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

Autor: Yanan Hou, Jiali Xiang, Huajie Dai, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Yu Xu, Jieli Lu, Yuhong Chen, Weiqing Wang, Guang Ning, Zhiyun Zhao, Yufang Bi, Min Xu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Diabetes, Vol 14, Iss 2, Pp 121-133 (2022)
Druh dokumentu: article
ISSN: 1753-0407
1753-0393
DOI: 10.1111/1753-0407.13244
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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