biostudies-literatureUnknown41(6)Tsoi STFHong Kong Government Health and Medical Research FundHong Kong Research Grants Council<h4>Aims</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.Diabetes/metabolism research and reviewse70087https://www.ebi.ac.uk/biostudies/studies/S-EPMC12445802biostudies-literatureDevelopment of a Chinese-Specific Clinical Model to Predict Maturity-Onset Diabetes of the Young.PMC12445802Chow EMa RCWSo WYFan YLau ESHKong APSFan BO CKChan JCNTsoi STFLim CKPLuk AOYfalseDevelopment of a Chinese-Specific Clinical Model to Predict Maturity-Onset Diabetes of the Young.<h4>Aims</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.2025-01-01T00:00:00Z2025 Sep2026-06-03T14:57:37.259Z2026-04-28T03:11:32.416ZS-EPMC124458024096638410.1002/dmrr.70087