Huang Min, Liang Qionglin, Li Ping, Xia Jianfei, Wang Yong, Hu Ping, Jiang Zhiting, He Yongxin, Pang Liqiong, Han Lida, Wang Yiming, Luo Guoan
Department of Chemistry, Tsinghua University, Beijing 100084, PR China.
Mol Biosyst. 2013 Aug;9(8):2134-41. doi: 10.1039/c3mb25543c. Epub 2013 May 30.
Diabetic nephropathy is a devastating disease that affects a growing number of diabetic patients. A complete cure is very hard to achieve once the disease has been diagnosed, therefore the diagnosis of early stages in diabetic nephropathy has become a hot area. Numbers of molecules have been proposed to be potential biomarkers for this purpose. However, some problems still remain, such as discovering effective biomarkers to diagnose the disease before obvious clinical evidence appears. Thus, the main purpose of this study was to find plasma biomarkers for early diagnosis of type 2 diabetic nephropathy stage 1 and stage 2, as well as separating them from diabetes. 182 subjects (Chinese) were recruited for this study, including 50 healthy controls, 33 type 2 diabetic patients and 99 type 2 diabetic nephropathy patients (33 of these were stage 3). Important clinical indicators including proteinuria, serum creatinine, and urea nitrogen were measured and the glomerular filtration rate was estimated to assess kidney function; fasting blood glucose, postprandial blood glucose and glycated hemoglobin were measured to assess the blood glucose control. Key metabolites and genes in plasma samples were identified and determined using -omic and quantitative techniques. The potential biomarkers were then combined and carefully screened to determine the most informative ones for early diagnosis of type 2 diabetic nephropathy. An integrated biomarker system (IBS) incorporating 6 clinical indicators, 40 metabolites and 5 genes was established. Correlation analysis results revealed that most of the potential biomarkers significantly correlated with the 6 clinical indicators. Discriminant analysis results showed that the developed IBS gave the highest total predictive accuracy (98.9%). Significant test and receiver operating characteristic analysis results indicated that inosine had the highest sensitivity (0.889), specificity (1.000), positive predictive rate (1.000) and negative predictive rate (0.900) amongst the 48 potential biomarkers when separating patients with diabetes from patients with diabetic nephropathy stage 3. Finally, inosine with a cutoff of 0.086 mg L(-1) was combined with estimated GFR to differentiate between diabetic nephropathy stages 1 and 2 from diabetes. The results demonstrate that IBS combined with a proper statistical analysis technique is a powerful tool for biomarker screening.
糖尿病肾病是一种破坏性疾病，影响着越来越多的糖尿病患者。一旦疾病被诊断，很难实现完全治愈，因此糖尿病肾病早期阶段的诊断已成为一个热门领域。许多分子已被提出作为潜在的生物标志物用于此目的。然而，一些问题仍然存在，例如在明显的临床证据出现之前发现有效的生物标志物来诊断疾病。因此，本研究的主要目的是寻找用于早期诊断2型糖尿病肾病1期和2期以及将它们与糖尿病区分开来的血浆生物标志物。本研究招募了182名受试者（中国人），包括50名健康对照、33名2型糖尿病患者和99名2型糖尿病肾病患者（其中33名是3期）。测量了包括蛋白尿、血清肌酐和尿素氮在内的重要临床指标，并估算了肾小球滤过率以评估肾功能；测量了空腹血糖、餐后血糖和糖化血红蛋白以评估血糖控制情况。使用组学和定量技术鉴定并确定了血浆样本中的关键代谢物和基因。然后将潜在的生物标志物进行组合并仔细筛选，以确定用于2型糖尿病肾病早期诊断的最具信息量的生物标志物。建立了一个包含6个临床指标、40种代谢物和5个基因的综合生物标志物系统（IBS）。相关性分析结果显示，大多数潜在生物标志物与这6个临床指标显著相关。判别分析结果表明，所开发的IBS具有最高的总预测准确率（98.9%）。显著性检验和受试者工作特征分析结果表明，在将糖尿病患者与3期糖尿病肾病患者区分开的48种潜在生物标志物中，肌苷具有最高的敏感性（0.889）、特异性（1.000）、阳性预测率（1.000）和阴性预测率（0.900）。最后，将截断值为0.086 mg L(-1)的肌苷与估算的肾小球滤过率相结合，以区分糖尿病肾病1期和2期与糖尿病。结果表明，IBS结合适当的统计分析技术是生物标志物筛选的有力工具。
