ArticleBMJ open diabetes research & care2022
Cluster analysis of Thai patients with newly diagnosed type 2 diabetes mellitus to predict disease progression and treatment outcomes : A prospective cohort study.
Article in BMJ open diabetes research & care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 20 citations in OpenAlex.
- Article
- Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.Diabetologia · 2026Article
- Cluster-specific genetic associations of CDKAL1, CDKN2A, CDKN2B, HHEX, KCNQ1, MTNR1B, PAX4, SLC30A8, TCF7L2, and UBE2E2 variants in new onset type 2 diabetes.Scientific reports · 2025Article
- Investigation of the degree of family history of diabetes in different clusters of newly diagnosed type 2 diabetes in Thailand.Annals of medicine · 2025Article
- Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.BMJ open · 2025Article
- Article
- Diabetes Phenotypes in Patients Presenting a Myocardial Infarction: Progress Towards Precision Medicine?Journal of personalized medicine · 2025Article
- Identification of pre-diabetes subphenotypes for type 2 diabetes, related vascular complications and mortality.BMJ open diabetes research & care · 2025Article
- Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study.Journal of clinical medicine · 2025Review
- Clinical application of cluster analysis in patients with newly diagnosed type 2 diabetes.Hormones (Athens, Greece) · 2025Article
- Risk Evaluation of Progression of Proteinuria and Renal Decline Based on a Novel Subgroup Classification in Chinese Patients with Type 2 Diabetes.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Article
- Article
- How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.Journal of epidemiology and global health · 2024Article
- Phenotypic characterization of nonautoimmune diabetes in adult Ugandans with low body mass index.Therapeutic advances in endocrinology and metabolism · 2024Article
- Precision Medicine in Type 2 Diabetes Mellitus: Utility and Limitations.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionType 2 diabetes mellitus (T2D) is highly heterogeneous in disease progression and risk of complications. This study aimed to categorize Thai T2D into subgroups using variables that are commonly available based on routine clinical parameters to predict disease progression and treatment outcomes. RESEARCH DESIGN AND
methodsThis was a cohort study. Data-driven cluster analysis was performed using a Python program in patients with newly diagnosed T2D (n=721) of the Siriraj Diabetes Registry using five variables (age, body mass index (BMI), glycated hemoglobin (HbA
resultsThe mean age was 53.4±11.3 years, 58.9% were women. The median follow-up time was 21.1 months (9.2-35.2). Four clusters were identified: cluster 1 (18.6%): high HbA
conclusionsWe were able to categorize Thai patients with newly diagnosed T2D into four clusters using five routine clinical parameters. This clustering method can help predict disease progression and risk of diabetic complications similar to previous studies using parameters including insulin resistance and insulin sensitivity markers.
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