Statistical issues in studying the relative importance of body mass index, waist circumference, waist hip ratio and waist stature ratio to predict type 2 diabetes

Systematic and appropriate statistical analysis is needed to examine the relative performance of anthropometrical indices, viz. body mass index (BMI), waist circumference (WC), waist hip ratio (WHR) and waist stature ratio (WSR) for predicting type 2 diabetes. Using information on socio-demographic, anthropometric and biochemical variables from 2148 males, we examined collinearity and non-linearity among the predictors before studying the association between anthropometric indices and type 2 diabetes. The variable involving in collinearity was removed from further analysis, and the relative importance of BMI, WC and WHR was examined by logistic regression analysis. To avoid non-interpretable odds ratios (ORs), cut point theory is used. Optimal cut points are derived and tested for significance. Multivariable fractional polynomial (MFP) algorithm is applied to reconcile non-linearity. As expected, WSR and WC were collinear with WHR and BMI. Since WSR was jointly as well as independently collinear, it was dropped from further analysis. The OR for WHR could not be interpreted meaningfully. Cut point theory was adopted. Deciles emerged as the optimal cut point. MFP recognized non-linearity effects on the outcome. Multicollinearity among the anthropometric indices was examined. Optimal cut points were identified and used to study the relative ORs. On the basis of the results of analysis, MFP is recommended to accommodate non-linearity among the predictors. WHR is relatively more important and significant than WC and BMI.