Effect of maternal diet on micronucleus frequency in pregnant women and newborns: a systematic review
Bibliographic record
Abstract
The effect of diet on maternal and infant genetic levels has been reported in the literature. Diet-associated DNA damage, such as the presence of micronuclei (MN), may be related to an increased risk of developing chronic diseases such as cancer. There is particular concern about this damage during pregnancy as it may affect the newborn (NB). Thus, this review aims to summarize the primary evidence of the impact of diet on the frequency of MN in the mother-infant population. This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42022302401. For elaboration, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) guidelines were considered. PubMed, Embase, Web of Science, Scopus, Science direct databases were used to search for observational studies. Google Scholar and manual search were required to perform the “grey literature” search. Inclusion criteria were observational studies that evaluated the diet consumed by pregnant women (without chronological and gestational age restriction) using a food consumption frequency questionnaire (FFQ) and investigated the frequency of MN in these women and their NBs. No restrictions were applied regarding year of publication and language. Data analysis and extraction was performed by three reviewers independently. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). The co-occurrence of the terms included in the articles was verified and a synthesis was carried out for the main findings of the selected ones. The search strategy retrieved 4558 records. Of these, 13 were read in full and 5 were included in the review. Most studies were of the cohort type (n= 4) and were carried out in the European region. A total of 875 pregnant women and 238 newborns were evaluated. Despite insufficient evidence to confirm that the diet changes the frequency of MN, the included studies found possible effects on the consumption of fried red meat and processed meats and the adequate consumption of vegetables and polyunsaturated fats. Future research is needed so that we can understand the effects of diet on genetic stability and have evidence to help plan public policies on food and nutrition or reinforce protective dietary patterns for this and future generations.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".