ZAG promotes exercise performance during endurance exercise by lipid utilization in skeletal muscle
Bibliographic record
Abstract
Endurance exercise significantly enhances energy expenditure with lipids serving as a crucial energy source for skeletal muscle during exercise. The adipocytokine zinc-α2-glycoprotein (ZAG) in endurance exercise remains largely uncertain. This study utilized ZAG knockout and overexpression mice to investigate ZAG's role in regulating lipid metabolism in skeletal muscle during endurance exercise. Results showed the serum ZAG level of mice was significantly increased after exercise, and ZAG knockout mice decreased the exercise performance. Subsequent research revealed that ZAG knockout notably elevated triglyceride (TG) level in skeletal muscle and reduced the expression of lipolysis-related factors such as adipose triglyceride lipase (ATGL), carnitine palmitoyl transferase-1b (CPT1b), and acyl-CoA synthetase long chain family member 1, while enhancing the expression of lipid synthesis factor fatty acid synthase during exercise. The expressions of mitochondrial energy metabolism related factors uncoupling protein 2 and cytochrome c oxidase subunit I were reduced in ZAG knockout mice during endurance exercise. To assess ZAG's impact on lipid metabolism in skeletal muscle, we used ZAG overexpression plasmid in mice and C2C12 cells. ZAG overexpression decreased TG levels, enhanced ATGL expression, and increased CPT1b expression. In conclusion, ZAG can improve the level of skeletal muscle lipid metabolism and mitochondrial function during exercise, and improve the endurance exercise performance of mice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".