The Impact of Gender Affirming Hormone Therapy on Measures of Kidney Function: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Gender affirming hormone therapy (GAHT) modifies body composition and lean muscle mass in transgender persons. We sought to characterize the change in serum creatinine, other kidney function biomarkers, and glomerular filtration rate (GFR) in transgender persons initiating masculinizing and feminizing GAHT. Methods: We searched PubMed, EMBASE, the Cochrane Library, and ClinicalTrials.gov from inception to September 16, 2020 for randomized controlled trials, observational studies, and case series that evaluated the change in serum creatinine, other kidney function biomarkers, and GFR before and after the initiation of GAHT in adult transgender persons. Two reviewers independently screened and abstracted data and disagreements were resolved by a third reviewer. Random effects meta-analysis was performed to determine the change in outcomes over follow-up of 3, 6 and 12 months. Results: Of the 4758 eligible studies, 26 met the inclusion criteria including 9 studies that recruited 488 transgender men and 593 women in which data was meta-analyzed. There was heterogeneity in study design, populations, GAHT routes, and dosing. At 12 months after initiating GAHT, serum creatinine increased by 0.15mg/dL (95% CI 0.00, 0.29) in 370 transgender men and decreased by -0.05mg/dL (95% CI -0.16, 0.05) in 361 transgender women. No study reported the impact of GAHT on albuminuria, proteinuria, cystatin C, or measured GFR. Conclusions: GAHT increases serum creatinine in transgender men and does not impact serum creatinine in transgender women. The impact on GAHT on other kidney function biomarkers and measured GFR is unknown.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".