Gestational thyroid hormones and autism‐related traits in the <scp>EARLI</scp> and <scp>HOME</scp> studies
Bibliographic record
Abstract
Thyroid hormones are essential for neurodevelopment. Few studies have considered associations with quantitatively measured autism spectrum disorder (ASD)-related traits, which may help elucidate associations for a broader population. Participants were drawn from two prospective pregnancy cohorts: the Early Autism Risk Longitudinal Investigation (EARLI), enrolling pregnant women who already had a child with ASD, and the Health Outcomes and Measures of the Environment (HOME) Study, following pregnant women from the greater Cincinnati, OH area. Gestational thyroid-stimulating hormone (TSH) and free thyroxine (FT4) were measured in mid-pregnancy 16 (±3) weeks gestation serum samples. ASD-related traits were measured using the Social Responsiveness Scale (SRS) at ages 3-8 years. The association was examined using quantile regression, adjusting for maternal and sociodemographic factors. 278 participants (132 from EARLI, 146 from HOME) were included. TSH distributions were similar across cohorts, while FT4 levels were higher in EARLI compared to HOME. In pooled analyses, particularly for those in the highest SRS quantile (95th percentile), higher FT4 levels were associated with increasing SRS scores (β = 5.21, 95% CI = 0.93, 9.48), and higher TSH levels were associated with decreasing SRS scores (β = -6.94, 95% CI = -11.04, -2.83). The association between TSH and SRS remained significant in HOME for the 95% percentile of SRS scores (β = -6.48, 95% CI = -12.16, -0.80), but not EARLI. Results for FT4 were attenuated when examined in the individual cohorts. Our results add to evidence that gestational thyroid hormones may be associated with ASD-related outcomes by suggesting that relationships may differ across the distribution of ASD-related traits and by familial likelihood of ASD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".