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Background <p>Intestinal congestion is a common pathophysiological feature of both liver cirrhosis and heart failure (HF). This study aimed to investigate whether intestinal congestion induces similar gut microbiota and metabolite alterations under both conditions, and to identify key microbial and metabolic signatures.</p> Methods <p>We analyzed 117 cirrhosis patients (uncomplicated cirrhosis, cirrhosis with hepatocellular carcinoma, transjugular intrahepatic portosystemic shunt, and liver transplantation), 75 HF patients, and 31 healthy controls (CG). We performed 16S rRNA sequencing on all samples to assess gut microbial diversity, and subjected six representative samples per group to metagenomic sequencing. We conducted untargeted metabolomics on 30 fecal samples each from the uncomplicated cirrhosis, HF with reduced ejection fraction (HFrEF), and CG groups to profile intestinal metabolites, followed by correlation analyses among representative taxa, clinical characteristics, and key metabolites.</p> Results <p>Intestinal congestion of different etiologies exhibits similar alterations in the gut microbiota, particularly in patients with uncomplicated cirrhosis and HFrEF. Alterations in <i>Bacteroides</i> were closely associated with the severity of congestion. <i>Veillonella</i> and <i>Lactobacillales</i> were enriched in cirrhotic patients, whereas <i>Coprococcus</i> was uniquely abundant in HFs. Metabolomic analysis revealed significant reductions in tripeptides, anti-inflammatory compounds, and prostaglandin analogs in patients with intestinal congestion. Musacin D and neopterin may serve as potential noninvasive biomarkers for HF and cirrhosis, respectively.</p> Conclusion <p>Intestinal congestion is associated with gut microbiota dysbiosis and metabolic disturbances in cirrhosis and HFs, with specific microbes and metabolites showing potential predictive value for distinguishing underlying diseases.</p>

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Intestinal congestion-driven gut dysbiosis: a cross-disease hemodynamic mechanism in liver cirrhosis and heart failure

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Yan Wang,
  • Zhongyuan Bai,
  • Jing Sun,
  • Qi Gong,
  • Wentao Miao,
  • Zhiqiang Niu,
  • Xiang Li,
  • Jun Xu,
  • Zhiyong Lai

摘要

Background

Intestinal congestion is a common pathophysiological feature of both liver cirrhosis and heart failure (HF). This study aimed to investigate whether intestinal congestion induces similar gut microbiota and metabolite alterations under both conditions, and to identify key microbial and metabolic signatures.

Methods

We analyzed 117 cirrhosis patients (uncomplicated cirrhosis, cirrhosis with hepatocellular carcinoma, transjugular intrahepatic portosystemic shunt, and liver transplantation), 75 HF patients, and 31 healthy controls (CG). We performed 16S rRNA sequencing on all samples to assess gut microbial diversity, and subjected six representative samples per group to metagenomic sequencing. We conducted untargeted metabolomics on 30 fecal samples each from the uncomplicated cirrhosis, HF with reduced ejection fraction (HFrEF), and CG groups to profile intestinal metabolites, followed by correlation analyses among representative taxa, clinical characteristics, and key metabolites.

Results

Intestinal congestion of different etiologies exhibits similar alterations in the gut microbiota, particularly in patients with uncomplicated cirrhosis and HFrEF. Alterations in Bacteroides were closely associated with the severity of congestion. Veillonella and Lactobacillales were enriched in cirrhotic patients, whereas Coprococcus was uniquely abundant in HFs. Metabolomic analysis revealed significant reductions in tripeptides, anti-inflammatory compounds, and prostaglandin analogs in patients with intestinal congestion. Musacin D and neopterin may serve as potential noninvasive biomarkers for HF and cirrhosis, respectively.

Conclusion

Intestinal congestion is associated with gut microbiota dysbiosis and metabolic disturbances in cirrhosis and HFs, with specific microbes and metabolites showing potential predictive value for distinguishing underlying diseases.