Advances in gestational diabetes mellitus screening: Emerging trends and future directions

Abstract

Gestational diabetes mellitus (GDM) is a multifactorial metabolic disorder first recognized during pregnancy, with rising global prevalence and significant implications for both maternal and neonatal outcomes. This review provides a comprehensive synthesis of current diagnostic strategies, including standard screening protocols such as the one-step and two-step oral glucose tolerance tests, and evaluates their limitations in terms of sensitivity, timing, and practicality. The complex pathogenesis of GDM-centered on β-cell dysfunction, insulin resistance, adipose tissue dysregulation, placental transport abnormalities, and neurohormonal imbalance-is explored in detail, highlighting the interplay of metabolic, inflammatory, and epigenetic mechanisms. Particular emphasis is placed on the emerging role of predictive biomarkers, encompassing metabolic, inflammatory, placental, urinary, and genetic indicators. These biomarkers, including adipokines, angiogenic factors, and microRNAs, offer promising avenues for early identification of at-risk individuals prior to the onset of hyperglycemia. The review also assesses recent advances in machine learning-based risk prediction models, which have demonstrated superior accuracy over traditional algorithms and may facilitate personalized screening and management strategies. Despite encouraging findings, challenges such as biomarker standardization, ethnic variability, and model validation persist. This review underscores the necessity for integrated, multi-omic, and patient-centered approaches to optimize GDM prediction, early diagnosis, and long-term risk reduction for both mother and child.

Keywords: Gestational diabetes mellitus; Predictive biomarkers; Insulin resistance; Β-cell dysfunction; Machine learning; Early screening

Core Tip: This review offers an integrative and up-to-date overview of gestational diabetes mellitus (GDM), emphasizing the evolving understanding of its pathophysiology and the emerging role of multi-modal biomarkers in early prediction. By combining evidence on metabolic, inflammatory, placental, genetic, and urinary biomarkers, alongside advanced machine learning-based models, this work underscores the shift toward precision diagnostics. It critically evaluates conventional screening strategies and highlights avenues for improving early detection and individualized care. The synthesis aims to support clinicians and researchers in refining GDM risk stratification and mitigating long-term maternal-fetal consequences.