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Prognostic Accuracy of Immunologic and Metabolic Markers for Type 1 Diabetes in a High-Risk Population: Receiver operating characteristic analysis
Diabetes Care, 07/16/2012
Clinical Article
Xu P et al. – The combination of metabolic markers derived from the oral glucose tolerance test improved accuracy in predicting progression to type 1 diabetes in a population with islet cell antibody (ICA) positivity and abnormal metabolism. The results indicate that the autoimmune activity may not alter the risk of type 1 diabetes after metabolic function has deteriorated. Future intervention trials may consider eliminating IVGTT measurements as an effective cost–reduction strategy for prognostic purposes.
Methods- A total of 339 subjects from the Diabetes Prevention Parenteral Trial–Type 1, who were islet cell antibody (ICA)–positive, with low first–phase insulin response (FPIR) and/or abnormal glucose tolerance at baseline, were followed until clinical diabetes onset or study end (5–year follow–up).
- The prognostic performance of biomarkers was estimated using receiver operating characteristic (ROC) curve analysis and compared with nonparametric testing of ROC curve areas.
- Pearson correlation was used to assess the relationship between the markers.
- Individually, insulin autoantibody titer, ICA512A titer, peak C–peptide, 2–h glucose, FPIR, and FPIR/homeostasis model assessment insulin resistance provided modest but significant prognostic values for 5–year risk with a similar level of area under ROC curve ranging between 0.61 and 0.67.
- The combination of 2–h glucose, peak C–peptide, and area under the curve C–peptide significantly improved the prognostic accuracy compared with any solitary index (P < 0.05) with an area under ROC curve of 0.76 (range 0.70–0.81).
- The addition of antibody titers and/or IVGTT markers did not increase the prognostic accuracy further (P = 0.46 and P = 0.66, respectively).