Maternal diet before and during pregnancy and risk of asthma and allergic rhinitis in children
Bibliographic record
Abstract
BACKGROUND: Consumption of certain foods during pregnancy has been shown to have beneficial effects on childhood asthma and allergic disease development and aggravation. However, most studies provide conflicting results and the relationships between maternal preconceptional diet and risks of childhood asthma and allergic disease have not previously been explored. The objective of this study was to assess maternal diet during the year before pregnancy and the last 3 months of pregnancy and investigate their associations with the risks of asthma, wheezing, allergic rhinitis and atopic dermatitis in young children. METHODS: The study sample consisted of 1140 mother-child pairs from the EDEN cohort. Mothers had responded to the food frequency questionnaires used to assess diet before and during pregnancy. Children were followed up using health questionnaires. The health outcomes studied were: asthma, wheezing, allergic rhinitis and atopic dermatitis by the age of 3 years. RESULTS: Using multivariable-adjusted logistic regression models, significant inverse associations were observed between cooked green vegetable consumption before pregnancy and childhood asthma; consumption of eggs and raw vegetables before and during pregnancy, consumption of grains before pregnancy, and consumption of cooked green vegetables during pregnancy and allergic rhinitis. For the first time, a significant positive association was found between meat intake during the preconceptional period and a risk of wheezing, allergic rhinitis and atopic dermatitis. CONCLUSIONS: Based on our findings, preconceptional and prenatal maternal intake of certain type of food groups may be preventive against asthma, wheezing and allergic rhinitis, whereas higher maternal intake of meat before pregnancy may increase the risk of wheezing, allergic rhinitis and atopic dermatitis in young children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".