Associations between maternal prenatal cortisol and fetal growth are specific to infant sex: findings from the Wirral Child Health and Development Study
Bibliographic record
Abstract
Recent findings highlight that there are prenatal risks for affective disorders that are mediated by glucocorticoid mechanisms, and may be specific to females. There is also evidence of sex differences in prenatal programming mechanisms and developmental psychopathology, whereby effects are in opposite directions in males and females. As birth weight is a risk for affective disorders, we sought to investigate whether maternal prenatal cortisol may have sex-specific effects on fetal growth. Participants were 241 mothers selected from the Wirral Child Health and Development Study (WCHADS) cohort (n=1233) using a psychosocial risk stratifier, so that responses could be weighted back to the general population. Mothers provided saliva samples, which were assayed for cortisol, at home over 2 days at 32 weeks gestation (on waking, 30-min post-waking and during the evening). Measures of infant birth weight (corrected for gestational age) were taken from hospital records. General population estimates of associations between variables were obtained using inverse probability weights. Maternal log of the area under the curve cortisol predicted infant birth weight in a sex-dependent manner (interaction term P=0.029). There was a positive and statistically significant association between prenatal cortisol in males, and a negative association in females that was not statistically significant. A sex interaction in the same direction was evident when using the waking (P=0.015), and 30-min post-waking (P=0.013) cortisol, but not the evening measure. There was no interaction between prenatal cortisol and sex to predict gestational age. Our findings add to an emerging literature that suggests that there may be sex-specific mechanisms that underpin fetal programming.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".