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Detection of Autologous Blood Transfusion by Metabolomics
Author(s) -
Bejder Jacob,
Gürdeniz Gözde,
Cuparencu Catalina,
Hall Frederikke,
Gybel-Brask Mikkel,
Andersen Andreas Breenfeldt,
Dragsted Lars Ove,
Secher Niels Henry,
Johansson Pär Ingemar,
Nordsborg Nikolai Baastrup
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.09104
Subject(s) - urine , metabolome , metabolomics , metabolite , medicine , phlebotomy , blood transfusion , placebo , whole blood , bioinformatics , biology , pathology , alternative medicine
Autologous blood transfusion (ABT) is illegal for competing athletes but misuse remains difficult to detect. Thus, the development of novel and sensitive methods is warranted. In this study, we explored whether ABT causes a shift of the urine metabolome. The hypothesis was that an untargeted metabolomics analysis of urine is able to identify novel biomarkers sensitive to ABT. In a randomized, double‐blinded, placebo‐controlled cross over design (3 month wash‐out), twelve trained males donated 900 ml blood or were sham phlebotomized. Four weeks later, a transfusion of the stored red blood cells or a sham transfusion was completed. Urine was collected before phlebotomy and 2 h, 1, 2, 3, 5 and 10 days after transfusion and analyzed by UPLC‐QTOF‐MS. Models of unique metabolites reflecting ABT was derived by partial least squares regression discriminant analysis. The strongest model appeared 2 h after transfusion (misclassification error: 6.3%, specificity: 98.8%). The remaining time points provided misclassification errors from ~20–50% with specificities of ~50–80%. Metabolite identification revealed secondary di‐2‐ethylhexyl phtalate metabolites as the strongest biomarkers for detection. In conclusion, untargeted metabolomics of urine identified plasticizers as the strongest metabolites for ABT detection. Future research should validate the proposed models. Support or Funding Information The study was funded by Partnership for Clean Competition