How gestational diabetes mellitus (GDM) is screened and monitored may be entering a new phase. A bibliometric analysis published in Journal of Diabetes Research found that global research is moving beyond traditional oral glucose tolerance test (OGTT)-based diagnosis toward biomarker-driven prediction and artificial intelligence (AI)-enabled monitoring during pregnancy.

The study analyzed publications indexed in the Web of Science Core Collection covering literature from 1961 to 2026 on GDM screening, assessment, and monitoring. Methods included annual publication and citation trends, collaboration mapping across countries and institutions, keyword co-occurrence analysis, reference co-citation analysis, historical trend mapping, and Latent Dirichlet Allocation topic modeling.

Publications in this field increased by 50% over the past 5 years. China ranked first in publication volume, while the United States led citation influence and international collaboration intensity. Keyword analysis identified three major research clusters and an evolving citation-burst pattern. Machine learning and adverse pregnancy outcomes were among the strongest ongoing emerging themes.

Topic modeling identified 16 research topics grouped into four domains: screening and diagnosis, pathophysiology and molecular mechanisms, environmental and behavioral determinants, and clinical management with health outcomes. These findings suggest future GDM care may increasingly rely on earlier risk prediction, personalized assessment, and digitally supported monitoring across pregnancy.