Iron deficiency in pregnancy and its association with blood lead and manganese levels in offspring in Benin, Sub-Saharan Africa
Bibliographic record
Abstract
INTRODUCTION: The impact of prenatal iron deficiency (ID) on offspring's blood lead level (BLL) and blood manganese level (BML) in infancy remains poorly understood. This study aimed to assess associations between prenatal ID and BLL/BML in offspring in Benin. We also investigated associations between infant ID and corresponding BLL and BML in infants. MATERIALS AND METHODS: Data on hematological parameters, metal blood concentrations, and socioeconomic status were obtained from a prospective mother-child cohort study in Allada, Benin. Blood samples were collected during pregnancy (n = 501), at delivery (n = 501), and from 12-month-old infants (n = 501) to assess iron deficiency and haemoglobin concentration. Additionally, BML was analyzed for a subset of 12-month-old infants (n = 271), and BLL was determined for the full cohort of 12-month-old infants (n = 501). Associations between ID and metal concentrations were examined using logistic regressions. RESULTS: Prenatal ID and IDA at the first and third antenatal care visit (ANC) were positively associated with infant BLL above 50 μg/L. Infants of mothers with prenatal ID and IDA had higher BLL. Moreover, prenatal ID and IDA at first and second ANC visits were positively associated with higher infant BML. In infancy, infants with ID and IDA had significantly higher BLL as compared to those without ID and IDA. Infant ID and IDA were positively associated with elevated BLL. CONCLUSION: Elevated BLL and BML in infants were positively associated with ID and/or IDA prenatally during at least one ANC visit, while in infancy, infant ID and IDA were positively associated with elevated BLL only. Infants with ID and IDA showed higher BLL but not BML. This suggests that ID prenatally and during infancy may contribute to high blood lead concentrations in infants, which can lead to neurotoxicity. Treating ID and IDA is critical to prevent toxicity caused by high BLL in infants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".