Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Nutrition and Health (including climate and ecological aspects)

Skeletal muscle estimation using magnetic-resonance-imaging-based equations for dual-energy X-ray absorptiometry and bioelectrical impedance analysis

European Journal of Clinical Nutrition volume 77, pages 1151–1159 (2023)Cite this article

Subjects

Abstract

Background/Objectives

Skeletal muscle mass (SMM) estimation is important but challenging in clinical settings. Criterion methods, such as magnetic resonance imaging (MRI), are often inaccessible. However, surrogate methods, such as dual-energy X-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA), can use MRI-based equations to estimate SMM, although the agreement between these methods is unclear.

Subjects/Methods

Total and segmental SMM were estimated with DXA and MFBIA using MRI-based equations in 313 healthy adults (120 M, 193 F; age 30.2 ± 13.0 y; BMI 24.6 ± 4.0 kg/m2). DXA total SMM was estimated using the Kim and McCarthy equations, and segmental SMM was estimated using the McCarthy equations. Relationships between DXA and MFBIA SMM were examined using Deming regression, Lin’s concordance correlation coefficient (CCC), equivalence testing, Bland-Altman analysis, and related tests.

Results

Strong linear relationships were observed for total (R2 0.95, CCC 0.96–0.97), leg (R2 0.90, CCC 0.85) and arm (R2 0.93, CCC 0.93) SMM in the entire sample. Kim equation SMM demonstrated statistical equivalence with MFBIA for total SMM, but the Deming regression slope differed from 1 and proportional bias was present. McCarthy equation total SMM exhibited a regression slope that did not differ from 1, and no proportional bias was present in the entire sample. However, equivalence with MFBIA was not observed. Systematically higher leg and arm SMM values were observed with DXA as compared to MFBIA.

Conclusions

While DXA and MFBIA total SMM generally exhibited strong agreement, higher appendicular SMM by DXA highlights technical differences between methods.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Total skeletal muscle mass estimated by dual-energy X-ray absorptiometry (Kim et al. 2004 equation) and multi-frequency bioelectrical impedance analysis.
Fig. 2: Total skeletal muscle mass estimated by dual-energy X-ray absorptiometry (McCarthy et al. 2023 equation) and multi-frequency bioelectrical impedance analysis.
Fig. 3: Leg skeletal muscle mass estimated by dual-energy X-ray absorptiometry and multi-frequency bioelectrical impedance analysis.
Fig. 4: Arm skeletal muscle mass estimated by dual-energy X-ray absorptiometry and multi-frequency bioelectrical impedance analysis.

Similar content being viewed by others

Data availability

Data may be available from corresponding author, upon reasonable request and pending relevant institutional approval.

References

  1. Gupta P, Lanca C, Gan ATL, Soh P, Thakur S, Tao Y, et al. The association between body composition using dual energy X-ray absorptiometry and type-2 diabetes: a systematic review and meta-analysis of observational studies. Sci Rep. 2019;9:12634.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575–75.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Heymsfield SB, Adamek M, Gonzalez MC, Jia G, Thomas DM. Assessing skeletal muscle mass: historical overview and state of the art. J Cachexia Sarcopenia Muscle. 2014;5:9–18.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wang ZM, Pierson RN Jr., Heymsfield SB. The five-level model: a new approach to organizing body-composition research. Am J Clin Nutr. 1992;56:19–28.

    Article  CAS  PubMed  Google Scholar 

  5. Erlandson M, Lorbergs A, Mathur S, Cheung A. Muscle analysis using pQCT, DXA and MRI. Eur J Radiol. 2016;85:1505–11.

    Article  CAS  PubMed  Google Scholar 

  6. Heymsfield SB, Gonzalez MC, Lu J, Jia G, Zheng J. Skeletal muscle mass and quality: evolution of modern measurement concepts in the context of sarcopenia. Proc Nutr Soc. 2015;74:355–66.

    Article  PubMed  Google Scholar 

  7. Kim J, Heshka S, Gallagher D, Kotler DP, Mayer L, Albu J, et al. Intermuscular adipose tissue-free skeletal muscle mass: estimation by dual-energy X-ray absorptiometry in adults. J Appl Physiol. 2004;97:655–60.

    Article  PubMed  Google Scholar 

  8. Bosy-Westphal A, Jensen B, Braun W, Pourhassan M, Gallagher D, Müller MJ. Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices. Eur J Clin Nutr. 2017;71:1061–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McCarthy C, Tinsley GM, Bosy-Westphal A, Müller MJ, Shepherd J, Gallagher D, et al. Total and regional appendicular skeletal muscle mass prediction from dual-energy X-ray absorptiometry body composition models. Sci Rep. 2023;13:2590.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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  PubMed  Google Scholar 

  11. Tinsley GM, Moore ML, Benavides ML, Dellinger JR, Adamson BT. 3-Dimensional optical scanning for body composition assessment: a 4-component model comparison of four commercially available scanners. Clin Nutr. 2020;39:3160–7.

    Article  CAS  PubMed  Google Scholar 

  12. Tinsley GM, Moore ML, Graybeal AJ, Paoli A, Kim Y, Gonzales JU, et al. Time-restricted feeding plus resistance training in active females: a randomized trial. Am J Clin Nutr. 2019;110:628–40.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tinsley GM, Harty PS, Stratton MT, Smith RW, Rodriguez C, Siedler MR. Tracking changes in body composition: comparison of methods and influence of pre-assessment standardisation. Br J Nutr. 2022;127:1656–74.

