Evidence map›Paper›PMID 35436338›Full record

ArticleThe Journal of clinical endocrinology and metabolism2022

Metabolomic Identification of a Novel, Externally Validated Predictive Test for Gestational Diabetes Mellitus.

Ulla Sovio, Gemma L Clayton, Emma Cook, Francesca Gaccioli, D Stephen Charnock-Jones, Deborah A Lawlor, Gordon C S Smith

Abstract read
In one paragraph

Article in The Journal of clinical endocrinology and metabolism, 2022. 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
–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

10 citing papers in PubMed.

  1. Article
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  3. Review
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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

7 authors.

Ulla SovioDepartment of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK.ORCID 0000-0002-0799-1105
Gemma L ClaytonNIHR Bristol Biomedical Research Centre, Bristol, UK.ORCID 0000-0002-9525-2758
Emma CookDepartment of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK.ORCID 0000-0002-6142-4443
Francesca GaccioliDepartment of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK.ORCID 0000-0001-7178-8921
D Stephen Charnock-JonesDepartment of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK.ORCID 0000-0002-2936-4890
Deborah A LawlorNIHR Bristol Biomedical Research Centre, Bristol, UK.ORCID 0000-0002-6793-2262
Gordon C S SmithDepartment of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK.ORCID 0000-0003-2124-0997

Funding

British Heart Foundation AA/18/7/34219British Heart Foundation CH/F/20/90003British Heart Foundation CS/16/4/32482European Research Council 669545Medical Research Council G1100221Medical Research Council MC_PC_21038Medical Research Council MC_UU_00011/6Medical Research Council MM_UU_00011/3Medical Research Council MR/N024397/1NIH HHS R01 DK10324Wellcome TrustWellcome Trust WT101597MA
6 · The paper itself

Abstract

contextUndiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m2.

objectiveTo improve the prediction of GDM using metabolomics.

methodsWe performed metabolomics on maternal serum from the Pregnancy Outcome Prediction (POP) study at 12 and 20 weeks of gestational age (wkGA; 185 GDM cases and 314 noncases). Predictive metabolites were internally validated using the 28 wkGA POP study serum sample and externally validated using 24- to 28-wkGA fasting plasma from the Born in Bradford (BiB) cohort (349 GDM cases and 2347 noncases). The predictive ability of a model including the metabolites was compared with BMI > 30 kg/m2 in the POP study.

resultsForty-seven predictive metabolites were identified using the 12- and 20-wkGA samples. At 28 wkGA, 4 of these [mannose, 4-hydroxyglutamate, 1,5-anhydroglucitol, and lactosyl-N-palmitoyl-sphingosine (d18:1/16:0)] independently increased the bootstrapped area under the receiver operating characteristic curve (AUC) by >0.01. All 4 were externally validated in the BiB samples (P = 2.6 × 10-12, 2.2 × 10-13, 6.9 × 10-28, and 2.6 × 10-17, respectively). In the POP study, BMI > 30 kg/m2 had a sensitivity of 28.7% (95% CI 22.3-36.0%) and a specificity of 85.4% whereas at the same level of specificity, a predictive model using age, BMI, and the 4 metabolites had a sensitivity of 60.2% (95% CI 52.6-67.4%) and an AUC of 0.82 (95% CI 0.78-0.86).

conclusionsWe identified 4 strongly and independently predictive metabolites for GDM that could have clinical utility in screening for GDM.

Indexed as

Diabetes, GestationalFemaleGestational AgeHumansMetabolomicsPregnancyPregnancy OutcomeROC Curvegestational diabetes mellitusmetabolomicspredictionpregnancy

Identifiers

PMID35436338
PMCPMC9282248

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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.