Department of Internal Medicine IV (Nephrology and Hypertension), Medical University of Innsbruck, Innsbruck, Austria.
Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Diabetes Care. 2017 Mar;40(3):391-397. doi: 10.2337/dc16-2202. Epub 2017 Jan 11.
Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large group of patients with type 2 diabetes and CKD at various stages.
We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serum samples from 1,765 patients recruited into two large clinical trials. eGFR decline was predicted based on molecular markers, clinical risk factors (including baseline eGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data.
The variability of annual eGFR loss explained by the biomarkers, indicated by the adjusted value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m, respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-the-large: -1.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively).
A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over a wide range of baseline eGFR values in patients with type 2 diabetes and CKD.
糖尿病慢性肾病（CKD）具有复杂的分子基础，可能具有多方面的病理生理学机制。我们旨在验证一组使用系统生物学方法鉴定的生物标志物，以预测不同阶段 2 型糖尿病和 CKD 患者的肾小球滤过率（eGFR）个体下降。
我们使用公开的“组学”数据开发了糖尿病 CKD 的分子过程模型，并确定了一组具有代表性的简约的 9 种分子生物标志物：几丁质酶 3 样蛋白 1、生长激素 1、肝细胞生长因子、基质金属蛋白酶（MMP）2、MMP7、MMP8、MMP13、酪氨酸激酶和肿瘤坏死因子受体-1。这些生物标志物在招募到两项大型临床试验的 1765 名患者的基线血清样本中进行了测量。基于分子标志物、临床危险因素（包括基线 eGFR 和蛋白尿）以及两者的组合来预测 eGFR 下降，并使用混合线性回归模型评估这些预测对纵向数据的影响。
生物标志物解释的每年 eGFR 损失的可变性，用调整后的 值表示，在 eGFR≥60 和 <60 mL/min/1.73 m 的患者中分别为 15%和 34%；临床预测因子解释的可变性分别为 20%和 31%。分子和临床预测因子的组合分别将调整后的 值提高到 35%和 64%。标志物模型的校准分析显示，与最佳校准（大校准：-1.125 和 0.95；两组校准斜率分别为 1.07 和 1.13）存在显著（均<0.0001）但基本无关的偏差。
使用系统生物学方法鉴定的一小组血清蛋白生物标志物，结合临床变量，可以增强对 2 型糖尿病和 CKD 患者广泛基线 eGFR 值范围内肾功能丧失的预测。
