Impact of maternal intrapartum antibiotics, method of birth and breastfeeding on gut microbiota during the first year of life: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Dysbiosis of the infant gut microbiota may have long-term health consequences. This study aimed to determine the impact of maternal intrapartum antibiotic prophylaxis (IAP) on infant gut microbiota, and to explore whether breastfeeding modifies these effects. DESIGN: Prospective pregnancy cohort of Canadian infants born in 2010-2012: the Canadian Healthy Infant Longitudinal Development (CHILD) Study. SETTING: General community. SAMPLE: Representative sub-sample of 198 healthy term infants from the CHILD Study. METHODS: Maternal IAP exposures and birth method were documented from hospital records and breastfeeding was reported by mothers. Infant gut microbiota was characterised by Illumina 16S rRNA sequencing of faecal samples at 3 and 12 months. MAIN OUTCOME MEASURES: Infant gut microbiota profiles. RESULTS: In this cohort, 21% of mothers received IAP for Group B Streptococcus prophylaxis or pre-labour rupture of membranes; another 23% received IAP for elective or emergency caesarean section (CS). Infant gut microbiota community structures at 3 months differed significantly with all IAP exposures, and differences persisted to 12 months for infants delivered by emergency CS. Taxon-specific composition also differed, with the genera Bacteroides and Parabacteroides under-represented, and Enterococcus and Clostridium over-represented at 3 months following maternal IAP. Microbiota differences were especially evident following IAP with emergency CS, with some changes (increased Clostridiales and decreased Bacteroidaceae) persisting to 12 months, particularly among non-breastfed infants. CONCLUSIONS: Intrapartum antibiotics in caesarean and vaginal delivery are associated with infant gut microbiota dysbiosis, and breastfeeding modifies some of these effects. Further research is warranted to explore the health consequences of these associations. TWEETABLE ABSTRACT: Maternal #antibiotics during childbirth alter the infant gut #microbiome.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".