Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Diabetologia. 2019 Sep;62(9):1616-1627. doi: 10.1007/s00125-019-4915-0. Epub 2019 Jun 20.
AIMS/HYPOTHESIS: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. METHODS: We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min[1.73 m], with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min[1.73 m] year) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone. RESULTS: For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and α1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well. CONCLUSIONS/INTERPRETATION: Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.
目的/假设：我们旨在确定一组稀疏的生物标志物，以改善 1 型糖尿病患者的肾脏疾病进展预测。
方法：我们考虑了来自苏格兰糖尿病研究网络 1 型生物资源（SDRNT1BIO）的 859 名参与者和芬兰糖尿病肾病（FinnDiane）研究的 315 名参与者。所有参与者的初始 eGFR 均在 30 至 75ml/min[1.73m]之间，其中来自 FinnDiane 的参与者的蛋白尿被过度采样。使用 Luminex 平台和 LC 电喷雾串联 MS（LC-MS/MS）在非禁食血清样本中测量了 297 种循环生物标志物（30 种蛋白质、121 种代谢物、146 种肽）。我们使用线性和逻辑回归模型研究了与最终 eGFR 的关联，这些最终 eGFR 是根据基线 eGFR 和快速进展（损失超过 3ml/min[1.73m]·年）进行调整的。使用惩罚贝叶斯方法确定了生物标志物的面板，并通过 10 倍交叉验证评估了它们的性能，并将其与仅使用临床记录数据进行了比较。
结果：对于最终 eGFR，SDRNT1BIO 中 16 种蛋白质和 30 种代谢物或肽与年龄、性别、糖尿病持续时间、研究日 eGFR 和随访时间（均 p<10）相关，芬兰的 FinnDiane 中 9 种蛋白质和 5 种代谢物或肽。最强的关联是与 CD27 抗原（CD27）、肾损伤分子 1（KIM-1）和α1-微球蛋白。在基线协变量的基础上增加 Luminex 生物标志物，可使 SDRNT1BIO 中最终 eGFR 的 r 值从 0.47 增加到 0.58，在 FinnDiane 中从 0.33 增加到 0.48。r 值增加的至少 75%归因于 CD27 和 KIM-1。然而，使用历史 eGFR 的加权平均值可提供与生物标志物类似的性能。LC-MS/MS 平台的性能较差。
结论/解释：在一组相关的生物标志物中，只有 CD27 和 KIM-1 的稀疏面板包含了 eGFR 进展的大部分预测信息。超越临床数据的预测增量虽然不大，但对于将快速疾病进展的个体纳入临床试验进行抽样可能非常有用，特别是在关于先前 eGFR 轨迹的信息很少的情况下。
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