Manganese and lead exposure and thyroid hormones during pregnancy in the Infants' Environmental Health Study (ISA) in Costa Rica
Bibliographic record
Abstract
Background: Metals may disrupt thyroid hormones (TH) like thyroid stimulating hormone (TSH), thyroxine (T4) and triiodothyronine (T3). We examined whether hair (MnH) and blood manganese (MnB), and blood lead (PbB) concentrations explained serum TH concentrations in pregnant women from the ISA birth cohort in Costa Rica.Methods: Using the first sample of each women (n=384), we ran separate multivariate linear regression models and General Additive Models (GAMs) adjusting for gestational age, maternal age, cotinine, prepregnancy Body Mass Index and severe vomiting. We also ran stratified analysis by trimester of pregnancy.Results: Median (p25-75) TSH, FT3, FT4 concentrations were: 1.47 (1.06-2.08) mIU/L, 4.60 (4.29-5.06) pmol/L, and 14.09 (13.00-15.65) pmol/L, respectively. Median (p25-75) MnH, MnB and PnB were 1.62 (0.86-3.49) μg/g, 23.44 (19.36-27.43), 6.66 (5.17-9.08) μg/L, respectively. Increased MnH was associated with increased TSH (% of change = 13,24, 95%CI -1,95, 30,79 for each 1% increase in MnH) during the second trimester of pregnancy. In addition, MnH showed a non-linear association with FT4 during the third trimester of pregnancy (GAM estimated degrees of freedom >2 with p<0.05). Overall, higher MnB concentrations were associated with increased FT4, ß=0,04 (95%CI 0, 0,07). Associations were strongest during the first trimester ß=0,13 95% CI: 0,05, 0,21). MnB also showed a non-linear association with FT3 during the first trimester. Finally, increased PbB concentrations were associated with higher FT4 [β=0.07 (95% CI: 0.00, 0.13)], particularly during the second trimester of pregnancy [β=0.10 (95% CI: 0.02, 0.19)]Conclusion: Our findings show that both manganese and lead explain TH concentrations during pregnancy. For Mn, being an essential element, this may reflect normal physiological functioning. Nevertheless, increased MnH is likely to reflect excess Mn and associations may also reflect endocrine disrupting effects. For lead, results show even very low concentrations might affect TH and possibly cause endocrine disrupting effects.
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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 | 0.001 | 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 | 0.001 |
| 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 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".