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Review
. 2022 Nov;65(11):1758-1769.
doi: 10.1007/s00125-022-05769-4. Epub 2022 Aug 12.

Phenotypic and genetic classification of diabetes

Aaron J Deutsch  1   2   3   4 , Emma Ahlqvist  5 , Miriam S Udler  6   7   8   9
Affiliations
Review

Phenotypic and genetic classification of diabetes

Aaron J Deutsch et al. Diabetologia. 2022 Nov.

Abstract

The historical subclassification of diabetes into predominantly types 1 and 2 is well appreciated to inadequately capture the heterogeneity seen in patient presentations, disease course, response to therapy and disease complications. This review summarises proposed data-driven approaches to further refine diabetes subtypes using clinical phenotypes and/or genetic information. We highlight the benefits as well as the limitations of these subclassification schemas, including practical barriers to their implementation that would need to be overcome before incorporation into clinical practice.

Keywords: Cluster analysis; Disease subtypes; Genetics; MODY; Personalised medicine; Polygenic score; Precision medicine; Review; Type 1 diabetes; Type 2 diabetes.

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Figures

Fig. 1
Fig. 1
Diabetes subtypes. Diabetes has historically been classified as type 1, type 2, gestational or secondary to other causes (monogenic disease, pancreatic disease, drug-induced, etc.). Increasingly, there is recognition that overlap exists between these categories. Subtypes representing an overlap between type 1 and type 2 diabetes include LADA and ketosis-prone diabetes (KPD). Various strategies have been proposed to further divide type 1 and type 2 diabetes into subtypes, including the example publications listed. This figure is available as part of a downloadable slideset
Fig. 2
Fig. 2
Strategies for identifying diabetes subtypes. (a) Hierarchical (‘hard’) clustering distributes people into discrete subtypes. These clusters are defined using a series of traits, which may include phenotypic and/or genotypic criteria. (b) In a ‘soft’ clustering approach, discrete subtypes are also defined using a series of traits; however, people may have features belonging to more than one cluster. Clusters that represent a distinct pathobiological mechanism may be referred to as endotypes. (c) Alternatively, clinical traits may be integrated into a regression model, yielding a continuous measurement of various outcomes (e.g. response to a certain drug or risk of developing a certain complication). Clinical decisions (e.g. to start a certain medication) are implemented for people who fall above a specified threshold. This figure is available as part of a downloadable slideset
See this image and copyright information in PMC

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