No abstract

cardiovascular disease (cVD) is the leading cause of death worldwide. one common factor that may affect CVD risk factors is sleep disturbance. The factors influencing an individual's sleep may vary among different cultures. The current study investigated sleep quality and quantity in the Fasa cohort population as an iranian population. in a cross-sectional study using the fasa peRSiAn cohort study data, 10,129 subjects aged 35-70 were entered. Self-reported sleep duration and cardiovascular events were recorded. the framingham risk score (fRS) is used to predict cardiovascular events. Adjusted logistic regression showed significant odds ratios in subjects who sleep less than 6 hours for cVD (oR = 1.23; 95% CI:1.03-1.48), coronary heart disease (CHD) (OR = 1.21; 95% CI:1.009-1.46), and hypertension (Htn) (oR = 1.37; 95% CI:1.16-1.62). Higher risk profiles were also seen in the FRS for short sleepers. The highest significant odds ratios in FRS profiles in the intermediate high-risk group compared with the low-risk group were (1.44; 95% CI:1.18-1.75) in CVD and (1.48; 95% CI:1.16-1.88) in CHD risk score profiles. It can be suggested that participants with short durations of sleep had significantly higher CVD, HTN prevalence, and 10-year FRS. Participants with long sleep durations had no increase in cVD, cHD, myocardial infarction (Mi), or Htn prevalence. Mi prevalence was at the lowest level in subjects who got 8 to 8.9 hours of sleep. Cardiovascular disease (CVD) comprises the most common disorders in both developed and developing countries. CVDs result in disability and reduced efficiency and can also have an economic burden on regional health systems 1. For example, more than $231 billion US were spent on personal healthcare due to CVD in 2013 2. Recent studies have shown that the prevalence of CVD is increasing in Central and Eastern Europe 3. In 2015, cardiovascular disease caused more than 176,000 deaths in Iran 4. There is no accurate data on CVD mortalities (including myocardial infarction (MI) and coronary heart disease (CHD)) in Iran; however changes in the lifestyle of Iranian people are related to a progressive increase in the prevalence of CVDs in Iran 5. The World Health Organization (WHO) recently recommended a global strategy to control and prevent non-communicable diseases (like CVDs) based on the reduction of exposure to risk factors 6. Age, gender, blood pressure, lipid profiles, physical activity, obesity, smoking, and type 2 diabetes are some of the common risk factors for CVD. Factors that may affect CVD directly or its risk factors are sleep disturbances 7,8. Sleep deprivation can be associated with obesity 9 , diabetes 10 , and hypertension (HTN) 11,12. This association can be a predisposing factor for the incidence of cardiac disease 13. According to the U.S. National Sleep Foundation, 31% of Americans in 2001 sleep 6 hours or less daily 14. People today sleep 1.5 fewer hours than people who lived in the past century. Evidence shows that inadequate sleep has massive effects on both physiolog...

Background: Metabolic syndrome (MetS) as a set of cardiac risk factors and its growing prevalence is one of the major concerns in different societies. In this study, we aimed to investigate the relationship between Mets and electrocardiogram (ECG) parameters and abnormalities as indicators for subclinical cardiovascular diseases (CVD). Methods: In this sub-analysis study, we used the data from Fasa PERSIAN Cohort Study which includes subjects age 35-70 years. Subjects with available ECG data included in the study (n=7002) and subjects with missing data on MetS components and non-sinus rhythm ECG were excluded (n=44). The MetS definition based on the Adult Treatment Panel (ATP) III guidelines and also a 12-lead ECG was obtained from all participants. Results: Our study population (n=6958) showed a mean age of 48.60±9.34 years and also 1656 (24.2%) subjects had MetS. Except for P duration, PR interval and S amplitude in men and P amplitude, S amplitude, Sokolow-Lyon Index, and QT interval in women, other ECG parameters differ significantly between subjects with and without Mets (P<0.05). Also among ECG abnormalities, prolonged P duration (≥120ms), QRS duration (≥100ms), and QTc interval (>450ms in male, >470ms in female) had a significant association with MetS in the total population. Waist circumferences (WC) showed the most count of significant relationship with ECG parameters in both genders. In males, WC more than ATP cut-points had significant associations with prolonged P and QRS duration, and also blood pressure (BP) had significant associations with prolonged P and QRS durations and QTc interval. In females, the MetS component except triglyceride had at least a significant relationship with prolonged P and/or QRS duration. Conclusion: MetS and its component especially WC and BP were associated with ECG parameters and abnormalities. These associations with ECG as a marker of subclinical CVD showed the importance of MetS and each component in our population to monitor in the further longitudinal studies.

