Research shows that diet, exercise, and medications can help prevent type 2 diabetes in people who are at high risk for the disease. Currently, the recommended method of identifying people at high risk for type 2 diabetes is an oral glucose tolerance test. This test involves: 1) having your blood glucose (sugar) measured after you have fasted overnight, 2) then drinking a glucose liquid, and 3) having your blood glucose measured again two hours later.
Because this test is time-consuming, inconvenient, and costly, researchers at the University of Texas Health Sciences Center in San Antonio have developed what may be a more efficient means of identifying people at risk for type 2 diabetes. Their method involves entering your medical information (such as height, weight, and cholesterol levels) into a computer program that can calculate your risk of type 2 diabetes. Their research was reported in a recent issue of the Annals of Internal Medicine .
The University of Texas researchers used data on 1791 Mexican Americans and 1112 non-Hispanic whites from the San Antonio Heart Study (SAHS). Participants entered the SAHS either between 1979 and 1982 or 1984 and 1988, at which time they were between the ages of 25 and 64 and did not have diabetes. Pregnant women were excluded from this study.
At the start of the study, participants underwent several tests, including an oral glucose tolerance test and measurements of cholesterol and blood pressure. They also answered questions about their family history of diabetes. Seven or eight years later, they were re-examined. At this time, people were diagnosed as having type 2 diabetes if they met one of the following criteria: 1) use of diabetes medication or 2) fasting or 2-hour oral glucose values that indicated diabetes.
To test the effectiveness of their mathematical model in identifying people at high risk for diabetes, the researchers plugged the following information about each participant into a computer program:
Finally, they compared the accuracy of the mathematical model in predicting who would develop type 2 diabetes with the accuracy of the oral glucose tolerance test. They also tested the predictive value of a scaled-down version of the mathematical model, called the clinical model.
The mathematical model did a better job of predicting which participants would develop type 2 diabetes than the oral glucose tolerance test did. Even the clinical model outperformed the oral glucose tolerance test in predicting future diabetes.
Although these results are interesting, there are limitations to this study. More than 50% of the participants were Mexican Americans and the rest were non-Hispanic whites. This model needs to be tested in other ethnic groups, such as Native Americans and African Americans, who are at particularly high risk for diabetes. In addition, a number of participants in the original San Antonio Heart Study could not be included in this analysis because their diabetes status was unknown as a result of missing data or death.
These findings suggest that your health care provider may be able to accurately assess your risk of developing type 2 diabetes without giving you an oral glucose tolerance test. In fact, all of the information needed for the mathematical model is information that is normally collected when you visit your health care provider. However, the accuracy of this model needs to be tested further before health care providers can begin to use it on a regular basis.
Why is this so exciting? Learning that you are at high risk for type 2 diabetes early on gives you a chance to take steps to prevent it. These might include dietary changes, weight loss, a regular exercise program, and perhaps even diabetes medication. If you are at increased risk for type 2 diabetes, your health care provider can recommend an appropriate regimen to help lower your risk of developing diabetes.
Source:
Stern MP, et al. Identification of persons at high risk for type 2 diabetes mellitus: Do we need the oral glucose tolerance test? Annals of Internal Medicine . April 16, 2002;136(8):575-581.
Last reviewed Apr 18, 2002 by Richard Glickman-Simon, MD
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