A Clinical Prediction Model to Distinguish Maturity-Onset Diabetes of the Young From Type 1 and Type 2 Diabetes in the Chinese Population

Genetic detection for the diagnosis of maturity-onset diabetes of the young (MODY) in China has low sensitivity and specificity. Better gene detection is urgently needed to distinguish testing subjects. We proposed to use numerous and weighted clinical traits as key indicators for reasonable genetic testing to predict the probability of MODY in the Chinese population.We created a prediction model based on data from 306 patients, including 140 patients with MODY, 84 patients with type 1 diabetes (T1D), and 82 patients with type 2 diabetes (T2D). This model was evaluated using receiver operating characteristic curves.Compared with patients with T1D, patients with MODY had higher C-peptide levels and negative antibodies, and most patients with MODY had a family history of diabetes. Different from T2D, MODY was characterized by lower body mass index and younger diagnostic age. A clinical prediction model was established to define the comprehensive probability of MODY by a weighted consolidation of the most distinguishing features, and the model showed excellent discrimination (areas under the curve of 0.916 in MODY vs T1D and 0.942 in MODY vs T2D). Further, high-sensitivity C-reactive protein, glycated hemoglobin A1c, 2-h postprandial glucose, and triglyceride were used as indicators for glucokinase-MODY, while triglyceride, high-sensitivity C-reactive protein, and hepatocellular adenoma were used as indicators for hepatocyte nuclear factor 1-α MODY.We developed a practical prediction model that could predict the probability of MODY and provide information to identify glucokinase-MODY and hepatocyte nuclear factor 1-α MODY. These results provide an advanced and more reasonable process to identify the most appropriate patients for genetic testing.