背景:尿液生物标志物可能改善慢性肾脏病(CKD)进展的预测。然而,报告大多数商业生物标志物测定对尿液中目标分析物检测的适用性以及对其预测性能的评估的数据很少。
方法:使用严格的(FDA批准的)验证标准,测试了30种商业测定(ELISA)定量尿液中目标分析物的能力。在探索性分析中,LASSO(最小绝对收缩和选择算子)逻辑回归分析用于确定预测CKD快速进展的潜在补充生物标志物。在229名CKD患者的子样本中,基于51CrEDTA清除率的测量肾小球滤过率(mGFR)下降(每年>10%)(平均年龄,61岁;66%男性;基线mGFR,38mL/min)来自肾病试验前瞻性队列。
结果:在30个检测中,针对24个候选生物标志物,涵盖CKD进展的不同病理生理机制,16种测定符合FDA批准的标准。LASSO逻辑回归确定了五种生物标志物的组合,包括CCL2,EGF,KIM1,NGAL,和TGF-α,与单独的肾衰竭风险方程变量相比,改善了mGFR快速下降的预测:年龄,性别,mGFR,和蛋白尿。在有100个重新样本的模型中,平均曲线下面积(AUC)高于没有这些生物标志物的模型。0.722(95%置信区间0.652-0.795)与0.682(0.614-0.748),分别。快速进展的完全调整比值比(95%置信区间)为1.87(1.22,2.98),1.86(1.23,2.89),0.43(0.25,0.70),1.10(0.71,1.83),0.55(0.33,0.89),白蛋白为2.99(1.89,5.01),CCL2,EGF,KIM1,NGAL,和TGF-α,分别。
结论:本研究为CKD进展的相关尿生物标志物的多种检测方法提供了严格的验证,这些联合检测方法可以改善CKD进展的预测。
背景:这项工作得到了美国国家研究所的支持,巴黎大学,援助巴黎公共图书馆,国家机构,MSDAVENIR,制药研究和早期发展罗氏实验室(巴塞尔,瑞士),和罗氏研究所(巴黎,法国)。
BACKGROUND: Urinary biomarkers may improve the prediction of chronic kidney disease (CKD) progression. Yet, data reporting the applicability of most commercial biomarker assays to the detection of their target analyte in urine together with an evaluation of their predictive performance are scarce.
METHODS: 30 commercial assays (ELISA) were tested for their ability to quantify the target analyte in urine using strict (FDA-approved) validation criteria. In an exploratory analysis, LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression analysis was used to identify potentially complementary biomarkers predicting fast CKD progression, determined as the 51CrEDTA clearance-based measured glomerular filtration rate (mGFR) decline (>10% per year) in a subsample of 229 CKD patients (mean age, 61 years; 66% men; baseline mGFR, 38 mL/min) from the NephroTest prospective cohort.
RESULTS: Among the 30 assays, directed against 24 candidate biomarkers, encompassing different pathophysiological mechanisms of CKD progression, 16 assays fulfilled the FDA-approved criteria. LASSO logistic regressions identified a combination of five biomarkers including CCL2, EGF, KIM1, NGAL, and TGF-α that improved the prediction of fast mGFR decline compared to the kidney failure risk equation variables alone: age, gender, mGFR, and albuminuria. Mean area under the curves (AUC) estimated from 100 re-samples was higher in the model with than without these biomarkers, 0.722 (95% confidence interval 0.652-0.795) vs. 0.682 (0.614-0.748), respectively. Fully-adjusted odds-ratios (95% confidence interval) for fast progression were 1.87 (1.22, 2.98), 1.86 (1.23, 2.89), 0.43 (0.25, 0.70), 1.10 (0.71, 1.83), 0.55 (0.33, 0.89), and 2.99 (1.89, 5.01) for albumin, CCL2, EGF, KIM1, NGAL, and TGF-α, respectively.
CONCLUSIONS: This study provides a rigorous validation of multiple assays for relevant urinary biomarkers of CKD progression which combination may improve the prediction of CKD progression.
BACKGROUND: This work was supported by Institut National de la Santé et de la Recherche Médicale, Université de Paris, Assistance Publique Hôpitaux de Paris, Agence Nationale de la Recherche, MSDAVENIR, Pharma Research and Early Development Roche Laboratories (Basel, Switzerland), and Institut Roche de Recherche et Médecine Translationnelle (Paris, France).