title: DXA: potential for creating a metabolic map of organ-tissue resting energy expenditure components.
abstract: OBJECTIVE
  This study tested the hypothesis that tissue-organ components can be derived from DXA measurements, and in turn, resting energy expenditure (REE) can be calculated from the summed heat productions of DXA-estimated brain, skeletal muscle mass (SM), adipose tissue, bone, and residual mass (RM).
  
  
  METHODS
  Subjects were divided into five groups of adults <50 years of age. The specific metabolic rate of RM was developed in 13 Group I healthy subjects and a DXA-brain mass prediction formula in 52 Group II subjects. SM, adipose tissue, and bone models were developed based on earlier reports. The composite REE prediction model (REEp) was tested in 154 Group III subjects in whom REEp was compared with measured REE (REEm). Features of the developed model were determined in 94 normal-weight men and women (Group IV) and seven spinal cord injury patients and healthy matched controls (Group V).
  
  
  RESULTS
  REEp and REEm in Group III were highly correlated (y = 0.85x + 233; r = 0.82, p < 0.001), and no bias was detected. Both REEm (mean +/- SD, 1,579 +/- 324 kcal/d) and REEp (1,585 +/- 316 kcal/d) were also highly correlated (r values = 0.85 to 0.98; p values < 0.001) and provided similar group values to REE estimated by the Harris-Benedict equations (1,597 +/- 279 kcal/d) and Wang's composite fat-free mass-based REE equation (1,547 +/- 248 kcal/d). New insights into the sources and distribution of REE were provided by analysis of the demonstration groups.
  
  
  CONCLUSIONS
  This approach offers a new practical and educational opportunity to examine REE in subject groups using modeling strategies that reveal the magnitude and distribution of fundamental somatic heat-producing units.
authors.affiliation: Department of Medicine, Obesity Research Center, St. Luke's/Roosevelt Hospital, Columbia University, College of Physicians and Surgeons, New York, New York 10025, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Hayes
authors.forename: Michael
authors.initials: M
authors.lastname: Chustek
authors.forename: ZiMian
authors.initials: Z
authors.lastname: Wang
authors.forename: Dympna
authors.initials: D
authors.lastname: Gallagher
authors.forename: Stanley
authors.initials: S
authors.lastname: Heshka
authors.forename: Ann
authors.initials: A
authors.lastname: Spungen
authors.forename: William
authors.initials: W
authors.lastname: Bauman
authors.forename: Steven B
authors.initials: SB
authors.lastname: Heymsfield
mesh_terms: D015502:Absorptiometry, Photon; D000273:Adipose Tissue / Q000378:metabolism; D000328:Adult; D001481:Basal Metabolism / Q000502:physiology*; D001823:Body Composition; D001842:Bone and Bones / Q000378:metabolism; D001921:Brain / Q000378:metabolism; D002153:Calorimetry, Indirect; D016022:Case-Control Studies; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000378:metabolism; D009929:Organ Size / Q000502:physiology; D011237:Predictive Value of Tests; D012044:Regression Analysis; D013119:Spinal Cord Injuries / Q000378:metabolism