Association between early-life antibiotic exposure and gut microbiome alterations linked to allergic diseases in children: a systematic review
Bibliographic record
Abstract
BACKGROUND: Early-life exposure to antibiotics has been implicated in the disruption of gut microbiota development, potentially contributing to the onset of allergic diseases in childhood. This systematic review aimed to evaluate the association between early antibiotic exposure, gut microbiome alterations, and the risk of developing allergic conditions such as asthma, atopic dermatitis, and allergic rhinitis. METHODS: This review followed PRISMA 2020 guidelines. A comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted up to [insert date]. Eligible studies included observational and interventional designs involving participants from the prenatal stage to 10 years of age. Data were extracted on antibiotic type, exposure timing, microbiome changes, and allergic outcomes. Study quality was assessed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias tool for randomized trials. RESULTS: Fifteen studies involving over 1.5 million children met the inclusion criteria. The majority reported that antibiotic exposure during the prenatal period or the first two years of life was significantly associated with an increased risk of allergic diseases, particularly asthma and atopic dermatitis. Several studies also documented alterations in gut microbiota composition, including reduced Bifidobacterium and increased Clostridium and Klebsiella spps. Antibiotic type, duration, and timing of exposure were key factors influencing microbiota disruption and allergy development. CONCLUSION: There is growing evidence that early-life antibiotic exposure may predispose children to allergic diseases through gut microbiota disturbances. These findings support the cautious use of antibiotics during pregnancy and early childhood and underscore the need for further research into microbiota-preserving interventions and long-term outcomes.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 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".