Hepatic carbohydrate and lipid metabolism are altered in rats fed creatine‐supplemented diets (LB151)
Bibliographic record
Abstract
Non‐alcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver damage. Insulin resistance often accompanies NAFLD and these phenomena are hallmarks of the metabolic syndrome. Recently, we reported that creatine supplementation prevents hepatic steatosis, lipid peroxidation and insulin resistance in rats fed a high‐fat diet. In order to examine potential mechanisms underlying these observations, we used McArdle RH‐7777 rat hepatoma cells treated with oleic acid as a model of hepatocyte lipid accumulation. We found that cells cultures with creatine had a dose dependent reduction in cellular TG accumulation. Using radiolabeled tracers we have demonstrated that incubation of hepatoma cells with creatine increases fatty acid oxidation and decreases both fatty acid and TG synthesis. In‐line with increased fatty acid oxidation, analysis of mRNA from hepatoma cells treated with creatine had increased expression of PPARα and its targets CPT1a and LCAD. We have also found that creatine treated hepatoma cells have increased expression of the PEPCK and pyruvate kinase. Our preliminary data suggests that rats fed a creatine‐supplemented high‐fat diet have significantly improved glucose tolerance compared to high‐fat diet fed control animals. Together these data suggest that dietary creatine influences carbohydrate metabolism as well as lipid metabolism. This research is funded by the CIHR Grant Funding Source : Supported by CIHR
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".