Impact of Early-Life Antibiotic Exposure on the Risk of Childhood Obesity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Childhood obesity is a growing global health challenge linked to long-term metabolic, cardiovascular, and psychosocial consequences. Emerging evidence suggests that early-life antibiotic exposure, a common disruptor of gut microbiota during critical developmental windows, may increase the risk of overweight and obesity.Objective: To systematically evaluate and quantify the association between antibiotic exposure in the first two years of life and the subsequent risk of overweight or obesity in childhood, with subgroup analyses by antibiotic type, timing, and sex.Methods: A systematic review and meta-analysis was conducted following PRISMA guidelines. PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to January 15, 2025, without language restrictions. Eligible studies included prospective or retrospective cohorts assessing systemic antibiotic exposure in children aged ≤24 months with later overweight/obesity outcomes. Data were extracted independently by two reviewers, and study quality was assessed using the Newcastle–Ottawa Scale (NOS). Random-effects models were used to generate pooled effect estimates, with heterogeneity assessed via I² statistics.Results: Three high-quality cohort studies (n = 365,306 participants) from China, Canada, and the USA met inclusion criteria. Antibiotic exposure within the first 24 months was consistently associated with increased BMI z-scores and higher odds of overweight/obesity. The largest study (n = 362,550) showed a dose–response relationship, with incremental BMI increases per additional antibiotic course (p < 0.001). Broad-spectrum antibiotics were linked to slightly higher BMI z-scores compared to narrow-spectrum agents, and cephalosporins/macrolides exhibited stronger associations. A sex-specific effect was noted in one study, with significant risk elevation among boys (aOR = 5.35, 95% CI: 1.94–14.72). Moderate heterogeneity was observed (I² range: 38–52%), but the direction of effect remained consistent across subgroups.Conclusion: Early-life antibiotic exposure, particularly broad-spectrum and repeated courses, is associated with an increased risk of overweight and obesity in childhood. These findings highlight the need for cautious antibiotic prescribing in infancy as part of obesity prevention strategies. Future research integrating microbiome profiling is warranted to elucidate causal pathways and identify high-risk subgroups.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".