Separating the Anti-Inflammatory and Diabetogenic Effects of Glucocorticoids Through LXRβ Antagonism
Bibliographic record
Abstract
Synthetic glucocorticoids (GCs), including dexamethasone (DEX), are powerful anti-inflammatory drugs. Long-term use of GCs, however, can result in metabolic side effects such as hyperglycemia, hepatosteatosis, and insulin resistance. The GC receptor (GR) and liver X receptors (LXRα and LXRβ) regulate overlapping genes involved in gluconeogenesis and inflammation. We have previously shown that Lxrβ-/- mice are resistant to the diabetogenic effects of DEX but still sensitive to its immunosuppressive actions. To determine whether this finding could be exploited for therapeutic intervention, we treated mice with GSK2033, a pan-LXR antagonist, alone or combined with DEX. GSK2033 suppressed GC-induced gluconeogenic gene expression without affecting immune-responsive GR target genes. The suppressive effect of GSK2033 on DEX-induced gluconeogenic genes was specific to LXRβ, was liver cell autonomous, and occurred in a target gene-specific manner. Compared with DEX treatment alone, the coadministration of GSK2033 with DEX decreased the recruitment of GR and its accessory factors MED1 and C/EBPβ to the phosphoenolpyruvate carboxykinase promoter. However, GSK2033 had no effect on DEX-mediated suppression of inflammatory genes expressed in the liver or in mouse primary macrophages stimulated with lipopolysaccharides. In conclusion, our study provides evidence that the gluconeogenic and immunosuppressive actions of GR activation can be mechanistically dissociated by pharmacological antagonism of LXRβ. Treatment with an LXRβ antagonist could allow the safer use of existing GC drugs in patients requiring chronic dosing of anti-inflammatory agents for the treatment of diseases such as rheumatoid arthritis and inflammatory bowel disease.
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 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) | 0.001 | 0.000 |
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.
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".