## 4 Discussion  
An important finding of the present study is that BIA showed a strong concordance with AP across the population studied, as shown by the CCC values. A moderate concordance was present in subjects with BMI \(<\) 25 kg/m\({}^{2}\) and in those without central obesity, as defined by the WHtR criteria. Although concordant, the two techniques had various degrees of agreement, depending on the different stratifications of the study group. The bias (mean difference between the two methods) of 3% in %BF had large limits of agreement when computed for the entire sample. However, it reached a value to up to 4.95% in women. The high value of the bias in females, especially when compared with the bias in men (1.28%), could be partly explained by the significant difference in the anthropometric parameters between the two subgroups, particularly the body mass and height. The male subjects were significantly taller and heavier. Furthermore, previous research has shown that in female subjects, subcutaneous fat is distributed more widely than in male subjects [37]. This observation is supported by the similar bias present in underweight and normal-weight subjects according to the BMI (5.07% and 4.04%, respectively), while the differences were not significant in the overweight and obese (1.68% and 2.63%, respectively). Figure 4: Concordance and Bland–Altman plots for the subjects without (**A**,**B**) and with central obesity (**C**,**D**), classified according to the WHtR criterion. The concordance plots also show the concordance correlation coefficients (CCC). On the Bland–Altman plots, the bias is indicated as a black solid line and its 95% confidence interval (CI) by black dashed lines. The limits of agreement (bias \(\pm\) 1.96 SD of the differences) are marked by red solid lines and their CIs by red dashed lines. The linear regression equation between the differences and the means is given together with the \(p\)-value and the coefficient of determination (R\({}^{2}\)). These results confirm previous research reporting BIA's tendency to overestimate the %BF in lean subjects and to underestimate it in obese ones [35; 38]. An important consideration in the validation of the body composition estimate by BIA is the predictive equation chosen to calculate %BF. Several prediction equations are based on linear regression [15; 39], and they use different reference methods. They all take account of subjects' characteristics such as age, sex, geographical provenance, health status, and the level of physical activity [15; 40]. Although the algorithms are increasingly sophisticated, BIA remains an indirect method to measure body composition, it depends on the measurement of the electrical response of the body when exposed to an electric current. The measured resistance is used to compute the fat-free mass by assuming a mean value for the resistivity and a hydration fraction, which is assumed to be equal to 0.73 for the healthy adult [41]. Therefore, the use of these estimated parameters in subjects who fall outside the \"normal, healthy\" ideal may contribute to prediction errors. Further errors can arise as the derivation %BF by BIA is calculated by the subtraction of the fat free mass from the body weight; thus, in lean subjects, an error in the fat free mass can have a greater impact on the fat mass prediction [21]. The main research question of the present study was to compare the two body composition methods in subjects with central obesity. To our knowledge, it is the first study to address this research question. The bioelectrical impedance (BIA) measurements are based on an empirical relationship between the impedance quotient (length\({}^{2}/\)R, where R is the resistance) and the volume of water able to conduct the electrical current due to its electrolyte content. The conductive length is approximated by the subject height. The lean body mass is then computed using the assumption that it has a 73% water content.