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Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Author

Listed:
  • Jiali Lv

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Shandong University)

  • Chang Pan

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

  • Yuping Cai

    (Chinese Academy of Sciences
    Shanghai Key Laboratory of Aging Studies)

  • Xinyue Han

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Shandong University)

  • Cheng Wang

    (Shandong University
    Shandong University)

  • Jingjing Ma

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

  • Jiaojiao Pang

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

  • Feng Xu

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

  • Shuo Wu

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

  • Tianzhang Kou

    (Chinese Academy of Sciences)

  • Fandong Ren

    (Chinese Academy of Sciences)

  • Zheng-Jiang Zhu

    (Chinese Academy of Sciences
    Shanghai Key Laboratory of Aging Studies)

  • Tao Zhang

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Shandong University)

  • Jiali Wang

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

  • Yuguo Chen

    (Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University
    Qilu Hospital of Shandong University)

Abstract

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Suggested Citation

  • Jiali Lv & Chang Pan & Yuping Cai & Xinyue Han & Cheng Wang & Jingjing Ma & Jiaojiao Pang & Feng Xu & Shuo Wu & Tianzhang Kou & Fandong Ren & Zheng-Jiang Zhu & Tao Zhang & Jiali Wang & Yuguo Chen, 2024. "Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50125-2
    DOI: 10.1038/s41467-024-50125-2
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    References listed on IDEAS

    as
    1. Inmaculada Martínez-Reyes & Navdeep S. Chandel, 2020. "Mitochondrial TCA cycle metabolites control physiology and disease," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    2. Xiaotao Shen & Ruohong Wang & Xin Xiong & Yandong Yin & Yuping Cai & Zaijun Ma & Nan Liu & Zheng-Jiang Zhu, 2019. "Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    3. Zhiwei Zhou & Mingdu Luo & Haosong Zhang & Yandong Yin & Yuping Cai & Zheng-Jiang Zhu, 2022. "Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

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