Evidence map›Paper›PMID 37415231›Full record

ArticleClinical epigenetics2023

Epigenetic marks associated with gestational diabetes mellitus across two time points during pregnancy.

Teresa Linares-Pineda, Nerea Peña-Montero, Nicolás Fragoso-Bargas, Carolina Gutiérrez-Repiso, Fuensanta Lima-Rubio, María Suarez-Arana, Antonio Sánchez-Pozo, Francisco J Tinahones, María Molina-Vega, María José Picón-César and 2 more

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 3 pooled it
5.6field-weighted citation impact, top 3% 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

15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 19 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

12 authors at 7 institutions in 2 countries.

Teresa Linares-PinedaUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
Nerea Peña-MonteroUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
Nicolás Fragoso-BargasDepartment of Endocrinology Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.
Carolina Gutiérrez-RepisoUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
Fuensanta Lima-RubioUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
María Suarez-AranaDepartamento de Obstetricia y Ginecología, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Regional Universitario de Málaga, 29009, Málaga, Spain.
Antonio Sánchez-PozoDepartamento de Bioquímica y Biología Molecular 2, Universidad de Granada, Granada, Spain.
Francisco J TinahonesUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
María Molina-VegaUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
María José Picón-CésarUnidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain. mjpiconcesar@gmail.com.
Christine Sommer *Department of Endocrinology Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.
Sonsoles Morcillo *Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain. sonsoles.morcillo@ibima.eu.
Andalusian Centre for Nanomedicine and Biotechnology · ESInstituto de Investigación Biomédica de Málaga · ESUniversidad de Granada · ESUniversity of Oslo · NOHospital Regional Universitario de Málaga · ESInstituto de Salud Carlos III · ESSpanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition · ES

Funding

Instituto de Salud Carlos III CP20/00066Instituto de Salud Carlos III FI19/00178Instituto de Salud Carlos III JR20-00040Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España POEJ-0039-18Servicio Andaluz de Salud RC-0008-202
6 · The paper itself

Abstract

An adverse intrauterine or periconceptional environment, such as hyperglycemia during pregnancy, can affect the DNA methylation pattern both in mothers and their offspring. In this study, we explored the epigenetic profile in maternal peripheral blood samples through pregnancy to find potential epigenetic biomarkers for gestational diabetes mellitus (GDM), as well as candidate genes involved in GDM development. We performed an epigenome-wide association study in maternal peripheral blood samples in 32 pregnant women (16 with GDM and 16 non-GDM) at pregnancy week 24-28 and 36-38. Biochemical, anthropometric, and obstetrical variables were collected from all the participants. The main results were validated in an independent cohort with different ethnic origin (European = 307; South Asians = 165). Two hundred and seventy-two CpGs sites remained significantly different between GDM and non-GDM pregnant women across two time points during pregnancy. The significant CpG sites were related to pathways associated with type I diabetes mellitus, insulin resistance and secretion. Cg01459453 (SELP gene) was the most differentiated in the GDM group versus non-GDM (73.6 vs. 60.9, p = 1.06E-11; FDR = 7.87E-06). Three CpG sites (cg01459453, cg15329406, and cg04095097) were able to discriminate between GDM cases and controls (AUC = 1; p = 1.26E-09). Three differentially methylated positions (DMPs) were replicated in an independent cohort. To conclude, epigenetic marks during pregnancy differed between GDM cases and controls suggesting a role for these genes in GDM development. Three CpGs were able to discriminate GDM and non-GDM groups with high specificity and sensitivity, which may be biomarker candidates for diagnosis or prediction of GDM.

Indexed as

Diabetes, GestationalHyperglycemiaBiomarkersDNA MethylationEpigenesis, GeneticEpigenomeFemaleHumansPregnancyBiomarkersDiabetesDNA methylationEpigeneticsEpigenome-wide association studyGestational diabetes mellitusPregnancyValidation

Identifiers

PMID37415231
PMCPMC10324212
OpenAlexW4383374410

What OpenQuestion holds

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LicenceCC BY
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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.