Evaluating the relationship between the nutrient intake of lactating women and their breast milk nutritional profile: a systematic review and narrative synthesis
Bibliographic record
Abstract
Abstract Maternal diet influences breast milk nutritional profile; however, it is unclear which nutrients and contaminants are particularly responsive to short- and long-term changes in maternal intake, and the impact of specific exclusion diets, such as vegan or vegetarian. This study systematically reviewed the literature on the effects of maternal nutrient intake, including exclusion diets, on both the nutrient and contaminant content of breast milk. The electronic databases, PubMed, CENTRAL, Web of Science and CINALH were systematically searched until 4 June 2023, with additionally searches of reference lists (PROSPERO, CRD42020221577). The quality of the studies was examined using Cochrane Risk of Bias tool and Newcastle–Ottawa scale. Eighty-eight studies (n 6577) met the search criteria. Due to high heterogeneity, meta-analysis was not possible. There was strong evidence of response to maternal intakes for DHA and EPA, vitamins A, E and K, iodine and Se in breast milk composition, some evidence of response for α-linolenic acid, B vitamins, vitamin C and D, ovalbumin, tyrosine and contaminants, and insufficient evidence to identify the effects arachidonic acid, Cu, Fe, Zn and choline. The paucity of evidence and high heterogeneity among studies reflects the need for more high-quality trials. However, this review identified the importance of maternal intake in the nutritional content of breast milk for a wide range of nutrients and supports the recommendation for supplementation of DHA and vitamin B12 for those on restrictive diets.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".