Invisible Links: Associations Between Micronutrient Deficiencies and Postpartum Depression—A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Following childbirth, up to 20% of women may have postpartum depression (PPD), which can adversely affect the mother's health, the infant's development, and familial connections. Numerous causes exist, although recent research indicates that micronutrient shortages are modifiable biological factors. This systematic review aims to consolidate existing knowledge regarding the relationship between micronutrient levels and the risk of PPD. METHODS: This review was conducted in accordance with PRISMA 2020 guidelines and registered with PROSPERO. We reviewed every study published up to 1 April 2025, on PubMed, Scopus, and Web of Science. Nineteen studies met the inclusion criteria. We employed the Newcastle-Ottawa Scale to assess bias. RESULTS: Nineteen studies were included in the analysis. Vitamin D was the most extensively researched vitamin. The majority of the studies (9 out of 13) identified a significant correlation between low serum 25(OH)D levels and PPD symptoms. Individuals with diminished levels of vitamin B12 and zinc had an elevated risk of PPD. There was insufficient evidence for folate, magnesium, iron, and selenium. This was frequently due to methodological discrepancies, insufficient control of confounding variables, and variations in biomarker timing. The majority of the studies exhibit a low to moderate likelihood of bias. CONCLUSIONS: Increasing evidence suggests that deficiencies in specific micronutrients, particularly vitamin D, vitamin B12, and zinc, may contribute to the onset of postpartum depression. The results indicate that targeted nutritional screening and management may be beneficial in perinatal mental health care, notwithstanding the inability to ascertain the exact causative factors. There is a necessity for more rigorous longitudinal investigations and randomised trials to enhance our understanding of processes and assist physicians in making informed judgements.
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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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".