Gut microbiota, dysbiosis and atrial fibrillation. Arrhythmogenic mechanisms and potential clinical implications
Bibliographic record
Abstract
Recent preclinical and observational cohort studies have implicated imbalances in gut microbiota composition as a contributor to atrial fibrillation (AF). The gut microbiota is a complex and dynamic ecosystem containing trillions of microorganisms, which produces bioactive metabolites influencing host health and disease development. In addition to host-specific determinants, lifestyle-related factors such as diet and drugs are important determinants of the gut microbiota composition. In this review, we discuss the evidence suggesting a potential bidirectional association between AF and gut microbiota, identifying gut microbiota-derived metabolites as possible regulators of the AF substrate. We summarize the effect of gut microbiota on the development and progression of AF risk factors, including heart failure, hypertension, obesity, and coronary artery disease. We also discuss the potential anti-arrhythmic effects of pharmacological and diet-induced modifications of gut microbiota composition, which may modulate and prevent the progression to AF. Finally, we highlight important gaps in knowledge and areas requiring future investigation. Although data supporting a direct relationship between gut microbiota and AF are very limited at the present time, emerging preclinical and clinical research dealing with mechanistic interactions between gut microbiota and AF is important as it may lead to new insights into AF pathophysiology and the discovery of novel therapeutic targets for AF.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".