Evidence map›Paper›PMID 34212051›Full record

ArticleJournal of diabetes research2021

Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling.

Xingjun Meng, Bo Zhu, Yan Liu, Lei Fang, Binbin Yin, Yanni Sun, Mengni Ma, Yuli Huang, Yuning Zhu, Yunlong Zhang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes research, 2021. 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
1.4field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 14 citations in OpenAlex.

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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

10 authors at 4 institutions in 1 country.

Xingjun MengDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Bo ZhuDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Yan LiuSchool of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.
Lei FangDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Binbin YinDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Yanni SunDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Mengni MaDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Yuli HuangDepartment of Cardiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528300, China.
Yuning ZhuDepartment of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.ORCID https://orcid.org/0000-0003-1134-4522
Yunlong ZhangKey Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 511436, China.ORCID https://orcid.org/0000-0002-2447-9138
Women's Hospital, School of Medicine, Zhejiang University · CNGuangzhou Medical University · CNJinan University · CNThe First People's Hospital of Shunde · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGestational diabetes mellitus (GDM) is a type of glucose intolerance disorder that first occurs during women's pregnancy. The main diagnostic method for GDM is based on the midpregnancy oral glucose tolerance test. The rise of metabolomics has expanded the opportunity to better identify early diagnostic biomarkers and explore possible pathogenesis.

methodsWe collected blood serum from 34 GDM patients and 34 normal controls for a LC-MS-based metabolomics study.

results184 metabolites were increased and 86 metabolites were decreased in the positive ion mode, and 65 metabolites were increased and 71 were decreased in the negative ion mode. Also, it was found that the unsaturated fatty acid metabolism was disordered in GDM. Ten metabolites with the most significant differences were selected for follow-up studies. Since the diagnostic specificity and sensitivity of a single differential metabolite are not definitive, we combined these metabolites to prepare a ROC curve. We found a set of metabolite combination with the highest sensitivity and specificity, which included eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, arachidonic acid, citric acid,

conclusionsOur results provide a direction for the mechanism of GDM research and demonstrate the feasibility of developing a diagnostic test that can distinguish between GDM and normal controls clearly. Our findings were helpful to develop novel biomarkers for precision or personalized diagnosis for GDM. In addition, we provide a critical insight into the pathological and biological mechanisms for GDM.

Indexed as

MetabolomicsAdultArachidonic AcidCase-Control StudiesChromatography, LiquidCitric AcidDiabetes, GestationalDocosahexaenoic AcidsEicosapentaenoic AcidFatty Acids, UnsaturatedFemaleGenisteinHumansKetoglutaric AcidsMass SpectrometryMetabolomeArachidonic AcidCitric AcidDocosahexaenoic Acidsdocosapentaenoic acidEicosapentaenoic AcidFatty Acids, UnsaturatedGenisteinKetoglutaric Acids

Identifiers

PMID34212051
PMCPMC8211500
OpenAlexW3169681407

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.