Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua City, Zhejiang Province, China.
Central Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua City, Zhejiang Province, China.
J Immunol Res. 2022 Apr 20;2022:6289261. doi: 10.1155/2022/6289261. eCollection 2022.
Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D.
This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated.
During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction ( < 0.05). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (C - index = 0.785, < 0.001). This model was validated using the -fold cross-validation method; the accuracy was 0.815 ± 0.013 in training sets and 0.784 ± 0.019 in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction.
The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.
过去十年的研究报告了许多基于基因组、代谢组和蛋白质组学技术的新型生物标志物，用于预测 2 型糖尿病（T2D）患者肾功能新发病或进展风险。然而，由于这些新型预测标志物技术含量高、稳定性差、成本高，短期内难以在临床上广泛应用。本研究旨在评估临床特征和六种传统肾脏标志物与 T2D 患者短期新发肾功能障碍风险的关系。
本研究纳入了 213 名基线时肾功能正常的 T2D 患者。分析了白蛋白与肌酐比值（ACR）、估算肾小球滤过率（eGFR）、α-1-微球蛋白与肌酐比值（A1MCR）、中性粒细胞明胶酶相关脂质运载蛋白与肌酐比值、转铁蛋白与肌酐比值（UTRF/Cr）和视黄醇结合蛋白与肌酐比值（URBP/Cr）的基线水平。建立并验证了多变量逻辑模型。
在两年的随访期间，23.01%的患者进展为肾功能障碍。ACR、A1MCR、UTRF/Cr 和 URBP/Cr 的基础水平是新发肾功能障碍的独立危险因素（<0.05）。构建了包含临床特征和这些肾脏标志物的几个逻辑模型，用于预测短期新发肾功能障碍的风险。比较而言，包含年龄、糖化血红蛋白（HbA1c）、高血压、ACR、A1MCR、UTRF/Cr 和 URBP/Cr 基线水平的模型具有最高的潜力（C 指数=0.785，<0.001）。该模型使用-折交叉验证法进行验证；在训练集中的准确性为 0.815±0.013，在验证集中的准确性为 0.784±0.019，表明该模型对预测新发肾功能障碍风险具有较好的一致性。最后，基于该模型构建了一个列线图，提供了一种评估短期新发肾功能障碍风险的定量工具。
该模型结合了这些标志物和临床特征，可能具有较高的预测短期新发肾功能障碍风险的潜力。
