Heart transplantation remains the gold standard therapy for patients with end-stage heart failure. However, post-transplant complications are considerable. Emerging evidence implicates the gut microbiome as a modifiable determinant of post–heart transplant outcomes through its influence on host immunity, metabolism, and inflammation. This Review synthesizes current understanding of gut microbiome dysregulation following solid organ transplantation, with particular emphasis on heart transplantation, examining mechanistic links underpinning important complications including allograft rejection, infection, metabolic dysfunction, and cardiac allograft vasculopathy. We critically evaluate bidirectional interactions between the gut microbiome and immunosuppressive drugs, assess the potential for microbiome profiling to serve as a predictive biomarker for post-transplant complications, and examine microbiome-targeted interventions including dietary modification, prebiotics, probiotics, and fecal microbiota transplant. Finally, we propose a translational roadmap to integrate microbiome science into heart transplant care to optimize immunosuppression, predict complications, and improve long-term outcomes for heart transplant recipients.
Ivan Ðuran, W.H. Wilson Tang, Petra Mamic
Usage data is cumulative from August 2026 through August 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 435 | 0 |
| 63 | 0 | |
| Figure | 78 | 0 |
| Table | 14 | 0 |
| Supplemental data | 14 | 0 |
| Citation downloads | 27 | 0 |
| Totals | 631 | 0 |
| Total Views | 631 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.