Threshold of Dopamine D<sub>2/3</sub> Receptor Occupancy for Hyperprolactinemia in Older Patients With Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Although hyperprolactinemia carries a long-term risk of morbidity, the threshold of dopamine D2/3 receptor (D2/3R) occupancy for hyperprolactinemia has not been investigated in older patients with schizophrenia. Data were taken from a positron emission tomography (PET) study conducted between August 2007 and August 2015. The present post hoc study included 42 clinically stable outpatients with schizophrenia (DSM-IV) (mean ± SD age = 60.2 ± 6.7 years) taking olanzapine or risperidone. Subjects underwent [¹¹C]-raclopride PET scans to measure D2/3R occupancy before and after reducing their dose of antipsychotic by up to 40%. Blood samples were collected before each PET scan to measure prolactin levels. METHODS: The relationship between prolactin levels and D2/3R occupancy was examined using stepwise linear regression analyses. The D2/3R occupancy thresholds for hyperprolactinemia were explored using Fisher exact tests. RESULTS: Prolactin levels decreased following dose reduction (mean ± SD = 24.1 ± 30.2 ng/mL to 17.2 ± 15.1 ng/mL; P < .001). Prolactin levels were associated with female gender (β = .32, P = .006, vs male), antipsychotics (β = .23, P = .02, risperidone vs olanzapine), and D2/3R occupancy (β = .23, P = .04). Those with D2/3R occupancy of 66% or higher were more likely to have hyperprolactinemia than those with D2/3R occupancy lower than 66% (P = .03). Sensitivity, specificity, positive predictive value, and negative predictive value of this threshold were 0.44, 0.81, 0.78, and 0.48, respectively. We identified a D2/3R occupancy threshold for hyperprolactinemia of 66% in older patients with schizophrenia, which is lower than that reported in younger patients (73%) by other researchers. CONCLUSIONS: Our results suggest a higher sensitivity to antipsychotics in older patients. Prolactin levels could assist in the determination of appropriate antipsychotic dosing to minimize adverse effects. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00716755.
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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 | 0.000 |
| 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) | 0.001 | 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".