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Background <p>Myocardial infarction (MI) has been linked to changes in the blood microbiome, yet the interplay between microbiome and metabolome remains poorly understood. This study integrates blood microbiome profiling and metabolomic analysis to uncover biomarkers and pathways associated with MI.</p> Methods <p>Using 16&#xa0;S rRNA sequencing and LC-MS metabolomics, blood samples from 24 MI patients and 24 healthy controls were analyzed. Microbial diversity, key taxa, metabolites, and their functional implications were evaluated.</p> Results <p>While alpha and beta diversity of the microbiome showed no significant differences, three bacterial taxa (Proteobacteria, Gammaproteobacteria, and Bacilli) and twenty metabolites (e.g., UPD-L-Ara4O, Urotensin-related peptide, and 9-hydroxy octadecanoic acid) were identified as potential biomarkers, achieving an AUC of 0.99-1. Functional pathway analysis revealed upregulation in glycerolipid metabolism and mTOR signaling pathways, which were significantly correlated with clinical markers of MI.</p> Conclusion <p>This integrative approach highlights the diagnostic potential of blood microbiome-metabolome dynamics in MI and suggests mechanistic pathways that could guide future interventions.</p>

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Integrated analysis of blood microbiome and metabolites reveals key biomarkers and functional pathways in myocardial infarction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月16日

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  • Ikram Khan,
  • Stefan Panaiotov,
  • Kotb A. Attia,
  • Arif Ahmed Mohammed,
  • Muhammad Uzair,
  • Imran Khan,
  • Zhiqiang Li,
  • Xiaodong Xie

摘要

Background

Myocardial infarction (MI) has been linked to changes in the blood microbiome, yet the interplay between microbiome and metabolome remains poorly understood. This study integrates blood microbiome profiling and metabolomic analysis to uncover biomarkers and pathways associated with MI.

Methods

Using 16 S rRNA sequencing and LC-MS metabolomics, blood samples from 24 MI patients and 24 healthy controls were analyzed. Microbial diversity, key taxa, metabolites, and their functional implications were evaluated.

Results

While alpha and beta diversity of the microbiome showed no significant differences, three bacterial taxa (Proteobacteria, Gammaproteobacteria, and Bacilli) and twenty metabolites (e.g., UPD-L-Ara4O, Urotensin-related peptide, and 9-hydroxy octadecanoic acid) were identified as potential biomarkers, achieving an AUC of 0.99-1. Functional pathway analysis revealed upregulation in glycerolipid metabolism and mTOR signaling pathways, which were significantly correlated with clinical markers of MI.

Conclusion

This integrative approach highlights the diagnostic potential of blood microbiome-metabolome dynamics in MI and suggests mechanistic pathways that could guide future interventions.