Malnutrition and depression in pregnancy and associations with child behaviour and cognitive function: a review of recent evidence on unique and joint effects
Bibliographic record
Abstract
Accumulating studies suggest that prenatal experiences can shape a child's neurodevelopment. Malnutrition and depression occur in pregnancy relatively often and may affect child neurodevelopment independently as well as synergistically. We aimed to provide an overview of recent studies that have examined malnutrition and (or) depression in pregnancy and associations with child behavioural problems and cognitive function. We conducted a literature search in PubMed, using the following main search terms: "depression", "nutrition", "BMI", "pregnancy", "offspring", "cognition", and "behaviour". We included studies in human populations published from 2013 onwards. The literature search yielded 1531 articles, of which 55 were included in the current review. We presented the evidence on the associations between prenatal markers of nutritional status and (or) depression and child behaviour and (or) cognitive function. We additionally discussed interventions and mechanisms. Both malnutrition and depression in pregnancy are associated with increased externalizing behavioural problems and attentional deficits, and to some extent with poorer cognitive function in the child, but the evidence is not conclusive. Studies on synergistic effects of both factors on child behaviour and cognitive function are still scarce, and more research is needed. Potential shared mechanisms include the hypothalamic-pituitary-adrenal axis, the immune system, epigenetics, and oxidative stress.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".