Early‐life risk factors for food allergy: Dietary and environmental factors revisited
Bibliographic record
Abstract
There appears a steep increase in the prevalence of food allergy worldwide in the past few decades. It is believed that, rather than genetic factors, the recently altered dietary and environmental factors are the driving forces behind the rapid increase of this disease. Accumulating evidence has implied that external exposures that occurred in prenatal and postnatal periods could affect the development of oral tolerance in later life. Understanding the potential risk factors for food allergy would greatly benefit the progress of intervention and therapy. In this review, we present updated knowledge on the dietary and environmental risk factors in early life that have been shown to impact the development of food allergy. These predominantly include dietary habits, microbial exposures, allergen exposure routes, environmental pollutants, and so on. The key evidence, conflicts, and potential research topics of each theory are discussed, and associated interventional strategies to prevent the disease development and ameliorate treatment burden are included. Accumulating evidence has supported the causative role of certain dietary and environmental factors in the establishment of oral tolerance in early life, especially the time of introducing allergenic foods, skin barrier function, and microbial exposures. In addition to certain immunomodulatory factors, increasing interest is raised toward modern dietary patterns, where adequately powered studies are required to identify contributions of those modifiable risk factors. This review broadens our understanding of the connections between diet, environment, and early-life immunity, thus benefiting the progress of intervention and therapy of food allergy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".