Impact of Reduced ATGL-Mediated Adipocyte Lipolysis on Obesity-Associated Insulin Resistance and Inflammation in Male Mice
Bibliographic record
Abstract
Emerging evidence suggests that impaired regulation of adipocyte lipolysis contributes to the proinflammatory immune cell infiltration of metabolic tissues in obesity, a process that is proposed to contribute to the development and exacerbation of insulin resistance. To test this hypothesis in vivo, we generated mice with adipocyte-specific deletion of adipose triglyceride lipase (ATGL), the rate-limiting enzyme catalyzing triacylglycerol hydrolysis. In contrast to previous models, adiponectin-driven Cre expression was used for targeted ATGL deletion. The resulting adipocyte-specific ATGL knockout (AAKO) mice were then characterized for metabolic and immune phenotypes. Lean and diet-induced obese AAKO mice had reduced adipocyte lipolysis, serum lipids, systemic lipid oxidation, and expression of peroxisome proliferator-activated receptor alpha target genes in adipose tissue (AT) and liver. These changes did not increase overall body weight or fat mass in AAKO mice by 24 weeks of age, in part due to reduced expression of genes involved in lipid uptake, synthesis, and adipogenesis. Systemic glucose and insulin tolerance were improved in AAKO mice, primarily due to enhanced hepatic insulin signaling, which was accompanied by marked reduction in diet-induced hepatic steatosis as well as hepatic immune cell infiltration and activation. In contrast, although adipocyte ATGL deletion reduced AT immune cell infiltration in response to an acute lipolytic stimulus, it was not sufficient to ameliorate, and may even exacerbate, chronic inflammatory changes that occur in AT in response to diet-induced obesity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".