Skeletal Muscle Triacylglycerol Hydrolysis Does Not Influence Metabolic Complications of Obesity
Bibliographic record
Abstract
Intramyocellular triacylglycerol (IMTG) accumulation is highly associated with insulin resistance and metabolic complications of obesity (lipotoxicity), whereas comparable IMTG accumulation in endurance-trained athletes is associated with insulin sensitivity (the athlete's paradox). Despite these findings, it remains unclear whether changes in IMTG accumulation and metabolism per se influence muscle-specific and systemic metabolic homeostasis and insulin responsiveness. By mediating the rate-limiting step in triacylglycerol hydrolysis, adipose triglyceride lipase (ATGL) has been proposed to influence the storage/production of deleterious as well as essential lipid metabolites. However, the physiological relevance of ATGL-mediated triacylglycerol hydrolysis in skeletal muscle remains unknown. To determine the contribution of IMTG hydrolysis to tissue-specific and systemic metabolic phenotypes in the context of obesity, we generated mice with targeted deletion or transgenic overexpression of ATGL exclusively in skeletal muscle. Despite dramatic changes in IMTG content on both chow and high-fat diets, modulation of ATGL-mediated IMTG hydrolysis did not significantly influence systemic energy, lipid, or glucose homeostasis, nor did it influence insulin responsiveness or mitochondrial function. These data argue against a role for altered IMTG accumulation and lipolysis in muscle insulin resistance and metabolic complications of 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".