A Potential Endurance Algorithm Prediction in the Field of Sports Performance

Abstract

Sport performance is influenced by several factors, including genetic susceptibility. In the past years, specific single nucleotide polymorphisms have been associated to sport performance; however, these effects should be considered in multivariable prediction systems since they are related to a polygenic inheritance. The aim of this study was to design a genetic endurance prediction score (GES) of endurance performance and analyze its association with anthropometric, nutritional and sport efficiency variables in a cross-sectional study within fifteen male cyclists. A statistically significant positive relationship between GES and the VO 2 maximum (P = 0.033), VO 2 VT1 (P = 0.049) and VO 2 VT2 (P < 0.001) was observed. Moreover, additional remarkable associations between genotype and the anthropometric, nutritional and sport performance variables, were achieved. In addition, an interesting link between the habit of consuming caffeinated beverages and the GES was observed. The outcomes of the present study indicate a potential use of this genetic prediction algorithm in the sports' field, which may facilitate the finding of genetically talented athletes, improve their training and food habits, as well as help in the improvement of physical conditions of amateurs.

Effects of the Trp64Arg Polymorphism in the ADRB3 Gene on Body Composition, Cardiorespiratory Fitness, and Physical Activity in Healthy Adults

“…The study involved a group of cyclists with correct body weight, BMI and body fat. However, they did not show an association between polymorphismTrp64Arg and somatotype [ 32 ].…”

Unlocking the Science of Physical Endurance: Training Techniques and Biological Factors

“…Genetic analysis, along with psychological interventions, shows potential for personalised programming in university sports programmes (Hao et al, 2023;Herbert, 2022). Universities can use genomic research developments to do genetic testing to pinpoint genetic variants linked to endurance performance parameters, including VO2 max, lactate threshold, and muscle fibre composition (De la Iglesia et al, 2020). Equipped with this information, coaches and sports scientists can customise training protocols to maximise players' genetic advantages and target areas that may need work.…”

A Study on Visualization of Tennis Players' Game Performance Based on Wavelet Analysis and ARIMA-LightGBM Model

“…Adam Maszczyk and Artur Golas, on the other hand, used nonlinear regression models and neural network models in javelin throw, and the results showed that the neural network models had a higher prediction accuracy. The team led by Rocio de la Iglesia [3] biologically perspective to analyze the genetics of athletes and to identify genetically gifted athletes through the Genetic Hardening Predictive Score (GES) to improve shortcomings and improve athlete performance.…”