Wang Yazhi, Zhang Mingkang, Wang Peng
The Second School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China.
The School of Pharmacy, Lanzhou University, Lanzhou, Gansu, China.
Front Med (Lausanne). 2025 Jul 30;12:1640017. doi: 10.3389/fmed.2025.1640017. eCollection 2025.
Type 2 diabetes mellitus (T2DM) is an endocrine and metabolic disorder that can lead to multi-organ damage and dysfunction, imposing significant financial burden on national healthcare systems. Currently, the early identification of high-risk individuals and the prevention of T2DM remain major challenges for clinicians. This study aimed to use easily obtainable clinical indicators to perform cluster analysis on healthy individuals, in order to accurately identify high-risk population requiring early intervention.
This study was a multicenter retrospective cohort study with a median follow-up period of 3 years. A total of 12,607 Chinese adult individuals without diabetes at baseline were included. The K-means clustering algorithm was applied to five standardized indicators: age, body mass index (BMI), fasting blood glucose (FBG), triglycerides (TG), and HDL-C (high-density lipoprotein cholesterol). After clustering, multivariate Cox proportional hazards regression analysis was used to evaluate and compare the risk of diabetes incidence among different clusters.
The study population comprising 12,607 subjects was clustered into four distinct groups: Cluster 1 (metabolic health cluster), Cluster 2 (low HDL-C cluster), Cluster 3 (old age and mild metabolic disorder cluster), and Cluster 4 (severe obesity and insulin resistance cluster). The proportional distributions of each cluster were 37.95, 29.99, 24.95, and 7.11%, respectively. The clinical characteristics and diabetes incidence risks varied significantly among the four clusters. Cluster 4 exhibited the highest diabetes incidence rate, followed by Cluster 3, Cluster 2, and Cluster 1. In all models adjusted for covariates, the diabetes incidence rates in Cluster 3 and Cluster 4 were significantly higher than those in Cluster 1 and Cluster 2. However, no significant difference was observed between Cluster 3 and Cluster 4.
Cluster-based analyses can effectively identify individuals at high risk of diabetes in the normal population. These high-risk groups (clusters 3 and 4) are often associated with aging, obesity, and insulin resistance (IR), necessitating early and targeted interventions.
2型糖尿病（T2DM）是一种内分泌和代谢紊乱疾病，可导致多器官损伤和功能障碍，给国家医疗保健系统带来巨大经济负担。目前，早期识别高危个体和预防T2DM仍然是临床医生面临的主要挑战。本研究旨在利用易于获得的临床指标对健康个体进行聚类分析，以准确识别需要早期干预的高危人群。
本研究是一项多中心回顾性队列研究，中位随访期为3年。共纳入12607名基线时无糖尿病的中国成年个体。将K均值聚类算法应用于五个标准化指标：年龄、体重指数（BMI）、空腹血糖（FBG）、甘油三酯（TG）和高密度脂蛋白胆固醇（HDL-C）。聚类后，采用多变量Cox比例风险回归分析评估和比较不同聚类中糖尿病发病风险。
由12607名受试者组成的研究人群被分为四个不同的组：第1组（代谢健康组）、第2组（低HDL-C组）、第3组（老年和轻度代谢紊乱组）和第4组（严重肥胖和胰岛素抵抗组）。每组的比例分布分别为37.95%、29.99%、24.95%和7.11%。四个聚类的临床特征和糖尿病发病风险差异显著。第4组糖尿病发病率最高，其次是第3组、第2组和第1组。在所有经协变量调整的模型中，第3组和第4组的糖尿病发病率显著高于第1组和第2组。然而，第3组和第4组之间未观察到显著差异。
基于聚类的分析可以有效地识别正常人群中糖尿病高危个体。这些高危组（第3组和第4组）通常与衰老、肥胖和胰岛素抵抗（IR）相关，需要早期和有针对性的干预。
