■本研究的目的是通过聚类分析对大连地区的2型糖尿病(T2DM)慢性并发症患者进行聚类。中国,并检查不同亚组之间不同慢性并发症和代谢水平的风险差异。
■2267例住院患者被纳入基于11个变量[体重指数(BMI),收缩压(SBP),舒张压(DBP),葡萄糖,甘油三酯(TG),总胆固醇(TC),尿酸(UA),微量白蛋白尿(mAlb),胰岛素,胰岛素敏感指数(ISI)和Homa胰岛素抵抗(Homa-IR)]。采用多因素logistic回归分析不同亚组T2DM各种慢性并发症的风险,Kruskal-WallisH检验和Nemenyi检验检查了不同亚簇之间代谢物的差异。
■通过聚类分析确定了四个子集群,每个亚簇都有显著的特征,并标记有不同的风险水平.第1组住院患者1112例(49.05%),标记为“低风险”;第2组包括859名(37.89%)住院患者,标签特征为“中低风险”;第3组包括134名(5.91%)住院患者,标记为“中等风险”;第4组包括162名(7.15%)住院患者,标签特征为“高风险”。此外,在不同的子集群中,多种慢性并发症的患者比例不同,相同慢性并发症的风险也有显著差异。与“低风险”群集相比,其他三个集群显示出更高的微血管病变的风险。在对20个协变量进行额外调整后,“中低风险”集群的比值比(OR)和95%置信区间(95CI),“中等风险”群集,“高风险”集群为1.369(1.042,1.799),2.188(1.496,3.201),和9.644(5.851,15.896)(所有p<0.05)。代表性地,“高风险”集群的DN风险最高[OR(95CI):11.510(7.139,18.557),(p<0.05)]和DR[OR(95CI):3.917(2.526,6.075),(p<0.05)]调整20个变量后。与其他亚簇[苏氨酸(Thr)相比,四种代谢物具有统计学上的显着分布差异,酪氨酸(Tyr),戊二酰肉碱(C5DC),和丁酰基肉碱(C4)]。
■T2DM慢性并发症患者具有显著的聚集性特征,不同亚簇的靶器官损害风险差异显著,代谢物的水平也是如此。这可能成为防治T2DM慢性并发症的新思路。
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 p<0.05). Representatively, the \"High-Risk\" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (p<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (p<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.