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Background: Metabolic syndrome (MetS) is a cluster of risk factors and the Framingham risk score (FRS) is a useful metric for measuring the 10-year cardiovascular disease (CVD) risk of the population. The present study aimed to determine the 10-year risk of cardiovascular disease using the Framingham risk score in people with and without MetS in a large Iranian cohort study. Methods: This cross-sectional study was done using the Fasa cohort. Participants aged ≥ 35 years old were recruited to the study from 2015 to 2016. The FRS was calculated using age, sex, current smoking, diabetes, systolic blood pressure (SBP), total cholesterol, and high-density lipoprotein (HDL) cholesterol. MetS was defined as the presence of three or more of the MetS risk factors including triglyceride (TG) level ≥ 150 mg dl− 1, HDL level < 40 mg dl− 1 in men and < 50 mg dl− 1 in women, systolic/diastolic blood pressure ≥ 130/≥85 mmHg or using medicine for hypertension, fasting blood sugar (FBS) level ≥ 100 mg dl− 1 or using diabetes medication and abdominal obesity considered as waist circumference (WC) ≥ 88 cm for women and ≥ 102 cm for men. Multiple logistic regressions were applied to estimate the 10- year CVD risk among people with and without MetS. Results: Of 8949 participants, 1928 people (21.6%) had MetS. The mean age of the participants with and without Mets was 50.4 ± 9.2 years and 46.9 ± 9.1 years respectively. In total 15.3% of participants with MetS and 8.0% of participants without MetS were in the high-risk category of 10-year CVD risk. Among participants with MetS gender, TG, SBP, FBS and in people without MetS gender, TG, SBP, FBS, and HDL showed strong associations with the predicted 10-year CVD risk. Conclusion: Male sex and increased SBP, TG, and FBS parameters were strongly associated with increased 10-year risk of CVD in people with and without MetS. In people without MetS, reduced HDL-cholestrol was strongly associated with increased 10-year risk of CVD. The recognition of participant's TG, blood pressure (BP), FBS and planning appropriate lifestyle interventions related to these characteristics is an important step towards prevention of CVD. [ABSTRACT FROM AUTHOR]

Background: Two versions of Framingham's 10-year risk score are defined for cardiovascular diseases, namely laboratory-based and office-based models. The former is mainly employed in high-income countries, but unfortunately, it is not cost-effective or practical to utilize it in countries with poor facilities. Therefore, the present study aims to identify the agreement and correlation between laboratory-based and office-based Framingham models. Methods: Using laboratory-based and office-based Framingham models, this cross-sectional study used data from 8944 participants without a history of CVDs and stroke at baseline in the Fasa cohort study to predict the 10-year risk of CVDs. The laboratory-based model included age, sex, diabetes, smoking status, systolic blood pressure (SBP), treatment of hypertension, total cholesterol, and high-density lipoprotein (HDL); and the office-based model included age, sex, diabetes, smoking status, SBP, treatment of hypertension, and body mass index (BMI). The agreement between risk categories of laboratory-based and office-based Framingham models (low [< 10%], moderate [from 10 to < 20%], high [≥ 20%]) was assessed by kappa coefficients and percent agreement. Then, the correlation between the risk scores was estimated using correlation coefficients and illustrated using scatter plots. Finally, agreements, correlation coefficient, and scatter plots for laboratory-based and office-based Framingham models were analyzed by stratified Framingham risk score factors including sex, age, BMI categories, hypertension, smoking, and diabetes status. Results: The two models showed substantial agreement at 89.40% with a kappa coefficient of 0.75. The agreement was substantial in all men (kappa = 0.73) and women (kappa = 0.72), people aged < 60 years (kappa = 0.73) and aged ≥ 60 years (kappa = 0.69), smokers (kappa = 0.70) and non-smokers (kappa = 0.75), people with hypertension (kappa = 0.73) and without hypertension (kappa = 0.75), diabetics (kappa = 0.71) and non-diabetics (kappa = 0.75), people with normal BMI (kappa = 0.75) and people with overweight and obesity (kappa = 0.76). There was also a very strong positive correlation (r ≥ 0.92) between laboratory-based and office-based models in terms of age, sex, BMI, hypertension, smoking status and diabetes status. Conclusions: The current study showed that there was a substantial agreement between the office-based and laboratory-based models, and there was a very strong positive correlation between the risk scores in the entire population as well across subgroups. Although differences were observed in some subgroups, these differences were small and not clinically relevant. Therefore, office-based models are suitable in low-middle-income countries (LMICs) with limited laboratory resources and facilities because they are more convenient and accessible. However, the validity of the office-based model must be assessed in longitudinal studies in LMICs. [ABSTRACT FROM AUTHOR]

Background: The WHO model has laboratory-based and non-laboratory-based versions for 10-year risk prediction of cardiovascular diseases. Due to the fact that in some settings, there may not be the necessary facilities for risk assessment with a laboratory-based model, the present study aimed to determine the agreement between laboratory-based and non-laboratory-based WHO cardiovascular risk equations. Methods: In this cross-sectional study, we used the baseline data of 6796 individuals without a history of cardiovascular disease and stroke who participated in the Fasa cohort study. The risk factors of the laboratory-based model included age, sex, systolic blood pressure (SBP), diabetes, smoking and total cholesterol, while the non-laboratory-based model included age, sex, SBP, smoking and BMI. Kappa coefficients was used to determine the agreement between the grouped risk and Bland–Altman plots were used to determine the agreement between the scores of the two models. Sensitivity and specificity of non-laboratory-based model were measured at the high-risk threshold. Results: In the whole population, the agreement between the grouped risk of the two models was substantial (percent agreement = 79.0%, kappa = 0.68). The agreement was better in males than in females. A substantial agreement was observed in all males (percent agreement = 79.8%, kappa = 0.70) and males < 60 years old (percent agreement = 79.9%, kappa = 0.67). The agreement in males ≥ 60 years old was moderate (percent agreement = 79.7%, kappa = 0.59). The agreement among females was also substantial (percent agreement = 78.3%, kappa = 0.66). The agreement for females < 60 years old, (percent agreement = 78.8%, kappa = 0.61) was substantial and for females ≥ 60 years old, (percent agreement = 75.8%, kappa = 0.46) was moderate. According to Bland–Altman plots, the limit of agreement was (95%CI: -4.2% to 4.3%) for males and (95%CI: -4.1% to 4.6%) for females. The range of agreement was suitable for both males < 60 years (95%CI: -3.8% to 4.0%) and females < 60 years (95%CI: -3.6% to 3.9%). However, it was not suitable for males ≥ 60 years (95% CI: -5.8% to 5.5%) and females ≥ 60 years (95%CI: -5.7% to 7.4%). At the high-risk threshold of 20% in non-laboratory and laboratory-based models, the sensitivity of the non-laboratory-based model was 25.7%, 70.7%, 35.7%, and 35.4% for males < 60 years, males ≥ 60 years, females < 60 years, and females ≥ 60 years, respectively. At the high-risk threshold of 10% in non-laboratory-based and 20% in laboratory-based models, the non-laboratory model has high sensitivity of 100% for males ≥ 60 years, females < 60 years, females ≥ 60 years, and 91.4% for males < 60 years. Conclusion: A good agreement was observed between laboratory-based and non-laboratory-based versions of the WHO risk model. Also, at the risk threshold of 10% to detect high-risk individuals, the non-laboratory-based model has acceptable sensitivity for practical risk assessment and the screening programs in settings where resources are limited and people do not have access to laboratory tests. [ABSTRACT FROM AUTHOR]