Lipoprotein subtypes after testosterone therapy in transmasculine adolescents
Abstract
Differences in lipoprotein-particle subclasses between men and women start in puberty and narrow after menopause, suggesting a role for sex steroids. In this cross-sectional cohort study, we examined lipoprotein subtype profiles in transmasculine adolescents treated with testosterone. Transmasculine adolescents (n = 17) had lipoprotein profiles that were similar to those of cisgender males (n = 33) and more atherogenic than those of cisgender females (n = 32), with higher concentrations of small low-density lipoprotein (LDL) particles (435 ± 222 nmol/L vs. 244 ± 163 nmol/L, p = 0.008) and lower concentrations of large high-density lipoprotein (HDL) particles (1.5 ± 1.3 μmol/L vs 2.7 ± 1.2 μmol/L, p = 0.003) when compared to cisgender females. Thus, testosterone appears to be a major contributor to differences in lipoprotein profiles, a surrogate for cardiovascular disease risk, between cisgender women and both transgender and cisgender men.
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 33 participants or records.
Read the paper
Related studies
- Induced osteoblast differentiation by amide derivatives of stilbene: The possible osteogenic agents
- On model selections for repeated measurement data in clinical studies
- Regulation of GLUT transporters by flavonoids in androgen-sensitive and -insensitive prostate cancer cells
- Region-specific microRNA signatures in the human epididymis
- Synthesis, biological evaluation and molecular docking of 4-Amino-2H-benzo[h]chromen-2-one (ABO) analogs containing the piperazine moiety
- In Vitro characterization of the endocrine disrupting effects of per- and poly-fluoroalkyl substances (PFASs) on the human androgen receptor
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.