Infant gut microbiome shows resilience against mode of delivery but is susceptible to feed type
Bibliographic record
Abstract
As cesarean section and formula feeding become increasingly popular, it is important to examine how mode of delivery and diet influence the development and composition of the infant gut microbiome. Awareness of the factors that influence an infant’s gut flora could help improve their short- and long-term health outcomes. With this in mind, here we investigate the influence of delivery and feeding method on the early infant gut microbiome; specifically, how vaginal versus cesarean section birth, and breastfeeding versus formula feeding impact gut flora. Additionally, we attempt to determine whether the type of feed and the method of delivery impact the vertical transmission of gut bacteria from mother to infant, and how the infant microbiome composition changes over time. We are able to show that breastfed and formula-fed infants had similar gut community richness but differed in microbial community composition at 6 months of age. Microbes belonging to the Veillonellaceae and Pasteurellaceae families were found to be more abundant in the gut microbiomes of breastfed infants, whereas microbes belonging to the Lachnospiraceae family were more abundant in formula-fed infants. Infants delivered via cesarean section and vaginal modes did not significantly differ in community richness and microbiome composition at 0.5 months of age. Microbes from the Lachnospiraceae family were found to be more abundant in the gut microbiomes of the cesarean section infants. All infant groups (cesarean section, vaginally delivered, breastfed and formula-fed infants) maintained stable community differences from their mothers over the 6-month period tested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".