Analysis of blood parameters in athletes taking into account hematocrit levels

Introduction. Assessment of hematological parameters in professional athletes requires a special approach. This includes analysis of hematocrit levels, which can be quite high due to their significant oscillations during intense muscular activity and under conditions of relative rest. Objective. Study of the additional influence of various hematocrit levels on morphological and biochemical blood parameters, determined within the framework of the comprehensive medical examination (CME) of athletes. Materials and methods. A retrospective study was conducted using data from athletes’ medical records. A total of 26,413 hematological parameters obtained during the CME of athletes from Russian national teams were analyzed. Hematocrit was calculated using an automatic Mindray BC-6200 analyzer as the ratio of red blood cell volume to total blood volume. Nonparametric reference intervals for hematocrit, hemoglobin, red blood cell count, and mean corpuscular volume were constructed according to the CLSI EP28-A3c standard. Associations between hematocrit and blood parameters (lipid and protein profiles, glucose, oxygen transport system, sodium, potassium, magnesium, and platelets) were assessed using Spearman’s rank correlations within extreme sub-samples (≤ p 25 and ≥ p 75 of hematocrit). Only correlations with Spearman’s coefficient above 0.5 were considered significant. Comparisons of parameter distributions between percentile groups of hematocrit were performed pairwise using the nonparametric Wilcoxon test with an FDR correction. Results. Irrespective of sex, within the normal hematocrit range (25–75 percentiles), a strong positive correlation is observed with hemoglobin levels ( r s = 0.836) and red blood cell count ( r s = 0.631). For hematocrit values above the 75th percentile, a strong positive correlation is maintained with hemoglobin concentration ( r s = 0.801) and red blood cell count ( r s = 0.603). Regarding hematocrit values below the 25th percentile, a near-significant correlation was identified with red blood cell count ( r s = 0.437), as well as a strong positive correlation with hemoglobin ( r s = 0.839). Conclusions. Across different percentile ranges of hematocrit, significant differences in numerous blood biochemical parameters were identified. Thus, the higher the hematocrit level, the higher the concentrations of the recorded parameters in the blood serum. This underscores the importance of taking into account, inter alia, the possible influence of the hematocrit level when evaluating hematological parameters in athletes.