The Effect of Maternal Vitamin D Status on Fetal Growth and Stunting: A Systematic Review
Bibliographic record
Abstract
Background and Objective: Impaired fetal growth and stunting remain immense public health problems involving maternal nutrition during pregnancy, as linear growth failure in children is the most common form of undernutrition across the world. Although both are preventable through adequate prenatal care and nutrition, impaired fetal growth and stunting continue to be implicated in multiple child health morbidities, physical, and psychological functioning. Recent knowledge and requirements for normal fetal and neonatal development are lacking. This systematic review investigates the effects of maternal vitamin D status on fetal growth and stunting. Methods: We reviewed three widely-used publications databases: the National Institutes of Health’s PubMed, Clarivate Analytics’ Web of Science, and Google Scholar using pre-established inclusion and exclusion criteria and keyword search strategy. Studies from 2010 to 2020 were included if they reported vitamin D levels on pregnant women, indicated growth outcomes and used quantitative measurements. We excluded non-English language studies, studies with ambiguous outcomes, studies that did not specify vitamin D intake, and studies that involved other maternal health complications. The search was implemented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Out of a total of 2,481 studies reviewed, we identified 8 studies on vitamin D and fetal growth and stunting. Published literature addressing maternal vitamin D status on fetal growth and stunting remains ambiguous. Five studies demonstrated improvements in fetal and humerus z-scores, which are known proxies for fetal growth, in groups with higher vitamin D status. Three studies found no statistical significance between vitamin D levels and fetal growth. Vitamin D status and ethnicity were correlated; vitamin D interacts with calcium levels in pregnant mothers to improve bone mineralization and fetal growth. Conclusion and Implications for Translation: Further studies are needed to understand the relationship between maternal vitamin D, ethnicity, and fetal growth and the long-term effects of maternal vitamin D levels on neonatal, early childhood, and adolescent growth. Copyright © 2021 Karmali et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".