Breast Milk and Brain: The Influence of Iodine and Neurotrophic and Growth Factors on Children’s Neurodevelopment-A Secondary Analysis
Bibliographic record
Abstract
Objective: This study targeted to investigate the potential role of breast milk iodine concentration (BMIC), insulin-like growth factor-1 (IGF-I), and brain-derived neurotrophic factor (BDNF) during the early stage of lactation in child neurocognitive development. Materials and methods: In this secondary analysis, we examined 122 breastfeeding mothers and their healthy children, all of whom were breastfed for at least six months. Levels of BDNF, IGF-1, and BMIC were assessed in breast milk samples obtained between the third and fifth days after lactation began (before any iodine supplementation intervention). Three-year-old children were administered the Bayley-III screening test to assess their cognitive, motor, and language development. Results: The median (interquartile range) concentrations of iodine, BDNF, and IGF-1 in breast milk during the starting few days of lactation were 285.0 (181.0-366.0) µg/l, 0.59 (0.52-0.76) ng/ml, and 12.5 (9.6-18.3) ng/ml, respectively. The mean (standard deviation) cognitive, motor, and language scores were 101.0 (10.8), 93.4 (14.6), 100.1 (13.5) and, respectively. Linear regression models revealed a negative relation between breast milk iodine and children’s cognitive development ((β unadjusted = -0.004 (P = 0.010); β adjusted = -0.003 (P = 0.024)). However, no associations were found between breast milk BDNF and IGF-1 and cognitive, language, or motor scores in three-year-olds. Conclusion: Our findings indicate that early exposure to iodine, BDNF, and IGF-1 in breast milk, measured prior to iodine supplementation, has no substantial association with neurodevelopment in three-year-old children. The weak negative association between BMIC and cognitive scores may reflect prenatal iodine status, warranting further research to explore long-term effects of supplementation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".