Prebiotics for the prevention of allergies: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Prevalence of allergic diseases in infants is approximately 10% reaching 20 to 30% in those with an allergic first-degree relative. Prebiotics are selectively fermented food ingredients that allow specific changes in composition/activity of the gastrointestinal microflora. They modulate immune responses, and their supplementation has been proposed as an intervention to prevent allergies. OBJECTIVE: To assess in pregnant women, breastfeeding mothers, and infants (populations) the effect of supplementing prebiotics (intervention) versus no prebiotics (comparison) on the development of allergic diseases and to inform the World Allergy Organization guidelines. METHODS: We performed a systematic review of studies assessing the effects of prebiotic supplementation with an intention to prevent the development of allergies. RESULTS: Of 446 unique records published until November 2016 in Cochrane, MEDLINE, and EMBASE, 22 studies fulfilled a priori specified criteria. We did not find any studies of prebiotics given to pregnant women or breastfeeding mothers. Prebiotic supplementation in infants, compared to placebo, had the following effects: risk of developing eczema (RR: 0.68, 95% CI: 0.40 to 1.15), wheezing/asthma (RR, 0.37; 95% CI: 0.17 to 0.80), and food allergy (RR: 0.28, 95% CI: 0.08 to 1.00). There was no evidence of an increased risk of any adverse effects (RR: 1.01, 95% CI: 0.92 to 1.10). Prebiotic supplementation had little influence growth rate (MD: 0.92 g per day faster with prebiotics, 95% CI: 0 to 1.84) and the final infant weight (MD: 0.10 kg higher with prebiotics, 95% CI: -0.09 to 0.29). The certainty of these estimates is very low due to risk of bias and imprecision of the results. CONCLUSIONS: Currently available evidence on prebiotic supplementation to reduce the risk of developing allergies is very uncertain.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".