Air pollution and endocrine disruptors induce human microbiome imbalances: A systematic review of recent evidence and possible biological mechanisms
Bibliographic record
Abstract
A rich body of literature indicates that environmental factors interact with the human microbiome and influence its composition and functions contributing to the pathogenesis of diseases in distal sites of the body. This systematic review examines the scientific evidence on the effect of environmental toxicants, air pollutants and endocrine disruptors (EDCs), on compositional and diversity of human microbiota. Articles from PubMed, Embase, WoS and Google Scholar where included if they focused on human populations or the SHIME® model, and assessed the effects of air pollutants and EDCs on human microbiome. Non-human studies, not written in English and not displaying original research were excluded. The Newcastle-Ottawa Scale was used to assess the quality of individual studies. Results were extracted and presented in tables. 31 studies were selected, including 24 related to air pollutants, 5 related to EDCs, and 2 related to EDC using the SHIME® model. 19 studies focussed on the respiratory system (19), gut (8), skin (2), vaginal (1) and mammary (1) microbiomes. No sufficient number of studies are available to observe a consistent trend for most of the microbiota, except for streptococcus and veillionellales for which 9 out of 10, and 3 out of 4 studies suggest an increase of abundance with exposure to air pollution. A limitation of the evidence reviewed is the scarcity of existing studies assessing microbiomes from individual systems. Growing evidence suggests that exposure to environmental contaminants could change the diversity and abundance of resident microbiota, e.g. in the upper and lower respiratory, gastrointestinal, and female reproductive system. Microbial dysbiosis might lead to colonization of pathogens and outgrowth of pathobionts facilitating infectious diseases. It also might prime metabolic dysfunctions disrupting the production of beneficial metabolites. Further studies should elucidate the role of environmental pollutants in the development of dysbiosis and dysregulation of microbiota-related immunological processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 teacher head, 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".