Glucocorticoids: Lipolytic or Lipogenic?
Bibliographic record
Abstract
The effects of glucocorticoids (GCs) on lipid metabolism are controversial with some studies indicating that GCs stimulate lipolysis, while others suggest lipogenesis. The purpose of this study was to clarify the role of GCs in lipolysis by utilizing a range of GC concentrations and to assess the long‐term effects of GCs on basal lipolytic rates. In study #1, both 3T3‐L1 were treated for 24h with increasing concentrations of GCs (0.01‐100 uM) and subsequently assayed for glycerol concentration as a marker of lipolysis. Lipolysis increased above control cells in a parabolic fashion to a maximum of 1.9±0.2 fold with 0.1 uM GC (P<0.01). In study #2, 3T3‐L1 were treated with varying GC doses for 24 hrs, then washed and resuspended in fresh GC‐free media. Glycerol was measured 1hr post GC removal. Basal lipolytic rates increased in a dose‐dependent and linear manner, with 100uM GC treatment resulting in a 4.6±0.47 fold increase in lipolysis. The responses in both studies were specifically related to GC treatment as co‐treatment with the GC receptor antagonist mifepristone abolished the effects. These results were confirmed in primary adipocytes (subcutaneous and epididymal). Based on these findings, we hypothesize that GCs can cause both in a lipolytic and anti‐lipolytic manner depending on the concentrations present and their duration of exposure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".