Impact of low-volume blood withdrawal on hematological biomarkers for the athlete biological passport.

This study investigated the impact of low-volume blood withdrawal on the hematological biomarkers currently considered for anti-doping purposes. After baseline measurement (D - 7), a 140 mL blood withdrawal was completed (D + 0) on 12 healthy volunteers, followed by weekly monitoring for 21 days (D + 7 - 21). Each visit consisted of a full blood count (Sysmex XN-1000) and duplicate blood volume measurements by CO-rebreathing. A significant decrease in total hemoglobin mass (Hbmass) (-2.3%, p = 0.007) and red blood cell volume (RBCV) (-2.8%, p = 0.028) was reported at D + 7. Despite no atypical passport finding (ATPF) when considering the athlete biological passport adaptive longitudinal model, hemoglobin concentration ([Hb]) increased significantly at D + 21 (+3.8%, p = 0.031). Besides, ferritin (FERR) was significantly downregulated at all points following blood withdrawal, with the largest decrease occurring at D + 7 (-26.6%, p < 0.001). Regardless of the presumable effect of blood reinfusion on ABP biomarkers, these results illustrate the challenge of monitoring hematological variables for the detection of low-volume blood withdrawal. Finally, this study outlines the sensitivity of FERR to altered erythropoiesis to support the implementation of iron markers as complementary variables for the longitudinal monitoring of blood doping, despite the potential influence of confounding factors (e.g., iron supplementations).

Prediction of plasma volume and total hemoglobin mass with machine learning.

Hemoglobin concentration ([Hb]) is used for the clinical diagnosis of anemia, and in sports as a marker of blood doping. [Hb] is however subject to significant variations mainly due to shifts in plasma volume (PV). This study proposes a newly developed model able to accurately predict total hemoglobin mass (Hbmass) and PV from a single complete blood count (CBC) and anthropometric variables in healthy subject. Seven hundred and sixty-nine CBC coupled to measures of Hbmass and PV using a CO-rebreathing method were used with a machine learning tool to calculate an estimation model. The predictive model resulted in a root mean square error of 33.2 g and 35.6 g for Hbmass, and 179 mL and 244 mL for PV, in women and men, respectively. Measured and predicted data were significantly correlated (p < 0.001) with a coefficient of determination (R2 ) ranging from 0.76 to 0.90 for Hbmass and PV, in both women and men. The Bland-Altman bias was on average 0.23 for Hbmass and 4.15 for PV. We herewith present a model with a robust prediction potential for Hbmass and PV. Such model would be relevant in providing complementary data in contexts such as the epidemiology of anemia or the individual monitoring of [Hb] in anti-doping.

Exposure to hypobaric hypoxia results in higher oxidative stress compared to normobaric hypoxia.

Sixteen healthy exercise trained participants underwent the following three, 10-h exposures in a randomized manner: (1) Hypobaric hypoxia (HH; 3450m terrestrial altitude) (2) Normobaric hypoxia (NH; 3450m simulated altitude) and (3) Normobaric normoxia (NN). Plasma oxidative stress (malondialdehyde, MDA; advanced oxidation protein products, AOPP) and antioxidant markers (superoxide dismutase, SOD; glutathione peroxidase, GPX; catalase; ferric reducing antioxidant power, FRAP) were measured before and after each exposure. MDA was significantly higher after HH compared to NN condition (+24%). SOD and GPX activities were increased (vs. before; +29% and +54%) while FRAP was decreased (vs. before; -34%) only after 10h of HH. AOPP significantly increased after 10h for NH (vs. before; +83%), and HH (vs. before; +99%) whereas it remained stable in NN. These results provide evidence that prooxidant/antioxidant balance was impaired to a greater degree following acute exposure to terrestrial (HH) vs. simulated altitude (NH) and that the chamber confinement (NN) did likely not explain these differences.