AimsThe role of high-intensity exercise and other emerging risk factors in lone atrial fibrillation (Ln-AF) epidemiology is still under debate. The aim of this study was to analyse the contribution of each of the emerging risk factors and the impact of physical activity dose in patients with Ln-AF.Methods and resultsPatients with Ln-AF and age- and sex-matched healthy controls were included in a 2:1 prospective case–control study. We obtained clinical and anthropometric data transthoracic echocardiography, lifetime physical activity questionnaire, 24-h ambulatory blood pressure monitoring, Berlin questionnaire score, and, in patients at high risk for obstructive sleep apnoea (OSA) syndrome, a polysomnography. A total of 115 cases and 57 controls were enrolled. Conditional logistic regression analysis associated height [odds ratio (OR) 1.06 [1.01–1.11]], waist circumference (OR 1.06 [1.02–1.11]), OSA (OR 5.04 [1.44–17.45]), and 2000 or more hours of cumulative high-intensity endurance training to a higher AF risk. Our data indicated a U-shaped association between the extent of high-intensity training and AF risk. The risk of AF increased with an accumulated lifetime endurance sport activity ≥2000 h compared with sedentary individuals (OR 3.88 [1.55–9.73]). Nevertheless, a history of <2000 h of high-intensity training protected against AF when compared with sedentary individuals (OR 0.38 [0.12–0.98]).ConclusionA history of ≥2000 h of vigorous endurance training, tall stature, abdominal obesity, and OSA are frequently encountered as risk factors in patients with Ln-AF. Fewer than 2000 total hours of high-intensity endurance training associates with reduced Ln-AF risk.

OBJECTIVE -Confirmatory factor analysis (CFA) was used to test the hypothesis that the components of the metabolic syndrome are manifestations of a single common factor. RESEARCH DESIGN AND METHODS-Three different datasets were used to test and validate the model. The Spanish and Mauritian studies included 207 men and 203 women and 1,411 men and 1,650 women, respectively. A third analytical dataset including 847 men was obtained from a previously published CFA of a U.S. population. The one-factor model included the metabolic syndrome core components (central obesity, insulin resistance, blood pressure, and lipid measurements). We also tested an expanded one-factor model that included uric acid and leptin levels. Finally, we used CFA to compare the goodness of fit of one-factor models with the fit of two previously published four-factor models.RESULTS -The simplest one-factor model showed the best goodness-of-fit indexes (comparative fit index 1, root mean-square error of approximation 0.00). Comparisons of one-factor with four-factor models in the three datasets favored the one-factor model structure. The selection of variables to represent the different metabolic syndrome components and model specification explained why previous exploratory and confirmatory factor analysis, respectively, failed to identify a single factor for the metabolic syndrome.CONCLUSIONS -These analyses support the current clinical definition of the metabolic syndrome, as well as the existence of a single factor that links all of the core components. Diabetes Care 29:113-122, 2006T he metabolic syndrome refers to the clustering, within individuals, of several cardiovascular risk factors (1,2). The metabolic syndrome is highly prevalent (3) and is a risk factor for cardiovascular diseases (CVD), chronic kidney disease, and type 2 diabetes (4 -6). Several definitions of the metabolic syndrome have been used, but all include insulin resistance or glucose intolerance, hypertension, dyslipidemia, and central obesity (7-9). Hyperuricemia and hyperleptinemia have also been proposed as components of the metabolic syndrome (1,10,11), and clinical, epidemiological, genetic, and physiologic studies have shown associations between these traits and both the metabolic syndrome components and CVD outcomes (10 -22).A central question in understanding the metabolic syndrome is why these traits cluster in individuals. For example, is there one or are there several factors, such as genetic or lifestyle characteristics, that influence the expression of metabolic syndrome traits in individuals? In an attempt to answer this question, many investigators have used exploratory factor analysis (EFA). This technique is used to analyze the interrelatedness of measured variables, so as to explain their observed correlations in terms of a smaller group of latent (i.e., unmeasured) variables, termed factors. For example, in the field of sociology, education level, income, and job status may all be related, and their relationship may best be explained by the presence of...

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IE in patients with BAV and MVP have higher rates of viridans group streptococci IE and IE from suspected odontologic origin than in other IE patients, with a clinical profile similar to that of high-risk IE patients. Our findings suggest that BAV and MVP should be classified as high-risk IE conditions and the case for IEAP should be reconsidered.