Adiposity-associated atrial fibrillation: molecular determinants, mechanisms, and clinical significance
Bibliographic record
Abstract
Obesity is an important contributing factor to the pathophysiology of atrial fibrillation (AF) and its complications by causing systemic changes, such as altered haemodynamic, increased sympathetic tone, and low-grade chronic inflammatory state. In addition, adipose tissue is a metabolically active organ that comprises various types of fat deposits with discrete composition and localization that show distinct functions. Fatty tissue differentially affects the evolution of AF, with highly secretory active visceral fat surrounding the heart generally having a more potent influence than the rather inert subcutaneous fat. A variety of proinflammatory, profibrotic, and vasoconstrictive mediators are secreted by adipose tissue, particularly originating from cardiac fat, that promote atrial remodelling and increase the susceptibility to AF. In this review, we address the role of obesity-related factors and in particular specific adipose tissue depots in driving AF risk. We discuss the distinct effects of key secreted adipokines from different adipose tissue depots and their participation in cardiac remodelling. The possible mechanistic basis and molecular determinants of adiposity-related AF are discussed, and finally, we highlight important gaps in current knowledge, areas requiring future investigation, and implications for clinical management.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".