Abstract
BACKGROUND: Accurate assessment of body composition is essential for monitoring health and performance in high-performance athletes. While anthropometric equations are widely used in conventional sports, their validity in athletes with unilateral lower-limb amputation remains unclear due to assumptions of symmetrical fat and muscle distribution. This study aimed to assess the concurrent validity and reliability of anthropometric equations for estimating body composition in this population, using Dual-energy X-ray Absorptiometry (DEXA) as the reference method. METHODS: A cross-sectional study was conducted with 27 athletes (22 men, 5 women; mean age 32 ± 7.4) from para-sports including athletics, swimming, triathlon, and others. Anthropometric measurements and DEXA were used to estimate body density (BD), fat percentage (FP), lean percentage (LP), lean mass (LM), and fat mass (FM). Forty-seven anthropometric equations were evaluated. Validity was assessed using Pearson or Spearman correlation coefficients, reliability with Intraclass Correlation Coefficients (ICC), and agreement through Bland-Altman analysis and Reduced Major Axis (RMA) regression. RESULTS: For FP, the Hastuti equation and the ISAK 5 components model showed the closest agreement with DEXA (mean differences 0.7 ± 4.55%, and 0.32 ± 4.8%, respectively; ICCs > 0.83). Durnin and Womersley with Siri also showed high reliability but a greater bias (2.6 ± 3.69%). For FM, the ISAK 5 components model and Lee DH equation demonstrated acceptable agreement (mean differences -0.71 ± 3.64% and 1.04 ± 4.35%, respectively; ICCs > 0.85). For LM, the Olshvang, Chien, and Lee DH equations showed the strongest agreement with DEXA (ICCs > 0.87). CONCLUSIONS: The Hastuti and the ISAK 5 components model are recommended to estimate FP. For FM, we recommend the ISAK 5 components model, followed by Lee DH equations, and for LM, the Olshvang, Chien and Lee DH. Finally, we do not recommend the use of any of the Lee, Poortsman, or ISAK 5 components model equations to estimate LP in this population.