Precision diabetes: learning from monogenic diabetes

Abstract

The precision medicine approach of tailoring treatment to the individual characteristics of each patient or subgroup has been a great success in monogenic diabetes subtypes, MODY and neonatal diabetes. This review examines what has led to the success of a precision medicine approach in monogenic diabetes (precision diabetes) and outlines possible implications for type 2 diabetes. For monogenic diabetes, the molecular genetics can define discrete aetiological subtypes that have profound implications on diabetes treatment and can predict future development of associated clinical features, allowing early preventative or supportive treatment. In contrast, type 2 diabetes has overlapping polygenic susceptibility and underlying aetiologies, making it difficult to define discrete clinical subtypes with a dramatic implication for treatment. The implementation of precision medicine in neonatal diabetes was simple and rapid as it was based on single clinical criteria (diagnosed <6 months of age). In contrast, in MODY it was more complex and slow because of the lack of single criteria to identify patients, but it was greatly assisted by the development of a diagnostic probability calculator and associated smartphone app. Experience in monogenic diabetes suggests that successful adoption of a precision diabetes approach in type 2 diabetes will require simple, quick, easily accessible stratification that is based on a combination of routine clinical data, rather than relying on newer technologies. Analysing existing clinical data from routine clinical practice and trials may provide early success for precision medicine in type 2 diabetes.

Keywords: GCK; HNF1A; HNF4A; KCNJ11; MODY; Maturity onset diabetes of the young; Monogenic diabetes; Neonatal diabetes; Precision diabetes; Precision medicine; Review; Type 2 diabetes.

Fig. 1

The paradigm shift: early non-selective genetic testing for neonatal diabetes. The figure shows the benefits of early comprehensive genetic testing in the management of neonatal diabetes. For example, genotype may: (1) predict treatment response: e.g. patients with mutations in KCNJ11/ABCC8 respond well to high-dose sulfonylureas; (2) explain pre-existing clinical features (e.g. heart defects with GATA4/6 mutation, microencephaly with IER3IP1 mutation and hypothyroidism with GLIS3 mutation); (3) shed light on anticipated clinical abnormalities (e.g. exocrine pancreas deficiency with mutations in GATA4, GATA6 or PDX1, and bone and liver disease with EIF2AK3 mutations); (4) lead to early intervention for co-morbidities, such as early treatment with high doses of sulfonylureas in KCNJ11-neonatal diabetes to improve the outcome when the mutation causes severe developmental delay, or early treatment with thiamine in thiamine-responsive megaloblastic anaemia (TRMA) neonatal diabetes

Fig. 2

Identification, important clinical features and treatment implications for common subtypes of MODY. An individual’s clinical features, treatment needs and parental history of diabetes may be suggestive of MODY. A MODY probability calculator can use these features to predict probability of disease, and this calculation of MODY, plus biomarkers of disease and other useful clinical information will determine if MODY genetic testing should be carried out. Genetic testing allows for stratification of patients into specific MODY subgroups based on their genotype, which may be used to identify present and predicted clinical features and treatment responses. For example, patients with GCK-MODY have a stable, raised fasting glucose, whereas those with HNF1A-, HNF4A- or HNF1B-MODY experience progressive deterioration of glucose over time. Furthermore, glycosuria is a known feature of HNF1A-MODY, whilst fetal macrosomia and neonatal hypoglycaemia often occur in HNF4A-MODY, and developmental disorders of the kidney and multiple other organs in HNF1B-MODY

Fig. 3

Molecular genetics-based approach for precision diabetes in monogenic and type 2 diabetes. An example of a precision diabetes approach in monogenic diabetes, in which molecular genetic aetiology defines subgroups that have differential features and treatment implications, is shown. A similar approach of precision diabetes in polygenic complex type 2 diabetes has failed to identify clear discrete aetiological subgroups and associated clinically useful treatment implications

Fig. 4

Clinical features of patients with MODY overlap with type 1 and type 2 diabetes. Percentage of (a) parent affected by diabetes (in black) and (b) treatment (diet, white; oral blood glucose lowering agents (OHA), black; insulin [± OHA], grey). Density plots for (c) age at diagnosis, (d) HbA_1c and (e) BMI (with child values converted to adult equivalent using reference charts [39]). For (c), (d) and (e), distributions for the four subtypes of diabetes are shown as: type 1 diabetes, black line; type 2 diabetes, red line; GCK-MODY, green line; HNF1A-/HNF4A-MODY, blue line. To convert values for HbA_1c in % into mmol/mol, subtract 2.15 and multiply by 10.929. All graphs are adopted from Shields et al [39] under the Creative Commons Attribution 4.0 International (CC BY) license (https://creativecommons.org/licenses/by/4.0) and amended to include colour

Fig. 5

Treatment response based approach for precision diabetes in type 2 diabetes. We propose to move the focus from defining subgroups based on molecular aetiology to defining subgroups based on differential treatment response to drugs. The aim will be to create a statistical probability calculator that will use simple clinical information (e.g. age, BMI, sex, eGFR etc.) to provide a likely HbA_1c response to existing drugs. Large-scale routine clinical data will be used to develop a statistical model and that will be validated in already completed clinical trials. Similar approaches can also be used for a drug’s side effects. The benefits of this approach are that it will be quick to develop, easy to implement, provide clinically useful treatment choices and may incorporate future ‘omic’ or physiological biomarker discoveries