Eremić-Kojić N, Đerić M, Govorčin M L, Balać D, Kresoja M, Kojić-Damjanov S
Centre for Laboratory Medicine, Clinical Centre of Vojvodina, Novi Sad, Serbia.
Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.
Hippokratia. 2018 Jan-Mar;22(1):10-16.
In order to optimize the identification of persons with non-alcoholic fatty liver disease (NAFLD), several algorithms for hepatic steatosis were developed. These available algorithms, as well as an algorithm, derived using biochemical and anthropometric data of our participants, are compared in a cross-sectional pilot study.
We included 77 participants with abdominal obesity: 43 with NAFLD and 33 without NAFLD. Body mass index (BMI), waist circumference (WC) and hip circumference (HC), systolic and diastolic blood pressure were assessed. Fibrinogen, high sensitive C-reactive protein (hsCRP), aspartate aminotransferase (AST), alanine transaminase (ALT), gamma-glutamyl transferase (GGT), uric acid, ferritin, glucose, insulin, homocysteine, lipid status parameters, apolipoprotein A-I, apolipoprotein B and Lp(a)-lipoprotein were measured. Fatty liver was assessed by ultrasound with the presence or absence of hepatic steatosis. Discovering the most significant factor in the presence of NAFLD is assessed through logistic regression modeling. The predictor variables were chosen according to an algorithm derived from conducted factor analysis and other available algorithms for hepatic steatosis.
Participants with NAFLD had significantly higher BMI (34.38 ± 9.73 vs 28.05 ± 4.79 kg/m, p =0.001), WC (108.05 ± 11.47 vs 96.15 ± 14.27 cm, p =0.001), HC (114.93 ± 11.01 vs 108.21 ± 9.82 cm, p =0.050), systolic (128.98 ± 8.67 vs 122.42 ± 10.62 mmHg, p =0.010) and diastolic blood pressure (83.64 ± 5.94 vs 78.33 ± 7.57 mmHg, p =0.001), AST (23.93 ± 6.91 vs 21.70 ± 5.21 U/L, p =0.014), ALT (30.50 ± 13.70 vs 23.00 ± 11.75 U/L, p =0.007), hsCRP (4.34 ± 5.56 vs 2.98 ± 2.34mg/l, p =0.004) and uric acid (358.02 ± 83.29 vs 296.78 ± 84.54µmol/l, p =0.001), in comparison non NAFLD. Logistic regression model with algorithm derived from factor analysis showed the best performance. From other available algorithms, only fatty liver index (FLI) and hepatic steatosis index (HSI) had statistically significant discriminatory power. Conclusions: Elevation of WC, HC, BMI, DBP, SBP, Fbg, hsCRP, glucose, and uric acid, incorporated in our hepatic steatosis prediction model, had the best predictive power among all assessed algorithms. HIPPOKRATIA 2018, 22(1): 10-16.
为了优化非酒精性脂肪性肝病（NAFLD）患者的识别，已开发出多种肝脂肪变性算法。在一项横断面试点研究中，对这些现有算法以及根据我们参与者的生化和人体测量数据得出的一种算法进行了比较。
我们纳入了77例腹部肥胖参与者：43例患有NAFLD，33例未患NAFLD。评估了体重指数（BMI）、腰围（WC）和臀围（HC）、收缩压和舒张压。测量了纤维蛋白原、高敏C反应蛋白（hsCRP）、天冬氨酸转氨酶（AST）、丙氨酸转氨酶（ALT）、γ-谷氨酰转移酶（GGT）、尿酸、铁蛋白、葡萄糖、胰岛素、同型半胱氨酸、血脂状态参数、载脂蛋白A-I、载脂蛋白B和脂蛋白（a）。通过超声评估脂肪肝并判断是否存在肝脂肪变性。通过逻辑回归模型评估在存在NAFLD的情况下最显著的因素。根据进行因子分析得出的算法以及其他现有的肝脂肪变性算法选择预测变量。
与未患NAFLD者相比，患NAFLD的参与者BMI（34.38±9.73 vs 28.05±4.79kg/m²，p =0.001）、WC（108.05±11.47 vs 96.15±14.27cm，p =0.001）、HC（114.93±11.01 vs 108.21±9.82cm，p =0.050）、收缩压（128.98±8.67 vs 122.42±10.62mmHg，p =0.010）和舒张压（83.64±5.94 vs 78.33±7.57mmHg，p =0.001）、AST（23.93±6.91 vs 21.70±5.21U/L，p =0.014）、ALT（30.50±13.70 vs 23.00±11.75U/L，p =0.007）、hsCRP（4.34±5.56 vs 2.98±2.34mg/l，p =0.004）和尿酸（358.02±83.29 vs 296.78±84.54µmol/l，p =0.001）显著更高。基于因子分析得出的算法的逻辑回归模型表现最佳。在其他现有算法中，只有脂肪肝指数（FLI）和肝脂肪变性指数（HSI）具有统计学上显著的鉴别能力。结论：纳入我们肝脂肪变性预测模型中的WC、HC、BMI、舒张压、收缩压、纤维蛋白原、hsCRP、葡萄糖和尿酸升高，在所有评估算法中具有最佳预测能力。《希波克拉底》2018年，22(1): 10 - 16。
