Immunomodulatory effects of antipsychotic treatment on gene expression in first-episode psychosis
Bibliographic record
Abstract
Abstract Previous studies suggest immunological alterations in patients with first-episode psychosis (FEP). Some studies show that antipsychotic compounds may cause immunomodulatory effects. To evaluate the immunological changes and the possible immunomodulatory effects in FEP, we recruited patients with FEP (n = 67) and matched controls (n = 38), aged 18–40 years, from the catchment area of the Helsinki University Hospital and the City of Helsinki, Finland. Fasting peripheral blood samples were collected between 8 and 10 a.m. in 10 ml PAXgene tubes. We applied the NanoString nCounter in-solution hybridization technology to determine gene expression levels of 147 candidate genes reflecting activation of the immune system. Cases had higher gene expression levels of BDKRB1 and SPP1/osteopontin compared with controls. Of the individual medications used as monotherapy, risperidone was associated with a statistically significant upregulation of 11 immune system genes, including cytokines and cytokine receptors (SPP1, IL1R1, IL1R2), pattern recognition molecules (TLR1, TLR2 and TLR6, dectin-1/CLEC7A), molecules involved in apoptosis (FAS), and some other molecules with functions in immune activation (BDKRB1, IGF1R, CR1). In conclusion, risperidone possessed strong immunomodulatory properties affecting mainly innate immune response in FEP patients, whereas the observed effects of quetiapine and olanzapine were only marginal. Our results further emphasize the importance of understanding the immunomodulatory mechanisms of antipsychotic treatment, especially in terms of specific compounds, doses and duration of medication in patients with severe mental illness. Future studies should evaluate the response pre- and post-treatment, and the possible role of this inflammatory activation for the progression of psychiatric and metabolic symptoms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".