Effect of Short-Chain Fatty Acids on Inflammatory and Metabolic Function in an Obese Skeletal Muscle Cell Culture Model
Bibliographic record
Abstract
The fermentation of non-digestible carbohydrates produces short-chain fatty acids (SCFAs), which have been shown to impact both skeletal muscle metabolic and inflammatory function; however, their effects within the obese skeletal muscle microenvironment are unknown. In this study, we developed a skeletal muscle in vitro model to mimic the critical features of the obese skeletal muscle microenvironment using L6 myotubes co-treated with 10 ng/mL lipopolysaccharide (LPS) and 500 µM palmitic acid (PA) for 24 h ± individual SCFAs, namely acetate, propionate and butyrate at 0.5 mM and 2.5 mM. At the lower SCFA concentration (0.5 mM), all three SCFA reduced the secreted protein level of RANTES, and only butyrate reduced IL-6 protein secretion and the intracellular protein levels of activated (i.e., ratio of phosphorylated–total) NFκB p65 and STAT3 (p < 0.05). Conversely, at the higher SCFA concentration (2.5 mM), individual SCFAs exerted different effects on inflammatory mediator secretion. Specifically, butyrate reduced IL-6, MCP-1 and RANTES secretion, propionate reduced IL-6 and RANTES, and acetate only reduced RANTES secretion (p < 0.05). All three SCFAs reduced intracellular protein levels of activated NFκB p65 and STAT3 (p < 0.05). Importantly, only the 2.5 mM SCFA concentration resulted in all three SCFAs increasing insulin-stimulated glucose uptake compared to control L6 myotube cultures (p < 0.05). Therefore, SCFAs exert differential effects on inflammatory mediator secretion in a cell culture model, recapitulating the obese skeletal muscle microenvironment; however, all three SCFAs exerted a beneficial metabolic effect only at a higher concentration via increasing insulin-stimulated glucose uptake, collectively exerting differing degrees of a beneficial effect on obesity-associated skeletal muscle dysfunction.
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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) | 0.001 | 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 | 0.000 |
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".