Search

Cardiovascular disease (CVD) is the leading cause of mortality globally. Low-density lipoprotein (LDL) plays an important role in CVD pathophysiology. Research has shown the safety and efficacy of keeping LDL at very low levels for CVD prevention. Therefore, experts recommend intense LDL-lowering approaches starting at young ages, promoting the mantras "the lower, the better" and "the earlier, the better." This commentary discusses the challenges regarding applying aggressive LDL-lowering approaches in the general population, including pharmacological efficacy and side effects, the cost-effectiveness of interventions, and patient adherence to treatment regimens., (© 2024. The Author(s).)

Background: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators., Methods: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software., Results: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD ( P <0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models., Conclusion: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)

Background: Globorisk is a novel risk prediction model for predicting cardiovascular disease (CVD). Globorisk is a country-specific risk prediction model that determines CVD risk for all countries. This model has two versions; laboratory-based and office-based. This study aimed to determine the agreement between laboratory-based and office-based models in a large sample of the general population., Methods: Baseline data from the Fasa cohort study was used for the current study. In total, 6810 participants ≥ 40 years without any history of cardiovascular disease or stroke were included in the study. To determine the laboratory-based risk model, factors include age, sex, current smoking status, history of diabetes, systolic blood pressure (SBP), and total cholesterol. To estimate the office-based risk model, factors were age, sex, current smoking status, SBP, and body mass index (BMI). Kappa statistics was used to distinguish the agreement between grouped scores in these two models. Additionally, correlation coefficients and scatter plots were used to determine the linear correlation between the two models., Results: In this study 46.53% of the participants were men. The mean age (SD) of participants was 51.08 (7.88) years. Agreements between the two models were moderate and substantial in all women and all men, respectively. The agreement between the two CVD risk groups was 90.15% (kappa = 0.717) in all men, 92.94% (kappa = 0.571) among men aged < 60 years and 77.60% (kappa = 0.645) in men aged ≥ 60 years. The agreement between the two CVD risk groups was 86.68% (kappa = 0.572) among all women, 93.96% (kappa = 0.274) among women aged < 60 years and 62.46% (kappa = 0.422) among women aged ≥ 60 years. A very strong positive correlation (r = 0.94) was found between the two risk scores in all men, and it was similar among men aged < 60 years (r = 0.84) and men aged > 60 years (r = 0.94). Among all women, there was a very strong positive correlation (r = 0.87), and the strong positive correlation remained among < 60 years old (r = 0.76) and women > 60 years old (r = 0.76)., Conclusion: The Globorisk office-based model which is easier to use as it does not require blood testing can determine the risk groups in this population. The Globorisk office-based model may be used for CVD risk screening in low-middle income countries where resources are limited., (© 2022. The Author(s).)

Backgrounds: Cardiovascular Diseases (CVDs) are the first leading cause of death worldwide. The present study aimed to investigate the relationship between demographics, anthropometrics, sleep duration, physical activity, and ECG parameters in the Fasa Persian cohort study., Methods: In this cross-sectional study, the basic information of 10,000 participants aged 35-70 years in the Fasa cohort study was used. The data used in this study included demographic data, main Electrocardiogram (ECG) parameters, anthropometric data, sleep duration, and physical activity. Data analysis was performed using t-test, chi-square, and linear regression model., Results: Based on multivariate linear regression analysis results, increased age was significantly associated with all study parameters. Nevertheless, gender and body mass index showed no significant relationship with SV3 and PR. Wrist circumference, hip circumference and waist circumference significantly increased the mean values of the ECG parameters. However, sleep duration was not significantly associated with the ECG parameters. In addition, hypertension was major comorbidity, which was shown to increase the mean values of the ECG parameters., Conclusion: Several factors affected the ECG parameters. Thus, to interpret ECGs, in addition to age and gender, anthropometric indices, physical activity, and previous history of comorbidities, such as hypertension and ischemic heart disease, should be taken into consideration., (© 2021. The Author(s).)

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.