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首页> 外文期刊>Medicine. >Pre-transplant Evaluation of Donor Urinary Biomarkers can Predict Reduced Graft Function After Deceased Donor Kidney Transplantation
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Pre-transplant Evaluation of Donor Urinary Biomarkers can Predict Reduced Graft Function After Deceased Donor Kidney Transplantation

机译:供体尿生物标志物的预移植评估可以预测死亡的供体肾移植后的降低的接枝函数

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摘要

Several recipient biomarkers are reported to predict graft dysfunction, but these are not useful in decision making for the acceptance or allocation of deceased donor kidneys; thus, it is necessary to develop donor biomarkers predictive of graft dysfunction. To address this issue, we prospectively enrolled 94 deceased donors and their 109 recipients who underwent transplantation between 2010 and 2013 at 4 Korean transplantation centers. We investigated the predictive values of donor urinary neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and L-type fatty acid binding protein (L-FABP) for reduced graft function (RGF). We also developed a prediction model of RGF using these donor biomarkers. RGF was defined as delayed or slow graft function. Multiple logistic regression analysis was used to generate a prediction model, which was internally validated using a bootstrapping method. Multiple linear regression analysis was used to assess the association of biomarkers with 1-year graft function. Notably, donor urinary NGAL levels were associated with donor AKI (P = 0.014), and donor urinary NGAL and L-FABP were predictive for RGF, with area under the receiver-operating characteristic curves (AUROC) of 0.758 and 0.704 for NGAL and L-FABP, respectively. The best-fit model including donor urinary NGAL, L-FABP, and serum creatinine conveyed a better predictive value for RGF than donor serum creatinine alone (P = 0.02). In addition, we generated a scoring method to predict RGF based on donor urinary NGAL, L-FABP, and serum creatinine levels. Diagnostic performance of the RGF prediction score (AUROC 0.808) was significantly better than that of the DGF calculator (AUROC 0.627) and the kidney donor profile index (AUROC 0.606). Donor urinary L-FABP levels were also predictive of 1-year graft function (P = 0.005). Collectively, these findings suggest donor urinary NGAL and L-FABP to be useful biomarkers for RGF, and support the use of a new scoring system based on donor biomarkers to facilitate decision-making in acceptance and allocation of deceased donor kidneys and contribute to maximal organ utilization.
机译:据报道了几种受体生物标志物预测移植物功能障碍,但这些不能用于验收或分配死者肾脏的决策;因此,有必要开发可移植功能障碍的供体生物标志物。为了解决这个问题,我们预计第94名已故的捐助者及其109名接受2010年至2013年间移植的收入,在4名韩国移植中心。我们研究了用于降低接枝函数(RGF)的供体尿中性粒细胞明胶酶相关脂素(Ngal),肾损伤分子-1(Kim-1)和L型脂肪酸结合蛋白(L-FABP)的预测值。我们还开发了使用这些捐赠者生物标志物的RGF的预测模型。 RGF被定义为延迟或缓慢的移植功能。多个逻辑回归分析用于生成预测模型,该预测模型是使用引导方法内部验证的。多元线性回归分析用于评估生物标志物与1年移植功能的关联。值得注意的是,供体尿NGAL水平与供体αKI(P = 0.014)相关(P = 0.014),并且供体尿NGAL和L-FABP对RGF预测,接收器操作特性曲线(AUROC)为0.758和0.704的接收器操作特性曲线(AUROC),对于NGAL和L. -FABP分别。包括供体尿NGAL,L-FABP和血清肌酐在内的最佳拟合模型为RGF单独提供了RGF的更好的预测值(P = 0.02)。此外,我们生成了基于供体尿NGAL,L-FABP和血清肌酐水平来预测RGF的评分方法。 RGF预测得分(AuCOC 0.808)的诊断性能明显优于DGF计算器(AuCOC 0.627)和肾脏供体轮廓指数(Auroc 0.606)。供体尿液L-FABP水平也预测1年接枝函数(P = 0.005)。集体,这些发现表明供体尿NGAL和L-FABP为RGF是有用的生物标志物,并支持基于供体生物标志物的新评分系统的使用,促进检验和分配死者肾脏的决策,并有助于最大器官利用率。

著录项

  • 来源
    《Medicine.》 |2016年第11期|共11页
  • 作者单位

    Seoul Natl Univ Hosp Transplantat Ctr Seoul 110744 South Korea;

    Ajou Univ Sch Med Dept Nephrol Suwon 441749 South Korea;

    Sungae Hosp Dept Cardiol Seoul South Korea;

    Gachon Univ Med &

    Sci Dept Prevent Med Inchon South Korea;

    Seoul Natl Univ Hosp Dept Pathol Seoul 110744 South Korea;

    Chonbuk Natl Univ Hosp Dept Nephrol Jeollabuk Do South Korea;

    Inje Univ Busan Paik Hosp Dept Nephrol Busan South Korea;

    Sungkyunkwan Univ Sch Med Dept Surg Samsung Med Ctr Seoul South Korea;

    Seoul Natl Univ Coll Med Dept Internal Med Seoul 110744 South Korea;

    Seoul Natl Univ Hosp Transplantat Ctr Seoul 110744 South Korea;

    Seoul Natl Univ Hosp Transplantat Ctr Seoul 110744 South Korea;

    Seoul Natl Univ Hosp Transplantat Ctr Seoul 110744 South Korea;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医药、卫生;
  • 关键词

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