Hu Huiyue, Mu Xiaodie, Zhao Shuya, Yang Min, Zhou Hua
Department of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, People's Republic of China.
Diabetes Metab Syndr Obes. 2025 Feb 10;18:383-398. doi: 10.2147/DMSO.S500992. eCollection 2025.
The aim of this study was to develop a predictive model for the progression of diabetic kidney disease (DKD) to end-stage renal disease (ESRD) and to evaluate the effectiveness of renal pathology and the kidney failure risk equation (KFRE) in this context.
The study comprised two parts. The first part involved 555 patients with clinically diagnosed DKD, while the second part focused on 85 patients with biopsy-proven DKD. Cox regression analysis and competing risk regression were employed to identify independent predictors. Time-dependent receiver operating characteristic (ROC) was used to evaluate prediction performance, and the area under the curve (AUC) was calculated to assess the model's accuracy.
The Cox regression model developed for the 555 patients clinically diagnosed with DKD identified 5 predictors (body mass index (BMI), estimated glomerular filtration rate (eGFR), 24-hour urinary total protein (UTP), systemic immune-inflammatory index (SII), and controlling nutritional status (CONUT), whereas the Competing risks model included 4 predictors (BMI, eGFR, UTP, CONUT). Among 85 patients with biopsy-proven diabetic DKD, the combined prognostic model integrating KFRE, interstitial fibrosis and tubular atrophy (IFTA), SII and BMI demonstrated enhanced predictive ability at 5 years. The developed models offer improved accuracy over existing methods by incorporating renal pathology and novel inflammatory indices, making them more applicable in clinical settings.
The predictive model proved to be effective in assessing the progression of DKD to ESRD. Additionally, the combined model of KFRE, IFTA, SII, and BMI demonstrates high predictive performance. Future studies should validate these models in larger cohorts and explore their integration into routine clinical practice to enhance personalized risk assessment and management.
本研究旨在建立一个预测糖尿病肾病(DKD)进展至终末期肾病(ESRD)的模型,并评估肾脏病理学及肾衰竭风险方程(KFRE)在此情况下的有效性。
本研究包括两个部分。第一部分纳入555例临床诊断为DKD的患者,第二部分聚焦于85例经活检证实为DKD的患者。采用Cox回归分析和竞争风险回归来确定独立预测因素。使用时间依赖性受试者工作特征曲线(ROC)评估预测性能,并计算曲线下面积(AUC)以评估模型的准确性。
为555例临床诊断为DKD的患者建立的Cox回归模型确定了5个预测因素(体重指数(BMI)、估算肾小球滤过率(eGFR)、24小时尿总蛋白(UTP)、全身免疫炎症指数(SII)和控制营养状况(CONUT),而竞争风险模型包括4个预测因素(BMI、eGFR、UTP、CONUT)。在85例经活检证实的糖尿病DKD患者中,整合KFRE、间质纤维化和肾小管萎缩(IFTA)、SII和BMI的联合预后模型在5年时显示出增强的预测能力。通过纳入肾脏病理学和新的炎症指标,所建立的模型比现有方法具有更高的准确性,使其在临床环境中更适用。
该预测模型被证明在评估DKD进展至ESRD方面是有效的。此外,KFRE、IFTA、SII和BMI的联合模型显示出较高的预测性能。未来的研究应在更大的队列中验证这些模型,并探索将其整合到常规临床实践中,以加强个性化风险评估和管理。