TY  - JOUR
AU  - Klisić, Aleksandra
AU  - Isaković, Aleksandra
AU  - Kocić, Gordana
AU  - Kavarić, Nebojša
AU  - Jovanović, Milovan
AU  - Zvrko, Elvir
AU  - Skerović, Verica
AU  - Ninić, Ana
PY  - 2018
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3233
AB  - Introduction/Aim Considering the high prevalence of nonalcoholic 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.
PB  - Johann Ambrosius Barth Verlag Medizinverlage Heidelberg Gmbh, Stuttgart
T2  - Experimental and Clinical Endocrinology & Diabetes
T1  - Relationship between Oxidative Stress, Inflammation and Dyslipidemia with Fatty Liver Index in Patients with Type 2 Diabetes Mellitus
VL  - 126
IS  - 6
SP  - 371
EP  - 378
DO  - 10.1055/s-0043-118667
ER  - 

@article{
author = "Klisić, Aleksandra and Isaković, Aleksandra and Kocić, Gordana and Kavarić, Nebojša and Jovanović, Milovan and Zvrko, Elvir and Skerović, Verica and Ninić, Ana",
year = "2018",
abstract = "Introduction/Aim Considering the high prevalence of nonalcoholic 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.",
publisher = "Johann Ambrosius Barth Verlag Medizinverlage Heidelberg Gmbh, Stuttgart",
journal = "Experimental and Clinical Endocrinology & Diabetes",
title = "Relationship between Oxidative Stress, Inflammation and Dyslipidemia with Fatty Liver Index in Patients with Type 2 Diabetes Mellitus",
volume = "126",
number = "6",
pages = "371-378",
doi = "10.1055/s-0043-118667"
}

Klisić, A., Isaković, A., Kocić, G., Kavarić, N., Jovanović, M., Zvrko, E., Skerović, V.,& Ninić, A.. (2018). Relationship between Oxidative Stress, Inflammation and Dyslipidemia with Fatty Liver Index in Patients with Type 2 Diabetes Mellitus. in Experimental and Clinical Endocrinology & Diabetes
Johann Ambrosius Barth Verlag Medizinverlage Heidelberg Gmbh, Stuttgart., 126(6), 371-378.
https://doi.org/10.1055/s-0043-118667

Klisić A, Isaković A, Kocić G, Kavarić N, Jovanović M, Zvrko E, Skerović V, Ninić A. Relationship between Oxidative Stress, Inflammation and Dyslipidemia with Fatty Liver Index in Patients with Type 2 Diabetes Mellitus. in Experimental and Clinical Endocrinology & Diabetes. 2018;126(6):371-378.
doi:10.1055/s-0043-118667 .

Klisić, Aleksandra, Isaković, Aleksandra, Kocić, Gordana, Kavarić, Nebojša, Jovanović, Milovan, Zvrko, Elvir, Skerović, Verica, Ninić, Ana, "Relationship between Oxidative Stress, Inflammation and Dyslipidemia with Fatty Liver Index in Patients with Type 2 Diabetes Mellitus" in Experimental and Clinical Endocrinology & Diabetes, 126, no. 6 (2018):371-378,
https://doi.org/10.1055/s-0043-118667 . .

TY  - JOUR
AU  - Klisić, Aleksandra
AU  - Kavarić, Nebojša
AU  - Jovanović, Milovan
AU  - Zvrko, Elvir
AU  - Skerović, Verica
AU  - Šćepanović, Anđelka
AU  - Medin, Darko
AU  - Ninić, Ana
PY  - 2017
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2835
AB  - Background: Recent studies hypothesize that dyslipidemia can predict glycated hemoglobin (HbA1c) and could be important contributing factor to the pathogenesis of type 2 diabetes mellitus (DM2). Therefore, we aimed to evaluate the influence of lipid parameters on long-term glycemic control in DM2. Materials and Methods: A total of 275 sedentary DM2 (mean [+/- standard deviation] age 60.6 [+/- 10.0] years) who volunteered to participate in this cross-sectional study were enrolled. Anthropometric (body weight, body hight, and waist circumference), biochemical parameters (fasting glucose, HbA1c, lipid parameters, creatinine), as well as blood pressure were obtained. Results: Total cholesterol (odds ratio [OR] = 1.30, 95% confidence interval [CI] [1.02-1.66], P = 0.032), triglycerides (OR = 1.34, 95% CI (1.07-1.67), P = 0.010), and low density lipoprotein cholesterol (OR = 1.42, 95% CI [1.10-1.83], P = 0.006) were the independent predictors of higher HBA1c, and as they increased by 1 mmol/L each, probabilities of higher HBA1c increased by 30%, 34%, and 42%, respectively. Low level of high-density lipoprotein cholesterol (HDL-c) was found to be the independent predictor of higher HBA1c (OR = 0.44, 95% CI [0.20-0.67], P = 0.039), and increase in HDL-c by 1 mmol/L, reduced the probability of higher HBA1c by 56%. Conclusion: Unfavorable lipid profile can predict HbA1c level in DM2 patients. Early diagnosis of dyslipidemia, as well as its monitoring and maintaining good lipids control can be used as a preventive measure for optimal long-term glycemic control.
PB  - Wolters Kluwer Medknow Publications, Mumbai
T2  - Journal of Research in Medical Sciences
T1  - Association between unfavorable lipid profile and glycemic control in patients with type 2 diabetes mellitus
VL  - 22
DO  - 10.4103/jrms.JRMS_284_17
ER  - 

