Model for predicting metabolic activity in athletes based on biochemical blood test analysis

Abstract

Improving the efficiency of athletic performance and reducing the likelihood of overtraining are primarily determined goals that can be achieved by the correct organization of the training process. The nature of adaptation to physical stress is associated with the specificity, focus, and degree of biochemical and functional changes that occur during muscular work. In this study, we aimed to develop a diagnostic model for predicting metabolic processes in athletes based on standard biochemical blood analysis indicators. The study involved athletes from the track and field athletics team (men, n ​= ​42, average age was [22.55 ​± ​3.68] years). Blood samples were collected in the morning at the beginning and end of the training week during the annual cycle. During the entire period, 3 625 laboratory parameter tests were conducted. Capillary blood sampling in athletes was conducted from the distal phalanx of the finger after overnight fasting, according to standard diagnostic procedures. To determine the predominance of anabolic or catabolic processes, equations were derived from a linear discriminant function. The discriminant function of predicting metabolic processes in athletes has a high information capacity (92.1%), as confirmed by the biochemical results of neuroendocrine system activity, which characterized the body's stage of adaptive regulatory mechanisms in response to stress factors. The classification matrix used to predict the metabolic processes based on the results of the discriminant function calculation demonstrates the statistical significance of the model (p ​< ​0.01). Consequently, an informative mathematical model was developed, which enabled the reliable and timely prediction of the prevalence of one of the metabolic activity phases in the athlete's body. The use of the developed model will also allow us to assess the nature of adaptation to specific muscular work, identify an athlete's weaknesses, forecast the success of their performance, and timely adjust both the training process and the recovery program.