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通过使用连续血糖监测数据,对2型糖尿病患者进行临床相关分层
Xiaopeng Shao1, Jingyi Lu2, Rui Tao1
1College of Information Science and Engineering, Northeastern University, Shenyang, China.
Diabetes, obesity & metabolism
|February 27, 2024
概括
持续血糖监测 (CGM) 数据可以揭示2型糖尿病 (T2DM) 亚型. 这种方法确定了四组患者,其中一组患有较高的白蛋白尿风险,有助于个性化护理.
科学领域:
- 内分泌学 在内分泌学.
- 代谢疾病 代谢疾病
- 在医疗保健中的数据科学.
背景情况:
- 持续血糖监测 (CGM) 产生了大量的数据,为识别2型糖尿病 (T2DM) 患者异质提供了潜力.
- 了解T2DM亚型对于有针对性的治疗和预测并发症至关重要.
研究的目的:
- 开发一种使用CGM数据用于T2DM亚型的新方法.
- 探索已识别的T2DM亚型和临床表型之间的关系,包括微血管并发症.
主要方法:
- 利用了来自3119名T2DM患者的CGM数据.
- 开发了一个葡萄糖符号模式 (GSP) 度量,将时间抽象和数值向量化结合起来.
- 在773名患者的独立队列中应用k-means集群到GSP进行子组识别和验证结果.
主要成果:
- 根据CGM的个人资料和参数,确定了四个不同的T2DM子组.
- 小组在临床表型中显示出显著差异,包括胰腺β细胞功能和胰岛素耐药性 (p < .001).
- 与最佳控制组 (D组) 相比,最耐胰岛素的子组 (C组) 患白蛋白尿风险明显更高 (OR=1.24) ,这在验证中得到证实.
结论:
- 通过CGM数据对T2DM患者进行亚型鉴定,可以识别患有微血管并发症高风险的个体.
- 这种方法提供了对T2DM病理生理学的见解,并支持精细的患者分层.
- 该方法有望为个性化糖尿病管理策略提供信息.
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