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. 2025;37(1):11-17.
doi: 10.5455/msm.2024.37.11-17.

Anthropometric Data by Using Bioelectrical Analysis as a Parameters for New Classification and Definition of Obesity

Affiliations

Anthropometric Data by Using Bioelectrical Analysis as a Parameters for New Classification and Definition of Obesity

Nizama Salihefendic et al. Mater Sociomed. 2025.

Abstract

Background: The prevalence of obesity and obesity-related clinical conditions, including metabolic-associated steatotic liver disease (MASLD), sarcopenia, and a wide spectrum of pathological manifestations, is rising globally. According to WHO, BMI is the only anthropometric measure currently used to classify obesity, overweight, and underweight. However, emerging research suggests that obesity is a complex pathological state influenced by multiple etiological factors. Given the limitations of BMI, there is a growing need for a more comprehensive assessment of body composition, particularly fat mass quantity and distribution. Bioelectrical impedance analysis (BIA) provides valuable anthropometric data that can help differentiate obesity phenotypes and guide improved therapeutic approaches.

Objective: This study aims to analyze body composition using BIA in a randomly selected sample of adults from primary healthcare settings in Bosnia and Herzegovina. The primary goal is to assess total body weight, fat mass quantity, fat distribution, and obesity types prevalent in this population. Additionally, the study seeks to establish reference values for further diagnostic, preventive, and therapeutic strategies to improve public health outcomes.

Methods: A cross-sectional study was conducted on adults (≥18 years) in Gračanica, Bosnia & Herzegovina (B6H), from January 2021 to January 2025. Inclusion criteria required participants to provide signed informed consent, while exclusion criteria included acute systemic diseases, severe dehydration, and fasting for more than 24 hours. Anthropometric parameters measured included age, height, weight, BMI, body fat mass (BFM), fat-free mass (FFM), percent body fat (PBF), waist-hip ratio (WHR), and bone mineral content (BMC). Data were analyzed using SPSS (version 18), with results presented as medians, interquartile ranges, and percentiles (5th, 25th, 50th, 75th, and 95th).

Results: A total of 4,628 adults participated in the study, of whom 2,824 (61.0%) were female and 1,804 (39.0%) were male. The median age was 45 years (IQR: 29 years). The findings revealed that over one-quarter of the B&H population is obese, with abdominal obesity being the predominant type. This phenotype is associated with the highest risk for metabolic syndrome and MASLD.

Conclusion: Our study highlights a high prevalence of obesity among the examined individuals in primary care settings in B&H, with abdominal obesity being the most common type. This phenotype is strongly associated with metabolic complications. BIA-derived parameters of fat distribution and visceral fat mass may serve as valuable tools for improving obesity classification and developing more effective preventive and therapeutic strategies.

Keywords: Adipose Tissue; Bioelectrical; Bosnia; Clinical Obesity; Herzegovina; Impedance Analysis; Obesity.

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Conflict of interest statement

None to declare.

Figures

Figure 1.
Figure 1.. Histogram of the BMI (n = 4,628).
Figure 2.
Figure 2.. Histogram of the Body Fat Mass (BFM) (n = 4,628).
Figure 3.
Figure 3.. Chart of average values of Skeletal Muscle Mass (SMM) (n = 4,628).
Figure 4.
Figure 4.. Histogram of the Skeletal Muscle Mass (SMM) (n = 4,628).
Figure 5.
Figure 5.. Histogram of the Visceral Fat Area (VFA) (n = 4,628).
Figure 6.
Figure 6.. Comparison Chart of average values of VFA and SMM (n = 4,628).
Figure 7.
Figure 7.. Histogram of the Percent Body Fat (PBF) (n = 4,628).
Figure 8.
Figure 8.. Histogram of the Waist-Hip Ratio (WHP) (n = 4,628).
Figure 9.
Figure 9.. Histogram of the Bone Mineral Content (BMC) (n = 4,628).
Figure 10.
Figure 10.. Algorithm of diagnostic procedures in the diagnosis of the type of obesity

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