Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes

Abstract

Abstract Since there is a high prevalence of type 2 diabetes mellitus (DM2), as well as CVD in Montenegro, we aimed to estimate CVD risk by United Kingdom Prospective Diabetes Study (UKPDS) risk engine algorithm in individuals with DM2. Furthermore, we aimed to explore whether non-traditional biomarker such as high sensitivity C-reactive protein (hsCRP) is superior for CVD risk prediction over old traditional risk factors. A total of 180 participants with DM2 (of them 50% females) were included in the current cross-sectional study. Biochemical and anthropometric parameters, and blood pressure were obtained. More males than females were classified at high UKPDS risk category (p<0.001). Also, about one third of diabetic patients (29.4%) were classified into the high-risk category. In multivariate regression analysis, triglycerides [Odds ratio (OR) =1.703, p=0.001] and creatinine concentration (OR=1.040, p<0.001) were independent predictors of CVD risk, whereas hsCRP was not correlated with CVD risk. HsCRP is not superior for CVD risk prediction by UKPDS risk engine algorithm over high triglyceride and creatinine levels in diabetic population, which suggests that the old traditional markers must not be underestimated when examining CVD risk in population with diabetes.