Glucocorticoids increase basal lipolysis, but not epinephrine stimulated lipolysis
Bibliographic record
Abstract
Glucocorticoids (GCs) are associated with central obesity, yet have been classically defined as lipolytic. We have recently shown that exercise training decreases fat mass while increasing GC action in adipocytes, perhaps by increasing adipose triglyceride lipase (ATGL) and hormone sensitive lipase (HSL). From this, we hypothesize that the lipolytic effects of GCs are mediated through changes in these adipose enzymes. To test this, we treated 3T3‐L1 adipocytes with GCs for 48 hours, and then allowed the cells to undergo basal or epinephrine stimulated lipolysis. 48 hours of GCs modestly increased lipolytic rates (1.6 fold vs con, p<0.05). However, the basal lipolysis following GC treatment was substantially increased (3.5 fold vs. con, p<0.01). The expression of ATGL was increased 3.5 fold over control (p<0.01), whereas HSL expression was increased only 1.5 fold (p<0.05). The phosphorylation of HSL at Ser563 and Ser660, sites that increase HSL activity, were also increased significantly (4.5‐and 2.2‐fold vs con, respectively; p<0.01). GC treatment only modestly increased epinephrine stimulated lipolysis (1.5 fold vs con, p<0.05). The phosphorylation of HSL at Ser563 and Ser565 by epinephrine was not affected by GC treatment. Thus, it appears GCs influence lipolysis mainly through alterations in basal lipolysis predominantly by altering ATGL expression and HSL activity. Supported by NSERC and CIHR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".