Evidence map›Paper›PMID 40663307›Full record

SynthesisReviews in endocrine & metabolic disorders2025

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

Xiaodong Wu, Henning Tiemeier, Tingting Xu

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Reviews in endocrine & metabolic disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiaodong WuSchool of Public Health, Capital Medical University, Beijing, China.
Henning TiemeierDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Tingting XuSchool of Public Health, Capital Medical University, Beijing, China. xtingting@ccmu.edu.cn.ORCID 0000-0001-5507-3061

Funding

National Natural Science Foundation of China 72204172
6 · The paper itself

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.

Indexed as

Diabetes, GestationalChinaFemaleHumansPregnancyPrevalenceRural Population

Identifiers

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.