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Record W2314477290 · doi:10.4161/adip.28308

Cross-tissue comparisons of leptin and adiponectin

2014· article· en· W2314477290 on OpenAlexaff
Andrée-Anne Houde, Cécilia Légaré, Frédéric‐Simon Hould, Stéfane Lebel, Picard Marceau, André Tchernof, Marie‐Claude Vohl, Marie‐France Hivert, Luigi Bouchard

Bibliographic record

VenueAdipocyte · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversité LavalCentre hospitalier universitaire de QuébecUniversité du QuébecAluminium Refining, Degassing and Filtering (Canada)Institut universitaire de cardiologie et de pneumologie de QuébecCégep de ChicoutimiUniversité de Sherbrooke
Fundersnot available
KeywordsAdipose tissueDNA methylationAdiponectinEpigeneticsLeptinBiologyMethylationGeneDNAGene expressionAdipokineEndocrinologyInternal medicineGeneticsMedicineObesityInsulin resistance

Abstract

fetched live from OpenAlex

DNA methylation has been mostly studied in circulating blood cells. Although being readily accessible, metabolically active tissues such as adipose tissue would be more informative for the study of metabolic disorders. However, whether or not the blood DNA methylation profile correlates with that of adipose tissue remains unknown. In this study, DNA methylation patterns of variation at LEP and ADIPOQ gene loci were similar between individual CpGs across the different tissues. We also report that DNA methylation levels at biologically relevant CpGs are correlated between blood, subcutaneous, and visceral adipose tissue, and that these nearby CpGs are associated with LEP and ADIPOQ gene expression in adipose tissues. These results will be highly relevant for future epigenetic studies in metabolic disorders.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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