An untargeted urine metabolomics approach for autologous blood transfusion detection
Research output: Contribution to journal › Journal article › Research › peer-review
- Bejder et al_(2020)_An untargeted urine metabolomics_(Accepted manuscript)
Accepted author manuscript, 597 KB, PDF document
Purpose: Autologous blood transfusion is performance enhancing and prohibited in sport but remains difficult to detect. This study explored the hypothesis that an untargeted urine metabolomics analysis can reveal one or more novel metabolites with high sensitivity and specificity for detection of autologous blood transfusion.
Methods: In a randomized, double-blinded, placebo-controlled, cross-over design, exercise-trained males (n=12) donated 900 ml blood or were sham phlebotomized. After four weeks, RBCs or saline were reinfused. Urine samples were collected before phlebotomy and 2 h, 1, 2, 3, 5 and 10 days after reinfusion and analyzed by UPLC-QTOF-MS. Models of unique metabolites reflecting autologous blood transfusion were attained by partial least squares discriminant analysis.
Results: The strongest model was obtained 2 h after reinfusion with a misclassification error of 6.3% and 98.8% specificity. However, combining only a few of the strongest metabolites selected by this model provided a sensitivity of 100% at days 1 and 2 and 66% at day 3 with 100% specificity. Metabolite identification revealed the presence of secondary di-2-ethylhexyl phtalate metabolites and putatively identified the presence of (iso)caproic acid glucuronide as the strongest candidate biomarker.
Conclusion: Untargeted urine metabolomics revealed several plasticizers as the strongest metabolic pattern for detection of autologous blood transfusion for up to 3 days. Importantly, no other metabolites in urine appear of value for anti-doping purposes.
|Journal||Medicine and Science in Sports and Exercise|
|Number of pages||8|
|Publication status||Published - 2021|
- Faculty of Science - Exercise, Blood transfusion, Blood doping, Antidoping, Metabolites
Number of downloads are based on statistics from Google Scholar and www.ku.dk