Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Journal: Cardiovascular diabetology
PMID: 40176039

Abstract

BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major adverse cardiovascular events (MACE). This study aimed to improve prediction and risk stratification by integrating traditional risk factors with biochemical and metabolomic biomarkers.

Authors

  • Xi-Ru Zhang
    Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong, China.
  • Wen-Fang Zhong
    Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Rui-Yan Liu
    Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Jie-Lin Huang
    Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong, China.
  • Jing-Xiang Fu
    Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong, China.
  • Jian Gao
  • Pei-Dong Zhang
  • Dan Liu
    Department of Bioengineering, Temple University, Philadelphia, PA, United States.
  • Zhi-Hao Li
    Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Yan He
    School of Biology & Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Hongwei Zhou
    Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
  • Zhuang Li
    Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong, China. jiandandjx@smu.edu.cn.

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