Ryu Ha-Eun, Heo Seok-Jae, Lee Jong Hee, Park Byoungjin, Han Taehwa, Kwon Yu-Jin
Department of Family Medicine, Yongin Severance Hospital, Gyeonggi-do, Republic of Korea.
Department of Family Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Endocrine. 2025 Apr;88(1):151-161. doi: 10.1007/s12020-024-04154-y. Epub 2025 Jan 2.
Early detection and intervention are vital for managing type 2 diabetes mellitus (T2DM) effectively. However, it's still unclear which risk factors for T2DM onset are most significant. This study aimed to use cluster analysis to categorize individuals based on six known risk factors, helping to identify high-risk groups requiring early intervention to prevent T2DM onset.
This study comprised 7402 Korean Genome and Epidemiology Study individuals aged 40 to 69 years. The hybrid hierarchical k-means clustering algorithm was employed on six variables normalized by Z-score-age, triglycerides, total cholesterol, non-high-density lipoprotein cholesterol, high-density lipoprotein cholesterol and C-reactive protein. Multivariable Cox proportional hazard regression analyses were conducted to assess T2DM incidence.
Four distinct clusters with significantly different characteristics and varying risks of new-onset T2DM were identified. Cluster 4 (insulin resistance) had the highest T2DM incidence, followed by Cluster 3 (inflammation and aging). Clusters 3 and 4 exhibited significantly higher T2DM incidence rates compared to Clusters 1 (healthy metabolism) and 2 (young age), even after adjusting for covariates. However, no significant difference was found between Clusters 3 and 4 after covariate adjustment.
Clusters 3 and 4 showed notably higher T2DM incidence rates, emphasizing the distinct risks associated with insulin resistance and inflammation-aging clusters.
早期检测和干预对于有效管理2型糖尿病（T2DM）至关重要。然而，T2DM发病的哪些风险因素最为显著仍不清楚。本研究旨在使用聚类分析根据六个已知风险因素对个体进行分类，以帮助识别需要早期干预以预防T2DM发病的高危人群。
本研究纳入了7402名年龄在40至69岁之间的韩国基因组与流行病学研究对象。采用混合层次k均值聚类算法对通过Z分数标准化的六个变量进行分析，这些变量包括年龄、甘油三酯、总胆固醇、非高密度脂蛋白胆固醇、高密度脂蛋白胆固醇和C反应蛋白。进行多变量Cox比例风险回归分析以评估T2DM发病率。
识别出四个具有显著不同特征和新发T2DM风险各异的不同聚类。聚类4（胰岛素抵抗）的T2DM发病率最高，其次是聚类3（炎症和衰老）。即使在调整协变量后，聚类3和聚类4的T2DM发病率仍显著高于聚类1（健康代谢）和聚类2（年轻）。然而，在调整协变量后聚类3和聚类4之间未发现显著差异。
聚类3和聚类4显示出明显更高的T2DM发病率，强调了与胰岛素抵抗和炎症-衰老聚类相关的独特风险。
