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<p>Arterial stiffness is an important biomarker of cardiovascular health, and vascular age (VA) prediction provides additional value beyond chronological age. Here we present a curated arterial stiffness dataset comprising 36,223 participants aged 30–80 years from China. To benchmark its utility for VA modelling, we evaluated the Klemera–Doubal Method (KDM) and six Artificial Intelligence (AI) models: multiple linear regression, LASSO, random forest, support vector regression, XGBoost, and a deep neural network. Results showed that the dataset enables VA prediction using both statistical and learning-based approaches. Across both male and female cohorts, KDM showed the lowest prediction error under the current benchmark setting, while several nonlinear learning-based models achieved better performance than the linear baselines. Among the learning-based methods evaluated here, SVR and XGBoost showed comparatively strong performance. This dataset provides a useful open resource for vascular aging research, cardiovascular risk assessment, and methodological benchmarking.</p>

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A curated arterial stiffness dataset for vascular age prediction in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Xiaohui Chen,
  • Pengcheng Ding,
  • Mengbo He,
  • Sai Zhang,
  • Qingfeng Tang,
  • Guangjun Wang,
  • Zhifeng Tian,
  • Hui An

摘要

Arterial stiffness is an important biomarker of cardiovascular health, and vascular age (VA) prediction provides additional value beyond chronological age. Here we present a curated arterial stiffness dataset comprising 36,223 participants aged 30–80 years from China. To benchmark its utility for VA modelling, we evaluated the Klemera–Doubal Method (KDM) and six Artificial Intelligence (AI) models: multiple linear regression, LASSO, random forest, support vector regression, XGBoost, and a deep neural network. Results showed that the dataset enables VA prediction using both statistical and learning-based approaches. Across both male and female cohorts, KDM showed the lowest prediction error under the current benchmark setting, while several nonlinear learning-based models achieved better performance than the linear baselines. Among the learning-based methods evaluated here, SVR and XGBoost showed comparatively strong performance. This dataset provides a useful open resource for vascular aging research, cardiovascular risk assessment, and methodological benchmarking.