Vitamin C as a nitrosation inhibitor: A modelling study across dietary patterns and water quality
Bibliographic record
Abstract
Rising dietary and drinking-water intake of nitrate (NO$_3^-$) and nitrite (NO$_2^-$) presents a significant public health concern. After ingestion, a portion of NO$_3^-$ enters the enterosalivary circulation, where oral bacteria reduce it to NO$_2^-$. When swallowed, NO$_2^-$ enters the acidic gastric environment, where it can react to form N-nitroso compounds (NOCs), many of which are suspected carcinogens. However, epidemiological evidence for this link remains mixed, likely due to the protective effects of antioxidants such as vitamin C, which is present in many high-NO$_3^-$ foods (e.g. leafy vegetables). To better understand and quantify these complex interactions, we develop a dynamic, compartmental quantitative systems pharmacology (QSP) model of human NO$_{3}^{-}$ and NO$_{2}^{-}$ metabolism and gastric chemistry. The framework tracks NO$_{3}^{-}$ and NO$_{2}^{-}$ fluxes across the stomach, intestine, plasma, and saliva, incorporates postprandial changes in gastric volume and pH, and includes mechanistic nitrosation pathways with vitamin C inhibition. Using this model, we evaluate NOC formation under different dietary and water-quality contexts, demonstrating the protective effect of dietary vitamin C and investigating the role of vitamin C supplementation in suppressing NOC formation. Our simulations suggest supplementation is most effective when administered shortly after each meal, with the greatest benefit observed for individuals consuming low vitamin C diets. These findings provide a mechanistic basis for understanding how diet, drinking-water NO$_{3}^{-}$ and NO$_{2}^{-}$, and vitamin C supplementation interact to shape endogenous NOC formation, with potential implications for nutritional guidelines and risk mitigation in vulnerable populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".