Commingling analysis of adiposity in an Indian population

Int J Obes Relat Metab Disord. 1994 Jan;18(1):1-8.

Abstract

The distributions of five adiposity phenotypes were assessed for the presence of commingling in a sample of 756 adults (> or = 30 years old), residing in the Chittoor district of Andhra Pradesh, India. Three measures of generalized fatness (body mass index, the sum of six skinfolds and the sum of three trunk skinfolds) and two indicators of fat patterning (the ratio of trunk to extremity subcutaneous fat and the ratio of the subscapular skinfold to the sum of the subscapular and the supra-iliac skinfolds) were analysed. Each phenotype was adjusted for the effects of (i) age within sex, and (ii) age, energy intake and energy expenditure within sex. The distribution of each phenotype, under both adjustment schemes, was assessed for evidence of commingling. The commingling analyses were performed separately for males (n = 397) and females (n = 359), and evidence for heterogeneity in the distribution of each phenotype, by sex, was evaluated. There is evidence of commingling in the distribution of each phenotype, under both adjustment schemes. Conclusions regarding the distributions of these phenotypes are, however, influenced by the specific adjustments made to the data. In general, the measures of generalized fatness are more sensitive than measures of fat patterning to the specific adjustments applied to the data. Interestingly, and in contrast with the majority of commingling analyses of adiposity, the smallest components of the commingled distributions often have the lowest mean phenotypic value.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adipose Tissue / anatomy & histology*
  • Adult
  • Age Factors
  • Body Constitution*
  • Body Mass Index
  • Chi-Square Distribution
  • Female
  • Humans
  • India
  • Male
  • Models, Biological
  • Models, Genetic
  • Normal Distribution
  • Obesity / epidemiology*
  • Phenotype
  • Regression Analysis
  • Sex Factors
  • Skinfold Thickness
  • Software