Both, arrhythmogenic right ventricular cardiomyopathy (ARVC) and regular training are associated with right ventricular (RV) remodelling. Cardiac magnetic resonance (CMR) is given an important role in the diagnosis of ARVC in current task force criteria (TFC), however, they contain no cut-off values for athletes. We aimed to confirm the added value of feature tracking and to provide new cut-off values to differentiate between ARVC and athlete’s heart. Healthy athletes with training of minimal 15 h/week (n = 34), patients with definite ARVC (n = 34) and highly trained athletes with ARVC (n = 8) were examined by CMR. Left and right ventricular volumes and masses were determined. Global right and left ventricular, and regional strain analysis for the RV free wall was performed using feature tracking on balanced steady-state free precession cine images. 94% of healthy athletes showed RV dilatation of the proposed TFC, 14.7% showed RV ejection fraction (RVEF) between 45–50%, none of them had RVEF < 45%. Although RVEF showed the highest accuracy in differentiating between athlete’s heart and ARVC, only 37.5% of athletes with ARVC showed RVEF < 45%. The only parameters falling in the pathological range (based on our established cut-off values: > − 25.6 and > − 1.4, respectively) in all athletes with ARVC were the strain and strain rate of the midventricular RV free wall. Establishing RVEF and RV strain analysis provides an important tool to distinguish ARVC from athlete’s heart. CMR based regional strain and strain rate values may help to identify ARVC even in highly trained athletes with preserved RVEF.

Body composition and cardiopulmonary exercise testing (CPET) are vital for optimizing sports performance, but the correlations between them are still underexplored. Our study aimed to investigate the relationships between body composition and specific CPET variables describing physical fitness in young athletes, also adjusting for age and height, in a less-studied, female population. Seventy players participated in our study (age: 16.10 ± 1.63 y). After determining body composition using dual-energy X-ray absorptiometry, we conducted treadmill-based maximal-intensity CPET. Data were analyzed in R using multivariate linear regression, accounting for age and height as confounders. Lean body mass (LBM), body fat mass (BFM), and bone mineral content (BMC) showed no effect on resting, maximum, or recovery heart rates and no correlation with resting or maximal lactate values. LBM positively correlated with maximum ventilation (VE-max) (Est: 1.3 × 10⁻³; SE: 6.1 × 10⁻⁴; p < 0.05) and maximum absolute oxygen consumption (VO2_abs-max) (Est: 7.710⁻⁵; SE: 6.9 × 10⁻⁶; p < 0.001)—with age as an influencing factor for VE-max and height as an influencing factor for VO2_abs-max. Conversely, BFM showed a negative correlation with maximum relative oxygen consumption (VO2_rel-max) (Est: −4.8 × 10⁻⁴; SE: 1.2 × 10⁻⁴; p < 0.001). Moreover, BFM and BMC were also negatively correlated with maximal exercise duration (Est: −2.2 × 10⁻⁴; SE: 8.0 × 10⁻⁵; p < 0.01; Est: −3.2 × 10⁻³; SE: 1.4 × 10⁻³; p < 0.05) with height as an influencing factor. Our findings indicate complex correlations between body composition and CPET parameters, providing important information for the analysis of individual ergospirometric data. Our results draw attention to the fact that body composition is more precise than weight and height in the evaluation of athletes’ physical fitness.

The effects of physical activity on skeletal muscle mass and cardiac function are well-documented, but there is limited information on the relationship between the two. Furthermore, differentiating between the ‘athlete’s heart’ and pathological cardiac conditions often presents challenges. We aimed to analyze resting echocardiographic parameters in elite water polo athletes, considering sex, anthropometrics, and body composition. We examined 161 youth and adult athletes (age: 19.7 ± 5.6 years, male: 50.9%). Data analysis was performed with R software (version 4.2), using multivariate linear regression models. Confounders besides the main predictor were sex, age, and height. Male players had higher weight (87.55 ± 12.83 vs. 69.77 ± 9.8 kg), height (188.59 ± 6.82 vs. 173.47 ± 6.76 cm), skeletal muscle mass (SMM, 43.87 ± 5.50 vs. 30.38 ± 3.95 kg), and fat-free mass (FFM, 76.60 ± 9.23 vs. 54.52 ± 6.68 kg) and lower percentage of body fat values (12.14 ± 4.00.vs 21.51 ± 4.76%) compared to the female players. Youth players had lower height (178.51 ± 9.53 vs. 186.74 ± 9.27 kg) and weight (74.34 ± 12.12. vs. 88.23 ± 14.66 kg) compared to adults. Left ventricular end-diastolic and end-systolic diameters correlated positively with SMM (Est: 0.38, StE: 0.08, p < 0.001 and Est: 0.42, StE: 0.11, p < 0.001) and FFM (Est: 0.25, StE: 0.05, p < 0.001 and Est: 0.25, StE: 0.06, p < 0.001). Right ventricular end-diastolic diameter correlated positively with SMM (Est: 0.18, StE: 0.08, p < 0.05) and FFM (Est: 0.12, StE: 0.05, p < 0.05). Interventricular septal wall thickness showed positive correlation with SMM (Est: 0.16, StE: 0.04, p < 0.001) and FFM (Est: 0.10, StE: 0.02, p < 0.001). Left ventricular posterior wall thickness correlated with SMM, with a stronger correlation in females (Est: 0.17, StE: 0.05, p < 0.001) than in males (Est:0.7, StE: 0.04, p < 0.05). The close relationship between body composition and cardiac dimensions provides an opportunity for professionals to distinguish between athlete’s heart and pathological conditions.

Body composition, bone mineral density, and cardiopulmonary exercise testing (CPET) are commonly used to assess aerobic fitness in athletes, but their interrelationships remain unclear. This study compared these parameters by sex and examined their associations in elite athletes. Our study included 145 youth water polo players (age: 15.7 ± 1.6 years; male: 75). Body composition was measured by DEXA, and treadmill CPET was performed using a sport-specific protocol. We analysed the correlations between the following factors by multivariate linear regression: lean body mass (LBM, LBM_index); body fat mass (BFM); percent body fat (PBF); bone mineral content (BMC); lumbar, femoral, and radial bone mineral density (LBMD, FNBMD, FTBMD, RBMD); exercise time; absolute and relative maximal oxygen uptake (VO_2absmax, VO_2relmax); maximal ventilation (VE_max). Exercise time was found to be negatively correlated with BFM, while VO_2relmax was found to be negatively correlated with BFM and PBF. VO_2absmax was found to be positively correlated with BFM, LBM, BMC, FNBMD, and RBMD. VE_max was found to be positively correlated with LBM and LBM_index. In males, VO_2absmax and VE_max were found to be positively correlated with LBMD and FTBMD. Correlations between bone density and CPET proved to be stronger in males. Our results indicate that body composition and bone density parameters influence CPET parameters, and their complex evaluation can support personalized diagnostics and athletes’ health.