Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression
Abstract
1. Introduction
- Gut dysbiosis contributes to HF via systemic inflammation and endotoxemia.
- Microbial metabolites, like SCFAs and TMAO, affect cardiac remodeling and function.
- Comorbidities, like obesity and diabetes, exacerbate gut dysbiosis in HF.
- Dietary and probiotic interventions hold potential for microbiome-targeted HF therapies.
2. Methodology
3. Science of the Gut Microbiome: Present Knowledge
3.1. Gut Microbiome: A Tool for Optimizing Heart Failure Therapy
3.2. The Gut Microbiota’s Role in Pathology and Physiology
4. Gut Metabolites Associated with HF
4.1. Short-Chain Fatty Acids: Protective Roles and Mechanisms in HF
4.2. Trimethylamine N-Oxide (TMAO): Pro-Inflammatory and Pro-Atherogenic Effects
4.3. Bile Acid
4.4. Phenylacetylglutamine
5. Clinical Evidence Linking the Microbiome to HF
6. Therapeutic Implications and Future Directions
6.1. Therapeutic Target
6.2. Dietary Interventions
6.3. Renal Denervation
6.4. Fecal Microbiota Transplantation (FMT)
6.5. Areas for Future Research
Longitudinal Studies and Clinical Trials
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| HF | Heart Failure |
| SCFAs | Short-Chain Fatty Acids |
| TMAO | Trimethylamine-N-oxide |
| LPS | Lipopolysaccharides |
| BAs | Bile Acids |
| HIF | Hypoxia-Inducible Factor |
| FMT | Fecal Microbiota Transplantation |
| GPR | G-Protein-Coupled Receptors |
| GDP | Gross Domestic Product |
| NF-κB | Nuclear Factor Kappa B |
| NO | Nitric Oxide |
| NHANES | National Health and Nutrition Examination Survey |
| CKD | Chronic Kidney Disease |
| CVDs | cardiovascular diseases |
| ICIs | Immune Checkpoint Inhibitors |
| RCT | Randomized Controlled Trial |
| HRQL | Health-related quality of life |
| FXR | Farnesoid X Receptor |
| TGR5 | Takeda G-Protein-Coupled Receptor 5 |
| GPCRs | G-Protein-Coupled Receptors |
| ADRs | Adrenergic Receptors |
| PERK | Protein Kinase RNA-Like Endoplasmic Reticulum Kinase |
| QALYs | Quality-Adjusted Life Years |
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| Method | Principle | Strengths | Limitations |
|---|---|---|---|
| Culturomics | Traditional approach where live microorganisms are cultivated on selective media under controlled conditions. | - Cost-effective - Widely accessible - Allows detailed phenotypic analysis (e.g., pathogenicity, antibiotic resistance, metabolic functions) - Suitable for aerotolerant organisms and rare bacteria poorly represented in databases. | - Labor-intensive - Limited to cultivable microbes - Results influenced by media and conditions - Challenges in studying strict anaerobes or capturing microbe interactions in mixed communities. |
| Polymerase Chain Reaction (PCR) | Sequencing-based method targeting specific microbial genes for detection and quantification (e.g., qPCR, RT-PCR). | - Rapid and widely available - Cost-effective - Provides absolute abundance of target taxa - High dynamic range. | - Targets limited number of genes/microbes - Unable to detect unknown taxa - Susceptible to PCR biases - Results depend on primer and probe specificity. |
| 16S rRNA Gene Sequencing | Amplification and sequencing of hypervariable regions of the 16S rRNA gene, followed by matching reads to reference databases. | - Commonly used for taxonomic profiling - Identifies cultured and uncultured microbes at the genus level - Relatively rapid and cost-efficient - Well-developed bioinformatic pipelines. | - Provides relative abundance only - Low resolution at species/strain level - Limited to bacteria and archaea; fungi require specialized primers - Cannot distinguish live versus dead microbes - Lacks functional insights. |
| Shotgun Metagenomics | High-throughput sequencing of all DNA fragments in a sample, followed by computational assembly and taxonomic/functional annotation. | - Identifies bacteria, archaea, viruses, and fungi - Provides species and strain-level resolution - Offers functional potential characterization - Requires minimal DNA input (<1 ng). | - Computationally intensive - Costly (though decreasing) - Requires advanced bioinformatics - Sequencing host DNA may confound results - Reproducibility remains uncertain. |
| Meta-transcriptomics | Sequencing of RNA transcripts to analyze microbiome gene expression. | - Enables real-time profiling of microbial activity - Quantitative and high resolution - Integrates functional insights with metagenomics for comprehensive analysis. | - Technically complex and costly - RNA instability poses challenges - Requires paired metagenomic data for optimal interpretation - Limited correlation with meta-proteomic outputs. |
