T16. SCHIZOPHRENIA SPECTRUM DISORDER: DEPRESSION TRAJECTORIES AND IMMUNE MARKERS
Bibliographic record
Abstract
Abstract Background Genetic findings imply a role of the immune system in the complex psychopathology of schizophrenia, and elevated serum levels of pro-inflammatory cytokines have been found in patients. Altered levels of cytokines are linked to severe depression and cognitive dysfunction, both of which are common among patients suffering from schizophrenia. Depression is important to diagnose in this patient population as consequences of untreated depression can be severe. In this study we will investigate if the level and change of immune markers in blood are related to depression in patients with schizophrenia spectrum disorders. Methods The study is part of the Bergen-Stavanger-Innsbruck-Trondheim study (BestIntro) which is a multicenter randomized controlled trial comparing treatment with amisulpride, aripirazole and olanzapine. The study included patients with schizophrenia spectrum disorders (ICD-10 F20-F29) above 18 years with a score of 4 or more one of the following items on the Positive and Negative Syndrome scale (PANSS): Delusions, hallucinations, grandiosity, suspiciousness/persecution and unusual thought content. Participants were followed throughout one year, and for this sub-study participants from all treatment arms were analyzed together. Blood samples were drawn at week 0, 1, 3, 6, 12, 26, 39 and 52. Depression was measured with the Calgary Depression Scale (CDSS) which distinguishes depression from negative symptoms. A panel of 9 immune markers were analyzed: interferon gamma (IFN-γ), interleukin 1-β (IL-1β), interleukin 10 (IL-10), interleukin 12p70 (IL-12p70), interleukin 17A (IL-17A), interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α). We examined whether the level and change in inflammation parameters could be predicted by latent classes describing CDSS trajectories. Results The preliminary results suggest three different CDSS trajectories: high, moderate and low level of depression. In the three class model, the different groups were found to be related to some differences in level and change in the inflammation parameters. Baseline differences were found with higher IL-10 in the high depression group. In the 0–1 week interval, the low depression trajectory group reduced their IL1-beta, while the other two groups did not. Discussion Different courses of change in depression were identified suggesting that trajectories exist. With regard to temporal patterns of inflammatory parameters, findings point in the opposite direction of the established links between pro-inflammatory cytokines and depression. Further studies should explore if cytokine alterations in schizophrenia per se can explain this difference, or if depression in schizophrenia differs in its underlying biology from regular depressive states.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".