pregnancies and poses significant risks to both mothers and offspring. This mini-review summarizes publicly available transcriptomic datasets related to GDM across tissues, data types, and platforms, highlighting critical gaps such as the underrepresentation of pancreatic islets and adipose tissue. It also outlines opportunities for integrative, multi-omics, and cell-type resolved approaches. By consolidating current resources, this review provides a roadmap for advancing biomarker discovery, therapeutic development, and mechanistic understanding of GDM pathogenesis.