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Record W4310368196 · doi:10.1038/s42003-022-04267-y

A meta-analysis of pre-pregnancy maternal body mass index and placental DNA methylation identifies 27 CpG sites with implications for mother-child health

2022· review· en· W4310368196 on OpenAlexaff
Nora Fernández‐Jiménez, Ruby Fore, Ariadna Cilleros‐Portet, Johanna Lepeule, Patrice Perron, Tuomas Kvist, Fu‐Ying Tian, Corina Lesseur, Alexandra M. Binder, Manuel Lozano, Jordi Martorell‐Marugán, Yuk Jing Loke, Kelly M. Bakulski, Yihui Zhu, Anne Forhan, Sara Sammallahti, Todd M. Everson, Jia Chen, Karin B. Michels, Thalía Belmonte, Pedro Carmona‐Sáez, Jane Halliday, M. Daniele Fallin, Janine M. LaSalle, Jörg Tost, Darina Czamara, Mariana F. Fernández, Antonio Gómez‐Martín, Jeffrey M. Craig, Beatriz González-Alzaga, Rebecca J. Schmidt, John Dou, Evelyne Muggli, Marina Lacasaña, Martine Vrijheid, Carmen J. Marsit, Margaret R. Karagas, Katri Räikkönen, Luigi Bouchard, Barbara Heude, Loreto Santa‐Marina, Mariona Bustamante, Marie‐France Hivert, José Ramón Bilbao

Bibliographic record

VenueCommunications Biology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de SherbrookeCentre Hospitalier Universitaire de Sherbrooke
FundersEuropean Regional Development FundJohns Hopkins Bloomberg School of Public HealthGenome Center, University of California, DavisUniversity of California, DavisNational Institute of Environmental Health SciencesUniversité de ParisUniversity of California Davis School of MedicineInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementInstitut National de la Santé et de la Recherche MédicaleFundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat ValencianaUniversidad de GranadaOsasun Saila, Eusko JaurlaritzakoUniversity of California, Los AngelesCentro Pfizer-Universidad de Granada-Junta de Andalucía de Genómica e Investigación OncológicSchool of Public Health, University of MichiganUniversitat Jaume IEuskal Herriko UnibertsitateaMinisterio de Economía y CompetitividadDeakin UniversityCentre d'Imagerie BioMédicaleJohns Hopkins UniversityMurdoch Children's Research InstituteUniversitat de ValènciaJoint Programming Initiative A healthy diet for a healthy lifeChildren’s Hospital of Wisconsin Research InstitutePfizerHelsingin YliopistoAlbert-Ludwigs-Universität FreiburgEmory UniversityCentre National de la Recherche ScientifiqueInstituto de Salud Carlos IIIEuropean Association of Social Psychology
KeywordsCpG siteDNA methylationMethylationMeta-analysisBody mass indexObstetricsPregnancyPlacentaBiologyMedicineGeneticsDNAFetusGeneInternal medicineGene expression

Abstract

fetched live from OpenAlex

Higher maternal pre-pregnancy body mass index (ppBMI) is associated with increased neonatal morbidity, as well as with pregnancy complications and metabolic outcomes in offspring later in life. The placenta is a key organ in fetal development and has been proposed to act as a mediator between the mother and different health outcomes in children. The overall aim of the present work is to investigate the association of ppBMI with epigenome-wide placental DNA methylation (DNAm) in 10 studies from the PACE consortium, amounting to 2631 mother-child pairs. We identify 27 CpG sites at which we observe placental DNAm variations of up to 2.0% per 10 ppBMI-unit. The CpGs that are differentially methylated in placenta do not overlap with CpGs identified in previous studies in cord blood DNAm related to ppBMI. Many of the identified CpGs are located in open sea regions, are often close to obesity-related genes such as GPX1 and LGR4 and altogether, are enriched in cancer and oxidative stress pathways. Our findings suggest that placental DNAm could be one of the mechanisms by which maternal obesity is associated with metabolic health outcomes in newborns and children, although further studies will be needed in order to corroborate these findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.131
GPT teacher head0.401
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2022
Admission routes1
Has abstractyes

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