Plasma proteome adaptations during feminizing gender-affirming hormone therapy
Bibliographic record
Abstract
Sex differences manifest in various traits, as well as in the risk of cardiovascular, metabolic and immunological conditions. Despite the clear physical changes induced by gender-affirming hormone therapy (GAHT), little is known about how it affects underlying physiological and biochemical processes. Here we examined plasma proteome changes over 6 months of feminizing GAHT in 40 transgender individuals treated with estradiol plus one of two antiandrogens: cyproterone acetate or spironolactone. Testosterone levels dropped markedly in the cyproterone group, but less so in those receiving spironolactone. Among 5,279 total proteins measured, feminizing GAHT changed the levels of 245 and 91, in the cyproterone and spironolactone groups, respectively, with most (>95%) showing a decrease. Proteins associated with male spermatogenesis showed a marked decrease in the cyproterone group, attributable specifically to loss of testosterone. Changes in body fat percentage and breast volume following GAHT were also reflected in the plasma proteome, including an increase in leptin expression. We show that feminizing GAHT remodels the proteome toward a cis-female profile, altering 36 (cyproterone) and 22 (spironolactone) of the top 100 sex-associated proteins in UK Biobank adult data. Moreover, 43% of cyproterone-affected proteins overlapped with those altered by menopausal hormone therapy in cis women, showing the same directional changes, with notable exceptions including CXCL13 and NOS3. Feminizing GAHT skewed the protein profile toward that linked to asthma and autoimmunity, while GAHT with cyproterone specifically skewed it away from an atherosclerosis-associated profile, suggesting a protective effect. These results reveal that feminizing GAHT reshapes the plasma proteome in a hormone-dependent manner, with implications for reproductive capacity, immune regulation and long-term health outcomes.
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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 | 0.000 |
| 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) | 0.001 | 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".