Childhood Chronic Physical Aggression Associates with Adult Cytokine Levels in Plasma
Bibliographic record
Abstract
BACKGROUND: An increasing number of animal and human studies are indicating that inflammation is associated with behavioral disorders including aggression. This study investigates the association between chronic physical aggression during childhood and plasma cytokine levels in early adulthood. METHODOLOGY/PRINCIPAL FINDINGS: Two longitudinal studies were used to select males on a chronic physical aggression trajectory from childhood to adolescence (n = 7) and a control group from the same background (n = 25). Physical aggression was assessed yearly by teachers from childhood to adolescence and plasma levels of 10 inflammatory cytokines were assessed at age 26 and 28 years. Compared to the control group, males on a chronic physical aggression trajectory from childhood to adolescence had consistently lower plasma levels of five cytokines: lower pro-inflammatory interleukins IL-1α (T(28.7) = 3.48, P = 0.002) and IL-6 (T(26.9) = 3.76, P = 0.001), lower anti-inflammatory interleukin IL-4 (T(27.1) = 4.91, P = 0.00004) and IL-10 (T(29.8) = 2.84, P = 0.008) and lower chemokine IL-8 (T(26) = 3.69, P = 0.001). The plasma levels of four cytokines accurately predicted aggressive and control group membership for all subjects. CONCLUSIONS/SIGNIFICANCE: Physical aggression of boys during childhood is a strong predictor of reduced plasma levels of cytokines in early adulthood. The causal and physiological relations underlying this association should be further investigated since animal data suggest that some cytokines such as IL-6 and IL-1β play a causal role in aggression.
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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 | 0.000 | 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) | 0.001 | 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".