Postnatal exposure to household disinfectants, infant gut microbiota and subsequent risk of overweight in children
Bibliographic record
Abstract
BACKGROUND: Emerging links between household cleaning products and childhood overweight may involve the gut microbiome. We determined mediating effects of infant gut microbiota on associations between home use of cleaning products and future overweight. METHODS: From the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort, we tested associations between maternal report of cleaning product use and overweight at age 3, and whether associations were mediated by microbial profiles of fecal samples in 3- to 4-month-old infants. RESULTS: Among 757 infants, the abundance of specific gut microbiota was associated with household cleaning with disinfectants and eco-friendly products in a dose-dependent manner. With more frequent use of disinfectants, Lachnospiraceae increasingly became more abundant (highest v. lowest quintile of use: adjusted odds ratio [AOR] 1.93, 95% confidence interval [CI] 1.08 to 3.45) while genus Haemophilus declined in abundance (highest v. lowest quintile of use: AOR 0.36, 95% CI 0.20 to 0.65). Enterobacteriaceae were successively depleted with greater use of eco-friendly products (AOR 0.45, 95% CI 0.27 to 0.74). Lachnospiraceae abundance significantly mediated associations of the top 30th centile of household disinfectant use with higher body mass index (BMI) z score (p = 0.02) and with increased odds of overweight or obesity (p = 0.04) at age 3. Use of eco-friendly products was associated with decreased odds of overweight or obesity independently of Enterobacteriaceae abundance (AOR 0.44, 95% CI 0.22 to 0.86), with no significant mediation (p = 0.2). INTERPRETATION: Exposure to household disinfectants was associated with higher BMI at age 3, mediated by gut microbial composition at age 3–4 months. Although child overweight was less common in households that cleaned with eco-friendly products, the lack of mediation by infant gut microbiota suggests another pathway for this association.
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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".