Deciphering the fundamental mechanisms of atrial fibrillation: a quest for over a century

Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease

Objective

To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model.

Methods

The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language "rms" package, and the model's differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve.

Results

A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963.

Conclusion

The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD.

Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.

Pulmonary Disease, Pulmonary Hypertension and Atrial Fibrillation

Atrial fibrillation is associated with aging, obesity, heart disease, diabetes, and/or hypertension. Recent evidence suggests that parenchymal and vascular lung diseases increase atrial fibrillation risk. We review the epidemiology, clinical features, pathophysiologic mechanisms, and treatment implications of atrial fibrillation associated with diseases of the lungs and their vasculature, especially pulmonary hypertension. We also consider other features of pulmonary disease-associated atrial fibrillation. A key mediator of these conditions is right heart disease and right atrial remodeling. We pay particular attention to the pathophysiology and treatment challenges in atrial fibrillation associated with right heart disease induced by pulmonary diseases, including pulmonary hypertension.

Ion Channel and Structural Remodeling in Obesity-Mediated Atrial Fibrillation

BACKGROUND

Epidemiological studies have established obesity as an independent risk factor for atrial fibrillation (AF), but the underlying pathophysiological mechanisms remain unclear. Reduced cardiac sodium channel expression is a known causal mechanism in AF. We hypothesized that obesity decreases Nav1.5 expression via enhanced oxidative stress, thus reducing INa, and enhancing susceptibility to AF.

METHODS

To elucidate the underlying electrophysiological mechanisms a diet-induced obese mouse model was used. Weight, blood pressure, glucose, F2-isoprostanes, NOX2 (NADPH oxidase 2), and PKC (protein kinase C) were measured in obese mice and compared with lean controls. Invasive electrophysiological, immunohistochemistry, Western blotting, and patch clamping of membrane potentials was performed to evaluate the molecular and electrophysiological phenotype of atrial myocytes.

RESULTS

Pacing-induced AF in 100% of diet-induced obese mice versus 25% in controls (P<0.01) with increased AF burden. Cardiac sodium channel expression, INa and atrial action potential duration were reduced and potassium channel expression (Kv1.5) and current (IKur) and F2-isoprostanes, NOX2, and PKC-α/δ expression and atrial fibrosis were significantly increased in diet-induced obese mice as compared with controls. A mitochondrial antioxidant reduced AF burden, restored INa, ICa,L, IKur, action potential duration, and reversed atrial fibrosis in diet-induced obese mice as compared with controls.

CONCLUSIONS

Inducible AF in obese mice is mediated, in part, by a combined effect of sodium, potassium, and calcium channel remodeling and atrial fibrosis. Mitochondrial antioxidant therapy abrogated the ion channel and structural remodeling and reversed the obesity-induced AF burden. Our findings have important implications for the management of obesity-mediated AF in patients. Graphic Abstract: A graphic abstract is available for this article.

Mechanism for Triggered Waves in Atrial Myocytes

Excitation-contraction coupling in atrial cells is mediated by calcium (Ca) signaling between L-type Ca channels and Ryanodine receptors that occurs mainly at the cell boundary. This unique architecture dictates essential aspects of Ca signaling under both normal and diseased conditions. In this study we apply laser scanning confocal microscopy, along with an experimentally based computational model, to understand the Ca cycling dynamics of an atrial cell subjected to rapid pacing. Our main finding is that when an atrial cell is paced under Ca overload conditions, Ca waves can then nucleate on the cell boundary and propagate to the cell interior. These propagating Ca waves are referred to as "triggered waves" because they are initiated by L-type Ca channel openings during the action potential. These excitations are distinct from spontaneous Ca waves originating from random fluctuations of Ryanodine receptor channels, and which occur after much longer waiting times. Furthermore, we argue that the onset of these triggered waves is a highly nonlinear function of the sarcoplasmic reticulum Ca load. This strong nonlinearity leads to aperiodic response of Ca at rapid pacing rates that is caused by the complex interplay between paced Ca release and triggered waves. We argue further that this feature of atrial cells leads to dynamic instabilities that may underlie atrial arrhythmias. These studies will serve as a starting point to explore the nonlinear dynamics of atrial cells and will yield insights into the trigger and maintenance of atrial fibrillation.