Folic Acid Supplementation Attenuates Hepatic Steatosis by Enhancing Choline Availability and Remodeling Fatty Acid Profiles in Mice Fed a High‐Fat Diet
Bibliographic record
Abstract
ABSTRACT High‐fat diets (HFDs) are a well‐established cause of hepatic steatosis, a condition associated with altered hepatic fatty acid metabolism and reduced choline availability. Folic acid (FA) deficiency can also promote steatosis, in part by impairing choline metabolism. Although FA supplementation has been found to reduce liver fat in mice with hepatic steatosis, it is unclear if this effect is due to increased hepatic choline levels, changes in fatty acid profiles, or a combination of both. In this study, four‐week‐old male C57BL/6J mice were fed 45 kcal% HFDs with total FA content adjusted to onefold, fivefold, or tenfold AIN‐93G recommended level (2 mg/kg diet) for 15 weeks ad libitum. Hepatic triacylglycerol (TAG), choline concentrations, expression of key genes in choline metabolism, and TAG‐bound fatty acid profiles were analyzed. Mice receiving tenfold FA had lower liver weight and hepatic TAG levels compared to the onefold control group ( p < 0.05). Both fivefold and tenfold FA supplementation increased hepatic choline concentrations and upregulated mRNA expression of choline‐metabolizing genes ( p < 0.05), suggesting enhanced choline utilization. Additionally, tenfold FA supplementation altered the hepatic TAG fatty acid profile, reducing levels of palmitoleic acid and oleic acid ( p < 0.05), fatty acids typically associated with de novo lipogenesis. A strong inverse correlation was observed between hepatic choline and TAG levels ( p < 0.001, adjusted R 2 = 0.56), supporting a potential role for choline availability in mediating FA's protective effects. Folic acid supplementation protects against hepatic steatosis by enhancing choline availability, modulating lipid metabolism, and reducing liver fat 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".