Disruption of Adipose Tissue Metabolism by Glucocorticoids is Attenuated with Loss of LXRβ or LXRβ Antagonism
Bibliographic record
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. GC treatment disrupts the thermogenic function of brown adipose tissue (BAT) and enhances futile cycling within white adipose tissue (WAT). 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.The liver x receptors (LXRα/β) are members of the nuclear receptor superfamily that are also implicated in regulating adipose tissue homeostasis. Lxrα/β-/- mice were previously shown to have smaller WAT depots with enhanced BAT activity compared to wildtype (WT) mice. We previously demonstrated that LXRβ is required to mediate GC-induced hyperglycemia and hepatic steatosis while sparing the therapeutic immunosuppressive effects. 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 adipose tissue. Herein, we demonstrated that pharmacological inhibition of LXRβ was able to protect against GC-mediated disruption in BAT and WAT homeostasis, both transcriptionally and functionally in mice. This is mediated, at least partially, by the flux of non-esterified fatty acids (NEFAs) from triglyceride-rich lipoproteins (TRLs) into the endogenous adipose tissue pool. Further, the protection afforded by LXRβ antagonism was confirmed to be cell-autonomous from studies performed in adipose-specific LXRβ-/- mice (LXRβATKO). The lipolytic and lipotoxic effects of GCs in adipose tissue and liver were largely abrogated by LXRβ antagonism or the loss of LXRβ in adipose tissue. Overall, our data suggest that LXRβ antagonism can reverse the disturbance in BAT and WAT (and indirectly liver) function caused by GC treatment in vivo and improved systemic insulin tolerance. The identification of this novel mechanism of interrupting GC adipose tissue action suggests that 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 diabetes and obesity as a result of drug treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".