Association between cytokines and suicidality in patients with psychosis: A multicentre longitudinal analysis
Bibliographic record
Abstract
Suicide is a common cause of death in all phases of schizophrenia spectrum disorder, particularly in the youngest patients. Clinical measures have demonstrated limited value in suicide prediction, spurring the search for potential biomarkers. The causes of suicidal behaviour are complex, but the immune system seems to be involved as it reflects or even causes mental suffering. We aimed to identify cytokines with associations to suicidality in a sample of patients with symptoms of active psychosis. Patients with schizophrenia spectrum disorder (N = 144) participating in a semi-randomized antipsychotic drug trial (the BeSt InTro study) were assessed with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS) at eight visits across 12 months. The Clinical Global Impression for Severity of Suicidality scale (CGI-SS) was used for assessing suicidality. Serum concentrations of tumour necrosis factor (TNF)-alpha, interferon (IFN)-gamma, interleukin (IL)-1beta, IL-2, IL-4, IL-6, and IL-10 were measured using immunoassays. A logistic regression model was used to investigate the association between cytokine levels and suicidality. To enhance clinical significance, the CGI-SS scores were dichotomized into two groups before analyses: low (=1) and high (≥2) risk for suicidality. Both uni- and multi-variate analyses revealed an inverse correlation between IL-2 and IL-10 serum levels and suicidality, where lower cytokine concentrations of IL-2 and IL-10 were associated with higher suicidality scores. The results were consistent when adjusted for depression and substance use. These results indicate that inflammatory processes are linked to the risk of suicidality in patients with schizophrenia spectrum disorders.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".