Microbiome and Heart Failure: A Comprehensive Review of Gut Health and Microbiota-Derived Metabolites in Heart Failure Progression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it