Changes in Inflammatory Markers in Clinical High Risk of Developing Psychosis
Bibliographic record
Abstract
INTRODUCTION: Immune alterations are associated with the progression of psychosis. However, there are few studies designed to longitudinally measure inflammatory biomarkers during psychotic episodes. We aimed to assess changes in biomarkers from the prodromal phase to psychotic episodes in individuals with clinical high risk (CHR) of psychosis and compare converters and non-converters to psychosis as well as healthy controls (HCs). METHODS: We enrolled 394 individuals with CHR and 100 HCs. A total of 263 individuals with CHR completed the 1-year follow-up, and 47 had converted to psychosis. Interleukin (IL)-1β, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor levels were measured at baseline and 1 year after completion of the clinical assessment. RESULTS: The baseline serum levels of IL-10, IL-2, and IL-6 were significantly lower in the conversion group than in the non-conversion group (IL-10, p = 0.010; IL-2, p = 0.023; IL-6, p = 0.012) and HC (IL-6: p = 0.034). Self-controlled comparisons showed that IL-2 changed significantly (p = 0.028), and IL-6 levels tended toward significance (p = 0.088) in the conversion group. In the non-conversion group, serum levels of TNF-α (p = 0.017) and VEGF (p = 0.037) changed significantly. Repeated measures analysis of variance revealed a significant time effect related to TNF-α (F = 4.502, p = 0.037, effect size (η2) = 0.051), a group effect related to IL-1β (F = 4.590, p = 0.036, η2 = 0.062), and IL-2 (F = 7.521, p = 0.011, η2 = 0.212), but no time × group effect. DISCUSSION: Alterations in the serum levels of inflammatory cytokines were found to precede the first episode of psychosis in the CHR population, particularly for those who later converted to psychosis. Longitudinal analysis supports the varied roles of cytokines in individuals with CHR with later psychotic conversion or non-conversion outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".