ArticleBMC medicine2022
Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes.
Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Prediabetes Subgroups, Type 2 Diabetes Risk, and Differential Effects of Preventive Interventions.The Journal of clinical endocrinology and metabolism · 2025 · on this mapTrial
- Large-Scale Proteomics Uncovers Pre-Disease Inflammation-Lipid Subtypes to Refine Risk Stratification and Prediction of Type 2 Diabetes.Diabetes, obesity & metabolism · 2026Article
- Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.Chinese medical journal · 2026Article
- Prediabetes and Risk of All-Cause and Cause-Specific Mortality: A Prospective Study of 114 062 Adults in Mexico City.The Journal of clinical endocrinology and metabolism · 2025Article
- Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis.Medical sciences (Basel, Switzerland) · 2025Article
- Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Identification of pre-diabetes subphenotypes for type 2 diabetes, related vascular complications and mortality.BMJ open diabetes research & care · 2025Article
- A life-course multisectoral approach to precision health in LMICs.Nature medicine · 2024Article
- Prevalence of prediabetes in Mexico: a retrospective analysis of nationally representative surveys spanning 2016-2022.Lancet regional health. Americas · 2023Article
- Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan.International journal of environmental research and public health · 2023Article
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes.
methodsData on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes.
resultsSix risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years.
conclusionsPhenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.
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