title: Body composition indices of a load-capacity model: gender- and BMI-specific reference curves.
abstract: OBJECTIVE
  Fat mass (FM) and fat-free mass (FFM) are frequently measured to define body composition phenotypes. The load-capacity model integrates the effects of both FM and FFM to improve disease-risk prediction. We aimed to derive age-, gender- and BMI-specific reference curves of load-capacity model indices in an adult population (≥18 years).
  
  
  METHODS
  Cross-sectional study. Dual-energy X-ray absorptiometry was used to measure FM, FFM, appendicular skeletal muscle mass (ASM) and truncal fat mass (TrFM). Two metabolic load-capacity indices were calculated: ratio of FM (kg) to FFM (kg) and ratio of TrFM (kg) to ASM (kg). Age-standardised reference curves, stratified by gender and BMI (<25.0 kg/m2, 25.0-29.9 kg/m2, ≥30.0 kg/m2), were constructed using an LMS approach. Percentiles of the reference curves were 5th, 15th, 25th, 50th, 75th, 85th and 95th.
  
  
  METHODS
  Secondary analysis of data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES).
  
  
  METHODS
  The population included 6580 females and 6656 males.
  
  
  RESULTS
  The unweighted proportions of obesity in males and females were 25.5 % and 34.7 %, respectively. The average values of both FM:FFM and TrFM:ASM were greater in female and obese subjects. Gender and BMI influenced the shape of the association of age with FM:FFM and TrFM:ASM, as a curvilinear relationship was observed in female and obese subjects. Menopause appeared to modify the steepness of the reference curves of both indices.
  
  
  CONCLUSIONS
  This is a novel risk-stratification approach integrating the effects of high adiposity and low muscle mass which may be particularly useful to identify cases of sarcopenic obesity and improve disease-risk prediction.
authors.affiliation: 1Human Nutrition Research Centre,Institute of Cellular Medicine,Newcastle University,Campus of Ageing and Vitality,Newcastle upon Tyne NE4 5PL,UK.
authors.forename: Mario
authors.initials: M
authors.lastname: Siervo
authors.affiliation: 2Department of Agricultural,Food and Nutritional Science,Division of Human Nutrition,University of Alberta,Edmonton,Alberta,Canada.
authors.forename: Carla M
authors.initials: CM
authors.lastname: Prado
authors.affiliation: 3Pennington Biomedical Research Center,Baton Rouge,LA,USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Mire
authors.affiliation: 3Pennington Biomedical Research Center,Baton Rouge,LA,USA.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Broyles
authors.affiliation: 4Childhood Nutrition Research Centre,UCL Institute of Child Health,London,UK.
authors.forename: Jonathan C K
authors.initials: JC
authors.lastname: Wells
authors.affiliation: 3Pennington Biomedical Research Center,Baton Rouge,LA,USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Heymsfield
authors.affiliation: 3Pennington Biomedical Research Center,Baton Rouge,LA,USA.
authors.forename: Peter T
authors.initials: PT
authors.lastname: Katzmarzyk
mesh_terms: D015502:Absorptiometry, Photon; D050154:Adiposity; D000328:Adult; D000465:Algorithms; D015992:Body Mass Index; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging* / Q000503:physiopathology; D009749:Nutrition Surveys; D009765:Obesity / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000503:physiopathology; D050177:Overweight / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000503:physiopathology; D015995:Prevalence; D018570:Risk Assessment; D055948:Sarcopenia / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000503:physiopathology; D012737:Sex Factors; D014481:United States / Q000453:epidemiology; D016474:Weight-Bearing; D051598:Whole Body Imaging
keywords: Appendicular skeletal mass