Influence of Birth Weight on Internalizing Traits Modulated by Serotonergic Genes
Bibliographic record
Abstract
OBJECTIVE: Fetal growth predicts childhood behavioral problems associated with brain serotonergic systems. We hypothesized that allelic variations in genes involved in serotonergic function would moderate associations between birth weight (BW) and internalizing traits in childhood. METHODS: The Child Behavior Checklist was administered to 545 healthy Singaporean children at 8 to 12 years. BW, corrected for gestational age, and candidate single-nucleotide polymorphisms (SNPs) in the TPH2, HTR2A, and SCL6A4 genes were investigated. RESULTS: There was no significant main effect of BW on internalizing T scores (F = 1.08; P = .36). After multiple corrections, significant main effects on internalizing T scores were found for HTR2A rs2296972 (adjusted: F = 2.85; P = .019) and HTR2A rs6313 (adjusted: F = 5.91; P = .0002). Significant interactions were found between BW and SNPs for the TPH2 gene (rs2171363: P = .008; rs7305115: P = .007) and the HTR2A gene (rs2770304: P = .001; rs6313: P = .026) for internalizing T scores. The CC genotype of TPH2 rs2171363, GG genotype of TPH2 rs7305115, CC genotype of HTR2A rs2770304, and CC genotype of HTR2A rs6313 were associated with reduced internalizing scores for children born in the quartile above the midpoint. No significant main effects or interactions were found for SCL6A4 SNPs. CONCLUSIONS: These findings suggest that sequence variations in genes involved in serotonergic functions modulate relationships between BW and internalizing traits and might be candidates for plasticity mechanisms that determine individual differences in responses to environmental influences over the course of development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".