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Precision subclassification of type 2 diabetes: a systematic review.
Shivani Misra1,2, Robert Wagner3,4,5, Bige Ozkan6,7
1Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK. s.misra@imperial.ac.uk.
Communications Medicine
|October 5, 2023
Summary
Precision medicine for type 2 diabetes (T2D) shows promise. Complex subclassification methods yield reproducible subtypes linked to outcomes, unlike simpler approaches, paving the way for tailored T2D treatments.
Area of Science:
- Endocrinology
- Genetics
- Computational Biology
Background:
- Type 2 diabetes (T2D) exhibits significant heterogeneity in presentation and progression.
- Precision medicine strategies may enhance clinical outcomes for T2D patients.
- Subclassification of T2D is being explored to personalize treatment.
Purpose of the Study:
- To systematically review subclassification strategies for type 2 diabetes.
- To assess the quality, reproducibility, and clinical relevance of T2D subtypes.
- To determine if subclassification is associated with improved patient outcomes.
Main Methods:
- Searched PubMed and Embase for studies on T2D subclassification.
- Included 'simple subclassification' (clinical characteristics) and 'complex subclassification' (machine learning, 'omics).
- Excluded studies on other diabetes subtypes or incident T2D prediction.
Main Results:
- 51 studies showed simple stratification approaches lacked replication and meaningful outcomes.
- 62 studies on complex stratification identified reproducible T2D subtypes associated with outcomes.
- Both approaches require higher evidence grades but support T2D subclassification.
Conclusions:
- Complex subclassification of T2D shows promise for identifying clinically meaningful subtypes.
- Future research must validate T2D subtypes across diverse ancestries.
- Tailoring interventions based on T2D subtypes requires further investigation to improve outcomes.
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