Browsing by Author "Kocic, Gordana (57203531432)"

Nitric oxide (NO) is oxidative stress biomarker which is regarded as one of the key determinants of energy metabolism and vascular tone. Considering the controversial reports on the association between nitric oxide products (NOx) and metabolic syndrome (MetS), the aim of the current study was to examine that potential relationship. Additionally, we aimed to evaluate a broad spectrum of other oxidative stress biomarkers [i.e., malondialdehyde (MDA), advanced oxidation protein products (AOPP), xanthine oxidoreductase (XOD), xanthine oxidase (XO) xanthine dehydrogenase (XDH)] in relation with MetS. A total of 109 volunteers (46.8% of them with MetS) were included in this cross-sectional study. Biohemical and anthropometric parameters, as well as blood pressure, were obtained. The MetS was diagnosed according to the International Diabetes Federation criteria. Multivariate logistic regression analysis showed that XOD (OR=1.011; 95% CI 1.002-1.019; p=0.016), XO (OR=1.014; 95% CI 1.003-1.026; p=0.016), MDA (OR=1.113; 95% CI 1.038-1.192; p=0.003) and AOPP (OR=1.022; 95% CI 1.005-1.039; p=0.012) were the independent predictors of MetS, whereas no association between NOx and MetS was found. As XOD rose for 1 U/L, XO for 1 U/L, MDA for 1 μmol/L and AOPP for 1 T/L, probability for MetS rose for 1.1%, 1.4%, 11.3% and 2.2%, respectively. Adjusted R2 for the Model was 0.531, which means that 53.1% of variation in MetS could be explained with this Model. Unlike XOD, MDA and AOPP, NOx is not associated with MetS. © 2019 Aleksandra Klisic, Gordana Kocic, Nebojsa Kavaric, Radmila Pavlovic, Ivan Soldatovic, Ana Ninic.

Nitric oxide (NO) is oxidative stress biomarker which is regarded as one of the key determinants of energy metabolism and vascular tone. Considering the controversial reports on the association between nitric oxide products (NOx) and metabolic syndrome (MetS), the aim of the current study was to examine that potential relationship. Additionally, we aimed to evaluate a broad spectrum of other oxidative stress biomarkers [i.e., malondialdehyde (MDA), advanced oxidation protein products (AOPP), xanthine oxidoreductase (XOD), xanthine oxidase (XO) xanthine dehydrogenase (XDH)] in relation with MetS. A total of 109 volunteers (46.8% of them with MetS) were included in this cross-sectional study. Biohemical and anthropometric parameters, as well as blood pressure, were obtained. The MetS was diagnosed according to the International Diabetes Federation criteria. Multivariate logistic regression analysis showed that XOD (OR=1.011; 95% CI 1.002-1.019; p=0.016), XO (OR=1.014; 95% CI 1.003-1.026; p=0.016), MDA (OR=1.113; 95% CI 1.038-1.192; p=0.003) and AOPP (OR=1.022; 95% CI 1.005-1.039; p=0.012) were the independent predictors of MetS, whereas no association between NOx and MetS was found. As XOD rose for 1 U/L, XO for 1 U/L, MDA for 1 μmol/L and AOPP for 1 T/L, probability for MetS rose for 1.1%, 1.4%, 11.3% and 2.2%, respectively. Adjusted R2 for the Model was 0.531, which means that 53.1% of variation in MetS could be explained with this Model. Unlike XOD, MDA and AOPP, NOx is not associated with MetS. © 2019 Aleksandra Klisic, Gordana Kocic, Nebojsa Kavaric, Radmila Pavlovic, Ivan Soldatovic, Ana Ninic.

Introduction/Aim Considering the high prevalence of non-alcoholic fatty liver disease (NAFLD) in individuals with type 2 diabetes mellitus (DM2), we aimed to investigate the potential benefit of determining markers of oxidative stress, inflammation and dyslipidemia for prediction of NAFLD, as estimated with fatty liver index (FLI) in individuals with DM2. Methods A total of 139 individuals with DM2 (of them 49.9% females) were enrolled in cross-sectional study. Anthropometric and biochemical parameters, as well as blood pressure were obtained. A FLI was calculated. Results Multivariate logistic regression analysis showed that high density lipoprotein cholesterol (HDL-c) and malondialdehyde (MDA) were independent predictors of higher FLI [Odds ratio (OR)=0.056, p=0.029; and OR=1.105, p=0.016, respectively]. In Receiver Operating Characteristic curve analysis, the addition of fatty liver risk factors (e. g., age, gender, body height, smoking status, diabetes duration and drugs metabolized in liver) to each analysed biochemical parameter [HDL-c, non-HDL-c, high sensitivity C-reactive protein (hsCRP), MDA and advanced oxidant protein products (AOPP)] in Model 1, increased the ability to discriminate patients with and without fatty liver [Area under the curve (AUC)=0.832, AUC=0.808, AUC=0.798, AUC=0.824 and AUC=0.743, respectively]. Model 2 (which included all five examined predictors, e. g., HDL-c, non-HDL-c, hsCRP, MDA, AOPP, and fatty liver risk factors) improved discriminative abilities for fatty liver status (AUC=0.909). Even more, Model 2 had the highest sensitivity and specificity (89.3% and 87.5%, respectively) together than each predictor in Model 1. Conclusion Multimarker approach, including biomarkers of oxidative stress, dyslipidemia and inflammation, could be of benefit in identifying patients with diabetes being at high risk of fatty liver disease. © J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart.New York.

Introduction/Aim Considering the high prevalence of non-alcoholic fatty liver disease (NAFLD) in individuals with type 2 diabetes mellitus (DM2), we aimed to investigate the potential benefit of determining markers of oxidative stress, inflammation and dyslipidemia for prediction of NAFLD, as estimated with fatty liver index (FLI) in individuals with DM2. Methods A total of 139 individuals with DM2 (of them 49.9% females) were enrolled in cross-sectional study. Anthropometric and biochemical parameters, as well as blood pressure were obtained. A FLI was calculated. Results Multivariate logistic regression analysis showed that high density lipoprotein cholesterol (HDL-c) and malondialdehyde (MDA) were independent predictors of higher FLI [Odds ratio (OR)=0.056, p=0.029; and OR=1.105, p=0.016, respectively]. In Receiver Operating Characteristic curve analysis, the addition of fatty liver risk factors (e. g., age, gender, body height, smoking status, diabetes duration and drugs metabolized in liver) to each analysed biochemical parameter [HDL-c, non-HDL-c, high sensitivity C-reactive protein (hsCRP), MDA and advanced oxidant protein products (AOPP)] in Model 1, increased the ability to discriminate patients with and without fatty liver [Area under the curve (AUC)=0.832, AUC=0.808, AUC=0.798, AUC=0.824 and AUC=0.743, respectively]. Model 2 (which included all five examined predictors, e. g., HDL-c, non-HDL-c, hsCRP, MDA, AOPP, and fatty liver risk factors) improved discriminative abilities for fatty liver status (AUC=0.909). Even more, Model 2 had the highest sensitivity and specificity (89.3% and 87.5%, respectively) together than each predictor in Model 1. Conclusion Multimarker approach, including biomarkers of oxidative stress, dyslipidemia and inflammation, could be of benefit in identifying patients with diabetes being at high risk of fatty liver disease. © J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart.New York.