Fat-free mass predictive equation using bioelectrical impedance and maturity offset in adolescent athletes: Development and cross-validation

• Published in: Nutrition (Burbank, Los Angeles County, Calif.) Los Angeles County, Calif.), 2024-07, Vol.123, p.112415-112415, Article 112415
• Main Authors: de Miranda, Andressa Cabral, Coelho, Gabriela Morgado de Oliveira, Cattem, Marcus Vinícius de Oliveira, Koury, Josely Correa
• Format: Article
• Language: English
• Subjects: Accuracy; Adolescent; Adolescents; Age; Athletes; Athletes, Fat-free mass; BIA; bioelectrical impedance; Bioelectricity; Body composition; Complex variables; Correlation coefficient; Correlation coefficients; cross-sectional studies; Dual energy X-ray absorptiometry; equations; Fat-free body mass; Frequency analyzers; Impedance; lean body mass; Males; Maturity; Nutrition; Predictive equation; Software; Teenagers; Variables
• ISSN: 0899-9007; 1873-1244
• DOI: 10.1016/j.nut.2024.112415

•Fat-free mass predictive equation was developed and cross-validated using single-frequency bioelectrical impedance and age at peak height velocity.•The new fat-free mass predictive equation is accurate for male adolescent athletes.•The new equation was developed using more economical and less complex variables. This is a cross-sectional study, aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using single-frequency bioelectrical impedance (BIA), considering the predicted age at peak height velocity (PHV) as a variable. Additionally, the study aims to test the FFM-BIA obtained using a previous predictive equation that used skeletal maturity as a variable. The participants (n = 169 male adolescent athletes) were randomly divided into two groups: development of a new predictive equation (n = 113), and cross-validation (n = 56). The concordance test between the FFM values obtained by Koury et al. predictive equation and DXA data was determined (n = 169). Bioelectrical data was obtained using a single-frequency analyzer. Among the models tested, the new predictive equation has resistance index (height2/resistance) and predictive age at PHV as variables and presented R2 = 0.918. The frequency of maturity status using skeletal maturity and PHV diagnosis was inadequate (Kappa = 0.4257; 95%CI = 0.298–0.553). Bland-Altman plots and concordance correlation coefficient showed substantial concordance between the FFM-DXA values (48.8 ± 11.2 kg) and the new predictive equation (CCC = 0.960). The results showed that the new equation performed better than the equation developed by Koury et al. (CCC = 0.901). Our results show that it is feasible to predict FFM in male adolescent athletes using predictive age at PHV, with moderate concordance. The calculation of FFM using more economical and less complex variables is viable and should be further explored.