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Record W2007154912 · doi:10.1111/nure.12057

Epigenetic programming of obesity and diabetes by in utero exposure to gestational diabetes mellitus

2013· review· en· W2007154912 on OpenAlexaff
Stéphanie-May Ruchat, Marie‐France Hivert, Luigi Bouchard

Bibliographic record

VenueNutrition Reviews · 2013
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsCégep de ChicoutimiUniversité de Sherbrooke
Fundersnot available
KeywordsGestational diabetesFetal programmingOffspringEpigeneticsDiabetes mellitusIn uteroObesityMedicinePregnancyAdipokineFetusBioinformaticsEndocrinologyInternal medicineGestationBiologyInsulin resistanceGeneticsGene

Abstract

fetched live from OpenAlex

It is now well accepted that offspring exposed to maternal undernutrition, obesity, or gestational diabetes mellitus have an increased risk for chronic diseases later in life, supporting the theory of the early origins of chronic diseases. However, the molecular mechanisms through which the exposure to an altered in utero environment translates into the development of chronic diseases are not yet well understood. Recently reported promising results help to resolve this issue. They suggest that epigenetic modifications are a potential mechanism for fetal metabolic programming. This review provides an overview of the relationship between the exposure to an altered intrauterine environment and fetal metabolic programming, focusing on gestational diabetes mellitus and epigenetic variations at adipokine candidate genes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.339
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations127
Published2013
Admission routes1
Has abstractyes

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