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Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis

INTRODUCTION: This study aimed to explore the novel classification of inpatients with new-onset diabetes in Eastern China by the cluster-based classification method and compare the clinical characteristics among the different subgroups. METHODS: A total of 1017 Inpatients with new-onset diabetes of...

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Autores principales: Song, Xueke, Lv, Yingqi, Huang, Nan, Sun, Jinfang, Yang, Ting, Wang, Xiaoyuan, Zhang, Jianan, Zhou, Ziwei, Gao, Huihua, Li, Jie, Zhang, Wei, Yin, Han, Wei, Qiong, Wang, Kun, Li, Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363570/
https://www.ncbi.nlm.nih.gov/pubmed/35966053
http://dx.doi.org/10.3389/fendo.2022.927661
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author Song, Xueke
Lv, Yingqi
Huang, Nan
Sun, Jinfang
Yang, Ting
Wang, Xiaoyuan
Zhang, Jianan
Zhou, Ziwei
Gao, Huihua
Li, Jie
Zhang, Wei
Yin, Han
Wei, Qiong
Wang, Kun
Li, Ling
author_facet Song, Xueke
Lv, Yingqi
Huang, Nan
Sun, Jinfang
Yang, Ting
Wang, Xiaoyuan
Zhang, Jianan
Zhou, Ziwei
Gao, Huihua
Li, Jie
Zhang, Wei
Yin, Han
Wei, Qiong
Wang, Kun
Li, Ling
author_sort Song, Xueke
collection PubMed
description INTRODUCTION: This study aimed to explore the novel classification of inpatients with new-onset diabetes in Eastern China by the cluster-based classification method and compare the clinical characteristics among the different subgroups. METHODS: A total of 1017 Inpatients with new-onset diabetes of five hospitals in Eastern China were included in the study. Clustering analysis was used to cluster the data into five subgroups according to six basic variables. The differences in clinical characteristics, treatments, and the prevalence of diabetes-related diseases among the five subgroups were analyzed by multiple groups comparisons and pairwise comparisons. The risk of diabetes-related diseases in the five subgroups was compared by calculating odd ratio (OR). P value < 0.05 was considered significant. RESULTS: Five subgroups were obtained by clustering analysis with the highest proportion of patients with severe insulin-deficient diabetes (SIDD) 451 (44.35%), followed by patients with mild age-related diabetes (MARD) 236 (23.21%), patients with mild obesity-related diabetes (MOD) 207 (20.35%), patients with severe insulin-resistant diabetes (SIRD) 81 (7.96%), and patients with severe autoimmune diabetes (SAID) 42 (4.13%). Five subtypes had their own unique characteristics and treatments. The prevalence and risk of diabetes-related complications and comorbidities were also significantly different among the five subtypes. Diabetic kidney disease (DKD) was the most common in SIRD group. Patients in SIDD, SIRD, and MARD groups were more likely to develop cardiovascular disease (CVD) and/or stroke, diabetic peripheral vascular disease (DPVD), and diabetic distal symmetric polyneuropathy (DSPN). The prevalence and risk of metabolic syndrome (MS) were the highest in MOD and SIRD groups. Patients in SAID group had the highest prevalence and risk of diabetic ketoacidosis (DKA). Patients with MOD were more likely to develop non-alcoholic fatty liver disease (NAFLD). CONCLUSIONS: The inpatients with new-onset diabetes in Eastern China had the unique clustering distribution. The clinical characteristics, treatments, and diabetes-related complications and comorbidities of the five subgroups were different, which may provide the basis for precise treatments of diabetes.
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spelling pubmed-93635702022-08-11 Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis Song, Xueke Lv, Yingqi Huang, Nan Sun, Jinfang Yang, Ting Wang, Xiaoyuan Zhang, Jianan Zhou, Ziwei Gao, Huihua Li, Jie Zhang, Wei Yin, Han Wei, Qiong Wang, Kun Li, Ling Front Endocrinol (Lausanne) Endocrinology INTRODUCTION: This study aimed to explore the novel classification of inpatients with new-onset diabetes in Eastern China by the cluster-based classification method and compare the clinical characteristics among the different subgroups. METHODS: A total of 1017 Inpatients with new-onset diabetes of five hospitals in Eastern China were included in the study. Clustering analysis was used to cluster the data into five subgroups according to six basic variables. The differences in clinical characteristics, treatments, and the prevalence of diabetes-related diseases among the five subgroups were analyzed by multiple groups comparisons and pairwise comparisons. The risk of diabetes-related diseases in the five subgroups was compared by calculating odd ratio (OR). P value < 0.05 was considered significant. RESULTS: Five subgroups were obtained by clustering analysis with the highest proportion of patients with severe insulin-deficient diabetes (SIDD) 451 (44.35%), followed by patients with mild age-related diabetes (MARD) 236 (23.21%), patients with mild obesity-related diabetes (MOD) 207 (20.35%), patients with severe insulin-resistant diabetes (SIRD) 81 (7.96%), and patients with severe autoimmune diabetes (SAID) 42 (4.13%). Five subtypes had their own unique characteristics and treatments. The prevalence and risk of diabetes-related complications and comorbidities were also significantly different among the five subtypes. Diabetic kidney disease (DKD) was the most common in SIRD group. Patients in SIDD, SIRD, and MARD groups were more likely to develop cardiovascular disease (CVD) and/or stroke, diabetic peripheral vascular disease (DPVD), and diabetic distal symmetric polyneuropathy (DSPN). The prevalence and risk of metabolic syndrome (MS) were the highest in MOD and SIRD groups. Patients in SAID group had the highest prevalence and risk of diabetic ketoacidosis (DKA). Patients with MOD were more likely to develop non-alcoholic fatty liver disease (NAFLD). CONCLUSIONS: The inpatients with new-onset diabetes in Eastern China had the unique clustering distribution. The clinical characteristics, treatments, and diabetes-related complications and comorbidities of the five subgroups were different, which may provide the basis for precise treatments of diabetes. Frontiers Media S.A. 2022-07-27 /pmc/articles/PMC9363570/ /pubmed/35966053 http://dx.doi.org/10.3389/fendo.2022.927661 Text en Copyright © 2022 Song, Lv, Huang, Sun, Yang, Wang, Zhang, Zhou, Gao, Li, Zhang, Yin, Wei, Wang and Li 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
Song, Xueke
Lv, Yingqi
Huang, Nan
Sun, Jinfang
Yang, Ting
Wang, Xiaoyuan
Zhang, Jianan
Zhou, Ziwei
Gao, Huihua
Li, Jie
Zhang, Wei
Yin, Han
Wei, Qiong
Wang, Kun
Li, Ling
Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis
title Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis
title_full Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis
title_fullStr Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis
title_full_unstemmed Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis
title_short Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis
title_sort clinical characteristics of inpatients with new-onset diabetes mellitus in eastern china: based on novel clustering analysis
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363570/
https://www.ncbi.nlm.nih.gov/pubmed/35966053
http://dx.doi.org/10.3389/fendo.2022.927661
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