Predicting future cardiovascular disease: do we need the oral glucose tolerance test?

Area of Science:

• Cardiovascular disease risk prediction
• Diabetes and metabolic health

Background:

• Cardiovascular disease (CVD) remains a leading cause of mortality worldwide.
• Accurate prediction of CVD risk is crucial for timely intervention.
• Oral glucose tolerance tests (OGTTs) are used to assess glucose metabolism but their role in CVD prediction requires further evaluation.

Purpose of the Study:

• To compare the predictive performance of OGTTs versus multivariate models using common clinical variables for future CVD.
• To determine if incorporating 2-hour plasma glucose from OGTTs enhances CVD risk prediction by multivariate models.

Main Methods:

• A cohort of 2,662 Mexican-Americans and 1,595 non-Hispanic whites (25-64 years) free of CVD and diabetes at baseline was studied.
• Baseline data included medical history, lifestyle factors, BMI, blood pressure, and various glucose, insulin, and lipid levels.
• Incident CVD was tracked over 7-8 years, and predictive models were assessed using receiver operating characteristic (ROC) curves.

Main Results:

• Fasting and 2-hour glucose levels from OGTTs were weak predictors of incident CVD.
• Multivariate models using readily available clinical variables significantly outperformed glucose measurements alone in predicting CVD.
• Adding 2-hour glucose data to the multivariate models did not substantially improve their predictive accuracy.

Conclusions:

• Simple predictive models utilizing common clinical variables are more effective for identifying individuals at high risk for CVD than OGTTs.
• The inclusion of OGTT results offers minimal additional benefit to the predictive power of established clinical risk models.