Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults
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BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise intervention. This study aimed to analyze common clinical indicators in a diabetes-free population using innovative clustering methods to identify characteristic subgroups and evaluate their utility in stratified prediction of diabetes risk and related complications. METHODS: This analysis included 13,829 adults without diabetes from the Kunshan Aging Research with E-health (KARE) cohort, a population-based longitudinal cohort of 51,400 community-dwelling residents from both urban and rural areas of Kunshan City, China, who have received annual health examinations between January 2014 and December 2023. A novel subtype classification method based on complication clustering and weighted naive Bayes classification was applied to select the most informative variables and categorize individuals into distinct diabetes subtypes. We then assessed 3-year risks of diabetes and complications, including cardiovascular disease (CVD), fatty liver disease (FLD), and stroke. To evaluate the influence of genetic factors, polygenic risk scores (PRS) were compared across all participants. External validation was performed using data from 6209 diabetes-free individuals in a cohort of 22,630 people who have been followed since 2014 at Beijing Jiuhua Hospital. RESULTS: Thirteen clinically relevant variables were identified: sex, age, body mass index (BMI), waist circumference, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), uric acid (UA), blood urea nitrogen (BUN), fasting blood glucose (FBG), systolic blood pressure (SBP), and heart rate. Three clusters were identified in the Kunshan cohort. Cluster 1 ( n = 6751) had favorable indicators and the lowest risks of diabetes (2.04%, 138/6751) and complications, including CVD (4.52%, 305/6751), FLD (15.30%, 1033/6751), and stroke (9.07%, 612/6751). Cluster 2 ( n = 4622) had the poorest glucose and lipid control, with the highest 3-year cumulative incidence of diabetes (9.95%, 460/4622) and FLD (52.14%, 2410/4622). Cluster 3 ( n = 2456) was characterized by the oldest age, highest SBP, BMI, and waist circumference, with intermediate diabetes risk (3.05%, 75/2456) and the highest risks of CVD (8.47%, 208/2456) and stroke (14.13%, 347/2456). In Cox survival analysis for FLD (adjusted), using Cluster 1 as reference, the hazard ratio (HR) was 2.357 (95% confidence interval [CI]: 2.161-2.571, P <0.001) for Cluster 2, and was 1.903 (95% CI: 1.718-2.108, P <0.001) for Cluster 3. In Cox survival analysis for CVD, HRs were 1.193 (95% CI: 0.975-1.459, P = 0.087) for Cluster 2 and 1.295 (95% CI: 1.041-1.611, P = 0.02) for Cluster 3. In stroke analysis, HRs were 1.058 (95% CI: 0.911-1.23, P = 0.46) for Cluster 2 and 1.212 (95% CI: 1.029-1.428, P = 0.021) for Cluster 3. The risks of diabetes and CVD predicted by PRS were consistent with those identified by clinical clustering. The findings were independently confirmed in the Beijing Jiuhua Hospital cohort. CONCLUSIONS: Phenotypes derived from clinical characteristic analysis using the new clustering method effectively identify and stratify the risk of diabetes and related complications, such as CVD, FLD, and stroke, in two large cohorts of diabetes-free Chinese adults, supporting the development of more precise and individualized prevention strategies.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults
- Date Crossref
- 10/02/2026
- Éditeur
- Ovid Technologies (Wolters Kluwer Health)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Chinese Academy of Medical Sciences & Peking Union Medical College pays non établi dans la noticeUniversité ou école supérieure
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First People's Hospital of Yunnan Province pays non établi dans la noticeÉtablissement de santé
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Peking University National Engineering Research Center For Software Engineering pays non établi dans la noticeUniversité ou école supérieure
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Beijing Aerospace General Hospital pays non établi dans la noticeÉtablissement de santé
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Jiangsu University Department of General Medicine pays non établi dans la noticeUniversité ou école supérieure
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First People's Hospital of Kunshan pays non établi dans la noticeÉtablissement de santé
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Beijing Academy of Artificial Intelligence pays non établi dans la noticeInstitution
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Beijing Hospital Department of Endocrinology pays non établi dans la noticeÉtablissement de santé
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The Affiliated Hospital of Kunming University of Science and Technology Department of Endocrinology and Metabolism pays non établi dans la noticeUniversité ou école supérieure
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School of Software and Microelectronics pays non établi dans la noticeUniversité ou école supérieure
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Guangzhou National Laboratory pays non établi dans la noticeStructure de recherche
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Beijing Jiuhua Hospital Department of General Medicine pays non établi dans la noticeÉtablissement de santé
Chinese Academy of Medical Sciences & Peking Union Medical College, First People's Hospital of Yunnan Province et National Engineering Research Center For Software Engineering — Peking University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.