Evidence map›Paper›PMID 41787260›Full record

ArticleBMC microbiology2026

Association between vaginal microbiota in early pregnancy and gestational diabetes mellitus: a nested case-control study.

Xiang Hong, Mengjie Zhao, Furong Tan, Hanyue Zheng, Xiaoling Ding, Jiechen Yin, Xuening Zhang, Bei Wang

Abstract read
In one paragraph

Article in BMC microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Xiang Hong *Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Mengjie Zhao *Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Furong TanKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Hanyue ZhengKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Xiaoling DingMaternal and Child Health Center of Gulou District, Nanjing, China.
Jiechen YinKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Xuening ZhangNational Health Commission Key Laboratory of Contraceptives Vigilance and Fertility Surveillance, Nanjing, 210036, China. jasonzxn@126.com.
Bei WangKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China. wangbeilxb@163.com.

Funding

Jiangsu Province Capability Improvement Project through Science, Technology and Education ZDXYS202210Jiangsu Traditional Chinese Medicine Science and Technology Development Program project MS2024145National Natural Science Foundation of China 82404284National Natural Science Foundation of China 82574109the Opening Foundation of Key Laboratory JSHD202301
6 · The paper itself

Abstract

backgroundTo investigate the association between vaginal microbiota structure in early pregnancy and gestational diabetes mellitus (GDM) and to characterize microbial signatures for early screening for GDM.

methodsThe present study was a nested case-control study recruiting pregnant women from the Nanjing Gulou Maternal-Child Health Center, China. Vaginal swabs were collected before 20 weeks of gestation for 16S rRNA sequencing. Following 1:3 propensity score matching, 45 GDM cases and 135 controls were enrolled. The final analysis included 42 GDM cases and 121 controls. A random forest model was used to explore the genera of vaginal differential microbiota associated with GDM. Based on these findings, latent profile analysis (LPA) was conducted to explore potential types of vaginal microbiota, and logistic regression was used to analyze the association between vaginal microbiota types and GDM.

resultsThe GDM group exhibited elevated alpha diversity (Chao1 index, P = 0.036), and the abundance of Aeromonas in early pregnancy was associated with the risk of GDM (aOR = 1.17, 95% CI: 1.01–1.29). LPA classified vaginal microbiota into five profiles: Type Ⅰ (high Aeromonas with low Lactobacillus), Type Ⅱ (Lactobacillus crispatus-dominant), Type Ⅲ (co-dominance of Lactobacillus iners and Lactobacillus jensenii), Type Ⅳ (co-dominance of Aeromonas and Lactobacillus crispatus), and Type Ⅴ (Lactobacillus iners-dominant). Compared with type II, type Ⅰ (aOR = 2.83, 95% CI: 1.25–6.40) or type IV (aOR = 3.02, 95% CI: 1.27–7.15) exhibited an elevated risk of GDM. Compared with Aeromonas abundance prediction, vaginal microbiota type-based prediction demonstrated superior diagnostic accuracy (Aeromonas-AUC = 0.629, 95% CI: 0.541–0.718; vaginal microbiota profiles-AUC = 0.775, 95% CI: 0.716–0.835).

conclusionThe composition and structure of vaginal microbiota in early pregnancy are different in the two groups. The vaginal microbiota in early pregnancy, which is characterized by co-dominated by Aeromonas and Lactobacillus crispatus, was associated with a higher risk of GDM. Vaginal microbiota type-based prediction demonstrated superior diagnostic accuracy than single bacteria.

Indexed as

BacteriaDiabetes, GestationalMicrobiotaVaginaAdultCase-Control StudiesChinaFemaleHumansLactobacillusLactobacillus crispatusPregnancyRNA, Ribosomal, 16SRNA, Ribosomal, 16SGestational diabetes mellitusLatent profile analysisPregnancyVaginal microbiota

Identifiers

PMID41787260
PMCPMC13077847

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.