Endocrine Abstracts

Introduction: Obesity is a public health concern, several evidence have linked excess body fat to various metabolic disorders such as type 2 diabetes. In clinical practice, body mass index (BMI) is the most commonly used anthropometric index to define overweight and obesity given its simplicity and affordability. However, it has many limitations that are now widely recognized. Indeed, BMI is unable to compartmentalize body weight. Moreover, its cut-off values for the diagnosis of overweight and obesity set by the World Health Organization (WHO) are also advised, regardless of age, gender or physical activity level.

Aim: The aim of this study was to test the validity of existing equations, retrieved from the literature, in the Algerian adult population. To develop, and validate, new predictive equations for body fat percentage (%BF) using simple and easy-to-measure anthropometric parameters.

Methods: This is a cross-sectional study including 877 Algerian adults who underwent a body composition assessment by the direct segmental multi-frequency bioelectrical impedance technique (Inbody770). Participants were randomly divided into two groups: the development group (n=577) and the validation group (n=300). To develop the equations, multiple linear regression models were analyzed. The predictive performance of the developed equations was compared with the direct technique. The following validation tests were used: Student’s t-test for paired samples, correlation, and BlandeAltman diagram. Diagnostic accuracy has also been assessed.

Results: Four existing equations were tested, and all showed statically significant bias. Four new equations were developed; all had satisfactory predictive performance, with a correlation coefficient ranging from 0.72 to 0.94 in men and 0.87 to 0.93 in women. The best-fitting equation was based on body mass index, waist-to-hip ratio, and chest circumference. The diagnostic accuracy of this equation was 96.7% in men and 95.3% in women.

Conclusion: The newly developed equations based on anthropometric parameters can serve as a simple tool for the accurate prediction of BF% in adult subjects, at both individual and epidemiological levels.