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维持性血液透析患者血清β2-微球蛋白对心脏瓣膜钙化的预测价值

Predictive value of serum β2-microglobulin in cardiac valve calcification in maintenance hemodialysis patients.

作者信息

Shen Yan, Chen Jia-Jia, Yao Wu-Bin, Feng Su-Juan, Yang Hong-Li, Chen Dong-Mei, Master Sankar Raj Vimal, Shen Liang-Lan, Huang Hua-Xing

机构信息

Department of Nephrology, First People's Hospital of Nantong City, the Second Affiliated Hospital of Nantong University, Nantong, China.

Department of Pediatric Nephrology, Children's Hospital of Illinois, University of Illinois College of Medicine at Peoria, Peoria, IL, USA.

出版信息

J Thorac Dis. 2023 Sep 28;15(9):4914-4924. doi: 10.21037/jtd-23-1185. Epub 2023 Sep 22.

DOI:10.21037/jtd-23-1185
PMID:37868894
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10586954/
Abstract

BACKGROUND

Cardiac valve calcification (CVC) is associated with adverse cardiovascular events. We studied the risk factors of CVC in maintenance hemodialysis (MHD) patients and the value of serum β2-microglobulin (β2-MG) levels in predicting the incidence of CVC. β2-MG is a middle molecular weight toxin. In recent years, researchers found that elevated blood β2-MG was associated with coronary, thoracic, and abdominal aortic calcifications with significant correlations. β2-MG has been emerging as a strong biomarker for cardiovascular mortality in uremic patients but its role in CVC is not well studied. This study looked specifically at CVC occurrence in relation to β2-MG for MHD patients.

METHODS

Patients who underwent MHD for more than 3 months in the First People's Hospital of Nantong City from November 2012 to November 2019 with complete available data were included in the study. The patients were divided into the CVC group and the non-CVC group. The general information and clinical laboratory indicators of the patients were collected in a retrospective manner. We analyzed the risk factors for developing CVC in MHD patients using binary logistic regression method. Receiver operating characteristic (ROC) curves were used to calculate the cut-off value of β2-MG for predicting CVC. The decision tree (DT) method was used to classify and explore the probability of CVC in patients with MHD.

RESULTS

The β2-MG in the CVC group was significantly higher than that in the non-CVC group (=6.750, P<0.001). Multivariate binary logistic regression analysis showed that gender, age, serum β2-MG, and hemodialysis (HD) adequacy (Kt/V urea) were independent risk factors for CVC in MHD patients. ROC analysis showed that a β2-MG value of 25 µg/L was the best cut-off point for predicting CVC in MHD patients. According to binary logistic regression analysis, the β2-MG ≥25 µg/L group was 3.39 times more likely to develop CVC than the β2-MG <25 µg/L group [odds ratio (OR), 3.39; 95% confidence interval (CI), 1.63-7.06; P=0.001]. The DT model determined that serum β2-MG ≥25 µg/L and age >69 years were important determinants for predicting CVC in MHD patients.

CONCLUSIONS

Serum β2-MG in MHD patients has a positive correlation with the severity and occurrence of CVC.

摘要

背景

心脏瓣膜钙化(CVC)与不良心血管事件相关。我们研究了维持性血液透析(MHD)患者CVC的危险因素以及血清β2-微球蛋白(β2-MG)水平在预测CVC发生率方面的价值。β2-MG是一种中分子量毒素。近年来,研究人员发现血液中β2-MG升高与冠状动脉、胸主动脉和腹主动脉钙化相关,且具有显著相关性。β2-MG已成为尿毒症患者心血管死亡的一个重要生物标志物,但其在CVC中的作用尚未得到充分研究。本研究专门探讨了MHD患者CVC的发生与β2-MG的关系。

方法

纳入2012年11月至2019年11月在南通市第一人民医院接受MHD治疗超过3个月且有完整可用数据的患者。将患者分为CVC组和非CVC组。回顾性收集患者的一般信息和临床实验室指标。采用二元逻辑回归方法分析MHD患者发生CVC的危险因素。使用受试者工作特征(ROC)曲线计算预测CVC的β2-MG临界值。采用决策树(DT)方法对MHD患者CVC发生的概率进行分类和探索。

结果

CVC组的β2-MG显著高于非CVC组(=6.750,P<0.001)。多因素二元逻辑回归分析显示,性别、年龄、血清β2-MG和血液透析(HD)充分性(Kt/V尿素)是MHD患者CVC的独立危险因素。ROC分析显示β2-MG值为25μg/L是预测MHD患者CVC的最佳临界点。根据二元逻辑回归分析,β2-MG≥25μg/L组发生CVC的可能性是β2-MG<25μg/L组的3.39倍[比值比(OR),3.39;95%置信区间(CI),1.63-7.06;P=0.001]。DT模型确定血清β2-MG≥25μg/L和年龄>69岁是预测MHD患者CVC的重要决定因素。

结论

MHD患者血清β2-MG与CVC的严重程度和发生呈正相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/19e4efc2f658/jtd-15-09-4914-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/04bb4a5832a7/jtd-15-09-4914-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/2ea8114d0182/jtd-15-09-4914-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/8ca6ede20c9d/jtd-15-09-4914-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/19e4efc2f658/jtd-15-09-4914-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/04bb4a5832a7/jtd-15-09-4914-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/2ea8114d0182/jtd-15-09-4914-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/8ca6ede20c9d/jtd-15-09-4914-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/299e/10586954/19e4efc2f658/jtd-15-09-4914-f4.jpg

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