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Record W2611306777 · doi:10.1038/ncomms14803

Temporal and tissue-specific requirements for T-lymphocyte IL-6 signalling in obesity-associated inflammation and insulin resistance

2017· article· en· W2611306777 on OpenAlexfundno aff
Elaine Xu, Mafalda M.A. Pereira, Ismene Karakasilioti, Sebastian Theurich, Mona Al-Maarri, Gunter Rappl, Ari Waisman, F. Thomas Wunderlich, Jens C. Brüning

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of TorontoDeutsche Forschungsgemeinschaft
KeywordsInsulin resistanceInflammationAdipose tissueInternal medicineEndocrinologyImmune systemWhite adipose tissueT cellBiologyInsulin receptorInsulinMedicineImmunology

Abstract

fetched live from OpenAlex

Abstract Low-grade inflammation links obesity to insulin resistance through the activation of tissue-infiltrating immune cells. Interleukin-6 (IL-6) is a crucial regulator of T cells and is increased in obesity. Here we report that classical IL-6 signalling in T cells promotes inflammation and insulin resistance during the first 8 weeks on a high-fat diet (HFD), but becomes dispensable at later stages (after 16 weeks). Mice with T cell-specific deficiency of IL-6 receptor-α (IL-6Rα T-KO ) exposed to a HFD display improved glucose tolerance, insulin sensitivity and inflammation in liver and EWAT after 8 weeks. However, after 16 weeks, insulin resistance in IL-6Rα T-KO epididymal white adipose tissue (EWAT) is comparable to that of controls, whereas the inflammatory profile is significantly worse. This coincided with a shift from classical T cell IL-6 signalling at 8 weeks, to enhanced IL-6 trans-signalling at 16 weeks. Collectively, our studies reveal that IL-6 action in T cells through classical IL-6 signalling promotes inflammation and insulin resistance early during obesity development, which can be compensated for by enhanced IL-6 trans-signalling at later stages.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.245
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.039
GPT teacher head0.328
Teacher spread0.289 · 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

Citations93
Published2017
Admission routes1
Has abstractyes

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