Air Pollution Intervention Changes the Gut Microbiome and Virome of Adult Women in Uganda
Bibliographic record
Abstract
Abstract RATIONALE: Emerging observational studies suggest that air pollution can influence the gut microbiome however air pollution exposure is often highly confounded. Recent studies suggest that the gut virome affects respiratory health independently of the gut microbiome. We have demonstrated in a randomized controlled trial (ClinicalTrials.govNCT03351504) that a clean lighting intervention reduced personal exposure to fine particulate matter and black carbon among adult women in rural Uganda. METHODS: Stool samples were collected from 80 adult women living in rural Uganda at baseline, 12- and 18-months post-randomization. Participants randomized to the intervention group received a solar lighting system at baseline, while those randomized to the control group received a solar lighting system at 12 months. Deep metagenomics sequencing was performed and profiled for non-viral and viral taxonomic composition. The most prevalent non-viral microbial constituents belonged to bacteria, archaea, and eukaryotic kingdoms, while the identified viral microbial constituents all belonged to the class Caudoviricetes, double-stranded DNA tailed phages whose hosts are bacteria and archaea. Post-intervention, non-viral and viral signatures of reduced air pollution exposure comparing pre- vs. post- intervention samples were identified. In exploratory analyses, mediation models were used to assess whether microbiome or virome signatures are mediators of the relationship between the solar lighting intervention and improved respiratory symptoms. RESULTS: Provision of solar lighting systems reduced personal exposureto PM2.5 from an average of 82.5 μg/m3 to 49.2 μg/m3 (p = 0.010) andreduced black carbon exposure from 11.5 μg/m3 to 6.3 μg/m3 (p = 0.013) with a reduction in reported respiratory symptoms from 57.1% to 36.1% (p = 0.002). The solar lighting intervention led to greater changes in viral compared to non-viral microbial community structure as well as differential abundance of bacteria, eukaryote, and viral members. Microbial (Figure 1B) and viral (Figure 1A) signatures of reduced air pollution exposure were identified. Bacteriome but not virome signatures mediated 21.3% of the protective effect of the clean lighting intervention on improved respiratory symptoms. CONCLUSIONS: A clean lighting intervention altered both non-viral and viral gut microbial community members, reduced air pollution exposure, and improved respiratory symptoms. One mechanism by which air pollution reduction interventions may improve respiratory symptoms is through changes in gut microbiota. Future large randomized controlled trials of air pollution interventions should investigate the potential of the gut microbiome as a target for interventions to reduce the harmful effects of household air pollution on lung health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".