Comparison of segmental lean tissue mass in individuals with spinal cord injury measured by dual energy X-ray absorptiometry and predicted by bioimpedance spectroscopy


Study design



To compare two methods for predicting segmental (arms, legs, trunk) lean tissue mass (LTM: non-bone fat-free mass) from bioimpedance spectroscopy (BIS) against LTM measured from dual energy X-ray absorptiometry (DXA) in individuals with acute spinal cord injury (SCI).


Austin Health Victorian Spinal Cord Service, Victoria, Australia.


Fourteen participants (two female), within 8 weeks of traumatic SCI had BIS measured following an overnight fast and within 24 h of DXA scanning. Total body fat-free mass (FFM, body weight minus fat mass) and segmental LTM were predicted from BIS using manufacturer’s proprietary software and a previously established SCI-specific prediction method. Appendicular LTM (ALM) was calculated from the sum of the LTM of the arms and legs. Agreement and strength of relationships with DXA for predicted LTM measures using both approaches were assessed using Lin’s concordance coefficient and limits of agreement analysis (LOA).


The BIS proprietary method performed better than the SCI-specific prediction method in predicting DXA LTM, demonstrating substantial concordance for total body FFM (rc = 0.80), ALM (rc = 0.78), arm (rc = 0.76) and leg LTM (rc = 0.65) and a smaller bias and LOA for ALM (+0.8 vs. −3.4 kg; LOA −4.9–6.4 vs. −11.9–5.1 kg), arm (+0.02 vs. −0.3 kg; LOA −1.1–1.1 kg vs. −2.2–1.6 kg) and leg (+0.4 vs. −1.4 kg; LOA −2.0–2.8 vs. −5.6–2.8) LTM.


BIS can be used to accurately predict total body FFM, segmental LTM and ALM in individuals with acute SCI.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Ratio of extracellular water (ECW) and intracellular water (ICW) volumes in body segments of spinal cord injury (SCI) participants (n = 14) and controls (n = 58) for this study.

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.


  1. 1.

    Castro MJ, Apple DF Jr, Hillegass EA, Dudley GA. Influence of complete spinal cord injury on skeletal muscle cross-sectional area within the first 6 months of injury. Eur J Appl Physiol Occup Physiol. 1999;80:373–8.

    CAS  Article  Google Scholar 

  2. 2.

    Gorgey A, Dudley G. Skeletal muscle atrophy and increased intramuscular fat after incomplete spinal cord injury. Spinal Cord. 2007;45:304.

    CAS  Article  Google Scholar 

  3. 3.

    Bauman WA, Spungen AM, Wang J, Pierson RN Jr. The relationship between energy expenditure and lean tissue in monozygotic twins discordant for spinal cord injury. J Rehabil Res Dev. 2004;41:1–8.

    Article  Google Scholar 

  4. 4.

    Felleiter P, Krebs J, Haeberli Y, Schmid W, Tesini S, Perret C. Post-traumatic changes in energy expenditure and body composition in patients with acute spinal cord injury. J Rehabil Med. 2017;49:579–84.

    Article  Google Scholar 

  5. 5.

    Teigen LM, Kuchnia AJ, Mourtzakis M, Earthman CP. The use of technology for estimating body composition: strengths and weaknesses of common modalities in a clinical setting. Nutr Clin Pract. 2017;32:20–9.

    Article  Google Scholar 

  6. 6.

    Beck LA, Lamb JL, Atkinson EJ, Wuermser LA, Amin S. Body composition of women and men with complete motor paraplegia. J Spinal Cord Med. 2014;37:359–65.

    Article  Google Scholar 

  7. 7.

    Buchholz AC, McGillivray CF, Pencharz PB. Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition. Am J Clin Nutr. 2003;77:371–8.

    CAS  Article  Google Scholar 

  8. 8.

    Maggioni M, Bertoli S, Testolin G, Merati G, Margonato V, Veicsteinas A. Body composition assessment in spinal cord injury subjects. Acta Diabetol. 2003;40:S183–6.

    Article  Google Scholar 

  9. 9.

    Spungen AM, Adkins RH, Stewart CA, Wang J, Pierson RN Jr, Waters RL, et al. Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol. 2003;95:2398.

    Article  Google Scholar 

  10. 10.

    Jones LM, Legge M, Goulding A. Healthy body mass index values often underestimate body fat in men with spinal cord injury. Arch Phys Med Rehabil. 2003;84:1068–71.

    Article  Google Scholar 

  11. 11.

    Cirnigliaro CM, La Fountaine MF, Emmons R, Kirshblum SC, Asselin P, Spungen AM, et al. Prediction of limb lean tissue mass from bioimpedance spectroscopy in persons with chronic spinal cord injury. J Spinal Cord Med. 2013;36:443–53.

    Article  Google Scholar 

  12. 12.

    Nuhlicek DN, Spurr GB, Barboriak JJ, Rooney CB, el Ghatit AZ, Bongard RD. Body composition of patients with spinal cord injury. Eur J Clin Nutr. 1988;42:765–73.

    CAS  PubMed  Google Scholar 

  13. 13.

    Singh R, Rohilla RK, Saini G, Kaur K. Longitudinal study of body composition in spinal cord injury patients. Indian J Orthop. 2014;48:168.

    Article  Google Scholar 

  14. 14.

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

    Article  Google Scholar 

  15. 15.

    Panisset MG, Desneves K, Ward LC, Rafferty J, Rodi H, Roff G, et al. Bedside quantification of fat-free mass in acute spinal cord injury using bioelectrical impedance analysis: a psychometric study. Spinal Cord. 2018;56:355.

    Article  Google Scholar 

  16. 16.

