Sex-Specific Effects of Birth Weight on Longitudinal Behavioral Outcomes: A Mendelian Randomization Approach Using Polygenic Scores
Bibliographic record
Abstract
Background: It is unclear whether sex differences in behavior arising from birth weight (BW) are genuine because of the cross-sectional nature and potential confounding in previous studies. We aimed to test whether sex differences associated with BW phenotype were reproducible using a Mendelian randomization approach, i.e., association between polygenic score (PGS) for BW and behavior outcomes across childhood and adolescence. Methods: Using data from the Raine Study, we had 1484 genotyped participants with a total of 6446 Child Behavior Checklist assessments from ages 5 to 17 years. We used BW-PGSs in linear mixed-effect models to predict parentally assessed attention, aggression, and social problems scales; we also derived estimates and significance for a sex-by-genotype interaction. We used a Bonferroni-corrected significance threshold and tested robustness of the results with teacher assessments of behavior and a second PGS. Results: We found a sex-by-genotype interaction with lower BW-PGSs associated with increased aggression in males compared with females. These findings were consistent across various analyses, including teacher assessments. Surprisingly, a lower BW-PGS showed protective effects in females, while a lower BW phenotype had detrimental effects in males with evidence of a genotype-phenotype mismatch increasing aggression problems in males only. Conclusions: This study underscores the genuine nature of behavioral sex differences arising from low BW and highlights the sex-dependent and diverging effects of environmental and genetic BW determinants.
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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 | 0.053 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".