Docosahexaenoic and eicosapentaenoic fatty acids differentially regulate glucose and fatty acid metabolism in L6 rat skeletal muscle cells
Bibliographic record
Abstract
The objective of this study was to investigate whether the n-3 polyunsaturated fatty acids (PUFAs) docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) can directly regulate glucose and fat metabolism in skeletal muscle besides exerting anti-inflammatory effects. To accomplish this, L6 skeletal muscle cells were treated with 50 µM of either DHA or EPA for 1, 3, and 5 days. Here, we report that basal and insulin-stimulated rates of glucose uptake, glycogen synthesis, protein kinase B (AKT), and glycogen synthase kinase 3 (GSK3) phosphorylation were not affected by DHA or EPA. However, glucose and palmitate oxidation were consistently elevated by DHA treatment, whereas EPA only increased this variable transiently. Similarly, only DHA caused significant and sustained increases in AMP-activated protein kinase (AMPK) phosphorylation and protein levels of carnitine-palmitoyl transferase-1b (CPT1b) and peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) in skeletal muscle cells. DHA also caused a larger anti-inflammatory effect than EPA in these cells. In conclusion, besides exerting anti-inflammatory effects, DHA and EPA directly regulated glucose and fat metabolism in skeletal muscle cells, although DHA was more effective in doing so than EPA. Thus, by directly enhancing glucose and fat oxidation, DHA may increase glucose disposal and reduce intramyocellular lipid accumulation.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
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".