ArticleDiabetologia2024
Machine learning-based reproducible prediction of type 2 diabetes subtypes.
Article in Diabetologia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Data-driven cluster analysis of adults with prediabetes: Findings from the Japan diabetes prevention study.Journal of diabetes investigation · 2026Trial
- Type 2 diabetes subtypes for precision medicine: methodological challenges and alternative prediction-based approaches.Diabetologia · 2026Review
- Insulin resistance in the 21st century: new takes on an old problem-2025 Diabetes, Diabetes Care, and Diabetologia Expert Forum.Diabetologia · 2026Review
- Subtyping metabolic dysfunction-associated steatotic liver disease using electronic health record-linked genomic cohorts reveals diverse etiologies and progression.Nature communications · 2026Article
- Single-cell profiling of pancreatic islets maps subtype-associated molecular alterations in type 2 diabetes.Diabetologia · 2026Article
- Identification of Patient Clusters with Distinct Disease Progression Patterns Utilizing a Nationwide Finnish Population with Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Article
- Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry.Diabetologia · 2026Article
- Validation of a Diabetes Subtype Classification Model Using Data from U.S. Adults Before and After the COVID-19 Pandemic.Metabolites · 2026Article
- Integrating trust into artificial intelligence for medicine: using diabetes as the exemplar disease.Journal of translational medicine · 2026Article
- Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.Journal of diabetes investigation · 2026Article
- Recognising, quantifying and accounting for classification uncertainty in type 2 diabetes subtypes.Diabetologia · 2025Observational
- Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict individuals at risk of microvascular disease.PLOS digital health · 2025Article
- The research progress and future directions in the pathophysiological mechanisms of type 2 diabetes mellitus from the perspective of precision medicine.Frontiers in medicine · 2025Article
- Energy landscape analysis of health checkup data clarified multiple pathways to diabetes development in obese and non-obese subjects.Frontiers in endocrinology · 2025Observational
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
aims/hypothesisClustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time.
methodsCohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2D
resultsT2D CONCLUSIONS/
interpretationThe new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.