Prenatal depression and 5‐ <scp>HTTLPR</scp> interact to predict dysregulation from 3 to 36 months – A differential susceptibility model
Bibliographic record
Abstract
BACKGROUND: Childhood dysregulation, which reflects deficits in the capacity to regulate or control one's thoughts, emotions and behaviours, is associated with psychopathology throughout childhood and into adulthood. Exposures to adversity during the prenatal period, including prenatal depression, can influence the development of dysregulation, and a number of candidate genes have been suggested as moderators of prenatal exposure, including polymorphisms in the promoter region of the serotonin transporter gene (5-HTTLPR). We examined whether prenatal depression and child 5-HTTLPR interact to predict childhood dysregulation. METHOD: Sample of N = 213 mother-child pairs from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) project. Mothers reported the IBQ-R at 3 and 6 months, and the ECBQ at 18 and 36 months, from which measures of dysregulation were extracted. Mothers' self-reported symptoms of depression on the CES-D at 24-36 weeks of gestation, and at 6, 12, 24 and 36 months postnatal. 5-HTTLPR genotype was extracted from buccal swabs. Mixed-model and confirmatory analyses were conducted. RESULTS: Prenatal depression and 5-HTTLPR interacted to predict dysregulation from 3 to 36 months, within a model of strong differential susceptibility. CONCLUSION: Children with S or LG alleles, when exposed to prenatal depression, have higher levels of dysregulation, and when exposed to lower or little prenatal depression, have higher capacity for regulation. Our findings support efforts to identify, support and treat prenatal depression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".