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<p>Infective endocarditis, a life-threatening condition, poses challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we performed proteomic profiling of plasma and vegetation samples from 238 patients with infective endocarditis and 100 controls, with validation in two external plasma cohorts (n = 328). We developed machine learning-based diagnostic and prognostic models for infective endocarditis, with area under the curve values of 0.98 and 0.87, respectively. Leucine-rich alpha-2-glycoprotein 1 and NADH:ubiquinone oxidoreductase subunit B4 are potential biomarkers associated with infection severity. Pathologically, protein networks characterized by glycometabolism, amino acid metabolism, and adhesion are linked to adverse events. Liver dysfunction may exacerbate the condition in patients with severe heart failure. Neutrophil extracellular traps emerge as promising therapeutic targets in <i>Streptococcus</i> or <i>Staphylococcus aureus</i> infections. Our findings provide insights into biomarker discovery and pathophysiological mechanisms in infective endocarditis, advancing early diagnosis and personalized medicine.</p>

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Integrated plasma and vegetation proteomic characterization of infective endocarditis for early diagnosis and treatment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • Shiman He,
  • Xuejiao Hu,
  • Jiajun Zhu,
  • Weiteng Wang,
  • Chi Ma,
  • Peng Ran,
  • Oudi Chen,
  • Fanyu Chen,
  • Hongkun Qing,
  • Jianhong Ma,
  • Danni Zeng,
  • Yunzhi Wang,
  • Weijiang Liu,
  • Jinwen Feng,
  • Lixi Gan,
  • Zhaoyu Qin,
  • Subei Tan,
  • Sha Tian,
  • Chen Ding,
  • Xuhua Jian,
  • Bing Gu

摘要

Infective endocarditis, a life-threatening condition, poses challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we performed proteomic profiling of plasma and vegetation samples from 238 patients with infective endocarditis and 100 controls, with validation in two external plasma cohorts (n = 328). We developed machine learning-based diagnostic and prognostic models for infective endocarditis, with area under the curve values of 0.98 and 0.87, respectively. Leucine-rich alpha-2-glycoprotein 1 and NADH:ubiquinone oxidoreductase subunit B4 are potential biomarkers associated with infection severity. Pathologically, protein networks characterized by glycometabolism, amino acid metabolism, and adhesion are linked to adverse events. Liver dysfunction may exacerbate the condition in patients with severe heart failure. Neutrophil extracellular traps emerge as promising therapeutic targets in Streptococcus or Staphylococcus aureus infections. Our findings provide insights into biomarker discovery and pathophysiological mechanisms in infective endocarditis, advancing early diagnosis and personalized medicine.