The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes

Aims/hypothesis Diagnosing MODY is difficult. To date, selection for molecular genetic testing for MODY has used discrete cut-offs of limited clinical characteristics with varying sensitivity and specificity. We aimed to use multiple, weighted, clinical criteria to determine an individual's probability of having MODY, as a crucial tool for rational genetic testing. Methods We developed prediction models using logistic regression on data from 1,191 patients with MODY (n=594), type 1 diabetes (n=278) and type 2 diabetes (n=319). Model performance was assessed by receiver operating characteristic (ROC) curves, cross-validation and validation in a further 350 patients. Results The models defined an overall probability of MODY using a weighted combination of the most discriminative characteristics. For MODY, compared with type 1 diabetes, these were: lower HbA(1c), parent with diabetes, female sex and older age at diagnosis. MODY was discriminated from type 2 diabetes by: lower BMI, younger age at diagnosis, female sex, lower HbA(1c), parent with diabetes, and not being treated with oral hypoglycaemic agents or insulin. Both models showed excellent discrimination (c-statistic = 0.95 and 0.98, respectively), low rates of cross-validated misclassification (9.2% and 5.3%), and good performance on the external test dataset (c-statistic = 0.95 and 0.94). Using the optimal cut-offs, the probability models improved the sensitivity (91% vs 72%) and specificity (94% vs 91%) for identifying MODY compared with standard criteria of diagnosis <25 years and an affected parent. The models are now available online at www.diabetesgenes.org. Conclusions/interpretation We have developed clinical prediction models that calculate an individual's probability of having MODY. This allows an improved and more rational approach to determine who should have molecular genetic testing.