title: Whole-body skeletal muscle mass: development and validation of total-body potassium prediction models.
abstract: BACKGROUND
  A substantial proportion of total body potassium (TBK) in humans is found in skeletal muscle (SM), thus affording a means of predicting total-body SM from whole-body counter-measured (40)K. There are now > 30 whole-body counters worldwide that have large cross-sectional and longitudinal TBK databases.
  
  
  OBJECTIVE
  We explored 2 SM prediction approaches, one based on the assumption that the ratio of TBK to SM is stable in healthy adults and the other on a multiple regression TBK-SM prediction equation.
  
  
  METHODS
  Healthy subjects aged >or= 20 y were recruited for body-composition evaluation. TBK and SM were measured by whole-body (40)K counting and multislice magnetic resonance imaging, respectively. A conceptual model with empirically derived data was developed to link TBK and adipose tissue-free SM as the ratio of TBK to SM.
  
  
  RESULTS
  A total of 300 subjects (139 men and 161 women) of various ethnicities with a mean (+/- SD) body mass index (in kg/m(2)) of 25.1 +/- 5.4 met the study entry criteria. The mean conceptual model-derived TBK-SM ratio was 122 mmol/kg, which was comparable to the measurement-derived TBK-SM ratios in men and women (119.9 +/- 6.7 and 118.7 +/- 8.4 mmol/kg, respectively), although the ratio tended to be lower in subjects aged >or= 70 y. A strong linear correlation was observed between TBK and SM (r = 0.98, P < 0.001), with sex, race, and age as small but significant prediction model covariates.
  
  
  CONCLUSIONS
  Two different types of prediction models were developed that provide validated approaches for estimating SM mass from (40)K measurements by whole-body counting. These methods afford an opportunity to predict SM mass from TBK data collected in healthy adults.
authors.affiliation: Obesity Research Center, St Luke's-Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York, USA. zw28@columbia.edu
authors.forename: ZiMian
authors.initials: Z
authors.lastname: Wang
authors.forename: Shankuan
authors.initials: S
authors.lastname: Zhu
authors.forename: Jack
authors.initials: J
authors.lastname: Wang
authors.forename: Richard N
authors.initials: RN
authors.lastname: Pierson
authors.forename: Steven B
authors.initials: SB
authors.lastname: Heymsfield
mesh_terms: D015502:Absorptiometry, Photon; D000328:Adult; D000367:Age Factors; D000368:Aged; D001823:Body Composition; D005006:Ethnicity; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000378:metabolism*; D011188:Potassium / Q000378:metabolism*; D011237:Predictive Value of Tests; D012737:Sex Factors; D014915:Whole-Body Counting / Q000379:methods
chemical_list: D011188:Potassium