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. 2012 May;55(5):1265-72.
doi: 10.1007/s00125-011-2418-8. Epub 2012 Jan 5.

The Development and Validation of a Clinical Prediction Model to Determine the Probability of MODY in Patients With Young-Onset Diabetes

Free PMC article

The Development and Validation of a Clinical Prediction Model to Determine the Probability of MODY in Patients With Young-Onset Diabetes

B M Shields et al. Diabetologia. .
Free PMC article


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 .

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.


Fig. 1
Fig. 1
Patient characteristics. Bar plots showing percentages of (a) parent affected by diabetes (in black) and (b) treatment (diet, white; OHA, black; insulin [± OHA], grey). Density plots for (c) age at diagnosis, (d) HbA1c and (e) BMI (with child values converted to adult equivalent). Distributions for the four subtypes of diabetes; type 1, solid black line; type 2, dashed line; GCK MODY, dotted line; HNF1A/4A MODY, solid grey line. To convert values for HbA1c in % into mmol/mol, subtract 2.15 and multiply by 10.929
Fig. 2
Fig. 2
a Boxplot of fitted probabilities for MODY from the type 2 diabetes vs MODY logistic regression model and (b) ROC curve showing the discriminative ability of the type 2 diabetes vs MODY logistic regression model; c-statistic = 0.98. Similar plots (c, d) are shown for the type 1 diabetes vs MODY model; c-statistic = 0.95

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