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.

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.

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 .

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.