Association of baseline inflammatory markers and the development of negative symptoms in individuals at clinical high risk for psychosis
Bibliographic record
Abstract
Negative symptoms are common in individuals at clinical high-risk (CHR) for psychosis and are associated with worse functional outcomes. Inflammation may be one mechanism underlying negative symptoms. Inflammatory markers are altered in individuals at CHR and are associated with negative symptoms in patients with schizophrenia. We thus hypothesized that baseline inflammatory markers would predict the development of negative symptoms in individuals at CHR for psychosis. Thirty seven individuals from the North American Prodromal Longitudinal Study who met CHR criteria were included in the study. Inflammatory cytokines, including interferon (IFN)-λ, Interleukin (IL)-1β, IL-1 receptor antagonist (IL-1RA), IL-4, IL-6, IL-8, IL-10, and tumor necrosis factor (TNF) were measured at baseline. Negative symptoms as measured by the Scale of Prodromal Symptoms, were measured at baseline and six and twelve months. Associations between inflammatory markers and the trajectory of negative symptoms (slope) over the first year of follow-up, were assessed using linear regression models controlling for age, sex, race and depressive symptom severity (as assessed by the Calgary Depression Scale for Schizophrenia). Baseline TNF (beta = 0.361, p = 0.007) and IL-6 (beta = −0.306, p = 0.026) predicted negative symptoms slopes, along with depressive symptom severity at baseline (beta = −0.596, p = 0.000). These findings demonstrate that inflammatory cytokines may underlie the development of negative symptoms in some individuals at CHR for psychosis. TNF predicted the development of negative symptoms independent of baseline depression. Given the heterogeneity of the CHR population, the comorbidity of negative symptoms and depression in this population, and the particular challenges in treating negative symptoms, immune markers could represent potential biomarkers that underlie the development of negative symptoms, representing a potential treatment target.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".