Associations of In Utero Polybrominated Diphenyl Ethers (PBDEs) and Polychlorinated Biphenyls (PCBs) with the Mid-childhood Gut Microbiome
Bibliographic record
Abstract
Background: The gut microbiome is influenced by early-life exposures, but—despite potentially enormous implications for child health—it is understudied in environmental health. This pilot study, the first to explore the effect of in utero exposures on long-term development of the microbiome, examined the association between first trimester and perinatal exposure to PBDEs and PCBs and the mid-childhood gut microbiome.Methods: We measured metabolites of PBDE-47, -99, -100, and -153 and PCB-138, -153, and -180 in maternal plasma during the first trimester (n=18) and at delivery (n=25) using gas chromatography coupled with tandem mass spectrometry in women from Sherbrooke, Quebec who identified as white and ever breastfed the child subject. The structure of the mid-childhood (6-8 years) fecal microbiome was measured using 16S rRNA sequencing. Sequences were processed using QIIME and paired to operational taxonomic units (OTUs) using SILVA v128. To test for differences at the OTU level, we used the MiCAM algorithm, adjusting for delivery mode and socioeconomic status.Results: Higher first trimester PCB-153, -180, and Σ3PCB blood 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, but the direction of the association varied by lower-level taxa. These OTU-level changes did not result in differences to within- or between-subject diversity. Exposures at delivery were not associated with differences in OTUs.Conclusion: Early-life exposure to PCBs and PBDEs was associated with differences in the mid-childhood gut microbiome. Larger studies are needed to confirm these results and explore health implications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".