Exploring the association of interleukin polymorphisms with aggression and internalizing behaviors in children and adolescents
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that inflammation influences mental health. Blood interleukin levels, which regulate inflammation, have been linked to aggression and internalizing behaviors. We performed a hypothesis-driven genetic study to (1) evaluate the association of IL1B, IL2, and IL6 gene variants with aggression and internalizing behaviors and (2) explore gene-environment interactions with childhood adversity in a deeply phenotyped childhood-onset aggression sample including 255 cases and 226 controls of European ancestry. METHODS: We evaluated the association of putative functional and tag SNPs within IL1B, IL2, and IL6 with aggression case status, parent-reported internalizing problems, self-reported anxiety symptoms, and self-reported depressive symptoms in our sample. We also performed exploratory GxE analyses within cases, testing for statistical interaction between interleukin SNP genotype and childhood adversity for depressive symptoms. RESULTS: ), and trends for an interaction between severe childhood adversity and SNPs in IL1B and IL2 for depressive symptoms. CONCLUSIONS: Our findings provide preliminary evidence that common variation in IL6 may be associated with depressive symptoms in children and adolescents, and that common variation in interleukin genes may sensitize individuals to the depressogenic effects of traumatic life experiences. Replication in independent samples is needed.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".