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Aims/hypothesis <p>Diabetes heterogeneity has been modelled as a continuum in European populations, but its phenotypes and long-term comorbidity risks remain unclear in Chinese individuals. This study aimed to identify distinct phenotypes and evaluate their links to future cardiometabolic risks in a large Chinese cohort.</p> Methods <p>The discriminative dimensionality reduction with trees (DDRTree) algorithm was used to develop a tree structure based on nine clinical variables. Cox proportional hazard models or logistic regression models were used to analyse probabilities of diabetes-related outcomes.</p> Results <p>This study included 19,612 individuals with newly diagnosed diabetes (36.8% male, mean age 59.01 years [SD 8.63]) from the China Cardiometabolic Disease and Cancer Cohort (4C) study. All nine clinical variables used for establishing DDRTree models were gradient distributed across the tree. By overlaying risks of diabetes-related outcomes, we show how these risks differ by participant phenotype. Participants characterised by hyperglycaemia, obesity and dyslipidaemia showed elevated risks of insulin initiation, hypoglycaemia and chronic kidney diseases, while those with hypertension and high creatinine, total cholesterol and alanine aminotransferase levels were associated with a higher risk of CVD. Notably, social determinants and lifestyle factors further contributed to the observed heterogeneity.</p> Conclusions/interpretation <p>These findings characterise the heterogeneity of diabetes phenotypes and complication risks in the Chinese population, suggesting potential implications for personalised diabetes care. Given the observed phenotypic differences, management strategies should consider population-specific characteristics.</p> Graphical Abstract <p></p>

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Heterogeneity of diabetes and disease progression with a tree-like representation: findings from the China Cardiometabolic Disease and Cancer Cohort (4C) study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月30日

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

摘要

Aims/hypothesis

Diabetes heterogeneity has been modelled as a continuum in European populations, but its phenotypes and long-term comorbidity risks remain unclear in Chinese individuals. This study aimed to identify distinct phenotypes and evaluate their links to future cardiometabolic risks in a large Chinese cohort.

Methods

The discriminative dimensionality reduction with trees (DDRTree) algorithm was used to develop a tree structure based on nine clinical variables. Cox proportional hazard models or logistic regression models were used to analyse probabilities of diabetes-related outcomes.

Results

This study included 19,612 individuals with newly diagnosed diabetes (36.8% male, mean age 59.01 years [SD 8.63]) from the China Cardiometabolic Disease and Cancer Cohort (4C) study. All nine clinical variables used for establishing DDRTree models were gradient distributed across the tree. By overlaying risks of diabetes-related outcomes, we show how these risks differ by participant phenotype. Participants characterised by hyperglycaemia, obesity and dyslipidaemia showed elevated risks of insulin initiation, hypoglycaemia and chronic kidney diseases, while those with hypertension and high creatinine, total cholesterol and alanine aminotransferase levels were associated with a higher risk of CVD. Notably, social determinants and lifestyle factors further contributed to the observed heterogeneity.

Conclusions/interpretation

These findings characterise the heterogeneity of diabetes phenotypes and complication risks in the Chinese population, suggesting potential implications for personalised diabetes care. Given the observed phenotypic differences, management strategies should consider population-specific characteristics.

Graphical Abstract