Interaction between peripheral and central immune markers in clinical high risk for psychosis
Bibliographic record
Abstract
Neuroinflammatory events prior to the diagnosis of schizophrenia may play a role in transition to illness. To date only one in-vivo study has investigated this association between peripheral proinflammatory cytokines and brain markers of inflammation (e.g., mitochondrial 18 kDa translocator protein, TSPO) in schizophrenia, but none in its putative prodrome. In this study, we primarily aimed to (Barron et al., 2017) test study group (clinical high-risk (CHR) and healthy controls) differences in peripheral inflammatory markers and test for any associations with symptom measures, (Hafizi et al., 2017a) investigate the interaction between brain TSPO levels (dorsolateral prefrontal cortex (DLPFC) and hippocampus) and peripheral inflammatory clusters (entire cohort and (CHR) group independently) within a relatively large group of individuals at CHR for psychosis (N = 38) and healthy controls (N = 20). Participants underwent structural brain magnetic resonance imaging (MRI) and TSPO [18F]FEPPA positron emission tomography (PET) scans. Serum samples were assessed for peripheral inflammatory markers (i.e., CRP and interleukins). For exploratory analysis, we aimed to examine cluster differences for symptom measures and identify independent peripheral predictors of brain TSPO expression. Here, we report increased IL-8 levels that are positively correlated with prodromal general symptom severity and showed trend-level association with apathy in CHR. We identified distinct inflammatory clusters characterized by inflammatory markers (IL-1 β, IL-2, IFN-γ) that were comparable between entire cohort and CHR. TSPO levels did not differ between inflammatory clusters (entire cohort or CHR). Finally, we show that CRP, IL-1 β, TNF-α, and IFN-γ levels were the independent peripheral predictors of brain TSPO expression. Thus, alterations in brain TSPO expression in response to inflammatory processes are not evident in CHR. Taken together, clustering by inflammatory status is a promising strategy to characterize the interaction between brain TSPO and peripheral markers of inflammation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".