ArticleFrontiers in endocrinology2022
Diabetes duration and types of diabetes treatment in data-driven clusters of patients with diabetes.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Enriched Metabolic Phenotyping Refines Phenotypic Resolution of Type 2 Diabetes.Diabetes, obesity & metabolism · 2026Article
- Prevalence and associated risk factors of Pseudomonas aeruginosa infection in Diabetic wounds in Duhok province, Iraq.PloS one · 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
- 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
- The differences of metabolic profiles, socioeconomic status and diabetic retinopathy in U.S. working-age and elderly adults with diabetes: results from NHANES 1999-2018.Acta diabetologica · 2025Article
- Disease severity-based subgrouping of type 2 diabetes does not parallel differences in quality of life: the Maastricht Study.Diabetologia · 2024Article
- The Role of Inflammation and Therapeutic Concepts in Diabetic Retinopathy-A Short Review.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to cluster patients with diabetes and explore the association between duration of diabetes and diabetes treatment choices in each cluster. Methods: A Two-Step cluster analysis was performed on 1332 Chinese patients with diabetes based on six parameters (glutamate decarboxylase antibodies, age at disease onset, body mass index, glycosylated hemoglobin, homeostatic model assessment 2 to estimate β-cell function and insulin resistance). Associations between the duration of diabetes and diabetes treatment choices in each cluster of patients were analyzed using Kaplan-Meier survival curves and logistic regression models. Results: The following five replicable clusters were identified: severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). There were significant differences in blood pressure, blood lipids, and diabetes-related complications among the clusters (all Conclusions: Clustering of patients with diabetes with a data-driven approach yields consistent results. Each diabetes cluster has significantly different disease characteristics and risk of diabetes complications. With the development of the disease course, each cluster receives different hypoglycemic treatments.
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