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Record W2170893222 · doi:10.1155/2011/168659

Variations in Adipokine Genes<i>AdipoQ</i>,<i>Lep</i>, and<i>LepR</i>Are Associated with Risk for Obesity-Related Metabolic Disease: The Modulatory Role of Gene-Nutrient Interactions

2011· article· en· W2170893222 on OpenAlexafffund
Jennifer Enns, Carla G. Taylor, Peter Zahradka

Bibliographic record

VenueJournal of Obesity · 2011
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdipokineAdiponectinLeptinMedicineObesityLeptin receptorEndocrinologyInternal medicineMetabolic syndromeDiseasePopulationDiabetes mellitusType 2 diabetesBioinformaticsInsulin resistanceBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Obesity rates are rapidly increasing worldwide and facilitate the development of many related disease states, such as cardiovascular disease, the metabolic syndrome, type 2 diabetes mellitus, and various types of cancer. Variation in metabolically important genes can have a great impact on a population's susceptibility to becoming obese and/or developing related complications. The adipokines adiponectin and leptin, as well as the leptin receptor, are major players in the regulation of body energy homeostasis and fat storage. This paper summarizes the findings of single nucleotide polymorphisms in these three genes and their effect on obesity and metabolic disease risk. Additionally, studies of gene-nutrient interactions involving adiponectin, leptin, and the leptin receptor are highlighted to emphasize the critical role of diet in susceptible populations.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations74
Published2011
Admission routes2
Has abstractyes

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