Early life exposures and the risk of inflammatory bowel disease: Systematic review and meta-analyses
Bibliographic record
Abstract
BACKGROUND: Early life exposures impact immune system development and therefore the risk of immune-mediated diseases, including inflammatory bowel disease (IBD). We systematically reviewed the impact of pre-, peri‑, and postnatal exposures up to the age of five years on subsequent IBD diagnosis. METHODS: We identified case-control and cohort studies reporting on the association between early life environmental factors and Crohn's disease (CD), ulcerative colitis (UC), or IBD overall. Databases were search from their inception until May 24th, 2019 until July 14th, 2020. We conducted meta-analyses for quantitative review of relevant risk factors that were comparable across studies and qualitative synthesis of the literature for a wide range of early life exposures, including maternal health and exposures during pregnancy, perinatal factors, birth month and related-factors, breastfeeding, hygiene-related factors and social factors, immigration, antibiotics, offspring health, including infections, and passive smoking. PROSPERO registration: CRD42019134980. FINDINGS: Prenatal exposure to antibiotics (OR 1.8; 95% CI 1.2-2.5) and tobacco smoke (OR 1.5; 95% CI 1.2-1.9), and early life otitis media (OR 2.1; 95% CI 1.2-3.6) were associated with IBD. There was a trend towards an association between exposure to antibiotics in infancy and IBD (OR: 1.7, 95% CI 0.97, 2.9), supported by positive data on population-based data. Breastfeeding was protective against IBD. Other early life risk factors had no association with IBD, but data were limited and heterogenous. INTERPRETATION: Early life is an important period of susceptibility for IBD development later in life. Tobacco smoke, infections and antibiotics were associated positively, and breastfeeding was associated negatively with IBD. Our findings offer an opportunity to develop primary prevention strategies. FUNDING: This study did not receive any funding.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".