## 1 Introduction  
The term 'body composition' refers to the ratio of fat mass to fat-free mass, composed of body water, bones, organs, and muscles, in the human body [1]. Body composition and dietary adequacy are recognized as potential factors affecting performance and physical health [2]. More specifically, an athlete's body composition phenotype indicates the complex interaction between genotype and sport-related metabolic demands, nutritional goals, and physical extent. This focus on functional body composition may favor both the health and performance of an athlete [3; 4]. Thus, in many sporting activities, athletes can turn to advantage by changing characteristics of their body composition such as their body weight, lean body mass, and body fat percentage (BF%). For example, whilst many sports such as boxing, Greco-Roman wrestling, judo, and takewondo are weight-classified, athletes should fall within a specific range of body weights [5]. Other weight-sensitive aerobic sports such as road cycling, swimming, skiing, endurance running, rowing, and modern pentathlons and biathlons are equally important [6]. As a result, both athletes' routines and diets are as adaptable to the specific demands of the cultivated sport as possible [7]. In this case, assessing and monitoring the body composition in high-performance athletes have become essential to their sporting success. Many quantification approaches, including 'indirect' methods, have been developed to measure body fat accurately [2]. An 'indirect' evaluation of physical characteristics such as the distribution of body fat mass and fat-free mass can be conducted by applying certain technologies, namely magnetic resonance imaging (MRI) [8; 9], computed X-ray tomography (CT) [8; 9], air displacement plethysmography (ADP) [10], and dual-energy X-ray absorptiometry (DXA) [11; 12; 13; 14]. However, high-priced body composition measurement techniques, referring to methods of the second level of validity, due to their large size, transportation difficulties, complexity, and the utilization of radiation, pose a challenge in terms of their deployment in many healthcare settings [15; 16]. In connection to this, approaches of the third level of validity, such as bioelectrical impedance analysis (BIA) [8; 17; 18] and anthropometry (skinfold thickness measures) [19], were established to assess human body composition [20]. Apart from this, diverse equations for the evaluation of body composition have been proposed for application to various population groups [15; 21; 22; 23; 24; 25; 26; 27]. Among this variety of equations, the Clinica Universidad de Navarra Body Adiposity Estimator (CUN-BAE) has made a distinction. Recently, the CUN-BAE has served as a newer algorithm broadly used for the analysis of body composition in clinical research. This is a body fat estimator determined by age, sex, and body mass index (BMI) in Caucasian adults [23; 28; 29; 30]. Furthermore, it should be emphasised that no body composition assessment techniques have an accuracy better than 1% [2]. Although DXA has been recognized as the gold standard, it still provides errors within the measurements (the estimated error for the prediction of BF% ranges from 2 to 3% [2]). Similarly, as the range of errors for the ADP test was detected at \(\pm\)1 to 2.7% [31], the ADP was recognized as a fast, accurate, and reliable method for estimating BF%. Meanwhile, the BIA, as a method of the third level of validity for assessing body composition, has a weaker accuracy in estimating BF% or body water content (the reported errors in its BF% estimates were up to ~5% [2]). In this context, while the CUN-BAE has been validated against ADP as the gold standard (test), widely applied as the first screening tool in clinical practice, and served as a proper tool for identifying patients at risk of cardiovascular diseases and type 2 diabetes [28; 29; 32], there is no consensus that similar effects could be expected in the testing of professional athletes using the CUN-BAE tool.