New aspects of endocrine control of atrial fibrillation and possibilities for clinical translation
Bibliographic record
Abstract
Hormones are potent endo-, para-, and autocrine endogenous regulators of the function of multiple organs, including the heart. Endocrine dysfunction promotes a number of cardiovascular diseases, including atrial fibrillation (AF). While the heart is a target for endocrine regulation, it is also an active endocrine organ itself, secreting a number of important bioactive hormones that convey significant endocrine effects, but also through para-/autocrine actions, actively participate in cardiac self-regulation. The hormones regulating heart-function work in concert to support myocardial performance. AF is a serious clinical problem associated with increased morbidity and mortality, mainly due to stroke and heart failure. Current therapies for AF remain inadequate. AF is characterized by altered atrial function and structure, including electrical and profibrotic remodelling in the atria and ventricles, which facilitates AF progression and hampers its treatment. Although features of this remodelling are well-established and its mechanisms are partly understood, important pathways pertinent to AF arrhythmogenesis are still unidentified. The discovery of these missing pathways has the potential to lead to therapeutic breakthroughs. Endocrine dysfunction is well-recognized to lead to AF. In this review, we discuss endocrine and cardiocrine signalling systems that directly, or as a consequence of an underlying cardiac pathology, contribute to AF pathogenesis. More specifically, we consider the roles of products from the hypothalamic-pituitary axis, the adrenal glands, adipose tissue, the renin-angiotensin system, atrial cardiomyocytes, and the thyroid gland in controlling atrial electrical and structural properties. The influence of endocrine/paracrine dysfunction on AF risk and mechanisms is evaluated and discussed. We focus on the most recent findings and reflect on the potential of translating them into clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".