Depression trajectories and cytokines in schizophrenia spectrum disorders - A longitudinal observational study
Bibliographic record
Abstract
Depression occurs frequently in all phases of schizophrenia spectrum disorders. Altered activity in the immune system is seen in both depression and schizophrenia. We aimed to uncover depressive trajectories in a sample of 144 adult individuals with schizophrenia spectrum disorders followed for one year, in order to identify possible cytokine profile differences. Patients were assessed longitudinally with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS), where a score above 6 predicts depression. The serum cytokine concentrations for tumor necrosis factor (TNF)-alpha, interferon (IFN)-gamma, interleukin (IL)-1beta, IL-2, IL-4, IL-6, IL-10, IL-12p70 and IL-17A were measured using immunoassays. Latent growth curve models, multilevel models and latent class growth analysis (LCGA) were applied. The LCGA model supported three latent classes (trajectories) with differing CDSS profiles during the one-year follow-up: a high CDSS group (40.8 % of participants), a moderate CDSS group (43.9 %) and a low CDSS group (15.3 %). Five single PANSS items predicted affiliation to depressive trajectory: hallucinations, difficulty in abstract thinking, anxiety, guilt feelings and tension. In the high CDSS group, despite diminishing psychotic symptoms, depressive symptoms persisted throughout one year. The pro-inflammatory cytokines IFN-γ, IL-1β and TNF-α were differentially distributed between the depressive trajectories, although levels remained remarkably stable throughout 12 months. Significant changes were found for the anti-inflammatory cytokine IL-10 at baseline with an accompanying difference in change over time. More research is required to optimize future treatment stratification and investigate the contribution of inflammation in depressed patients with schizophrenia spectrum disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".