1. Introduction
Obesity is a global health problem and most prevalent in developed countries. According to Eurostat data, the prevalence of overweight in the European region was 52.7% in the adult population in 2019. The prevalence of overweight seems to grow in eastern European countries; in the Czech Republic and Hungary it is 60%, in Slovakia 59% of adults. In Poland, 58% of the adult population is overweight based on their BMI (body mass index) [
1]. According to data of the Organisation for Economic Cooperation and Development (OECD), 18.5% of the adult Polish population was obese in 2019, which surpasses the European average of 16% [
2]. Evaluation of body fat content and its distribution in different body regions is of great importance in predicting various health risk factors [
3].
Certain anthropometric measurements considered surrogates for obesity have long been used in medical settings for obesity-associated health risk evaluation [
4]. Anthropometric measures are simple, cheap, non-invasive, and portable tools for assessing human body size or composition [
5,
6]. The core components of anthropometric measurements are height; weight; body circumference of waist, hip, and limbs; and skinfold thickness. They are used in different situations to compute established and known indices as BMI, which has been commonly used to assess body weight [
7]; but it does not reflect actual body composition, or the accumulation and distribution of fat mass. The definition and diagnosis of obesity based on “obesity/overfat” and classification based on BMI is increasingly being questioned [
8], due to its low predictive sensitivity [
9] and the lack of consideration of gender, age, and race, which determine the amount of body fat [
10]. Therefore, fat distribution indices, including waist-height ratio (WHtR) and waist-hip ratio (WHR), are proposed to diagnose the risk of obesity or metabolic disorders. In addition, there are new indices based on the existing ones, such as the body shape index (ABSI) [
11], body adiposity index (BAI) [
12], body roundness index (BRI) [
13], abdominal volume index (AVI) [
12], and the relative fat mass (RFM) [
14].
On the one hand, body fat mass/percentage is an essential measure of body composition and is strongly associated with obesity and metabolic syndrome. It can be derived from body density obtained manually through equations, or instrumentally using devices for body composition measurements [
15]. On the other hand, the results of studies in various population groups indicate that the negative impact on health and survival is associated not only with excessive adipose tissue, but above all with lower lean mass/muscle mass, which is not assessed using the above mentioned indicators [
8].
Increased adipose tissue may also be accompanied by muscle loss (leading to sarcopenia), which is diagnosed as sarcopenic obesity [
16]. Both sarcopenia and sarcopenic obesity are risk factors for higher mortality [
17] and cannot be quantified using the above-mentioned measures/indicators, which focus solely on adipose tissue. Therefore, a better approach is to measure body composition, which identifies the two main components: fat and free mass/muscle mass, and the relationships between them. Simple and non-invasive methods of assessing muscle mass include measuring the arm circumference together with tricep skinfold thickness as the mid-upper arm circumference (MUAC), arm muscle area (AMA), or muscle-arm circumference (MAC) [
18].
One of the technologies commonly used to assess body composition, also in clinical trials, is bioelectric impedance analysis (BIA). It allows determining the fat mass (FM) and fat free (lean) mass (FFM) [
19]. It is recommended to relate the obtained values to themselves (FM/FFM) [
20] or to body height [
21]. Both fat and lean mass (kg) should be normalized by height squared (m
2), as the fat mass index (FMI) and fat free mass index (FFMI) [
8,
21]. Then, these results are used to assess the risk of obesity, sarcopenia, or sarcopenic obesity. Moreover, additional research is necessary in populations where the varieties of anthropometric measures, especially those newly proposed, have not been expansively analyzed and studied. Due to the lack of full agreement as to the best indicators of obesity diagnosis and type, the aim of this study was to assess their comparative classification capabilities, with the use of BIA results and selected anthropometric indices in individuals aged 20–60 years.
