# The gut microbiome in colorectal anastomotic leakage: from mechanisms to precision

## TL;DR

This review explores how the gut microbiome influences anastomotic leakage after colorectal cancer surgery, highlighting its role in healing and potential interventions.

## Contribution

The paper introduces a biological framework for anastomotic leakage, emphasizing microbiome-host interactions and multi-omics prediction models.

## Key findings

- The gut microbiome regulates inflammation and epithelial repair, impacting anastomotic healing.
- Microbial dysbiosis involving specific bacteria correlates with extracellular matrix degradation and leakage.
- Multi-omics and machine learning models may improve risk prediction over traditional clinical scores.

## Abstract

Anastomotic leakage after curative colorectal cancer resection remains a frequent and severe complication that increases short-term mortality, worsens long-term oncologic outcomes, and places substantial burdens on individuals and health systems despite advances in surgical technique and perioperative care. Emerging evidence redefines anastomotic failure as not only a technical event but also a biologically driven process in which the gut microbiome regulates inflammation, epithelial repair, and barrier integrity at the healing interface. This review summarizes current data on the dual role of the intestinal microbiome in promoting physiological anastomotic healing and driving pathological leakage when perioperative stressors cause dysbiosis. Mechanistic sections describe how a diverse, metabolically active community supports collagen stability through short-chain fatty acid production, immune regulation, and maintenance of mucus and tight junction architecture. In contrast, the enrichment of microbial groups such as Enterococcus faecalis, Fusobacterium nucleatum, and Alistipes onderdonkii together with fungal and viral shifts, has been associated with extracellular matrix degradation and excessive inflammation. Furthermore, the review examines microbiome-related biomarkers for risk assessment, including DNA-based microbial signatures, metabolite profiles, and host immune markers. It also discusses how integrated multi-omics models combined with machine learning may outperform traditional clinical scores for preoperative and early postoperative prediction. Finally, the article critically evaluates perioperative microbiome-directed strategies ranging from dietary prehabilitation and microbial supplementation to selective decontamination and fecal microbiota transplantation, highlighting promising signals, variability of effect, safety considerations, and key methodological limitations that currently prevent routine implementation. In summary, this review addresses three interconnected domains—mechanisms of microbiome-driven anastomotic failure, microbiome-derived biomarkers for risk stratification, and perioperative intervention strategies—underscoring that AL is best understood as a host–microbiome interaction rather than a purely technical failure. This framing offers surgeons and perioperative teams a biologically rational basis for prevention, yet clinical translation will require causal validation, standardized intervention algorithms, and interpretable computational tools embedded into real-world perioperative practice.