Early and frequent exposure to antibiotics in children and the risk of obesity: systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Background: This study aimed to systematically evaluate the available evidence on prenatal and early infancy antibiotic exposure and the association with overweight and obesity in later childhood. Methods: We conducted a comprehensive search of Embase, MEDLINE, and Web of Science for observational studies assessing prenatal and early antibiotic exposure on the risk of overweight and obesity. We independently assessed the risk of bias using the ROBINS instrument and the overall quality of evidence using the GRADE approach. Results: Our search identified thirteen observational studies including 554,983 participants; most studies were at moderate risk of bias. We found a statistically significant impact of early antibiotic exposure and the risk of being overweight later in childhood (OR 1.18; 95% CI 1.05 to 1.34) (very low quality evidence). We also found that early childhood antibiotic exposure was associated with the risk for childhood obesity (OR 1.14; 95% CI 1.04 to 1.24) (very low quality evidence). Conclusions: Very low quality evidence suggests that exposure to antibiotics early in life may be associated with an increased risk of being overweight and obese in later childhood. However, very low quality evidence raises serious questions about the plausibility of prenatal and early infancy antibiotic exposure being causally related to weight in children. PROSPERO registration : CRD42016050011 (14/12/2016)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.006 | 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".