Skeletal muscle is extremely adaptable to various stresses which can be placed upon it. In spite of importance of skeletal muscles, little is known about genetic factors which demonstrate high influence to muscle size, function, strength and adaptation to various environmental factors. Because endurance performance is a multifactorial trait, the list of candidate genes which could account for human variation in related phenotypes is extensive. One of the first characterized and most frequently studied genetic variant is a polymorphism in the angiotensin converting enzyme I gene. The ACTN3 gene is the first structural skeletal muscle gene with a relation between its genotype and elite sprinterí performance. Nevertheless, current genetic testing cannot provide an extra advantage over existing testing methods in determining sports selection in young athletes. The main challenge still remains to identify other, complex polygenetic variants and their interactions with environmental factors which could provide benefit in the sports selection and existing talent identification.

The aim of this study was to evaluate body fat content (BF) of elite athletes obtained by two different field methods for body composition measurements and to compare it with body mass index (BMI) values. The research was conducted on 40 male athletes (20 runners and 20 handball players) and 30 non athletes. BF was calculated from the skinfold values (BFsft) and estimated using a hand-held impedance analyzer (BFbia%). Body mass index, waist to hip ratio (WHR) and waist to stature ratio (WSR) were calculated from adequate anthropometric values. Comparing the BF content between non athletes and two different sport groups, significant difference was found in all parameters between runners and non athletes (p < 0.05). Significant difference was found between BF values of runners and handball players (p < 0.05). Runners have had significantly lower BF, estimated by both methods. They also have had significantly lower WHR and WSR (p < 0.05). In the group of athletes and non athletes with BMI higher than 25 kg/m 2 , or lower than 20 kg/m 2 , comparing with others, no significant difference was found in BFsft and WHR. BMI is not a good predictor of BF, because it does not provide specific information about body fatness, but rather body heaviness. Bioimpedance and anthropometry methods could be used to monitor non obese subjects in clinical routine and population based studies. For BF estimation in athletes, we recommend anthropometry, rather than bioimpedance because of inter individual and inter sports variations in arms length and regional masculinity.