Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression
Bibliographic record
Abstract
A multifaceted clinical disease, heart failure (HF) is typified by decreased cardiac function and systemic symptoms caused by anatomical or functional abnormalities in the heart. Although traditional studies have concentrated on hemodynamic and neurohormonal processes, new data highlight the vital role that the gut microbiota and its byproducts play in the pathogenesis of HF. An imbalance in the microbial structure known as gut dysbiosis is common in HF patients and is linked to increased gut permeability, systemic inflammation, and changed bioactive metabolite synthesis. Prominent metabolites generated by the microbiota, including phenylacetylglutamine, short-chain fatty acids (SCFAs), secondary bile acids, and trimethylamine N-oxide (TMAO), have a major impact on endothelial function, cardiac remodeling, and inflammation. Together with gut-derived lipopolysaccharides, these metabolites interact with host systems to exacerbate the course of HF. Further impacting HF outcomes are comorbidities such as diabetes, obesity, and chronic renal disease, which intensify gut dysbiosis. The importance of metabolites originating from the microbiota in the progression of HF is highlighted in this review, which summarizes recent findings regarding the gut-heart axis. Additionally, it investigates how dietary changes, probiotics, prebiotics, and multi-omics techniques can all be used to improve the management of HF. This thorough analysis emphasizes the necessity of integrative therapy approaches and longitudinal research to better address the complex link between HF and the gut microbiota.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".