BACKGROUND: Total body volume (TBV), with the exclusion of internal air voids, is necessary to quantify body composition in Lohman's 4-compartment (4C) model. OBJECTIVE: This investigation sought to derive a novel, TBV measure with the use of only dual-energy X-ray absorptiometry (DXA) attenuation values for use in Lohman's 4C body composition model. DESIGN: Pixel-specific masses and volumes were calculated from low- and high-energy attenuation values with the use of first principle conversions of mass attenuation coefficients. Pixel masses and volumes were summed to derive body mass and total body volume. As proof of concept, 11 participants were recruited to have 4C measures taken: DXA, air-displacement plethysmography (ADP), and total body water (TBW). TBV measures with the use of only DXA (DXA-volume) and ADP-volume measures were compared for each participant. To see how body composition estimates were affected by these 2 methods, we used Lohman's 4C model to quantify percentage fat measures for each participant and compared them with conventional DXA measures. RESULTS: DXA-volume and ADP-volume measures were highly correlated (R(2) = 0.99) and showed no statistically significant bias. Percentage fat by DXA volume was highly correlated with ADP-volume percentage fat measures and DXA software-reported percentage fat measures (R(2) = 0.96 and R(2) = 0.98, respectively) but were slightly biased. CONCLUSIONS: A novel method to calculate TBV with the use of a clinical DXA system was developed, compared against ADP as proof of principle, and used in Lohman's 4C body composition model. The DXA-volume approach eliminates many of the inherent inaccuracies associated with displacement measures for volume and, if validated in larger groups of participants, would simplify the acquisition of 4C body composition to a single DXA scan and TBW measure.
BACKGROUND: Total body volume (TBV), with the exclusion of internal air voids, is necessary to quantify body composition in Lohman's 4-compartment (4C) model. OBJECTIVE: This investigation sought to derive a novel, TBV measure with the use of only dual-energy X-ray absorptiometry (DXA) attenuation values for use in Lohman's 4C body composition model. DESIGN: Pixel-specific masses and volumes were calculated from low- and high-energy attenuation values with the use of first principle conversions of mass attenuation coefficients. Pixel masses and volumes were summed to derive body mass and total body volume. As proof of concept, 11 participants were recruited to have 4C measures taken: DXA, air-displacement plethysmography (ADP), and total body water (TBW). TBV measures with the use of only DXA (DXA-volume) and ADP-volume measures were compared for each participant. To see how body composition estimates were affected by these 2 methods, we used Lohman's 4C model to quantify percentage fat measures for each participant and compared them with conventional DXA measures. RESULTS: DXA-volume and ADP-volume measures were highly correlated (R(2) = 0.99) and showed no statistically significant bias. Percentage fat by DXA volume was highly correlated with ADP-volume percentage fat measures and DXA software-reported percentage fat measures (R(2) = 0.96 and R(2) = 0.98, respectively) but were slightly biased. CONCLUSIONS: A novel method to calculate TBV with the use of a clinical DXA system was developed, compared against ADP as proof of principle, and used in Lohman's 4C body composition model. The DXA-volume approach eliminates many of the inherent inaccuracies associated with displacement measures for volume and, if validated in larger groups of participants, would simplify the acquisition of 4C body composition to a single DXA scan and TBW measure.
Authors: Nicole E Marshall; Elizabeth J Murphy; Janet C King; E Kate Haas; Jeong Y Lim; Jack Wiedrick; Kent L Thornburg; Jonathan Q Purnell Journal: Am J Clin Nutr Date: 2016-02-17 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: B Bourgeois; B K Ng; D Latimer; C R Stannard; L Romeo; X Li; J A Shepherd; S B Heymsfield Journal: Eur J Clin Nutr Date: 2017-09-06 Impact factor: 4.016
Authors: L Soileau; D Bautista; C Johnson; C Gao; K Zhang; X Li; S B Heymsfield; D Thomas; J Zheng Journal: Eur J Clin Nutr Date: 2015-09-16 Impact factor: 4.016
Authors: Abbie E Smith-Ryan; Meredith G Mock; Eric D Ryan; Gena R Gerstner; Eric T Trexler; Katie R Hirsch Journal: Clin Nutr Date: 2016-05-15 Impact factor: 7.324
Authors: Serghei Malkov; Peggy M Cawthon; Kathy Wilt Peters; Jane A Cauley; Rachel A Murphy; Marjolein Visser; Joseph P Wilson; Tamara Harris; Suzanne Satterfield; Steve Cummings; John A Shepherd Journal: J Bone Miner Res Date: 2015-05-21 Impact factor: 6.741