| Meta-proteomics | Mass spectrometry (MS)-based quantification of microbiome-derived proteins and peptides. | - Provides functional insights into microbial protein expression. - Can reveal host-microbe protein interactions. | - Technically challenging - Requires high-quality reference libraries - Limited sensitivity for low-abundance proteins. |
| Metabolomics | Identification and quantification of microbial metabolites using chromatography and MS. | - Rapid and versatile - Suitable for various sample types (e.g., feces, urine, plasma) - Facilitates discovery of both known and novel metabolites - Low sample requirement. | - Targeted metabolomics identifies known compounds but misses unknowns - Untargeted methods are less quantitative and challenging for metabolite annotation - Limited ability to link metabolites to specific microbial taxa. |
| Multiomics | Integration of multiple data types (e.g., genomics, transcriptomics, proteomics, metabolomics) from the same or concurrent samples. | - Offers a comprehensive systems-level view of microbiome-host interactions - Generates novel hypotheses. | - Costly and computationally demanding - Analytic methods and pipelines are still being standardized. |
| Reference | Patient Type | Age (Years) | Sample Size | Method | Key Findings |
|---|---|---|---|---|---|
| [89] | Acute HF or exacerbation of chronic HF | 47.4 ± 2.8 (younger HF) 73.8 ± 2.8 (older HF) | HF < 60: n = 12 HF > 60: n = 10 Controls: n = 12 | 16S rRNA | ↓ Eubacterium rectale, Dorea longicatena Depletion of Faecalibacterium in older patients |
| [90] | Chronic HF | 67 ± 2 | Chronic HF: n = 22 Controls: n = 22 | Fluorescence in situ hybridization | ↑ Eubacterium rectale, Faecalibacterium |
| [91] | Chronic HF | 65 ± 1.2 | HF: n= 60 Controls: n = 20 | Traditional culture techniques | ↑ Campylobacter, Shigella, Salmonella, Yersinia enterolytica, Candida |
| [92] | Chronic HF | 60.69 | HF: n = 29 Controls: n = 30 | 16S rRNA | ↓ Ruminococcaceae, Lachnospiraceae, Dialister ↑ Enterococcus, Enterococcaceae |
| [92] | Chronic HF (NYHA III-IV) | 65–86 | NYHA III HF: n = 29 NYHA IV HF: n = 29 Controls: n = 22 | 16S rRNA | NYHA III: ↑ Escherichia, Bifidobacterium NYHA IV: ↑ Klebsiella, Lactobacillus |
| [93] | Chronic HF (70% exacerbation, 30% stable) | 65 ± 3.2 | HF: n = 20 Controls: n = 20 | 16S rRNA | ↓ Coriobacteriaceae, Erysipelotrichaceae, Ruminococcaceae (family level) ↓ Blautia (genus level) |
| [85] | Chronic HF | NA | Discovery: n = 40 Validation: n = 44 Controls: n = 266 | 16S rRNA | ↓ Lachnospiraceae family |
| [94] | Stable chronic HF (ischemic/dilated cardiomyopathy) | 58.1 ± 13.3 | HF: n = 53 Controls: n = 41 | 16S rRNA | ↑ Ruminococcus gnavus ↓ Faecalibacterium prausnitzii |
| [95] | HF with preserved ejection fraction (HFpEF) | 40–70 | HFpEF: n = 26 Controls: n = 67 | 16S rRNA | ↓ Ruminococcus spp. |
| [96] | Chronic HF | 65 ± 3.17 | HF: n = 26 Controls: n = 26 | 16S rRNA | ↑ Escherichia, Shigella, Ruminococcaceae, Lactobacillus, Atopobium, Romboutsia, Streptococcus, Haemophilus, Klebsiella |
| [97] | Non-ischemic HF with reduced ejection fraction (HFrEF) | 18–70 | HFrEF: n = 28 Controls: n = 19 | 16S rRNA | ↑ Streptococcus spp., Veillonella spp. ↓ SMB53 |
| [98] | Acute decompensated HF/acute worsening of chronic HF | 72 ± 18 | HF: n = 22 Controls: n = 11 | 16S rRNA | ↑ Actinomycetota (phylum), Bifidobacterium (genus) ↓ Megamonas (genus) |
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Orjichukwu, C.K.; Orjichukwu, R.O.; Akpunonu, P.K.; Ugwu, P.C.; Nnabuife, S.G. Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression. Med. Sci. 2025, 13, 302. https://doi.org/10.3390/medsci13040302
Orjichukwu CK, Orjichukwu RO, Akpunonu PK, Ugwu PC, Nnabuife SG. Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression. Medical Sciences. 2025; 13(4):302. https://doi.org/10.3390/medsci13040302
Chicago/Turabian StyleOrjichukwu, Chukwudi Kingsley, Rita Ogochukwu Orjichukwu, Peter Kanayochukwu Akpunonu, Paul Chikwado Ugwu, and Somtochukwu Godfrey Nnabuife. 2025. "Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression" Medical Sciences 13, no. 4: 302. https://doi.org/10.3390/medsci13040302
APA StyleOrjichukwu, C. K., Orjichukwu, R. O., Akpunonu, P. K., Ugwu, P. C., & Nnabuife, S. G. (2025). Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression. Medical Sciences, 13(4), 302. https://doi.org/10.3390/medsci13040302

