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

TY - JOUR

T1 - Precision phenotyping of type 2 diabetes in chinese populations using a variational autoencoder-informed tree model

AU - Yue, Tong

AU - Zhang, Wenhao

AU - Ding, Yu

AU - Zheng, Xueying

AU - Ma, Yunjie

AU - Chan, Juliana C.N.

AU - Lau, Eric S.H.

AU - Lui, Juliana N.M.

AU - Jin, Guoxi

AU - Xu, Wen

AU - Bi, Yan

AU - Wang, Zuocheng

AU - Nie, Sheng

AU - Gong, Mengchun

AU - Pearson, Ewan R.

AU - Luo, Sihui

AU - Weng, Jianping

N1 - Publisher Copyright: © The Author(s) 2026.

PY - 2026/2/9

Y1 - 2026/2/9

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105029567091

U2 - 10.1038/s41467-025-68211-4

DO - 10.1038/s41467-025-68211-4

M3 - Article

C2 - 41535268

AN - SCOPUS:105029567091

SN - 2041-1723

VL - 17

SP - 1

EP - 16

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 1475

ER -