    Yamada Y, Watanabe Y, Ikenaga M, Yokoyama K, Yoshida T, Morimoto T, et al. Comparison of single-or multifrequency bioelectrical impedance analysis and spectroscopy for assessment of appendicular skeletal muscle in the elderly. J Appl Physiol. 2013;115:812–8.

    Article  Google Scholar 

  17. 17.

    Sheean P, Gonzalez MC, Prado CM, McKeever L, Hall AM, Braunschweig CA. American society for parenteral and enteral nutrition clinical guidelines: the validity of body composition assessment in clinical populations. J Parenter Enter Nutr. 2020;44:12–43.

    Article  Google Scholar 

  18. 18.

    Kirshblum S, Waring W III. Updates for the International Standards for Neurological Classification of Spinal Cord Injury. Phys Med Rehabil Clin N Am. 2014;25:505–17.

    Article  Google Scholar 

  19. 19.

    Garshick E, Ashba J, Tun GC, Lieberman LS, Brown R. Assessment of stature in spinal cord injury. J Spinal Cord Med. 1997;20:36–42.

    CAS  Article  Google Scholar 

  20. 20.

    Pelletier CA, Miyatani M, Giangregorio L, Craven BC. Original research: sarcopenic obesity in adults with spinal cord injury: a cross-sectional study. Arch Phys Med Rehabil. 2016;97:1931–7.

    Article  Google Scholar 

  21. 21.

    Cornish B, Jacobs A, Thomas B, Ward L. Optimizing electrode sites for segmental bioimpedance measurements. Physiol Meas. 1999;20:241.

    CAS  Article  Google Scholar 

  22. 22.

    Thomas B, Cornish B, Ward L. Bioelectrical impedance analysis for measurement of body fluid volumes: a review. J Clin Eng. 1992;17:505–10.

    CAS  Article  Google Scholar 

  23. 23.

    Ward L, Isenring E, Dyer J, Kagawa M, Essex T. Resistivity coefficients for body composition analysis using bioimpedance spectroscopy: effects of body dominance and mixture theory algorithm. Physiol Meas. 2015;36:1529.

    CAS  Article  Google Scholar 

  24. 24.

    Seoane F, Abtahi S, Abtahi F, Ellegård L, Johannsson G, Bosaeus I, et al. Mean expected error in prediction of total body water: a true accuracy comparison between bioimpedance spectroscopy and single frequency regression equations. BioMed Res Int. 2015:1–11;2015.

  25. 25.

    WHO. Physical status: the use of and interpretation of anthropometry. Report of a WHO Expert Committee. 854:1–452;1995.

  26. 26.

    Desneves KJ, Panisset MG, Rafferty J, Rodi H, Ward LC, Nunn A, et al. Comparison of estimated energy requirements using predictive equations with total energy expenditure measured by the doubly labelled water method in acute spinal cord injury. Spinal Cord. 2019:57;562–70.

  27. 27.

    Buchholz AC, Pencharz PB, McGillivray CF. The use of bioelectric impedance analysis to measure fluid compartments in subjects with chronic paraplegia. Arch Phys Med Rehabil. 2003;84:854–61.

    Article  Google Scholar 

  28. 28.

    Cornish BH, Thomas BJ, Ward LC, Hirst C, Bunce IH. A new technique for the quantification of peripheral edema with application in both unilateral and bilateral cases. Angiology. 2002;53:41–7.

    Article  Google Scholar 

  29. 29.

    Tanaka T, Yamada Y, Ohata K, Yabe K. Intra-and extra-cellular water distribution in the limbs after cervical spinal cord injury. J Exerc Sports Physiol. 2008;15:11–7.

    Google Scholar 

  30. 30.

    Fuhrman MP, Charney P, Mueller CM. Hepatic proteins and nutrition assessment. J Am Diet Assoc. 2004;104:1258–64.

    CAS  Article  Google Scholar 

  31. 31.

    Steihaug OM, Bogen B, Ranhoff AH, Kristoffersen MH. Bones, blood and steel: How bioelectrical impedance analysis is affected by hip fracture and surgical implants. J Electr Bioimpedance. 2017;8:54–9.

    Article  Google Scholar 

Download references


The authors acknowledge the contributions of Dr. Andrew Nunn of the Victorian Spinal Cord Service for facilitating participant access, dietitians Helena Rodi and Jillian Rafferty for assistance with data collection, as well as the contributions of the participants and their families.


Original data collection was funded by a grant from the Transport Accident Commission through the Institute for Safety, Compensation and Recovery Research (ISCRR project #NGE-E-13-078). Author KJD was supported by a grant from the Austin Medical Research Foundation and MGP by an Australian Postgraduate Award.

Author information




KJD participated in the design of the study, performed data collection, data entry, and analysis and contributed to paper preparation. MGP participated in the design of the study, performed data collection and contributed to paper preparation. LCW advised on the design of data collection protocol, performed quality assurance on the data, contributed to statistical analyses and paper preparation. MPG developed the concept for and designed the study, contributed to grant procurement and paper preparation. NK and RMD contributed to the interpretation of the results and preparation of the paper. All authors read and approved the final version of the paper.

Corresponding author

Correspondence to Katherine J. Desneves.

Ethics declarations

Conflict of interest

Author LCW consults to Impedimed Ltd. All other authors have no conflicts of interest to declare.

Ethical approval

Ethical approval for this study was obtained from the Austin Health Human Research Ethics Committee (H2013/05117) and all applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Desneves, K.J., Panisset, M.G., Galea, M.P. et al. Comparison of segmental lean tissue mass in individuals with spinal cord injury measured by dual energy X-ray absorptiometry and predicted by bioimpedance spectroscopy. Spinal Cord (2020).

Download citation


Quick links