We have presented models showing excellent discrimination between MODY and both type 1 and type 2 diabetes in patients diagnosed ≤35 years, with c-statistics >0.94 in both training and test datasets, and cross-validated prediction errors of <10%. The models show considerable improvement in prediction of MODY compared with traditional clinical criteria. The models are validated, and provide post-test probabilities for MODY that would be helpful in clinical practice, particularly as supported by an online calculator. Probabilities are produced, rather than classifications, allowing decisions regarding testing to vary in light of other information.
Genetic testing is highly specific and sensitive and represents the gold standard for diagnosing MODY. However, the cost limits its use in all individuals. The decision on whether to test is a clinical judgement that depends on the likelihood and impact of a positive result, weighted against the expense of the test. The current pick-up rate based on UK referrals in the Exeter genetics diagnostic laboratory is 27% [3
], which is based on pre-selection of patients by healthcare professionals in line with best-practice guidelines [14
]. We would advise that this model should be used in all patients diagnosed with diabetes under the age of 35. In patients who are not treated with insulin within 6 months of diagnosis, post-test probabilities of >25% would be appropriate to trigger genetic testing. In patients who are treated with insulin within 6 months of diagnosis, a lower post-test probability of >10% could be considered appropriate as the impact of finding a mutation is greater than for a non-insulin-treated patient, both financially, through saved treatment costs, and in terms of improved quality of life. In these cases, further testing of C-peptide and pancreatic autoantibodies could be carried out prior to genetic testing, with a positive C-peptide and negative autoantibody result being strongly suggestive of MODY compared with type 1 diabetes [19
]. As the cost of genetic testing decreases, the threshold at which to refer patients will lower. A full health-economic model would be required to formally explore the relative trade-offs.
The pre-test probability of MODY we used is likely to be an underestimate, particularly in the paediatric age group. In a study of white non-insulin-treated patients under 17, it was proposed that the probability of MODY was similar to that of type 2 diabetes [21
], much higher than 4.6% as used in our study. In all studies to date, prevalence estimates have been restricted by initial selection on clinical criteria before genetic testing, and the proportion of patients missed has not been quantified. Small increases in pre-test probabilities could make a considerable difference to the post-test probabilities.
We have provided internal and external validation of the model, but further testing is needed. The model should be validated in different settings and populations, particularly as our dataset comprised solely white Europeans. The performance of the model in other populations could vary considerably as the incidence of type 2 diabetes in adolescents and young adults is more common in high-risk ethnic groups [22
]. Further validation on a community-based population cohort would remove the biases associated with being referred to the diagnostic service, and would elucidate whether the female predominance is genuine.
We cannot exclude the possibility that patients in the type 1 diabetes or type 2 diabetes groups did not have MODY, as genetic testing was not carried out on them all. It is also possible that a few of the confirmed MODY patients may also have coincidental type 1 or type 2 diabetes. These misclassified patients will be a tiny minority of cases and would have little impact on the final model.
As genetic testing becomes cheaper, its use in diagnosis will be more commonplace. One consequence of this will be that more variants will be identified with greater uncertainty as to whether they are pathogenic mutations or rare polymorphisms of no clinical significance. Probability models based on clinical criteria, such as those presented here, may then have a different but equally important role in producing prior probabilities for MODY to inform the likelihood of pathogenicity of a novel mutation.
Incorporating other characteristics into the model could improve its diagnostic ability. Only basic clinical characteristics were used in these models and data were limited to information available for all participants. Some important clinical features known to be indicative of MODY, type 1 diabetes and type 2 diabetes were not included. Pancreatic autoantibodies [23
] and measures of endogenous insulin secretion, such as persistent C-peptide [24
], are highly sensitive and specific biomarkers of type 1 diabetes, which have been shown to discriminate well between MODY and type 1 diabetes [19
]. Young-onset type 2 diabetes is more common in non-white populations and is often characterised by signs of insulin resistance, such as acanthosis nigricans [22
], which is rare in MODY. HNF1A MODY patients show sensitivity to sulfonylureas [4
], have a low renal threshold for glucose [25
], and lower high-sensitivity C-reactive protein (hsCRP) levels [13
]. A small glucose increment in an OGTT is predictive of a diagnosis of GCK MODY [26
]. Information on these additional factors is not always collected. The advantage of the clinical criteria we have used is that it should be routinely available for all patients. We have already shown excellent discrimination (ROC AUC
0.94) using clinical characteristics alone. The next important step will be to assess the additional value of other biomarkers, which would be most useful in aiding decisions in insulin-treated patients. Provided that they are independent, it is possible to account for other information by combining likelihood ratios for any additional test with that produced by the model.
In conclusion, we have produced a clinical prediction model that shows good discrimination between MODY and the more common type 1 diabetes and type 2 diabetes in patients diagnosed under the age of 35. This should provide a useful aid for selecting patients for diagnostic genetic testing for MODY who may benefit from improved treatment and management if a mutation is identified.