Tepel Martin, Beck Hans C, Tan Qihua, Borst Christoffer, Rasmussen Lars M
Department of Nephrology, Odense University Hospital, and University of Southern Denmark, Institute of Molecular Medicine, Cardiovascular and Renal Research, Institute of Clinical Research.
Department of Clinical Biochemistry and Pharmacology, Centre for Individualized Medicine in Arterial Diseases (Odense University Hospital), and Centre for Clinical Proteomics (Odense University Hospital/University of Southern Denmark).
Sci Rep. 2015 Oct 8;5:14882. doi: 10.1038/srep14882.
The objective of the study was to define the specific plasma protein signature that predicts the increase of the inflammation marker C-reactive protein from index day to next-day using proteome analysis and novel bioinformatics tools. We performed a prospective study of 91 incident kidney transplant recipients and quantified 359 plasma proteins simultaneously using nano-Liquid-Chromatography-Tandem Mass-Spectrometry in individual samples and plasma C-reactive protein on the index day and the next day. Next-day C-reactive protein increased in 59 patients whereas it decreased in 32 patients. The prediction model selected and validated 82 plasma proteins which determined increased next-day C-reactive protein (area under receiver-operator-characteristics curve, 0.772; 95% confidence interval, 0.669 to 0.876; P < 0.0001). Multivariable logistic regression showed that 82-plex protein signature (P < 0.001) was associated with observed increased next-day C-reactive protein. The 82-plex protein signature outperformed routine clinical procedures. The category-free net reclassification index improved with 82-plex plasma protein signature (total net reclassification index, 88.3%). Using the 82-plex plasma protein signature increased net reclassification index with a clinical meaningful 10% increase of risk mainly by the improvement of reclassification of subjects in the event group. An 82-plex plasma protein signature predicts an increase of the inflammatory marker C-reactive protein.
本研究的目的是利用蛋白质组分析和新型生物信息学工具,确定能够预测从指数日到次日炎症标志物C反应蛋白升高的特定血浆蛋白特征。我们对91例新发肾移植受者进行了一项前瞻性研究,使用纳升液相色谱-串联质谱法同时对个体样本中的359种血浆蛋白以及指数日和次日的血浆C反应蛋白进行定量。次日,59例患者的C反应蛋白升高,32例患者的C反应蛋白降低。预测模型筛选并验证了82种血浆蛋白,这些蛋白可确定次日C反应蛋白升高(受试者操作特征曲线下面积为0.772;95%置信区间为0.669至0.876;P < 0.0001)。多变量逻辑回归显示,82种蛋白特征(P < 0.001)与观察到的次日C反应蛋白升高相关。82种蛋白特征优于常规临床程序。无类别净重新分类指数因82种血浆蛋白特征而提高(总净重新分类指数为88.3%)。使用82种血浆蛋白特征可提高净重新分类指数,主要通过改善事件组中受试者的重新分类,使风险有临床意义地增加10%。82种血浆蛋白特征可预测炎症标志物C反应蛋白升高。