Screening for endogenous anabolic-androgenic steroid abuse has long relied on the urinary testosterone/epitestosterone (T/E) ratio. However, this metric is significantly influenced by genetic polymorphisms such as copy number variations in the UGT2B17 gene. Because the distribution of this genetic heterogeneity varies across ethnic populations, individuals with the del/del genotype face a higher risk of being missed in conventional testing. Drawing on current research progress, this paper proposes several potential strategies: integrating genotyping data into the athlete biological passport to calibrate individual metabolic baselines, employing alternative urinary markers such as the androsterone to etiocholanolone ratio and 5α-diol-3-glucuronide, and utilizing blood-based testing methods, including direct detection of intact steroid esters in dried blood spots to circumvent interference from the UGT2B17-dependent metabolic pathway. In addition, this paper attempts to construct a tiered decision-making framework that combines genotype status with marker selection, advancing testing strategies from single-biomarker thresholds toward multiomics and machine learning models. By reviewing current research across genetics, metabolomics, and artificial intelligence, this paper aims to provide analytical perspectives and potential solutions for addressing the impact of genetic polymorphisms on doping detection.
Cheng et al. (2026) studied this question.