Iodine Levels and Thyroid Hormones in Pregnant Women and Neonatal Outcomes: A Systematic Review
Bibliographic record
Abstract
Iodine plays a vital role in the synthesis of thyroid hormones, which are essential for fetal growth and brain development. During pregnancy, maternal iodine needs to increase. Both deficiency and excess can impair maternal thyroid function and lead to complications such as hypothyroxinemia, fetal growth restriction, or thyroid dysfunction in the mother or child. Objectives: To assess the relationship between maternal iodine levels, thyroid function, and neonatal outcomes, and highlight the risks associated with both iodine deficiency and excess. Methods: A systematic search was conducted in PubMed, Web of Science, Scopus, Springer, and MDPI for studies published from 2021 to 2025. Inclusion criteria involved studies assessing iodine status (Urinary Iodine Concentration (UIC) or Serum Iodine Concentration (SIC)), maternal thyroid function (TSH, FT4, FT3), and neonatal outcomes. Articles were screened using PRISMA guidelines. The risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool. Due to heterogeneity in methods and outcomes, results were narratively synthesized. Results: Ten studies were included. Iodine deficiency was consistently associated with low birth weight and disrupted thyroid hormone levels, while iodine excess particularly at levels ≥500 µg/L was linked to transient neonatal hyperthyrotropinemia. Environmental exposures such as endocrine-disrupting chemicals also influence maternal thyroid function. Conclusions: It was concluded that both iodine deficiency and excess pose risks to maternal and neonatal thyroid health. Routine monitoring and individualized supplementation based on regional dietary patterns and environmental exposures are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".