Abstract
In the fight against doping, disciplinary sanctions have up to now been primarily based on the discovery of an exogenous substance in a biological fluid of the athlete. However, indirect markers of altered erythropoiesis can provide enough evidence to differentiate between natural variations and blood doping. Forensic techniques for the evaluation of the evidence, and more particularly Bayesian networks, allow antidoping authorities to take into account firstly the natural variations of indirect markers – through a mathematical formalism based on probabilities – and secondly the complexity due to the multiplicity of causes and confounding effects – through a distributed and flexible graphical representation. The information stored in an athlete’s biological passport may be then sufficient to launch a disciplinary procedure against the athlete. The strength of the passport is that it relies on a statistical approach based on sound empirical testing on large populations and justifiable protocols. Interestingly, its introduction coincides with the paradigm shift that is materializing today in forensic identification science, from archaic assumptions of absolute certainty and perfection to a more defensible empirical and probabilistic foundation.
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Notes
- 1.
Any racing cyclist whose result is above 50% is declared ineligible and is excluded from the race.
- 2.
As of 2008, the OFF score remains among the best indirect markers of blood doping. Even though it was originally developed to detect solely the interruption of rHuEPO doping, it is also sensitive to slow, long-acting erythropoietic agents such as insulin-growth factor 1 (IGF-1), human growth hormone (hGH) and microdosing with rHuEPO.
- 3.
The RET# marker offers an enormous advantage: it responds both to rHuEPO microdoses and to hemodilution, the latter actually reinforcing the RET# drop.
- 4.
For example, an athlete may spend 8 days training at sea level during the day and sleeping at an altitude higher than 2,000 m and then 6 days at an altitude of 800 m. Such a pattern is extremely complex and simplification is unavoidable.
- 5.
This terminology may be misleading: intra-individual variations are not restricted to biological variations occurring in an individual. They include all variations with the exception of inter-individual variations, whether their origin is biological or analytical. Non-inter-individual variations may be a better term in the current context.
- 6.
Not all conditions should necessarily be met. The higher the number of conditions fulfilled, the smaller the variability of the marker. If one condition is not met, the component of variance should be adapted accordingly.
- 7.
This is particularly important for quantitative tests, such as Hgb and Hct, used to establish the athlete’s biological passport.
- 8.
Only one tube is necessary if blood is drawn only to establish a blood profile.
- 9.
This procedure makes it possible to calculate the mean and the coefficient of variation (CV) and to compare the data to the target values provided by the manufacturer of the controls.
- 10.
The terms distribution and density are often used interchangeably in mathematics.
- 11.
As a reminder, specificity refers to the capacity for a marker to correctly identify negative cases, i.e. nondoped athletes. Specificity equals one minus the rate of false positives.
- 12.
To avoid confusion, the notation \( \Pr ( \cdot ) \) is used to represent the probability of a state or of an event, whereas \( P( \cdot ) \) represents the density of probability.
- 13.
No evidence against normality has been found for Hgb, OFF score and ABPS if time-dependent factors are taken into account and if samples are taken at least 5 days apart (Sharpe et al. 2006, Sottas et al. 2008b). The non-linear response of ABPS to changes in hematopoeisis may however lead to a departure from normality if the athlete changes altitude.
- 14.
Such a situation may occur if the athlete is tested positive for rHuEPO in the urine or for homologous blood transfusion and if an analysis of indirect blood markers has been carried out at the same time.
- 15.
This hypothesis is wrong if for instance the prevalence of doping is higher in athletes with ABPS that is naturally lower compared to athletes with high ABPS.
- 16.
A mathematical formulation may be found in the book by Gelman et al. (2004) ((1.3), page 8)
- 17.
These methods are usually based on maximum likelihood techniques applied to a distance measure between two distributions, such as the Kolmogorov–Smirnov distance.
- 18.
In statistics, the positive predictive value is among the most important parameters in decision making. It is the only statistical entity that measures the probability that a positive test actually proves the tested hypothesis, such as the fact, in our case, that an athlete is truly doped.
- 19.
A low level of evidence, such as 50%, may be chosen for the purpose of targeting the athletes who may then be tested for the presence of rHuEPO in the urine, homologous blood transfusion and/or human growth hormone. This is true in both paradigms.
- 20.
For the ease of interpretation, the likelihood function as expressed in the formula (3) of a previous article (Sottas et al. 2008a,b) has been converted in a probability density function that is independent of the length n of the sequence. The likelihood follows a gamma distribution function with scale 1/n and shape n/2.