4. Discussion
This study was designed to initially assess the predictive power of obesity indices in comparison to using FM(FMI) and/or FFM(FFMI). The results of our study confirmed that they can be implemented for obesity diagnosis and replace the questioned BMI. Considering the results of AUC, sensitivity, specificity, and Youden index (>0.9), fat mass index and percentage of fat mass could be considered the best marker for obesity screening in adults, regardless of sex. In women, the results also confirmed the need of a BIA analysis for sarcopenic obesity diagnosis. Undoubtedly, the current criteria for the diagnosis of obesity for commonly used indicators such as BMI, WC, WHtR, or WHR should be verified taking into account biological differences in body size and composition, including those determined by sex. ABSI had the lowest predictive value for obesity among the analyzed indices, regardless of sex. For the measurement of arm circumference and indicators based on its measurement, a positive moderate correlation with FFM was confirmed; but as an easy-to-use screening tool for the diagnosis of sarcopenic obesity or sarcopenia, this requires further investigation.
We found that the optimal BMI cut-offs were slightly below the obesity threshold for both sexes (BMI > 29.99 kg/m
2) and were sex-specific. This is in line with the results of Macek et al. [
32], who identified the cut-off points for BMI at 27.5 kg/m
2 in women and 28.1 kg/m
2 in men for screening cardiometabolic risk in an older group of adults. It is necessary to underline that, according to standard BMI quantification, both estimated thresholds indicate overweight, not obesity. Due to the small difference between the common threshold for normal BMI values and the proposed limits for obesity, an increased risk of cardiometabolic disorders may be potentially misclassified, especially in women [
32].
For indices of abdominal obesity estimation such as WC, WHtR, and WHR, the optimal cut-off values were close to the established ones (WC > 88 cm for women and >102 cm for men; WHtR ≥ 0.5; WHR > 0.8 for women and 0.9 for men). WHtR lower than 0.5 was previously established as the universal cut-off point for assessing abdominal obesity and cardiometabolic risk [
33], but our results confirmed the need for further research, to establish sex-specific cut-off points for WHtR. Similarly, as indicated by other authors, this index is also ethnic-specific [
30].
The optimal cut-off point of BRI was 4.71 in women and 5.59 (or 4.80 according to FM/FFM) in men. These values are lower than those given by Walczyk [
34], according to whom values above 4.9 for women and 4.612 for men can be used as diagnostic criteria of metabolic disorders. Liu [
35] indicated that the cut-off values of BRI reach values in the range 3.18 and 6.20, and even 1–16 [
34], which may be due to differences in the study group, race, and diagnostic criteria. BRI is a novel index used to assess metabolic disorders, which may also be used for detecting high cardiometabolic risk [
36], metabolic syndrome risk [
37], or diabetes risk [
35], and could be a better predictor than BMI.
AVI is used to assess general volume, and it has been highly associated with the dysfunction of glucose metabolism [
38]. For Iranian men, it was determined at 16.6, and for women 17.0 [
39]. Lower values of AVI, 14.25 for men and 13.03 for women, were established by Wang et al. [
40] for predictive metabolic syndrome in the Chinese population. In our findings, the cut-off values for AVI were higher, but the AUC for AVI indicated excellent predictive power for obesity.
Compared with other indices, the values of AUC of ABSI showed the lowest predictive power in obesity diagnosis in our study group. ABSI was proposed by Krakauer and Krakauer, and based on WC, height, and BMI [
11,
41]. In previous studies, ABSI appeared to be better than traditional measures, such as BMI, as a measure of metabolic changes [
3]. 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 Ji 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 Macek [
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 sarcopenic 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 Xiao 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 Kagawa [
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]. However, on the other hand, BMI is also widely used, although it does not reflect body fat distribution. Due to the increasing risk of weight gain and central obesity caused by estrogen deficiency in postmenopausal women, future studies to establish cut-off values for known and new indicators for assessing the risk of obesity and related metabolic disorders, should also take into account menstrual status in women [
35]. The main strength of this study is the inclusion of a sex-specific analysis of ROC and the use of two obesity diagnosis criteria for further comparisons with anthropometric indices (AVI, BAI, BMI, BRI, MAC, AMA, MUAC/H, RFM, WC, WHR, WHtR, and ABSI). To our knowledge, it is the first study assessing the ability of these anthropometric indices for predicting obesity and its type based on FM(FMI) and FFM(FFMI). The highly standardized procedures of anthropometric measurements were also major strengths of this study.