Differences in Epidemiology and Risk Factors for Atrial Fibrillation Between Women and Men

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is one of the most frequent cardiovascular diseases among both women and men. Although age-adjusted AF incidence and prevalence is larger among men, women are older at the time of AF diagnosis and have larger risk for AF-associated adverse outcomes such as morality and stroke. Based on evidence from epidemiological studies, elevated body mass index seems to confer a higher risk of AF among men. However, evidence regarding sex differences in the association between diabetes mellitus, elevated blood pressure, and dysglycemia with AF remains conflicting. While men with AF have larger burden of coronary artery disease, women with AF tend to have a larger prevalence of heart failure and valvular heart disease. Recently, several women-specific risk factors including pregnancy and its complications and number of children have been associated with AF. Earlier age at menopause, despite being a strong marker of adverse cardiometabolic risk, does not seem to be associated with increased risk of AF. To reduce the AF burden in both genders, better understanding of the differences between women and men with regard to AF is central. Large-scale studies are needed to separately investigate and report on women and men. Besides observations from epidemiological and clinical studies, to improve our understanding of sexual dimorphism in AF, sufficiently large genome-wide association studies as well as well-powered Mendelian randomization studies are essential to shed light on the sex-specific nature of the associations of risk factors with AF

Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, has a large impact on quality of life and is associated with increased risk of hospitalization, morbidity, and mortality. Over the past two decades advances regarding the clinical epidemiology and management of AF have been established. Moreover, sex differences in the prevalence, incidence, prediction, pathophysiology, and prognosis of AF have been identified. Nevertheless, AF remains to be a complex and heterogeneous disorder and a comprehensive sex- and gender-specific approach to predict new-onset AF is lacking. The exponential growth in various sources of big data such as electrocardiograms, electronic health records, and wearable devices, carries the potential to improve AF risk prediction. Leveraging these big data sources by artificial intelligence (AI)-enabled approaches, in particular in a sex- and gender-specific manner, could lead to substantial advancements in AF prediction and ultimately prevention. We highlight the current status, premise, and potential of big data to improve sex- and gender-specific prediction of new-onset AF

Subclinical Measures of Atherosclerosis: Genetics and Cardiovascular Risk Prediction

__Abstract__ Atherosclerosis is a chronic, progressive, systematic condition with a long asymptomatic phase. Atherosclerosis develops gradually as a subclinical condition over the life course and eventually becomes clinically apparent as ischemic heart disease, cerebrovascular disease, or peripheral arterial disease. Subclinical atherosclerosis, or preclinical atherosclerosis, refers to the early stage of the atherosclerosis process when within the vascular walls “something has started to change”, yet the cardiovascular disease is not clinically evident. Detecting the forthcoming disease at this stage, before the clinical manifestations, has gained interest over the past decade. Coronary artery calcifi cation, carotid intima-media thickness, and ankle- brachial index are three measures of subclinical atherosclerosis burden that can be detected and quantifi ed non-invasively

Aortic Valve Calcium in Relation to Subclinical Cardiac Dysfunction and Risk of Heart Failure

Background: The link between (mild) aortic valve calcium (AVC) with subclinical cardiac dysfunction and with risk of heart failure (HF) remains unclear. This research aims to determine the association of computed tomography-assessed AVC with echocardiographic measurements of cardiac dysfunction, and with HF in the general population. Methods: We included 2348 participants of the Rotterdam Study cohort (mean age 68.5 years, 52% women), who had AVC measurement between 2003 and 2006, and without history of HF at baseline. Linear regression models were used to explore relationship between AVC and echocardiographic measures at baseline. Participants were followed until December 2016. Fine and Gray subdistribution hazard models were used to assess the association of AVC with incident HF, accounting for death as a competing risk. Results: The presence of AVC or greater AVC were associated with larger mean left ventricular mass and larger mean left atrial size. In particular, AVC ≥800 showed a strong association (body surface area indexed left ventricular mass, β coefficient: 22.01; left atrium diameter, β coefficient: 0.17). During a median of 9.8 years follow-up, 182 incident HF cases were identified. After accounting for death events and adjusting for cardiovascular risk factors, one-unit larger log (AVC+1) was associated with a 10% increase in the subdistribution hazard of HF (subdistribution hazard ratio, 1.10 [95% CI, 1.03-1.18]), but the presence of AVC was not significantly associated with HF risk in fully adjusted models. Compared with the AVC=0, AVC between 300 and 799 (subdistribution hazard ratio, 2.36 [95% CI, 1.32-4.19]) and AVC ≥800 (subdistribution hazard ratio, 2.54 [95% CI, 1.31-4.90]) were associated with a high risk of HF. Conclusions: Presence and high levels of AVC were associated with markers of left ventricular structure, independent of traditional cardiovascular risk factors. Larger computed tomography-assessed AVC is an indicative of increased risk for the development of HF.</p

Genetic Research and Women’s Heart Disease: a Primer

PURPOSE OF REVIEW: This review provides a brief synopsis of sexual dimorphism in atherosclerosis with an emphasis on genetic studies aimed to better understand the atherosclerotic process and clinical outcomes in women. Such studies are warranted because development of atherosclerosis, impact of several traditional risk factors, and burden of coronary heart disease (CHD) differ between women and men. RECENT FINDINGS: While most candidate gene studies pool women and men and adjust for sex, some sex-specific studies provide evidence of association between candidate genes and prevalent and incident CHD in women. So far, most genome-wide association studies (GWAS) also failed to consider sex-specific associations. The few GWAS focused on women tended to have small sample sizes and insufficient power to reject the null hypothesis of no association even if associations exist. SUMMARY: Few studies consider that sex can modify the effect of gene variants on CHD. Sufficiently large-scale genetic studies in women of different race/ethnic groups, taking into account possible gene-gene and gene-environment interactions as well as hormone-mediated epigenetic mechanisms, are needed. Using the same disease definition for women and men might not be appropriate. Accurate phenotyping and inclusion of relevant outcomes in women, together with targeting the entire spectrum of atherosclerosis, could help address the contribution of genes to sexual dimorphism in atherosclerosis. Discovered genetic loci should be taken forward for replication and functional studies to elucidate the plausible underlying biological mechanisms. A better understanding of the etiology of atherosclerosis in women would facilitate future prevention efforts and interventions

A study on effects of organizational learning on organizational innovation: A case study of insurance industry

This paper presents an empirical investigation to study the relationship between organization learning and organization innovation in one of Iranian insurance firm. The proposed study selects a sample of 300 employees who work for different positions for the case study of this paper and using Pearson correlation as well as Freedman tests determines the relationship and ranks different components of the survey. The results of this implementation have indicated that organization innovation influences on organizational learning, data distribution, interpretation and memory significantly but the effect of organizational innovation on data collection cannot be confirmed when the level of significance is five or even ten percent. The implementation of Freedman test has also indicated that Information interpretation is number priority followed by information learning, organizational distribution and organizational memory