Associations of prenatal exposure to polybrominated diphenyl ethers and polychlorinated biphenyls with long-term gut microbiome structure: a pilot study
Bibliographic record
Abstract
Background: The gut microbiome is influenced by early-life exposures, but—despite potentially enormous implications for child health—is understudied in environmental epidemiology. This pilot study is one of the first to explore in utero exposures and long-term gut microbiome profiles. We examined the association between exposure to polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) during pregnancy and the mid-childhood gut microbiome. Methods: We measured levels of PBDE-47, PBDE-99, PBDE-100, and PBDE-153 and PCB-138, PCB-153, and PCB-180 in maternal plasma during early pregnancy (n = 18) and at delivery (n = 25) in women of European descent who breastfed the child participant of the Gestation and Environment cohort in Sherbrooke, Québec (recruited 2007–2009). Bacteria in the mid-childhood (6–8 years) fecal microbiome were detected with 16S rRNA sequencing. To test for differences at the taxon level, we used the Microbiome Comprehensive Association Mapping algorithm. Results: Early pregnancy PCB-153, PCB-180, and the sum of PCBs (Σ3PCB) concentrations were associated with a higher relative abundance of Propionibacteriales and Propionibacteriaceae in mid-childhood. Higher PCB-180 and Σ3PCB were associated with higher relative abundance of Bacillales Family XI. Higher PBDE-99 exposure was associated with a decrease in uncultured bacteria within the Ruminococcaceae NK4A214 group and PBDE-47 was associated with differences in Ruminococcus 2. These taxon-level changes did not result in differences within- or between-subject diversity. Exposures at delivery were not associated with differences in taxa. Conclusions: Prenatal exposure to PCBs and PBDEs is associated with mid-childhood gut microbiome profiles. Larger studies are needed to confirm these results and explore health implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".