Cumulative prenatal exposure to adversity reveals associations with a broad range of neurodevelopmental outcomes that are moderated by a novel, biologically informed polygenetic score based on the serotonin transporter solute carrier family C6, member 4 (<i>SLC6A4</i>) gene expression
Bibliographic record
Abstract
While many studies focus on the association between early life adversity and the later risk for psychopathology, few simultaneously explore diverse forms of environmental adversity. Moreover, those studies that examined the cumulative impact of early life adversity focus uniquely on postnatal influences. The objective of this study was to focus on the fetal period of development to construct and validate a cumulative prenatal adversity score in relation to a wide range of neurodevelopmental outcomes. We also examined the interaction of this adversity score with a biologically informed genetic score based on the serotonin transporter gene. Prenatal adversities were computed in two community birth cohorts using information on health during pregnancy, birth weight, gestational age, income, domestic violence/sexual abuse, marital strain, as well as maternal smoking, anxiety, and depression. A genetic score based on genes coexpressed with the serotonin transporter in the amygdala, hippocampus, and prefrontal cortex during prenatal life was constructed with an emphasis on functionally relevant single nucleotide polymorphisms, that is, expression quantitative trait loci. Prenatal adversities predicted a wide range of developmental and behavioral alterations in children as young as 2 years of age in both cohorts. There were interactions between the genetic score and adversities for several domains of the Child Behavior Checklist (CBCL), with pervasive developmental problems remaining significant adjustment for multiple comparisons. Scores combining different prenatal adverse exposures predict childhood behavior and interact with the genetic background to influence the risk for psychopathology.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".