Abbreviations
- 3G:
-
Third generation
- ABPS:
-
Abnormal blood profile score
- BN:
-
Bayesian network
- CDF:
-
Cumulative distribution function
- CSCQ:
-
Swiss Quality Control Center
- CV:
-
Coefficient of variation
- EPO:
-
Erythropoietin
- Hct:
-
Hematocrit
- Hgb:
-
Hemoglobin
- IGF-1:
-
Insulin growth factor-1
- OOC:
-
Out-of-competition
- PIO2 :
-
Partial pressure of inspired oxygen
- PREC:
-
Pre-competition
- RET#:
-
Reticulocyte count
- RET%:
-
Reticulocyte percentage
- rHuEPO:
-
Recombinant human erythropoietin
- WHO:
-
World Health Organization
References
Aitken CGG, Taroni F (2004) Statistics and the evaluation of evidence for forensic scientists, 2nd edn. Wiley, Chichester
Ashenden MJ (2002) A strategy to deter blood doping in sport. Haematologica 87:225–232
Ashenden MJ, Sharpe K, Damsgaard R et al (2004) Standardization of reticulocyte values in an antidoping context. Am J Clin Pathol 121:816–825
Audran M, Gareau R, Matecki S et al (1999) Effects of erythropoietin administration in training athletes and possible indirect detection in doping control. Med Sci Sports Exerc 31:639–645
Berger JO, Berry DA (1988) Statistical analysis and the illusion of objectivity. Am Scient 76:159–165
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, Chichester
Evett IW (1996) Expert evidence and forensic misconceptions of the nature of exact science. Sci & Just 36:118–122
Gelman A, Carlin JB, Stern HS et al (2004) Bayesian data analysis, 2nd edn. Chapman & Hall, Boca Raton
Gore CJ, Parisotto R, Ashenden MJ et al (2003) Second-generation blood tests to detect erythropoietin abuse by athletes. Haematologica 88:333–344
Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton
Harris EK (1974) Effects of intra- and inter-individual variation on the appropriate use of normal ranges. Clin Chem 20:1535–1542
Leppänen EA, Gräsbeck R (1998) Experimental basis of standardized specimen collection: effect of posture on blood picture. Eur J Haematol 40:222–226
Malcovati L, Pascutto C, Cazzola M (2003) Hematologic passport for athletes competing in endurance sports: a feasibility study. Haematologica 88:570–581
Parisotto R, Wu M, Ashenden M et al (2001) Detection of recombinant human erythropoietin abuse in athletes utilizing markers of altered erythropoiesis. Haematologica 86:128–137
Robinson N (2007) Lausanne blood protocol. Swiss Laboratory for Doping Analyses technical document, Epalinges
Robinson N, Mangin P, Saugy M (2004) Time and temperature dependant changes in red blood cell analytes used for testing recombinant erythropoietin abuse in sports. Clin Lab 50:317–323
Robinson N, Schattenberg L, Zorzoli M et al (2005) Haematological analysis conducted at the departure of the Tour de France 2001. Int J Sports Med 26:200–207
Robinson N, Sottas PE, Mangin P et al (2007) Bayesian detection of abnormal haematological values to introduce a “no-start” rule for heterogeneous populations of athletes. Haematologica 92:1143–1144
Saks MJ, Koehler JJ (2005) The coming paradigm shift in forensic identification science. Science 309:892
Scarpino V, Arrigo A, Benzi G et al (1990) Evaluation of prevalence of doping among Italian athletes. Lancet 336:1048–1050
Schmidt W, Prommer N (2005) The optimised CO-rebreathing method: a new tool to determine total haemoglobin mass routinely. Eur J Appl Physiol 95:486–495
Sharpe K, Hopkins W, Emslie KR et al (2002) Development of reference ranges in elite athletes for markers of altered erythropoiesis. Haematologica 87:1248–1257
Sharpe K, Ashenden MJ, Schumacher YO (2006) A third generation approach to detect erythopoetin abuse in athletes. Haematologica 91:356–363
Sottas PE, Robinson N, Giraud S et al (2006) Statistical classification of abnormal blood profiles in athletes. Int J Biostat 2:3
Sottas PE, Baume N, Saudan C et al (2007) Bayesian detection of abnormal values in longitudinal biomarkers with an application to T/E ratio. Biostatistics 2:285–296
Sottas PE, Saudan C, Schweizer C et al (2008a) From population- to subject-based limits of T/E ratio to detect testosterone abuse in elite sports. Forensic Sci Int 174:166–172
Sottas PE, Robinson N, Niggli O et al (2008b) A forensic approach to the interpretation of blood doping markers. Law Prob & Risk 7:191–210
Taroni F, Aitken C, Garbolino P et al (2006) Bayesian networks and probabilistic inference in forensic science. Wiley, Chichester
Varlet-Marie E, Audran M, Lejeune M et al (2004) Analysis of human reticulocyte genes reveals altered erythropoiesis: potential use to detect recombinant human erythropoietin doping. Haematologica 89:991–997
World Health Organisation (2001) Iron deficiency anemia: assessment, prevention and control, a guide for programme managers. WHO publications, Geneva
Acknowledgements
A major portion of this work was made possible by a grant from the Swiss National Fund for Scientific Research (grant #320000-111771) and the World Anti-Doping Agency (grants #R06C1MS and #R07D0MS).
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Sottas, PE., Robinson, N., Saugy, M. (2010). The Athlete’s Biological Passport and Indirect Markers of Blood Doping. In: Thieme, D., Hemmersbach, P. (eds) Doping in Sports: Biochemical Principles, Effects and Analysis. Handbook of Experimental Pharmacology, vol 195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79088-4_14
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