Dissemin is shutting down on January 1st, 2025

Published in

Taylor and Francis Group, European Journal of Sport Science, 4(24), p. 431-439, 2024

DOI: 10.1002/ejsc.12073

Links

Tools

Export citation

Search in Google Scholar

Are pre‐race serum blood biomarkers associated with the 24‐h ultramarathon race performance?

Distributing this paper is prohibited by the publisher
Distributing this paper is prohibited by the publisher

Full text: Unavailable

Red circle
Preprint: archiving forbidden
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

AbstractThe 24‐h ultramarathon (UM) race is one of the most demanding competitive sports in terms of muscular and physiological exertion. In this context, predictors of UM athletes' physical performance are in high demand; however, data on the predictive capabilities of hematological variables are still sparse. In the present paper, we retrospectively took into consideration the pre‐race blood biomarker levels (including basic blood count, leukocyte subpopulations, markers of inflammation and organ function, metabolic profile, and electrolytes) of 50 UM athletes (M = 33, F = 17) who completed a 24‐h competition in order to identify a combination of analytes capable of predicting the athletic performance in terms of distance covered during the 24‐h run. The multiple regression analysis produced a model that explained a significant portion of the variance in the dependent variable, with an adjusted R‐squared value of 0.783 (F(13, 36) = 14.58, p < 0.001). A greater race distance was correlated with higher pre‐race values of hematocrit, lactate dehydrogenase (LDH), total cholesterol, HDL/LDL ratio, and triglycerides and lower levels of monocytes, eosinophils, alanine aminotransferase (ALT), gamma‐glutamyl transferase (GGT), total proteins, and sodium. This study represents the first of its kind conducted on 24‐h UM athletes that investigated the association between blood markers and endurance performance. Our model, given its promising predictive power, would serve as a starting point that will require refinement and integration with other traditional performance prediction measures, in order to support athletes and coaches in better managing the training loads during the race‐approaching phases.