mac -2结合蛋白糖基化异构体与肝炎病毒相关肝病的控制营养状况评分密切相关

H. Nishikawa, R. Takata, Kazunori Yoh, H. Enomoto, N. Ishii, Y. Iwata, Takashi Nishimura, Nobuhiro Aizawa, Yoshiyuki Sakai, Naoto Ikeda, Kunihiro Hasegawa, Yukihisa Yuri, Tomoyuki Takashima, H. Iijima, S. Nishiguchi
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摘要

目的:研究mac -2结合蛋白糖基化异构体(M2BPGi)的临床意义,M2BPGi是最近被引入的一种新的肝纤维化生物标志物,在慢性肝病患者中具有独特的纤维化相关乙二醇链蛋白改变,而不是肝纤维化标志物。与其他肝纤维化标志物相比,我们试图检查乙型肝炎和丙型肝炎病毒相关患者(hbv相关队列(br -队列,n=249)和丙型肝炎相关队列(cr -队列,n=386)的M2BPGi与控制营养(CONUT)评分之间的相关性。患者和方法:在两个队列中,我们检查了CONUT评分与四种肝纤维化标志物(M2BPGi、FIB-4指数、透明质酸和血小板计数)的相关性。受试者工作特征(ROC)分析与CONUT评分升高(CONUT评分≥1、2、3、4或5)相关。结果:br队列中位CONUT评分(范围)为1 (0-5),cr队列中位CONUT评分(范围)为2 (0-8)(P<0.0001)。在br队列中,60例(24.1%)患者出现晚期纤维化或以上(F3或F4),而在cr队列中,212例(54.9%)患者出现晚期纤维化。在br队列中,FIB-4指数相关系数最高(r=0.436, P<0.0001),其次是M2BPGi (r=0.376, P<0.0001)。在cr队列中,相关系数最高的是M2BPGi (r=0.690, P<0.0001),其次是FIB-4指数(r=0.598, P<0.0001)。对于与CONUT评分升高相关的ROC分析,在cr -队列中,M2BPGi在所有ROC分析中产生最高的AUC,而在br -队列中,没有注意到这种趋势。结论:M2BPGi可作为预测慢性丙型肝炎患者营养状况的有效指标。
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Mac-2-binding Protein Glycosylation isomer well correlates with the Controlling Nutritional Status Score in Hepatitis Viruses-related Liver Diseases
Purpose: Examining the clinical significance of Mac-2-binding protein glycosylation isomer (M2BPGi), which was recently introduced as a novel liver fibrotic biomarker in chronic liver disease patients with unique fibrosis associated glycol chain protein alteration, other than liver fibrotic marker appears to be of importance. We sought to examine the relevance between M2BPGi and the Controlling Nutrition (CONUT) score in hepatitis B and C viruses-related patients (the HBVrelated cohort (Br-cohort, n=249) and the HCV-related cohort (Cr-cohort, n=386)) comparing with other liver fibrotic markers. Patients and Methods: We checked the correlation between the CONUT score and four liver fibrotic markers (M2BPGi, FIB-4 index, hyaluronic acid, and platelet count) in the two cohorts. Receiver operating characteristics (ROC) analyses associated with elevated CONUT score (CONUT score ≥ 1,2,3,4 or 5) were also conducted. Results: The median CONUT score (range) were 1 (0-5) in the Br-cohort and 2 (0-8) in the Crcohort (P<0.0001). In the Br-cohort, advanced fibrosis or more (F3 or F4) was noted in 60 patients (24.1%), while in the Cr-cohort, it was noted in 212 patients (54.9%). In the Br-cohort, the highest correlation coefficient was identified in the FIB-4 index (r=0.436, P<0.0001), followed by M2BPGi (r=0.376, P<0.0001). In the Cr-cohort, the highest correlation coefficient was noted in M2BPGi (r=0.690, P<0.0001), followed by the FIB-4 index (r=0.598, P<0.0001). For the ROC analyses linked to the elevated CONUT score, in the Cr-cohort, M2BPGi yielded the highest AUC in all ROC analyses, whereas in the Br-cohort, such tendencies were not noted. Conclusion: M2BPGi can be a useful marker for predicting nutritional condition as determined by the CONUT score especially in chronic hepatitis C patients.
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