Relationship between Infant Feeding and the Microbiome: Implications for Allergies and Food Intolerances
Bibliographic record
Abstract
Childhood is a critical period for immune system development, which is greatly influenced by the gut microbiome. Likewise, a number of factors affect the gut microbiome composition and diversity, including breastfeeding, formula feeding, and solid foods introduction. In this regard, several studies have previously demonstrated that breastfeeding promotes a favorable microbiome. In contrast, formula feeding and the early incorporation of certain solid foods may adversely affect microbiome development. Additionally, there is increasing evidence that disruptions in the early microbiome can lead to allergic conditions and food intolerances. Thus, developing strategies to promote optimal infant nutrition requires an understanding of the relationship between infant nutrition and long-term health. The present review aims to examine the relationship between infant feeding practices and the microbiome, as well as its implications on allergies and food intolerances in infants. Moreover, this study synthesizes existing evidence on how different eating habits influence the microbiome. It highlights their implications for the prevention of allergies and food intolerances. In conclusion, introducing allergenic solid foods before six months, alongside breastfeeding, may significantly reduce allergies and food intolerances risks, being also associated with variations in gut microbiome and related complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".