Gender‐affirming hormone therapy decreases d‐dimer but worsens insulin sensitivity in transgender women
Bibliographic record
Abstract
OBJECTIVES: Gender-affirming hormonal therapies (GAHT) and HIV increase cardiovascular risk for transgender women (TW), yet there is a paucity of data quantifying cardiometabolic changes following GAHT initiation, particularly among TW with HIV. METHODS: The Féminas study enrolled TW from October 2016 to March 2017 in Lima, Peru. Participants reported sexual activity that was high risk for HIV acquisition or transmission. All were tested for HIV/ sexually transmitted infection and were given access to GAHT (oestradiol valerate and spironolactone), HIV pre-exposure prophylaxis (PrEP) or antiretroviral therapy (ART) for 12 months. Biomarker measurement was done on stored serum, whereas fasting glucose and lipids were measured in real time. RESULTS: In all, 170 TW (32 with HIV, 138 without HIV) had median age 27 years and 70% prior GAHT use. At baseline, PCSK9, sCD14, sCD163, IL-6, sTNFRI/II, CRP and EN-RAGE levels were significantly higher in TW with HIV than in TW without HIV. High-density lipoprotein and total cholesterol were lower and insulin and glucose parameters were similar. All TW with HIV started ART, but only five achieved virological suppression at any time. No TW without HIV initiated PrEP. Over 6 months, all participants initiated GAHT and had worsening insulin, glucose and HOMA-IR. Large d-dimer decreases also occurred. Similar changes occurred in TW with and without HIV. CONCLUSIONS: In this unique cohort of TW, GAHT decreased d-dimer but worsened insulin sensitivity. Because PrEP uptake and ART adherence were very low, observed effects are primarily attributed to GAHT use. Further study is needed to better understand cardiometabolic changes in TW by HIV serostatus.
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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) | 0.003 | 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".