Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH 45229-3039, USA.
Adv Chronic Kidney Dis. 2010 Nov;17(6):469-79. doi: 10.1053/j.ackd.2010.09.002.
There is a paucity of sensitive and specific biomarkers for the early prediction of CKD progression. The recent application of innovative technologies such as functional genomics, proteomics, and biofluid profiling has uncovered several new candidates that are emerging as predictive biomarkers of CKD. The most promising among these include urinary proteins such as neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and liver-type fatty acid binding protein. In addition, an improved understanding of the complex pathophysiologic processes underlying CKD progression has also provided discriminatory biomarkers of CKD progression that are being actively evaluated. Candidates included in this category are plasma proteins such as asymmetric dimethylarginine, adiponectin, apolipoprotein A-IV, fibroblast growth factor 23, neutrophil gelatinase-associated lipocalin, and the natriuretic peptides, as well as urinary N-acetyl-β-d-glucosaminidase. This review represents a critical appraisal of the current status of these emerging CKD biomarkers. Currently, none of these are ready for routine clinical use. Additional large, multicenter prospective studies are needed to validate the biomarkers, identify thresholds and cut-offs for prediction of CKD progression and adverse events, assess the effects of confounding variables, and establish the ideal assays.
目前，用于早期预测 CKD 进展的敏感且特异的生物标志物十分匮乏。创新性技术如功能基因组学、蛋白质组学和生物体液分析的最新应用，揭示了几种新的候选物，它们正在成为 CKD 的预测性生物标志物。其中最有前景的包括尿中的蛋白质，如中性粒细胞明胶酶相关脂质运载蛋白、肾损伤分子-1 和肝型脂肪酸结合蛋白。此外，对 CKD 进展背后复杂的病理生理过程的深入理解，也为 CKD 进展的鉴别性生物标志物提供了依据，目前这些标志物正在积极评估中。该类别中的候选物包括血浆蛋白，如不对称二甲基精氨酸、脂联素、载脂蛋白 A-IV、成纤维细胞生长因子 23、中性粒细胞明胶酶相关脂质运载蛋白和利钠肽，以及尿 N-乙酰-β-D-氨基葡萄糖苷酶。这篇综述对这些新兴的 CKD 生物标志物的现状进行了批判性评估。目前，这些生物标志物都还没有准备好用于常规临床应用。需要开展更多大型、多中心的前瞻性研究，以验证这些生物标志物，确定预测 CKD 进展和不良事件的阈值和临界点，评估混杂变量的影响，并建立理想的检测方法。
