Chronic AICAR-induced AMP-kinase activation regulates adipocyte lipolysis in a time-dependent and fat depot-specific manner in rats
Bibliographic record
Abstract
This study investigated the effects of chronic in vivo AMP-kinase activation with 5-aminoimidazole-4-carboxamide-1-β-d-ribofuranoside (AICAR) on lipolysis in subcutaneous inguinal, epididymal, and retroperitoneal fat pads. Male Wistar rats received daily single intraperitoneal injections of either saline or AICAR (0.7 g/kg body wt) for a period of 8 wk. The fat pads were used either to isolate adipocytes and measure basal and catecholamine-stimulated lipolysis or to assess signaling steps of lipolysis after 4 and 8 wk of AICAR treatment. Blood was sampled weekly to measure nonesterified fatty acids (NEFAs). AICAR treatment reduced basal and catecholamine-stimulated lipolysis at week 4 in adipocytes from all fat depots. However, at week 8, catecholamine-induced lipolysis significantly increased in inguinal and retroperitoneal adipocytes. Interestingly, plasma levels of NEFAs were also decreased and subsequently increased at 4 and 8 wk, respectively. The lipolytic cascade of the inguinal fat pad was the most drastically affected by the treatment, since the phosphorylation and content of most proteins involved in lipolysis were consistently undetected in this tissue after 4 and 8 wk of AICAR treatment. The enhancement of catecholamine-induced lipolysis in inguinal and retroperitoneal adipocytes after 8 wk of AICAR treatment was accompanied by increased contents of adipose triglyceride lipase (ATGL) and perilipin A in these fat depots. In summary, despite depot-specific regulation of the lipolytic cascade, catecholamine-induced lipolysis in isolated adipocytes correlated well with plasma NEFA concentrations in the course of chronic AICAR-induced AMPK activation. The mechanisms underlying these effects also involved time-dependent and depot-specific regulation of hormone-sensitive lipase, ATGL, and perilipin.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".