Prediction of Non-Alcoholic Fatty Liver Disease and Liver Fat Using Metabolic and Genetic Factors
Prediction of Non-Alcoholic Fatty Liver Disease and Liver Fat Using Metabolic and Genetic Factors
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DOI：
10.1053/j.gastro.2009.06.005
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发表时间：
2009-09-01
期刊：
影响因子：
29.4
通讯作者：
Yki-Jarvinen, Hannele
中科院分区：
文献类型：
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作者：
Kotronen, Anna;Peltonen, Markku;Yki-Jarvinen, Hannele
背景与目的：我们的目的是开发一种方法，根据常规的临床和实验室数据准确预测非酒精性脂肪肝（NAFLD）和肝脏脂肪含量，并测试最近发现的PNPLA 3基因（rs738409）遗传变异的知识是否增加了预测的准确性。方法：使用质子磁共振波谱法测量470名受试者的肝脏脂肪含量，这些受试者被随机分为估计组（三分之二的受试者，n = 313）和验证组（三分之一的受试者，n = 157）。使用多变量逻辑和线性回归分析来创建NAFLD肝脂肪评分以诊断NAFLD和肝脂肪方程以估计每个个体的肝脂肪百分比。研究结果：代谢综合征和2型糖尿病的存在、空腹血清（fS）胰岛素、FS-天冬氨酸氨基转移酶（AST）和AST/丙氨酸氨基转移酶比值是NAFLD的独立预测因子。该评分的受试者工作特征曲线下面积在估计组为0.87，在验证组为0.86。最佳临界点-0.640预测肝脏脂肪含量增加，灵敏度为86%，特异性为71%。将遗传信息添加到分数中，仅将预测的准确性提高了1.5%。
BACKGROUND & AIMS: Our aims were to develop a method to accurately predict non-alcoholic fatty liver disease (NAFLD) and liver fat content based on routinely available clinical and laboratory data and to test whether knowledge of the recently discovered genetic variant in the PNPLA3 gene (rs738409) increases accuracy of the prediction. METHODS: Liver fat content was measured using proton magnetic resonance spectroscopy in 470 subjects, who were randomly divided into estimation (two thirds of the subjects, n = 313) and validation (one third of the subjects, n = 157) groups. Multivariate logistic and linear regression analyses were used to create an NAFLD liver fat score to diagnose NAFLD and liver fat equation to estimate liver fat percentage in each individual. RESULTS: The presence of the metabolic syndrome and type 2 diabetes, fasting serum (fS) insulin, FS-aspartate aminotransferase (AST), and the AST/alanine aminotransferase ratio were independent predictors of NAFLD. The score had an area under the receiver operating characteristic curve of 0.87 in the estimation and 0.86 in the validation group. The optimal cut-off point of -0.640 predicted increased liver fat content with sensitivity of 86% and specificity of 71%. Addition of the genetic information to the score improved the accuracy of the prediction by only