Two-step approach for the prediction of future type 2 diabetes risk

Muhammad A. Abdul-Ghani, Tamam Abdul-Ghani, Michael P. Stern, Jasmina Karavic, Tiinamaija Tuomi, Insoma Bo, Ralph A. DeFronzo, Leif Groop

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


OBJECTIVE - To develop a model for the prediction of type 2 diabetes mellitus (T2DM) risk on the basis of a multivariate logistic model and 1-h plasma glucose concentration (1-h PG). RESEARCH DESIGN AND METHODS - The model was developed in a cohort of 1,562 nondiabetic subjects from the San Antonio Heart Study (SAHS) and validated in 2,395 non-diabetic subjects in the Botnia Study. A risk score on the basis of anthropometric parameters, plasma glucose and lipid profile, and blood pressure was computed for each subject. Subjects with a risk score above a certain cut point were considered to represent high-risk individuals, and their 1-h PG concentration during the oral glucose tolerance test was used to further refine their future T2DM risk. RESULTS - We used the San Antonio Diabetes Prediction Model (SADPM) to generate the initial risk score. A risk-score value of 0.065 was found to be an optimal cut point for initial screening and selection of high-risk individuals. A 1-h PG concentration >140 mg/dL in highrisk individuals (whose risk score was >0.065) was the optimal cut point for identification of subjects at increased risk. The two cut points had 77.8, 77.4, and 44.8% (for the SAHS) and 75.8, 71.6, and 11.9% (for the Botnia Study) sensitivity, specificity, and positive predictive value, respectively, in the SAHS and Botnia Study. CONCLUSIONS - 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.

Original languageEnglish (US)
Pages (from-to)2108-2112
Number of pages5
JournalDiabetes care
Issue number9
StatePublished - Sep 2011

ASJC Scopus subject areas

  • Advanced and Specialized Nursing
  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism


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