Impact of Maternal Microbiota Composition on Neonatal Immunity and Early Childhood Allergies: A Systematic Review
Bibliographic record
Abstract
Background: The maternal microbiota serve as a key regulator of neonatal immune development and early-life health outcomes. This systematic review aims to find out how the makeup of the maternal microbiota affects newborn immunity and the risk of allergies, identify which microbes are linked to a higher or lower chance of allergies, and assess treatments that could improve newborn immune health. Methods: We conducted a systematic search in PubMed, MEDLINE, and Web of Science, adhering to the PRISMA guidelines. We included randomized controlled trials (RCTs), cohort studies, and observational studies that looked at how the makeup of the maternal microbiota affects newborn immune responses or allergic outcomes in early life. We conducted a systematic search, and the quality of the studies was evaluated using the GRADE system and tools to check for bias (RoB 2, Newcastle–Ottawa Scale, MINORS). Results: We included a total of 74 studies. The main findings showed that having a cesarean delivery and using certain antibiotics during pregnancy increased the risk of allergies, while breastfeeding, taking probiotics, and changing the mother’s diet helped to protect against allergies. Maternal stress had a negative association with the microbiota composition (OR = 1.9–2.4) and neonatal immune regulation. Moreover, the study noted significant geographic variation in the microbiota’s influence, underscoring the importance of contextualized interventions. Conclusions: The composition of the maternal microbiota has a major impact on neonatal immunity and the risk of early-life allergy. Adverse factors include cesarean birth, antibiotic exposure, and maternal stress, all of which have been associated with alterations in neonatal immunity. More studies are required to validate promising microbiota-targeted strategies and develop evidence-based guidelines to improve maternal and neonatal immune health.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".