Cardiovascular disease risk prediction among Iranian patients with diabetes mellitus in Isfahan Province, Iran, in 2014, by using Framingham risk score, atherosclerotic cardiovascular disease risk score, and high-sensitive C-reactive protein

Abstract

BACKGROUND: Risk assessment in clinical practice plays an important role in classifying population for appropriate preventive medicine for each category. Several multivariable risk predictor algorithms and inflammatory biomarkers are developed for assessing risk for cardiovascular diseases (CVDs). We aimed to depict a picture of the cardiovascular risk profiles in the Iranian population with diabetes mellitus (DM) through three risk predictors for the first time, as the patients with DM have an increased risk for CVDs. METHODS: In this cross-sectional study, the sample size consisted of 418 patients with DM from Diabetes Clinic of Shariati hospital, Isfahan, Iran, in February to July, 2014. We collected the latest information, and then calculated the 10-year CVD risk using Framingham risk score (FRS) and atherosclerotic cardiovascular disease (ASCVD) risk score; while high-sensitivity C-reactive protein (hs-CRP) was measured for them based on their physicians' prescription. Finally, all data were analyzed using SPSS software. RESULTS: The mean 10-year risk prediction of CVDs in the 30- to 74-year-old Iranian patients with DM was high in all three predictors based on their cut-off points, 16.31, 12.39, and 3.46 mg/l for FRS, ASCVD risk score, and hs-CRP level, respectively. Although the mean FRS and ASCVD risk scores were significantly higher among men than women (P < 0.0500), the mean hs-CRP level was slightly lower in men than women (P > 0.0500). CONCLUSION: Mean FRS and ASCVD risk scores and hs-CRP in patients were high, and a considerable proportion of patients with DM in our study were at intermediate and high risk for CVDs in the next 10 years. Future cohort studies would investigate the accuracy of different predictors in upcoming years, and also help to derive a specific model or recalibrate existing predictors with characteristic of Iranian populations and specific target groups.