Probiotics supplementation during pregnancy or infancy on multiple food allergies and gut microbiota: a systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: Probiotics show promise in preventing and managing food allergies, but the impact of supplementation during pregnancy or infancy on children's allergies and gut microbiota remains unclear. OBJECTIVE: This study aimed to assess the effects of maternal or infant probiotic supplementation on food allergy risk and explore the role of gut microbiota. DATA SOURCES: A systematic search of databases (PubMed, Cochrane Library, Embase, and Medline) identified 37 relevant studies until May 20, 2023. DATA EXTRACTION: Two independent reviewers extracted data, including probiotics intervention details, gut microbiota analysis, and food allergy information. DATA ANALYSIS: Probiotics supplementation during pregnancy and infancy reduced the risk of total food allergy (relative risk [RR], 0.79; 95% CI, 0.63-0.99), cow-milk allergy (RR, 0.51; 95% CI, 0.29-0.88), and egg allergy (RR, 0.57; 95% CI, 0.39-0.84). Infancy-only supplementation lowered cow-milk allergy risk (RR, 0.69; 95% CI, 0.49-0.96), while pregnancy-only had no discernible effect. Benefits were observed with over 2 probiotic species, and a daily increase of 1.8 × 109 colony-forming units during pregnancy and infancy correlated with a 4% reduction in food allergy risk. Children with food allergies had distinct gut microbiota profiles, evolving with age. CONCLUSIONS: Probiotics supplementation during pregnancy and infancy reduces food allergy risk and correlates with age-related changes in gut microbial composition in children. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42023425988.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".