The association between antibiotic use in infancy and childhood overweight or obesity: a systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: Antibiotic use is associated with alteration of the gut microbiome and metabolic activity. As childhood obesity is a predisposing factor for adult obesity, addressing childhood risk factors to weight gain in early life is important. This review aims to investigate the association between infant antibiotic exposure (aged < 24 months) and childhood obesity or overweight. METHODS: Articles were retrieved from CINAHL, Cochrane CENTRAL, Embase and MEDLINE. Eligible articles investigated antibiotic use in exposed versus unexposed infants and measured childhood weight change. Data were synthesized narratively and meta-analysed where possible. RESULTS: After title/abstract and full-text screening, 17 articles representing 15 unique studies were included for narrative synthesis. We found a small association between antibiotic exposure in infancy (<24 months) and childhood overweight or obesity. The strongest associations were observed in boys versus girls and children exposed to multiple antibiotic courses or broad-spectrum drugs. Meta-analysis of 12 sets of results comparing the earliest age of exposure to any antibiotic with overweight or obesity at the latest age of outcome found a pooled odds ratio of 1.05 (95% confidence interval: 1.00-1.11). CONCLUSIONS: Antibiotic exposure in infants, aged < 24 months, was associated with a small increase in odds of childhood overweight or obesity in some subgroups of children.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".