Innate Immune Cells and C-Reactive Protein in Acute First-Episode Psychosis and Schizophrenia: Relationship to Psychopathology and Treatment
Bibliographic record
Abstract
Innate immunity has been linked to initiation of Alzheimer's disease and multiple sclerosis. Moreover, risk of first-episode psychosis (FEP) and schizophrenia (Sz) is increased after various infections in predisposed individuals. Thus, we hypothesized an analogous role of innate immunity with increased C-reactive protein (CRP) in non-affective psychosis. Differential blood count, CRP, neutrophil and monocyte-macrophage activation markers, cortisol and psychotic symptoms (Positive and Negative Syndrome Scale [PANSS]) were assessed in controls (n = 294) and acutely ill unmedicated FEP (n = 129) and Sz (n = 124) patients at baseline and after 6 weeks treatment. Neutrophils, monocytes, and CRP were increased in patients vs controls at baseline (P < .001), and neutrophil and monocyte counts correlated positively with activation markers. Eosinophils were lower at baseline in FEP (P < .001) and Sz (P = .021) vs controls. Differences in neutrophils (P = .023), eosinophils (P < .001), and CRP (P < .001) were also present when controlling for smoking and cortisol, and partially remitted after antipsychotic treatment. FEP patients with high neutrophils (P = .048) or monocytes (P = .021) had higher PANSS-P scores at baseline but similar disease course. CRP correlated with PANSS-P at baseline (ρ = 0.204, P = .012). Improvement of positive symptoms after treatment correlated with declining neutrophils (ρ = 0.186, P = .015) or CRP (ρ = 0.237, P = .002) and rising eosinophils (ρ = -0.161, P = .036). In FEP, normalization of neutrophils (ρ = -0.231, P = .029) and eosinophils (ρ = 0.209, P = .048) correlated with drug dosage. In conclusion, innate immune system activation correlated with PANSS-P, supporting the immune hypothesis of psychosis. Neutrophil and monocyte counts and CRP levels may be useful markers of disease acuity, severity, and treatment response.
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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 | 0.000 | 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) | 0.001 | 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".