@article{
author = "Klisić, Aleksandra and Kavarić, Nebojša and Jovanović, Milovan and Zvrko, Elvir and Skerović, Verica and Šćepanović, Anđelka and Medin, Darko and Ninić, Ana",
year = "2017",
abstract = "Background: Recent studies hypothesize that dyslipidemia can predict glycated hemoglobin (HbA1c) and could be important contributing factor to the pathogenesis of type 2 diabetes mellitus (DM2). Therefore, we aimed to evaluate the influence of lipid parameters on long-term glycemic control in DM2. Materials and Methods: A total of 275 sedentary DM2 (mean [+/- standard deviation] age 60.6 [+/- 10.0] years) who volunteered to participate in this cross-sectional study were enrolled. Anthropometric (body weight, body hight, and waist circumference), biochemical parameters (fasting glucose, HbA1c, lipid parameters, creatinine), as well as blood pressure were obtained. Results: Total cholesterol (odds ratio [OR] = 1.30, 95% confidence interval [CI] [1.02-1.66], P = 0.032), triglycerides (OR = 1.34, 95% CI (1.07-1.67), P = 0.010), and low density lipoprotein cholesterol (OR = 1.42, 95% CI [1.10-1.83], P = 0.006) were the independent predictors of higher HBA1c, and as they increased by 1 mmol/L each, probabilities of higher HBA1c increased by 30%, 34%, and 42%, respectively. Low level of high-density lipoprotein cholesterol (HDL-c) was found to be the independent predictor of higher HBA1c (OR = 0.44, 95% CI [0.20-0.67], P = 0.039), and increase in HDL-c by 1 mmol/L, reduced the probability of higher HBA1c by 56%. Conclusion: Unfavorable lipid profile can predict HbA1c level in DM2 patients. Early diagnosis of dyslipidemia, as well as its monitoring and maintaining good lipids control can be used as a preventive measure for optimal long-term glycemic control.",
publisher = "Wolters Kluwer Medknow Publications, Mumbai",
journal = "Journal of Research in Medical Sciences",
title = "Association between unfavorable lipid profile and glycemic control in patients with type 2 diabetes mellitus",
volume = "22",
doi = "10.4103/jrms.JRMS_284_17"
}

Klisić, A., Kavarić, N., Jovanović, M., Zvrko, E., Skerović, V., Šćepanović, A., Medin, D.,& Ninić, A.. (2017). Association between unfavorable lipid profile and glycemic control in patients with type 2 diabetes mellitus. in Journal of Research in Medical Sciences
Wolters Kluwer Medknow Publications, Mumbai., 22.
https://doi.org/10.4103/jrms.JRMS_284_17

Klisić A, Kavarić N, Jovanović M, Zvrko E, Skerović V, Šćepanović A, Medin D, Ninić A. Association between unfavorable lipid profile and glycemic control in patients with type 2 diabetes mellitus. in Journal of Research in Medical Sciences. 2017;22.
doi:10.4103/jrms.JRMS_284_17 .

Klisić, Aleksandra, Kavarić, Nebojša, Jovanović, Milovan, Zvrko, Elvir, Skerović, Verica, Šćepanović, Anđelka, Medin, Darko, Ninić, Ana, "Association between unfavorable lipid profile and glycemic control in patients with type 2 diabetes mellitus" in Journal of Research in Medical Sciences, 22 (2017),
https://doi.org/10.4103/jrms.JRMS_284_17 . .