Klisic Aleksandra, Isakovic Aleksandra, Kocic Gordana, Kavaric Nebojsa, Jovanovic Milovan, Zvrko Elvir, Skerovic Verica, Ninic Ana
Primary Health Care Center, Podgorica, Montenegro.
Institute of Medical and Clinical Biochemistry, University of Belgrade - School of Medicine, Belgrade, Serbia.
Exp Clin Endocrinol Diabetes. 2018 Jun;126(6):371-378. doi: 10.1055/s-0043-118667. Epub 2017 Sep 11.
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.
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.
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.
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.
引言/目的：鉴于2型糖尿病（DM2）患者中非酒精性脂肪性肝病（NAFLD）的高患病率，我们旨在研究确定氧化应激、炎症和血脂异常标志物对于预测NAFLD的潜在益处，NAFLD通过DM2患者的脂肪肝指数（FLI）进行评估。
共有139例DM2患者（其中49.9%为女性）纳入横断面研究。获取人体测量和生化参数以及血压。计算FLI。
多因素逻辑回归分析显示，高密度脂蛋白胆固醇（HDL-c）和丙二醛（MDA）是FLI升高的独立预测因素[比值比（OR）=0.056，p=0.029；OR=1.105，p=0.016]。在受试者工作特征曲线分析中，在模型1中，将脂肪肝危险因素（如年龄、性别、身高、吸烟状况、糖尿病病程和在肝脏代谢的药物）添加到每个分析的生化参数[HDL-c、非HDL-c、高敏C反应蛋白（hsCRP）、MDA和晚期氧化蛋白产物（AOPP）]中，可提高区分有无脂肪肝患者的能力[曲线下面积（AUC）分别为=0.832、AUC=0.808、AUC=0.798、AUC=0.824和AUC=0.743]。模型2（包括所有五个检测的预测因素，即HDL-c、非HDL-c、hsCRP、MDA、AOPP和脂肪肝危险因素）提高了对脂肪肝状态的鉴别能力（AUC=0.909）。此外，模型2的敏感性和特异性最高（分别为89.3%和87.5%），高于模型1中的每个预测因素。
包括氧化应激、血脂异常和炎症生物标志物的多标志物方法可能有助于识别有脂肪肝疾病高风险的糖尿病患者。
