BACKGROUND: Muscle wasting is a consequence of many primary conditions including sarcopenia, cachexia, osteoporosis, HIV/AIDS, and chronic kidney disease. Unfortunately, there is not a clinically accessible method to measure total body protein, which is the functional mass of muscle. OBJECTIVE: We sought to derive a simple method to measure total body protein by using dual-energy X-ray absorptiometry (DXA) and bioimpedance analysis (BIA). DESIGN: We retrospectively analyzed a clinical convenience sample of individuals with numerous metabolic conditions from the Monash Medical Centre, Melbourne, Australia, who had a concurrent protein measure by using neutron activation analysis-derived protein (NAA-TBPro), water measure by using BIA, and whole-body DXA scan. The study was split into calibration and validation data sets by using simple random sampling stratified by sex, BMI category, and age decade. We generated a protein estimate direct-calibration protein (DC-TBPro) derived from BIA water, bone mass, and body volume. We compared NAA-TBPro with DC-TBPro and 2 protein estimates from the literature, one that used the DC-TBPro equation with fixed coefficients [4-compartment Lohman method for analysis of total body protein (4CL-TBPro)] and another that used fat-free mass, age, and sex [Wang equation-derived protein (W-TBPro)]. RESULTS: A total of 187 participants [119 women; mean (±SD) age: 37.0 ± 15.4 y; mean (±SD) BMI (in kg/m(2)) 24.5 ± 7.7] were included. When plotted against NAA-TBPro, DC-TBPro had the highest correlation [coefficient of determination (R(2)) = 0.87], lowest root mean squared error (RMSE; 0.87 kg), and fewest outliers compared with 4CL-TBPro (R(2) = 0.75; RMSE = 1.22 kg) and W-TBPro (R(2) = 0.80; RMSE = 1.10 kg). CONCLUSIONS: A simple method to measure total body protein by using a DXA system and BIA unit was developed and compared with NAA as proof of principle. With additional validation, this method could provide a clinically useful way to monitor muscle-wasting conditions.
BACKGROUND: Muscle wasting is a consequence of many primary conditions including sarcopenia, cachexia, osteoporosis, HIV/AIDS, and chronic kidney disease. Unfortunately, there is not a clinically accessible method to measure total body protein, which is the functional mass of muscle. OBJECTIVE: We sought to derive a simple method to measure total body protein by using dual-energy X-ray absorptiometry (DXA) and bioimpedance analysis (BIA). DESIGN: We retrospectively analyzed a clinical convenience sample of individuals with numerous metabolic conditions from the Monash Medical Centre, Melbourne, Australia, who had a concurrent protein measure by using neutron activation analysis-derived protein (NAA-TBPro), water measure by using BIA, and whole-body DXA scan. The study was split into calibration and validation data sets by using simple random sampling stratified by sex, BMI category, and age decade. We generated a protein estimate direct-calibration protein (DC-TBPro) derived from BIAwater, bone mass, and body volume. We compared NAA-TBPro with DC-TBPro and 2 protein estimates from the literature, one that used the DC-TBPro equation with fixed coefficients [4-compartment Lohman method for analysis of total body protein (4CL-TBPro)] and another that used fat-free mass, age, and sex [Wang equation-derived protein (W-TBPro)]. RESULTS: A total of 187 participants [119 women; mean (±SD) age: 37.0 ± 15.4 y; mean (±SD) BMI (in kg/m(2)) 24.5 ± 7.7] were included. When plotted against NAA-TBPro, DC-TBPro had the highest correlation [coefficient of determination (R(2)) = 0.87], lowest root mean squared error (RMSE; 0.87 kg), and fewest outliers compared with 4CL-TBPro (R(2) = 0.75; RMSE = 1.22 kg) and W-TBPro (R(2) = 0.80; RMSE = 1.10 kg). CONCLUSIONS: A simple method to measure total body protein by using a DXA system and BIA unit was developed and compared with NAA as proof of principle. With additional validation, this method could provide a clinically useful way to monitor muscle-wasting conditions.
Authors: Grant M Tinsley; M Lane Moore; Austin J Graybeal; Antonio Paoli; Youngdeok Kim; Joaquin U Gonzales; John R Harry; Trisha A VanDusseldorp; Devin N Kennedy; Megan R Cruz Journal: Am J Clin Nutr Date: 2019-09-01 Impact factor: 7.045
Authors: S B Heymsfield; C B Ebbeling; J Zheng; A Pietrobelli; B J Strauss; A M Silva; D S Ludwig Journal: Obes Rev Date: 2015-02-03 Impact factor: 9.213
Authors: Bennett K Ng; Yong E Liu; Wei Wang; Thomas L Kelly; Kevin E Wilson; Dale A Schoeller; Steven B Heymsfield; John A Shepherd Journal: Am J Clin Nutr Date: 2018-10-01 Impact factor: 7.045
Authors: Grant M Tinsley; M Lane Moore; Jacob R Dellinger; Brian T Adamson; Marqui L Benavides Journal: Eur J Clin Nutr Date: 2019-11-04 Impact factor: 4.016