Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data
IntroductionThe 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.Methods2267 hospitalize...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-10-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1230921/full |
| _version_ | 1797654370108571648 |
|---|---|
| author | Cuicui Wang Cuicui Wang Yan Li Jun Wang Kunjie Dong Chenxiang Li Guiyan Wang Xiaohui Lin Hui Zhao |
| author_facet | Cuicui Wang Cuicui Wang Yan Li Jun Wang Kunjie Dong Chenxiang Li Guiyan Wang Xiaohui Lin Hui Zhao |
| author_sort | Cuicui Wang |
| collection | DOAJ |
| description | IntroductionThe 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.Methods2267 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.ResultsFour 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)].ConclusionPatients 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. |
| first_indexed | 2024-03-11T16:57:57Z |
| format | Article |
| id | doaj.art-9e4588863bf5472ba6e067a9596bb68a |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-11T16:57:57Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-9e4588863bf5472ba6e067a9596bb68a2023-10-20T13:05:39ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-10-011410.3389/fendo.2023.12309211230921Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real dataCuicui Wang0Cuicui Wang1Yan Li2Jun Wang3Kunjie Dong4Chenxiang Li5Guiyan Wang6Xiaohui Lin7Hui Zhao8Department of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, ChinaDepartment of Gastroenterology, The 986th Hospital of Xijing Hospital, Air Force Military Medical University, Xi’an, ChinaState Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, ChinaDepartment of Gastroenterology, The 986th Hospital of Xijing Hospital, Air Force Military Medical University, Xi’an, ChinaSchool of Computer Science & Technology, Dalian University of Technology, Dalian, ChinaSchool of Computer Science & Technology, Dalian University of Technology, Dalian, ChinaSchool of Information Engineering, Dalian Ocean University, Dalian, ChinaSchool of Computer Science & Technology, Dalian University of Technology, Dalian, ChinaDepartment of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, ChinaIntroductionThe 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.Methods2267 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.ResultsFour 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)].ConclusionPatients 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.https://www.frontiersin.org/articles/10.3389/fendo.2023.1230921/fullT2DMchronic complicationsK-meanscluster analysismetabolite |
| spellingShingle | Cuicui Wang Cuicui Wang Yan Li Jun Wang Kunjie Dong Chenxiang Li Guiyan Wang Xiaohui Lin Hui Zhao Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data Frontiers in Endocrinology T2DM chronic complications K-means cluster analysis metabolite |
| title | Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data |
| title_full | Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data |
| title_fullStr | Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data |
| title_full_unstemmed | Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data |
| title_short | Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data |
| title_sort | unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of t2dm an observational study of real data |
| topic | T2DM chronic complications K-means cluster analysis metabolite |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1230921/full |
| work_keys_str_mv | AT cuicuiwang unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT cuicuiwang unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT yanli unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT junwang unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT kunjiedong unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT chenxiangli unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT guiyanwang unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT xiaohuilin unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata AT huizhao unsupervisedclusteranalysisofclinicalandmetabolitecharacteristicsinpatientswithchroniccomplicationsoft2dmanobservationalstudyofrealdata |