Evidence map›Paper›PMID 30290597›Full record

ArticleMedicine2018

Normal pregnancy induced glucose metabolic stress in a longitudinal cohort of healthy women: Novel insights generated from a urine metabolomics study.

Mu Wang, Wei Xia, Han Li, Fang Liu, Yuanyuan Li, Xiaojie Sun, Songfeng Lu, Shunqing Xu

Open access · goldAbstract read
In one paragraph

Article in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

19 citing papers in PubMed, 27 citations in OpenAlex.

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  11. Hyperglycemia in Pregnancy and Women's Health in the 21st Century.International journal of environmental research and public health · 2022
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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

8 authors at 1 institution in 1 country.

Mu WangSchool of Computer Science and Technology, Huazhong University of Science and Technology.
Wei XiaKey Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Wuhan, Hubei, China.
Han LiKey Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Wuhan, Hubei, China.
Fang LiuSchool of Computer Science and Technology, Huazhong University of Science and Technology.
Yuanyuan LiKey Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Wuhan, Hubei, China.
Xiaojie SunKey Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Wuhan, Hubei, China.
Songfeng LuSchool of Computer Science and Technology, Huazhong University of Science and Technology.
Shunqing XuKey Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Wuhan, Hubei, China.
Huazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During normal pregnancy, mothers face a unique physiological challenge in the adaptation of glucose metabolism in preparation for the metabolic stress presented by fetal development. However, the responsible mechanism remains elusive. The purpose of this study is to investigate the mechanism of the metabolic stress of glucose metabolism in pregnant women using metabolomics method.A Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometer-based untargeted metabolomics study was performed to investigate the dynamic urinary signature of the intermediates of glucose metabolism in a longitudinal cohort of 232 healthy pregnant women in their first, second, and third trimesters.Twelve glucose metabolic intermediates were screened out from hundreds of candidate metabolites using partial least squares discriminant analysis models. These 12 markers were mainly involved in the metabolic pathways of insulin resistance, glycolysis/gluconeogenesis, tricarboxylic acid cycle, nonabsorbable carbohydrate metabolism, and N-glycan biosynthesis. In particular, L-acetylcarnitine, a metabolite that is beneficial for the amelioration of insulin resistance, decreased in a time-dependent manner during normal pregnancy. Moreover, thiamine pyrophosphate, an intermediate product of glycolysis/gluconeogenesis, significantly increased in the second trimester, and argininosuccinic acid and oxalosuccinic acid, intermediates involved in the tricarboxylic acid cycle, significantly decreased in the third trimester, suggesting an increased glucose demand in the maternal body during fetal development.These findings provide novel insight into the normal pregnancy-induced elevation of insulin resistance and glycolysis/gluconeogenesis, as well as the observed reduction in the aerobic oxidation of glucose.

Indexed as

AdultBiomarkersCarbohydrate MetabolismChromatography, LiquidCitric Acid CycleDiscriminant AnalysisFemaleGlycolysisGlycosuriaHealthy VolunteersHumansInsulin ResistanceLongitudinal StudiesMetabolomicsPeptide-N4-(N-acetyl-beta-glucosaminyl) Asparagine AmidasePregnancyBiomarkersPeptide-N4-(N-acetyl-beta-glucosaminyl) Asparagine Amidase

Identifiers

PMID30290597
PMCPMC6200460
OpenAlexW2894679862

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.