Effect of Household Air Pollution on the Gut Microbiome and Virome of Adult Women Living in Uganda
Bibliographic record
Abstract
BACKGROUND: Emerging observational studies suggest air pollution can influence the gut microbiome. However, this association is often highly confounded by factors such as diet and poverty. The gut virome may influence respiratory health independent of the gut microbiome. We recently demonstrated in a randomized waitlist-controlled trial (ClinicalTrials.gov NCT03351504) that a clean lighting intervention reduced personal exposure to air pollution among adult women in rural Uganda. OBJECTIVES: To determine the effect of a solar lighting intervention on changes to the gut microbiome and virome and secondarily to determine association between these changes on lung health. METHODS: Between 2018 and 2019, we collected stool samples and assessed respiratory symptoms and spirometry from 80 adult women living in rural Uganda at baseline, 12 and 18 months post-randomization. The intervention group received a solar lighting system after randomization, while the waitlist-controlled group received one at 12 months. Deep metagenomics sequencing of stool was performed and profiled for non-viral and viral taxonomic composition. The primary analysis focused on pre- vs. post-intervention changes due power considerations, adjusting for potential confounding by age, diet, antibiotic use, and season. A sensitivity analysis was conducted using intention-to-treat principles. When comparing pre- vs. post-intervention periods, we used sparse partial least squares models to identify non-viral and viral signatures of reduced air pollution exposure. Mixed effects models were used to evaluate changes in health outcomes as well as associations between microbial signatures of reduced air pollution exposure and health. RESULTS: The average age was 39.2 years. The solar lighting intervention led to larger changes in viral compared to non-viral microbial community structure and differential abundance of bacteria, eukaryotes, and viruses. Provision of solar lighting systems was associated with a reduction in the presence of respiratory symptoms from 57.1% to 36.1% (p = 0.002) while there was no impact on lung function. Microbiome and virome signatures had AUCs of 0.74 and 0.76 respectively, in predicting pre- vs. post-intervention stool samples. Microbiome signatures were associated with a lower risk of respiratory symptoms (OR 0.68 (0.49-0.94), p = 0.020). CONCLUSION: Among adult women living in rural Uganda, both non-viral and viral components of the gut microbial community changed after a clean lighting intervention. Microbiome signatures reflective of lower air pollution exposures were associated with improved respiratory symptoms. These observations suggest that air pollution may influence lung health through the gut-lung axis, warranting further exploration in future intervention studies. https://doi.org/10.1289/EHP16002.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".