Evidence map›Paper›PMID 39347390›Full record

ArticleHeliyon2024

Clinical significance of the genetically variable landscape of the gut microbiome in patients with gestational diabetes mellitus patients.

Kunna Zhang, Menglu Hu, Wentao Yang, Zhexia Hu, Yun Rong, Biyun Luo, Mengjia Wang, Yajuan Cheng, Rui Zhang, Ning Lv and 2 more

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025
    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

12 authors.

Kunna ZhangDepartment of Obstetrics, the First Hospital of Yongnian District, Handan, Hebei Province, China.
Menglu HuSchool of Medicine, Southeast University, Nanjing Province, China.
Wentao YangSchool of Medicine, Southeast University, Nanjing Province, China.
Zhexia HuDepartment of Obstetrics and Gynecology, the Fourth Hospital of Shijiazhuang, Shijiazhuang, Hebei Province, China.
Yun RongDepartment of Obstetrics and Gynecology, the Fourth Hospital of Shijiazhuang, Shijiazhuang, Hebei Province, China.
Biyun LuoDepartment of Obstetrics and Gynecology, the Fourth Hospital of Shijiazhuang, Shijiazhuang, Hebei Province, China.
Mengjia WangDepartment of Obstetrics and Gynecology, the Fourth Hospital of Shijiazhuang, Shijiazhuang, Hebei Province, China.
Yajuan ChengDepartment of Obstetrics, the First Hospital of Yongnian District, Handan, Hebei Province, China.
Rui ZhangDepartment of Obstetrics, the First Hospital of Yongnian District, Handan, Hebei Province, China.
Ning LvDepartment of Obstetrics & Gynecology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, China.
Qian ZhouDepartment of Obstetrics & Gynecology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, China.
Xueling ZhangDepartment of Obstetrics and Gynecology, the Fourth Hospital of Shijiazhuang, Shijiazhuang, Hebei Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The composition of the gut microbiome has been recorted to be strongly associated with gestational diabetes mellitus (GDM), but mutational characterization of the microbiome in patients with GDM has been overlooked. Here, we revealed the genetic variation landscape of the gut microbiome and assessed its clinical significance in a cohort of patients with GDM. Methods: We employed a macrogenomic dataset made up of a discovery cohort of 54 cases and a validation cohort of 220 cases to screen for high-abundance microbial flora and identified single nucleotide variants (SNVs) and insertions/deletions (indels). Subsequently, we analyzed the mutation spectra of genomes of the intestinal flora by using the previously identified SNVs and identified mutation signatures. Additionally, we utilized the Random Forest algorithm to identify key differential SNVs and elucidated their biological functions and associations with the clinicopathological parameters of GDM. Results: We screened 15 key microbial flora and found that the GDM group had more SNVs and indels in the intestinal flora than the control group, with a significant increase in C > T and T > C base mutations and were more susceptible to sequence mutations. Compared to the control group, the GDM group underwent a more significant evolution, as evidenced by the presence of a unique mutational spectrum and mutational characteristics. Random Forest algorithm analysis showed that the combined characterization of five gut microbial species and 21 SNV-related markers was effective in distinguishing between GDM and control subjects in both discovery (area under the curve (AUC) = 0.86) and validation (AUC = 0.73) sets. These markers also revealed that GDM is strongly associated with sphingolipids, galactose, and proteins containing the DUF structural domain. Conclusions: The GDM intestinal flora has unique mutational features that correlate significantly with clinicopathological involvement and may be involved in the development of the disease.

Indexed as

Gestational diabetes mellitusGut microbiotaMutation characteristicsSingle nucleotide variation

Identifiers

PMID39347390
PMCPMC11438002

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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.