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Record W4393854150 · doi:10.1530/joe-23-0361

The interplay of glucose-dependent insulinotropic polypeptide in adipose tissue

2024· article· en· W4393854150 on OpenAlexafffund
Samrin Kagdi, Sulayman A. Lyons, Jacqueline L. Beaudry

Bibliographic record

VenueJournal of Endocrinology · 2024
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersCanadian Institutes of Health ResearchBanting and Best Diabetes Centre, University of TorontoNovo Nordisk
KeywordsAdipose tissueEndocrinologyInternal medicineWhite adipose tissueBiologyChemistryMedicine

Abstract

fetched live from OpenAlex

Adipose tissue was once known as a reservoir for energy storage but is now considered a crucial organ for hormone and energy flux with important effects on health and disease. Glucose-dependent insulinotropic polypeptide (GIP) is an incretin hormone secreted from the small intestinal K cells, responsible for augmenting insulin release, and has gained attention for its independent and amicable effects with glucagon-like peptide 1 (GLP-1), another incretin hormone secreted from the small intestinal L cells. The GIP receptor (GIPR) is found in whole adipose tissue, whereas the GLP-1 receptor (GLP-1R) is not, and some studies suggest that GIPR action lowers body weight and plays a role in lipolysis, glucose/lipid uptake/disposal, adipose tissue blood flow, lipid oxidation, and free-fatty acid (FFA) re-esterification, which may or may not be influenced by other hormones such as insulin. This review summarizes the research on the effects of GIP in adipose tissue (distinct depots of white and brown) using cellular, rodent, and human models. In doing so, we explore the mechanisms of GIPR-based medications for treating metabolic disorders, such as type 2 diabetes and obesity, and how GIPR agonism and antagonism contribute to improvements in metabolic health outcomes, potentially through actions in adipose tissues.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.013
GPT teacher head0.315
Teacher spread0.303 · 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
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

Citations19
Published2024
Admission routes2
Has abstractyes

Explore more

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