Two Steps to Predict Future Risk for Type 2 Diabetes

Muhammad A. Abdul-Ghani, MD, PhD, from the Division of Diabetes, University of Texas Health Science Center at San Antonio in Texas writes, "Accurate identification of subjects at increased risk for future type 2 diabetes is essential for every prevention program." "It minimizes the number of subjects in the intervention program and improves its efficacy and cost-effectiveness. All previous intervention trials that have tested the efficacy of various prevention strategies have recruited subjects with [impaired glucose tolerance] and/or impaired fasting glucose."

The goal of this study was to develop a model to predict risk for type 2 diabetes based on a multivariate logistic model and 1-hour plasma glucose (1-h PG) concentration in a cohort of 1562 nondiabetic participants in the San Antonio Heart Study (SAHS). The model was then validated in a sample of 2395 nondiabetic participants in the Botnia Study.

Risk score for each participant was calculated from anthropometric parameters, plasma glucose and lipid profile, and blood pressure. High-risk individuals were defined as those with a risk score above a certain cut point, and their future risk for type 2 diabetes was further refined using 1-h PG concentration during the oral glucose tolerance test.

The initial risk score was generated using the San Antonio Diabetes Prediction Model (SADPM). For initial screening and identification of high-risk individuals, the optimal cut point was a risk score value of 0.065. In these high-risk individuals, a 1-h PG concentration greater than 140 mg/dL was the optimal cut point to define subjects at additional risk.

In the SAHS, sensitivity, specificity, and positive predictive value of the 2 cut points were 77.8%, 77.4%, and 44.8%, respectively. In the Botnia Study, these values were 75.8%, 71.6%, and 11.9%, respectively.

"A two-step model, based on the combination of the SADPM and 1-h PG, is a useful tool for the identification of high-risk Mexican-American and Caucasian individuals," the study authors write.

Limitations of this model, in contrast to simply measuring hemoglobin A_1C and fasting plasma glucose, include the need to test glucose load in a subgroup of the population, which is inconvenient and expensive and requires a special office visit.

"Tools to ascertain the risk for future diabetes are valuable to the extent one believes that it is important to detect diabetes as soon as it exists," the study authors conclude. "Although there are no trials to inform us as to the urgency to detect diabetes, it is clear that hyperglycemia is the principal cause of microvascular complications, so any test or algorithm that might help to reduce the time spent with undetected hyperglycemia should intuitively be of benefit. Future studies should address the value of early detection and, by implication, the value of sensitive and specific risk-assessment tools for future diabetes, such as the one described here."

Diabetes Care. Published July 25, 2011