Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua city, Zhejiang Province, China.
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 Diabetes Complications. 2021 Dec;35(12):108058. doi: 10.1016/j.jdiacomp.2021.108058. Epub 2021 Oct 5.
Numerous biomarkers have been shown to be associated with albuminuria. However, few of them are valuable separate predictors of albuminuria development. This study aimed to develop a model for predicting the short-term risk of new-onset albuminuria in normoalbuminuric patients with type 2 diabetes (T2D).
213 patients with T2D who were normoalbuminuric at the baseline were enrolled in this study. Basal levels of clinical characteristics and renal biomarkers including urinary orosomucoid (alpha-1-acid-glycoprotein, UORM), neutrophil gelatinase-associated lipocalin, retinol-binding protein, alpha-1-microglobulin, transferrin, and albumin-to-creatinine ratio (ACR) were utilized to analyze the association with the short-term risk of new-onset albuminuria.
19.72% of normoalbuminuric subjects at baseline progressed to albuminuria over the 2-year follow-up period. Except for NGAL, the basal levels of the other five renal biomarkers were significantly associated with new-onset albuminuria risk in the univariate analysis. In the multivariate logistic regression analysis using Forward: LR method, a model incorporating UORM/Cr, ACR, and HbA1c was established. Comparatively, this model had a higher potential to predict new-onset albuminuria risk compared with the single use of renal markers. In the validation of this model performed by 5-fold cross-validation method, the accuracy of this model was 0.818 ± 0.008 in the training sets, 0.827 ± 0.062 in the test sets, indicating a good capability for assessing albuminuria risk. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice.
The combined analysis of UORM/Cr, ACR and HbA1c may be of potential value for predicting the short-term risk of new-onset albuminuria in such patients.
已有大量生物标志物被证实与白蛋白尿相关。然而，其中很少有能单独作为白蛋白尿发展的有价值的预测指标。本研究旨在建立一个模型，以预测 2 型糖尿病（T2D）患者中正常白蛋白尿患者新发白蛋白尿的短期风险。
本研究共纳入 213 例基线时正常白蛋白尿的 T2D 患者。分析了基础临床特征和肾生物标志物（包括尿唾液酸糖蛋白（α-1-酸性糖蛋白，UORM）、中性粒细胞明胶酶相关载脂蛋白、视黄醇结合蛋白、α-1-微球蛋白、转铁蛋白和白蛋白/肌酐比值（ACR））与短期新发白蛋白尿风险的相关性。
在 2 年的随访期间，有 19.72%的正常白蛋白尿患者进展为白蛋白尿。除了 NGAL 外，在单因素分析中，其他五种肾生物标志物的基础水平与新发白蛋白尿风险显著相关。在使用 Forward：LR 方法的多变量逻辑回归分析中，建立了一个包含 UORM/Cr、ACR 和 HbA1c 的模型。与单一使用肾标志物相比，该模型具有更高的预测新发白蛋白尿风险的潜力。在通过 5 折交叉验证方法验证该模型时，该模型在训练组中的准确率为 0.818±0.008，在测试组中的准确率为 0.827±0.062，表明该模型具有良好的评估白蛋白尿风险的能力。最后，基于该模型构建了一个列线图，以方便其在临床实践中的应用。
联合分析 UORM/Cr、ACR 和 HbA1c 可能对预测此类患者新发白蛋白尿的短期风险具有潜在价值。
