Cumulative Influence of Inflammatory Response Genetic Variation on Long-Term Neurobehavioral Outcomes after Pediatric Traumatic Brain Injury Relative to Orthopedic Injury: An Exploratory Polygenic Risk Score
Bibliographic record
Abstract
The addition of genetic factors to prognostic models of neurobehavioral recovery following pediatric traumatic brain injury (TBI) may account for unexplained heterogeneity in outcomes. The present study examined the cumulative influence of candidate genes involved in the inflammatory response on long-term neurobehavioral recovery in children with early childhood TBI relative to children with orthopedic injuries (OI). Participants were drawn from a prospective, longitudinal study evaluating outcomes of children who sustained TBI (n = 67) or OI (n = 68) between the ages of 3 and 7 years. Parents completed ratings of child executive function and behavior at an average of 6.8 years after injury. Exploratory unweighted and weighted polygenic risk scores (PRS) were constructed from single nucleotide polymorphisms (SNPs) across candidate inflammatory response genes (i.e., angiotensin converting enzyme [ACE], brain-derived neurotrophic factor [BDNF], interleukin-1 receptor antagonist [IL1RN], and 5’-ectonucleotidase [NT5E]) that showed nominal (p ≤ 0.20) associations with outcomes in the TBI group. Linear regression models tested the PRS × injury group (TBI vs. OI) interaction term and post-hoc analyses examined the effect of PRS within each injury group. Higher inflammatory response PRS were associated with more executive dysfunction and behavior problems in children with TBI but not in children with OI. The cumulative influence of inflammatory response genes as measured by PRS explained additional variance in long-term neurobehavioral outcomes, over and above well-established predictors and single candidate SNPs tested individually. The results suggest that some of the unexplained heterogeneity in long-term neurobehavioral outcomes following pediatric TBI may be attributable to a child's genetic predisposition to a greater or lesser inflammatory response to TBI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".