Prolactin and Estrogen Levels in Postmenopausal Women Receiving Aripiprazole Augmentation Treatment for Depression
Bibliographic record
Abstract
BACKGROUND: Antipsychotic drugs are well established to alter serum prolactin levels, often resulting in adverse effects including amenorrhea, galactorrhea, osteoporosis, and loss of libido. There is growing preclinical evidence that prolactin-elevating drugs can instigate the progression of precancerous lesions to breast cancer and that genes activated by prolactin are associated with the development and proliferation of breast cancer. Current guides advise a cautious approach (weighing risks and benefits) to the administration of prolactin-elevating antipsychotic drugs in women with a previously detected breast cancer. Aripiprazole is known to be a prolactin-sparing antipsychotic; however, data regarding its effects on prolactin and estrogens in postmenopausal women are lacking. METHODS: We examined serum hormone levels in n = 66 women who participated in a randomized, double-blind, placebo-controlled, multicenter trial of aripiprazole (high and low doses) added to an antidepressant in adults older than 60 years. Aripiprazole or placebo tablets were administered for 12 weeks as an augmentation strategy in venlafaxine-treated women. The primary outcomes were the difference in prolactin and estrogen levels. RESULTS: There was no significant effect of aripiprazole treatment on prolactin or estrogen levels, including in models that divided groups into low and high doses: prolactin (P = 0.075), estrone (P = 0.67), and estradiol (P = 0.96). CONCLUSIONS: Aripiprazole addition to an antidepressant did not alter serum estrogens or prolactin. These findings may be relevant in the treatment of some postmenopausal women with depression.
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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) | 0.001 | 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".