<i>In vivo</i> immunomodulatory effects of antipsychotics on inflammatory mediators: A review
Bibliographic record
Abstract
Background: Substantial evidence demonstrates the presence of an inflammatory syndrome in schizophrenia, which is manifested by increased peripheral levels of interleukin-6 (IL-6), soluble interleukin-2 receptor (sIL-2R), and interleukin-1 receptor antagonist (IL-1RA). Unfortunately, the immunomodulatory effects of antipsychotics on peripheral cytokine levels remain poorly understood. Objectives: The objectives of the current systematic review are to determine if antipsychotics have anti-inflammatory effects in patients with schizophrenia-spectrum disorders and to examine the relationships between antipsychotic-induced cytokine changes and drug response or common side effects. Method: A systematic search was performed in the electronic databases PubMed and EMBASE. Results: We identified 39 studies measureing the effects of 8 antipsychotics on 13 inflammatory mediators and 4 cytokine receptors. This literature suggests that antipsychotics (especially clozapine) consistently decrease peripheral interleukin-2 (IL-2) levels and increase sIL-2R and soluble tumor-necrosis factor (sTNF-R) receptor levels. Changes in IL-2/sIL- 2R levels seem to correlate with changes in positive symptoms. Preliminary results suggest that antipsychotics decrease interferon-γ (IFN-γ) and transforming growth factor-β (TGF-β) levels, and increase interleukin-4 (IL-4) levels. Antipsychotic-induced changes in IL-6, C-reactive protein (CRP) and tumor-necrosis factor-α (TNF-α) have been linked with common antipsychotic side effects (metabolic, fever). Discussion: Despite significant clinical heterogeneity across studies, this review has evidenced that antipsychotics can produce both anti- and pro-inflammatory effects that may partially contribute to drug response and drug-induced side effects. In the future, a better understanding of the molecular mechanisms of action of antipsychotics on inflammatory mediators will help to identify novel therapeutic strategies as well as novel biomarkers of treatment response and of drug-induced side effects in schizophrenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".