Creatine Supplementation may prevent NAFLD by stimulating fatty acid oxidation
Bibliographic record
Abstract
Creatine is a naturally occurring metabolite responsible for maintaining ATP levels in tissues with high and rapidly fluctuating energy demands. Recently, we reported the novel and surprising finding that dietary creatine supplementation prevents hepatic steatosis in rats fed a high‐fat diet. The primary goal of this project is to investigate the mechanism by which creatine influences hepatic lipid metabolism. To accomplish this goal we incubated McArdle RH‐7777 hepatoma cells with oleic acid (OA) and creatine. We found that creatine reduces cellular TG levels by 50% (P<0.05). Incorporation of 3H‐oleate into TG was reduced by creatine by 40% (P<0.05). Creatine supplementation resulted in a 2‐fold increase (P<0.05) in 14CO2 production from 14C‐oleate, and a 20% increase (P<0.05) in levels of the ketone body, â‐hydroxybutyrate. To determine whether dietary creatine supplementation can improve liver function in mice susceptible to NAFLD, we fed the liver‐specific CTP:phosphocholine cytidylyltransferase‐α knockout (LCTαKO) and control mice a HFD with or without 1% creatine. After 3 weeks of feeding, creatine supplementation prevented the 3‐fold increase (P<0.05) in hepatic TG in the LCTαKO mice. The data suggests that creatine may be a potential and novel therapy for Non‐Alcoholic Fatty Liver Disease in humans.
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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 | 0.000 |
| 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 | 0.001 | 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".