Food allergy prevention in progeny by prebiotics supplementation during pregnancy in a preclinical study
Bibliographic record
Abstract
Background: Food allergies (FAs) are associated with alterations in the gut microbiota, epithelial barrier and immune tolerance. These dysfunctions are observed in the first month of life, revealing that early intervention is crucial for disease prevention. Nutritional strategies such as prebiotics may reduce FAs in children. Indeed some prebiotics such as galacto-oligosaccharides (GOS) and inulin are able to induce tolerance, epithelial barrier reinforcementand gut microbiota modulation, but the ideal period for intervention is unknown. Herein, we investigated whether GOS/inulin supplementation during gestation could protect progeny against FAs in mice. Methods: The mothers received a control diet or an enriched diet with GOS/inulin exclusively during the pregnancy. At the weaning, pups were intraperitoneally sensitized and orally challenged with a wheat allergen. After the challenge pups symptoms were evaluated and we analyzed allergic and tolerogenic parameters. Moreover, mothers and pups fecal microbiota and short chain fatty acids (SCFAs) were analyzed throughout the protocol. Results: We demonstrated that prebiotics supplementation induced a strong restructuration of the fecal microbiota of mice toward beneficial strains during gestation and partially during mid-lactation. This specific microbiota was transferred to pups and maintained to adulthood. Moreover, B and T regulator subsets were increased in pups born from supplemented mothers, inducing a tolerogenic environment and protecting them against FAs. Conclusions: Our study demonstrates that prebiotics supplementation during pregnancy induces on the offspring a tolerogenic environment and a microbial imprint, leading to a reduction of FA development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".