在中国人群中使用变异性自编码器告知树模型模型进行2型糖尿病的精确表型识别
Tong Yue1,2, Wenhao Zhang1,2, Yu Ding1,2
1Department of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China.
Nature communications
|January 14, 2026
概括
现有的2型糖尿病 (T2D) 模型在不同人群中失败. 这项研究开发了一个中国特有的T2D分类框架,揭示了人口特异性,并使个性化风险预测成为更精确的糖尿病学.
科学领域:
- 内分泌学和新陈代谢学
- 遗传学和人口研究研究.
- 计算生物学和生物信息学
背景情况:
- 2型糖尿病 (T2D) 具有显著的临床异质性,使管理复杂化.
- 目前的T2D分类模型主要基于欧洲队列,对其他祖先的概括性有限.
- 解决人口特异性T2D异质性对于推进精确糖尿病学的发展至关重要.
研究的目的:
- 评估现有的T2D分类模型在不同祖先中的通用性.
- 开发和验证一种新的,针对中国群体的T2D人口特异性分类框架.
- 确定推动中国人T2D异质性的关键临床特征.
主要方法:
- 来自苏格兰数据的树状图形结构在一个大型的多中心中国队列 (32,501名患者) 上进行了测试.
- 用一个变异自编码器 (VAE) 框架来识别中国队列中的关键临床特征.
- 区分维度减小树 (DDRTree) 算法被用来构建一个中国特有的T2D树模型,在外部队列中验证.
主要成果:
- 虽然心血管和的结果显示出类似的分布,但糖尿病视网膜病变在类似的表型中的祖先之间有所不同.
- 开发的中国T2D树模型捕获了特定人口的异质性.
- 纵向分析显示,中国队列中的表型变化趋向于分类树的高风险分支.
结论:
- 现有的T2D分类框架需要适应不同种群,因为祖先特定的表型变异.
- 对于准确的T2D风险预测,一个特定于人群的分类框架,比如为中国队列开发的一种,是必不可少的.
- 实施量身定制的分类系统将促进个性化的风险分层和精密糖尿病学的专业治疗指南.
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