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Background <p>This study aims to analyze the composition, diversity, and metabolic functions of fecal microbiome (FM)-derived outer membrane vesicles (OMVs) in patients with vascular dementia (VaD), to identify potential biomarkers for VaD diagnosis.</p> Methods <p>FM-derived OMVs were isolated from 29 VaD patients and 28 matched controls via ultracentrifugation and characterized using transmission electron microscopy and nanoparticle tracking analysis. PKH26-labeled OMVs were used for in vivo tracking in mouse brains. Microbial composition was profiled by 16&#xa0;S rRNA sequencing, combined with diversity analysis and machine learning.</p> Results <p>VaD-OMVs were widely distributed in multiple cognitive function-related regions of mouse brains. The VaD group showed a decreased Chao1 index and increased coverage. β-diversity (PCoA/PLS-DA) revealed significant structural differences. Conditional pathogens (e.g., <i>Pseudomonas</i>, <i>Acinetobacter</i>) were enriched, while beneficial bacteria (e.g., <i>Bifidobacterium</i>) were reduced. Correlation analysis indicated promoting effects of Pseudomonadaceae and inhibitory effects of Faecalibacterium. Metabolic pathways including amino acid, carbohydrate, and nucleotide metabolism were enriched. A random forest model achieved an AUC of 0.74 (95% CI: 0.59–0.88) in classifying VaD.</p> Conclusion <p>VaD is associated with distinct OMV microbial and functional profiles. OMV-based biomarkers show potential for VaD diagnosis.</p>

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Altered microbial cargo in fecal microbiome-derived outer membrane vesicles as novel biomarkers for vascular dementia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Xin Li,
  • Wei Wei,
  • Shouchao Wei,
  • Wenwei Xu,
  • Lang Mo,
  • Junjun Wang,
  • He Zhu,
  • Zhou Liu,
  • Fengri Jin

摘要

Background

This study aims to analyze the composition, diversity, and metabolic functions of fecal microbiome (FM)-derived outer membrane vesicles (OMVs) in patients with vascular dementia (VaD), to identify potential biomarkers for VaD diagnosis.

Methods

FM-derived OMVs were isolated from 29 VaD patients and 28 matched controls via ultracentrifugation and characterized using transmission electron microscopy and nanoparticle tracking analysis. PKH26-labeled OMVs were used for in vivo tracking in mouse brains. Microbial composition was profiled by 16 S rRNA sequencing, combined with diversity analysis and machine learning.

Results

VaD-OMVs were widely distributed in multiple cognitive function-related regions of mouse brains. The VaD group showed a decreased Chao1 index and increased coverage. β-diversity (PCoA/PLS-DA) revealed significant structural differences. Conditional pathogens (e.g., Pseudomonas, Acinetobacter) were enriched, while beneficial bacteria (e.g., Bifidobacterium) were reduced. Correlation analysis indicated promoting effects of Pseudomonadaceae and inhibitory effects of Faecalibacterium. Metabolic pathways including amino acid, carbohydrate, and nucleotide metabolism were enriched. A random forest model achieved an AUC of 0.74 (95% CI: 0.59–0.88) in classifying VaD.

Conclusion

VaD is associated with distinct OMV microbial and functional profiles. OMV-based biomarkers show potential for VaD diagnosis.