医学
蛋白尿
优势比
内科学
2型糖尿病
糖尿病
亚型
2型糖尿病
人口
胰岛素抵抗
1型糖尿病
置信区间
胰岛素
内分泌学
计算机科学
环境卫生
程序设计语言
作者
Xiaopeng Shao,Jingyi Lu,Jian Zhou,Liang Wu,Yaxin Wang,Wei Lu,Hongru Li,Jian Zhou,Xia Yu
摘要
Abstract Aim The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications. Methods The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge‐based temporal abstraction with numerical vectorization. The k‐means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM. Results By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic β‐cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03‐1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set. Conclusion Subtyping patients with T2DM using CGM data may help identify high‐risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.
科研通智能强力驱动
Strongly Powered by AbleSci AI