PREDICTING DIABETES - MOVING BEYOND IMPAIRED GLUCOSE-TOLERANCE

We developed predictive models for type II diabetes using stepwise mul tiple logistic regression analyses of a cohort of 844 Mexican American s and 641 non-Hispanic whites who were nondiabetic at baseline and who were then followed for 8 yr. Models were developed for the overall po pulation and separately for each sex and ethnic group. For optimal mod els, the multiple logistic regression program selected potential risk factors from a panel of 5 categorical and 14 continuous demographic, a nthropometric, metabolic, and hemodynamic variables. For reduced model s, the list of candidate variables was restricted to those commonly us ed in ordinary clinical practice, i.e., skinfolds, and serum insulin a nd postoral 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 mode ls with the performance of impaired glucose tolerance (15% prevalence) as a predictor of diabetes. The relative risk of being high risk rang ed from 12.16 to 35.29, whereas the relative risk of having impaired g lucose tolerance ranged from 7.11 to 10.0. The sensitivity of the mult iple logistic regression models ranged from 67.7 to 83.3% compared wit h 56.5 to 62.1% for impaired glucose tolerance. The results indicate t hat multivariate predictive models perform at least as well, if not be tter than impaired glucose tolerance in predicting type II diabetes bu t need not require an oral glucose load. Moreover, the models highligh t the complex metabolic and hemodynamic syndrome that precedes diabete s.