Excess Folic Acid Increases Lipid Storage, Weight Gain, and Adipose Tissue Inflammation in High Fat Diet-Fed Rats
Bibliographic record
Abstract
Folic acid intake has increased to high levels in many countries, raising concerns about possible adverse effects, including disturbances to energy and lipid metabolism. Our aim was to investigate the effects of excess folic acid (EFA) intake compared to adequate folic acid (AFA) intake on metabolic health in a rodent model. We conducted these investigations in the setting of either a 15% energy low fat (LF) diet or 60% energy high fat (HF) diet. There was no difference in weight gain, fat mass, or glucose tolerance in EFA-fed rats compared to AFA-fed rats when they were fed a LF diet. However, rats fed EFA in combination with a HF diet had significantly greater weight gain and fat mass compared to rats fed AFA (p < 0.05). Gene expression analysis showed increased mRNA levels of peroxisome proliferator-activated receptor γ (PPARγ) and some of its target genes in adipose tissue of high fat-excess folic acid (HF-EFA) fed rats. Inflammation was increased in HF-EFA fed rats, associated with impaired glucose tolerance compared to high fat-adequate folic acid (HF-AFA) fed rats (p < 0.05). In addition, folic acid induced PPARγ expression and triglyceride accumulation in 3T3-L1 cells. Our results suggest that excess folic acid may exacerbate weight gain, fat accumulation, and inflammation caused by consumption of a HF diet.
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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 | 0.001 | 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".