Evidence map›Paper›PMID 37538307›Full record

ArticleFrontiers in cellular and infection microbiology2023

Untargeted metabolomics of saliva in pregnant women with and without gestational diabetes mellitus and healthy non-pregnant women.

Yueheng Li, Yang Feng, Zhengyan Yang, Zhi Zhou, Dan Jiang, Jun Luo

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Identifying Biomarkers for Chronic Obstructive Pulmonary Disease in the Salivary Metabolome.International journal of chronic obstructive pulmonary disease · 2026
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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

6 authors at 4 institutions in 1 country.

Yueheng LiDepartment of Preventive Dentistry, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
Yang FengChongqing Changshou Health Center for Women and Children, Chongqing, China.
Zhengyan YangDepartment of Preventive Dentistry, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
Zhi ZhouDepartment of Preventive Dentistry, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
Dan JiangDepartment of Preventive Dentistry, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
Jun LuoDepartment of Preventive Dentistry, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
First Affiliated Hospital of Chongqing Medical University · CNStomatological Hospital of Chongqing Medical University · CNChongqing Maternal and Child Health Hospital · CNChongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this study was to compare the differences in salivary metabolites between pregnant women with gestational diabetes mellitus (GDM), healthy pregnant women (HPW), and healthy non-pregnant women (HNPW), and analyze the possible associations between the identified metabolites and gingivitis. Method: The study included women with GDM ( Results: In positive ion mode, a total of 2,529 molecular features were detected in all samples, 166 differential metabolites were identified between the GDM and HPW groups (89 upregulated and 77 downregulated), 823 differential metabolites were identified between the GDM and HNPW groups (402 upregulated and 421 downregulated), and 647 differential metabolites were identified between the HPW and HNPW groups (351 upregulated and 296 downregulated). In negative ion mode, 983 metabolites were detected in all samples, 49 differential metabolites were identified between the GDM and HPW groups (29 upregulated and 20 downregulated), 341 differential metabolites were identified between the GDM and HNPW groups (167 upregulated and 174 downregulated), and 245 differential metabolites were identified between the HPW and HNPW groups (112 upregulated and 133 downregulated). A total of nine differential metabolites with high confidence levels were identified in both the positive and negative ion modes, namely, L-isoleucine, D-glucose 6-phosphate, docosahexaenoic acid, arachidonic acid, adenosine, adenosine-monophosphate, adenosine 5'-monophosphate, xanthine, and hypoxanthine. Among all pathways enriched by the upregulated differential metabolites, the largest number of pathways were enriched by four differential metabolites, adenosine, adenosine 5'-monophosphate, D-glucose 6-phosphate, and adenosine-monophosphate, and among all pathways enriched by the downregulated differential metabolites, the largest number of pathways were enriched by three differential metabolites, L-isoleucine, xanthine, and arachidonic acid. Conclusion: Untargeted metabolomic analysis of saliva samples from pregnant women with GDM, HPW, and HNPW identified nine differential metabolites with high confidence. The results are similar to findings from previous metabolomics studies of serum and urine samples, which offer the possibility of using saliva for regular noninvasive testing in the population of pregnant women with and without GDM. Meanwhile, the associations between these identified differential metabolites and gingivitis need to be further validated by subsequent studies.

Indexed as

Diabetes, GestationalGingivitisAdenosineAdultArachidonic AcidChromatography, LiquidFemaleGlucoseHumansInfantIsoleucineMetabolomicsPhosphatesPregnancySalivaTandem Mass SpectrometryAdenosineArachidonic AcidGlucoseIsoleucinePhosphatesgestational diabetes mellitusgingivitispregnant womensalivauntargeted metabolomics

Identifiers

PMID37538307
PMCPMC10394705
OpenAlexW4384822540

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.