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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".