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

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.

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Publication Details

Journal
Nature Communications
Published
2025-05-30
DOI
https://doi.org/10.1038/s41467-025-60184-8
Citations
5
Primary Topic
Infective Endocarditis Diagnosis and Management
Type
article
Field-Weighted Citation Impact
6.77

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article

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

Oudi Chen, Chi Ma, Danni Zeng, Hongkun Qing et al.
5 citations
Nature Communications
Infective Endocarditis Diagnosis and Management
6.77
article

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

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

Abstract

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.

Nature CommunicationsVol. 16(1)
Xinjiang Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Fudan University (CN), Tumor Hospital of Xinjiang Medical University (CN), Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Southern Medical University (CN)
National Natural Science Foundation of China, Fudan University, Guangdong Provincial People's Hospital, National Science and Technology Major Project, National Key Research and Development Program of China, Program of Shanghai Academic Research Leader
Good health and well-being
Openalex Percentile: Top 3%
Infective Endocarditis Diagnosis and Management
6.77
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