title: Assessment of sarcopenia: longitudinal versus cross sectional body composition data.
abstract: OBJECTIVE
  An accurate diagnosis of sarcopenia is required. The aim of this study is to correlate the results of two methods to define sarcopenia using cross sectional body composition data, with actual loss of fat free mass.
  
  
  METHODS
  Healthy older subjects (926 females and 381 males aged 70 years or more) and healthy young adults (425 females and 151 males aged 20 to 40 years) were studied. Body composition was assessed by double beam X ray absorptiometry (DEXA). Among older subjects, a contemporary subsample of 148 females and 45 males had two or more measurements, separated by 4.8+/-1.5 years and loss of fat free mass per year was calculated. In the whole sample, total and appendicular lean body mass index were calculated as total or appendicular lean body mass/height. Using data from young people, sex specific t scores were obtained. In older subjects residuals were derived from a regression equation, using total or appendicular fat free mass as the dependent variable and height, fat free mass and age as independent variables.
  
  
  RESULTS
  The concordance between residuals and t scores to define sarcopenia was 68 and 72%, respectively. Among subjects with two or more measurements, men and women lost a mean of 521+/-454 and 221+/-399 g/year of fat free mass, respectively. The odds ratio of losing more than 822 g lean body mass /year among men or 514 g lean body mass /year among women was 2.63 and 2.64 for subjects classified in the two lowest quintiles of sarcopenia, using t scores or residuals, respectively.
  
  
  CONCLUSIONS
  Cross sectional body composition data can predict loss of fat free mass among older people.
authors.affiliation: INTA, University of Chile, 138-11 Santiago, Chile. dbunout@inta.cl
authors.forename: Daniel
authors.initials: D
authors.lastname: Bunout
authors.forename: María Pía
authors.initials: MP
authors.lastname: de la Maza
authors.forename: Gladys
authors.initials: G
authors.lastname: Barrera
authors.forename: Laura
authors.initials: L
authors.lastname: Leiva
authors.forename: Vivien
authors.initials: V
authors.lastname: Gattas
authors.forename: Sandra
authors.initials: S
authors.lastname: Hirsch
mesh_terms: D015502:Absorptiometry, Photon; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology* / Q000502:physiology; D001823:Body Composition / Q000502:physiology*; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D018482:Muscle, Skeletal / Q000473:pathology* / Q000503:physiopathology; D012044:Regression Analysis