Association between serum cytokines and timeframe for conversion from clinical high‐risk to psychosis
Bibliographic record
Abstract
AIM: Although many studies have explored the link between inflammatory markers and psychosis, there is a paucity of research investigating the temporal progression in individuals at clinical high-risk (CHR) who eventually develop full psychosis. To address this gap, we investigated the correlation between serum cytokine levels and Timeframe for Conversion to Psychosis (TCP) in individuals with CHR. METHODS: We enrolled 53 individuals with CHR who completed a 5-year follow-up with a confirmed conversion to psychosis. Granulocyte macrophage-colony stimulating factor (GM-CSF), interleukin (IL)-1β, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor (VEGF) levels were measured at baseline and 1-year. Correlation and quantile regression analyses were performed. RESULTS: The median TCP duration was 14 months. A significantly shorter TCP was associated with higher levels of TNF-α (P = 0.022) and VEGF (P = 0.016). A negative correlation was observed between TCP and TNF-α level (P = 0.006) and VEGF level (P = 0.04). Quantile regression indicated negative associations between TCP and GM-CSF levels below the 0.5 quantile, IL-10 levels below the 0.3 quantile, IL-2 levels below the 0.25 quantile, IL-6 levels between the 0.65 and 0.75 quantiles, TNF-α levels below the 0.8 quantile, and VEGF levels below the 0.7 quantile. A mixed linear effects model identified significant time effects for IL-10 and IL-2, and significant group effects for changes in IL-2 and TNF-α. CONCLUSIONS: Our findings underscore that a more pronounced baseline inflammatory state is associated with faster progression of psychosis in individuals with CHR. This highlights the importance of considering individual inflammatory profiles during early intervention and of tailoring preventive measures for risk profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.000 | 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".