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Body composition, energy expenditure and physical activity

Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes

Abstract

Background/objective

Knowing the distribution of lean soft tissue (LST) among the body segments is of relevance for optimizing athletic performance, monitoring response to training, and for evaluating injury risk. Bioelectrical impedance (BIA) is a portable, low cost, and easy technique to assess body composition. However, most equations used by BIA to predict LST are not specific for the athlete population. The aim of this investigation was to develop and validate equations to estimate dual-energy X-ray absorptiometry (DXA) appendicular LST of the arms and legs based on whole-body BIA in athletes.

Methods

Arms and legs LST were assessed by DXA and whole-body reactance (Xc) and resistance (R) were measured by BIA in athletes from various sports. Using measures of height, the resistance index (RI) (RI = height2/R) was calculated. Prediction equations were established using a cross-validation method where 177 athletes (2/3 of sample) were used for equation development and the remaining 88 athletes (1/3 of sample) were used for equation validation.

Results

The developed prediction equations were as follows: arm LST = 0.940 × sex (0 = male; 1 = female) + 0.042 × total body weight (kg) + 0.080 × RI + 0.024 × Xc − 3.927; leg LST = 1.983 × sex (0 = male; 1 = female) + 0.154 × total body weight (kg) + 0.127 × RI − 1.147. Both equations validated well for the arms (mean difference = 0.11 kg, R2 = 0.89, pure error (PE) = 0.61) and for the legs (mean difference = 0.05 kg, R2 = 0.81, PE = 1.93 kg). There were no differences (p > 0.05) in the mean observed and predicted LST for the arms and legs.

Conclusion

The developed BIA-based prediction equations provide a valid estimation of upper and lower body LST in athletes.

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Fig. 1: LST lean soft tissue, RMSE root mean squared error, LOA 95% limits of agreement.

References

  1. Raymond CJ, Dengel DR, Bosch TA. Total and segmental body composition examination in collegiate football players using multifrequency bioelectrical impedance analysis and dual X-ray absorptiometry. J Strength Cond Res. 2018;32:772–82.

    Article  Google Scholar 

  2. Gonzalez MC, Barbosa-Silva TG, Heymsfield SB. Bioelectrical impedance analysis in the assessment of sarcopenia. Curr Opin Clin Nutr Metab Care. 2018;21:366–74.

    Article  Google Scholar 

  3. Bilsborough JC, Greenway K, Opar D, Livingstone S, Cordy J, Coutts AJ. The accuracy and precision of DXA for assessing body composition in team sport athletes. J Sports Sci. 2014;32:1821–8.

    Article  Google Scholar 

  4. Fuller NJ, Laskey MA, Elia M. Assessment of the composition of major body regions by dual-energy X-ray absorptiometry (DEXA), with special reference to limb muscle mass. Clin Physiol. 1992;12:253–66.

    Article  CAS  Google Scholar 

  5. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gomez J, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. 2004;23:1430–53.

    Article  Google Scholar 

  6. Prior BM, Modlesky CM, Evans EM, Sloniger MA, Saunders MJ, Lewis RD, et al. Muscularity and the density of the fat-free mass in athletes. J Appl Physiol. 1985;90:1523–31.

    Article  Google Scholar 

  7. Beaudart C, Bruyère O, Geerinck A, Hajaoui M, Scafoglieri A, Perkisas S, et al. Equation models developed with bioelectric impedance analysis tools to assess muscle mass: a systematic review. Clin Nutr. 2019;35:47–62.

    Google Scholar 

  8. Fornetti WC, Pivarnik JM, Foley JM, Fiechtner JJ. Reliability and validity of body composition measures in female athletes. J Appl Physiol. 1985;87:1114–22.

    Article  Google Scholar 

  9. Stewart AD, Hannan WJ. Prediction of fat and fat-free mass in male athletes using dual X-ray absorptiometry as the reference method. J Sports Sci. 2000;18:263–74.

    Article  CAS  Google Scholar 

  10. Yannakoulia M, Keramopoulos A, Tsakalakos N, Matalas AL. Body composition in dancers: the bioelectrical impedance method. Med Sci Sports Exerc. 2000;32:228–34.

