ArticleScientific reports2019
Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.Chinese medical journal · 2026Article
- Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis.Medical sciences (Basel, Switzerland) · 2025Article
- Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis.JMIR public health and surveillance · 2024Article
- Artificial intelligence in endocrinology: a comprehensive review.Journal of endocrinological investigation · 2024Review
- 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
- Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data.PLOS digital health · 2023Article
- Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes.BMC medicine · 2022Article
- Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.Nutrition & diabetes · 2022Article
- Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease.The Journal of clinical endocrinology and metabolism · 2021Review
- Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.Frontiers in public health · 2021Article
- Current Pharmacological Treatment of Type 2 Diabetes Mellitus in Undocumented Migrants: Is It Appropriate for the Phenotype of the Disease?International journal of environmental research and public health · 2020Article
- Observational
- Silencing of the tRNA Modification Enzyme Cdkal1 Effects Functional Insulin Synthesis in NIT-1 Cells: tRNAFrontiers in molecular biosciences · 2020Article
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3 authors.
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Abstract
Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC identified six population clusters with significantly different prevalence rates of type 2 diabetes in the discovery data (N = 10,023), ranging from 0.09 to 0.44 (Chi-square test, P < 0.001). The machine learning method identified six clusters in the validation data (N = 215,083), which also showed the heterogeneity of prevalence between the clusters (P < 0.001). In addition to the prevalence of type 2 diabetes, the clusters showed different clinical features including biochemical profiles and prediction performance with the risk factors. SOur results seem to implicate a heterogeneous mechanism in the development of type 2 diabetes. These results will provide new insights for the development of more precise management strategy for type 2 diabetes.
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