Lin Zishan, Hong Tao, Wang Wenfeng, Xie Shidong, Chen Caiming, Yang Feng, Jiang Dewen, Wan Jianxin, Xie Zugang, Xu Yanfang
Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Research Center for Metabolic Chronic Kidney Disease, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Front Nutr. 2025 Jun 2;12:1605841. doi: 10.3389/fnut.2025.1605841. eCollection 2025.
Type 2 diabetes mellitus (T2DM) significantly exacerbates the global health burden, with diabetic nephropathy (DN) emerging as one of the most common causes of chronic kidney disease. In T2DM patients with kidney disease, it is particularly important to distinguish DN from non-diabetic nephropathy (NDN), as treatment strategies differ markedly. However, the gold standard, renal biopsy, is often impractical due to its invasive nature. This multicenter study aims to develop a non-invasive diagnostic model to distinguish DN from NDN in T2DM patients.
From January 2014 to December 2023, T2DM patients undergoing percutaneous renal biopsies at three hospitals in Fujian were enrolled. The model was formulated using logistic regression analysis based on clinical and laboratory parameters. A visual predictive nomogram was developed and subsequently evaluated for its predictive performance.
A total of 292 patients were included, with 164 diagnosed with DN and 128 with NDN. Diabetic retinopathy, duration of diabetes, HbA1c, systolic blood pressure, neutrophil-to-lymphocyte ratio, kidney volume, triglycerides, estimated glomerular filtration rate, and urinary red blood cell count were identified as independent predictors of DN. A nomogram was then constructed. The model demonstrated high diagnostic accuracy with an AUC of 0.941, validated by an independent cohort yielding an AUC of 0.923. Calibration curves showed good agreement between predicted and actual outcomes, and decision curve analysis confirmed notable clinical utility.
The developed model offers a non-invasive, reliable alternative to renal biopsy for distinguishing between DN and NDN in T2DM patients. This tool proves especially valuable in clinical settings where renal biopsy is impractical, helping guide more appropriate treatment decisions.
2型糖尿病（T2DM）显著加剧了全球健康负担，糖尿病肾病（DN）已成为慢性肾脏病最常见的病因之一。在患有肾脏疾病的T2DM患者中，区分DN与非糖尿病肾病（NDN）尤为重要，因为治疗策略差异显著。然而，金标准肾活检由于其侵入性往往不切实际。这项多中心研究旨在开发一种非侵入性诊断模型，以区分T2DM患者中的DN和NDN。
2014年1月至2023年12月，纳入在福建三家医院接受经皮肾活检的T2DM患者。基于临床和实验室参数，使用逻辑回归分析制定模型。开发了可视化预测列线图，并随后评估其预测性能。
共纳入292例患者，其中164例诊断为DN，128例诊断为NDN。糖尿病视网膜病变、糖尿病病程、糖化血红蛋白、收缩压、中性粒细胞与淋巴细胞比值、肾脏体积、甘油三酯、估计肾小球滤过率和尿红细胞计数被确定为DN的独立预测因素。然后构建了列线图。该模型显示出较高的诊断准确性，AUC为0.941，独立队列验证后的AUC为0.923。校准曲线显示预测结果与实际结果之间具有良好的一致性，决策曲线分析证实了显著的临床实用性。
所开发的模型为区分T2DM患者中的DN和NDN提供了一种非侵入性、可靠的肾活检替代方法。在肾活检不切实际的临床环境中，该工具被证明特别有价值，有助于指导更合适的治疗决策。
