SynthesisInternational journal of environmental research and public health2020
The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review.
Synthesis in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Ethnic differences between Asians and non-Asians in clustering-based phenotype classification of adult-onset diabetes mellitus: A systematic narrative review.Primary care diabetes · 2022Pooled it
- Subtypes of pre-diabetes in rural adolescent girls from India (DERVAN-11).BMJ open diabetes research & care · 2026Article
- Article
- Data-Driven Multidimensional Clinical Phenotypes and Longitudinal Changes in Type 2 Diabetes Mellitus: A Retrospective Cohort Study.Biomedicines · 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
- Clinically distinct metabotypes of pediatric MASLD identified through unsupervised clustering of NASH CRN data.Nature communications · 2026Article
- Distinct metabolic phenotypes in adolescents with obesity identified by unsupervised learning: associations with insulin resistance and resting energy expenditure.Frontiers in endocrinology · 2026Article
- Clinical risk phenotypes in diabetes and their associations with adverse cardiovascular events: A report from the Silesia Diabetes-Heart Project.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- The Prevalence of Diabetic Retinopathy in the 21st Century: New Insights From a Portuguese Center.Cureus · 2025Article
- Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study.Journal of clinical medicine · 2025Review
- Survival Tree Analysis of Interactions Among Factors Associated With Colorectal Cancer Risk in Patients With Type 2 Diabetes: Retrospective Cohort Study.JMIR public health and surveillance · 2025Article
- Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.Expert review of pharmacoeconomics & outcomes research · 2025Article
- Heterogeneity of type 2 diabetes in rural India.Frontiers in endocrinology · 2025Article
- Article
- Hypertension Phenotypes and Mortality Risk in the United States of America: A Data-Driven Cluster Analysis.International journal of hypertension · 2025Article
- Mortality and Years of Life Lost from Diabetes Mellitus in Poland: A Register-Based Study (2000-2022).Nutrients · 2024Article
- Clinical characteristics and complication risks in data-driven clusters among Chinese community diabetes populations.Journal of diabetes · 2024Article
- Identification and validation of gestational diabetes subgroups by data-driven cluster analysis.Diabetologia · 2024Article
- Novel subgroups of obesity and their association with outcomes: a data-driven cluster analysis.BMC public health · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.
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Registered trials
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.