ArticleFrontiers in endocrinology2021
Clinical Characteristics and Risk of Diabetic Complications in Data-Driven Clusters Among Type 2 Diabetes.
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- 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
- Yoga practice duration and its influence on type 2 diabetes management: A meta-analysis of Asian population.iScience · 2026Article
- Targeting risk factors and strategies for prevention of peripheral neuropathy: focus on B-vitamin deficiencies and metabolic diseases.Neurological research and practice · 2026Review
- Validation of a Diabetes Subtype Classification Model Using Data from U.S. Adults Before and After the COVID-19 Pandemic.Metabolites · 2026Article
- Metabolic heterogeneity at the onset of type 2 diabetes: phenotypes of insulin resistance and β-cell function in the Kazakh population.Frontiers in endocrinology · 2026Article
- Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.BMJ open · 2025Article
- Article
- Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models.Journal of clinical medicine · 2025Article
- Risk factors for progression to albuminuria in individuals with newly diagnosed type 2 diabetes: a 5-year cohort study.BMC endocrine disorders · 2025Observational
- Characterizing trajectories of diabetes-related health parameters before diabetes diagnosis in diabetes subtypes: analysis of a 20-year long prospective cohort study in Sweden.Cardiovascular diabetology · 2025Article
- Severe insulin-deficient diabetes is at the opposite end of the neuropathy spectrum to mild aged-related diabetes.Journal of diabetes investigation · 2025Article
- Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study.Journal of clinical medicine · 2025Review
- Causal associations between gut microbiota and type 2 diabetes mellitus subtypes: a mendelian randomization analysis.BMC endocrine disorders · 2025Article
- 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
- Clinical characteristics and complication risks in data-driven clusters among Chinese community diabetes populations.Journal of diabetes · 2024Article
- Disease severity-based subgrouping of type 2 diabetes does not parallel differences in quality of life: the Maastricht Study.Diabetologia · 2024Article
- Article
- Characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups: a retrospective study.Lipids in health and disease · 2023Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to cluster newly diagnosed patients and patients with long-term diabetes and to explore the clinical characteristics, risk of diabetes complications, and medication treatment related to each cluster. Research Design and Methods: K-means clustering analysis was performed on 1,060 Chinese patients with type 2 diabetes based on five variables (HbA1c, age at diagnosis, BMI, HOMA2-IR, and HOMA2-B). The clinical features, risk of diabetic complications, and the utilization of elven types of medications agents related to each cluster were evaluated with the chi-square test and the Tukey-Kramer method. Results: Four replicable clusters were identified, severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). In terms of clinical characteristics, there were significant differences in blood pressure, renal function, and lipids among clusters. Furthermore, individuals in SIRD had the highest prevalence of stages 2 and 3 chronic kidney disease (CKD) (57%) and diabetic peripheral neuropathy (DPN) (67%), while individuals in SIDD had the highest risk of diabetic retinopathy (32%), albuminuria (31%) and lower extremity arterial disease (LEAD) (13%). Additionally, the difference in medication treatment of clusters were observed in metformin (p = 0.012), α-glucosidase inhibitor (AGI) (p = 0.006), dipeptidyl peptidase 4 inhibitor (DPP-4) (p = 0.017), glucagon-like peptide-1 (GLP-1) (p <0.001), insulin (p <0.001), and statins (p = 0.006). Conclusions: The newly diagnosed patients and patients with long-term diabetes can be consistently clustered into featured clusters. Each cluster had significantly different patient characteristics, risk of diabetic complications, and medication treatment.
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