Vol. 11, Issue 2, Part D (2025)
The role of BMI in predicting body fat percentage and trunk lean mass: A linear approach
The role of BMI in predicting body fat percentage and trunk lean mass: A linear approach
Author(s)
Shilpa Dutta and Priya Nandy
Abstract
Bioelectrical impedance analysis is a widely used, non-invasive technique for evaluating body composition, including estimating body fat percentage and trunk lean mass. This study investigates the correlation between body mass index, body fat percentage, and trunk lean mass in young, trained female athletes. It explores the potential to predict these parameters using body mass index alone. A total of 54 participants, aged 15 to 20 years, were assessed for body mass index, body fat percentage, and trunk lean mass using bioelectrical impedance analysis. Pearson’s correlation analysis revealed significant positive correlations between body mass index and both body fat percentage (r=0.787) and trunk lean mass (r=0.785). Regression analysis further established linear equations to predict body fat percentage and trunk lean mass from body mass index values: Fat percentage= 2.45 + 1.34 × Body mass index and Trunk lean mass= 9 + 1.52 × Body mass index. These findings indicate that body mass index can serve as a reliable predictor of body fat percentage and trunk lean mass, eliminating the need for specialized equipment in routine assessments. This development offers a practical method for athletes to estimate their body fat percentage and trunk lean mass using only height and weight, potentially streamlining performance monitoring and health assessments. The study underscores the utility of body mass index as a simple, accessible tool for estimating body composition in sports and health contexts, with broad implications for improving athletic performance and health management.
How to cite this article:
Shilpa Dutta, Priya Nandy. The role of BMI in predicting body fat percentage and trunk lean mass: A linear approach. Int J Appl Res 2025;11(2):254-257. DOI:
10.22271/allresearch.2025.v11.i2d.12366