Evidence map›Paper›PMID 34385555›Full record

ArticleScientific reports2021

Metabolic signatures in the conversion from gestational diabetes mellitus to postpartum abnormal glucose metabolism: a pilot study in Asian women.

Xi-Meng Wang, Yan Gao, Johan G Eriksson, Weiqing Chen, Yap Seng Chong, Kok Hian Tan, Cuilin Zhang, Lei Zhou, Ling-Jun Li

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
3.3field-weighted citation impact, top 7% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 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

9 authors at 8 institutions in 4 countries.

Xi-Meng Wang *Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore.
Yan Gao *Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Johan G ErikssonSingapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore.
Weiqing ChenDepartment of Epidemiology, School of Public Health, Sun-Yat Sen University, Guangzhou, Guangdong, China.
Yap Seng ChongSingapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore.
Kok Hian TanDivision of Obstetrics and Gynecology, KK Women's and Children's Hospital, Singapore, Singapore.
Cuilin ZhangEpidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Lei ZhouSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Ling-Jun LiSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. obgllj@nus.edu.sg.
National University of Singapore · SGDuke-NUS Medical School · SGEunice Kennedy Shriver National Institute of Child Health and Human Development · USGuangdong Provincial People's Hospital · CNSingapore Eye Research Institute · SGSingapore Institute for Clinical Sciences · SGSingapore National Eye Center · SGSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aimed to identify serum metabolites related to abnormal glucose metabolism (AGM) among women with gestational diabetes mellitus (GDM). The study recruited 50 women diagnosed with GDM during mid-late pregnancy and 50 non-GDM matchees in a Singapore birth cohort. At the 5-year post-partum follow-up, we applied an untargeted approach to investigate the profiles of serum metabolites among all participants. We first employed OPLS-DA and logistic regression to discriminate women with and without follow-up AGM, and then applied area under the curve (AUC) to assess the incremental indicative value of metabolic signatures on AGM. We identified 23 candidate metabolites that were associated with postpartum AGM among all participants. We then narrowed down to five metabolites [p-cresol sulfate, linoleic acid, glycocholic acid, lysoPC(16:1) and lysoPC(20:3)] specifically associating with both GDM and postpartum AGM. The combined metabolites in addition to traditional risks showed a higher indicative value in AUC (0.92-0.94 vs. 0.74 of traditional risks and 0.77 of baseline diagnostic biomarkers) and R

Indexed as

Asian PeoplePostpartum PeriodAdultCohort StudiesDiabetes, GestationalFemaleGlucoseHumansPilot ProjectsPregnancySingaporeGlucose

Identifiers

PMID34385555
PMCPMC8361021
OpenAlexW3191489627

What OpenQuestion holds

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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.