Inflammation biomarkers and neurobehavioral performance in rural adolescents
Bibliographic record
Abstract
Background: Systemic inflammation has been associated with lower neurobehavioral performance in diverse populations, yet the evidence in adolescents remains lacking. Cytokines can alter neural network activity to induce neurocognitive changes. This work seeks to investigate the association between inflammation and neurobehavior in adolescents living in a rural region of Ecuador. Methods: We examined 535 adolescents in rural communities of Ecuador (ESPINA study), 508 of which had neurobehavioral assessments (NEPSY-II) and circulating plasma levels of inflammatory markers (CRP, IL-6, TNF-⍺, sICAM-1, sVCAM-1, SAA, and sCD14). Associations between inflammatory biomarker concentrations and neurobehavioral scores were examined using adjusted bivariate semi-parametric models with generalized estimating equations. A partial least squares regression approach was used to create composite variables from multiple inflammation biomarkers and model their association with cognitive outcomes. Results: Higher sCD14 and TNF-α concentrations were significantly associated with lower social perception scores, by -0.465 units (95% CI: -0.80, -0.13) and -0.418 units (-0.72, -0.12) for every 50% increase in inflammatory marker concentration, respectively. Similarly, every 50% increase in the inflammation summary score was associated with a significantly lower Social Perception score by -0.112 units (-0.19, -0.03). A greater inflammatory composite variable from seven markers was associated with lower scores in language (β = -0.11, p = 0.043), visuospatial processing (β = -0.15, p = 0.086), and social perception (β = -0.22, p = 0.005) domains. Conclusions: Higher levels of inflammation were associated with lower neurobehavioral performance in adolescents, especially with social perception. In addition, using a robust analytic method to examine an association between a composite inflammatory variable integrating seven markers led to additional findings, including the domains of language and visuospatial processing. A longitudinal follow-up of such investigations could unveil potential changes in inflammation-neurobehavior performance links through developmental stages and intervention opportunities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".