Social Risk Factors and Atrial Fibrillation

Dietary interventions in the management of atrial fibrillation

Atrial fibrillation (AF) represents the most common cardiac arrhythmia with significant morbidity and mortality implications. It is a common cause of hospital admissions, significantly impacts quality of life, increases morbidity and decreases life expectancy. Despite advancements in treatment options, prevalence of AF remains exceptionally high. AF is a challenging disease to manage, not just clinically but also financially. Evidence suggests lifestyle modification, including dietary changes, plays a significant role in the treatment of AF. This review aims to analyze the existing literature on the effects of dietary modifications on the incidence, progression, and outcomes of atrial fibrillation. It examines various dietary components, including alcohol, caffeine, omega-3 polyunsaturated fatty acids and minerals, and their impact on AF incidence, progression, and outcomes. The evidence surrounding the effects of dietary patterns, such as the Mediterranean and low carbohydrate diets, on AF is also evaluated. Overall, this review underscores the importance of dietary interventions as part of a comprehensive approach to AF management and highlights the need for further research in this emerging field.

An application of the Bayesian network model based on the EN-ESL-GA algorithm: Exploring the predictors of heart disease in middle-aged and elderly people in China

BACKGROUND

The morbidity and mortality of heart disease are increasing in middle-aged and elderly people in China. It is necessary to explore relationships and interactive associations between heart disease and its risk factors in order to prevent heart disease.

OBJECTIVE

To establish a Bayesian network model of heart disease and its influencing factors in middle-aged and elderly people in China, and explore the applicability of the elite-based structure learner using genetic algorithm based on ensemble learning (EN-ESL-GA) algorithm in etiology analysis and disease prediction.

METHODS

Based on the 2013 national tracking survey data from China Health and Retirement Longitudinal Study (CHARLS) database, EN-ESL-GA algorithm was used to learn the Bayesian network structure. Then we input the data and the learned network structure into the Netica software for parameter learning and inference analysis.

RESULTS

The Bayesian network model based on the EN-ESL-GAalgorithm can effectively excavate the complex network relationships and interactive associations between heart disease and its risk factors in middle-aged and elderly people in China.

CONCLUSIONS

The Bayesian network model based on the EN-ESL-GA algorithm has good applicability and application prospect in the prediction of diseases prevalence risk.

Environmental Exposome and Atrial Fibrillation: Emerging Evidence and Future Directions

There has been increased awareness of the linkage between environmental exposures and cardiovascular health and disease. Atrial fibrillation is the most common sustained cardiac arrhythmia, affecting millions of people worldwide and contributing to substantial morbidity and mortality. Although numerous studies have explored the role of genetic and lifestyle factors in the development and progression of atrial fibrillation, the potential impact of environmental determinants on this prevalent condition has received comparatively less attention. This review aims to provide a comprehensive overview of the current evidence on environmental determinants of atrial fibrillation, encompassing factors such as air pollution, temperature, humidity, and other meteorologic conditions, noise pollution, greenspace, and the social environment. We discuss the existing evidence from epidemiological and mechanistic studies, critically evaluating the strengths and limitations of these investigations and the potential underlying biological mechanisms through which environmental exposures may affect atrial fibrillation risk. Furthermore, we address the potential implications of these findings for public health and clinical practice and identify knowledge gaps and future research directions in this emerging field.

Air Pollution and Arrhythmias

Air pollution is commonly defined as the contamination of the air we breathe by any chemical, physical, or biological agent that is potentially threatening to human and ecosystem health. The common pollutants known to be disease-causing are particulate matter, ground-level ozone, sulphur dioxide, nitrogen dioxide, and carbon monoxide. Although the association between increasing concentrations of these pollutants and cardiovascular disease is now accepted, the association of air pollution and arrhythmias is less well established. In this review we provide an in-depth discussion of the association of acute and chronic air pollution exposure and arrhythmia incidence, morbidity, and mortality, and the purported pathophysiological mechanisms. Increases in concentrations of air pollutants have multiple proarrhythmic mechanisms including systemic inflammation (via increases in reactive oxygen species, tumour necrosis factor, and direct effects from translocated particulate matter), structural remodelling (via an increased risk of atherosclerosis and myocardial infarction or by affecting the cell-to-cell coupling and gap junction function), and mitochondrial and autonomic dysfunction. Furthermore, we describe the associations of air pollution and arrhythmias. There is a strong correlation of acute and chronic air pollutant exposure and the incidence of atrial fibrillation. Acute increases in air pollution increase the risk of emergency room visits and hospital admissions for atrial fibrillation and the risk of stroke and mortality in patients with atrial fibrillation. Similarly, there is a strong correlation of increases of air pollutants and the risk of ventricular arrhythmias, out-of-hospital cardiac arrest, and sudden cardiac death.