Associations of Stool Metal Exposures with Childhood Gut Microbiome Multiomics Profiles in a Prospective Birth Cohort Study
Bibliographic record
Abstract
Metal exposures are closely related to childhood developmental health. However, their effects on the childhood gut microbiome, which also impacts health, are largely unexplored using microbiome multiomics including the metagenome and metatranscriptome. This study examined the associations of fecal profiles of metal/element exposures with gut microbiome species and active functional pathways in 8- to 12-year-old children ( N = 116) participating in the GESTation and Environment (GESTE) cohort study. We analyzed 19 stool metal and element concentrations (B, Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Mo, Cd, Ba, and Pb). Covariate-adjusted linear regression models identified several significant microbiome associations with continuous stool metal/element concentrations. For instance, Zn was positively associated with Turicibacter sanguinis (coef = 1.354, q -value = 0.039) and negatively associated with Eubacterium eligens (coef = −0.794, q -value = 0.044). Higher concentrations of Cd were associated with lower Eubacterium eligens (coef = −0.774, q -value = 0.045). Additionally, a total of 490 significant functional pathways such as biosynthesis and degradation/utilization/assimilation were identified, corresponding to different functions, including amino acid synthesis and carbohydrate degradation. Our results suggest links among metal exposures, pediatric gut microbiome multiomics, and potential health implications. Future work will further explore their relation to childhood 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".