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Record W3157601772 · doi:10.1210/jendso/bvab048.1674

Disruption of Adipose Tissue Metabolism by Glucocorticoids Is Attenuated With LXRβ Antagonism

2021· article· en· W3157601772 on OpenAlexaff
Jia Xu Li, Carolyn L. Cummins

Bibliographic record

VenueJournal of the Endocrine Society · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEndocrinologyLiver X receptorInternal medicineGlucocorticoid receptorGlucocorticoidDownregulation and upregulationWhite adipose tissueAdipose tissueBrown adipose tissueAgonistDexamethasoneChemistryAdipocyteNuclear receptorReceptorBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract Excessive exposure to glucocorticoids (GCs), either from endogenous overproduction of cortisol, or exogenous pharmacological GC treatment, potentiates the development of diabetes and obesity in a fat depot-specific manner. Undesirable metabolic side effects resulting from the activation of the glucocorticoid receptor (GR) remain a key limitation to the long-term therapeutic use of GCs as immunosuppressants. GC treatment disrupts the thermogenic function of brown adipose tissue (BAT) and enhances futile cycling within white adipose tissue (WAT). Mice lacking the liver X receptors (LXRs) were previously shown to have smaller AT depots with enhanced BAT activity compared to wildtype (WT) mice. We previously demonstrated that LXRβ is required to mediate the side effects of GCs in the liver but not the beneficial anti-inflammatory effects of GCs. The discovery of this GC/LXRβ cross-talk led to the hypothesis that LXRβ antagonism may be therapeutically beneficial to prevent GC-induced dysfunction in AT. To test this idea, LXRα-/- mice were treated for 5 days with vehicle, 5 mg/kg dexamethasone (Dex, a synthetic GC agonist), and/or 40 mg/kg GSK2033 (GSK, non-selective LXR antagonist). As expected, Dex-treated mice showed significant accumulation of lipids in BAT and were unable to maintain their body temperature when exposed to cold, yet GSK was able to protect against these effects. Dex increased body fat mass and caused adipocyte enlargement in WAT which was not observed in mice co-treated with GSK. At the transcriptional level, GSK attenuated Dex-mediated downregulation of thermogenic genes in BAT, and upregulation of lipogenic genes in WAT. Similar beneficial changes were confirmed in WT mice. The protection afforded by GSK against Dex-induced fat accumulation was confirmed to be cell-autonomous from studies in adipose-specific LXRβ-/- mice (AdβKO). Dex-dependent increases in lipolysis in gonadal WAT and plasma free fatty acids (FFA) were also attenuated by GSK co-treatment. With GSK co-treatment, Dex-induced liver steatosis was diminished suggesting that LXRβ antagonism attenuated FFA shuttling to the liver. The lipolytic and lipotoxic effects of Dex in AT and liver were largely abrogated in AdβKO along with improved systemic insulin sensitivity. Overall, our data suggest that LXRβ antagonism prevents disruption of BAT and WAT (and indirectly liver) function caused by GC treatment in an in vivo model, highlighting the potential role of LXRβ antagonists in combating the negative effects of excessive GC exposure on the development of diabetes and obesity. The identification of this novel mechanism of interrupting GC adipose tissue action suggests therapeutic targeting of LXRβ with an antagonist could improve the health of patients currently taking GCs to control inflammation but suffer the detrimental side effects of drug treatment.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.279
Teacher spread0.268 · 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 designBench or experimental
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

Citations1
Published2021
Admission routes1
Has abstractyes

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