3-F-394 - Early life adversity and a sex-specific polygenic risk for fasting insulin are associated with variations in childhood executive functioning
Bibliographic record
Abstract
Authors: Aashita Batra¹, Lawrence Chen², Zihan Wang², Carine Parent², Irina Pokhvisneva¹, Sachin Patel², Michael Meaney³, Patricia Silveira¹ ¹McGill University, ²Douglas Mental Health University Institute, ³Singapore Institute for Clinical Sciences Abstract: As insulin is an important hormone for childhood growth and development and has implications for adult psychopathology in both males and females, we hypothesized that 1) the genetic background associated with altered fasting insulin (FI) and ADHD would be shared; 2) if (1) is rejected, the genetic background associated with altered fasting insulin would perform better in interaction models, G by E (childhood adversity), as opposed to main effect models to predict child psychosocial problems and adult psychopathology. Using conjunctional false discovery rate (FDR), we saw that no SNPs were shared between the FI GWAS and ADHD GWAS. (2) We calculated polygenic risk scores (PRS) from the sex-specific FI GWAS at different thresholds and identified one that best predicted peripheral insulin levels in male and female children in the ALSPAC cohort, further refining it to only include SNPs significantly associated with the peripheral insulin levels (p-refined<0.05). As hypothesized, these PRS predicted childhood total problems and ADHD (CBCL) in children from the MAVAN cohort (76 females, 74 males), in pre-adolescents of the ABCD cohort (3684 females, 4037 males), as well as mood disorders in adults from the UK Biobank (44638 females, 26576 males) in a sex-specific manner at each age in response to childhood adversity. The genetic background associated with higher fasting insulin levels is linked to psychopathology, but this effect is dependent on the exposure to adversity. The findings reported here have implications for identification and treatment of psychopathology at different ages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".