Scholarlink

<p>Type 2 diabetes (T2D) exhibits clinical heterogeneity, yet most existing classification models are derived from European populations and face challenges in clinical application. Here, we evaluate the generalizability of a tree-like graph structure from Scottish data to 32,501 newly diagnosed T2D patients from a multi-center Chinese cohort comprising over 8.6 million individuals. We observe similar distribution between the Scottish and Chinese individuals in heart and kidney outcomes, but diabetic retinopathy varies across ancestries even within similar phenotypes. To capture T2D Chinese-specific heterogeneity, we apply a variational autoencoder (VAE) framework to identify key clinical features and construct a tree structure using the Discriminative Dimensionality Reduction Tree (DDRTree) algorithm. This Chinese tree model is validated in two independent external cohorts and revealed longitudinal phenotypic shifts trending toward higher-risk branches. Our findings emphasize the need for population-specific classification frameworks to advance precision diabetology through individualized risk prediction and specialized treatment guidelines.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Precision phenotyping of type 2 diabetes in chinese populations using a variational autoencoder-informed tree model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月14日

查看全文
  • Tong Yue,
  • Wenhao Zhang,
  • Yu Ding,
  • Xueying Zheng,
  • Yunjie Ma,
  • Juliana C. N. Chan,
  • Eric S. H. Lau,
  • Juliana N. M. Lui,
  • Guoxi Jin,
  • Wen Xu,
  • Yan Bi,
  • Zuocheng Wang,
  • Sheng Nie,
  • Mengchun Gong,
  • Ewan R. Pearson,
  • Sihui Luo,
  • Jianping Weng

摘要

Type 2 diabetes (T2D) exhibits clinical heterogeneity, yet most existing classification models are derived from European populations and face challenges in clinical application. Here, we evaluate the generalizability of a tree-like graph structure from Scottish data to 32,501 newly diagnosed T2D patients from a multi-center Chinese cohort comprising over 8.6 million individuals. We observe similar distribution between the Scottish and Chinese individuals in heart and kidney outcomes, but diabetic retinopathy varies across ancestries even within similar phenotypes. To capture T2D Chinese-specific heterogeneity, we apply a variational autoencoder (VAE) framework to identify key clinical features and construct a tree structure using the Discriminative Dimensionality Reduction Tree (DDRTree) algorithm. This Chinese tree model is validated in two independent external cohorts and revealed longitudinal phenotypic shifts trending toward higher-risk branches. Our findings emphasize the need for population-specific classification frameworks to advance precision diabetology through individualized risk prediction and specialized treatment guidelines.