Plasma volume and red cell mass are key health markers used to monitor numerous disease states, such as heart failure, kidney disease, or sepsis. Nevertheless, there is currently no practically applicable method to easily measure absolute plasma or red cell volumes in a clinical setting. Here, a novel marker for plasma volume and red cell mass was developed through analysis of the observed variability caused by plasma volume shifts in common biochemical measures, selected based on their propensity to present with low variations over time. Once a month for 6 months, serum and whole blood samples were collected from 33 active males. Concurrently, the CO-rebreathing method was applied to determine target levels of hemoglobin mass (HbM) and blood volumes. The variability of 18 common chemistry markers and 27 Full Blood Count variables was investigated and matched to the observed plasma volume variation. After the removal of between-subject variations using a Bayesian model, multivariate analysis identified two sets of 8 and 15 biomarkers explaining 68% and 69% of plasma volume variance, respectively. The final multiparametric model contains a weighting function to allow for isolated abnormalities in single biomarkers. This proof-of-concept investigation describes a novel approach to estimate absolute vascular volumes, with a simple blood test. Despite the physiological instability of critically ill patients, it is hypothesized the model, with its multiparametric approach and weighting function, maintains the capacity to describe vascular volumes. This model has potential to transform volume management in clinical settings.

The haematological module of the Athlete's Biological Passport (ABP) has significantly impacted the prevalence of blood manipulations in elite sports. However, the ABP relies on a number of concentration-based markers of erythropoiesis, such as haemoglobin concentration ([Hb]), which are influenced by shifts in plasma volume (PV). Fluctuations in PV contribute to the majority of biological variance associated with volumetric ABP markers. Our laboratory recently identified a panel of common chemistry markers (from a simple blood test) capable of describing ca 67% of PV variance, presenting an applicable method to account for volume shifts within anti-doping practices. Here, this novel PV marker was included into the ABP adaptive model. Over a six-month period (one test per month), 33 healthy, active males provided blood samples and performed the CO-rebreathing method to record PV (control). In the final month participants performed a single maximal exercise effort to promote a PV shift (mean PV decrease -17%, 95% CI -9.75 to -18.13%). Applying the ABP adaptive model, individualized reference limits for [Hb] and the OFF-score were created, with and without the PV correction. With the PV correction, an average of 66% of [Hb] within-subject variance is explained, narrowing the predicted reference limits, and reducing the number of atypical ABP findings post-exercise. Despite an increase in sensitivity there was no observed loss of specificity with the addition of the PV correction. The novel PV marker presented here has the potential to improve the ABP's rate of correct doping detection by removing the confounding effects of PV variance.

The purpose of this study was to observe and report variations in several haematological and biochemical markers throughout an entire athletic season in a large cohort of adolescent athletes of Arab origin. Blood samples were collected from 72 adolescent male athletes at 4 selected time points during their training season. Results expressed in relation to plasma volume were corrected accordingly and significant variations in several variables emerged. Initial uncorrected haematological results revealed that haematocrit (Hct) and mean cell volume (MCV) concentrations noticeably increased at the competitive period (T3) and before the start of the following preseason (T4), whereas reticulocytes equivalent (Ret-He) only rose at T4 phase (p < 0.01). Conversely, corrected red blood cells (RBC), haemoglobin (Hb) and mean cell haemoglobin concentration (MCHC) progressively decreased over the year (p < 0.001). From the electrolytes panel, sodium and chloride considerably reduced at the peak of the training period (T2) to the start of the next preseason (T4), while a significant fall in potassium was mainly observed during the competitive period (T3) (p < 0.001). Coaches and sport scientists could use the results of this study to evaluate typical variations of each age group in order to diagnose potential adverse effects of high training loads, assist in the design of training programs and/or clinical interventions that will safeguard athletes’ health, and consider the important role of plasma volume for the interpretation of results.

Background: There is a shortage of longitudinal surveys in highly trained adolescent table tennis athletes reporting typical changes in performance related, hematological, and biochemical parameters. Objectives: The purpose of the study was to: (1) examine yearly variations of performance related parameters, and (2) report the variability of selected hematological and biochemical markers utilized to ascertain health status, and training influences in a group of young Arab table tennis athletes. Methods: Data were collected from ten male adolescent players with a mean age of 14.2 ± 1.3 years and a mean table tennis training experience of 6 years. Physical and biochemical measures were carried out at three important time points throughout the training season: a baseline measure (PRE-September); a mid-season (transition) measure (MID-February); an end of season measure (END-May). Results: Performance related parameters tended to significantly improve over time (P = 0.05), with lowest values displayed during PRE-measure. Hematocrit (P = 0.05), serum testosterone (P = 0.01), mean cell volume (MCV) (P = 0.001) and reticulocytes (RET) (P = 0.017) significantly increased at the END training period compared to PRE. Moreover, 25(OH)D levels (P = 0.001) showed a drop during MID to then significantly increase at the END whilst sex hormone binding globulin (SHBG; P = 0.005) decreased at the END period compared to MID. Conclusions: In conclusion, based on our findings, changes in certain physical parameters and, hematological and biochemical markers, take place during the training period. Therefore, coaches, sports scientists and nutritionists should take into consideration these fluctuations and plan and alter their training programs accordingly.

No abstract