MétaCan
Menu
Back to cohort
Record W4404197680 · doi:10.1016/j.isci.2024.111292

Adipocyte Angptl8 deletion improves glucose and energy metabolism and obesity associated inflammation in mice

2024· article· en· W4404197680 on OpenAlexafffund
Anindya Ghosh, Isabelle Chénier, Yat Hei Leung, Abel Oppong, Marie‐Line Peyot, S.R. Murthy Madiraju, Irina Al‐Khairi, Jehad Abubaker, Fahd Al‐Mulla, Marc Prentki, Mohamed Abu‐Farha

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchDasman Diabetes Institute
KeywordsInflammationAdipocyteEnergy metabolismCarbohydrate metabolismObesityMetabolismEndocrinologyFat accumulationInternal medicineChemistryBiologyMedicineAdipose tissue

Abstract

fetched live from OpenAlex

Angiopoietin-like protein 8 (Angptl8), expressed in the liver and adipocytes, forms a complex with Angptl3 or Angptl4, which regulates lipoprotein lipase and triglyceride metabolism. However, the precise functions of adipocyte Angptl8 remain elusive. Here we report that adipocyte-specific inducible Angptl8-knockout (AT-A8-KO) male mice on normal diet showed minor phenotypic changes, but after a high-fat high fructose (HFHF) diet, exhibited decreased body weight gain and glycemia, elevated rectal temperature and early dark phase energy expenditure compared to the Cre controls. AT-A8-KO mice also displayed improved glucose tolerance, a trend for better insulin sensitivity, improved insulin-stimulated glucose uptake in adipose tissues, and reduced visceral adipose tissue crown-like structures, plasma MCP-1 and leptin levels. The results indicate the importance of adipose Angptl8 in the context of nutri-stress and obesity, as its deletion in mice promotes a metabolically healthy obese phenotype by slightly ameliorating obesity, improving glucose and energy homeostasis, and mitigating inflammation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.293

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 integrity0.0000.000
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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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 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

Citations6
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicLipid metabolism and disordersFrench-language works237,207