Purpose: Plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and cardiac troponin T levels show a transient increase after marathon running. The aim of this study was to investigate whether running duration influences the patterns of changes in cardiac biomarkers. Methods: Twenty participants with fast and slow finishing times were included in the study. Blood samples were taken before the marathon race, immediately after, and 24 hours after the race. Samples were analyzed for NT-proBNP and cardiac troponin T concentration. Furthermore, a complete blood cell count was performed. Results: After the marathon race, the fast and slow runners showed similar changes of NT-proBNP and cardiac troponin T (ie, a transient increase). Curve estimation regression analysis showed a curvilinear relationship (quadratic model) between running times and NT-proBNP increments immediately after the race, with less of an increase in the very fast and the very slow runners (r 2 = .359, P = .023). NT-proBNP increments immediately after the race were correlated to the decline 24 hours after the marathon (r = −.612, P = .004). Conclusions: This study indicates that NT-proBNP release immediately after marathon running varies in a curvilinear fashion with running time. It is speculated that low NT-proBNP release is associated with training adaptation in most elite runners and the relatively low cardiac stress in the slowest (but experienced) runners. The combination of less adaptation and relatively large cardiac wall and metabolic stress may explain the highest NT-proBNP values in runners with average running times. In addition, NT-proBNP decrements 24 hours after the race depend primarily on the values reached after the marathon and not on running time.
Natthapon Traiperm, Rungchai Chaunchaiyakul, Martin Burtscher, and Hannes Gatterer
Lachlan P. James, Haresh Suppiah, Michael R. McGuigan, and David L. Carey
Purpose: Dozens of variables can be derived from the countermovement jump (CMJ). However, this does not guarantee an increase in useful information because many of the variables are highly correlated. Furthermore, practitioners should seek to find the simplest solution to performance testing and reporting challenges. The purpose of this investigation was to show how to apply dimensionality reduction to CMJ data with a view to offer practitioners solutions to aid applications in high-performance settings. Methods: The data were collected from 3 cohorts using 3 different devices. Dimensionality reduction was undertaken on the extracted variables by way of principal component analysis and maximum likelihood factor analysis. Results: Over 90% of the variance in each CMJ data set could be explained in 3 or 4 principal components. Similarly, 2 to 3 factors could successfully explain the CMJ. Conclusions: The application of dimensional reduction through principal component analysis and factor analysis allowed for the identification of key variables that strongly contributed to distinct aspects of jump performance. Practitioners and scientists can consider the information derived from these procedures in several ways to streamline the transfer of CMJ test information.
Gennaro Boccia, Marco Cardinale, and Paolo Riccardo Brustio
Purpose: This study investigated (1) the transition rate of elite world-class throwers, (2) the age of peak performance in either elite junior and/or elite senior athletes, and (3) if relative age effect (RAE) influences the chance of being considered elite in junior and/or senior category. Methods: The career performance trajectories of 5108 throwers (49.9% females) were extracted from the World Athletics database. The authors identified throwers who had reached the elite level (operationally defined as the World all-time top 50 ranked for each age category) in either junior and/or senior category and calculated the junior-to-senior transition rate. The age of peak performance and the RAE were also investigated. Results: The transition rate at 16 and 18 years of age was 6% and 12% in males and 16% and 24% in females, respectively. Furthermore, elite senior throwers reached their personal best later in life than elite junior throwers. The athletes of both genders considered elite in the junior category showed a large RAE. Interestingly, male athletes who reached the elite level in senior category also showed appreciable RAE. Conclusions: Only a few of the athletes who reach the top 50 in the world at 16 or 18 years of age manage to become elite senior athletes, underlining that success at the beginning of an athletic career does not predict success in the athlete’s senior career. Moreover, data suggest that being relatively older may confer a benefit across the whole career of male throwers.
Gabriele Gallo, Luca Filipas, Michele Tornaghi, Mattia Garbin, Roberto Codella, Nicola Lovecchio, and Daniele Zaccaria
Purpose: To analyze the anthropometric and physiological characteristics of competitive 15- to 16-year-old young male road cyclists and scale them according to a dichotomous category of successful/unsuccessful riders. Methods: A total of 103 15- to 16-year-old male road cyclists competing in the Italian national under 17 category performed a laboratory incremental exercise test during the in-season period. Age, height, body mass, body mass index, peak height velocity, and absolute and relative power output at 2 mmol/L and 4 mmol/L of blood lactate concentration were compared between 2 subgroups, including those scoring at least 1 point (successful, n = 70) and those that did not score points (unsuccessful, n = 61) in the general season ranking. Results: Successful and unsuccessful riders did not differ anthropometrically. Successful riders recorded significantly higher absolute and relative power output at 2 mmol/L and 4 mmol/L of blood lactate concentration compared with unsuccessful riders. Successful riders were also significantly older and had advanced biological maturation compared with their unsuccessful counterparts. Conclusion: Power associated with blood lactate profiles, together with chronological age and peak height velocity, plays an important role in determining race results in under 17 road cycling. Physiological tests could be helpful for coaches to measure these performance predictors.