Perinatal diet and offspring anxiety: A scoping review
Bibliographic record
Abstract
Health behaviors during pregnancy have an impact on the developing offspring. Dietary factors play a role in the development of mental illness: however, less is known about the impact of diet factors during pre-conception, gestation, and lactation on anxiety levels in offspring. This scoping review sought to systematically map the available research involving human and animal subjects to identify nutritional interventions which may have a harmful or protective effect, as well as identify gaps. Studies investigating an association between any perinatal diet pattern or diet constituent and offspring anxiety were included. The number of studies reporting an association with increased or decreased levels of anxiety were counted and presented in figures. A total of 55,914 results were identified as part of a larger scoping review, and 120 articles met the criteria for inclusion. A greater intake of phytochemicals and vitamins were associated with decreased offspring anxiety whereas maternal caloric restriction, protein restriction, reduced omega-3 consumption, and exposure to a high fat diet were associated with higher levels of offspring anxiety. Results were limited by a very large proportion of animal studies. High quality intervention studies involving human subjects are warranted to elucidate the precise dietary factors or constituents that modulate the risk of anxiety in offspring.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".