Genomic and Non-Genomic Actions of Glucocorticoids on Adipose Tissue Lipid Metabolism
Bibliographic record
Abstract
Glucocorticoids (GCs) are hormones that aid the body under stress by regulating glucose and free fatty acids. GCs maintain energy homeostasis in multiple tissues, including those in the liver and skeletal muscle, white adipose tissue (WAT), and brown adipose tissue (BAT). WAT stores energy as triglycerides, while BAT uses fatty acids for heat generation. The multiple genomic and non-genomic pathways in GC signaling vary with exposure duration, location (adipose tissue depot), and species. Genomic effects occur directly through the cytosolic GC receptor (GR), regulating the expression of proteins related to lipid metabolism, such as ATGL and HSL. Non-genomic effects act through mechanisms often independent of the cytosolic GR and happen shortly after GC exposure. Studying the effects of GCs on adipose tissue breakdown and generation (lipolysis and adipogenesis) leads to insights for treatment of adipose-related diseases, such as obesity, coronary disease, and cancer, but has led to controversy among researchers, largely due to the complexity of the process. This paper reviews the recent literature on the genomic and non-genomic effects of GCs on WAT and BAT lipolysis and proposes research to address the many gaps in knowledge related to GC activity and its effects on disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".