How Increased Dietary Folic Acid Intake Impacts Health Outcomes Through Changes in Inflammation, Angiogenesis, and Neurotoxicity
Bibliographic record
Abstract
Dietary folic acid supplementation is well known for playing a crucial role in the closure of the neural tube. Individuals have continued to increase dietary intake of folic acid in counties with mandatory fortication laws in place. Some studies have demonstrated adverse health effects in individuals with high dietary intake of folic acid. Nutrition is a modifiable risk factor for ischemic stroke. Specifically, elevated levels of homocysteine, they can be reduced by increasing intake of vitamins, such as folic acid, a B-vitamin. Hypoxia, when levels of oxygen are reduced, is a major component of cardiovascular diseases. The aim of this review paper was to summarize how increased dietary intake of folic acid interaction with hypoxia to impact health outcomes. Our survey of the literature found that increased dietary intake of folic acid promotes inflammation, angiogenesis, and neurotoxicity. We also report negative actions of increased dietary intake of folic acid with vitamin B12 and genetic deficiencies in one-carbon metabolism. Increased dietary intake of folic acid also results in elevated levels of unmetabolized folic acid in the population, of which the impact on health risks has not yet been determined. Our review of the literature emphasizes that a more comprehensive understanding of the action between increased dietary intake of folic acid on disease outcomes could pave the way for improved public health guidelines. Furthermore, adequate knowledge of an individual's one-carbon metabolism status can inform proactive management for patients at higher risk of experiencing negative health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".