Associations of Childhood and Perinatal Blood Metals with Children’s Gut Microbiomes in a Canadian Gestation Cohort
Bibliographic record
Abstract
BACKGROUND: The gut microbiome is important in modulating health in childhood. Metal exposures affect multiple health outcomes, but their ability to modify bacterial communities in children is poorly understood. OBJECTIVES: We assessed the associations of childhood and perinatal blood metal levels with childhood gut microbiome diversity, structure, species, gene family-inferred species, and potential pathway alterations. METHODS: ). We used multiple covariate-adjusted statistical models to determine microbiome associations with continuous blood metal levels, including linear regression (Shannon and Pielou alpha diversity indexes), permutational multivariate analysis of variance (adonis; beta diversity distance matrices), and multivariable association model (MaAsLin2; phylum, family, species, gene family-inferred species, and pathways). RESULTS: ). Finally, neither perinatal nor childhood metal concentrations were associated with children's gut microbial inter- and intrasubject diversity. DISCUSSION: Our findings suggest both long- and short-term associations between metal exposure and the childhood gut microbiome, with stronger associations observed with more recent exposure. Future epidemiologic analyses may elucidate whether the observed changes in the microbiome relate to children's health. https://doi.org/10.1289/EHP9674.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".