    Article  CAS  Google Scholar 

  11. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM, et al. Bioelectrical impedance analysis-part I: review of principles and methods. Clin Nutr. 2004;23:1226–43.

    Article  Google Scholar 

  12. Andreoli A, Monteleone M, Van Loan M, Promenzio L, Tarantino U, De Lorenzo A. Effects of different sports on bone density and muscle mass in highly trained athletes. Med Sci Sports Exerc. 2001;33:507–11.

    Article  CAS  Google Scholar 

  13. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign, IL: Human Kinetics Books; 1988.

    Google Scholar 

  14. Matias CN, Júdice PB, Santos DA, Magalhães JP, Minderico CS, Fields DA, et al. Suitability of bioelectrical based methods to assess water compartments in recreational and elite athletes. J Am Coll Nutr. 2016;35:413–21.

    Article  CAS  Google Scholar 

  15. Silva AM, Santos DA, Matias CN, Rocha PM, Petroski EL, Minderico CS, et al. Changes in regional body composition explain increases in energy expenditure in elite junior basketball players over the season. Eur J Appl Physiol. 2012;112:2727–37.

    Article  Google Scholar 

  16. Sun S, Chumlea W. Statistical methods. In: Human body composition. Champaign, IL: Human Kinetics; 2005. p. 151–60.

  17. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.

    Article  CAS  Google Scholar 

  18. Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255–68.

    Article  CAS  Google Scholar 

  19. Pichard C, Kyle UG, Gremion G, Gerbase M, Slosman DO. Body composition by X-ray absorptiometry and bioelectrical impedance in female runners. Med Sci Sports Exerc. 1997;29:1527–34.

    Article  CAS  Google Scholar 

  20. Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr. 2002;76:378–83.

    Article  CAS  Google Scholar 

  21. Pietrobelli A, Faith MS, Wang J, Brambilla P, Chiumello G, Heymsfield SB. Association of lean tissue and fat mass with bone mineral content in children and adolescents. Obes Res. 2002;10:56–60.

    Article  Google Scholar 

  22. De Rui M, Veronese N, Bolzetta F, Berton L, Carraro S, Bano G, et al. Validation of bioelectrical impedance analysis for estimating limb lean mass in free-living Caucasian elderly people. Clin Nutr. 2017;36:577–84.

    Article  Google Scholar 

  23. Esco MR, Snarr RL, Leatherwood MD, Chamberlain NA, Redding ML, Flatt AA, et al. Comparison of total and segmental body composition using DXA and multifrequency bioimpedance in collegiate female athletes. J Strength Cond Res. 2015;29:918–25.

    Article  Google Scholar 

  24. Sergi G, De Rui M, Stubbs B, Veronese N, Manzato E. Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017;29:591–7.

    Article  Google Scholar 

  25. Fields JB, Merrigan JJ, White JB, Jones MT. Body composition variables by sport and sport-position in elite collegiate athletes. J Strength Cond Res. 2018;32:3153–9.

    Article  Google Scholar 

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Acknowledgements

The authors are thankful for all the athletes who contributed to this study.

Funding

Supported by the Fundação para a Ciência e Tecnologia, under Grant UIDB/00447/2020 to CIPER—Centro Interdisciplinar para o Estudo da Performance Humana (unit 447). PBJ is supported by the Fundação para a Ciência e Tecnologia under SFRH/BPD/115977/2016.

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Contributions

LBS and AMS contributed to the conception and design of the study. IRC, JPM, PBJ, and MHR were responsible for data collection and acquisition. MHR was responsible for the data analysis and interpretation. MHR drafted the paper. LBS, AMS, IRC, JPM, PBJ, and MHR contributed to reviewing and editing the paper. LBS and MHR gave approval of the final paper and take responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding author

Correspondence to Luís B. Sardinha.

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Sardinha, L.B., Correia, I.R., Magalhães, J.P. et al. Development and validation of BIA prediction equations of upper and lower limb lean soft tissue in athletes. Eur J Clin Nutr 74, 1646–1652 (2020). https://doi.org/10.1038/s41430-020-0666-8

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