Gut Microbiota Alterations in Heart Failure Patients: Insights from a Systematic Review
Bibliographic record
Abstract
Background/Objectives: Heart failure (HF) is associated with chronic systemic inflammation, resulting in increased mortality. The intestinal microbiota can modulate systemic inflammation, and changes in the microbiota have been observed in patients with HF. Methods: A systematic search was performed in PubMed/MEDLINE up until July 2025 for studies comparing the intestinal microbiota between patients with HF and healthy controls (HCs). The PRISMA (Preferred Reporting Items for Systemic reviews and Meta-Analyses) criteria were used. The risk of bias was evaluated with the Newcastle–Ottawa scale for cross-sectional studies. Results: Fourteen studies with 1167 participants (550 patients with HF and 617 HC) were included. The patients with HF had less alpha and beta diversity compared with HC. In turn, the patients with HF presented an increase in proinflammatory bacteria belonging to the genera Streptococcus and Escherichia-Shigella, and a decrease in bacteria with anti-inflammatory effects, pertaining to the genera Faecalibacterium, Blautia and Lachnospira. Conclusions: Patients with HF present an altered intestinal microbiota, favoring the growth of bacteria that increase systemic inflammation through their metabolic activity. Modulation of the intestinal microbiota through different approaches is seen as a new therapeutic target in HF.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".