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An Ultrasound Prediction Equation to Estimate DXA-Derived Body Fatness for Middle-Aged and Older Caucasian Adults

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Abstract

Background & Objectives

Currently, only one study has used dual-energy X-ray absorptiometry (DXA)-derived percent body fat (BF%) as the criterion measure to develop ultrasound prediction equations to estimate BF% in adults between the ages of 50 and 80 years. The aim of this study was to examine the relationship between BF% estimated from subcutaneous fat thickness using a previously published Japanese-based prediction equation and DXA-derived BF% in Caucasian middle-aged and older adults. A secondary aim was to develop a new prediction equation for Caucasian adults if the previously published equation did not predict BF% well in Caucasians.

Design

Cross-sectional study.

Participants & Measurements

One-hundred and two Caucasian adults aged 50–76 years (59 men and 43 women) had ultrasound fat thickness and DXA values measured. A new BF% prediction model was developed using ordinary least squares multiple linear regression.

Results

There was a strong correlation between ultrasound predicted and DXA-derived BF% (r = 0.882, p<0.001). Bland-Altman analysis did not indicate a bias in the prediction of BF% for Caucasian adults (r = −0.092, p>0.05). However, the predicted BF% was significantly higher compared to DXA-derived BF% (approximately 4%). A newly developed nonlinear prediction model used to estimate BF% was significant [F(17,84) = 33.44, p<0.001] with an R2 of 0.871 and an adjusted R2 of 0.845. When examining the stability of the model, bootstrapping (n=1000) resulted in an optimism value of 0.1135 so that the corrected R2 was 0.758. After removing an outlier, the model was significant [F(17,83) = 34.82, p<0.001] and it’s R2 was 0.877 and adjusted R2 was 0.852.

Conclusion

The developed equation was stable with a high degree of variance compared to results from previous studies. The results of this study also suggest that ethnicity should be considered when choosing which prediction equations should be used to estimate BF%.

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References

  1. Finelli C, Sommella L, Gioia S, La Sala N, Tarantino G. Should visceral fat be reduced to increase longevity? Aging Res Rev 2013;12:996–1004.

    Article  Google Scholar 

  2. Patel TP, Rawal K, Bagchi AK, et al. Insulin resistance: an additional risk factor in the pathogenesis of cardiovascular disease in type 2 diabetes. Heart Fail Rev 2016;21:11–23.

    Article  CAS  PubMed  Google Scholar 

  3. Ellis KJ. Human body composition: in vivo methods. Physiol Rev 2000;80:649–680.

    Article  CAS  PubMed  Google Scholar 

  4. Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis — part I: review of principles and methods. Clin Nutr 2004;23:1226–1243.

    Article  PubMed  Google Scholar 

  5. Muller W, Horn M, Furhapter-Rieger A, et al. Body composition in sport: interobserver reliability of a novel ultrasound measure of subcutaneous fat tissue. Br J Sports Med 2013;47:1036–1043.

    Article  PubMed  Google Scholar 

  6. Abe T, Loenneke JP, Thiebaud RS. The use of ultrasound for the estimation of muscle mass: one site fits most? J Cachexia Sarcopenia Muscle 2018;9:213–214.

    Article  PubMed  Google Scholar 

  7. Buckner SL, Abe T, Counts BR, Dankel SJ, Barnett BE, Loenneke JP. Muscle and fat mapping of the trunk: A case study. J Ultrasound 2015;18:399–405.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Thiebaud RS, Abe T, Loenneke JP, Fijita E, Akamine T. Body fat percentage assessment by ultrasound subcutaneous fat thickness measurements in middle-aged and older adults. Clin Nutr [published online ahead of print November 30, 2018]. https://doi.org/10.1016/j.clnu.2018.11.017

  9. Kadowaki T, Sekikawa A, Murata K, et al. Japanese men have large areas of visceral adipose tissue than Caucasian men in the same levels of waist circumference in a population-based study. Int J Obes (Lond) 2006;30:1163–1165.

    Article  CAS  Google Scholar 

  10. Park YW, Allison DB, Heymsfield SB, Gallagher D. Large amounts of visceral adipose tissue in Asian Americans. Obes Res 2001;9:381–387.

    Article  CAS  PubMed  Google Scholar 

  11. Abe T, Thiebaud RS, Loenneke JP, Young KC. Prediction and validation of DXA-derived appendicular lean soft tissue mass by ultrasound in older adults. Age (Dordr) 2015;37:114.

    Article  PubMed Central  Google Scholar 

  12. Abe T, Kondo M, Kawakami Y, Fukunaga T. Prediction equations for body composition of Japanese adults by B-mode ultrasound. Am J Hum Biol 1994;6:161–170.

    Article  PubMed  Google Scholar 

  13. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;i:307–310.

    Article  Google Scholar 

  14. Harrell FE Jr. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. 2015. Springer.

    Google Scholar 

  15. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2018. Vienna, Austria. URL https://doi.org/www.R-project.org/

    Google Scholar 

  16. Ishida Y, Kanehisa H, Kondo M, et al. Body fat and muscle thickness in Japanese and Caucasian females. Am J Hum Biol 1994;6:711–718.

    Article  CAS  PubMed  Google Scholar 

  17. Leahy S, Toomey C, McCreesh K, O’Neill C, Jakeman P. Ultrasound measurement of subcutaneous adipose tissue thickness accurately predicts total and segmental body fat of young adults. Ultrasound Med Biol 2012;38:28–34.

    Article  PubMed  Google Scholar 

  18. Hyde PN, Kendall KL, Fairman CM, Coker NA, Yarbrough ME, Rossi SJ. Use of B-Mode Ultrasound as a Body Fat Estimate in Collegiate Football Players. J Strength Cond Res 2016;30:3525–3530.

    Article  PubMed  Google Scholar 

  19. Saito K, Nakaji S, Umeda T, Shimoyama T, Sugawara K, Yamamoto Y. Development of predictive equations for body density of sumo wrestlers using B-mode ultrasound for the determination of subcutaneous fat thickness. Br J Sports Med 2003;37:144–148.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Health, Exercise Science, & Recreation Management, Kevser Ermin Applied Physiology Laboratory, The University of Mississippi, University, Oxford, MS, 38677, USA

    Takashi Abe & J. P. Loenneke

  2. Department of Kinesiology, Texas Wesleyan University, Fort Worth, TX, 76105, USA

    R. S. Thiebaud

  3. 224 Turner Center, University, Oxford, MS, 38677, USA

    Takashi Abe

Authors
  1. Takashi Abe
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  2. J. P. Loenneke
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  3. R. S. Thiebaud
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Correspondence to Takashi Abe.

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Abe, T., Loenneke, J.P. & Thiebaud, R.S. An Ultrasound Prediction Equation to Estimate DXA-Derived Body Fatness for Middle-Aged and Older Caucasian Adults. J Frailty Aging 8, 79–84 (2019). https://doi.org/10.14283/jfa.2019.8

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  • DOI: https://doi.org/10.14283/jfa.2019.8

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