Neonatal Diet and Gut Microbiome Development After C-Section During the First Three Months After Birth: A Systematic Review
Bibliographic record
Abstract
Background Cesarean section (C-section) delivery imprints fundamentally on the gut microbiota composition with potential health consequences. With the increasing incidence of C-sections worldwide, there is a need for precise characterization of neonatal gut microbiota to understand how to restore microbial imbalance after C-section. After birth, gut microbiota development is shaped by various factors, especially the infant’s diet and antibiotic exposure. Concerning diet, current research has proposed that breastfeeding can restore the characteristic gut microbiome after C-section. Objectives In this systematic review, we provide a comprehensive summary of the current literature on the effect of breastfeeding on gut microbiota development after C-section delivery in the first 3 months of life. Methods The retrieved data from PubMed, Scopus, and Web of Science were evaluated according to the PICO/PECO strategy. Quality assessment was conducted by the Newcastle–Ottawa Scale. Results After critical selection, we identified 14 out of 4,628 studies for the evaluation of the impact of the diet after C-section delivery. The results demonstrate consistent evidence that C-section and affiliated intrapartum antibiotic exposure affect Bacteroidetes abundance and the incapacity of breastfeeding to reverse their reduction. Furthermore, exclusive breastfeeding shows a positive effect on Actinobacteria and Bifidobacteria restoration over the 3 months after birth. None of the included studies detected any significant changes in Lactobacillus abundance in breastfed infants after C-section. Conclusion C-section and intrapartum antibiotic exposure influence an infant’s gut microbiota by depletion of Bacteroides, regardless of the infant’s diet in the first 3 months of life. Even though breastfeeding increases the presence of Bifidobacteria, further research with proper feeding classification is needed to prove the restoration effect on some taxa in infants after C-section. Systematic Review Registration: [ www.crd.york.ac.uk/prospero/ ], identifier [CRD42021287672].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".