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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.
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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.