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Machine learning-based reproducible prediction of type 2 diabetes subtypes
Hayato Tanabe1,2, Masahiro Sato1, Akimitsu Miyake3
1Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan.
Diabetologia
|August 21, 2024
Summary
A new machine learning model accurately classifies type 2 diabetes subtypes, offering consistent, long-term predictions of complications. This approach aids personalized treatment by overcoming limitations of existing methods, even with missing clinical data.
Area of Science:
- Endocrinology and Metabolism
- Computational Biology and Bioinformatics
- Genetics and Genomics
Background:
- Clustering-based subclassification of type 2 diabetes (T2D) shows promise for personalized therapy by reflecting pathophysiology and genetic predisposition.
- Ahlqvist's classification is validated for predicting complications but lacks temporal consistency and requires non-routine HOMA2 indices.
- There is a need for a robust method to consistently classify T2D subtypes over time using routinely available clinical data.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for consistent classification of T2D into Ahlqvist's subtypes.
- To assess the model's performance using readily available variables, including in cases with missing HOMA2 indices.
- To evaluate the long-term consistency and predictive ability of the ML-derived subtypes for diabetes complications.
Main Methods:
- A random forest (RF) algorithm (T2DRF15) was trained on 15 variables from a Japanese T2D cohort (Cohort 1) pre-classified using k-means clustering (T2Dkmeans).
- The model was externally validated on a separate cohort (Cohort 2) and tested on a subset with missing insulin-related variables.
- Kaplan-Meier analysis assessed complication risks, and subtype consistency was evaluated over time.
Main Results:
- T2DRF15 achieved 94% accuracy in predicting T2Dkmeans subtypes, with high performance in external validation (86.3%) and imputation scenarios (82.9%).
- The ML-derived subtypes showed distinct risks for diabetic retinopathy (SIDD) and chronic kidney disease (SIRD), mirroring T2Dkmeans findings.
- Excluding an 'undecidable' cluster improved predictive accuracy and demonstrated significantly higher subtype consistency over time compared to T2Dkmeans.
Conclusions:
- The developed ML model offers a consistent and clinically applicable method for classifying T2D subtypes using routinely available data.
- This approach facilitates personalized treatment strategies by predicting glycaemic control, complications, and outcomes with long-term stability.
- Further multiethnic validation is warranted to assess broader applicability in research and clinical practice.
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