Cytokine regulation of skeletal muscle fatty acid metabolism: effect of interleukin-6 and tumor necrosis factor-α
Bibliographic record
Abstract
IL-6 and TNF-alpha have been associated with insulin resistance and type 2 diabetes. Furthermore, abnormalities in muscle fatty acid (FA) metabolism are strongly associated with the development of insulin resistance. However, few studies have directly examined the effects of either IL-6 or TNF-alpha on skeletal muscle FA metabolism. Here, we used a pulse-chase technique to determine the effect of IL-6 (50-5,000 pg/ml) and TNF-alpha (50-5,000 pg/ml) on FA metabolism in isolated rat soleus muscle. IL-6 (5,000 pg/ml) increased exogenous and endogenous FA oxidation by approximately 50% (P < 0.05) but had no effect on FA uptake or incorporation of FA into endogenous lipid pools. In contrast, TNF-alpha had no effect on FA oxidation but increased FA incorporation into diacylglycerol (DAG) by 45% (P < 0.05). When both IL-6 (5,000 pg/ml) and insulin (10 mU/ml) were present, IL-6 attenuated insulin's suppressive effect on FA oxidation, increasing exogenous FA oxidation (+37%, P < 0.05). Furthermore, in the presence of insulin, IL-6 reduced the esterification of FA to triacylglycerol by 22% (P < 0.05). When added in combination with IL-6 or leptin (10 microg/ml), the TNF-alpha-induced increase in DAG synthesis was inhibited. In conclusion, the results demonstrate that IL-6 plays an important role in regulating fat metabolism in muscle, increasing rates of FA oxidation, and attenuating insulin's lipogenic effects. In contrast, TNF-alpha had no effect on FA oxidation but increased FA incorporation into DAG, which may be involved in the development of TNF-alpha-induced insulin resistance in skeletal muscle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".