The Role of Cardiovascular and Metabolic Comorbidities in the Link between Atrial Fibrillation and Cognitive Impairment: An Appraisal of Current Scientific Evidence
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice with implications on long-term outcomes. Metabolic disorders including diabetes mellitus and obesity are independent predictors of atrial fibrillation and present therapeutic targets to reduce both the incidence and duration burden of atrial fibrillation. The presence of pericardial fat in direct contact with cardiac structures, as well the subsequent release of proinflammatory cytokines, may play an important role in this connection. Atrial fibrillation is an independent predictor of cognitive impairment and dementia. While clinical stroke is a major contributor, other factors such as cerebral hypoperfusion and microbleeds play important roles. New evidence suggests that atrial fibrillation and cognitive impairment may be downstream events of atrial cardiomyopathy, which may be caused by several factors including metabolic syndrome, obesity, and obstructive sleep apnea. The mechanisms linking these comorbidities to cognitive impairment are not yet fully elucidated. A clearer understanding of the association of AF with dementia and cognitive impairment is imperative. Future studies should focus on the predictors of cognitive impairment among those with AF and aim to understand the potential mechanisms underlying these associations. This would inform strategies for the management of AF aiming to prevent continued cognitive impairment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".