Trends in gestational diabetes prevalence in China from 1990 to 2024: a systematic review and meta-analysis

Rev Endocr Metab Disord. 2025 Dec;26(6):1009-1021. doi: 10.1007/s11154-025-09987-0. Epub 2025 Jul 15.

Abstract

This study examines the 34-year trends in gestational diabetes mellitus (GDM) prevalence in China, analyzing diagnostic shifts, regional variations, and urban-rural disparities. A comprehensive search of PubMed, Scopus, EMBASE, Google Scholar, WanFang, and China National Knowledge Infrastructure was conducted, ultimately including 1,105 articles after screening and data extraction. The study used multilevel meta-analysis to calculate pooled GDM prevalence and meta-regression to estimate trends and identify sources of heterogeneity. The results show that GDM prevalence based on the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria was 2 to 3 times higher (15.6%, [95% CI 14.9-16.2%]) in pregnant women post-2010, compared to other criteria (e.g., World Health Organization: 7.1%, [95%CI 4.1-10.0%]). A consistent upward trend in GDM prevalence was observed across all criteria except for ADA/C&C samples. Notably, the urban-rural gap in GDM prevalence is narrowing, with rural areas experiencing a faster increase in GDM rates (rural: 0.90% per year, urban: 0.60% per year). Additionally, regional differences are becoming less pronounced. The study highlights the significant and rapid rise in GDM prevalence in China over the past 34 years, with a notable shift in regional patterns. The narrowing of the urban-rural disparity and the diminishing regional differences underscore the need for unified national guidelines and targeted healthcare strategies to address the growing prevalence of GDM across diverse populations.Protocol registration.Systematic review registration PROSPERO CRD42019135521.

Publication types

  • Systematic Review
  • Meta-Analysis
  • Research Support, Non-U.S. Gov't

MeSH terms

  • China / epidemiology
  • Diabetes, Gestational* / epidemiology
  • Female
  • Humans
  • Pregnancy
  • Prevalence
  • Rural Population / statistics & numerical data