Background Previous studies suggested that obesity and fat mass are associated with QT interval prolongation, but the role of different body parts' fat mass is unclear. The associations between total and regional fat mass (FM) and corrected QT interval (QTc) were investigated for the first time in this study. Methods In this sub-analysis of Fasa PERSIAN cohort Study data, 3217 subjects aged 35–70 entered our study. Body fat mass was assessed by bioelectrical impedance analysis and QTc interval calculated by the QT interval measured by Cardiax® software from ECGs and Bazett’s formula. Uni- and multi-variable linear and logistic regression was performed in IBM SPSS Statistics v23. Results In males, the fat mass to fat-free mass (FM/FFM) ratio in the trunk, arms, total body, and legs were significantly higher in the prolonged QTc group (QTc > 450 ms). Trunk (B = 0.148), total (B = 0.137), arms (B = 0.124), legs (B = 0.107) fat mass index (FMI) showed significant positive relationship with continuous QTc (P-value < 0.001). Also, just the fat-free mass index of legs had significant positive associations with QTc interval (P-value < 0.05). Surprisingly, in females, the mean of FM/FFM ratio in trunk and legs in the normal QTc group had higher values than the prolonged QTc group (QTc > 470 ms). Also, none of the body composition variables had a significant correlation with continuous QTc. Conclusion Our study suggested that FMI ratios in the trunk, total body, arms, and legs were positively associated with QTc interval in males, respectively, from a higher to a lower beta-coefficient. Such associations were not seen in females. Our study implies that body fat mass may be an independent risk factor for higher QTc interval and, consequently, more cardiovascular events that should be investigated.

Background QT interval as an indicator of ventricular repolarization is a clinically important parameter on an electrocardiogram (ECG). QT prolongation predisposes individuals to different ventricular arrhythmias and sudden cardiac death. The current study aimed to identify the best heart rate corrected QT interval for a non-hospitalized Iranian population based on cardiovascular mortality. Methods Using Fasa PERSIAN cohort study data, this study enrolled 7071 subjects aged 35–70 years. Corrected QT intervals (QTc) were calculated by the QT interval measured by Cardiax® software from ECGs and 6 different correction formulas (Bazett, Fridericia, Dmitrienko, Framingham, Hodges, and Rautaharju). Mortality status was checked using an annual telephone-based follow-up and a minimum 3-year follow-up for each participant. Bland–Altman, QTc/RR regression, sensitivity analysis, and Cox regression were performed in IBM SPSS Statistics v23 to find the best QT. Also, for calculating the upper and lower limits of normal of different QT correction formulas, 3952 healthy subjects were selected. Results In this study, 56.4% of participants were female, and the mean age was 48.60 ± 9.35 years. Age, heart rate in females, and QT interval in males were significantly higher. The smallest slopes of QTc/RR analysis were related to Fridericia in males and Rautaharju followed by Fridericia in females. Thus, Fridericia’s formula was identified as the best mathematical formula and Bazett’s as the worst in males. In the sensitivity analysis, however, Bazett’s formula had the highest sensitivity (23.07%) among all others in cardiac mortality. Also, in the Cox regression analysis, Bazett’s formula was better than Fridericia’s and was identified as the best significant cardiac mortality predictor (Hazard ratio: 4.31, 95% CI 1.73–10.74, p value = 0.002). Conclusion Fridericia was the best correction formula based on mathematical methods. Bazett’s formula despite its poorest performance in mathematical methods, was the best one for cardiac mortality prediction. Practically, it is suggested that physicians use QTcB for a better evaluation of cardiac mortality risk. However, in population-based studies, QTcFri might be the one to be used by researchers.