May Sulfites in Wine Affect Gut Microbiota? An In Vitro Study of Their Digestion and Interplay with Wine Polyphenols
Bibliographic record
Abstract
Sulfites are widely used in the wine industry, but their human health effects remain debated. This study is the first to investigate the interaction between wine sulfites and gut microbiota under simulated gastrointestinal conditions. Using the simgi model, red and synthetic wines─with and without SO 2 (200 mg/L)─underwent gastrointestinal digestion and colonic fermentation with fecal microbiota from three healthy donors ( n = 3). SO 2 -treated wines slightly modified gut microbiota composition, decreasing beneficial bacteria like Bacteroides and Ruminococcus, while increasing Coprococcus and pro-inflammatory Escherichia / Shigella, although the overall microbiome of each individual seems to condition its resilience toward SO 2 . These effects were partially mitigated in red wine, suggesting a protective role of wine polyphenols. Additionally, SO 2 treatment in red wines enhanced phenolic metabolism at the gut level, increasing low-molecular-weight phenolic compounds, such as valerolactones, bioavailable in the small intestine and colon. For instance, 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone concentrations were consistently higher in SO 2 -treated red wine (0.86–1.28 mg/L) than in the untreated wine (<0.78 mg/L) after 6 h of fermentation. This pioneering study reveals a complex interplay between sulfites, wine components, and gut microbiota, with potential health implications, especially for sulfite-sensitive individuals.
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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) | 0.000 | 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".