T7. PHARMACOGENETIC OF TARDIVE DYSKINESIA -- A FOLLOW-UP ON THE VALBENAZINE TARGET VMAT2/SLC18A2
Bibliographic record
Abstract
Tardive dyskinesia (TD) is a motor side effect that may arise after long-term treatment of antipsychotic drugs. Its etiology is not well understood, but a number of risk factors have been associated with TD. TD occurrence appears to be familial, thus suggesting a genetic component. We previously reported on an association between the SLC18A2 gene that codes for the vesicular monoamine transporter 2 (VMAT2) that packages monoamines including dopamine from the cytoplasm into synaptic vesicles (Zai et al, 2013). In the present study, we examined the dopamine transporter gene SLC6A3 by itself and in conjunction with SLC18A2 for possible association with TD. We genotyped and analyzed the variable-number tandem repeat (VNTR) polymorphism in the 3’ untranslated region of the SLC6A3 gene in our European sample of 187 schizophrenia/schizoaffective disorder patients assessed for TD occurrence based on the Abnormal Involuntary Movement Scale (AIMS). We also explored the interaction between the VNTR and the TD-associated SLC18A2 marker rs363224. Our preliminary analysis did not show the SLC6A3 VNTR to be associated with TD occurrence or severity. There also appeared to be no significant interaction between SLC6A3 VNTR and SLC18A2 rs363224 in TD occurrence or severity (p>0.05). Our findings did not support a major role of the dopamine transporter gene in TD risk or severity, but we will examine additional putative functional markers in this gene.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".