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Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data

INTRODUCTION: 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. METHODS: 2267 hospit...

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Autores principales: Wang, Cuicui, Li, Yan, Wang, Jun, Dong, Kunjie, Li, Chenxiang, Wang, Guiyan, Lin, Xiaohui, Zhao, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623421/
https://www.ncbi.nlm.nih.gov/pubmed/37929026
http://dx.doi.org/10.3389/fendo.2023.1230921
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author Wang, Cuicui
Li, Yan
Wang, Jun
Dong, Kunjie
Li, Chenxiang
Wang, Guiyan
Lin, Xiaohui
Zhao, Hui
author_facet Wang, Cuicui
Li, Yan
Wang, Jun
Dong, Kunjie
Li, Chenxiang
Wang, Guiyan
Lin, Xiaohui
Zhao, Hui
author_sort Wang, Cuicui
collection PubMed
description INTRODUCTION: 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. METHODS: 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. RESULTS: 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)]. CONCLUSION: 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.
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spelling pubmed-106234212023-11-04 Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data Wang, Cuicui Li, Yan Wang, Jun Dong, Kunjie Li, Chenxiang Wang, Guiyan Lin, Xiaohui Zhao, Hui Front Endocrinol (Lausanne) Endocrinology INTRODUCTION: 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. METHODS: 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. RESULTS: 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)]. CONCLUSION: 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. Frontiers Media S.A. 2023-10-20 /pmc/articles/PMC10623421/ /pubmed/37929026 http://dx.doi.org/10.3389/fendo.2023.1230921 Text en Copyright © 2023 Wang, Li, Wang, Dong, Li, Wang, Lin and Zhao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Wang, Cuicui
Li, Yan
Wang, Jun
Dong, Kunjie
Li, Chenxiang
Wang, Guiyan
Lin, Xiaohui
Zhao, Hui
Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data
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 Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623421/
https://www.ncbi.nlm.nih.gov/pubmed/37929026
http://dx.doi.org/10.3389/fendo.2023.1230921
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