Maternal Dietary Patterns and Risk of Postpartum Depression: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Postpartum depression (PPD) has deleterious effects on both maternal and child outcomes. Poor maternal nutrition during pregnancy has been implicated in the development of PPD. This review aimed to explore the association between the overall dietary intake patterns during pregnancy and the development of PPD. METHODS: A literature search was performed in PubMed, Embase, Scopus, CINAHL, and PsycINFO databases for relevant randomized controlled trials, cohort and cross-sectional studies published up to 17th September 2020. Included studies assessed at least one dietary pattern during pregnancy and reported on PPD. The Newcastle Ottawa Scale and the Joanna Briggs Institute critical appraisal tools were used to assess the quality of methodology. A narrative analysis was conducted. RESULTS: Ten studies (eight cohort and two cross-sectional) were included with substantial heterogeneity in measurements of dietary intake exposures and PPD. The studies identified several types of healthy dietary patterns, including a 'healthy', 'health conscious', 'Japanese', 'high-glycemic index/glycemic load', 'Vegetable', 'Nut-Fruit', 'Seafood', and 'compliance with the Australian Dietary Guidelines'. The 'Western', 'unhealthy', 'Beverage', 'Cereal-Meat', and 'Egg' were labelled as unhealthy dietary patterns. Four of the eight studies showed an inverse association between adherence to healthy diets and risk of PPD, whereas only one of the seven studies showed that adherence to unhealthy diets was associated with increased risk of PPD. Methodological quality of the studies varied across the sample. CONCLUSIONS: Our findings indicate that adherence to a healthy diet may be beneficial for PPD. However, the relationship between unhealthy diets and PPD needs to be corroborated by more high-quality studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".