Associations of Maternal Nutritional Status and Supplementation with Fetal, Newborn, and Infant Outcomes in Low-Income and Middle-Income Settings: An Overview of Reviews
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Despite advances in maternal nutritional knowledge, the effect of maternal diet, micronutrient status and undernutrition, and the effect of maternal supplementation on fetal, neonatal and infant outcomes still have gaps in the literature. This overview of reviews is intended to assess the available information on these issues and identify the main maternal nutritional factors associated with offspring outcomes in low- and middle-income countries as possible targets for public health interventions. METHODS: The literature search was performed in Medline (PubMed) and Cochrane Library datasets in June 2024. Pre-specified outcomes in offspring were pooled using standard meta-analytical methods. RESULTS: We found consistent evidence on the impact of maternal undernutrition indicated by low body mass index (BMI), mid-upper arm circumference (MUAC), and stature, but not of individual micronutrient status, on intrauterine-growth retardation, preterm birth, low birth weight, and small for gestational age, with research showing a possible effect of maternal undernutrition in later child nutritional status. Studies on micronutrient supplementation showed possible beneficial effects of iron, vitamin D, and multiple micronutrients on birthweight and/or decreasing small for gestational age, as well as a possible effect of calcium on preterm birth reduction. Interventions showing more consistent beneficial outcomes were balanced protein-energy and lipid base supplements, which demonstrated improved weight in newborns from supplemented mothers and a decreased risk of adverse neonatal outcomes. CONCLUSIONS: Further research is needed to identify the benefits and risks of maternal individual micronutrient supplementation on neonatal and further child outcomes.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".