通过计算脂肪变性指数诊断非酒精性脂肪肝的方法

I. Bulatova, A. Sobol, I. L. Gulyaeva
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摘要

目的利用一般可用的标记物,确定诊断绝经后代谢综合征(MS)妇女非酒精性脂肪肝(NAFLD)的指标。材料和方法。62 名绝经后早期患有非酒精性脂肪肝和代谢综合征的女性参加了研究。她们与 24 名绝经后未患肥胖症的相对健康女性进行了比较。患者的平均年龄为 49.9±1.1 岁。肝脏脂肪变性是通过超声波检查诊断出来的。肝脏脂肪变性指数(HSI)计算的数学模型包括:体重指数(BMI)、腰围(WS)、甘油三酯和低密度脂蛋白(LDL)。结果与相对健康的女性相比,绝经后多发性硬化症和非酒精性脂肪肝患者的体重指数和腰围明显增大。脂肪变性患者的高甘油三酯血症和血清中低密度脂蛋白增加。生物测定和实验室数据已被纳入计算 HSI 的数学公式中。当 HSI 值大于等于 0.5 时,非酒精性脂肪肝可被诊断为绝经后妇女的非酒精性脂肪肝;当 HSI 值小于 0.5 时,则不能被诊断为非酒精性脂肪肝。该方法的灵敏度和特异性指标分别为 98.4 % 和 95.85 %。结论上述建议的微创方法可以发现生育后早期多发性硬化症女性患者的肝脏脂肪变性。值得一提的是,该方法具有很高的诊断特性,而且由于采用了简单的生物测量和实验室数据,因此有可能得到广泛应用。
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A method of diagnosing non-alcoholic fatty liver disease with the calculation of the steatosis index
Objective. To work out the index for diagnosing non-alcoholic fatty liver disease (NAFLD) in women with metabolic syndrome (MS) in postmenopause using generally available markers. Materials and methods. 62 females with NAFLD and MS in early postreproductive period took part in the study. They were compared to 24 relatively healthy females not suffering from obesity in postmenopause. The average age of the patients was 49,9 ± 1,1. Hepatic steatosis was diagnosed by ultrasound examination. The mathematic model of hepatic steatosis index (HSI) calculation included: body mass index (BMI), waist size (WS), triglycerides and low-density lipoproteins (LDL). Results. BMI and WS in patients with MS and NAFLD in postmenopause were considerably more than in relatively healthy women. Hypertriglyceridemia and an increase of LDL in blood serum were noted in patients with steatosis. Biometric and laboratory data were included in the mathematical formula which allows to calculate HSI. With HSI 0,5 and more NAFLD is diagnosed in women in post menopause, when HSI is lower than 0,5 it is not diagnosed. Indicators of sensitivity and specificity of the method were 98,4 % and 95,85 % respectively. Conclusions. The minimally invasive method suggested above allows to reveal hepatic steatosis in females with MS in early postreproductive period. High diagnostic characteristics of the method are worth mentioning, as well as the possibility to be widely used thanks to simple biometric and laboratory data employed.
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