Open Access聚类分析显示血清电解质簇有助于糖尿病及代谢异常风险人群分层
Author(s) -
Hou Yanan,
Xiang Jiali,
Dai Huajie,
Wang Tiange,
Li Mian,
Lin Hong,
Wang Shuangyuan,
Xu Yu,
Lu Jieli,
Chen Yuhong,
Wang Weiqing,
Ning Guang,
Zhao Zhiyun,
Bi Yufang,
Xu Min
Publication year - 2022
Publication title -
journal of diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.949
H-Index - 43
eISSN - 1753-0407
pISSN - 1753-0393
DOI - 10.1111/1753-0407.13244
Subject(s) - diabetes mellitus , medicine , cluster (spacecraft) , endocrinology , body mass index , potassium , chemistry , computer science , programming language , organic chemistry
Background Serum electrolytes were found to associate with type 2 diabetes. Our study aimed to stratify nondiabetes by clusters based on multiple serum electrolytes and evaluate their associations with risk of developing diabetes and longitudinal changes in glucose and lipid metabolic traits. Methods We performed a data‐driven cluster analysis in 4937 nondiabetes individuals aged ≥40 years at baseline from a cohort follow‐up for an average of 4.4 years. Cluster analysis was based on seven commonly measured serum electrolytes (iron, chlorine, magnesium, sodium, potassium, calcium, and phosphorus) by using the k‐means method. Results A total of 4937 nondiabetes individuals were classified into three distinct clusters, with 1635 (33.1%) assigned to Cluster A, 1490 (30.2%) to Cluster B, and 1812 (36.7%) to Cluster C. Individuals in Cluster A had higher serum chlorine, were older, and more were women. Individuals in Cluster B had higher serum iron and body mass index (BMI). Individuals in Cluster C had higher serum phosphorus, were younger, and had lower BMI. Cluster B had 1.41‐fold higher risk of developing diabetes and Cluster C’s risk was 1.33‐fold higher compared with Cluster A. Over an average follow‐up of 4.4 years, Cluster A showed a moderate and stable BMI, Cluster B showed an accelerated deterioration in glucose metabolism, and Cluster C showed the most sharply increased serum low‐density lipoprotein cholesterol level. Conclusions Clusters based on seven common serum electrolytes differed in diabetes risk and progression of glucose and lipid metabolic traits. Serum electrolytes clusters could provide a powerful tool to differentiate individuals into different risk stratification for developing type 2 diabetes.