Department of Endocrinology, Key Laboratory of Endocrinology of the Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China; Department of Endocrinology, Beijing Institute of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing, China.
Department of Endocrinology, Key Laboratory of Endocrinology of the Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China.
Endocr Pract. 2021 Aug;27(8):776-782. doi: 10.1016/j.eprac.2021.05.002. Epub 2021 May 12.
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.
中国用于诊断青少年发病的成年型糖尿病（MODY）的基因检测灵敏度和特异性较低。迫切需要更好的基因检测来区分检测对象。我们建议使用众多加权的临床特征作为关键指标，进行合理的基因检测，以预测中国人群中 MODY 的可能性。
我们基于包括 140 名 MODY 患者、84 名 1 型糖尿病（T1D）患者和 82 名 2 型糖尿病（T2D）患者在内的 306 名患者的数据创建了一个预测模型。该模型通过接受者操作特征曲线进行评估。
与 T1D 患者相比，MODY 患者的 C 肽水平更高且抗体阴性，大多数 MODY 患者有糖尿病家族史。与 T2D 不同，MODY 的特征是体重指数较低和诊断年龄较小。通过加权整合最具鉴别特征，建立了一个用于定义 MODY 综合概率的临床预测模型，该模型显示出良好的判别能力（MODY 与 T1D 比较的曲线下面积为 0.916，MODY 与 T2D 比较的曲线下面积为 0.942）。此外，高敏 C 反应蛋白、糖化血红蛋白 A1c、餐后 2 小时血糖和三酰甘油可作为葡萄糖激酶-MODY 的指标，而三酰甘油、高敏 C 反应蛋白和肝细胞腺瘤可作为肝细胞核因子 1-α MODY 的指标。
我们开发了一种实用的预测模型，可以预测 MODY 的可能性，并提供信息来识别葡萄糖激酶-MODY 和肝细胞核因子 1-α MODY。这些结果为识别最适合进行基因检测的患者提供了一种先进且更合理的方法。
