ReviewDrug testing and analysis2026
The Evolution of Precision Anti-Doping From UGT2B17 Polymorphism Insights to Integrated Detection Frameworks.
Review in Drug testing and analysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
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