Predicting Diabetes: Moving Beyond Impaired Glucose Tolerance

We developed predictive models for type II diabetes using stepwise multiple logistic regression analyses of a cohort of 844 Mexican Americans and 641 non-Hispanic whites who were nondiabetic at baseline and who were then followed for 8 yr. Models were developed for the overall population and separately for each sex and ethnic group. For optimal models, the multiple logistic regression program selected potential risk factors from a panel of 5 categorical and 14 continuous demographic, anthropometric, metabolic, and hemodynamic variables. For reduced models, the list of candidate variables was restricted to those commonly used in ordinary clinical practice, i.e., skinfolds, and serum insulin and postural glucose load variables were excluded. For all models, the stepwise process selected a mixture of anthropometric, glucose, lipid, and hemodynamic variables. The top 15% of the risk continuum for each model was defined as high risk to compare the performance of the models with the performance of impaired glucose tolerance (15% prevalence) as a predictor of diabetes. The relative risk of being high risk ranged from 12.16 to 35.29, whereas the relative risk of having impaired glucose tolerance ranged from 7.11 to 10.0. The sensitivity of the multiple logistic regression models ranged from 67.7 to 83.3% compared with 56.5 to 62.1% for impaired glucose tolerance. The results indicate that multivariate predictive models perform at least as well, if not better than impaired glucose tolerance in predicting type II diabetes but need not require an oral glucose load. Moreover, the models highlight the complex metabolic and hemodynamic syndrome that precedes diabetes.