Breastfeeding enrichment of B. longum subsp. infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk
Bibliographic record
Abstract
BACKGROUND: Early antibiotic exposure is linked to persistent disruption of the infant gut microbiome and subsequent elevated pediatric asthma risk. Breastfeeding acts as a primary modulator of the gut microbiome during early life, but its effect on asthma development has remained unclear. METHODS: We harnessed the CHILD cohort to interrogate the influence of breastfeeding on antibiotic-associated asthma risk in a subset of children (n = 2,521). We then profiled the infant microbiomes in a subset of these children (n = 1,338) using shotgun metagenomic sequencing and compared human milk oligosaccharide and fatty acid composition from paired maternal human milk samples for 561 of these infants. FINDINGS: Children who took antibiotics without breastfeeding had 3-fold higher asthma odds, whereas there was no such association in children who received antibiotics while breastfeeding. This benefit was associated with widespread "re-balancing" of taxonomic and functional components of the infant microbiome. Functional changes associated with asthma protection were linked to enriched Bifidobacterium longum subsp. infantis colonization. Network analysis identified a selection of fucosylated human milk oligosaccharides in paired maternal samples that were positively associated with B. infantis and these broader functional changes. CONCLUSIONS: Our data suggest that breastfeeding and antibiotics have opposing effects on the infant microbiome and that breastfeeding enrichment of B. infantis is associated with reduced antibiotic-associated asthma risk. FUNDING: This work was supported in part by the Canadian Institutes of Health Research; the Allergy, Genes and Environment Network of Centres of Excellence; Genome Canada; and Genome British Columbia.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".