Treatment-Resistant Schizophrenia, Clozapine Resistance, Genetic Associations, and Implications for Precision Psychiatry: A Scoping Review
Bibliographic record
Abstract
Treatment-resistant schizophrenia (TRS) is often associated with severe burden of disease, poor quality of life and functional impairment. Clozapine is the gold standard for the treatment of TRS, although it is also known to cause significant side effects in some patients. In view of the burgeoning interest in the role of genetic factors in precision psychiatry, we conducted a scoping review to narratively summarize the current genetic factors associated with TRS, clozapine resistance and side effects to clozapine treatment. We searched PubMed from inception to December 2022 and included 104 relevant studies in this review. Extant evidence comprised associations between TRS and clozapine resistance with genetic factors related to mainly dopaminergic and serotoninergic neurotransmitter systems, specifically, TRS and rs4680, rs4818 within COMT, and rs1799978 within DRD2; clozapine resistance and DRD3 polymorphisms, CYP1A2 polymorphisms; weight gain with LEP and SNAP-25 genes; and agranulocytosis risk with HLA-related polymorphisms. Future studies, including replication in larger multi-site samples, are still needed to elucidate putative risk genes and the interactions between different genes and their correlations with relevant clinical factors such as psychopathology, psychosocial functioning, cognition and progressive changes with treatment over time in TRS and clozapine resistance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".