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

Abstract

Aims/hypothesisDiagnosing 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.MethodsWe 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.ResultsThe 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 HbA1c, 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 HbA1c, 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/interpretationWe 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-011-2418-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Clinical utility gene card for: Maturity-onset diabetes of the young

“…Approximately 52% of confirmed UK MODY families have HNF1A mutations, 32% have mutations in GCK and 10% have mutations in HNF4A. 18 HNF1A mutations are more frequent in Euro-Caucasian patients (87.5%) compared with patients with other geographic origins (22.5%). 19 …”

To test, or not to test: time for a MODY calculator?

“…The article by Shields and colleagues [5] in the current issue of Diabetologia is a significant contribution in this regard. Using logistic regression, the authors have developed a prediction model that discriminates between MODY and type 1 diabetes, and between MODY and type 2 diabetes.…”

“…Hence, a misreported diagnosis in a second degree relative causes greater distortion of the prediction if the first degree relative is affected rather than unaffected [6]. In the questionnaire of Shields et al [5], the issue regarding a precise family history has been replaced by the simple question: Parent with diabetes-yes or no! It will be interesting to see whether this simplification makes the predictive model even more robust.…”

“…The study populations in the article by Shields et al [5] Although a mutation in any of the genes HNF1A, HNF4A and GCK is present in more than three of four individuals with genetically confirmed MODY, a limitation of the MODY calculator is that presence of extra-pancreatic features is ignored. If this was included, the model would probably have picked up, for example, HNF1B MODY, which is a systemic disease including renal cysts, renal failure, genital abnormalities, abnormal liver function and dorsal pancreas aplasia [7].…”

“…The prediction model of Shields et al [5] is based solely on white people and may not be applicable to other populations. A study has shown that the prediction power of risk prediction models widely used for BRCA1 and BRCA2 mutations had lower sensitivity, specificity and positivepredictive value in a Malayan than in a white population [9].…”