Differential Effects of Cyproterone Acetate vs Spironolactone on Serum High-Density Lipoprotein and Prolactin Concentrations in the Hormonal Treatment of Transgender Women
Bibliographic record
Abstract
INTRODUCTION: Spironolactone and cyproterone acetate (CPA) are the two main antiandrogen medications used in feminizing hormone therapy in transgender women. Previous studies have suggested that these two agents might have opposite effects on high-density lipoprotein (HDL) level when used in this context, and limited data have suggested CPA increases prolactin more than spironolactone. AIM: To compare the effects of spironolactone and CPA on HDL and prolactin serum concentrations in transgender women. METHODS: A retrospective chart review was conducted at three clinical sites in Toronto, Ontario, Canada. Patients were selected if they (i) identified as a transgender woman, (ii) had newly started spironolactone or CPA with estrogen or restarted spironolactone or CPA after a washout period of at least 6 months, and (iii) had not used other antiandrogens within the previous 6 months. MAIN OUTCOME MEASURES: HDL and prolactin concentrations between the two treatment groups at baseline and at 12 months. RESULTS: Eighty-two patients were included in the spironolactone group and 31 patients were included in the CPA group. Baseline HDL and prolactin levels were not significantly different between the two groups. At 12 months, HDL increased by 0.10 mmol/L (SD = 0.24) in the spironolactone group but decreased by 0.07 mmol/L (SD = 0.21) in the CPA group (P = .002). The difference remained significant after adjusting for baseline HDL, use of lipid-lowering drugs, and age. The change in prolactin was +3.10 μg/L (SD = 5.70) in the spironolactone group and +11.8 μg/L (SD = 8.63) in the CPA group (P < 0.001). This difference also remained significant after adjusting for baseline prolactin level. CONCLUSION: These data suggest that spironolactone use in transgender women increases HDL levels and that CPA has the opposite effect. CPA also is associated with a larger increase in prolactin. These factors should be considered when choosing between these two antiandrogen agents.
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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".