Exploration of phenotypic age in gender-affirming hormone therapy

Jeroen Vervalcke; Gustavo Rodrigues; Lorenzo Marinelli; Joeri Walravens; Dorte Glintborg; Guy T

doi:10.1530/endoabs.112.008

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Endocrine Abstracts (2025) 112 008 | DOI: 10.1530/endoabs.112.008

BES2025 BES 2025 CLINICAL STUDIES (21 abstracts)

Exploration of phenotypic age in gender-affirming hormone therapy

Jeroen Vervalcke ^1,2 , Gustavo Rodrigues ³ , Lorenzo Marinelli ^2,4 , Joeri Walravens ^1,2 , Dorte Glintborg ⁵ & Guy T’Sjoen ^1,2

Author affiliations

¹Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; ²Service of Endocrinology, Dep. of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent; ³Service of Endocrinology, Coimbra University Hospital, Coimbra, Portugal; ⁴Division of Endocrinology, Diabetes, and Metabolism, Department of Medical Sciences, University of Turin, 10126, Turin, Italy; ⁵Department of Endocrinology, Odense University Hospital, Odense Denmark

Introduction: Biological or ‘epigenetic’ age reflects physiological integrity more accurately than chronological age, with established associations to morbidity and mortality. While DNA methylation-based estimators are the gold standard for assessing biological ageing, surrogate models using standard blood biomarkers, such as PhenoAge®, offer a clinically feasible alternative (1). This study applies PhenoAge ® and its ageing acceleration metric, PhenoAccel, to individuals undergoing gender-affirming hormone therapy (GAHT).

Methods: A retrospective analysis was performed on data from the Ghent arm of the European Network for the Investigation of Gender Incongruence (ENIGI) cohort, a longitudinal study of gender-diverse individuals. PhenoAge® was calculated in a hormone-naïve state at baseline and after 36 months of GAHT using apanel of nine routinely-measured biomarkers (1). Data gaps were addressed through imputation, andadjustment factors were applied to account for GAHT-induced changes in muscle mass. PhenoAccelwas derived from residuals of a regression model comparing PhenoAge to chronological age, built usingmerged data from ENIGI and NHANES (National Health and Nutrition Examination Survey), a large,population-based study in the United States. Multiple regression analysis was used to identify predictorsof accelerated ageing. Analyses were conducted in RStudio.

Results: Of 651 participants, 97 (53 on feminizing GAHT, 44 on masculinizing GAHT) had complete data for both time points. At baseline, ageing rates did not differ significantly between groups. After 36 months, individuals on masculinizing GAHT showed a significant reduction in ageing rate, with median PhenoAccel decreasing from 0.14 to -0.12 standardized residuals (P = 0.003), while those on feminizing GAHT showed no significant change (from -0.05 to -0.02 residuals). Older age at baseline predicted greater changes in ageing rate (P <0.001), explaining 63% of the variance. Smoking status was associated with higher baseline ageing acceleration. Rapid agers tended to have medical comorbidities or substance use.

Conclusion: PhenoAge® and PhenoAccel offer promising, though preliminary, insights into biological ageing duringGAHT. During the first three years of GAHT, aging rates remained stable in those receiving feminizingtherapy, whilst they significantly decreased in participants using masculinizing GAHT. These findingssupport the safety of GAHT from a biological ageing perspective and highlight the potential of DNAmbasedtools in future gender-diverse health research.

References: 1. Levine, M.E. An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573–591. (2018)

Keywords: Epigenetic Age, Aging, Transgender, Biomarkers