Development of a Chinese‐Specific Clinical Model to Predict Maturity‐Onset Diabetes of the Young

Aims Accurate identification of individuals with maturity‐onset diabetes of the young (MODY) can support precision diabetes management. However, diagnosing MODY is challenging due to overlapping clinical features with type 2 diabetes. We aimed to develop a prediction model for identifying Chinese with high likelihood of MODY for further genetic testing. Methods We developed a logistic regression model using clinical data from an unselected cohort of 1021 Chinese with young‐onset (age at diagnosis ≤ 40) non‐type 1 diabetes enrolled in the Hong Kong Diabetes Register, 1.9% ( n = 19) of whom had MODY ( GCK ‐, HNF1A ‐, HNF4A ‐ and HNF1B ‐MODY) by molecular confirmation. We validated the model in an independent local cohort of 822 Chinese with young‐onset non‐type 1 diabetes. We compared the performance of the new Chinese‐specific MODY prediction model with an existing MODY probability calculator in the validation cohort. Results The prediction model comprised the following clinical variables: current age, age at diagnosis, sex, body mass index, systolic blood pressure, HDL‐cholesterol, LDL‐cholesterol, triglyceride and fasting C‐peptide. It demonstrated acceptable discrimination of patients with MODY in the validation dataset, with an area under the curve of 0.813 (95% confidence interval 0.647–0.979). At the probability cut‐off of 50%, the model achieved a sensitivity of 72.7% and a specificity of 92.4%. It allows identification of one MODY case in every nine genetic tests conducted. Conclusion We developed a comprehensive Chinese‐specific MODY prediction model. This model can be used in unselected Chinese with young‐onset non‐type 1 diabetes to identify high‐risk individuals for genetic testing.