Prolactin as a biomarker for treatment response and tardive dyskinesia in schizophrenia subjects: old thoughts revisited from a genetic perspective
Bibliographic record
Abstract
Previous studies investigated whether prolactin (PRL) serum level was a biomarker of antipsychotic response, schizophrenia symptomatology, and tardive dyskinesia. Most of the findings support that antipsychotic drugs modulate PRL levels but PRL is not a steady indicator. Recent results suggest a genetic effect of PRL and PRL receptor (PRLR) polymorphisms in PRL levels indicating that independently of antipsychotic therapy subjects could have altered PRL levels due to their genetic background.We evaluated whether PRL and PRLR variants were associated with treatment outcome and tardive dyskinesia. We observed no association of PRL/PRLR polymorphism with treatment response (best genotypic results include PRL rs849885 and PRLR rs4703509 permuted p=0.326). Regarding tardive dyskinesia, the major allele of PRL rs37364 was nominally associated with risk for tardive dyskinesia in the European ancestry sub-sample (permuted p=0.183). Although we reported no significant associations, it is definitely worthy of investigation to see if together (genetic variants in the PRL system and PRL serum measures) could be a reliable biomarker for antipsychotic response and TD prevalence. Our results suggest that more studies in this context are required to shed light in the molecular mechanisms underlying antipsychotic response and tardive dyskinesia occurrence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".