Evidence map›Paper›PMID 40590552›Full record

ArticleMicrobiology spectrum2025

Gut microbiota composition in early pregnancy as a diagnostic tool for gestational diabetes mellitus.

Weirong Yao, Ruijing Wen, Zhufeng Huang, Xuhong Huang, Kai Chen, Yuchao Hu, Qianbei Li, Weiqian Zhu, Dejin Ou, Huanlan Bai

Abstract read
In one paragraph

Article in Microbiology spectrum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. Special Issue "Molecular Insight into Gestational Diabetes Mellitus".International journal of molecular sciences · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
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

10 authors.

Weirong Yao *The Second Hospital of Zhangzhou, Zhangzhou, China.
Ruijing Wen *Nanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0009-0001-7505-8325
Zhufeng Huang *The Second Hospital of Zhangzhou, Zhangzhou, China.
Xuhong HuangThe Second Hospital of Zhangzhou, Zhangzhou, China.
Kai ChenThe Second Hospital of Zhangzhou, Zhangzhou, China.
Yuchao HuThe Second Hospital of Zhangzhou, Zhangzhou, China.
Qianbei LiNanfang Hospital, Southern Medical University, Guangzhou, China.
Weiqian ZhuThe Second Hospital of Zhangzhou, Zhangzhou, China.ORCID 0009-0008-7782-9710
Dejin OuThe Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.ORCID 0009-0009-4977-2531
Huanlan BaiNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0009-0005-1284-5937

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gestational diabetes mellitus (GDM) is a metabolic disorder that poses substantial risks to both maternal and fetal health. Early intervention has been shown to effectively reduce various complications. Gut microbiota dysbiosis is strongly linked to the onset and progression of GDM and may serve as a critical early-warning biomarker. In this study, we systematically analyzed the fecal microbiota of 61 pregnant women during the first trimester using 16S rRNA sequencing. These microbial profiles were correlated with oral glucose tolerance test (OGTT) results at 24-28 weeks of gestation and clinical delivery outcomes. Our analysis identified significant differences in gut microbiota composition between GDM and healthy pregnancies, observed at both the phylum and genus levels early in gestation. Leveraging these microbial distinctions, we developed an early diagnostic model based on genus-level markers, achieving an area under the curve (AUC) of 98.23, indicating high diagnostic precision. This study highlights early-pregnancy microbiota signatures associated with GDM and provides a robust scientific basis for developing microbiota-based diagnostic tools, offering new avenues for GDM prevention and management. IMPORTANCE: Gestational diabetes mellitus (GDM) poses significant risks to both maternal and fetal health, but early intervention can reduce complications. This study identifies gut microbiota signatures associated with GDM in the first trimester, providing a potential early diagnostic biomarker. By analyzing fecal microbiota profiles, we developed a diagnostic model with high accuracy (AUC = 98.23). These findings suggest that microbiota-based tools could enable early, non-invasive detection of GDM, offering new opportunities for prevention and personalized management. This research highlights the role of the gut microbiome in pregnancy and has important implications for improving maternal and fetal health outcomes.

Indexed as

BacteriaDiabetes, GestationalGastrointestinal MicrobiomeAdultBiomarkersDysbiosisFecesFemaleGlucose Tolerance TestHumansPregnancyPregnancy Trimester, FirstRNA, Ribosomal, 16SBiomarkersRNA, Ribosomal, 16Sbiomarkersearly diagnostic modelgestational diabetes mellitusgut microbiota

Identifiers

PMID40590552
PMCPMC12349467

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LicenceCC BY
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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.