Prenatal serotonin reuptake inhibitor antidepressant exposure, <i>SLC6A4</i> genetic variations, and cortisol activity in 6‐year‐old children of depressed mothers: A cohort study
Bibliographic record
Abstract
Abstract Prenatal exposure to maternal depression and serotonin reuptake inhibitor (SRI) antidepressants both affect the development of the hypothalamic–pituitary–adrenal (HPA) system, possibly via the neurotransmitter serotonin (5HT). In a community cohort, we investigated the impact of two factors that shape prenatal 5HT signaling (prenatal SRI [pSRI] exposure and child SLC6A4 genotype) on HPA activity at age 6 years. Generalized estimating equation (GEE) models were used to study associations between cortisol reactivity, pSRI exposure, and child SLC6A4 genotype, controlling for maternal depression, child age, and sex (48 pSRI exposed, 74 nonexposed). Salivary cortisol levels were obtained at five time points during a laboratory stress challenge: arrival at the laboratory, following two sequential developmental assessments, and then 20 and 40 min following the onset of a stress‐inducing cognitive/social task. Cortisol decreased from arrival across both developmental assessments, and then increased across both time points following the stress challenge in both groups. pSRI‐exposed children had lower cortisol levels across all time points. In a separate GEE model, we observed a lower cortisol stress response among children with LG/S alleles compared with children with La/La alleles, and this was particularly evident among children of mothers reporting greater third trimester depressed mood. Our findings suggest that pSRI exposure and a genetic factor associated with modulating 5HT signaling shaped HPA reactivity to a laboratory stress challenge at school age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".