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

full-text ► large
To test, or not to test: time for a MODY calculator?


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

P. R. Njølstad^{1, 2}  and A. Molven^{3, 4}

(1)	Department of Clinical Medicine, University of Bergen, Bergen, Norway

(2)	Department of Pediatrics, Haukeland University Hospital, N-5021 Bergen, Norway

(3)	The Gade Institute, University of Bergen, Bergen, Norway

(4)	Department of Pathology, Haukeland University Hospital, Bergen, Norway

P. R. Njølstad Email: pal.njolstad@uib.no

Received: 13 January 2012  Accepted: 8 February 2012  Published online: 2 March 2012 

Abstract  

To test, or not to test, that is often the question in diabetes genetics. This is why the paper of Shields et al in the current issue of Diabetologia is so warmly welcomed. MODY is the most common form of monogenic diabetes. Nevertheless, the optimal way of identifying MODY families still poses a challenge both for researchers and clinicians. Hattersley’s group in Exeter, UK, have developed an easy-to-use MODY prediction model that can help to identify cases appropriate for genetic testing. By answering eight simple questions on the internet (www.diabetesgenes.org/content/mody-probability-calculator), the doctor receives a positive predictive value in return: the probability that the patient has MODY. Thus, the classical binary (yes/no) assessment provided by clinical diagnostic criteria has been substituted by a more rational, quantitative estimate. The model appears to discriminate well between MODY and type 1 and type 2 diabetes when diabetes is diagnosed before the age of 35 years. However, the performance of the MODY probability calculator should now be validated in other settings than where it was developed—and, as always, there is room for some improvements and modifications.

Keywords  Diabetes – Genetic testing – MODY – Monogenic diabetes – Prediction model 

Traditionally, MODY was considered if a proband had an age of diabetes onset less than 25 years, there was diabetes in one of his/her parents and the phenotype was characterised by beta cell failure and no obesity [1]. These criteria have been challenged in recent years. Obesity is now frequent in the general population and cannot be used to exclude MODY. Diabetes molecular genetics has provided at least ten forms of MODY, which differ in clinical presentation, including hallmarks such as age of onset after 25 years (mild HNF4A mutations), pancreatic exocrine failure (CEL MODY) and renal dysfunction (HNF1B MODY) [2]. Thus, some authors have argued that the term MODY should be considered obsolete and substituted by ‘monogenic diabetes’ [3]. MODY is, however, still widely used, due both to tradition and to the fact that most cases do indeed fit the classical criteria.

Identification of a MODY mutation is important for correct diagnosis, appropriate genetic counselling, evaluation of prognosis and selection of the best treatment [2, 3]. The current diagnostic approach when MODY is suspected includes Sanger sequencing of candidate genes, one after another, based on expected clinical and genetic correlations, and dosage analysis where appropriate. Many diagnostic laboratories—including our own—find, however, a mutation in one of the three most common MODY genes in only some 50% of the probands, with the percentage depending on how stringently the clinical criteria used for inclusion in the screening are set. Most laboratories will then end the screening, although additional genetic investigations are sometimes performed based on a specific phenotype or as part of a research project. The cost of this strategy is obviously not trivial because of the number of candidate genes and the iterative screening approach. Although next-generation sequencing may change this picture, we are likely to perform Sanger sequencing in the traditional way for several years to come. In the molecular evaluation of MODY, it is very important to exclude type 1 and type 2 diabetes to avoid unnecessary screening. There are some biomarkers associated with either type 1 diabetes or type 2 diabetes, but it can be difficult to define MODY in clinical practice [4].

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. The authors used data from 594 individuals with known mutations in the most common genes causing MODY (HNF1A, GCK, HNF4A), 278 individuals with type 1 diabetes and 319 individuals with type 2 diabetes. The model was validated in a set of 350 patients with these three types of diabetes. MODY could best be discriminated from type 1 diabetes by lower HbA_1c, parent with diabetes, being female and older age at diagnosis, while MODY was discriminated from type 2 diabetes by lower BMI, younger age at diagnosis, being female, lower HbA_1c, parent with diabetes, and not being treated with tablets or insulin. The model showed very good discrimination and a low rate of misclassification, and performed well on the external data set. Using optimal cut-offs, the prediction model improved the sensitivity from 72% to 91% and the specificity from 91% to 94% for identifying MODY compared with the standard criteria.

A very attractive feature of the proposed model is its ease of use (Fig. 1). By punching eight straightforward features of the diabetes patient into the MODY calculator on the website (www.diabetesgenes.org/content/mody-probability-calculator), a positive predictive value, i.e. the probability that the patient has MODY, is calculated. An important point is that the physician will receive a quantitative estimate for the diagnosis rather than the conventional yes/no evaluation provided by clinical diagnostic criteria.