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Record W2180709391 · doi:10.2217/epi.15.72

<i>LRP1B, BRD2</i> and <i>CACNA1D</i> : New Candidate Genes in Fetal Metabolic Programming of Newborns Exposed to Maternal Hyperglycemia

2015· article· en· W2180709391 on OpenAlexaff
Andrée-Anne Houde, Stéphanie-May Ruchat, Catherine Allard, Jean‐Patrice Baillargeon, Julie St‐Pierre, Patrice Perron, Daniel Gaudet, Diane Brisson, Marie‐France Hivert, Luigi Bouchard

Bibliographic record

VenueEpigenomics · 2015
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversité du Québec à ChicoutimiCégep de ChicoutimiUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsBiologyDNA methylationPlacentaGestational diabetesCord bloodEndocrinologyInternal medicineEpigeneticsEpigenomeFetusPregnancyGeneticsGeneGestationMedicineGene expression

Abstract

fetched live from OpenAlex

AIM: To assess the associations between gestational diabetes mellitus (GDM) and DNA methylation levels at genes related to energy metabolism. PATIENTS & METHODS: Ten loci were selected from our recent epigenome-wide association study on GDM. DNA methylation levels were quantified by bisulfite pyrosequencing in 80 placenta and cord blood samples (20 exposed to GDM) from an independent birth cohort (Gen3G). RESULTS: We did not replicate association between DNA methylation and GDM. However, in normoglycemic women, glucose levels were associated with DNA methylation changes at LRP1B and BRD2 and at CACNA1D and LRP1B gene loci in placenta and cord blood, respectively. CONCLUSION: These results suggest that maternal glucose levels, within the normal range, are associated with DNA methylation changes at genes related to energy metabolism and previously associated with GDM. Maternal glycemia might thus be involved in fetal metabolic programming.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations45
Published2015
Admission routes1
Has abstractyes

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