Diabetes, Vol 42, Issue 5 706-714, Copyright © 1993 by American Diabetes Association
Predicting diabetes. Moving beyond impaired glucose tolerance
MP Stern, PA Morales, RA Valdez, A Monterrosa, SM Haffner, BD Mitchell and HP Hazuda
Department of Medicine, University of Texas Health Science Center, San Antonio 78284-7873.
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.