Regulation of Hepatic Inflammation by Folic Acid in Non‐alcoholic Fatty Liver Disease (NAFLD)
Bibliographic record
Abstract
Non‐alcoholic fatty liver disease (NAFLD) is a multifaceted liver disorder that often exists with the comorbidities of obesity and type 2 diabetes. The histopathology of NAFLD is characterized by steatosis, inflammation, fibrosis, and liver injury. Chronic hepatic inflammation is an important pathogenic mediator of NAFLD that contributes to disease severity. The progression of NAFLD may be associated with development of cardiovascular disease which is the primary cause of death in patients. Dietary supplementation of vitamins is important for health maintenance, and has been suggested to be a beneficial strategy for NAFLD management. Folic acid is a water soluble B vitamin that has been demonstrated to have lipid‐lowering and anti‐oxidant effects. The objective of our study was to investigate the effects of folic acid supplementation on hepatic inflammation and to identify the underlying mechanisms. Male C57Bl/6J mice were fed a control diet (10% kcal fat), a high‐fat diet (60% kcal fat), or a high‐fat diet supplemented with folic acid (26mg/kg diet) for a 5 week period. High‐fat diet feeding induced a significant body weight gain and increased aggregation of inflammatory foci in the liver. Folic acid supplementation did not alter the body weight of mice fed a high‐fat diet. However, folic acid supplementation reduced the number of inflammatory foci as well as lipid vacuoles in the liver of high‐fat diet fed mice. This correlated with reduced expression of pro‐inflammatory cytokines mediated through attenuation of NF‐κB transcription activity, an inflammatory transcriptional regulator. Our results suggest that folic acid supplementation can alleviate the hepatic inflammatory response induced by chronic consumption of high‐fat diets, which may contribute to the hepatoprotective effect by folic acid. Support or Funding Information This study was supported, in part, by NSERC and 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) | 0.000 | 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 | 0.001 | 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".