A new simple mathematical model performs as well as the OGTT to predict a person’s risk of developing type 2 diabetes and could change diabetes care. hat’s what a new study, by researchers at the University of Texas Health Science Center, suggests. Along with his colleagues, statistician Ken Williams collected data on blood pressure, medical history and sugar levels after fasting and during an OGT test for 1,791 Mexican Americans and 1,112 whites. None had diabetes, and all were checked again 7.5 years later.
Williams then compared the predictive accuracy of three models: one that included only the OGT test results; one that used only the other clinical data; and a third that combined both the clinical information and the OGT test data.
For OGT data alone, the predictive accuracy was 77.5 percent, while the clinical data’s predictive accuracy reached 84.3 percent. If both were used together, the predictive accuracy peaked at 85.7 percent.
"Physicians can do a better job of assessing risk for developing diabetes by looking at the variety of indicators at their disposal from a standard physical exam than they can by focusing entirely on the results of an oral glucose tolerance test," Williams says.
Williams adds patients might also prefer the mathematical model over the OGT test, which requires that they fast for 12 hours, take a blood test, then wait at their medical provider’s office for another two hours for another blood test. "That costs the patient two hours of their time," Williams says.