An Untargeted Urine Metabolomics Approach for Autologous Blood Transfusion Detection / Jacob Bejder, Gözde Gürdeniz, Cătălina Cuparencu, Frederikke Hall, Mikkel Gybel-Brask, Andreas Breenfeldt Andersen, Lars Ove Dragsted, Niels H. Secher, Pär I. Johansson, Nikolai Baastrup Nordsborg. - (Medicine & Science in Sports & Exercise (2020) 16 July)
- PMID: 32694367
- DOI: 10.1249/MSS.0000000000002442
Abstract
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.
