Folic Acid Supplementation Alters Adipose Tissue Lipid Metabolism
Bibliographic record
Abstract
Mandatory folate fortification polices in Canada and the United States has resulted in high levels of circulating folate in a large proportion of the population. At the same time, a trend toward increased energy intake and reduced energy expenditure has lead to an increased prevalence of obesity, characterized by an accumulation of TAG in adipose tissue. Folate plays an essential role in maintaining S ‐adenosylmethionine (AdoMet), an important biological methyl‐donor. AdoMet functions in the formation of phosphatidylcholine from phosphatidylethanolamine catalyzed by the enzyme phosphatidylethanolamine N ‐methyltransferase (PEMT). PEMT activity contributes to storage of triacylglycerol (TAG) in adipocytes. The objective of this study was to investigate the effect of dietary folic acid supplementation on adipose tissue lipid metabolism. Sprague Dawley rats were fed a high fat diet containing 0.75 mg or 7.5 mg folic acid for 12 weeks. Adipose tissue weight was significantly greater in rats fed excess folic acid. This was accompanied by an increased expression of genes involved in re‐esterification of fatty acids to glycerol, suggesting elevated TAG formation. Up‐regulation of genes involved in phospholipid biosynthesis supports increased storage of TAG in adipocytes upon folate supplementation. In summary, folate supplementation enhances TAG storage in adipose tissue of high fat diet‐fed rats. Grant Funding Source : CIHR, Food and Health Innovation Initiative
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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 | 0.000 | 0.000 |
| 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".