Department of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Department of Gastroenterology, The 986th Hospital of Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Front Endocrinol (Lausanne). 2023 Oct 20;14:1230921. doi: 10.3389/fendo.2023.1230921. eCollection 2023.
The aim of this study was to cluster patients with chronic complications of type 2 diabetes mellitus (T2DM) by cluster analysis in Dalian, China, and examine the variance in risk of different chronic complications and metabolic levels among the various subclusters.
2267 hospitalized patients were included in the K-means cluster analysis based on 11 variables [Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Glucose, Triglycerides (TG), Total Cholesterol (TC), Uric Acid (UA), microalbuminuria (mAlb), Insulin, Insulin Sensitivity Index (ISI) and Homa Insulin-Resistance (Homa-IR)]. The risk of various chronic complications of T2DM in different subclusters was analyzed by multivariate logistic regression, and the Kruskal-Wallis H test and the Nemenyi test examined the differences in metabolites among different subclusters.
Four subclusters were identified by clustering analysis, and each subcluster had significant features and was labeled with a different level of risk. Cluster 1 contained 1112 inpatients (49.05%), labeled as "Low-Risk"; cluster 2 included 859 (37.89%) inpatients, the label characteristics as "Medium-Low-Risk"; cluster 3 included 134 (5.91%) inpatients, labeled "Medium-Risk"; cluster 4 included 162 (7.15%) inpatients, and the label feature was "High-Risk". Additionally, in different subclusters, the proportion of patients with multiple chronic complications was different, and the risk of the same chronic complication also had significant differences. Compared to the "Low-Risk" cluster, the other three clusters exhibit a higher risk of microangiopathy. After additional adjustment for 20 covariates, the odds ratios (ORs) and 95% confidence intervals (95%CI) of the "Medium-Low-Risk" cluster, the "Medium-Risk" cluster, and the"High-Risk" cluster are 1.369 (1.042, 1.799), 2.188 (1.496, 3.201), and 9.644 (5.851, 15.896) (all <0.05). Representatively, the "High-Risk" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (<0.05)] after 20 variables adjusted. Four metabolites with statistically significant distribution differences when compared with other subclusters [Threonine (Thr), Tyrosine (Tyr), Glutaryl carnitine (C5DC), and Butyryl carnitine (C4)].
Patients with chronic complications of T2DM had significant clustering characteristics, and the risk of target organ damage in different subclusters was significantly different, as were the levels of metabolites. Which may become a new idea for the prevention and treatment of chronic complications of T2DM.
本研究旨在通过聚类分析对中国大连的 2 型糖尿病慢性并发症患者进行分组，并检验不同亚组之间不同慢性并发症和代谢水平的风险差异。
基于 11 个变量（体重指数（BMI）、收缩压（SBP）、舒张压（DBP）、血糖、甘油三酯（TG）、总胆固醇（TC）、尿酸（UA）、微量白蛋白尿（mAlb）、胰岛素、胰岛素敏感性指数（ISI）和 Homa 胰岛素抵抗（Homa-IR）），对 2267 例住院患者进行 K-均值聚类分析。采用多变量 logistic 回归分析不同亚组 2 型糖尿病慢性并发症的风险，采用 Kruskal-Wallis H 检验和 Nemenyi 检验检验不同亚组间代谢物的差异。
通过聚类分析确定了 4 个亚组，每个亚组都有显著的特征，并标记了不同的风险水平。第 1 组包含 1112 名住院患者（49.05%），标记为“低风险”；第 2 组包括 859 名住院患者（37.89%），标记为“中低风险”；第 3 组包括 134 名住院患者（5.91%），标记为“中风险”；第 4 组包括 162 名住院患者（7.15%），标记为“高风险”。此外，在不同的亚组中，患有多种慢性并发症的患者比例不同，同一慢性并发症的风险也存在显著差异。与“低风险”组相比，其他三个亚组发生微血管并发症的风险更高。在额外调整 20 个协变量后，“中低风险”组、“中风险”组和“高风险”组的比值比（OR）和 95%置信区间（95%CI）分别为 1.369（1.042，1.799）、2.188（1.496，3.201）和 9.644（5.851，15.896）（均<0.05）。代表性地，“高风险”组在调整 20 个变量后，DN 的风险最高[OR（95%CI）：11.510（7.139，18.557），（<0.05）]和 DR [OR（95%CI）：3.917（2.526，6.075），（<0.05）]。与其他亚组相比，有 4 种代谢物的分布差异具有统计学意义[苏氨酸（Thr）、酪氨酸（Tyr）、戊二酰肉碱（C5DC）和丁酰肉碱（C4）]。
2 型糖尿病慢性并发症患者具有明显的聚类特征，不同亚组的靶器官损害风险显著不同，代谢物水平也存在显著差异。这可能成为 2 型糖尿病慢性并发症防治的新思路。
