Protective Roles of Prebiotics and Probiotics Against Environmental Pollutant‐Induced Toxicities: A Systematic Review of Animal Studies
Bibliographic record
Abstract
Environmental pollutants, including heavy metals, organic pollutants, and mycotoxins, cause various adverse health effects linked to inflammation and oxidative stress. These toxicities disrupt gut microbiota composition and function, which prebiotics and probiotics may counteract. This study systematically reviewed the effects of prebiotics, probiotics, synbiotics, and commensal gut microbiota on pollutant-induced toxicities in animal models. A literature search was conducted using Scopus, PubMed, Embase, Web of Science, and Google Scholar databases up to January 2025, following PRISMA guidelines. Study quality was assessed using the SYRCLE risk of bias tool, and reliability was evaluated with the ToxRTool. Fifty-four studies investigating prebiotics, probiotics, synbiotics, or commensal microbiota in rats, mice, or zebrafish were included. While 98% of studies were considered reliable, only 18% were rated as good quality due to biases in selection, performance, and detection. Probiotics, prebiotics, synbiotics, and gut microbiota reduced heavy metal absorption and increased fecal excretion. They also improved gut barrier integrity and reduced intestinal permeability in animals exposed to heavy metals. Additionally, probiotics and prebiotics enhanced antioxidant activity and triggered anti-inflammatory responses in animals exposed to heavy metals, mycotoxins, and organic pollutants. Overall, this review supports the role of microbiota, probiotics, prebiotics, and synbiotics in mitigating pollutant-induced toxicities, though future studies should address common bias issues.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".