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Background <p>The heterogeneous and complex nature of prediabetes presents a major challenge in identifying individuals predisposed to developing incident diabetes and related complications. We aimed to identify phenotypic subgroups of prediabetes at risk and to explore their distinct associations with cardiometabolic outcomes.</p> Methods <p>This study included 79,000 individuals with prediabetes from the three large-scale prospective cohorts in China. Phenotypic heterogeneity was identified using a soft-clustering algorithm based on the proximity network derived from uniform manifold approximation and projection (UMAP), combined with graph-clustering and Gaussian mixture models. Associations between phenotype probabilities and the incidence of type 2 diabetes (T2D), cardiovascular disease (CVD), and kidney events were assessed to evaluate risk differences across the identified profiles.</p> Results <p>Six phenotypic profiles were identified, including five with distinct metabolic features (representing ~ 70% of the total population), and one without significant features. These profiles demonstrated substantial differences in both baseline cardiometabolic burden and future disease risk. For instance, individuals with a 20% higher probability of belonging to the hypertensive profile had a 9, 6, and 12% higher risk of T2D, CVD, and CKD, respectively, while the profile with high lipids, creatinine, and liver enzyme was associated with an 10% increased risk of T2D and kidney events. Moreover, incorporating phenotypic probabilities into multivariable models significantly improved the prediction of disease risks (likelihood ratio test, <i>P</i> &lt; 0.05).</p> Conclusions <p>Prediabetes exhibits substantial phenotypic heterogeneity, and delineation of distinct metabolic profiles enables refined risk stratification and informs precision prevention strategies.</p>

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Data-driven phenotypic profiling of prediabetes reveals heterogeneous cardiometabolic risks in Chinese adults

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Xiaojing Jia,
  • Shuangyuan Wang,
  • Jinfeng Wang,
  • Yilan Ding,
  • Mian Li,
  • Yiting Lin,
  • Ruizhi Zheng,
  • Feiyue Huang,
  • Huapeng Wei,
  • Chunyan Hu,
  • Yu Xu,
  • Hong Lin,
  • Min Xu,
  • Tiange Wang,
  • Hong Qiao,
  • Guijun Qin,
  • Yingfen Qin,
  • Xulei Tang,
  • Zhen Ye,
  • Ruying Hu,
  • Lixin Shi,
  • Qing Su,
  • Xuefeng Yu,
  • Li Yan,
  • Qin Wan,
  • Gang Chen,
  • Zhengnan Gao,
  • Guixia Wang,
  • Feixia Shen,
  • Xuejiang Gu,
  • Zuojie Luo,
  • Li Chen,
  • Xinguo Hou,
  • Qiang Li,
  • Yanan Huo,
  • Yinfei Zhang,
  • Tianshu Zeng,
  • Chao Liu,
  • Youmin Wang,
  • Shengli Wu,
  • Tao Yang,
  • Huacong Deng,
  • Donghui Li,
  • Shenghan Lai,
  • Lulu Chen,
  • Jiajun Zhao,
  • Yiming Mu,
  • Guang Ning,
  • Yufang Bi,
  • Weiqing Wang,
  • Jieli Lu

摘要

Background

The heterogeneous and complex nature of prediabetes presents a major challenge in identifying individuals predisposed to developing incident diabetes and related complications. We aimed to identify phenotypic subgroups of prediabetes at risk and to explore their distinct associations with cardiometabolic outcomes.

Methods

This study included 79,000 individuals with prediabetes from the three large-scale prospective cohorts in China. Phenotypic heterogeneity was identified using a soft-clustering algorithm based on the proximity network derived from uniform manifold approximation and projection (UMAP), combined with graph-clustering and Gaussian mixture models. Associations between phenotype probabilities and the incidence of type 2 diabetes (T2D), cardiovascular disease (CVD), and kidney events were assessed to evaluate risk differences across the identified profiles.

Results

Six phenotypic profiles were identified, including five with distinct metabolic features (representing ~ 70% of the total population), and one without significant features. These profiles demonstrated substantial differences in both baseline cardiometabolic burden and future disease risk. For instance, individuals with a 20% higher probability of belonging to the hypertensive profile had a 9, 6, and 12% higher risk of T2D, CVD, and CKD, respectively, while the profile with high lipids, creatinine, and liver enzyme was associated with an 10% increased risk of T2D and kidney events. Moreover, incorporating phenotypic probabilities into multivariable models significantly improved the prediction of disease risks (likelihood ratio test, P < 0.05).

Conclusions

Prediabetes exhibits substantial phenotypic heterogeneity, and delineation of distinct metabolic profiles enables refined risk stratification and informs precision prevention strategies.