title: Lower leg length as an index of stature in adults.
abstract: OBJECTIVE
  To derive regression equations using lower leg length (knee height) or arm span to predict height, and equations using ratios of weight/lower leg length or weight/arm span to predict body mass index and % body fat.
  
  
  METHODS
  Determination sample of 78 men and 82 women aged 17-70 years, and validation sample of 53 men and 121 women aged 18-82 years.
  
  
  METHODS
  Height, weight, lower leg length measured from the top of the patella with knee flexed at 90 degrees, arm span, % body fat by densitometry, and age.
  
  
  RESULTS
  Lower leg length gave good prediction of height (men: r2 = 79%, SEE = 3.2 cm; women: r2 = 73%, SEE = 3.4 cm). Weight/lower leg length ratio was highly predictive of body mass index (men: r2 = 95%, SEE = 1.1 kg/m2, women: r2 = 94%, SEE = 1.1 kg/m2), and gave an estimation comparable to that of body mass index for predicting % body fat (men: r200 = 68%, SEE = 5.0% of body weight; women: r2 = 72%, SEE = 4.6% of body weight). Applying these equations based on lower leg length and weight/lower leg length ratio to a separate sample of men and women showed 95% of the errors of height estimate were within 6.5 cm, and of body mass index estimate were within 2 kg/m2. The same analysis of arm span showed the errors of body composition prediction were unacceptably high.
  
  
  CONCLUSIONS
  Lower leg length is useful for estimating body composition when height measurement is not available.
authors.affiliation: Department of Human Nutrition, University of Glasgow, UK.
authors.forename: T S
authors.initials: TS
authors.lastname: Han
authors.forename: M E
authors.initials: ME
authors.lastname: Lean
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000886:Anthropometry; D001132:Arm / Q000033:anatomy & histology; D001823:Body Composition; D001827:Body Height; D015992:Body Mass Index; D005260:Female; D006801:Humans; D007866:Leg / Q000033:anatomy & histology*; D008297:Male; D008433:Mathematics; D008875:Middle Aged; D012044:Regression Analysis