## 4 Discussion
Currently, more and more research results have indicated the lowest AUC of ABSI for metabolic syndrome and other cardiometabolic risks [30; 42; 43]. As found by [PERSON] et al. [44], ABSI can be used in predicting premature mortality risk, but is poor in predicting chronic diseases, including obesity. For Europeans, for the early diagnosis of a metabolic disorder, the proposed cut-off points: 0.076 for women and 0.080 for men [34], are similar to our findings: 0.074 and 0.081, respectively. In our study group, the cut-off points for obesity assessment for BAI were similar to FM-BIA and ranged from 31.6-37.5% for women and 25-26.1% for men. BAI uses hip circumference and height as basic anthropometric measures and estimates body adipose tissue as a percentage [12]. BAI can be applicable in both sexes and all ethnicities, but others indicated that results can be inaccurate in subjects with adiposity at the extreme ends of body fat percentages [3; 45; 46; 47]. Our fat percentage cut-off thresholds are similar with those published by [PERSON] [32], where the cut-offs were established as 25.8% for men and 37.1% for women. Our findings confirmed that the results of body composition analysis, including mainly fat mass and fat percentage, should be the basis for the determination of obesity risk and type. Despite the good and excellent AUC values for the majority of the tested indices, the results of the Youden index showed that both fat mass index and percentage of fat and fat free mass had the highest predictive potential for obesity. Considering the types of obesity, metabolic obesity with normal body weight is increasingly commonly diagnosed [48]. As in the case of sarcopenia obesity, it is accompanied by metabolic disorders, and diagnostics with the use of routine anthropometric measures are insufficient, and it requires body composition assessment. As found by [PERSON] et al. [49], FM to FFM ratio may be a good index for the evaluation of abnormal body composition, but this also requires establishing cut-off points for sexes. Non-invasive techniques consisting of a set of anthropometric measurements and indices are essential in the diagnosis of body adiposity and obesity, as well as body composition. These screening tools and indicators are fundamental to public health. Many novel body indexes, based on anthropometric measurement or based on BIA results have been studied in different population groups and have shown promise for clinical use. Undoubtedly, further studies to confirm the proposed cut-off points, using more advanced methods of body composition analysis, are warranted. As shown by [PERSON] [50], in a study examining the usefulness of anthropometric parameters for obesity screening and indicators of adiposity obtained from dual energy x-ray absorptiometry (DXA), the new indicators such as BRI, BAI, or ABSI correlated poorly with DXA results and had poor screening abilities. Hence, further research is needed in this field. The present study has some limitations and strengths that should be mentioned. First, our participants were of Caucasian ethnicity and mainly lived in a large city and around central Poland; therefore, the use of our results may be limited to this population. The classification of obesity based on the established cut-off points needs to be checked on large populations. Further studies with larger samples should be conducted, also for including the confounding effects from potential covariates such as age or physical activity. Due to the size of our study group and the distribution of sex and age categories, we did not undertake an age-specific analysis. Undoubtedly, this indicates the need for further research in sex- and age-specific groups. Second, for more accurate estimates of body components,modern technologies such as dual-energy X-ray absorptiometry (DXA) or air displacement plethysmography (ADP) should be used. The BIA method is non-invasive and low-cost, and the results are commonly used as a screening tool, but have also met with criticism among scientists [48].