The Gut Microbiota Profile in Heart Failure Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Traditional cardiovascular risk factors are established predictors of heart failure (HF). However, the human gut microbiota is suggested to potentially interact with the cardiovascular system through the "gut-heart axis", which induces inflammation and contributes to HF pathogenesis. This systematic review aims to confirm the interconnection between the gut microbiome in HF patients. METHODS: Peer-reviewed human studies comparing the gut microbiota profile in adult patients with HF and healthy controls (HCs) up to April 18, 2022, were searched in Ovid MEDLINE, Ovid EMBASE, SCOPUS, and the Cochrane Library. The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: A total of nine studies, including 317 HF patients and 510 HCs, were included in the review. Decreased gut microbiota richness and similar microbial diversity (alpha diversity), and significantly different gut microbiota composition (beta diversity) were observed between HF patients and HCs. In comparison to HCs, HF patients had a greater abundance of Actinobacteria, Proteobacteria, and Synergistetes phyla; Enterococcus, Escherichia, Klebsiella, Lactobacillus, Streptococcus, and Veilonella genera and Ruminococcus gnavus, Streptococcus sp., and Veilonella sp. species. In contrast, there was decreased abundance of Firmicutes phylum; Blautia, Eubacterium, Faecalibacterium, and Lachnospiraceae FCS020 genera; and Dorea longicatena, Eubacterium rectale, Faecalibacterium prausnitzii, Oscillibacter sp., and Sutterella wadsworthensis species in HF patients. CONCLUSIONS: Gut microbiota diversity, richness, and composition in HF patients differ significantly from the healthy population. Overall, short-chain fatty acid (SCFA)-producing gut microbiota was depleted in HF patients. However, different underlying comorbidities, environments, lifestyles, and dietary choices could affect gut microbiota heterogeneity.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".