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Predicting diabetes. Moving beyond impaired glucose tolerance
M P Stern1, P A Morales, R A Valdez
1Department of Medicine, University of Texas Health Science Center, San Antonio 78284-7873.
Diabetes
|May 1, 1993
Summary
Predictive models for type II diabetes accurately identify high-risk individuals. These models outperform impaired glucose tolerance testing and do not require an oral glucose load, highlighting a complex metabolic syndrome preceding diabetes.
Area of Science:
- Epidemiology
- Biostatistics
- Metabolic Diseases
Background:
- Type II diabetes is a growing public health concern.
- Accurate prediction of type II diabetes risk is crucial for early intervention.
- Existing predictive methods may have limitations.
Purpose of the Study:
- To develop and evaluate multivariate predictive models for type II diabetes.
- To compare the performance of these models against impaired glucose tolerance.
- To identify key risk factors for type II diabetes.
Main Methods:
- Stepwise multiple logistic regression analysis was used.
- A cohort of Mexican Americans and non-Hispanic whites was followed for 8 years.
- Models were developed using demographic, anthropometric, metabolic, and hemodynamic variables.
Main Results:
- Multivariate models demonstrated high predictive accuracy, with relative risks ranging from 12.16 to 35.29.
- Model sensitivity (67.7–83.3%) exceeded that of impaired glucose tolerance (56.5–62.1%).
- Predictive models identified a complex interplay of metabolic and hemodynamic factors.
Conclusions:
- Multivariate predictive models are effective for identifying individuals at high risk of type II diabetes.
- These models offer an alternative to oral glucose tolerance testing.
- The findings underscore the complex syndrome preceding type II diabetes development.