    Article  CAS  PubMed  Google Scholar 

  14. Stratton MT, Siedler MR, Harty PS, Rodriguez C, Boykin JR, Green JJ, et al. The influence of caffeinated and non-caffeinated multi-ingredient pre-workout supplements on resistance exercise performance and subjective outcomes. J Int Soc Sports Nutr. 2022;19:126–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Siedler MR, Rodriguez C, Stratton MT, Harty PS, Keith DS, Green JJ, et al. Assessing the reliability and cross-sectional and longitudinal validity of 15 bioelectrical impedance analysis devices. Br J Nutr. 2022:1–29. e-pub ahead of print 2022/11/21; https://doi.org/10.1017/s0007114522003749

  16. Bosy-Westphal A, Schautz B, Later W, Kehayias JJ, Gallagher D, Muller MJ. What makes a BIA equation unique? Validity of eight-electrode multifrequency BIA to estimate body composition in a healthy adult population. Eur J Clin Nutr. 2013;67:S14–21.

    Article  PubMed  Google Scholar 

  17. Seca gmbh & co. seca 515/514 Product Manual v. 1.1: Hamburg, Germany, 2016.

  18. Nana A, Slater GJ, Hopkins WG, Burke LM. Effects of daily activities on dual-energy X-ray absorptiometry measurements of body composition in active people. Med Sci Sports Exerc. 2012;44:180–9.

    Article  PubMed  Google Scholar 

  19. Moore ML, Benavides ML, Dellinger JR, Adamson BT, Tinsley GM. Segmental body composition evaluation by bioelectrical impedance analysis and dual-energy X-ray absorptiometry: Quantifying agreement between methods. Clin Nutr. 2020;39:2802–10.

    Article  CAS  PubMed  Google Scholar 

  20. Tinsley GM, Moore ML, Graybeal AJ. Precision of dual-energy X-ray absorptiometry reflection scans in muscular athletes. J Clin Densitom. 2020;23:647–55.

    Article  PubMed  Google Scholar 

  21. Hangartner TN, Warner S, Braillon P, Jankowski L, Shepherd J. The Official Positions of the International Society for Clinical Densitometry: acquisition of dual-energy X-ray absorptiometry body composition and considerations regarding analysis and repeatability of measures. J Clin Densitom. 2013;16:520–36.

    Article  PubMed  Google Scholar 

  22. Therneau T. deming: Deming, Theil-Sen, Passing-Bablock and Total Least Squares Regression. 2018. https://CRAN.R-project.org/package=deming

  23. Lakens D. Equivalence tests: a practical primer for t-tests, correlations, and meta-analyses. Social Psycholog Personal Sci. 2017;8:355–62.

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  25. Signorell A. DescTools: Tools for Descriptive Statistics. 2022. https://CRAN.R-project.org/package=DescTools

  26. Tinsley GM, Moore ML, Rafi Z, Griffiths N, Harty PS, Stratton MT, et al. Explaining discrepancies between total and segmental DXA and BIA body composition estimates using Bayesian regression. J Clin Densitom. 2021;24:294–307.

    Article  PubMed  Google Scholar 

  27. Jiang F, Tang S, Eom JJ, Song KH, Kim H, Chung S, et al. Accuracy of estimated bioimpedance parameters with octapolar segmental bioimpedance analysis. Sensors (Basel). 2022;22:2681.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge the research team members and participants involved in the studies yielding the datasets used in the present analysis.

Funding

No funding was received for the present analysis. Funding for individual studies whose data were included in the analysis was provided by Texas Tech University, MTI Biotech Inc., and Legion Athletics Inc. None of these entities played any role in the present analysis.

Author information

Authors and Affiliations

  1. Energy Balance & Body Composition Laboratory, Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, USA

    Grant M. Tinsley, Christian LaValle, Christian Rodriguez & Madelin R. Siedler

  2. Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA

    Steven B. Heymsfield

Authors
  1. Grant M. Tinsley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian LaValle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Madelin R. Siedler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steven B. Heymsfield
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GMT and CL conceived the work. GMT, CR, and MRS contributed to data collection. GMT performed the statistical analysis. GMT and CL wrote the initial draft of the manuscript. All authors contributed to revision of the manuscript, approved the final version, and agree to be accountable for the work.

Corresponding author

Correspondence to Grant M. Tinsley.

Ethics declarations

Competing interests

GMT has received support for his research laboratory, in the form of research grants or equipment loan or donation, from manufacturers of body composition assessment devices, including Size Stream LLC; Naked Labs Inc.; Prism Labs Inc.; RJL Systems; MuscleSound; and Biospace, Inc. SBH reports his role on the Medical Advisory Boards of Tanita Corporation, Amgen, and Medifast. The remaining authors declare no conflicts of interest.

Ethical approval

This research was performed in accordance with the Declaration of Helsinki and was approved by the Texas Tech University Institutional Review Board (IRB2017-912, IRB2018-417, IRB2019-356, IRB2020-813, and IRB2021-107).

Informed consent

Informed consent was obtained from all participants.

Additional information

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tinsley, G.M., LaValle, C., Rodriguez, C. et al. Skeletal muscle estimation using magnetic-resonance-imaging-based equations for dual-energy X-ray absorptiometry and bioelectrical impedance analysis. Eur J Clin Nutr 77, 1151–1159 (2023). https://doi.org/10.1038/s41430-023-01331-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-023-01331-6

Search

Advanced search

Quick links