An untargeted urine metabolomics approach for autologous blood transfusion detection

Research output: Contribution to journalJournal articleResearchpeer-review

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An untargeted urine metabolomics approach for autologous blood transfusion detection. / Bejder, Jacob; Gürdeniz, Gözde; Cuparencu, Catalina; Hall, Frederikke; Gybel-Brask, Mikkel; Andersen, Andreas Breenfeldt; Dragsted, Lars Ove; Secher, Niels H; Johansson, Pär I; Nordsborg, Nikolai Baastrup.

In: Medicine and Science in Sports and Exercise, 16.07.2020.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Bejder, J, Gürdeniz, G, Cuparencu, C, Hall, F, Gybel-Brask, M, Andersen, AB, Dragsted, LO, Secher, NH, Johansson, PI & Nordsborg, NB 2020, 'An untargeted urine metabolomics approach for autologous blood transfusion detection', Medicine and Science in Sports and Exercise. https://doi.org/10.1249/MSS.0000000000002442

APA

Bejder, J., Gürdeniz, G., Cuparencu, C., Hall, F., Gybel-Brask, M., Andersen, A. B., ... Nordsborg, N. B. (Accepted/In press). An untargeted urine metabolomics approach for autologous blood transfusion detection. Medicine and Science in Sports and Exercise. https://doi.org/10.1249/MSS.0000000000002442

Vancouver

Bejder J, Gürdeniz G, Cuparencu C, Hall F, Gybel-Brask M, Andersen AB et al. An untargeted urine metabolomics approach for autologous blood transfusion detection. Medicine and Science in Sports and Exercise. 2020 Jul 16. https://doi.org/10.1249/MSS.0000000000002442

Author

Bejder, Jacob ; Gürdeniz, Gözde ; Cuparencu, Catalina ; Hall, Frederikke ; Gybel-Brask, Mikkel ; Andersen, Andreas Breenfeldt ; Dragsted, Lars Ove ; Secher, Niels H ; Johansson, Pär I ; Nordsborg, Nikolai Baastrup. / An untargeted urine metabolomics approach for autologous blood transfusion detection. In: Medicine and Science in Sports and Exercise. 2020.

Bibtex

@article{d81545b136ad414e9acc41e9edf0e4c9,
title = "An untargeted urine metabolomics approach for autologous blood transfusion detection",
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.",
keywords = "Faculty of Science, Exercise, Blood transfusion, Blood doping, Antidoping, Metabolites",
author = "Jacob Bejder and G{\"o}zde G{\"u}rdeniz and Catalina Cuparencu and Frederikke Hall and Mikkel Gybel-Brask and Andersen, {Andreas Breenfeldt} and Dragsted, {Lars Ove} and Secher, {Niels H} and Johansson, {P{\"a}r I} and Nordsborg, {Nikolai Baastrup}",
note = "Afventer publicering som [Epub ahead of print] samt tildeling af CURIS-nummer.",
year = "2020",
month = "7",
day = "16",
doi = "10.1249/MSS.0000000000002442",
language = "English",
journal = "Medicine and Science in Sports and Exercise",
issn = "0195-9131",
publisher = "Lippincott Williams & Wilkins",

}

RIS

TY - JOUR

T1 - An untargeted urine metabolomics approach for autologous blood transfusion detection

AU - Bejder, Jacob

AU - Gürdeniz, Gözde

AU - Cuparencu, Catalina

AU - Hall, Frederikke

AU - Gybel-Brask, Mikkel

AU - Andersen, Andreas Breenfeldt

AU - Dragsted, Lars Ove

AU - Secher, Niels H

AU - Johansson, Pär I

AU - Nordsborg, Nikolai Baastrup

N1 - Afventer publicering som [Epub ahead of print] samt tildeling af CURIS-nummer.

PY - 2020/7/16

Y1 - 2020/7/16

N2 - 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.

AB - 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.

KW - Faculty of Science

KW - Exercise

KW - Blood transfusion

KW - Blood doping

KW - Antidoping

KW - Metabolites

U2 - 10.1249/MSS.0000000000002442

DO - 10.1249/MSS.0000000000002442

M3 - Journal article

C2 - 32694367

JO - Medicine and Science in Sports and Exercise

JF - Medicine and Science in Sports and Exercise

SN - 0195-9131

ER -

ID: 245233896