Hyperuricemia has been described as associated with the risk of development metabolic syndrome; however the relationship between the uric acid level and particular parameters of metabolic syndrome remained unclear. We performed a cross-sectional study on a cohort of 833 dyslipidemic patients and correlated their levels of uric acid with parameters of insulin resistance, lipid metabolism, C-reactive protein, anthropometric parameters. We also defined patients with hypertriglyceridemic waist phenotype and compered their uric acid levels with those without this phenotype. We found that levels of uric acid are associated with parameters of metabolic syndrome. Specifically, dyslipidemia characteristic for metabolic syndrome (low HDL cholesterol and high triglycerides) correlates better with uric acid levels than parameters of insulin resistance. Also waist circumference correlates better with uric acid levels than body mass index. Patients with hypertriglyceridemic waist phenotype had higher levels of uric acid when compared with patients without this phenotype. Serum uric acid levels are even in low levels linearly correlated with parameters of metabolic syndrome (better with typical lipid characteristics than with parameters of insulin resistance) and could be associated with higher cardiovascular risk.

Background and objectives: The visceral adiposity index (VAI), estimating visceral adiposity dysfunction through a simple formula, could serve as a useful tool for identifying individuals at higher cardiometabolic risk. Its relationship with insulin resistance (IR), assessed using the homeostasis model assessment of IR (HOMA-IR), and metabolic syndrome (MetS) components remains unclear. The study aimed to investigate the association of VAI with both HOMA-IR and MetS. Materials and Methods: After undergoing anthropometric and biochemical studies, 783 individuals were divided into three groups according to a number of present MetS components. The VAI cut-offs signaling MetS and HOMA-IR were determined by maximizing the sum of the sensitivity and specificity. Correlation analysis was performed to explore the associations between VAI and other tested parameters. A logistic stepwise regression analysis was applied to identify statistically significant determinants of HOMA-IR. Given the variability of reference values, two thresholds of HOMA-IR were applied, namely 2.0 and 3.8. Results: VAI increased significantly between the groups with a rising number of MetS components. The VAI cut-off for MetS was 2.37, with a sensitivity of 0.86 and a specificity of 0.78. The same cut-off point identified subjects with HOMA-IR = 3.8, with a sensitivity of 0.79 and a specificity of 0.66. The VAI cut-off for HOMA-IR = 2.0 was 1.89, with a sensitivity of 0.74 and a specificity of 0.68. The strongest correlations of VAI were noted with HOMA-IR (r = 0.51) and insulin (r = 0.49), respectively, while the strongest correlation of HOMA-IR was with waist circumference (r = 0.54). Not one of the routine parameters was a significant predictor in the regression analysis. Conclusions: The obtained results show an existing association of VAI with HOMA-IR. The high sensitivity and specificity of the cut-offs may allow the application of VAI in common clinical practice.

Coronary risk evaluation by conventional factors (age, gender, smoking, blood pressure and cholesterol) may further be specified by facets of the metabolic syndrome, namely insulin resistance, hypertriglyceridemia and obesity. Although obesity is usually defined as elevated body mass index (BMI), recent data indicate a superior role of waist circumference or hypertri-glyceridemic waist (HTGW) over BMI in the assessment of cardiometabolic risk. In dyslipidemic patients, the specific contributions of risky waist, HTGW or BMI have not been evaluated as yet. 686 dyslipidemic subjects (322 males and 364 females) were enrolled into a cross-sectional study. In each subject basic antropometry (i.e. waist circumference, HTGW, BMI) and laboratory parameters of lipid profile and insulin resistance were determined. Cardiometabolic risk was given by fulfilling the criteria (harmonized definition) of metabolic syndrome. The significance of risky waist, HTGW and BMI were assessed by comparing the respective predictive values for the presence of metabolic syndrome. Dyslipidemic patients with risky waist, HTGW or high BMI have a more atherogenic lipid profile and higher insulin resistance compared to those without risky waist, HTGW or high BMI. Risky waist is stronger predictor of metabolic syndrome (PPV 66 %, NPV 90 %) and thus posesa greater cardiometabolic risk than higher BMI per se does (PPV 42 %, NPV 97 %). The contribution of triglycerides (i.e. HTGW) to these predictive values is marginal (PPV 66 %, NPV 92 %). The present results highlight the superior role of waist circumference as a screening tool over BMI for the evaluation of cardiometabolic risk in dyslipidemic subjects. HTGW brings little additional benefit in risk stratification. Lower BMI proved to be optimal for identifying the subjects with inferior risk.